From 56997519fa5f6f12bdd1552fcc82c9861f997728 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Fri, 25 Apr 2025 18:56:19 -0400 Subject: [PATCH 01/97] Initial commit --- .gitignore | 174 ++++++++++++++++++++++++++++++++++++++++++++++ LICENSE | 201 +++++++++++++++++++++++++++++++++++++++++++++++++++++ README.md | 1 + 3 files changed, 376 insertions(+) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..0a19790 --- /dev/null +++ b/.gitignore @@ -0,0 +1,174 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000..8a98da3 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +# icefabric \ No newline at end of file From 19ef30156a9ec3b4ca7c039c4e5d0adae87f249e Mon Sep 17 00:00:00 2001 From: taddyb Date: Fri, 25 Apr 2025 19:05:29 -0400 Subject: [PATCH 02/97] initial commit --- .gitignore | 4 ++ .pre-commit-config.yaml | 23 +++++++ docs/index.md | 17 +++++ mkdocs.yml | 1 + pyproject.toml | 126 ++++++++++++++++++++++++++++++++++++++ src/icefabric/__init__.py | 0 6 files changed, 171 insertions(+) create mode 100644 .pre-commit-config.yaml create mode 100644 docs/index.md create mode 100644 mkdocs.yml create mode 100644 pyproject.toml create mode 100644 src/icefabric/__init__.py diff --git a/.gitignore b/.gitignore index 0a19790..11510c8 100644 --- a/.gitignore +++ b/.gitignore @@ -172,3 +172,7 @@ cython_debug/ # PyPI configuration file .pypirc + +.qodo +_version.py +./data diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..1f55ba1 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.6.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: debug-statements + + - repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.11.5 + hooks: + # Run the linter. + - id: ruff + args: [ --fix ] + # Run the formatter. + - id: ruff-format + + # - repo: https://github.com/pre-commit/mirrors-mypy + # rev: v1.15.0 + # hooks: + # - id: mypy diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..000ea34 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,17 @@ +# Welcome to MkDocs + +For full documentation visit [mkdocs.org](https://www.mkdocs.org). + +## Commands + +* `mkdocs new [dir-name]` - Create a new project. +* `mkdocs serve` - Start the live-reloading docs server. +* `mkdocs build` - Build the documentation site. +* `mkdocs -h` - Print help message and exit. + +## Project layout + + mkdocs.yml # The configuration file. + docs/ + index.md # The documentation homepage. + ... # Other markdown pages, images and other files. diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..c97182f --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1 @@ +site_name: My Docs diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..49021f1 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,126 @@ +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling", "hatch-vcs"] + +[tool.hatch] +version.source = "vcs" +build.hooks.vcs.version-file = "./src/icefabric/_version.py" + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.build.targets.wheel] +packages = ["src/icefabric"] + +[tool.hatch.build.targets.sdist] +packages = ["src/icefabric"] + +[project] +name = "icefabric" +description = "A tool for training deep learning models for FIM" +readme = "README.md" +dynamic = ["version"] +requires-python = ">=3.11.0" +license = { file = "LICENSE" } +authors = [{ name = "Tadd Bindas" }] +maintainers = [{ name = "Tadd Bindas", email = "taddbindas@gmail.com" }] + +dependencies = [ + "numpy==2.2.5", + "pandas==2.2.3", + "cubed-xarray==0.0.7", + "geopandas==1.0.1", + "pydantic==2.10.6", + "xarray==2025.3.1", + "scipy==1.15.1", + "tqdm==4.67.1", + "zarr==3.0.7", + "sympy==1.13.1", + "scikit-learn==1.6.1", + "matplotlib==3.10.0", + "s3fs==2025.3.2", +] + +dev = ["pre-commit", "ruff", "black", "mypy"] + +docs = [ + "pillow", + "cairosvg", + "mkdocs-material", + "mkdocs-git-committers-plugin", + "mkdocs-material-extensions", + "mkdocs-git-authors-plugin", + "mkdocs-git-revision-date-localized-plugin", +] + +jupyter = ["ipykernel", "jupyterlab"] + +[tool.black] +line-length = 110 +skip-string-normalization = true +preview = true +target-version = ['py311'] + +[tool.ruff] +line-length = 110 +lint.select = [ + "F", # Errors detected by Pyflakes + "E", # Error detected by Pycodestyle + "W", # Warning detected by Pycodestyle + "I", # isort + "D", # pydocstyle + "B", # flake8-bugbear + "TID", # flake8-tidy-imports + "C4", # flake8-comprehensions + "BLE", # flake8-blind-except + "UP", # pyupgrade + "RUF100", # Report unused noqa directives +] +lint.ignore = [ + # line too long -> we accept long comment lines; black gets rid of long code lines + "E501", + # Do not assign a lambda expression, use a def -> lambda expression assignments are convenient + "E731", + # allow I, O, l as variable names -> I is the identity matrix + "E741", + # Missing docstring in public package + "D104", + # Missing docstring in public module + "D100", + # Missing docstring in __init__ + "D107", + # Errors from function calls in argument defaults. These are fine when the result is immutable. + "B008", + # __magic__ methods are are often self-explanatory, allow missing docstrings + "D105", + # first line should end with a period [Bug: doesn't work with single-line docstrings] + "D400", + # First line should be in imperative mood; try rephrasing + "D401", + ## Disable one in each pair of mutually incompatible rules + # We don't want a blank line before a class docstring + "D203", + # We want docstrings to start immediately after the opening triple quote + "D213", +] + +[tool.ruff.lint.pydocstyle] +convention = "numpy" + +[tool.ruff.lint.per-file-ignores] +"docs/*" = ["I"] +"tests/*" = ["D"] +"*/__init__.py" = ["F401"] + +[tool.mypy] +python_version = "3.11" +warn_return_any = true +warn_unused_configs = true +strict_optional = true +ignore_missing_imports = true +disallow_any_unimported = true +check_untyped_defs = true +disallow_untyped_defs = true +no_implicit_optional = true +show_error_codes = true +warn_unused_ignores = true diff --git a/src/icefabric/__init__.py b/src/icefabric/__init__.py new file mode 100644 index 0000000..e69de29 From 1fa9f5aab88c07172d4ece299a2e1e32aa9e9ab3 Mon Sep 17 00:00:00 2001 From: taddyb Date: Fri, 25 Apr 2025 21:26:26 -0400 Subject: [PATCH 03/97] wrote an example for building my warehouse DB --- .gitignore | 2 +- examples/build.ipynb | 229 +++++++++++++++++++++++++++++++++++++ pyproject.toml | 5 + scripts/gpkg_to_parquet.py | 14 +++ 4 files changed, 249 insertions(+), 1 deletion(-) create mode 100644 examples/build.ipynb create mode 100644 scripts/gpkg_to_parquet.py diff --git a/.gitignore b/.gitignore index 11510c8..ce6cd93 100644 --- a/.gitignore +++ b/.gitignore @@ -175,4 +175,4 @@ cython_debug/ .qodo _version.py -./data +data/ diff --git a/examples/build.ipynb b/examples/build.ipynb new file mode 100644 index 0000000..87e8279 --- /dev/null +++ b/examples/build.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from pyiceberg.catalog import load_catalog\n", + "import pyarrow.parquet as pq\n", + "import os\n", + "from pathlib import Path\n", + "import geopandas as gpd\n", + "from shapely import wkb" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def add_parquet_to_catalog(file_path, table_name):\n", + " # Check if table already exists\n", + " if catalog.table_exists(f\"hydrofabric.{table_name}\"):\n", + " print(f\"Table {table_name} already exists, loading it\")\n", + " return catalog.load_table(f\"hydrofabric.{table_name}\")\n", + " \n", + " # Read the parquet file\n", + " arrow_table = pq.read_table(file_path)\n", + " \n", + " # Create the table in the catalog\n", + " iceberg_table = catalog.create_table(\n", + " f\"hydrofabric.{table_name}\",\n", + " schema=arrow_table.schema,\n", + " )\n", + " \n", + " # Append the data to the table\n", + " iceberg_table.append(arrow_table)\n", + " \n", + " print(f\"Added {file_path} as table {table_name}\")\n", + " return iceberg_table" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Added ../data/parquet/network.parquet as table network\n", + "Added ../data/parquet/nexus.parquet as table nexus\n", + "Added ../data/parquet/flowpath-attributes.parquet as table flowpath-attributes\n", + "Added ../data/parquet/divides.parquet as table divides\n", + "Added ../data/parquet/pois.parquet as table pois\n", + "Added ../data/parquet/flowpath-attributes-ml.parquet as table flowpath-attributes-ml\n", + "Added ../data/parquet/divide-attributes.parquet as table divide-attributes\n", + "Added ../data/parquet/flowpaths.parquet as table flowpaths\n", + "Added ../data/parquet/hydrolocations.parquet as table hydrolocations\n", + "Added ../data/parquet/lakes.parquet as table lakes\n" + ] + } + ], + "source": [ + "# Get all parquet files from the directory\n", + "parquet_dir = \"../data/parquet\"\n", + "parquet_files = list(Path(parquet_dir).glob(\"*.parquet\"))\n", + "\n", + "# Dictionary to store all tables\n", + "tables = {}\n", + "\n", + "# Add each parquet file to the catalog\n", + "for parquet_file in parquet_files:\n", + " table_name = parquet_file.stem # Get filename without extension\n", + " tables[table_name] = add_parquet_to_catalog(str(parquet_file), table_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tables in the catalog:\n", + "- ('hydrofabric', 'divide-attributes')\n", + "- ('hydrofabric', 'divides')\n", + "- ('hydrofabric', 'flowpath-attributes')\n", + "- ('hydrofabric', 'flowpath-attributes-ml')\n", + "- ('hydrofabric', 'flowpaths')\n", + "- ('hydrofabric', 'hydrolocations')\n", + "- ('hydrofabric', 'lakes')\n", + "- ('hydrofabric', 'network')\n", + "- ('hydrofabric', 'nexus')\n", + "- ('hydrofabric', 'pois')\n" + ] + } + ], + "source": [ + "print(\"Tables in the catalog:\")\n", + "for table_id in catalog.list_tables(\"hydrofabric\"):\n", + " print(f\"- {table_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def create_geodataframe(df):\n", + " if 'geometry' in df.columns:\n", + " df['geometry'] = df['geometry'].apply(\n", + " lambda x: wkb.loads(x) if x is not None else None\n", + " )\n", + " return gpd.GeoDataFrame(df, geometry='geometry', crs=\"EPSG:5070\")\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " divide_id toid type ds_id areasqkm vpuid id lengthkm \\\n", + "0 cat-5539 nex-5540 network NaN 10.4985 01 wb-5539 6.449001 \n", + "\n", + " tot_drainage_areasqkm has_flowline \\\n", + "0 2247.957002 True \n", + "\n", + " geometry \n", + "0 POLYGON ((2059095 2991255, 2059095 2990985, 20... " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "divides_df = tables['divides'].scan(\n", + " row_filter=\"divide_id == 'cat-5539'\",\n", + ").to_pandas()\n", + "divides_gdf = create_geodataframe(divides_df)\n", + "divides_gdf" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyproject.toml b/pyproject.toml index 49021f1..e71d5e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ maintainers = [{ name = "Tadd Bindas", email = "taddbindas@gmail.com" }] dependencies = [ "numpy==2.2.5", + "fiona==1.10.1", "pandas==2.2.3", "cubed-xarray==0.0.7", "geopandas==1.0.1", @@ -39,8 +40,12 @@ dependencies = [ "scikit-learn==1.6.1", "matplotlib==3.10.0", "s3fs==2025.3.2", + "pyarrow==19.0.1", + "pyiceberg==0.9.0", ] +[project.optional-dependencies] +test = ["pytest", "pytest-cov"] dev = ["pre-commit", "ruff", "black", "mypy"] docs = [ diff --git a/scripts/gpkg_to_parquet.py b/scripts/gpkg_to_parquet.py new file mode 100644 index 0000000..85f28a7 --- /dev/null +++ b/scripts/gpkg_to_parquet.py @@ -0,0 +1,14 @@ +"""A simple script to convert the v2.2 hydrofabric to parquet""" + +import geopandas as gpd +import fiona + +input_file = "data/conus_nextgen.gpkg" +available_layers = fiona.listlayers(input_file) +print(f"Layers in GeoPackage: {available_layers}") + +for layer in available_layers: + gdf = gpd.read_file(input_file, layer=layer) + output_file = f"data/parquet/{layer}.parquet" + gdf.to_parquet(output_file) + print(f"Converted layer '{layer}' to {output_file}") From 08f6dd24e93cb2cbf48b1f1571ea47c7e3644154 Mon Sep 17 00:00:00 2001 From: taddyb Date: Sat, 26 Apr 2025 16:01:12 -0400 Subject: [PATCH 04/97] edited build example to add a get_origin() function --- examples/build.ipynb | 170 ++++++++++++++++++++++++++++++------------- 1 file changed, 119 insertions(+), 51 deletions(-) diff --git a/examples/build.ipynb b/examples/build.ipynb index 87e8279..2aa3eec 100644 --- a/examples/build.ipynb +++ b/examples/build.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -11,12 +11,34 @@ "import os\n", "from pathlib import Path\n", "import geopandas as gpd\n", - "from shapely import wkb" + "from shapely import wkb\n", + "import numpy as np\n", + "import networkx as nx\n", + "from tqdm import tqdm" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from pyiceberg.catalog import load_catalog\n", + "\n", + "warehouse_path = \"../data/warehouse\"\n", + "catalog = load_catalog(\n", + " \"default\",\n", + " **{\n", + " 'type': 'sql',\n", + " \"uri\": f\"sqlite:///{warehouse_path}/icefabric_catalog.db\",\n", + " \"warehouse\": f\"file://{warehouse_path}\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -44,23 +66,23 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Added ../data/parquet/network.parquet as table network\n", - "Added ../data/parquet/nexus.parquet as table nexus\n", - "Added ../data/parquet/flowpath-attributes.parquet as table flowpath-attributes\n", - "Added ../data/parquet/divides.parquet as table divides\n", - "Added ../data/parquet/pois.parquet as table pois\n", - "Added ../data/parquet/flowpath-attributes-ml.parquet as table flowpath-attributes-ml\n", - "Added ../data/parquet/divide-attributes.parquet as table divide-attributes\n", - "Added ../data/parquet/flowpaths.parquet as table flowpaths\n", - "Added ../data/parquet/hydrolocations.parquet as table hydrolocations\n", - "Added ../data/parquet/lakes.parquet as table lakes\n" + "Table network already exists, loading it\n", + "Table nexus already exists, loading it\n", + "Table flowpath-attributes already exists, loading it\n", + "Table divides already exists, loading it\n", + "Table pois already exists, loading it\n", + "Table flowpath-attributes-ml already exists, loading it\n", + "Table divide-attributes already exists, loading it\n", + "Table flowpaths already exists, loading it\n", + "Table hydrolocations already exists, loading it\n", + "Table lakes already exists, loading it\n" ] } ], @@ -80,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -124,7 +146,59 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def find_origin(network_table, identifier, id_type=\"hl_uri\"):\n", + " \"\"\"Find an origin point in the hydrofabric network.\n", + " \n", + " This function handles the case where multiple records match the identifier.\n", + " It follows the R implementation to select a single origin point based on \n", + " the minimum hydroseq value.\n", + " \"\"\"\n", + " # Filter network table by the identifier\n", + " if id_type == \"hl_uri\":\n", + " row_filter = f\"{id_type} == '{identifier}'\"\n", + " # elif id_type == \"comid\":\n", + " # row_filter = f\"hf_id == {identifier}\"\n", + " # elif id_type == \"id\":\n", + " # row_filter = f\"id == '{identifier}'\"\n", + " # elif id_type == \"poi_id\":\n", + " # row_filter = f\"poi_id == '{identifier}'\"\n", + " else:\n", + " raise ValueError(f\"Identifier type {id_type} not supported\")\n", + " \n", + " # Get all matching records\n", + " origin_candidates = network_table.scan(row_filter=row_filter).to_pandas()\n", + " \n", + " if len(origin_candidates) == 0:\n", + " raise ValueError(f\"No origin found for {id_type}='{identifier}'\")\n", + " \n", + " # Select relevant columns for the origin\n", + " origin_cols = ['id', 'toid', 'vpuid', 'topo', 'hydroseq']\n", + " available_cols = [col for col in origin_cols if col in origin_candidates.columns]\n", + " \n", + " # Select only the relevant columns and drop duplicates\n", + " origin = origin_candidates[available_cols].drop_duplicates()\n", + " \n", + " # Find the record with minimum hydroseq\n", + " if 'hydroseq' in origin.columns:\n", + " # For consistency with R, check if there are unique hydroseq values\n", + " if len(origin['hydroseq'].unique()) > 1:\n", + " # Sort by hydroseq and take the minimum\n", + " origin = origin.sort_values('hydroseq').iloc[0:1]\n", + " \n", + " # If we still have multiple records, it's a problem\n", + " if len(origin) > 1:\n", + " raise ValueError(f\"Multiple origins found: {origin['id'].tolist()}\")\n", + " \n", + " return origin" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { @@ -148,60 +222,54 @@ " \n", " \n", " \n", - " divide_id\n", + " id\n", " toid\n", - " type\n", - " ds_id\n", - " areasqkm\n", " vpuid\n", - " id\n", - " lengthkm\n", - " tot_drainage_areasqkm\n", - " has_flowline\n", - " geometry\n", + " topo\n", + " hydroseq\n", " \n", " \n", " \n", " \n", - " 0\n", - " cat-5539\n", - " nex-5540\n", - " network\n", - " NaN\n", - " 10.4985\n", - " 01\n", - " wb-5539\n", - " 6.449001\n", - " 2247.957002\n", - " True\n", - " POLYGON ((2059095 2991255, 2059095 2990985, 20...\n", + " 1\n", + " wb-87646\n", + " nex-87404\n", + " 02\n", + " fl-nex\n", + " 26923.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " divide_id toid type ds_id areasqkm vpuid id lengthkm \\\n", - "0 cat-5539 nex-5540 network NaN 10.4985 01 wb-5539 6.449001 \n", - "\n", - " tot_drainage_areasqkm has_flowline \\\n", - "0 2247.957002 True \n", - "\n", - " geometry \n", - "0 POLYGON ((2059095 2991255, 2059095 2990985, 20... " + " id toid vpuid topo hydroseq\n", + "1 wb-87646 nex-87404 02 fl-nex 26923.0" ] }, - "execution_count": 41, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "divides_df = tables['divides'].scan(\n", - " row_filter=\"divide_id == 'cat-5539'\",\n", - ").to_pandas()\n", - "divides_gdf = create_geodataframe(divides_df)\n", - "divides_gdf" + "identifier = \"gages-01563500\"\n", + "id_type = \"hl_uri\"\n", + "origin = find_origin(network_table, identifier, id_type)\n", + "origin" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "network_df = tables[\"network\"].scan().to_pandas()\n", + "nexus_df = tables[\"nexus\"].scan().to_pandas()\n", + "\n", + "terminal_id = origin['id'].iloc[0]\n", + "terminal_nexus = origin['toid'].iloc[0]" ] } ], From 9db758d32369729fa3afdb088fea4527d4d63b05 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Sun, 27 Apr 2025 10:20:35 -0400 Subject: [PATCH 05/97] Update LICENSE --- LICENSE | 741 +++++++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 540 insertions(+), 201 deletions(-) diff --git a/LICENSE b/LICENSE index 261eeb9..b03120f 100644 --- a/LICENSE +++ b/LICENSE @@ -1,201 +1,540 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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From 78db7b810826967a3c87589346a8e328b2280429 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Sun, 27 Apr 2025 10:22:24 -0400 Subject: [PATCH 06/97] Update README.md --- README.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 8a98da3..a7a51d2 100644 --- a/README.md +++ b/README.md @@ -1 +1,11 @@ -# icefabric \ No newline at end of file +# icefabric + +An Apache Iceberg implementation of the Hydrofabric to disseminate continental hydrologic data + +#### Credits: + +Lynker and its contributors +``` +Johnson, J. M. (2022). National Hydrologic Geospatial Fabric (hydrofabric) for the Next Generation (NextGen) Hydrologic Modeling Framework, +HydroShare http://www.hydroshare.org/resource/129787b468aa4d55ace7b124ed27dbde +``` From 039ace7afea5e8561f32004d03edc159af36bdcb Mon Sep 17 00:00:00 2001 From: taddyb Date: Sun, 27 Apr 2025 10:58:25 -0400 Subject: [PATCH 07/97] updated readme --- README.md | 5 +++++ docs/img/icefabric.png | Bin 0 -> 1447330 bytes 2 files changed, 5 insertions(+) create mode 100644 docs/img/icefabric.png diff --git a/README.md b/README.md index a7a51d2..77e388b 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,10 @@ # icefabric +icefabric + +[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) + + An Apache Iceberg implementation of the Hydrofabric to disseminate continental hydrologic data #### Credits: diff --git a/docs/img/icefabric.png b/docs/img/icefabric.png new file mode 100644 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zEVpyA%k9!mp{Re%y;UpDhxr+RQSJj$tXd?=fZNV6!JdDI2w;Q56clG$Op#G__kF#m ztjIIBmkKcdo_s+==_yd%%cjrZAYvJ^jfp+^KWc;3>pADgezzFFSD_@)@HDj|yxj%+ z)736ZC!eg}mJlcJZ<(`smf1m@Cv?2I%>CSYmQ1j3CS$67GGe>nV+7sU63f7g>NduW zi2cfMUESE7pxfV5Y6J}d5BQZzskMTzJ|vg0vl8-u|0yK)N`p2tNyANK@9~?!k`=LI e0Rjbcg8v`Xw*f$h%16fl0000 Date: Mon, 28 Apr 2025 09:31:41 -0400 Subject: [PATCH 08/97] creating management repo for iceberg catalog --- .pre-commit-config.yaml | 9 +- README.md | 2 +- examples/build.ipynb | 441 +++++++++++++++++- pyproject.toml | 73 ++- scripts/gpkg_to_parquet.py | 2 +- src/icefabric_manage/.pyiceberg.yaml | 5 + .../README.md} | 0 src/icefabric_manage/pyproject.toml | 33 ++ .../src/icefabric_manage/__init__.py | 3 + .../src/icefabric_manage/_api.py | 55 +++ 10 files changed, 571 insertions(+), 52 deletions(-) create mode 100644 src/icefabric_manage/.pyiceberg.yaml rename src/{icefabric/__init__.py => icefabric_manage/README.md} (100%) create mode 100644 src/icefabric_manage/pyproject.toml create mode 100644 src/icefabric_manage/src/icefabric_manage/__init__.py create mode 100644 src/icefabric_manage/src/icefabric_manage/_api.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 1f55ba1..55c7d48 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -17,7 +17,8 @@ repos: # Run the formatter. - id: ruff-format - # - repo: https://github.com/pre-commit/mirrors-mypy - # rev: v1.15.0 - # hooks: - # - id: mypy + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.5.7 + hooks: + - id: uv-lock + diff --git a/README.md b/README.md index 77e388b..1e2efd3 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) -An Apache Iceberg implementation of the Hydrofabric to disseminate continental hydrologic data +An [Apache Iceberg](https://py.iceberg.apache.org/) implementation of the Hydrofabric to disseminate continental hydrologic data #### Credits: diff --git a/examples/build.ipynb b/examples/build.ipynb index 2aa3eec..45c6838 100644 --- a/examples/build.ipynb +++ b/examples/build.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -100,6 +100,71 @@ " tables[table_name] = add_parquet_to_catalog(str(parquet_file), table_name)" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "NoSuchTableError", + "evalue": "Table does not exist: hydrofabric.d", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNoSuchTableError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcatalog\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload_table\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mhydrofabric.d\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.13/site-packages/pyiceberg/catalog/sql.py:300\u001b[39m, in \u001b[36mSqlCatalog.load_table\u001b[39m\u001b[34m(self, identifier)\u001b[39m\n\u001b[32m 298\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[32m 299\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._convert_orm_to_iceberg(result)\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m NoSuchTableError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTable does not exist: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnamespace\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtable_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mNoSuchTableError\u001b[39m: Table does not exist: hydrofabric.d" + ] + } + ], + "source": [ + "catalog.load_table(\"hydrofabric.d\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "network(\n", + " 1: id: optional string,\n", + " 2: toid: optional string,\n", + " 3: divide_id: optional string,\n", + " 4: ds_id: optional double,\n", + " 5: mainstem: optional double,\n", + " 6: hydroseq: optional double,\n", + " 7: hf_source: optional string,\n", + " 8: hf_id: optional double,\n", + " 9: lengthkm: optional double,\n", + " 10: areasqkm: optional double,\n", + " 11: tot_drainage_areasqkm: optional double,\n", + " 12: type: optional string,\n", + " 13: vpuid: optional string,\n", + " 14: hf_hydroseq: optional double,\n", + " 15: hf_lengthkm: optional double,\n", + " 16: hf_mainstem: optional double,\n", + " 17: topo: optional string,\n", + " 18: poi_id: optional double,\n", + " 19: hl_uri: optional string\n", + "),\n", + "partition by: [],\n", + "sort order: [],\n", + "snapshot: Operation.APPEND: id=3803992390522077902, schema_id=0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tables[\"network\"]" + ] + }, { "cell_type": "code", "execution_count": 7, @@ -261,16 +326,386 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "network_df = tables[\"network\"].scan().to_pandas()\n", "nexus_df = tables[\"nexus\"].scan().to_pandas()\n", + "flowpaths_df = tables[\"flowpaths\"].scan().to_pandas()\n", "\n", "terminal_id = origin['id'].iloc[0]\n", "terminal_nexus = origin['toid'].iloc[0]" ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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" + ], + "text/plain": [ + " id toid divide_id ds_id mainstem hydroseq \\\n", + "84513 nex-1000 wb-1000 None NaN NaN NaN \n", + "84514 nex-10000 wb-10000 None NaN NaN NaN \n", + "84515 nex-10004 wb-10004 None NaN NaN NaN \n", + "84516 nex-10008 wb-10008 None NaN NaN NaN \n", + "84517 nex-1001 wb-1001 None NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "3461032 tnx-1000014142 wb-0 None NaN NaN NaN \n", + "3461033 tnx-1000014143 wb-0 None NaN NaN NaN \n", + "3461034 tnx-1000014144 wb-0 None NaN NaN NaN \n", + "3461035 tnx-1000014145 wb-0 None NaN NaN NaN \n", + "3461036 tnx-1000014146 wb-0 None NaN NaN NaN \n", + "\n", + " hf_source hf_id lengthkm areasqkm \\\n", + "84513 NOAA Reference Fabric NaN NaN NaN \n", + "84514 NOAA Reference Fabric NaN NaN NaN \n", + "84515 NOAA Reference Fabric NaN NaN NaN \n", + "84516 NOAA Reference Fabric NaN NaN NaN \n", + "84517 NOAA Reference Fabric NaN NaN NaN \n", + "... ... ... ... ... \n", + "3461032 NOAA Reference Fabric NaN NaN NaN \n", + "3461033 NOAA Reference Fabric NaN NaN NaN \n", + "3461034 NOAA Reference Fabric NaN NaN NaN \n", + "3461035 NOAA Reference Fabric NaN NaN NaN \n", + "3461036 NOAA Reference Fabric NaN NaN NaN \n", + "\n", + " tot_drainage_areasqkm type vpuid hf_hydroseq hf_lengthkm \\\n", + "84513 NaN nexus 01 NaN NaN \n", + "84514 NaN nexus 01 NaN NaN \n", + "84515 NaN nexus 01 NaN NaN \n", + "84516 NaN nexus 01 NaN NaN \n", + "84517 NaN nexus 01 NaN NaN \n", + "... ... ... ... ... ... \n", + "3461032 NaN terminal 18 NaN NaN \n", + "3461033 NaN terminal 18 NaN NaN \n", + "3461034 NaN terminal 18 NaN NaN \n", + "3461035 NaN terminal 18 NaN NaN \n", + "3461036 NaN terminal 18 NaN NaN \n", + "\n", + " hf_mainstem topo poi_id hl_uri \n", + "84513 NaN fl-nex NaN None \n", + "84514 NaN fl-nex NaN None \n", + "84515 NaN fl-nex NaN None \n", + "84516 NaN fl-nex NaN None \n", + "84517 NaN fl-nex NaN None \n", + "... ... ... ... ... \n", + "3461032 NaN fl-nex NaN None \n", + "3461033 NaN fl-nex NaN None \n", + "3461034 NaN fl-nex NaN None \n", + "3461035 NaN fl-nex NaN None \n", + "3461036 NaN fl-nex NaN None \n", + "\n", + "[404808 rows x 19 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wb_rows = network_df[network_df['toid'].str.contains('wb-', na=False)]\n", + "nex_rows = network_df[network_df['toid'].str.contains('nex-', na=False)]\n", + "wb_rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/pyproject.toml b/pyproject.toml index e71d5e9..a804791 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,53 +1,17 @@ -[build-system] -build-backend = "hatchling.build" -requires = ["hatchling", "hatch-vcs"] - -[tool.hatch] -version.source = "vcs" -build.hooks.vcs.version-file = "./src/icefabric/_version.py" - -[tool.hatch.metadata] -allow-direct-references = true - -[tool.hatch.build.targets.wheel] -packages = ["src/icefabric"] - -[tool.hatch.build.targets.sdist] -packages = ["src/icefabric"] - [project] name = "icefabric" -description = "A tool for training deep learning models for FIM" +version = "0.1.0" +description = "An Apache Iceberg implementation of the Hydrofabric" readme = "README.md" dynamic = ["version"] -requires-python = ">=3.11.0" +requires-python = ">=3.11" license = { file = "LICENSE" } -authors = [{ name = "Tadd Bindas" }] -maintainers = [{ name = "Tadd Bindas", email = "taddbindas@gmail.com" }] +authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] +maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] -dependencies = [ - "numpy==2.2.5", - "fiona==1.10.1", - "pandas==2.2.3", - "cubed-xarray==0.0.7", - "geopandas==1.0.1", - "pydantic==2.10.6", - "xarray==2025.3.1", - "scipy==1.15.1", - "tqdm==4.67.1", - "zarr==3.0.7", - "sympy==1.13.1", - "scikit-learn==1.6.1", - "matplotlib==3.10.0", - "s3fs==2025.3.2", - "pyarrow==19.0.1", - "pyiceberg==0.9.0", -] +dependencies = [] [project.optional-dependencies] -test = ["pytest", "pytest-cov"] -dev = ["pre-commit", "ruff", "black", "mypy"] - docs = [ "pillow", "cairosvg", @@ -58,7 +22,30 @@ docs = [ "mkdocs-git-revision-date-localized-plugin", ] -jupyter = ["ipykernel", "jupyterlab"] +[tool.uv.sources] +icefabric_tools = { workspace = true } +icefabric_manage = { workspace = true } +examples = { workspace = true } + +[tool.uv.workspace] +members = [ + "examples/*", "src/*" +] + +[tool.uv] +dev-dependencies = [ + "pre-commit>=3.8.0", + "ipykernel>=6.29.5", + "jupyterlab", + "pytest>=8.3.2", + "ruff", + "black", + "mypy", + "icefabric_manage", + "icefabric_tools", + "examples", +] +package = false [tool.black] line-length = 110 diff --git a/scripts/gpkg_to_parquet.py b/scripts/gpkg_to_parquet.py index 85f28a7..17c4911 100644 --- a/scripts/gpkg_to_parquet.py +++ b/scripts/gpkg_to_parquet.py @@ -10,5 +10,5 @@ for layer in available_layers: gdf = gpd.read_file(input_file, layer=layer) output_file = f"data/parquet/{layer}.parquet" - gdf.to_parquet(output_file) + gdf.to_parquet(output_file) print(f"Converted layer '{layer}' to {output_file}") diff --git a/src/icefabric_manage/.pyiceberg.yaml b/src/icefabric_manage/.pyiceberg.yaml new file mode 100644 index 0000000..0cef7d0 --- /dev/null +++ b/src/icefabric_manage/.pyiceberg.yaml @@ -0,0 +1,5 @@ +catalog: + hydrofabric: + type: sql + uri: sqlite:///../..//warehouse/icefabric_catalog.db + warehouse: file:///../..//warehouse diff --git a/src/icefabric/__init__.py b/src/icefabric_manage/README.md similarity index 100% rename from src/icefabric/__init__.py rename to src/icefabric_manage/README.md diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml new file mode 100644 index 0000000..a4712a2 --- /dev/null +++ b/src/icefabric_manage/pyproject.toml @@ -0,0 +1,33 @@ +[project] +name = "icefabric_manage" +version = "0.1.0" +description = "All functionality for creating, updating, and deleting information from the icefabric catalog" +authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] +maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] +requires-python = ">=3.11" +readme = "README.md" +dependencies = [ + "numpy==2.2.5", + "fiona==1.10.1", + "pandas==2.2.3", + "cubed-xarray==0.0.7", + "geopandas==1.0.1", + "pydantic==2.10.6", + "xarray==2025.3.1", + "scipy==1.15.1", + "tqdm==4.67.1", + "zarr==3.0.7", + "sympy==1.13.1", + "scikit-learn==1.6.1", + "matplotlib==3.10.0", + "s3fs==2025.3.2", + "pyarrow==19.0.1", + "pyiceberg==0.9.0", +] + +[project.scripts] +icefabric_manage = "icefabric_manage._api:build" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/src/icefabric_manage/src/icefabric_manage/__init__.py b/src/icefabric_manage/src/icefabric_manage/__init__.py new file mode 100644 index 0000000..3803004 --- /dev/null +++ b/src/icefabric_manage/src/icefabric_manage/__init__.py @@ -0,0 +1,3 @@ +from icefabric_manage._api import build + +__all__ = ["build"] diff --git a/src/icefabric_manage/src/icefabric_manage/_api.py b/src/icefabric_manage/src/icefabric_manage/_api.py new file mode 100644 index 0000000..67d2c87 --- /dev/null +++ b/src/icefabric_manage/src/icefabric_manage/_api.py @@ -0,0 +1,55 @@ +"""Contains all api functions that can be called outside of the icefabric_manage package""" + +from pyiceberg.catalog import Catalog, load_catalog, load_table +from pyiceberg.excpetions import NoSuchTableError +from pyiceberg.table import Table +import pyarrow.parquet as pq +from pathlib import Path + +def _add_parquet_to_catalog(catalog: Catalog, file_path: Path, table_name: str) -> Table: + """Adding a parquet file to the hydrofabric catalog + + Parameters + ---------- + catalog : Catalog + A PyIceberg catalog + file_path : Path + The path to the parquet file + table_name : str + The table name that's wanted + + Returns + ------- + Table + A PyIceberg table + + Raises + ------ + FileNotFoundError + The parquet file given doesn't exist + """ + if file_path.exists(): + arrow_table = pq.read_table(file_path) + iceberg_table = catalog.create_table( + f"hydrofabric.{table_name}", + schema=arrow_table.schema, + ) + iceberg_table.append(arrow_table) + return iceberg_table + else: + raise FileNotFoundError(f"Cannot find file: {file_path}") + +def build(file_dir: Path): + catalog = load_catalog("hydrofabric") + parquet_files = list(file_dir.glob("*.parquet")) + + tables = {} + for parquet_file in parquet_files: + table_name = parquet_file.stem # Get filename without extension + tables[table_name] = _add_parquet_to_catalog(catalog, parquet_file, table_name) + +def load_table_from_catalog(identifier: str) -> Table: + try: + return load_table(identifier) + except NoSuchTableError as e: + raise NoSuchTableError from e From 65797348ae76151d495cac5c0db43d91e5877b67 Mon Sep 17 00:00:00 2001 From: taddyb Date: Mon, 28 Apr 2025 12:49:49 -0400 Subject: [PATCH 09/97] adding new workspaces into the monorepo --- .../.pyiceberg.yaml => .pyiceberg.yaml | 0 examples/__build.ipynb | 730 +++ examples/build.ipynb | 675 +-- pyproject.toml | 4 +- scripts/gpkg_to_parquet.py | 2 +- src/icefabric_manage/pyproject.toml | 3 +- .../src/icefabric_manage/__init__.py | 4 +- .../src/icefabric_manage/_api.py | 63 +- src/icefabric_tools/README.md | 0 src/icefabric_tools/pyproject.toml | 33 + .../src/icefabric_tools/__init__.py | 3 + .../src/icefabric_tools/_api.py | 53 + uv.lock | 3990 +++++++++++++++++ 13 files changed, 4887 insertions(+), 673 deletions(-) rename src/icefabric_manage/.pyiceberg.yaml => .pyiceberg.yaml (100%) create mode 100644 examples/__build.ipynb create mode 100644 src/icefabric_tools/README.md create mode 100644 src/icefabric_tools/pyproject.toml create mode 100644 src/icefabric_tools/src/icefabric_tools/__init__.py create mode 100644 src/icefabric_tools/src/icefabric_tools/_api.py create mode 100644 uv.lock diff --git a/src/icefabric_manage/.pyiceberg.yaml b/.pyiceberg.yaml similarity index 100% rename from src/icefabric_manage/.pyiceberg.yaml rename to .pyiceberg.yaml diff --git a/examples/__build.ipynb b/examples/__build.ipynb new file mode 100644 index 0000000..30d6379 --- /dev/null +++ b/examples/__build.ipynb @@ -0,0 +1,730 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyiceberg.catalog import load_catalog\n", + "import pyarrow.parquet as pq\n", + "import os\n", + "from pathlib import Path\n", + "import geopandas as gpd\n", + "from shapely import wkb\n", + "import numpy as np\n", + "import networkx as nx\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyiceberg.catalog import load_catalog\n", + "\n", + "warehouse_path = \"../data/warehouse\"\n", + "catalog = load_catalog(\n", + " \"default\",\n", + " **{\n", + " \"type\": \"sql\",\n", + " \"uri\": f\"sqlite:///{warehouse_path}/icefabric_catalog.db\",\n", + " \"warehouse\": f\"file://{warehouse_path}\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def add_parquet_to_catalog(file_path, table_name):\n", + " # Check if table already exists\n", + " if catalog.table_exists(f\"hydrofabric.{table_name}\"):\n", + " print(f\"Table {table_name} already exists, loading it\")\n", + " return catalog.load_table(f\"hydrofabric.{table_name}\")\n", + "\n", + " # Read the parquet file\n", + " arrow_table = pq.read_table(file_path)\n", + "\n", + " # Create the table in the catalog\n", + " iceberg_table = catalog.create_table(\n", + " f\"hydrofabric.{table_name}\",\n", + " schema=arrow_table.schema,\n", + " )\n", + "\n", + " # Append the data to the table\n", + " iceberg_table.append(arrow_table)\n", + "\n", + " print(f\"Added {file_path} as table {table_name}\")\n", + " return iceberg_table" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table network already exists, loading it\n", + "Table nexus already exists, loading it\n", + "Table flowpath-attributes already exists, loading it\n", + "Table divides already exists, loading it\n", + "Table pois already exists, loading it\n", + "Table flowpath-attributes-ml already exists, loading it\n", + "Table divide-attributes already exists, loading it\n", + "Table flowpaths already exists, loading it\n", + "Table hydrolocations already exists, loading it\n", + "Table lakes already exists, loading it\n" + ] + } + ], + "source": [ + "# Get all parquet files from the directory\n", + "parquet_dir = \"../data/parquet\"\n", + "parquet_files = list(Path(parquet_dir).glob(\"*.parquet\"))\n", + "\n", + "# Dictionary to store all tables\n", + "tables = {}\n", + "\n", + "# Add each parquet file to the catalog\n", + "for parquet_file in parquet_files:\n", + " table_name = parquet_file.stem # Get filename without extension\n", + " tables[table_name] = add_parquet_to_catalog(str(parquet_file), table_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "NoSuchTableError", + "evalue": "Table does not exist: hydrofabric.d", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNoSuchTableError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcatalog\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload_table\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mhydrofabric.d\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.13/site-packages/pyiceberg/catalog/sql.py:300\u001b[39m, in \u001b[36mSqlCatalog.load_table\u001b[39m\u001b[34m(self, identifier)\u001b[39m\n\u001b[32m 298\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[32m 299\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._convert_orm_to_iceberg(result)\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m NoSuchTableError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTable does not exist: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnamespace\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtable_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mNoSuchTableError\u001b[39m: Table does not exist: hydrofabric.d" + ] + } + ], + "source": [ + "catalog.load_table(\"hydrofabric.d\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "network(\n", + " 1: id: optional string,\n", + " 2: toid: optional string,\n", + " 3: divide_id: optional string,\n", + " 4: ds_id: optional double,\n", + " 5: mainstem: optional double,\n", + " 6: hydroseq: optional double,\n", + " 7: hf_source: optional string,\n", + " 8: hf_id: optional double,\n", + " 9: lengthkm: optional double,\n", + " 10: areasqkm: optional double,\n", + " 11: tot_drainage_areasqkm: optional double,\n", + " 12: type: optional string,\n", + " 13: vpuid: optional string,\n", + " 14: hf_hydroseq: optional double,\n", + " 15: hf_lengthkm: optional double,\n", + " 16: hf_mainstem: optional double,\n", + " 17: topo: optional string,\n", + " 18: poi_id: optional double,\n", + " 19: hl_uri: optional string\n", + "),\n", + "partition by: [],\n", + "sort order: [],\n", + "snapshot: Operation.APPEND: id=3803992390522077902, schema_id=0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tables[\"network\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tables in the catalog:\n", + "- ('hydrofabric', 'divide-attributes')\n", + "- ('hydrofabric', 'divides')\n", + "- ('hydrofabric', 'flowpath-attributes')\n", + "- ('hydrofabric', 'flowpath-attributes-ml')\n", + "- ('hydrofabric', 'flowpaths')\n", + "- ('hydrofabric', 'hydrolocations')\n", + "- ('hydrofabric', 'lakes')\n", + "- ('hydrofabric', 'network')\n", + "- ('hydrofabric', 'nexus')\n", + "- ('hydrofabric', 'pois')\n" + ] + } + ], + "source": [ + "print(\"Tables in the catalog:\")\n", + "for table_id in catalog.list_tables(\"hydrofabric\"):\n", + " print(f\"- {table_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def create_geodataframe(df):\n", + " if \"geometry\" in df.columns:\n", + " df[\"geometry\"] = df[\"geometry\"].apply(lambda x: wkb.loads(x) if x is not None else None)\n", + " return gpd.GeoDataFrame(df, geometry=\"geometry\", crs=\"EPSG:5070\")\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def find_origin(network_table, identifier, id_type=\"hl_uri\"):\n", + " \"\"\"Find an origin point in the hydrofabric network.\n", + "\n", + " This function handles the case where multiple records match the identifier.\n", + " It follows the R implementation to select a single origin point based on\n", + " the minimum hydroseq value.\n", + " \"\"\"\n", + " # Filter network table by the identifier\n", + " if id_type == \"hl_uri\":\n", + " row_filter = f\"{id_type} == '{identifier}'\"\n", + " # elif id_type == \"comid\":\n", + " # row_filter = f\"hf_id == {identifier}\"\n", + " # elif id_type == \"id\":\n", + " # row_filter = f\"id == '{identifier}'\"\n", + " # elif id_type == \"poi_id\":\n", + " # row_filter = f\"poi_id == '{identifier}'\"\n", + " else:\n", + " raise ValueError(f\"Identifier type {id_type} not supported\")\n", + "\n", + " # Get all matching records\n", + " origin_candidates = network_table.scan(row_filter=row_filter).to_pandas()\n", + "\n", + " if len(origin_candidates) == 0:\n", + " raise ValueError(f\"No origin found for {id_type}='{identifier}'\")\n", + "\n", + " # Select relevant columns for the origin\n", + " origin_cols = [\"id\", \"toid\", \"vpuid\", \"topo\", \"hydroseq\"]\n", + " available_cols = [col for col in origin_cols if col in origin_candidates.columns]\n", + "\n", + " # Select only the relevant columns and drop duplicates\n", + " origin = origin_candidates[available_cols].drop_duplicates()\n", + "\n", + " # Find the record with minimum hydroseq\n", + " if \"hydroseq\" in origin.columns:\n", + " # For consistency with R, check if there are unique hydroseq values\n", + " if len(origin[\"hydroseq\"].unique()) > 1:\n", + " # Sort by hydroseq and take the minimum\n", + " origin = origin.sort_values(\"hydroseq\").iloc[0:1]\n", + "\n", + " # If we still have multiple records, it's a problem\n", + " if len(origin) > 1:\n", + " raise ValueError(f\"Multiple origins found: {origin['id'].tolist()}\")\n", + "\n", + " return origin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id toid vpuid topo hydroseq\n", + "1 wb-87646 nex-87404 02 fl-nex 26923.0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identifier = \"gages-01563500\"\n", + "id_type = \"hl_uri\"\n", + "origin = find_origin(network_table, identifier, id_type)\n", + "origin" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "network_df = tables[\"network\"].scan().to_pandas()\n", + "nexus_df = tables[\"nexus\"].scan().to_pandas()\n", + "flowpaths_df = tables[\"flowpaths\"].scan().to_pandas()\n", + "\n", + "terminal_id = origin[\"id\"].iloc[0]\n", + "terminal_nexus = origin[\"toid\"].iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idtoiddivide_idds_idmainstemhydroseqhf_sourcehf_idlengthkmareasqkmtot_drainage_areasqkmtypevpuidhf_hydroseqhf_lengthkmhf_mainstemtopopoi_idhl_uri
84513nex-1000wb-1000NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84514nex-10000wb-10000NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84515nex-10004wb-10004NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84516nex-10008wb-10008NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84517nex-1001wb-1001NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
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3461032tnx-1000014142wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461033tnx-1000014143wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461034tnx-1000014144wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461035tnx-1000014145wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461036tnx-1000014146wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
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404808 rows × 19 columns

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" + ], + "text/plain": [ + " id toid divide_id ds_id mainstem hydroseq \\\n", + "84513 nex-1000 wb-1000 None NaN NaN NaN \n", + "84514 nex-10000 wb-10000 None NaN NaN NaN \n", + "84515 nex-10004 wb-10004 None NaN NaN NaN \n", + "84516 nex-10008 wb-10008 None NaN NaN NaN \n", + "84517 nex-1001 wb-1001 None NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "3461032 tnx-1000014142 wb-0 None NaN NaN NaN \n", + "3461033 tnx-1000014143 wb-0 None NaN NaN NaN \n", + "3461034 tnx-1000014144 wb-0 None NaN NaN NaN \n", + "3461035 tnx-1000014145 wb-0 None NaN NaN NaN \n", + "3461036 tnx-1000014146 wb-0 None NaN NaN NaN \n", + "\n", + " hf_source hf_id lengthkm areasqkm \\\n", + "84513 NOAA Reference Fabric NaN NaN NaN \n", + "84514 NOAA Reference Fabric NaN NaN NaN \n", + "84515 NOAA Reference Fabric NaN NaN NaN \n", + "84516 NOAA Reference Fabric NaN NaN NaN \n", + "84517 NOAA Reference Fabric NaN NaN NaN \n", + "... ... ... ... ... \n", + "3461032 NOAA Reference Fabric NaN NaN NaN \n", + "3461033 NOAA Reference Fabric NaN NaN NaN \n", + "3461034 NOAA Reference Fabric NaN NaN NaN \n", + "3461035 NOAA Reference Fabric NaN NaN NaN \n", + "3461036 NOAA Reference Fabric NaN NaN NaN \n", + "\n", + " tot_drainage_areasqkm type vpuid hf_hydroseq hf_lengthkm \\\n", + "84513 NaN nexus 01 NaN NaN \n", + "84514 NaN nexus 01 NaN NaN \n", + "84515 NaN nexus 01 NaN NaN \n", + "84516 NaN nexus 01 NaN NaN \n", + "84517 NaN nexus 01 NaN NaN \n", + "... ... ... ... ... ... \n", + "3461032 NaN terminal 18 NaN NaN \n", + "3461033 NaN terminal 18 NaN NaN \n", + "3461034 NaN terminal 18 NaN NaN \n", + "3461035 NaN terminal 18 NaN NaN \n", + "3461036 NaN terminal 18 NaN NaN \n", + "\n", + " hf_mainstem topo poi_id hl_uri \n", + "84513 NaN fl-nex NaN None \n", + "84514 NaN fl-nex NaN None \n", + "84515 NaN fl-nex NaN None \n", + "84516 NaN fl-nex NaN None \n", + "84517 NaN fl-nex NaN None \n", + "... ... ... ... ... \n", + "3461032 NaN fl-nex NaN None \n", + "3461033 NaN fl-nex NaN None \n", + "3461034 NaN fl-nex NaN None \n", + "3461035 NaN fl-nex NaN None \n", + "3461036 NaN fl-nex NaN None \n", + "\n", + "[404808 rows x 19 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wb_rows = network_df[network_df[\"toid\"].str.contains(\"wb-\", na=False)]\n", + "nex_rows = network_df[network_df[\"toid\"].str.contains(\"nex-\", na=False)]\n", + "wb_rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/build.ipynb b/examples/build.ipynb index 45c6838..1ee57e7 100644 --- a/examples/build.ipynb +++ b/examples/build.ipynb @@ -2,71 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from pyiceberg.catalog import load_catalog\n", - "import pyarrow.parquet as pq\n", - "import os\n", "from pathlib import Path\n", - "import geopandas as gpd\n", - "from shapely import wkb\n", - "import numpy as np\n", - "import networkx as nx\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyiceberg.catalog import load_catalog\n", - "\n", - "warehouse_path = \"../data/warehouse\"\n", - "catalog = load_catalog(\n", - " \"default\",\n", - " **{\n", - " 'type': 'sql',\n", - " \"uri\": f\"sqlite:///{warehouse_path}/icefabric_catalog.db\",\n", - " \"warehouse\": f\"file://{warehouse_path}\",\n", - " },\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def add_parquet_to_catalog(file_path, table_name):\n", - " # Check if table already exists\n", - " if catalog.table_exists(f\"hydrofabric.{table_name}\"):\n", - " print(f\"Table {table_name} already exists, loading it\")\n", - " return catalog.load_table(f\"hydrofabric.{table_name}\")\n", - " \n", - " # Read the parquet file\n", - " arrow_table = pq.read_table(file_path)\n", - " \n", - " # Create the table in the catalog\n", - " iceberg_table = catalog.create_table(\n", - " f\"hydrofabric.{table_name}\",\n", - " schema=arrow_table.schema,\n", - " )\n", - " \n", - " # Append the data to the table\n", - " iceberg_table.append(arrow_table)\n", - " \n", - " print(f\"Added {file_path} as table {table_name}\")\n", - " return iceberg_table" + "from icefabric_manage import build, load_table_from_catalog\n", + "from icefabric_tools import to_geopandas" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -87,617 +34,37 @@ } ], "source": [ - "# Get all parquet files from the directory\n", - "parquet_dir = \"../data/parquet\"\n", - "parquet_files = list(Path(parquet_dir).glob(\"*.parquet\"))\n", - "\n", - "# Dictionary to store all tables\n", - "tables = {}\n", - "\n", - "# Add each parquet file to the catalog\n", - "for parquet_file in parquet_files:\n", - " table_name = parquet_file.stem # Get filename without extension\n", - " tables[table_name] = add_parquet_to_catalog(str(parquet_file), table_name)" + "warehouse_path = \"../data/warehouse\"\n", + "catalog = build(\n", + " Path(\"/Users/taddbindas/projects/NGWPC/icefabric/data/parquet\"),\n", + " catalog_settings = {\n", + " 'type': 'sql',\n", + " \"uri\": f\"sqlite:///{warehouse_path}/pyiceberg_catalog.db\",\n", + " \"warehouse\": f\"file://{warehouse_path}\",\n", + " },\n", + ")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { - "ename": "NoSuchTableError", - "evalue": "Table does not exist: hydrofabric.d", + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'load_table'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNoSuchTableError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcatalog\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload_table\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mhydrofabric.d\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.13/site-packages/pyiceberg/catalog/sql.py:300\u001b[39m, in \u001b[36mSqlCatalog.load_table\u001b[39m\u001b[34m(self, identifier)\u001b[39m\n\u001b[32m 298\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[32m 299\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._convert_orm_to_iceberg(result)\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m NoSuchTableError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTable does not exist: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnamespace\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtable_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mNoSuchTableError\u001b[39m: Table does not exist: hydrofabric.d" + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mload_table_from_catalog\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcatalog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mnetwork\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/src/icefabric_manage/src/icefabric_manage/_api.py:91\u001b[39m, in \u001b[36mload_table_from_catalog\u001b[39m\u001b[34m(catalog, identifier)\u001b[39m\n\u001b[32m 71\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mload_table_from_catalog\u001b[39m(catalog: Catalog, identifier: \u001b[38;5;28mstr\u001b[39m) -> Table:\n\u001b[32m 72\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"_summary_\u001b[39;00m\n\u001b[32m 73\u001b[39m \n\u001b[32m 74\u001b[39m \u001b[33;03m Parameters\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 89\u001b[39m \u001b[33;03m The table does not exist in the catalog\u001b[39;00m\n\u001b[32m 90\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m91\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 92\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m catalog.load_table(identifier)\n\u001b[32m 93\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m NoSuchTableError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[31mAttributeError\u001b[39m: 'NoneType' object has no attribute 'load_table'" ] } ], "source": [ - "catalog.load_table(\"hydrofabric.d\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "network(\n", - " 1: id: optional string,\n", - " 2: toid: optional string,\n", - " 3: divide_id: optional string,\n", - " 4: ds_id: optional double,\n", - " 5: mainstem: optional double,\n", - " 6: hydroseq: optional double,\n", - " 7: hf_source: optional string,\n", - " 8: hf_id: optional double,\n", - " 9: lengthkm: optional double,\n", - " 10: areasqkm: optional double,\n", - " 11: tot_drainage_areasqkm: optional double,\n", - " 12: type: optional string,\n", - " 13: vpuid: optional string,\n", - " 14: hf_hydroseq: optional double,\n", - " 15: hf_lengthkm: optional double,\n", - " 16: hf_mainstem: optional double,\n", - " 17: topo: optional string,\n", - " 18: poi_id: optional double,\n", - " 19: hl_uri: optional string\n", - "),\n", - "partition by: [],\n", - "sort order: [],\n", - "snapshot: Operation.APPEND: id=3803992390522077902, schema_id=0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tables[\"network\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tables in the catalog:\n", - "- ('hydrofabric', 'divide-attributes')\n", - "- ('hydrofabric', 'divides')\n", - "- ('hydrofabric', 'flowpath-attributes')\n", - "- ('hydrofabric', 'flowpath-attributes-ml')\n", - "- ('hydrofabric', 'flowpaths')\n", - "- ('hydrofabric', 'hydrolocations')\n", - "- ('hydrofabric', 'lakes')\n", - "- ('hydrofabric', 'network')\n", - "- ('hydrofabric', 'nexus')\n", - "- ('hydrofabric', 'pois')\n" - ] - } - ], - "source": [ - "print(\"Tables in the catalog:\")\n", - "for table_id in catalog.list_tables(\"hydrofabric\"):\n", - " print(f\"- {table_id}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "def create_geodataframe(df):\n", - " if 'geometry' in df.columns:\n", - " df['geometry'] = df['geometry'].apply(\n", - " lambda x: wkb.loads(x) if x is not None else None\n", - " )\n", - " return gpd.GeoDataFrame(df, geometry='geometry', crs=\"EPSG:5070\")\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def find_origin(network_table, identifier, id_type=\"hl_uri\"):\n", - " \"\"\"Find an origin point in the hydrofabric network.\n", - " \n", - " This function handles the case where multiple records match the identifier.\n", - " It follows the R implementation to select a single origin point based on \n", - " the minimum hydroseq value.\n", - " \"\"\"\n", - " # Filter network table by the identifier\n", - " if id_type == \"hl_uri\":\n", - " row_filter = f\"{id_type} == '{identifier}'\"\n", - " # elif id_type == \"comid\":\n", - " # row_filter = f\"hf_id == {identifier}\"\n", - " # elif id_type == \"id\":\n", - " # row_filter = f\"id == '{identifier}'\"\n", - " # elif id_type == \"poi_id\":\n", - " # row_filter = f\"poi_id == '{identifier}'\"\n", - " else:\n", - " raise ValueError(f\"Identifier type {id_type} not supported\")\n", - " \n", - " # Get all matching records\n", - " origin_candidates = network_table.scan(row_filter=row_filter).to_pandas()\n", - " \n", - " if len(origin_candidates) == 0:\n", - " raise ValueError(f\"No origin found for {id_type}='{identifier}'\")\n", - " \n", - " # Select relevant columns for the origin\n", - " origin_cols = ['id', 'toid', 'vpuid', 'topo', 'hydroseq']\n", - " available_cols = [col for col in origin_cols if col in origin_candidates.columns]\n", - " \n", - " # Select only the relevant columns and drop duplicates\n", - " origin = origin_candidates[available_cols].drop_duplicates()\n", - " \n", - " # Find the record with minimum hydroseq\n", - " if 'hydroseq' in origin.columns:\n", - " # For consistency with R, check if there are unique hydroseq values\n", - " if len(origin['hydroseq'].unique()) > 1:\n", - " # Sort by hydroseq and take the minimum\n", - " origin = origin.sort_values('hydroseq').iloc[0:1]\n", - " \n", - " # If we still have multiple records, it's a problem\n", - " if len(origin) > 1:\n", - " raise ValueError(f\"Multiple origins found: {origin['id'].tolist()}\")\n", - " \n", - " return origin" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idtoidvpuidtopohydroseq
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" - ], - "text/plain": [ - " id toid vpuid topo hydroseq\n", - "1 wb-87646 nex-87404 02 fl-nex 26923.0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identifier = \"gages-01563500\"\n", - "id_type = \"hl_uri\"\n", - "origin = find_origin(network_table, identifier, id_type)\n", - "origin" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "network_df = tables[\"network\"].scan().to_pandas()\n", - "nexus_df = tables[\"nexus\"].scan().to_pandas()\n", - "flowpaths_df = tables[\"flowpaths\"].scan().to_pandas()\n", - "\n", - "terminal_id = origin['id'].iloc[0]\n", - "terminal_nexus = origin['toid'].iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idtoiddivide_idds_idmainstemhydroseqhf_sourcehf_idlengthkmareasqkmtot_drainage_areasqkmtypevpuidhf_hydroseqhf_lengthkmhf_mainstemtopopoi_idhl_uri
84513nex-1000wb-1000NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84514nex-10000wb-10000NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84515nex-10004wb-10004NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84516nex-10008wb-10008NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84517nex-1001wb-1001NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
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3461032tnx-1000014142wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
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3461034tnx-1000014144wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461035tnx-1000014145wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461036tnx-1000014146wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
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" - ], - "text/plain": [ - " id toid divide_id ds_id mainstem hydroseq \\\n", - "84513 nex-1000 wb-1000 None NaN NaN NaN \n", - "84514 nex-10000 wb-10000 None NaN NaN NaN \n", - "84515 nex-10004 wb-10004 None NaN NaN NaN \n", - "84516 nex-10008 wb-10008 None NaN NaN NaN \n", - "84517 nex-1001 wb-1001 None NaN NaN NaN \n", - "... ... ... ... ... ... ... \n", - "3461032 tnx-1000014142 wb-0 None NaN NaN NaN \n", - "3461033 tnx-1000014143 wb-0 None NaN NaN NaN \n", - "3461034 tnx-1000014144 wb-0 None NaN NaN NaN \n", - "3461035 tnx-1000014145 wb-0 None NaN NaN NaN \n", - "3461036 tnx-1000014146 wb-0 None NaN NaN NaN \n", - "\n", - " hf_source hf_id lengthkm areasqkm \\\n", - "84513 NOAA Reference Fabric NaN NaN NaN \n", - "84514 NOAA Reference Fabric NaN NaN NaN \n", - "84515 NOAA Reference Fabric NaN NaN NaN \n", - "84516 NOAA Reference Fabric NaN NaN NaN \n", - "84517 NOAA Reference Fabric NaN NaN NaN \n", - "... ... ... ... ... \n", - "3461032 NOAA Reference Fabric NaN NaN NaN \n", - "3461033 NOAA Reference Fabric NaN NaN NaN \n", - "3461034 NOAA Reference Fabric NaN NaN NaN \n", - "3461035 NOAA Reference Fabric NaN NaN NaN \n", - "3461036 NOAA Reference Fabric NaN NaN NaN \n", - "\n", - " tot_drainage_areasqkm type vpuid hf_hydroseq hf_lengthkm \\\n", - "84513 NaN nexus 01 NaN NaN \n", - "84514 NaN nexus 01 NaN NaN \n", - "84515 NaN nexus 01 NaN NaN \n", - "84516 NaN nexus 01 NaN NaN \n", - "84517 NaN nexus 01 NaN NaN \n", - "... ... ... ... ... ... \n", - "3461032 NaN terminal 18 NaN NaN \n", - "3461033 NaN terminal 18 NaN NaN \n", - "3461034 NaN terminal 18 NaN NaN \n", - "3461035 NaN terminal 18 NaN NaN \n", - "3461036 NaN terminal 18 NaN NaN \n", - "\n", - " hf_mainstem topo poi_id hl_uri \n", - "84513 NaN fl-nex NaN None \n", - "84514 NaN fl-nex NaN None \n", - "84515 NaN fl-nex NaN None \n", - "84516 NaN fl-nex NaN None \n", - "84517 NaN fl-nex NaN None \n", - "... ... ... ... ... \n", - "3461032 NaN fl-nex NaN None \n", - "3461033 NaN fl-nex NaN None \n", - "3461034 NaN fl-nex NaN None \n", - "3461035 NaN fl-nex NaN None \n", - "3461036 NaN fl-nex NaN None \n", - "\n", - "[404808 rows x 19 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wb_rows = network_df[network_df['toid'].str.contains('wb-', na=False)]\n", - "nex_rows = network_df[network_df['toid'].str.contains('nex-', na=False)]\n", - "wb_rows" + "load_table_from_catalog(catalog, \"network\")" ] }, { diff --git a/pyproject.toml b/pyproject.toml index a804791..b7d9c9a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,6 @@ name = "icefabric" version = "0.1.0" description = "An Apache Iceberg implementation of the Hydrofabric" readme = "README.md" -dynamic = ["version"] requires-python = ">=3.11" license = { file = "LICENSE" } authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] @@ -25,11 +24,10 @@ docs = [ [tool.uv.sources] icefabric_tools = { workspace = true } icefabric_manage = { workspace = true } -examples = { workspace = true } [tool.uv.workspace] members = [ - "examples/*", "src/*" + "src/*" ] [tool.uv] diff --git a/scripts/gpkg_to_parquet.py b/scripts/gpkg_to_parquet.py index 17c4911..1f06b13 100644 --- a/scripts/gpkg_to_parquet.py +++ b/scripts/gpkg_to_parquet.py @@ -1,7 +1,7 @@ """A simple script to convert the v2.2 hydrofabric to parquet""" -import geopandas as gpd import fiona +import geopandas as gpd input_file = "data/conus_nextgen.gpkg" available_layers = fiona.listlayers(input_file) diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index a4712a2..8cb240f 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -22,7 +22,8 @@ dependencies = [ "matplotlib==3.10.0", "s3fs==2025.3.2", "pyarrow==19.0.1", - "pyiceberg==0.9.0", + "pyiceberg[sql-sqlite]", + "python-dotenv==1.1.0" ] [project.scripts] diff --git a/src/icefabric_manage/src/icefabric_manage/__init__.py b/src/icefabric_manage/src/icefabric_manage/__init__.py index 3803004..45625fb 100644 --- a/src/icefabric_manage/src/icefabric_manage/__init__.py +++ b/src/icefabric_manage/src/icefabric_manage/__init__.py @@ -1,3 +1,3 @@ -from icefabric_manage._api import build +from icefabric_manage._api import build, load_table_from_catalog -__all__ = ["build"] +__all__ = ["build", "load_table_from_catalog"] diff --git a/src/icefabric_manage/src/icefabric_manage/_api.py b/src/icefabric_manage/src/icefabric_manage/_api.py index 67d2c87..c8fe044 100644 --- a/src/icefabric_manage/src/icefabric_manage/_api.py +++ b/src/icefabric_manage/src/icefabric_manage/_api.py @@ -1,12 +1,14 @@ """Contains all api functions that can be called outside of the icefabric_manage package""" -from pyiceberg.catalog import Catalog, load_catalog, load_table -from pyiceberg.excpetions import NoSuchTableError -from pyiceberg.table import Table -import pyarrow.parquet as pq from pathlib import Path -def _add_parquet_to_catalog(catalog: Catalog, file_path: Path, table_name: str) -> Table: +import pyarrow.parquet as pq +from pyiceberg.catalog import Catalog, load_catalog +from pyiceberg.exceptions import NoSuchTableError +from pyiceberg.table import Table + + +def _add_parquet_to_catalog(catalog: Catalog, file_path: Path, table_name: str): """Adding a parquet file to the hydrofabric catalog Parameters @@ -35,21 +37,58 @@ def _add_parquet_to_catalog(catalog: Catalog, file_path: Path, table_name: str) schema=arrow_table.schema, ) iceberg_table.append(arrow_table) - return iceberg_table else: raise FileNotFoundError(f"Cannot find file: {file_path}") -def build(file_dir: Path): - catalog = load_catalog("hydrofabric") + +def build(file_dir: Path, catalog_settings: dict[str, str]) -> Catalog: + """Builds the hydrofabric catalog based on the .pyiceberg.yaml config and defined parquet files. + + Parameters + ---------- + file_dir : Path + The path to the parquet files to add into the iceberg catalog + catalog_settings : dict[str, str] + The settings for the catalog entry + """ + catalog = load_catalog("hydrofabric", **catalog_settings) + + if not any(ns == ('hydrofabric',) for ns in catalog.list_namespaces()): + catalog.create_namespace('hydrofabric') + print("Created 'hydrofabric' namespace") + parquet_files = list(file_dir.glob("*.parquet")) - tables = {} for parquet_file in parquet_files: table_name = parquet_file.stem # Get filename without extension - tables[table_name] = _add_parquet_to_catalog(catalog, parquet_file, table_name) + if catalog.table_exists(f"hydrofabric.{table_name}"): + print(f"Table {table_name} already exists. Skipping build") + else: + _add_parquet_to_catalog(catalog, parquet_file, table_name) + return catalog -def load_table_from_catalog(identifier: str) -> Table: + +def load_table_from_catalog(catalog: Catalog, identifier: str) -> Table: + """_summary_ + + Parameters + ---------- + catalog : Catalog + The Iceberg catalog + identifier : str + The catalog name and table. Ex: hydrofabric.network + + Returns + ------- + Table + The requested table + + Raises + ------ + NoSuchTableError + The table does not exist in the catalog + """ try: - return load_table(identifier) + return catalog.load_table(identifier) except NoSuchTableError as e: raise NoSuchTableError from e diff --git a/src/icefabric_tools/README.md b/src/icefabric_tools/README.md new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml new file mode 100644 index 0000000..abf3353 --- /dev/null +++ b/src/icefabric_tools/pyproject.toml @@ -0,0 +1,33 @@ +[project] +name = "icefabric_tools" +version = "0.1.0" +description = "All functionality for creating, updating, and deleting information from the icefabric catalog" +authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] +maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] +requires-python = ">=3.11" +readme = "README.md" +dependencies = [ + "numpy==2.2.5", + "fiona==1.10.1", + "pandas==2.2.3", + "cubed-xarray==0.0.7", + "geopandas==1.0.1", + "pydantic==2.10.6", + "xarray==2025.3.1", + "scipy==1.15.1", + "tqdm==4.67.1", + "zarr==3.0.7", + "sympy==1.13.1", + "scikit-learn==1.6.1", + "matplotlib==3.10.0", + "s3fs==2025.3.2", + "pyarrow==19.0.1", + "pyiceberg==0.9.0", +] + +[project.scripts] +icefabric_manage = "icefabric_manage._api:build" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/src/icefabric_tools/src/icefabric_tools/__init__.py b/src/icefabric_tools/src/icefabric_tools/__init__.py new file mode 100644 index 0000000..1b90e20 --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/__init__.py @@ -0,0 +1,3 @@ +from icefabric_tools._api import to_geopandas + +__all__ = ["to_geopandas"] diff --git a/src/icefabric_tools/src/icefabric_tools/_api.py b/src/icefabric_tools/src/icefabric_tools/_api.py new file mode 100644 index 0000000..a25974b --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/_api.py @@ -0,0 +1,53 @@ +"""Contains all api functions that can be called outside of the icefabric_tools package""" + +import geopandas as gpd +from pyiceberg.table import Table +from shapely import wkb + + +def to_geopandas( + table: Table, + row_filter: str | None = "", + case_sensitive: bool | None = True, + snapshot_id: int | None = None, + limit: int | None = None +) -> gpd.DataFrame: + """Converts a table to a geopandas dataframe + + Parameters + ---------- + table : Table + The iceberg table you are trying to read from + row_filter : str | None, optional + A string or BooleanExpression that describes the desired rows, by default "" + case_sensitive : bool | None, optional + If True column matching is case sensitive, by default True + snapshot_id : int | None, optional + Optional Snapshot ID to time travel to. + If None, scans the table as of the current snapshot ID, by default None + limit : int | None, optional + An integer representing the number of rows to return in the scan result. + If None, fetches all matching rows., by default None + + Returns + ------- + gpd.DataFrame + The resulting queried row, but in a geodataframe + + Raises + ------ + ValueError + Raised if the table does not have a geometry column + """ + df = table.scan( + row_filter=row_filter, + case_sensitive=case_sensitive, + snapshot_id=snapshot_id, + limit=limit, + ).to_pandas() + if "geometry" in df.columns: + df["geometry"] = df["geometry"].apply(lambda x: wkb.loads(x) if x is not None else None) + return gpd.GeoDataFrame(df, geometry="geometry", crs="EPSG:5070") + else: + raise ValueError("The provided table does not have a geometery column.") + return df diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..58a72f3 --- /dev/null +++ b/uv.lock @@ -0,0 +1,3990 @@ +version = 1 +revision = 1 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.12'", + "python_full_version < '3.12'", +] + 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size = 203937 }, +] From 0d0db9c675e15ba8cf590c31e607ccd5e6a041b0 Mon Sep 17 00:00:00 2001 From: taddyb Date: Mon, 28 Apr 2025 14:09:39 -0400 Subject: [PATCH 10/97] updating the example notebook and internal workspace repos so we can properly query the HF --- examples/__build.ipynb | 730 ------------------ examples/build.ipynb | 99 --- examples/build_hf_catalog.ipynb | 366 +++++++++ src/icefabric_manage/README.md | 3 + src/icefabric_manage/pyproject.toml | 5 +- .../src/icefabric_manage/__init__.py | 4 +- .../src/icefabric_manage/_api.py | 37 +- src/icefabric_tools/README.md | 3 + src/icefabric_tools/pyproject.toml | 5 +- .../src/icefabric_tools/__init__.py | 4 +- .../src/icefabric_tools/_api.py | 72 +- uv.lock | 6 +- 12 files changed, 456 insertions(+), 878 deletions(-) delete mode 100644 examples/__build.ipynb delete mode 100644 examples/build.ipynb create mode 100644 examples/build_hf_catalog.ipynb diff --git a/examples/__build.ipynb b/examples/__build.ipynb deleted file mode 100644 index 30d6379..0000000 --- a/examples/__build.ipynb +++ /dev/null @@ -1,730 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pyiceberg.catalog import load_catalog\n", - "import pyarrow.parquet as pq\n", - "import os\n", - "from pathlib import Path\n", - "import geopandas as gpd\n", - "from shapely import wkb\n", - "import numpy as np\n", - "import networkx as nx\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyiceberg.catalog import load_catalog\n", - "\n", - "warehouse_path = \"../data/warehouse\"\n", - "catalog = load_catalog(\n", - " \"default\",\n", - " **{\n", - " \"type\": \"sql\",\n", - " \"uri\": f\"sqlite:///{warehouse_path}/icefabric_catalog.db\",\n", - " \"warehouse\": f\"file://{warehouse_path}\",\n", - " },\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def add_parquet_to_catalog(file_path, table_name):\n", - " # Check if table already exists\n", - " if catalog.table_exists(f\"hydrofabric.{table_name}\"):\n", - " print(f\"Table {table_name} already exists, loading it\")\n", - " return catalog.load_table(f\"hydrofabric.{table_name}\")\n", - "\n", - " # Read the parquet file\n", - " arrow_table = pq.read_table(file_path)\n", - "\n", - " # Create the table in the catalog\n", - " iceberg_table = catalog.create_table(\n", - " f\"hydrofabric.{table_name}\",\n", - " schema=arrow_table.schema,\n", - " )\n", - "\n", - " # Append the data to the table\n", - " iceberg_table.append(arrow_table)\n", - "\n", - " print(f\"Added {file_path} as table {table_name}\")\n", - " return iceberg_table" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Table network already exists, loading it\n", - "Table nexus already exists, loading it\n", - "Table flowpath-attributes already exists, loading it\n", - "Table divides already exists, loading it\n", - "Table pois already exists, loading it\n", - "Table flowpath-attributes-ml already exists, loading it\n", - "Table divide-attributes already exists, loading it\n", - "Table flowpaths already exists, loading it\n", - "Table hydrolocations already exists, loading it\n", - "Table lakes already exists, loading it\n" - ] - } - ], - "source": [ - "# Get all parquet files from the directory\n", - "parquet_dir = \"../data/parquet\"\n", - "parquet_files = list(Path(parquet_dir).glob(\"*.parquet\"))\n", - "\n", - "# Dictionary to store all tables\n", - "tables = {}\n", - "\n", - "# Add each parquet file to the catalog\n", - "for parquet_file in parquet_files:\n", - " table_name = parquet_file.stem # Get filename without extension\n", - " tables[table_name] = add_parquet_to_catalog(str(parquet_file), table_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "NoSuchTableError", - "evalue": "Table does not exist: hydrofabric.d", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNoSuchTableError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcatalog\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload_table\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mhydrofabric.d\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.13/site-packages/pyiceberg/catalog/sql.py:300\u001b[39m, in \u001b[36mSqlCatalog.load_table\u001b[39m\u001b[34m(self, identifier)\u001b[39m\n\u001b[32m 298\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[32m 299\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._convert_orm_to_iceberg(result)\n\u001b[32m--> \u001b[39m\u001b[32m300\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m NoSuchTableError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTable does not exist: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnamespace\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtable_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", - "\u001b[31mNoSuchTableError\u001b[39m: Table does not exist: hydrofabric.d" - ] - } - ], - "source": [ - "catalog.load_table(\"hydrofabric.d\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "network(\n", - " 1: id: optional string,\n", - " 2: toid: optional string,\n", - " 3: divide_id: optional string,\n", - " 4: ds_id: optional double,\n", - " 5: mainstem: optional double,\n", - " 6: hydroseq: optional double,\n", - " 7: hf_source: optional string,\n", - " 8: hf_id: optional double,\n", - " 9: lengthkm: optional double,\n", - " 10: areasqkm: optional double,\n", - " 11: tot_drainage_areasqkm: optional double,\n", - " 12: type: optional string,\n", - " 13: vpuid: optional string,\n", - " 14: hf_hydroseq: optional double,\n", - " 15: hf_lengthkm: optional double,\n", - " 16: hf_mainstem: optional double,\n", - " 17: topo: optional string,\n", - " 18: poi_id: optional double,\n", - " 19: hl_uri: optional string\n", - "),\n", - "partition by: [],\n", - "sort order: [],\n", - "snapshot: Operation.APPEND: id=3803992390522077902, schema_id=0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tables[\"network\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tables in the catalog:\n", - "- ('hydrofabric', 'divide-attributes')\n", - "- ('hydrofabric', 'divides')\n", - "- ('hydrofabric', 'flowpath-attributes')\n", - "- ('hydrofabric', 'flowpath-attributes-ml')\n", - "- ('hydrofabric', 'flowpaths')\n", - "- ('hydrofabric', 'hydrolocations')\n", - "- ('hydrofabric', 'lakes')\n", - "- ('hydrofabric', 'network')\n", - "- ('hydrofabric', 'nexus')\n", - "- ('hydrofabric', 'pois')\n" - ] - } - ], - "source": [ - "print(\"Tables in the catalog:\")\n", - "for table_id in catalog.list_tables(\"hydrofabric\"):\n", - " print(f\"- {table_id}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "def create_geodataframe(df):\n", - " if \"geometry\" in df.columns:\n", - " df[\"geometry\"] = df[\"geometry\"].apply(lambda x: wkb.loads(x) if x is not None else None)\n", - " return gpd.GeoDataFrame(df, geometry=\"geometry\", crs=\"EPSG:5070\")\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def find_origin(network_table, identifier, id_type=\"hl_uri\"):\n", - " \"\"\"Find an origin point in the hydrofabric network.\n", - "\n", - " This function handles the case where multiple records match the identifier.\n", - " It follows the R implementation to select a single origin point based on\n", - " the minimum hydroseq value.\n", - " \"\"\"\n", - " # Filter network table by the identifier\n", - " if id_type == \"hl_uri\":\n", - " row_filter = f\"{id_type} == '{identifier}'\"\n", - " # elif id_type == \"comid\":\n", - " # row_filter = f\"hf_id == {identifier}\"\n", - " # elif id_type == \"id\":\n", - " # row_filter = f\"id == '{identifier}'\"\n", - " # elif id_type == \"poi_id\":\n", - " # row_filter = f\"poi_id == '{identifier}'\"\n", - " else:\n", - " raise ValueError(f\"Identifier type {id_type} not supported\")\n", - "\n", - " # Get all matching records\n", - " origin_candidates = network_table.scan(row_filter=row_filter).to_pandas()\n", - "\n", - " if len(origin_candidates) == 0:\n", - " raise ValueError(f\"No origin found for {id_type}='{identifier}'\")\n", - "\n", - " # Select relevant columns for the origin\n", - " origin_cols = [\"id\", \"toid\", \"vpuid\", \"topo\", \"hydroseq\"]\n", - " available_cols = [col for col in origin_cols if col in origin_candidates.columns]\n", - "\n", - " # Select only the relevant columns and drop duplicates\n", - " origin = origin_candidates[available_cols].drop_duplicates()\n", - "\n", - " # Find the record with minimum hydroseq\n", - " if \"hydroseq\" in origin.columns:\n", - " # For consistency with R, check if there are unique hydroseq values\n", - " if len(origin[\"hydroseq\"].unique()) > 1:\n", - " # Sort by hydroseq and take the minimum\n", - " origin = origin.sort_values(\"hydroseq\").iloc[0:1]\n", - "\n", - " # If we still have multiple records, it's a problem\n", - " if len(origin) > 1:\n", - " raise ValueError(f\"Multiple origins found: {origin['id'].tolist()}\")\n", - "\n", - " return origin" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idtoidvpuidtopohydroseq
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" - ], - "text/plain": [ - " id toid vpuid topo hydroseq\n", - "1 wb-87646 nex-87404 02 fl-nex 26923.0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "identifier = \"gages-01563500\"\n", - "id_type = \"hl_uri\"\n", - "origin = find_origin(network_table, identifier, id_type)\n", - "origin" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "network_df = tables[\"network\"].scan().to_pandas()\n", - "nexus_df = tables[\"nexus\"].scan().to_pandas()\n", - "flowpaths_df = tables[\"flowpaths\"].scan().to_pandas()\n", - "\n", - "terminal_id = origin[\"id\"].iloc[0]\n", - "terminal_nexus = origin[\"toid\"].iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idtoiddivide_idds_idmainstemhydroseqhf_sourcehf_idlengthkmareasqkmtot_drainage_areasqkmtypevpuidhf_hydroseqhf_lengthkmhf_mainstemtopopoi_idhl_uri
84513nex-1000wb-1000NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84514nex-10000wb-10000NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84515nex-10004wb-10004NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84516nex-10008wb-10008NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
84517nex-1001wb-1001NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNnexus01NaNNaNNaNfl-nexNaNNone
............................................................
3461032tnx-1000014142wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461033tnx-1000014143wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461034tnx-1000014144wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461035tnx-1000014145wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
3461036tnx-1000014146wb-0NoneNaNNaNNaNNOAA Reference FabricNaNNaNNaNNaNterminal18NaNNaNNaNfl-nexNaNNone
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404808 rows × 19 columns

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" - ], - "text/plain": [ - " id toid divide_id ds_id mainstem hydroseq \\\n", - "84513 nex-1000 wb-1000 None NaN NaN NaN \n", - "84514 nex-10000 wb-10000 None NaN NaN NaN \n", - "84515 nex-10004 wb-10004 None NaN NaN NaN \n", - "84516 nex-10008 wb-10008 None NaN NaN NaN \n", - "84517 nex-1001 wb-1001 None NaN NaN NaN \n", - "... ... ... ... ... ... ... \n", - "3461032 tnx-1000014142 wb-0 None NaN NaN NaN \n", - "3461033 tnx-1000014143 wb-0 None NaN NaN NaN \n", - "3461034 tnx-1000014144 wb-0 None NaN NaN NaN \n", - "3461035 tnx-1000014145 wb-0 None NaN NaN NaN \n", - "3461036 tnx-1000014146 wb-0 None NaN NaN NaN \n", - "\n", - " hf_source hf_id lengthkm areasqkm \\\n", - "84513 NOAA Reference Fabric NaN NaN NaN \n", - "84514 NOAA Reference Fabric NaN NaN NaN \n", - "84515 NOAA Reference Fabric NaN NaN NaN \n", - "84516 NOAA Reference Fabric NaN NaN NaN \n", - "84517 NOAA Reference Fabric NaN NaN NaN \n", - "... ... ... ... ... \n", - "3461032 NOAA Reference Fabric NaN NaN NaN \n", - "3461033 NOAA Reference Fabric NaN NaN NaN \n", - "3461034 NOAA Reference Fabric NaN NaN NaN \n", - "3461035 NOAA Reference Fabric NaN NaN NaN \n", - "3461036 NOAA Reference Fabric NaN NaN NaN \n", - "\n", - " tot_drainage_areasqkm type vpuid hf_hydroseq hf_lengthkm \\\n", - "84513 NaN nexus 01 NaN NaN \n", - "84514 NaN nexus 01 NaN NaN \n", - "84515 NaN nexus 01 NaN NaN \n", - "84516 NaN nexus 01 NaN NaN \n", - "84517 NaN nexus 01 NaN NaN \n", - "... ... ... ... ... ... \n", - "3461032 NaN terminal 18 NaN NaN \n", - "3461033 NaN terminal 18 NaN NaN \n", - "3461034 NaN terminal 18 NaN NaN \n", - "3461035 NaN terminal 18 NaN NaN \n", - "3461036 NaN terminal 18 NaN NaN \n", - "\n", - " hf_mainstem topo poi_id hl_uri \n", - "84513 NaN fl-nex NaN None \n", - "84514 NaN fl-nex NaN None \n", - "84515 NaN fl-nex NaN None \n", - "84516 NaN fl-nex NaN None \n", - "84517 NaN fl-nex NaN None \n", - "... ... ... ... ... \n", - "3461032 NaN fl-nex NaN None \n", - "3461033 NaN fl-nex NaN None \n", - "3461034 NaN fl-nex NaN None \n", - "3461035 NaN fl-nex NaN None \n", - "3461036 NaN fl-nex NaN None \n", - "\n", - "[404808 rows x 19 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wb_rows = network_df[network_df[\"toid\"].str.contains(\"wb-\", na=False)]\n", - "nex_rows = network_df[network_df[\"toid\"].str.contains(\"nex-\", na=False)]\n", - "wb_rows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/build.ipynb b/examples/build.ipynb deleted file mode 100644 index 1ee57e7..0000000 --- a/examples/build.ipynb +++ /dev/null @@ -1,99 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "from icefabric_manage import build, load_table_from_catalog\n", - "from icefabric_tools import to_geopandas" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Table network already exists, loading it\n", - "Table nexus already exists, loading it\n", - "Table flowpath-attributes already exists, loading it\n", - "Table divides already exists, loading it\n", - "Table pois already exists, loading it\n", - "Table flowpath-attributes-ml already exists, loading it\n", - "Table divide-attributes already exists, loading it\n", - "Table flowpaths already exists, loading it\n", - "Table hydrolocations already exists, loading it\n", - "Table lakes already exists, loading it\n" - ] - } - ], - "source": [ - "warehouse_path = \"../data/warehouse\"\n", - "catalog = build(\n", - " Path(\"/Users/taddbindas/projects/NGWPC/icefabric/data/parquet\"),\n", - " catalog_settings = {\n", - " 'type': 'sql',\n", - " \"uri\": f\"sqlite:///{warehouse_path}/pyiceberg_catalog.db\",\n", - " \"warehouse\": f\"file://{warehouse_path}\",\n", - " },\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'load_table'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mload_table_from_catalog\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcatalog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mnetwork\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/src/icefabric_manage/src/icefabric_manage/_api.py:91\u001b[39m, in \u001b[36mload_table_from_catalog\u001b[39m\u001b[34m(catalog, identifier)\u001b[39m\n\u001b[32m 71\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mload_table_from_catalog\u001b[39m(catalog: Catalog, identifier: \u001b[38;5;28mstr\u001b[39m) -> Table:\n\u001b[32m 72\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"_summary_\u001b[39;00m\n\u001b[32m 73\u001b[39m \n\u001b[32m 74\u001b[39m \u001b[33;03m Parameters\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 89\u001b[39m \u001b[33;03m The table does not exist in the catalog\u001b[39;00m\n\u001b[32m 90\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m91\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 92\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m catalog.load_table(identifier)\n\u001b[32m 93\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m NoSuchTableError \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "\u001b[31mAttributeError\u001b[39m: 'NoneType' object has no attribute 'load_table'" - ] - } - ], - "source": [ - "load_table_from_catalog(catalog, \"network\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/build_hf_catalog.ipynb b/examples/build_hf_catalog.ipynb new file mode 100644 index 0000000..fe712df --- /dev/null +++ b/examples/build_hf_catalog.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to build your own catalog\n", + "Author: Tadd Bindas" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "from pyiceberg.catalog import load_catalog\n", + "\n", + "from icefabric_manage import build\n", + "from icefabric_tools import find_origin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, you will need to define where your local data warehouse will live. This file will be located on disk and requires you to already have some geoparquet files to load" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table network already exists. Skipping build\n", + "Table nexus already exists. Skipping build\n", + "Table flowpath-attributes already exists. Skipping build\n", + "Table divides already exists. Skipping build\n", + "Table pois already exists. Skipping build\n", + "Table flowpath-attributes-ml already exists. Skipping build\n", + "Table divide-attributes already exists. Skipping build\n", + "Table flowpaths already exists. Skipping build\n", + "Table hydrolocations already exists. Skipping build\n", + "Table lakes already exists. Skipping build\n" + ] + } + ], + "source": [ + "warehouse_path = Path(\"../data/warehouse\")\n", + "warehouse_path.mkdir(exist_ok=True)\n", + "\n", + "catalog_settings = {\n", + " 'type': 'sql',\n", + " \"uri\": f\"sqlite:///{warehouse_path}/pyiceberg_catalog.db\",\n", + " \"warehouse\": f\"file://{warehouse_path}\",\n", + "}\n", + "\n", + "catalog = load_catalog(\"hydrofabric\", **catalog_settings)\n", + "build(catalog, Path(\"/Users/taddbindas/projects/NGWPC/icefabric/data/parquet\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the catalog is created, you can load the table into memory, scan the file based on a query, then return the object via pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idtoiddivide_idds_idmainstemhydroseqhf_sourcehf_idlengthkmareasqkmtot_drainage_areasqkmtypevpuidhf_hydroseqhf_lengthkmhf_mainstemtopopoi_idhl_uri
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" + ], + "text/plain": [ + " id toid divide_id ds_id mainstem hydroseq \\\n", + "0 wb-20469 tnx-1000000125 cat-20469 NaN 2613576.0 20283.0 \n", + "1 wb-20469 tnx-1000000125 cat-20469 NaN 2613576.0 20283.0 \n", + "2 wb-20469 tnx-1000000125 cat-20469 NaN 2613576.0 20283.0 \n", + "3 wb-20479 tnx-1000000697 cat-20479 NaN 2613603.0 20277.0 \n", + "4 wb-20479 tnx-1000000697 cat-20479 NaN 2613603.0 20277.0 \n", + "\n", + " hf_source hf_id lengthkm areasqkm \\\n", + "0 NOAA Reference Fabric 166196253.0 7.006821 13.96665 \n", + "1 NOAA Reference Fabric 166196257.0 7.006821 13.96665 \n", + "2 NOAA Reference Fabric 166196256.0 7.006821 13.96665 \n", + "3 NOAA Reference Fabric 4599061.0 1.445138 5.88735 \n", + "4 NOAA Reference Fabric 4599715.0 1.445138 5.88735 \n", + "\n", + " tot_drainage_areasqkm type vpuid hf_hydroseq hf_lengthkm \\\n", + "0 72.707401 terminal 01 2613576.0 7.006821 \n", + "1 72.707401 terminal 01 2613602.0 0.992107 \n", + "2 72.707401 terminal 01 2613601.0 0.940432 \n", + "3 42.698700 terminal 01 2613604.0 0.793282 \n", + "4 42.698700 terminal 01 2613605.0 0.205191 \n", + "\n", + " hf_mainstem topo poi_id hl_uri \n", + "0 2613576.0 fl-nex NaN None \n", + "1 2613601.0 fl-nex NaN None \n", + "2 2613601.0 fl-nex NaN None \n", + "3 2613603.0 fl-nex NaN None \n", + "4 2613603.0 fl-nex NaN None " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "network = catalog.load_table(\"hydrofabric.network\")\n", + "network.scan().to_pandas().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have these tables in memory, we can use predefined functions/services to get more insight from our data/methods" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id toid vpuid topo hydroseq\n", + "0 wb-1795006 nex-1795007 10U fl-nex 84196.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "find_origin(network_table=network, identifier=7226556, id_type=\"comid\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/icefabric_manage/README.md b/src/icefabric_manage/README.md index e69de29..e0d26b6 100644 --- a/src/icefabric_manage/README.md +++ b/src/icefabric_manage/README.md @@ -0,0 +1,3 @@ +# Icefabric Manage + +A repo which contains scripts for building the icefabric from scratch diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index 8cb240f..47e0ef7 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -22,13 +22,10 @@ dependencies = [ "matplotlib==3.10.0", "s3fs==2025.3.2", "pyarrow==19.0.1", - "pyiceberg[sql-sqlite]", + "pyiceberg[sql-sqlite]==0.9.0", "python-dotenv==1.1.0" ] -[project.scripts] -icefabric_manage = "icefabric_manage._api:build" - [build-system] requires = ["hatchling"] build-backend = "hatchling.build" diff --git a/src/icefabric_manage/src/icefabric_manage/__init__.py b/src/icefabric_manage/src/icefabric_manage/__init__.py index 45625fb..3803004 100644 --- a/src/icefabric_manage/src/icefabric_manage/__init__.py +++ b/src/icefabric_manage/src/icefabric_manage/__init__.py @@ -1,3 +1,3 @@ -from icefabric_manage._api import build, load_table_from_catalog +from icefabric_manage._api import build -__all__ = ["build", "load_table_from_catalog"] +__all__ = ["build"] diff --git a/src/icefabric_manage/src/icefabric_manage/_api.py b/src/icefabric_manage/src/icefabric_manage/_api.py index c8fe044..9c14e54 100644 --- a/src/icefabric_manage/src/icefabric_manage/_api.py +++ b/src/icefabric_manage/src/icefabric_manage/_api.py @@ -3,7 +3,7 @@ from pathlib import Path import pyarrow.parquet as pq -from pyiceberg.catalog import Catalog, load_catalog +from pyiceberg.catalog import Catalog from pyiceberg.exceptions import NoSuchTableError from pyiceberg.table import Table @@ -41,18 +41,16 @@ def _add_parquet_to_catalog(catalog: Catalog, file_path: Path, table_name: str): raise FileNotFoundError(f"Cannot find file: {file_path}") -def build(file_dir: Path, catalog_settings: dict[str, str]) -> Catalog: +def build(catalog: Catalog, file_dir: Path) -> None: """Builds the hydrofabric catalog based on the .pyiceberg.yaml config and defined parquet files. Parameters ---------- + catalog: Catalog + The Apache Iceberg Catalog file_dir : Path The path to the parquet files to add into the iceberg catalog - catalog_settings : dict[str, str] - The settings for the catalog entry """ - catalog = load_catalog("hydrofabric", **catalog_settings) - if not any(ns == ('hydrofabric',) for ns in catalog.list_namespaces()): catalog.create_namespace('hydrofabric') print("Created 'hydrofabric' namespace") @@ -65,30 +63,3 @@ def build(file_dir: Path, catalog_settings: dict[str, str]) -> Catalog: print(f"Table {table_name} already exists. Skipping build") else: _add_parquet_to_catalog(catalog, parquet_file, table_name) - return catalog - - -def load_table_from_catalog(catalog: Catalog, identifier: str) -> Table: - """_summary_ - - Parameters - ---------- - catalog : Catalog - The Iceberg catalog - identifier : str - The catalog name and table. Ex: hydrofabric.network - - Returns - ------- - Table - The requested table - - Raises - ------ - NoSuchTableError - The table does not exist in the catalog - """ - try: - return catalog.load_table(identifier) - except NoSuchTableError as e: - raise NoSuchTableError from e diff --git a/src/icefabric_tools/README.md b/src/icefabric_tools/README.md index e69de29..c522fc6 100644 --- a/src/icefabric_tools/README.md +++ b/src/icefabric_tools/README.md @@ -0,0 +1,3 @@ +# Icefabric tools + +A collection of compute functions/services build on top of existing Iceberg data diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index abf3353..175a8db 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -22,12 +22,9 @@ dependencies = [ "matplotlib==3.10.0", "s3fs==2025.3.2", "pyarrow==19.0.1", - "pyiceberg==0.9.0", + "pyiceberg[sql-sqlite]==0.9.0", ] -[project.scripts] -icefabric_manage = "icefabric_manage._api:build" - [build-system] requires = ["hatchling"] build-backend = "hatchling.build" diff --git a/src/icefabric_tools/src/icefabric_tools/__init__.py b/src/icefabric_tools/src/icefabric_tools/__init__.py index 1b90e20..792b227 100644 --- a/src/icefabric_tools/src/icefabric_tools/__init__.py +++ b/src/icefabric_tools/src/icefabric_tools/__init__.py @@ -1,3 +1,3 @@ -from icefabric_tools._api import to_geopandas +from icefabric_tools._api import find_origin, to_geopandas -__all__ = ["to_geopandas"] +__all__ = ["find_origin", "to_geopandas"] diff --git a/src/icefabric_tools/src/icefabric_tools/_api.py b/src/icefabric_tools/src/icefabric_tools/_api.py index a25974b..165d8a7 100644 --- a/src/icefabric_tools/src/icefabric_tools/_api.py +++ b/src/icefabric_tools/src/icefabric_tools/_api.py @@ -1,6 +1,7 @@ """Contains all api functions that can be called outside of the icefabric_tools package""" import geopandas as gpd +import pandas as pd from pyiceberg.table import Table from shapely import wkb @@ -11,7 +12,7 @@ def to_geopandas( case_sensitive: bool | None = True, snapshot_id: int | None = None, limit: int | None = None -) -> gpd.DataFrame: +) -> gpd.GeoDataFrame: """Converts a table to a geopandas dataframe Parameters @@ -51,3 +52,72 @@ def to_geopandas( else: raise ValueError("The provided table does not have a geometery column.") return df + + +def find_origin(network_table: Table, identifier: str, id_type: str ="hl_uri") -> pd.DataFrame: + """Find an origin point in the hydrofabric network. + + This function handles the case where multiple records match the identifier. + It follows the R implementation to select a single origin point based on + the minimum hydroseq value. + + Parameters + ---------- + network_table : Table + The HF network table from the hydrofabric catalog + identifier : str + The unique identifier you want to find the origin of + id_type : str, optional + The network table column you can query from, by default "hl_uri" + + Returns + ------- + pd.DataFrame + The origin row from the network table + + Raises + ------ + ValueError + The provided identifier is not supported + ValueError + No origin for the point is found + ValueError + Multiple origins for the point are found + """ + # Filter network table by the identifier + if id_type == "hl_uri": + row_filter = f"{id_type} == '{identifier}'" + elif id_type == "comid": + row_filter = f"hf_id == {identifier}" + elif id_type == "id": + row_filter = f"id == '{identifier}'" + elif id_type == "poi_id": + row_filter = f"poi_id == '{identifier}'" + else: + raise ValueError(f"Identifier {id_type} not supported") + + # Get all matching records + origin_candidates = network_table.scan(row_filter=row_filter).to_pandas() + + if len(origin_candidates) == 0: + raise ValueError(f"No origin found for {id_type}='{identifier}'") + + # Select relevant columns for the origin + origin_cols = ["id", "toid", "vpuid", "topo", "hydroseq"] + available_cols = [col for col in origin_cols if col in origin_candidates.columns] + + # Select only the relevant columns and drop duplicates + origin = origin_candidates[available_cols].drop_duplicates() + + # Find the record with minimum hydroseq + if "hydroseq" in origin.columns: + # For consistency with R, check if there are unique hydroseq values + if len(origin["hydroseq"].unique()) > 1: + # Sort by hydroseq and take the minimum + origin = origin.sort_values("hydroseq").iloc[0:1] + + # If we still have multiple records, it's a problem + if len(origin) > 1: + raise ValueError(f"Multiple origins found: {origin['id'].tolist()}") + + return origin diff --git a/uv.lock b/uv.lock index 58a72f3..50fde3b 100644 --- a/uv.lock +++ b/uv.lock @@ -1254,7 +1254,7 @@ requires-dist = [ { name = "pandas", specifier = "==2.2.3" }, { name = "pyarrow", specifier = "==19.0.1" }, { name = "pydantic", specifier = "==2.10.6" }, - { name = "pyiceberg", extras = ["sql-sqlite"] }, + { name = "pyiceberg", extras = ["sql-sqlite"], specifier = "==0.9.0" }, { name = "python-dotenv", specifier = "==1.1.0" }, { name = "s3fs", specifier = "==2025.3.2" }, { name = "scikit-learn", specifier = "==1.6.1" }, @@ -1278,7 +1278,7 @@ dependencies = [ { name = "pandas" }, { name = "pyarrow" }, { name = "pydantic" }, - { name = "pyiceberg" }, + { name = "pyiceberg", extra = ["sql-sqlite"] }, { name = "s3fs" }, { name = "scikit-learn" }, { name = "scipy" }, @@ -1298,7 +1298,7 @@ requires-dist = [ { name = "pandas", specifier = "==2.2.3" }, { name = "pyarrow", specifier = "==19.0.1" }, { name = "pydantic", specifier = "==2.10.6" }, - { name = "pyiceberg", specifier = "==0.9.0" }, + { name = "pyiceberg", extras = ["sql-sqlite"], specifier = "==0.9.0" }, { name = "s3fs", specifier = "==2025.3.2" }, { name = "scikit-learn", specifier = "==1.6.1" }, { name = "scipy", specifier = "==1.15.1" }, From b0abf1187c68dd0c69f483bd1613548410c70828 Mon Sep 17 00:00:00 2001 From: taddyb Date: Mon, 28 Apr 2025 15:11:38 -0400 Subject: [PATCH 11/97] fixed the geopandas function as there was a missing default value for scans --- examples/build_hf_catalog.ipynb | 523 ++++++++++++++++-- .../src/icefabric_tools/_api.py | 5 +- 2 files changed, 489 insertions(+), 39 deletions(-) diff --git a/examples/build_hf_catalog.ipynb b/examples/build_hf_catalog.ipynb index fe712df..d1fdeeb 100644 --- a/examples/build_hf_catalog.ipynb +++ b/examples/build_hf_catalog.ipynb @@ -19,7 +19,7 @@ "from pyiceberg.catalog import load_catalog\n", "\n", "from icefabric_manage import build\n", - "from icefabric_tools import find_origin" + "from icefabric_tools import find_origin, to_geopandas" ] }, { @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -230,48 +230,207 @@ " NaN\n", " None\n", " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 3461362\n", + " None\n", + " cnx-3299511\n", + " cat-3299511\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NOAA Reference Fabric\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " None\n", + " None\n", + " NaN\n", + " NaN\n", + " NaN\n", + " fl-nex\n", + " NaN\n", + " None\n", + " \n", + " \n", + " 3461363\n", + " None\n", + " cnx-3299510\n", + " cat-3299510\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NOAA Reference Fabric\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " None\n", + " None\n", + " NaN\n", + " NaN\n", + " NaN\n", + " fl-nex\n", + " NaN\n", + " None\n", + " \n", + " \n", + " 3461364\n", + " None\n", + " cnx-3299509\n", + " cat-3299509\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NOAA Reference Fabric\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " None\n", + " None\n", + " NaN\n", + " NaN\n", + " NaN\n", + " fl-nex\n", + " NaN\n", + " None\n", + " \n", + " \n", + " 3461365\n", + " None\n", + " cnx-3299508\n", + " cat-3299508\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NOAA Reference Fabric\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " None\n", + " None\n", + " NaN\n", + " NaN\n", + " NaN\n", + " fl-nex\n", + " NaN\n", + " None\n", + " \n", + " \n", + " 3461366\n", + " None\n", + " cnx-3299507\n", + " cat-3299507\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NOAA Reference Fabric\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " None\n", + " None\n", + " NaN\n", + " NaN\n", + " NaN\n", + " fl-nex\n", + " NaN\n", + " None\n", + " \n", " \n", "\n", + "

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" hf_source hf_id lengthkm areasqkm \\\n", - "0 NOAA Reference Fabric 166196253.0 7.006821 13.96665 \n", - "1 NOAA Reference Fabric 166196257.0 7.006821 13.96665 \n", - "2 NOAA Reference Fabric 166196256.0 7.006821 13.96665 \n", - "3 NOAA Reference Fabric 4599061.0 1.445138 5.88735 \n", - "4 NOAA Reference Fabric 4599715.0 1.445138 5.88735 \n", + " tot_drainage_areasqkm type vpuid hf_hydroseq hf_lengthkm \\\n", + "0 72.707401 terminal 01 2613576.0 7.006821 \n", + "1 72.707401 terminal 01 2613602.0 0.992107 \n", + "2 72.707401 terminal 01 2613601.0 0.940432 \n", + "3 42.698700 terminal 01 2613604.0 0.793282 \n", + "4 42.698700 terminal 01 2613605.0 0.205191 \n", + "... ... ... ... ... ... \n", + "3461362 NaN None None NaN NaN \n", + "3461363 NaN None None NaN NaN \n", + "3461364 NaN None None NaN NaN \n", + "3461365 NaN None None NaN NaN \n", + "3461366 NaN None None NaN NaN \n", "\n", - " tot_drainage_areasqkm type vpuid hf_hydroseq hf_lengthkm \\\n", - "0 72.707401 terminal 01 2613576.0 7.006821 \n", - "1 72.707401 terminal 01 2613602.0 0.992107 \n", - "2 72.707401 terminal 01 2613601.0 0.940432 \n", - "3 42.698700 terminal 01 2613604.0 0.793282 \n", - "4 42.698700 terminal 01 2613605.0 0.205191 \n", + " hf_mainstem topo poi_id hl_uri \n", + "0 2613576.0 fl-nex NaN None \n", + "1 2613601.0 fl-nex NaN None \n", + "2 2613601.0 fl-nex NaN None \n", + "3 2613603.0 fl-nex NaN None \n", + "4 2613603.0 fl-nex NaN None \n", + "... ... ... ... ... \n", + "3461362 NaN fl-nex NaN None \n", + "3461363 NaN fl-nex NaN None \n", + "3461364 NaN fl-nex NaN None \n", + "3461365 NaN fl-nex NaN None \n", + "3461366 NaN fl-nex NaN None \n", "\n", - " hf_mainstem topo poi_id hl_uri \n", - "0 2613576.0 fl-nex NaN None \n", - "1 2613601.0 fl-nex NaN None \n", - "2 2613601.0 fl-nex NaN None \n", - "3 2613603.0 fl-nex NaN None \n", - "4 2613603.0 fl-nex NaN None " + "[3461367 rows x 19 columns]" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "network = catalog.load_table(\"hydrofabric.network\")\n", - "network.scan().to_pandas().head()" + "network.scan().to_pandas()" ] }, { @@ -281,9 +440,16 @@ "Now that we have these tables in memory, we can use predefined functions/services to get more insight from our data/methods" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finding an Origin point" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -317,28 +483,311 @@ " \n", " \n", " 0\n", - " wb-1795006\n", - " nex-1795007\n", - " 10U\n", + " wb-824123\n", + " nex-824124\n", + " 05\n", " fl-nex\n", - " 84196.0\n", + " 3565.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id toid vpuid topo hydroseq\n", - "0 wb-1795006 nex-1795007 10U fl-nex 84196.0" + " id toid vpuid topo hydroseq\n", + "0 wb-824123 nex-824124 05 fl-nex 3565.0" ] }, - "execution_count": 5, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "find_origin(network_table=network, identifier=18471000, id_type=\"comid\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting tables into geopandas dataframes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id toid mainstem order hydroseq lengthkm \\\n", + "0 wb-20466 nex-20467 2613576.0 1.0 20292 2.443793 \n", + "1 wb-20474 nex-20475 2613597.0 1.0 20291 3.982518 \n", + "2 wb-20475 nex-20468 2613597.0 1.0 20290 1.006645 \n", + "3 wb-20473 nex-20467 2613587.0 1.0 20289 1.892781 \n", + "4 wb-20467 nex-20468 2613576.0 2.0 20288 1.616463 \n", + "... ... ... ... ... ... ... \n", + "828283 wb-3054945 nex-3054946 2523667.0 2.0 236 3.515287 \n", + "828284 wb-3054946 nex-3054947 2523667.0 2.0 235 4.365587 \n", + "828285 wb-3054961 nex-3054958 2523700.0 1.0 234 2.032194 \n", + "828286 wb-3054958 nex-3054959 2523693.0 1.0 233 6.466212 \n", + "828287 wb-3054960 nex-3054959 2523698.0 1.0 232 2.242424 \n", + "\n", + " areasqkm tot_drainage_areasqkm has_divide divide_id poi_id \\\n", + "0 4.304250 4.304250 True cat-20466 19534 \n", + "1 4.829400 4.829400 True cat-20474 None \n", + "2 6.713101 11.542501 True cat-20475 None \n", + "3 4.486050 4.486050 True cat-20473 None \n", + "4 2.583900 22.916701 True cat-20467 1193 \n", + "... ... ... ... ... ... \n", + "828283 9.335699 84.047850 True cat-3054945 None \n", + "828284 13.306500 97.354350 True cat-3054946 None \n", + "828285 5.235750 5.235750 True cat-3054961 None \n", + "828286 10.670850 15.906600 True cat-3054958 None \n", + "828287 3.640500 3.640500 True cat-3054960 None \n", + "\n", + " vpuid geometry \n", + "0 01 MULTILINESTRING ((1873700.565 2684810.523, 187... \n", + "1 01 MULTILINESTRING ((1875378.459 2678583.977, 187... \n", + "2 01 MULTILINESTRING ((1875781.183 2682056.744, 187... \n", + "3 01 MULTILINESTRING ((1875252.558 2684695.906, 187... \n", + "4 01 MULTILINESTRING ((1875278.618 2683667.5, 18754... \n", + "... ... ... \n", + "828283 17 MULTILINESTRING ((-2219263.794 2463211.731, -2... \n", + "828284 17 MULTILINESTRING ((-2222330.448 2464615.711, -2... \n", + "828285 17 MULTILINESTRING ((-2218810.457 2467656.224, -2... \n", + "828286 17 MULTILINESTRING ((-2217798.39 2468518.649, -22... \n", + "828287 17 MULTILINESTRING ((-2220749.538 2467137.344, -2... \n", + "\n", + "[828288 rows x 13 columns]" + ] + }, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "find_origin(network_table=network, identifier=7226556, id_type=\"comid\")" + "flowpaths = catalog.load_table(\"hydrofabric.flowpaths\")\n", + "to_geopandas(flowpaths)" ] } ], diff --git a/src/icefabric_tools/src/icefabric_tools/_api.py b/src/icefabric_tools/src/icefabric_tools/_api.py index 165d8a7..a6f32ed 100644 --- a/src/icefabric_tools/src/icefabric_tools/_api.py +++ b/src/icefabric_tools/src/icefabric_tools/_api.py @@ -2,13 +2,14 @@ import geopandas as gpd import pandas as pd -from pyiceberg.table import Table +from pyiceberg.expressions import BooleanExpression +from pyiceberg.table import ALWAYS_TRUE, Table from shapely import wkb def to_geopandas( table: Table, - row_filter: str | None = "", + row_filter: str | BooleanExpression = ALWAYS_TRUE, case_sensitive: bool | None = True, snapshot_id: int | None = None, limit: int | None = None From f462d2fa7e373f866f8cd392c832b0c41a2b1f97 Mon Sep 17 00:00:00 2001 From: taddyb Date: Tue, 29 Apr 2025 15:01:31 -0400 Subject: [PATCH 12/97] created the rnr.py file for replace and route hydrofabric functionality --- ...ild_hf_catalog.ipynb => quick_start.ipynb} | 358 ++++-------------- .../src/icefabric_manage/_api.py | 2 - .../src/icefabric_tools/__init__.py | 4 +- .../src/icefabric_tools/_api.py | 60 ++- .../src/icefabric_tools/rnr.py | 83 ++++ 5 files changed, 197 insertions(+), 310 deletions(-) rename examples/{build_hf_catalog.ipynb => quick_start.ipynb} (60%) create mode 100644 src/icefabric_tools/src/icefabric_tools/rnr.py diff --git a/examples/build_hf_catalog.ipynb b/examples/quick_start.ipynb similarity index 60% rename from examples/build_hf_catalog.ipynb rename to examples/quick_start.ipynb index d1fdeeb..a5475d0 100644 --- a/examples/build_hf_catalog.ipynb +++ b/examples/quick_start.ipynb @@ -4,13 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### How to build your own catalog\n", + "### Icefabric QuickStart\n", "Author: Tadd Bindas" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -19,7 +19,7 @@ "from pyiceberg.catalog import load_catalog\n", "\n", "from icefabric_manage import build\n", - "from icefabric_tools import find_origin, to_geopandas" + "from icefabric_tools import find_origin, table_to_geopandas" ] }, { @@ -31,26 +31,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Table network already exists. Skipping build\n", - "Table nexus already exists. Skipping build\n", - "Table flowpath-attributes already exists. Skipping build\n", - "Table divides already exists. Skipping build\n", - "Table pois already exists. Skipping build\n", - "Table flowpath-attributes-ml already exists. Skipping build\n", - "Table divide-attributes already exists. Skipping build\n", - "Table flowpaths already exists. Skipping build\n", - "Table hydrolocations already exists. Skipping build\n", - "Table lakes already exists. Skipping build\n" - ] - } - ], + "outputs": [], "source": [ "warehouse_path = Path(\"../data/warehouse\")\n", "warehouse_path.mkdir(exist_ok=True)\n", @@ -65,6 +48,26 @@ "build(catalog, Path(\"/Users/taddbindas/projects/NGWPC/icefabric/data/parquet\"))" ] }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "hydrofabric ()" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catalog" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -449,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -498,7 +501,7 @@ "0 wb-824123 nex-824124 05 fl-nex 3565.0" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -511,284 +514,51 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Converting tables into geopandas dataframes" + "### Reading a geopandas dataframe" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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- "4 2.583900 22.916701 True cat-20467 1193 \n", - "... ... ... ... ... ... \n", - "828283 9.335699 84.047850 True cat-3054945 None \n", - "828284 13.306500 97.354350 True cat-3054946 None \n", - "828285 5.235750 5.235750 True cat-3054961 None \n", - "828286 10.670850 15.906600 True cat-3054958 None \n", - "828287 3.640500 3.640500 True cat-3054960 None \n", - "\n", - " vpuid geometry \n", - "0 01 MULTILINESTRING ((1873700.565 2684810.523, 187... \n", - "1 01 MULTILINESTRING ((1875378.459 2678583.977, 187... \n", - "2 01 MULTILINESTRING ((1875781.183 2682056.744, 187... \n", - "3 01 MULTILINESTRING ((1875252.558 2684695.906, 187... \n", - "4 01 MULTILINESTRING ((1875278.618 2683667.5, 18754... \n", - "... ... ... \n", - "828283 17 MULTILINESTRING ((-2219263.794 2463211.731, -2... \n", - "828284 17 MULTILINESTRING ((-2222330.448 2464615.711, -2... \n", - "828285 17 MULTILINESTRING ((-2218810.457 2467656.224, -2... \n", - "828286 17 MULTILINESTRING ((-2217798.39 2468518.649, -22... \n", - "828287 17 MULTILINESTRING ((-2220749.538 2467137.344, -2... \n", - "\n", - "[828288 rows x 13 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "flowpaths = catalog.load_table(\"hydrofabric.flowpaths\")\n", - "to_geopandas(flowpaths)" + "flowpaths.scan().to_pandas()\n", + "table_to_geopandas(flowpaths)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting a Replace and Route Segment Geopackage" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from icefabric_tools import load_hydrofabric\n", + "from icefabric_tools.rnr import get_rnr_segment\n", + "\n", + "catalog_settings = {\n", + " 'type': 'sql',\n", + " \"uri\": \"sqlite:///../data/warehouse/pyiceberg_catalog.db\",\n", + " \"warehouse\": \"file://../data/warehouse\",\n", + "}\n", + "catalog = load_hydrofabric(catalog_settings)\n", + "get_rnr_segment(catalog, 18471000, \"../data/test_rnr.gpkg\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/icefabric_manage/src/icefabric_manage/_api.py b/src/icefabric_manage/src/icefabric_manage/_api.py index 9c14e54..4aa8600 100644 --- a/src/icefabric_manage/src/icefabric_manage/_api.py +++ b/src/icefabric_manage/src/icefabric_manage/_api.py @@ -4,8 +4,6 @@ import pyarrow.parquet as pq from pyiceberg.catalog import Catalog -from pyiceberg.exceptions import NoSuchTableError -from pyiceberg.table import Table def _add_parquet_to_catalog(catalog: Catalog, file_path: Path, table_name: str): diff --git a/src/icefabric_tools/src/icefabric_tools/__init__.py b/src/icefabric_tools/src/icefabric_tools/__init__.py index 792b227..0451357 100644 --- a/src/icefabric_tools/src/icefabric_tools/__init__.py +++ b/src/icefabric_tools/src/icefabric_tools/__init__.py @@ -1,3 +1,3 @@ -from icefabric_tools._api import find_origin, to_geopandas +from icefabric_tools._api import find_origin, load_hydrofabric, table_to_geopandas, to_geopandas -__all__ = ["find_origin", "to_geopandas"] +__all__ = ["find_origin", "load_hydrofabric", "to_geopandas", "table_to_geopandas"] diff --git a/src/icefabric_tools/src/icefabric_tools/_api.py b/src/icefabric_tools/src/icefabric_tools/_api.py index a6f32ed..cefb45f 100644 --- a/src/icefabric_tools/src/icefabric_tools/_api.py +++ b/src/icefabric_tools/src/icefabric_tools/_api.py @@ -2,12 +2,28 @@ import geopandas as gpd import pandas as pd +from pyiceberg.catalog import Catalog, load_catalog from pyiceberg.expressions import BooleanExpression from pyiceberg.table import ALWAYS_TRUE, Table from shapely import wkb -def to_geopandas( +def load_hydrofabric(catalog_settings: dict[str, str]) -> Catalog: + """A function to read in the hydrofabric catalog + + Parameters + ---------- + catalog_settings : dict[str, str] + The settings to read the hydrofabric catalog + + Returns + ------- + Catalog + The Iceberg catalog + """ + return load_catalog("hydrofabric", **catalog_settings) + +def table_to_geopandas( table: Table, row_filter: str | BooleanExpression = ALWAYS_TRUE, case_sensitive: bool | None = True, @@ -35,11 +51,6 @@ def to_geopandas( ------- gpd.DataFrame The resulting queried row, but in a geodataframe - - Raises - ------ - ValueError - Raised if the table does not have a geometry column """ df = table.scan( row_filter=row_filter, @@ -47,12 +58,37 @@ def to_geopandas( snapshot_id=snapshot_id, limit=limit, ).to_pandas() - if "geometry" in df.columns: - df["geometry"] = df["geometry"].apply(lambda x: wkb.loads(x) if x is not None else None) - return gpd.GeoDataFrame(df, geometry="geometry", crs="EPSG:5070") - else: - raise ValueError("The provided table does not have a geometery column.") - return df + return to_geopandas(df) + + +def to_geopandas( + df: pd.DataFrame, + crs: str = "EPSG:5070" +) -> gpd.GeoDataFrame: + """Converts the geometries in a pandas df to a geopandas dataframe + + Parameters + ---------- + df: pd.DataFrame + The iceberg table you are trying to read from + crs: str, optional + A string representing the CRS to set in the gdf, by default "EPSG:5070" + + Returns + ------- + gpd.DataFrame + The resulting queried row, but in a geodataframe + + Raises + ------ + ValueError + Raised if the table does not have a geometry column + """ + if "geometry" not in df.columns: + raise ValueError("The provided table does not have a geometry column.") + + geometry = df["geometry"].apply(lambda x: wkb.loads(x) if x is not None else None) + return gpd.GeoDataFrame(df, geometry=geometry, crs=crs) def find_origin(network_table: Table, identifier: str, id_type: str ="hl_uri") -> pd.DataFrame: diff --git a/src/icefabric_tools/src/icefabric_tools/rnr.py b/src/icefabric_tools/src/icefabric_tools/rnr.py new file mode 100644 index 0000000..7fca089 --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/rnr.py @@ -0,0 +1,83 @@ +"""A file to hold all replace and route (RnR) geospatial scripts""" + +import geopandas as gpd +import numpy as np +import pandas as pd +from pyiceberg.catalog import Catalog +from pyiceberg.expressions import And, EqualTo, In, LessThanOrEqual + +from icefabric_tools import table_to_geopandas, to_geopandas + + +def get_rnr_segment(catalog: Catalog, reach_id: str, output_file: str) -> gpd.GeoDataFrame: + """Returns a geopackage subset from the hydrofabric based on RnR rules + + Parameters + ---------- + catalog : Catalog + The iceberg catalog of the hydrofabric + reach_id : str + The reach_id, or hf_id, from the NWPS API + output_file : str + The output file where we want to save the geopackage + + Returns + ------- + gpd.GeoDataFrame + _description_ + """ + network = catalog.load_table("hydrofabric.network") + divides = catalog.load_table("hydrofabric.divides") + flowpaths = catalog.load_table("hydrofabric.flowpaths") + divides_attr = catalog.load_table("hydrofabric.divide-attributes") + flowpaths = catalog.load_table("hydrofabric.flowpaths") + flowpath_attr = catalog.load_table("hydrofabric.flowpath-attributes") + flowpath_attr_ml = catalog.load_table("hydrofabric.flowpath-attributes-ml") + nexus = catalog.load_table("hydrofabric.nexus") + pois = catalog.load_table("hydrofabric.pois") + hydrolocations = catalog.load_table("hydrofabric.hydrolocations") + + origin_row = network.scan(row_filter=f"hf_id = {reach_id}").to_pandas() + mainstem_expression = EqualTo("hf_mainstem", origin_row['hf_mainstem'].values[0]) + hydroseq_expression = LessThanOrEqual("hydroseq", origin_row['hydroseq'].values[0]) + combined_filter = And(mainstem_expression, hydroseq_expression) + + # Find all streams with the same stream order + # TODO Determine lakes to break segments + mainstem_features = network.scan(row_filter=combined_filter).to_pandas() + segment_flowpaths = flowpaths.scan(row_filter=In("divide_id", mainstem_features["divide_id"].drop_duplicates().values)).to_pandas() + joined_df = pd.merge( + mainstem_features, + segment_flowpaths, + on="divide_id", + how="outer" + ) + stream_order = joined_df[joined_df["hf_id"] == reach_id]["order"].values[0] + filtered_flowpaths = segment_flowpaths[segment_flowpaths["order"] == stream_order] + filtered_poi_list = [int(_id) for _id in segment_flowpaths[segment_flowpaths["order"] == stream_order]["poi_id"].values if _id is not None] + + # Get full river network + filtered_nexus_points = table_to_geopandas(table=nexus, row_filter=In("id", filtered_flowpaths["toid"])) + filtered_divides = table_to_geopandas(table=divides, row_filter=In("divide_id", filtered_flowpaths["divide_id"])) + filtered_network = network.scan(row_filter=In("id", np.concat([filtered_flowpaths["toid"].values, filtered_flowpaths["id"].values]))).to_pandas() + filtered_divide_attr = divides_attr.scan(row_filter=In("divide_id", filtered_flowpaths["divide_id"])).to_pandas() + filtered_flowpath_attr = flowpath_attr.scan(row_filter=In("id", filtered_flowpaths["toid"])).to_pandas() + filtered_flowpath_attr_ml = flowpath_attr_ml.scan(row_filter=In("id", filtered_flowpaths["toid"])).to_pandas() + filtered_pois = pois.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() + filtered_hydrolocations = hydrolocations.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() + filtered_flowpaths = to_geopandas(filtered_flowpaths) + + layers = { + "flowpaths": filtered_flowpaths, + "nexus": filtered_nexus_points, + "divides": filtered_divides, + "divide-attributes": filtered_divide_attr, + "network": filtered_network, + "pois": filtered_pois, + "flowpath-attributes-ml": filtered_flowpath_attr_ml, + "flowpath-attributes": filtered_flowpath_attr, + # "lakes": filtered_water_bodies, + "hydrolocations": filtered_hydrolocations, + } + for table, layer in layers.items(): + gpd.GeoDataFrame(layer).to_file(output_file, layer=table, driver='GPKG') From 67ab989e7a20e99ab9f2c306feb157cbcf97904c Mon Sep 17 00:00:00 2001 From: taddyb Date: Tue, 29 Apr 2025 21:55:23 -0400 Subject: [PATCH 13/97] updated pyproject.toml to reduce python version --- examples/quick_start.ipynb | 2 +- pyproject.toml | 2 +- src/icefabric_manage/pyproject.toml | 1 - src/icefabric_tools/pyproject.toml | 1 - 4 files changed, 2 insertions(+), 4 deletions(-) diff --git a/examples/quick_start.ipynb b/examples/quick_start.ipynb index a5475d0..0726b38 100644 --- a/examples/quick_start.ipynb +++ b/examples/quick_start.ipynb @@ -532,7 +532,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Getting a Replace and Route Segment Geopackage" + "### Getting a Replace and Route Segment as a Geopackage" ] }, { diff --git a/pyproject.toml b/pyproject.toml index b7d9c9a..3883d48 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "icefabric" version = "0.1.0" description = "An Apache Iceberg implementation of the Hydrofabric" readme = "README.md" -requires-python = ">=3.11" +requires-python = ">=3.10" license = { file = "LICENSE" } authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index 47e0ef7..e1d73fb 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -4,7 +4,6 @@ version = "0.1.0" description = "All functionality for creating, updating, and deleting information from the icefabric catalog" authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] -requires-python = ">=3.11" readme = "README.md" dependencies = [ "numpy==2.2.5", diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index 175a8db..b0a2267 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -4,7 +4,6 @@ version = "0.1.0" description = "All functionality for creating, updating, and deleting information from the icefabric catalog" authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] -requires-python = ">=3.11" readme = "README.md" dependencies = [ "numpy==2.2.5", From cb76842e29719554141237bc455bb3a36c85e119 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Tue, 29 Apr 2025 21:57:26 -0400 Subject: [PATCH 14/97] Update pyproject.toml --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 3883d48..9947498 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,8 @@ icefabric_manage = { workspace = true } [tool.uv.workspace] members = [ - "src/*" + "src/icefabric_manage", + "src/icefabric_tools", ] [tool.uv] From 1c28decdc591e77db4828225f6b2d9939b893278 Mon Sep 17 00:00:00 2001 From: taddyb Date: Wed, 30 Apr 2025 17:15:27 -0400 Subject: [PATCH 15/97] removed dependencies --- src/icefabric_manage/pyproject.toml | 10 - src/icefabric_tools/pyproject.toml | 8 - uv.lock | 940 +++++++++++++++------------- 3 files changed, 513 insertions(+), 445 deletions(-) diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index e1d73fb..b1e5a93 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -9,20 +9,10 @@ dependencies = [ "numpy==2.2.5", "fiona==1.10.1", "pandas==2.2.3", - "cubed-xarray==0.0.7", "geopandas==1.0.1", - "pydantic==2.10.6", - "xarray==2025.3.1", - "scipy==1.15.1", - "tqdm==4.67.1", - "zarr==3.0.7", - "sympy==1.13.1", - "scikit-learn==1.6.1", - "matplotlib==3.10.0", "s3fs==2025.3.2", "pyarrow==19.0.1", "pyiceberg[sql-sqlite]==0.9.0", - "python-dotenv==1.1.0" ] [build-system] diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index b0a2267..17ff640 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -9,15 +9,7 @@ dependencies = [ "numpy==2.2.5", "fiona==1.10.1", "pandas==2.2.3", - "cubed-xarray==0.0.7", "geopandas==1.0.1", - "pydantic==2.10.6", - "xarray==2025.3.1", - "scipy==1.15.1", - "tqdm==4.67.1", - "zarr==3.0.7", - "sympy==1.13.1", - "scikit-learn==1.6.1", "matplotlib==3.10.0", "s3fs==2025.3.2", "pyarrow==19.0.1", diff --git a/uv.lock b/uv.lock index 50fde3b..c1b4409 100644 --- a/uv.lock +++ b/uv.lock @@ -1,9 +1,10 @@ version = 1 revision = 1 -requires-python = ">=3.11" +requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.12'", - "python_full_version < '3.12'", + "python_full_version == '3.11.*'", + "python_full_version < '3.11'", ] [manifest] @@ -47,6 +48,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, { name = "aiosignal" }, + { name = "async-timeout", marker = "python_full_version < 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"https://files.pythonhosted.org/packages/0d/67/971d9a661b66cc1c2fe2472e38085f5025a393ee1c1822b0155cc3f357b7/zarr-3.0.7-py3-none-any.whl", hash = "sha256:db0ca28f12a59be681813ac719e4d5436591e2b315e89c5ae6e2d255715be960", size = 203937 }, -] From dcda1c4ec2705c2522bf8d0844036ab813171124 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Sat, 3 May 2025 18:33:44 -0400 Subject: [PATCH 16/97] Update __init__.py --- src/icefabric_tools/src/icefabric_tools/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/icefabric_tools/src/icefabric_tools/__init__.py b/src/icefabric_tools/src/icefabric_tools/__init__.py index 0451357..de371af 100644 --- a/src/icefabric_tools/src/icefabric_tools/__init__.py +++ b/src/icefabric_tools/src/icefabric_tools/__init__.py @@ -1,3 +1,4 @@ from icefabric_tools._api import find_origin, load_hydrofabric, table_to_geopandas, to_geopandas +from icefabric_tools import rnr -__all__ = ["find_origin", "load_hydrofabric", "to_geopandas", "table_to_geopandas"] +__all__ = ["find_origin", "load_hydrofabric", "rnr", "to_geopandas", "table_to_geopandas"] From 526a8041cd23f25e14090020d679063ce0796d7d Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Sat, 3 May 2025 21:41:24 -0400 Subject: [PATCH 17/97] Update pyproject.toml --- src/icefabric_tools/pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index 17ff640..e26c828 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -6,9 +6,9 @@ authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] readme = "README.md" dependencies = [ - "numpy==2.2.5", + "numpy==1.26.3", "fiona==1.10.1", - "pandas==2.2.3", + "pandas==2.2.0", "geopandas==1.0.1", "matplotlib==3.10.0", "s3fs==2025.3.2", From c953ccbad843ec1307a09be767d916471eccc9a3 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Sat, 3 May 2025 21:41:53 -0400 Subject: [PATCH 18/97] Update pyproject.toml --- src/icefabric_manage/pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index b1e5a93..8617b5c 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -6,9 +6,9 @@ authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] readme = "README.md" dependencies = [ - "numpy==2.2.5", + "numpy==1.26.3", "fiona==1.10.1", - "pandas==2.2.3", + "pandas==2.2.0", "geopandas==1.0.1", "s3fs==2025.3.2", "pyarrow==19.0.1", From 5917c305d1b9acd90d1381f4a184edf8bf0c1710 Mon Sep 17 00:00:00 2001 From: taddyb Date: Sat, 3 May 2025 22:09:47 -0400 Subject: [PATCH 19/97] lowered numpy versions due to t-route inconsistencies --- examples/quick_start.ipynb | 522 +++++++++++++++++- pyproject.toml | 2 +- src/icefabric_manage/pyproject.toml | 2 +- src/icefabric_tools/pyproject.toml | 2 +- .../src/icefabric_tools/rnr.py | 2 +- uv.lock | 155 ++---- 6 files changed, 571 insertions(+), 114 deletions(-) diff --git a/examples/quick_start.ipynb b/examples/quick_start.ipynb index 0726b38..6caaa00 100644 --- a/examples/quick_start.ipynb +++ b/examples/quick_start.ipynb @@ -10,10 +10,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ + "import os\n", "from pathlib import Path\n", "\n", "from pyiceberg.catalog import load_catalog\n", @@ -31,9 +32,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table network already exists. Skipping build\n", + "Table nexus already exists. Skipping build\n", + "Table flowpath-attributes already exists. Skipping build\n", + "Table divides already exists. Skipping build\n", + "Table pois already exists. Skipping build\n", + "Table flowpath-attributes-ml already exists. Skipping build\n", + "Table divide-attributes already exists. Skipping build\n", + "Table flowpaths already exists. Skipping build\n", + "Table hydrolocations already exists. Skipping build\n", + "Table lakes already exists. Skipping build\n" + ] + } + ], "source": [ "warehouse_path = Path(\"../data/warehouse\")\n", "warehouse_path.mkdir(exist_ok=True)\n", @@ -50,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -59,7 +77,7 @@ "hydrofabric ()" ] }, - "execution_count": 76, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -426,7 +444,7 @@ "[3461367 rows x 19 columns]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -452,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -501,7 +519,7 @@ "0 wb-824123 nex-824124 05 fl-nex 3565.0" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -519,9 +537,275 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import geopandas as gpd\n", + "gpd.read_file(\"../data/test_rnr.gpkg\", layer=\"flowpaths\")" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/pyproject.toml b/pyproject.toml index 9947498..bbe14f0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "icefabric" version = "0.1.0" description = "An Apache Iceberg implementation of the Hydrofabric" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">3.10,<3.13" license = { file = "LICENSE" } authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index 8617b5c..27e1557 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] readme = "README.md" dependencies = [ - "numpy==1.26.3", + "numpy==1.26.4", "fiona==1.10.1", "pandas==2.2.0", "geopandas==1.0.1", diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index e26c828..1bfd306 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] readme = "README.md" dependencies = [ - "numpy==1.26.3", + "numpy==1.26.4", "fiona==1.10.1", "pandas==2.2.0", "geopandas==1.0.1", diff --git a/src/icefabric_tools/src/icefabric_tools/rnr.py b/src/icefabric_tools/src/icefabric_tools/rnr.py index 7fca089..39b79ac 100644 --- a/src/icefabric_tools/src/icefabric_tools/rnr.py +++ b/src/icefabric_tools/src/icefabric_tools/rnr.py @@ -59,7 +59,7 @@ def get_rnr_segment(catalog: Catalog, reach_id: str, output_file: str) -> gpd.Ge # Get full river network filtered_nexus_points = table_to_geopandas(table=nexus, row_filter=In("id", filtered_flowpaths["toid"])) filtered_divides = table_to_geopandas(table=divides, row_filter=In("divide_id", filtered_flowpaths["divide_id"])) - filtered_network = network.scan(row_filter=In("id", np.concat([filtered_flowpaths["toid"].values, filtered_flowpaths["id"].values]))).to_pandas() + filtered_network = network.scan(row_filter=In("id", np.concatenate([filtered_flowpaths["toid"].values, filtered_flowpaths["id"].values]))).to_pandas() filtered_divide_attr = divides_attr.scan(row_filter=In("divide_id", filtered_flowpaths["divide_id"])).to_pandas() filtered_flowpath_attr = flowpath_attr.scan(row_filter=In("id", filtered_flowpaths["toid"])).to_pandas() filtered_flowpath_attr_ml = flowpath_attr_ml.scan(row_filter=In("id", filtered_flowpaths["toid"])).to_pandas() diff --git a/uv.lock b/uv.lock index c1b4409..56a2b66 100644 --- a/uv.lock +++ b/uv.lock @@ -1238,8 +1238,8 @@ dependencies = [ requires-dist = [ { name = "fiona", specifier = "==1.10.1" }, { name = "geopandas", specifier = "==1.0.1" }, - { name = "numpy", specifier = "==2.2.5" }, - { name = "pandas", specifier = "==2.2.3" }, + { name = "numpy", specifier = "==1.26.4" }, + { name = "pandas", specifier = "==2.2.0" }, { name = "pyarrow", specifier = "==19.0.1" }, { name = "pyiceberg", extras = ["sql-sqlite"], specifier = "==0.9.0" }, { name = "s3fs", specifier = "==2025.3.2" }, @@ -1265,8 +1265,8 @@ requires-dist = [ { name = "fiona", specifier = "==1.10.1" }, { name = "geopandas", specifier = "==1.0.1" }, { name = "matplotlib", specifier = "==3.10.0" }, - { name = "numpy", specifier = "==2.2.5" }, - { name = "pandas", specifier = "==2.2.3" }, + { name = "numpy", specifier = "==1.26.4" }, + { name = "pandas", specifier = "==2.2.0" }, { name = "pyarrow", specifier = "==19.0.1" }, { name = "pyiceberg", extras 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attributes (#1) --- examples/quick_start.ipynb | 276 +++++----- .../src/icefabric_tools/rnr.py | 7 +- uv.lock | 503 +----------------- 3 files changed, 140 insertions(+), 646 deletions(-) diff --git a/examples/quick_start.ipynb b/examples/quick_start.ipynb index 6caaa00..6980cf7 100644 --- a/examples/quick_start.ipynb +++ b/examples/quick_start.ipynb @@ -17,6 +17,7 @@ "import os\n", "from pathlib import Path\n", "\n", + "import geopandas as gpd\n", "from pyiceberg.catalog import load_catalog\n", "\n", "from icefabric_manage import build\n", @@ -821,7 +822,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -865,182 +866,168 @@ " \n", " id\n", " toid\n", + " divide_id\n", + " ds_id\n", " mainstem\n", - " order\n", " hydroseq\n", + " hf_source\n", + " hf_id\n", " lengthkm\n", " areasqkm\n", " tot_drainage_areasqkm\n", - " has_divide\n", - " divide_id\n", - " poi_id\n", + " type\n", " vpuid\n", - " geometry\n", + " hf_hydroseq\n", + " hf_lengthkm\n", + " hf_mainstem\n", + " topo\n", + " poi_id\n", + " hl_uri\n", " \n", " \n", " \n", " \n", " 0\n", - " wb-824123\n", - " nex-824124\n", - " 1547279.0\n", - " 5.0\n", - " 3565\n", - " 9.516844\n", - " 17.663400\n", - " 12878.231399\n", - " True\n", - " cat-824123\n", + " wb-1321815\n", + " tnx-1000005117\n", + " cat-1321815\n", + " NaN\n", + " 473130.0\n", + " 9408.0\n", + " NOAA Reference Fabric\n", + " 22812067.0\n", + " 7.573456\n", + " 7.18740\n", + " 129645.996881\n", + " terminal\n", + " 08\n", + " 473132.0\n", + " 3.072944\n", + " 473130.0\n", + " fl-nex\n", + " NaN\n", " None\n", - " 05\n", - " MULTILINESTRING ((752258.435 1788547.25, 75221...\n", " \n", " \n", " 1\n", - " wb-824124\n", - " nex-824125\n", - " 1547279.0\n", - " 5.0\n", - " 3564\n", - " 4.186372\n", - " 4.710151\n", - " 12997.003050\n", - " True\n", - " cat-824124\n", - " 93863\n", - " 05\n", - " MULTILINESTRING ((753323.441 1781497.25, 75323...\n", + " wb-1321815\n", + " 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areasqkm \\\n", - "0 wb-824123 nex-824124 1547279.0 5.0 3565 9.516844 17.663400 \n", - "1 wb-824124 nex-824125 1547279.0 5.0 3564 4.186372 4.710151 \n", - "2 wb-824125 nex-824126 1547279.0 5.0 3562 7.335880 6.629850 \n", - "3 wb-824126 nex-824127 1547279.0 5.0 3561 7.164683 20.938500 \n", - "4 wb-824127 nex-824128 1547279.0 5.0 3558 1.754889 5.896801 \n", - "5 wb-824128 nex-824129 1547279.0 5.0 3557 8.993345 21.132900 \n", - "6 wb-824129 nex-824130 1547279.0 5.0 3556 8.903493 8.900550 \n", - "7 wb-824130 nex-824131 1547279.0 5.0 3542 2.378809 1.035000 \n", + " id toid divide_id ds_id mainstem hydroseq \\\n", + "0 wb-1321815 tnx-1000005117 cat-1321815 NaN 473130.0 9408.0 \n", + "1 wb-1321815 tnx-1000005117 cat-1321815 NaN 473130.0 9408.0 \n", + "2 wb-1321815 tnx-1000005117 cat-1321815 NaN 473130.0 9408.0 \n", + "3 wb-1321754 nex-1321755 cat-1321754 NaN 473130.0 9562.0 \n", + "4 wb-1321754 nex-1321755 cat-1321754 NaN 473130.0 9562.0 \n", + "\n", + " hf_source hf_id lengthkm areasqkm \\\n", + "0 NOAA Reference Fabric 22812067.0 7.573456 7.18740 \n", + "1 NOAA Reference Fabric 22811613.0 7.573456 7.18740 \n", + "2 NOAA Reference Fabric 22811611.0 7.573456 7.18740 \n", + "3 NOAA Reference Fabric 19269180.0 5.946802 9.06615 \n", + "4 NOAA Reference Fabric 19269466.0 5.946802 9.06615 \n", "\n", - " tot_drainage_areasqkm has_divide divide_id poi_id vpuid \\\n", - "0 12878.231399 True cat-824123 None 05 \n", - "1 12997.003050 True cat-824124 93863 05 \n", - "2 13098.629700 True cat-824125 None 05 \n", - "3 13119.568200 True cat-824126 74964 05 \n", - "4 13549.954953 True cat-824127 None 05 \n", - "5 13571.087853 True cat-824128 73141 05 \n", - "6 13680.138604 True cat-824129 None 05 \n", - "7 13875.044853 True cat-824130 None 05 \n", + " tot_drainage_areasqkm type vpuid hf_hydroseq hf_lengthkm \\\n", + "0 129645.996881 terminal 08 473132.0 3.072944 \n", + "1 129645.996881 terminal 08 473131.0 2.692083 \n", + "2 129645.996881 terminal 08 473130.0 1.808428 \n", + "3 127052.965484 nexus 08 473933.0 0.149239 \n", + "4 127052.965484 nexus 08 473932.0 2.638347 \n", "\n", - " geometry \n", - "0 MULTILINESTRING ((752258.435 1788547.25, 75221... \n", - "1 MULTILINESTRING ((753323.441 1781497.25, 75323... \n", - "2 MULTILINESTRING ((751418.443 1779097.245, 7514... \n", - "3 MULTILINESTRING ((751778.441 1774552.25, 75185... \n", - "4 MULTILINESTRING ((754178.441 1771642.252, 7542... \n", - "5 MULTILINESTRING ((754396.812 1770074.121, 7543... \n", - "6 MULTILINESTRING ((754523.437 1764907.249, 7544... \n", - "7 MULTILINESTRING ((754338.731 1760185.448, 7544... " + " hf_mainstem topo poi_id hl_uri \n", + "0 473130.0 fl-nex NaN None \n", + "1 473130.0 fl-nex NaN None \n", + "2 473130.0 fl-nex NaN None \n", + "3 473130.0 fl-nex NaN None \n", + "4 473130.0 fl-nex NaN None " ] }, "execution_count": 8, @@ -1050,7 +1037,8 @@ ], "source": [ "import geopandas as gpd\n", - "gpd.read_file(\"../data/test_rnr.gpkg\", layer=\"flowpaths\")" + "gdf = gpd.read_file(\"../data/test_ds_rnr.gpkg\", layer=\"network\")\n", + "gdf.head()" ] }, { diff --git a/src/icefabric_tools/src/icefabric_tools/rnr.py b/src/icefabric_tools/src/icefabric_tools/rnr.py index 39b79ac..d9ae9e0 100644 --- a/src/icefabric_tools/src/icefabric_tools/rnr.py +++ b/src/icefabric_tools/src/icefabric_tools/rnr.py @@ -59,13 +59,14 @@ def get_rnr_segment(catalog: Catalog, reach_id: str, output_file: str) -> gpd.Ge # Get full river network filtered_nexus_points = table_to_geopandas(table=nexus, row_filter=In("id", filtered_flowpaths["toid"])) filtered_divides = table_to_geopandas(table=divides, row_filter=In("divide_id", filtered_flowpaths["divide_id"])) - filtered_network = network.scan(row_filter=In("id", np.concatenate([filtered_flowpaths["toid"].values, filtered_flowpaths["id"].values]))).to_pandas() filtered_divide_attr = divides_attr.scan(row_filter=In("divide_id", filtered_flowpaths["divide_id"])).to_pandas() - filtered_flowpath_attr = flowpath_attr.scan(row_filter=In("id", filtered_flowpaths["toid"])).to_pandas() - filtered_flowpath_attr_ml = flowpath_attr_ml.scan(row_filter=In("id", filtered_flowpaths["toid"])).to_pandas() + filtered_flowpath_attr = flowpath_attr.scan(row_filter=In("id", filtered_flowpaths["id"])).to_pandas() + filtered_flowpath_attr_ml = flowpath_attr_ml.scan(row_filter=In("id", filtered_flowpaths["id"])).to_pandas() filtered_pois = pois.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() filtered_hydrolocations = hydrolocations.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() filtered_flowpaths = to_geopandas(filtered_flowpaths) + filtered_network = network.scan(row_filter=In("id", np.concatenate([filtered_flowpaths["toid"].values, filtered_flowpaths["id"].values]))).to_pandas() + layers = { "flowpaths": filtered_flowpaths, diff --git a/uv.lock b/uv.lock index 56a2b66..5dd3bbe 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,6 @@ version = 1 revision = 1 -requires-python = ">=3.10" +requires-python = ">3.10, <3.13" resolution-markers = [ "python_full_version >= '3.12'", "python_full_version == '3.11.*'", @@ -105,22 +105,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/a5/71/f9cd2fed33fa2b7ce4d412fb7876547abb821d5b5520787d159d0748321d/aiohttp-3.11.18-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ab2ef72f8605046115bc9aa8e9d14fd49086d405855f40b79ed9e5c1f9f4faea", size = 1695131 }, { url = "https://files.pythonhosted.org/packages/97/97/d1248cd6d02b9de6aa514793d0dcb20099f0ec47ae71a933290116c070c5/aiohttp-3.11.18-cp312-cp312-win32.whl", hash = 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Catalog for Parquet files" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7e3f71a8-a823-4df9-90df-8d6e62969faa", + "metadata": {}, + "outputs": [], + "source": [ + "import pyarrow as pa\n", + "import pyiceberg\n", + "from pyiceberg.catalog import load_catalog\n", + "from pyiceberg.schema import Schema\n", + "from pyiceberg.types import StringType, IntegerType\n", + "import pyarrow.parquet as pq\n", + "from pyiceberg.schema import Schema\n", + "from pyiceberg.types import NestedField, IntegerType, StringType, DoubleType, LongType\n", + "from pyiceberg.expressions import EqualTo, LessThan, And, GreaterThan, GreaterThanOrEqual\n", + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "class IcebergTable(object):\n", + " '''\n", + " Create a Iceberg table per parquet file w/ its inherited schema set.\n", + " \n", + " '''\n", + " def __init__(self):\n", + " \n", + " # Set location of where the iceberg catalog will reside\n", + " os.environ['PYICEBERG_HOME'] = os.getcwd()\n", + " \n", + " def read_data_dirs(self, data_dir):\n", + " '''\n", + " Extract the list of parquet directories.\n", + " \n", + " Args:\n", + " data_dir (str): Parent directory of the parquet files.\n", + " Note: All the ml_auxiliary_data parquet \n", + " files are save under same filenames, \n", + " but categorized by 'vpuid' conditions.\n", + "\n", + " Return (list): List of directories associated with each parquet file.\n", + " \n", + " '''\n", + " parquet_list = []\n", + " for folder, subfolders, files in os.walk(data_dir):\n", + " if folder != data_dir:\n", + " for file in files:\n", + " parquet_list.append(f'{folder}/{file}')\n", + " \n", + " return parquet_list\n", + " \n", + " def read_data(self, parquet_file_path):\n", + " '''\n", + " Load a single parquet as a Pyarrow table.\n", + " \n", + " Args: \n", + " parquet_file_path (str): Directory of a single parquet.\n", + " \n", + " Return: None\n", + " \n", + " '''\n", + " data = pq.read_table(parquet_file_path)\n", + " \n", + " return data\n", + "\n", + " def establish_catalog(self, name='dev'):\n", + " '''\n", + " Creates a new Iceberg catalog.\n", + " \n", + " Args:\n", + " name (str): Name of the catalog to be created.\n", + " Default: 'dev' for development catalog\n", + "\n", + " Return: None\n", + " \n", + " ''' \n", + " # Establish a new Iceberg catalog & its configuration\n", + " self.catalog = load_catalog(name=name,\n", + " **{'uri': f'sqlite:///iceberg_catalog/{name}_catalog.db'})\n", + " \n", + " return\n", + "\n", + " def convert_pyarrow_to_iceberg_schema(self, arrow_schema):\n", + " \"\"\"\n", + " Translate a given Pyarrow schema into a schema acceptable by Iceberg.\n", + "\n", + " Args:\n", + " arrow_schema (object): Pyarrow schema read from the loaded \n", + " parquet of interest.\n", + "\n", + " Return (object): Iceberg schema\n", + " \n", + " \"\"\"\n", + " fields = []\n", + " for idx in range(len(arrow_schema)):\n", + " \n", + " # Extraction of the datatype & name of each schema row\n", + " field_name = arrow_schema.field(idx).name\n", + " arrow_type = arrow_schema.field(idx).type\n", + "\n", + " # Iceberg datatypes to pyarrow datatypes\n", + " if pa.types.is_int32(arrow_type):\n", + " iceberg_type = LongType()\n", + " elif pa.types.is_string(arrow_type):\n", + " iceberg_type = StringType()\n", + " elif pa.types.is_float64(arrow_type):\n", + " iceberg_type = DoubleType()\n", + " elif pa.types.is_int64(arrow_type):\n", + " iceberg_type = LongType()\n", + " elif pa.types.is_dictionary(arrow_type): \n", + " if pa.types.is_string(arrow_type.value_type):\n", + " iceberg_type = StringType()\n", + " elif pa.types.is_int32(arrow_type.value_type):\n", + " iceberg_type = LongType()\n", + " else:\n", + " raise ValueError(f\"Unsupported PyArrow type: {arrow_type}\")\n", + "\n", + " # Establish the new schema acceptable to Iceberg\n", + " fields.append(NestedField(field_id=idx + 1,\n", + " required=False,\n", + " name=field_name,\n", + " field_type=iceberg_type))\n", + " # Iceberg schema\n", + " schema = Schema(*fields)\n", + " \n", + " return schema\n", + "\n", + " def create_table_for_parquet(self, iceberg_tablename, data_table, schema, namespace):\n", + " '''\n", + " Convert parquet Pyarrow table to iceberg table & allocate Iceberg catalog under the ./iceberg_catalog directory.\n", + "\n", + " Args:\n", + " iceberg_tablename (str): Name of the Iceberg table to be created.\n", + " \n", + " data_table (object): Pyarrow table\n", + " \n", + " schema (object): Unique Iceberg schema to be set for the Iceberg table.\n", + " \n", + " namespace (str): Namespace for which the Iceberg table will reside within\n", + " the Iceberg catalog.\n", + "\n", + " Return: None\n", + " \n", + " '''\n", + " \n", + " # Create an Iceberg table\n", + " self.catalog.create_namespace_if_not_exists(namespace)\n", + " iceberg_table = self.catalog.create_table(identifier=f\"{namespace}.{iceberg_tablename}\",\n", + " schema=schema,\n", + " location=f\"{os.environ['PYICEBERG_HOME']}/iceberg_catalog\")\n", + " \n", + " # Updates the Iceberg table with data of interest.\n", + " iceberg_table.append(data_table)\n", + " \n", + " return\n", + "\n", + " def create_table_for_all_parquets(self, parquet_files, namespace):\n", + " '''\n", + " Convert parquets to Iceberg tables - each w/ their inherited schema.\n", + "\n", + " Args:\n", + " parquet_files (list): List of directories of the parquet files.\n", + " \n", + " namespace (str): Namespace for which the Iceberg table will reside within\n", + " the Iceberg catalog.\n", + "\n", + " Return: None\n", + " \n", + " '''\n", + " for idx, parquet_file in enumerate(parquet_files):\n", + " data_table = self.read_data(parquet_file)\n", + " data_pyarrow_schema = data_table.schema\n", + " schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema)\n", + " self.create_table_for_parquet(os.path.split(os.path.split(parquet_file)[0])[1],\n", + " data_table, \n", + " schema,\n", + " namespace) \n", + " return\n", + " " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e6e15027-cc49-4508-b434-fc4362ffb44e", + "metadata": {}, + "source": [ + "#### Demo:\n", + "#### Establish A New Iceberg Catalog & Generate Tables For All Parquets" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c74f8432-f07f-430d-830f-d0910a699ea8", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n" + ] + } + ], + "source": [ + "# Instantiate class\n", + "obj = IcebergTable()\n", + "\n", + "# Extract list of parquets\n", + "parquet_list = obj.read_data_dirs(data_dir=f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/')\n", + "\n", + "# Establish new Iceberg catalog\n", + "obj.establish_catalog('dev')\n", + "\n", + "# Generate tables for all parquets detected w/in the new Iceberg catalog\n", + "obj.create_table_for_all_parquets(parquet_list, \n", + " namespace='bathymetry_ml_auxiliary')" + ] + }, + { + "cell_type": "markdown", + "id": "71949e8b-36f2-4e74-afb2-741a884d95dd", + "metadata": {}, + "source": [ + "#### Demo:\n", + "#### Load Iceberg Catalog & Table of Interest" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f449ec30-027e-45c8-91f2-9ad5402b6d17", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pyarrow.Table\n", + "hf_source: large_string\n", + "owp_ahg_c: double\n", + "owp_ahg_f: double\n", + "owp_ahg_a: double\n", + "owp_ahg_b: double\n", + "owp_ahg_k: double\n", + "owp_ahg_m: double\n", + "owp_dingman_r: double\n", + "LENGTHKM: double\n", + "slope: double\n", + "owp_bf_channel_area: double\n", + "owp_inchan_channel_area: double\n", + "owp_bf_channel_perimeter: double\n", + "owp_inchan_channel_perimeter: double\n", + "owp_bf_channel_volume: double\n", + "owp_inchan_channel_volume: double\n", + "owp_bf_channel_bed_area: double\n", + "owp_inchan_channel_bed_area: double\n", + "owp_y_bf: double\n", + "owp_y_inchan: double\n", + "owp_tw_bf: double\n", + "owp_tw_inchan: double\n", + "owp_roughness_bathy: double\n", + "owp_roughness_bathy_lower_bound: double\n", + "owp_roughness_bathy_upper_bound: double\n", + "hf_id: int64\n", + "owp_roughness_no_bathy: double\n", + "hand_source: large_string\n", + "vpuid: int64\n", + "----\n", + "hf_source: [[\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",...,\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\"]]\n", + "owp_ahg_c: [[0.2783956527709961,0.26645627617836,0.2778419256210327,0.2656676173210144,0.2778984308242798,...,0.24669642746448517,0.2410922795534134,0.21650205552577972,0.23524419963359833,0.2453560084104538]]\n", + "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.46750980615615845,0.46937182545661926,0.45957159996032715,0.4573363959789276,0.46542882919311523]]\n", + "owp_ahg_a: [[15.474262237548828,15.589872360229492,15.27043628692627,15.695556640625,15.373209953308105,...,14.39522933959961,15.831462860107422,17.09531593322754,15.73424243927002,15.698975563049316]]\n", + "owp_ahg_b: [[0.21297027170658112,0.20660685002803802,0.22359369695186615,0.21279571950435638,0.21274369955062866,...,0.22100567817687988,0.20211540162563324,0.1899542361497879,0.19244758784770966,0.20660847425460815]]\n", + "owp_ahg_k: [[0.2632668912410736,0.2649047374725342,0.2646264433860779,0.26899534463882446,0.26056915521621704,...,0.30960214138031006,0.3056097626686096,0.3103371262550354,0.3096386194229126,0.2898109257221222]]\n", + "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.31428271532058716,0.34859946370124817,0.38616055250167847,0.37841135263442993,0.31649747490882874]]\n", + "owp_dingman_r: [[34.94359157777524,78.50221647993335,23.816824026591785,71.37071616896621,68.16839964475069,...,87.87201916851028,120.53569949076702,196.1542010519919,183.4062312745436,144.50062488154873]]\n", + "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,11.041326186301118,2.2220571382098906,2.3020617663270158,7.381776970395373,10.90124605539771]]\n", + "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.00064764,0.00001,0.00286086,0.0031118,0.00064093]]\n", + "owp_bf_channel_area: [[80.46187884864187,90.44316537020227,89.3267744543078,89.62238381994659,89.90283819898646,...,33.88476675445253,48.125833094134954,45.14128117526434,41.78322001139718,56.363562127191784]]\n", + "owp_inchan_channel_area: [[56.33064405881224,47.85337776245516,45.59987281142967,48.37917782823691,50.59816670544206,...,25.20392164626514,32.69610800625728,32.75638185253578,31.798663807897064,41.19407020582869]]\n", + "owp_bf_channel_perimeter: [[59.02193299271193,56.93125233080745,56.69193540611867,56.50078213602541,55.71884696596583,...,23.818806528270343,30.790736238597308,31.668208182122697,30.244473860557726,30.83026535966577]]\n", + "owp_inchan_channel_perimeter: [[45.902837801519674,42.88972958711348,42.781428840029896,42.25541060346861,42.74371656180321,...,21.110362040213978,26.22321774860116,29.594636105720035,28.74937461636522,29.077815788520574]]\n", + "owp_bf_channel_volume: [[238808.31641383746,122992.17939660752,186691.7464348059,47767.11633624437,114784.993280073,...,374132.76248264225,106938.35095912036,103918.0174765935,308434.4112290948,614433.0593072133]]\n", + "owp_inchan_channel_volume: [[167187.57332894605,65075.024723013536,95303.11538032124,25785.23039753066,64602.07866200221,...,278284.72007038887,72652.62018698554,75407.21426593073,234730.64418647942,449066.69533706637]]\n", + "owp_bf_channel_bed_area: [[2892.2022978763703,2606.358246231509,2120.7761872525807,986.0789590461588,2397.4053647652536,...,25676.071091443406,5707.898767447053,5221.871474208872,16154.579945017962,35426.56632826127]]\n", + "owp_inchan_channel_bed_area: [[2882.6693726661933,1727.420220530991,1302.426453449232,684.7218800959778,1690.876524247671,...,20277.60153281499,4218.877531759497,3638.538549084465,11664.69130783148,24593.30687386722]]\n", + "owp_y_bf: [[1.4270710945129395,1.6684650778770447,1.6728723049163818,1.6662447452545166,1.6976696848869324,...,1.6160700917243958,1.7370615005493164,1.5624184012413025,1.5164253115653992,2.082498550415039]]\n", + "owp_y_inchan: [[1.2915553450584412,1.1687858998775482,1.128367155790329,1.2016823887825012,1.2436135411262512,...,1.3436493873596191,1.3731055855751038,1.1905770599842072,1.1925222873687744,1.5661699175834656]]\n", + "...\n" + ] + } + ], + "source": [ + "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", + "catalog_name = 'dev'\n", + "namespace = 'bathymetry_ml_auxiliary'\n", + "tablename = 'vpuid=01'\n", + "catalog = load_catalog(name=catalog_name, \n", + " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", + "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "print(iceberg_table.to_string(preview_cols=20))" + ] + }, + { + "cell_type": "markdown", + "id": "d69402cf-d224-45e8-9680-74b28205ea44", + "metadata": {}, + "source": [ + "#### Observe List of Tables under Catalog of Interest" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "aec525f7-6753-4ca5-ba5e-916c8c6659ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "List of tables under bathymetry_ml_auxiliary within dev:\n", + "\n", + "[('bathymetry_ml_auxiliary', 'vpuid=01'), ('bathymetry_ml_auxiliary', 'vpuid=02'), ('bathymetry_ml_auxiliary', 'vpuid=03N'), ('bathymetry_ml_auxiliary', 'vpuid=03S'), ('bathymetry_ml_auxiliary', 'vpuid=03W'), ('bathymetry_ml_auxiliary', 'vpuid=04'), ('bathymetry_ml_auxiliary', 'vpuid=05'), ('bathymetry_ml_auxiliary', 'vpuid=06'), ('bathymetry_ml_auxiliary', 'vpuid=07'), ('bathymetry_ml_auxiliary', 'vpuid=08'), ('bathymetry_ml_auxiliary', 'vpuid=09'), ('bathymetry_ml_auxiliary', 'vpuid=10L'), ('bathymetry_ml_auxiliary', 'vpuid=10U'), ('bathymetry_ml_auxiliary', 'vpuid=11'), ('bathymetry_ml_auxiliary', 'vpuid=12'), ('bathymetry_ml_auxiliary', 'vpuid=13'), ('bathymetry_ml_auxiliary', 'vpuid=14'), ('bathymetry_ml_auxiliary', 'vpuid=15'), ('bathymetry_ml_auxiliary', 'vpuid=16'), ('bathymetry_ml_auxiliary', 'vpuid=17'), ('bathymetry_ml_auxiliary', 'vpuid=18')]\n" + ] + } + ], + "source": [ + "# All tables created within the namespace of interest.\n", + "print(f\"List of tables under {namespace} within {catalog_name}:\\n\\n{catalog.list_tables(namespace)}\") " + ] + }, + { + "cell_type": "markdown", + "id": "c5b0cacc-8f92-49db-b080-5c1ecb72f57b", + "metadata": {}, + "source": [ + "#### Query Iceberg Catalog's Table by Conditions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "28153d1a-46de-43f0-962a-5b613bdbaa73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "hf_source: large_string\n", + "owp_ahg_c: double\n", + "owp_ahg_f: double\n", + "owp_ahg_a: double\n", + "owp_ahg_b: double\n", + "owp_ahg_k: double\n", + "owp_ahg_m: double\n", + "owp_dingman_r: double\n", + "LENGTHKM: double\n", + "slope: double\n", + "owp_bf_channel_area: double\n", + "owp_inchan_channel_area: double\n", + "owp_bf_channel_perimeter: double\n", + "owp_inchan_channel_perimeter: double\n", + "owp_bf_channel_volume: double\n", + "owp_inchan_channel_volume: double\n", + "owp_bf_channel_bed_area: double\n", + "owp_inchan_channel_bed_area: double\n", + "owp_y_bf: double\n", + "owp_y_inchan: double\n", + "owp_tw_bf: double\n", + "owp_tw_inchan: double\n", + "owp_roughness_bathy: double\n", + "owp_roughness_bathy_lower_bound: double\n", + "owp_roughness_bathy_upper_bound: double\n", + "hf_id: int64\n", + "owp_roughness_no_bathy: double\n", + "hand_source: large_string\n", + "vpuid: int64\n", + "----\n", + "hf_source: [[\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",...,\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\"]]\n", + "owp_ahg_c: [[0.24283623695373535,0.24679405987262726,0.24796637892723083,0.24558784067630768,0.2416917383670807,...,0.24669642746448517,0.2410922795534134,0.21650205552577972,0.23524419963359833,0.2453560084104538]]\n", + "owp_ahg_f: [[0.44655272364616394,0.45140621066093445,0.45361408591270447,0.4597383141517639,0.46237292885780334,...,0.46750980615615845,0.46937182545661926,0.45957159996032715,0.4573363959789276,0.46542882919311523]]\n", + "owp_ahg_a: [[16.292556762695312,16.71356201171875,15.591700553894043,17.455158233642578,18.136899948120117,...,14.39522933959961,15.831462860107422,17.09531593322754,15.73424243927002,15.698975563049316]]\n", + "owp_ahg_b: [[0.1877126842737198,0.18782523274421692,0.19405822455883026,0.1825941652059555,0.17806944251060486,...,0.22100567817687988,0.20211540162563324,0.1899542361497879,0.19244758784770966,0.20660847425460815]]\n", + "owp_ahg_k: [[0.2711319327354431,0.26460614800453186,0.28298744559288025,0.2645978033542633,0.25294309854507446,...,0.30960214138031006,0.3056097626686096,0.3103371262550354,0.3096386194229126,0.2898109257221222]]\n", + "owp_ahg_m: [[0.4176878035068512,0.40634584426879883,0.3849862217903137,0.4011600911617279,0.39922311902046204,...,0.31428271532058716,0.34859946370124817,0.38616055250167847,0.37841135263442993,0.31649747490882874]]\n", + "owp_dingman_r: [[163.10582763253834,121.4828569903629,109.42969777415078,131.98548478571865,144.5459281918684,...,87.87201916851028,120.53569949076702,196.1542010519919,183.4062312745436,144.50062488154873]]\n", + "LENGTHKM: [[1.1828318619868186,3.453164835389148,3.7720762279021254,2.444968856687704,2.735238005445334,...,11.041326186301118,2.2220571382098906,2.3020617663270158,7.381776970395373,10.90124605539771]]\n", + "slope: [[0.00489451,0.00399073,0.00354641,0.00291035,0.00222913,...,0.00064764,0.00001,0.00286086,0.0031118,0.00064093]]\n", + "..." + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Filter Iceberg table based on threshold set for a given feature\n", + "feature_name = 'owp_ahg_c'\n", + "thresh_val = 0.25\n", + "catalog.load_table(f'{namespace}.{tablename}').scan(row_filter=LessThan(feature_name, thresh_val)).to_arrow()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6320114d-8115-4e1f-b8fd-3c80aeaa07de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "hf_source: large_string\n", + "owp_ahg_c: double\n", + "owp_ahg_f: double\n", + "owp_ahg_a: double\n", + "owp_ahg_b: double\n", + "owp_ahg_k: double\n", + "owp_ahg_m: double\n", + "owp_dingman_r: double\n", + "LENGTHKM: double\n", + "slope: double\n", + "owp_bf_channel_area: double\n", + "owp_inchan_channel_area: double\n", + "owp_bf_channel_perimeter: double\n", + "owp_inchan_channel_perimeter: double\n", + "owp_bf_channel_volume: double\n", + "owp_inchan_channel_volume: double\n", + "owp_bf_channel_bed_area: double\n", + "owp_inchan_channel_bed_area: double\n", + "owp_y_bf: double\n", + "owp_y_inchan: double\n", + "owp_tw_bf: double\n", + "owp_tw_inchan: double\n", + "owp_roughness_bathy: double\n", + "owp_roughness_bathy_lower_bound: double\n", + "owp_roughness_bathy_upper_bound: double\n", + "hf_id: int64\n", + "owp_roughness_no_bathy: double\n", + "hand_source: large_string\n", + "vpuid: int64\n", + "----\n", + "hf_source: [[\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",...,\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\"]]\n", + "owp_ahg_c: [[0.2783956527709961,0.26645627617836,0.2778419256210327,0.2656676173210144,0.2778984308242798,...,0.26968732476234436,0.262911856174469,0.2565535306930542,0.27186259627342224,0.2509637176990509]]\n", + "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.4091697037220001,0.4002934396266937,0.3982747495174408,0.41463810205459595,0.4620860517024994]]\n", + "owp_ahg_a: [[15.474262237548828,15.589872360229492,15.27043628692627,15.695556640625,15.373209953308105,...,13.57089614868164,15.591622352600098,17.110021591186523,15.198208808898926,16.834657669067383]]\n", + "owp_ahg_b: [[0.21297027170658112,0.20660685002803802,0.22359369695186615,0.21279571950435638,0.21274369955062866,...,0.22416476905345917,0.20449289679527283,0.20677334070205688,0.2223099172115326,0.20692850649356842]]\n", + "owp_ahg_k: [[0.2632668912410736,0.2649047374725342,0.2646264433860779,0.26899534463882446,0.26056915521621704,...,0.29988086223602295,0.27692529559135437,0.2727428376674652,0.28107815980911255,0.2823072373867035]]\n", + "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.3715367913246155,0.4299010634422302,0.4299301207065582,0.40392443537712097,0.3803613483905792]]\n", + "owp_dingman_r: [[34.94359157777524,78.50221647993335,23.816824026591785,71.37071616896621,68.16839964475069,...,66.4125603175732,47.97490105010014,49.70555612325059,44.49719316488429,221.7325794960283]]\n", + "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,1.3556982962034625,1.7761013055059551,3.1182578724544983,5.662275879263775,0.760075523461262]]\n", + "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.0154941,0.02633596,0.01911396,0.04333816,0.00001]]\n", + "..." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_name = 'owp_ahg_c'\n", + "thresh_lower, thresh_upper = 0.25, 0.28\n", + "catalog.load_table(f'{namespace}.{tablename}').scan(row_filter=And(GreaterThan(feature_name, thresh_lower), \n", + " LessThan(feature_name, thresh_upper))).to_arrow()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3acdfde0-d34d-442e-87eb-724dbcef5d1b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "owp_ahg_f: double\n", + "owp_ahg_m: double\n", + "LENGTHKM: double\n", + "slope: double\n", + "----\n", + "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.4067888855934143,0.3982747495174408,0.4000049829483032,0.41463810205459595,0.4620860517024994]]\n", + "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.3738446533679962,0.4299301207065582,0.4191203713417053,0.40392443537712097,0.3803613483905792]]\n", + "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,2.891987367172746,3.1182578724544983,3.6554421303734443,5.662275879263775,0.760075523461262]]\n", + "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.02001729,0.01911396,0.02483032,0.04333816,0.00001]]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_name = 'owp_ahg_c'\n", + "thresh = 0.25\n", + "catalog.load_table(f'{namespace}.{tablename}').scan(row_filter=GreaterThanOrEqual(feature_name, thresh),\n", + " selected_fields=(\"owp_ahg_m\", \"slope\", \"LENGTHKM\", \"owp_ahg_f\")).to_arrow()\n" + ] + }, + { + "cell_type": "markdown", + "id": "01dc1ff0-bfd5-4b7a-82d0-1d60e237e2cd", + "metadata": {}, + "source": [ + "# Cross-Check & Verify Parquet Data Has Been Written To Its Associated Iceberg Table Sucessfully" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b365d647-bbfa-45a9-8673-52c564cf0ea7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "hf_source: string\n", + "owp_ahg_c: double\n", + "owp_ahg_f: double\n", + "owp_ahg_a: double\n", + "owp_ahg_b: double\n", + "owp_ahg_k: double\n", + "owp_ahg_m: double\n", + "owp_dingman_r: double\n", + "LENGTHKM: double\n", + "slope: double\n", + "owp_bf_channel_area: double\n", + "owp_inchan_channel_area: double\n", + "owp_bf_channel_perimeter: double\n", + "owp_inchan_channel_perimeter: double\n", + "owp_bf_channel_volume: double\n", + "owp_inchan_channel_volume: double\n", + "owp_bf_channel_bed_area: double\n", + "owp_inchan_channel_bed_area: double\n", + "owp_y_bf: double\n", + "owp_y_inchan: double\n", + "owp_tw_bf: double\n", + "owp_tw_inchan: double\n", + "owp_roughness_bathy: double\n", + "owp_roughness_bathy_lower_bound: double\n", + "owp_roughness_bathy_upper_bound: double\n", + "hf_id: int64\n", + "owp_roughness_no_bathy: double\n", + "hand_source: string\n", + "vpuid: dictionary\n", + "----\n", + "hf_source: [[\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",...,\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\"]]\n", + "owp_ahg_c: [[0.2783956527709961,0.26645627617836,0.2778419256210327,0.2656676173210144,0.2778984308242798,...,0.24669642746448517,0.2410922795534134,0.21650205552577972,0.23524419963359833,0.2453560084104538]]\n", + "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.46750980615615845,0.46937182545661926,0.45957159996032715,0.4573363959789276,0.46542882919311523]]\n", + "owp_ahg_a: [[15.474262237548828,15.589872360229492,15.27043628692627,15.695556640625,15.373209953308105,...,14.39522933959961,15.831462860107422,17.09531593322754,15.73424243927002,15.698975563049316]]\n", + "owp_ahg_b: [[0.21297027170658112,0.20660685002803802,0.22359369695186615,0.21279571950435638,0.21274369955062866,...,0.22100567817687988,0.20211540162563324,0.1899542361497879,0.19244758784770966,0.20660847425460815]]\n", + "owp_ahg_k: [[0.2632668912410736,0.2649047374725342,0.2646264433860779,0.26899534463882446,0.26056915521621704,...,0.30960214138031006,0.3056097626686096,0.3103371262550354,0.3096386194229126,0.2898109257221222]]\n", + "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.31428271532058716,0.34859946370124817,0.38616055250167847,0.37841135263442993,0.31649747490882874]]\n", + "owp_dingman_r: [[34.94359157777524,78.50221647993335,23.816824026591785,71.37071616896621,68.16839964475069,...,87.87201916851028,120.53569949076702,196.1542010519919,183.4062312745436,144.50062488154873]]\n", + "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,11.041326186301118,2.2220571382098906,2.3020617663270158,7.381776970395373,10.90124605539771]]\n", + "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.00064764,0.00001,0.00286086,0.0031118,0.00064093]]\n", + "..." + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the Parquet file into a DataFrame\n", + "parquet_file_path = f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/vpuid=01/ml_auxiliary_data.parquet'\n", + "df = pq.read_table(parquet_file_path)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86ec5e38-ab4c-4185-bc0f-3306e670f968", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "iceberg", + "language": "python", + "name": "iceberg" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 11e99b3e8b5500d56ac658791676768bea42e7d5 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 6 May 2025 14:02:43 -0400 Subject: [PATCH 22/97] added S3 capability to demo --- examples/Demo_Iceberg.ipynb | 204 +++++++++++++++++++++++++++++------- 1 file changed, 169 insertions(+), 35 deletions(-) diff --git a/examples/Demo_Iceberg.ipynb b/examples/Demo_Iceberg.ipynb index 3538302..0a8ad2f 100644 --- a/examples/Demo_Iceberg.ipynb +++ b/examples/Demo_Iceberg.ipynb @@ -20,9 +20,10 @@ "import pyiceberg\n", "from pyiceberg.catalog import load_catalog\n", "from pyiceberg.schema import Schema\n", - "from pyiceberg.types import StringType, IntegerType\n", "import pyarrow.parquet as pq\n", "from pyiceberg.schema import Schema\n", + "import boto3\n", + "import s3fs\n", "from pyiceberg.types import NestedField, IntegerType, StringType, DoubleType, LongType\n", "from pyiceberg.expressions import EqualTo, LessThan, And, GreaterThan, GreaterThanOrEqual\n", "import os\n", @@ -66,12 +67,13 @@ " \n", " Args: \n", " parquet_file_path (str): Directory of a single parquet.\n", + "\n", " \n", - " Return: None\n", + " Return: A Pyarrow table.\n", " \n", " '''\n", " data = pq.read_table(parquet_file_path)\n", - " \n", + " \n", " return data\n", "\n", " def establish_catalog(self, name='dev'):\n", @@ -165,7 +167,7 @@ " \n", " return\n", "\n", - " def create_table_for_all_parquets(self, parquet_files, namespace):\n", + " def create_table_for_all_parquets(self, parquet_files, namespace, source='local'):\n", " '''\n", " Convert parquets to Iceberg tables - each w/ their inherited schema.\n", "\n", @@ -185,8 +187,41 @@ " self.create_table_for_parquet(os.path.split(os.path.split(parquet_file)[0])[1],\n", " data_table, \n", " schema,\n", - " namespace) \n", + " namespace)\n", + " return\n", + "\n", + " def create_table_for_all_s3parquets(self, namespace, bucket_name='ngwpc-bathymetry', profile_name='default'):\n", + " '''\n", + " Convert parquets from S3 to Iceberg tables - each w/ their inherited schema.\n", + " \n", + " Args:\n", + " bucket_name (list): S3 bucket name.\n", + " \n", + " namespace (str): Namespace for which the Iceberg table will reside within\n", + " the Iceberg catalog.\n", + "\n", + " profile_name (str: Profile name declared in the AWS configuration file. \n", + " Default: 'default' \n", + " \n", + " Return: None\n", + " '''\n", + " # Instantiate bucket of interest.\n", + " session = boto3.Session(profile_name=profile_name)\n", + " s3 = session.resource('s3')\n", + " bucket_ob = s3.Bucket(bucket_name)\n", + " pyarrow_table = {}\n", + " for s3obj in bucket_ob.objects.all():\n", + " pyarrow_table[s3obj.key]= pq.read_table(f's3://{bucket_name}/{s3obj.key}',\n", + " filesystem=s3fs.S3FileSystem())\n", + " for key, data_table in pyarrow_table.items():\n", + " data_pyarrow_schema = data_table.schema\n", + " schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema)\n", + " self.create_table_for_parquet(os.path.split(os.path.split(key)[0])[1],\n", + " data_table, \n", + " schema,\n", + " namespace) \n", " return\n", + " \n", " " ] }, @@ -197,7 +232,7 @@ "metadata": {}, "source": [ "#### Demo:\n", - "#### Establish A New Iceberg Catalog & Generate Tables For All Parquets" + "#### Establish A New Iceberg Catalog & Generate Tables For All Parquets in Local" ] }, { @@ -309,11 +344,130 @@ "# Establish new Iceberg catalog\n", "obj.establish_catalog('dev')\n", "\n", - "# Generate tables for all parquets detected w/in the new Iceberg catalog\n", + "# Generate tables w/in the new Iceberg catalog for all parquets detected \n", "obj.create_table_for_all_parquets(parquet_list, \n", " namespace='bathymetry_ml_auxiliary')" ] }, + { + "cell_type": "markdown", + "id": "b7f5dba5-6892-45a2-81b8-1a80a3ad9f49", + "metadata": {}, + "source": [ + "#### Demo\n", + "#### Establish A New Iceberg Catalog & Generate Tables For All Parquets in S3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a1c58d4f-59e5-41d8-be31-536f3f790216", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as string on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", + "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n" + ] + } + ], + "source": [ + "# Instantiate class\n", + "obj = IcebergTable()\n", + "\n", + "# Establish new Iceberg catalog\n", + "obj.establish_catalog('dev2')\n", + "\n", + "# Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", + "obj.create_table_for_all_s3parquets(namespace='bathymetry_ml_auxiliary',\n", + " bucket_name='ngwpc-bathymetry',\n", + " profile_name='default')" + ] + }, { "cell_type": "markdown", "id": "71949e8b-36f2-4e74-afb2-741a884d95dd", @@ -325,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "f449ec30-027e-45c8-91f2-9ad5402b6d17", "metadata": {}, "outputs": [ @@ -409,7 +563,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "aec525f7-6753-4ca5-ba5e-916c8c6659ec", "metadata": {}, "outputs": [ @@ -438,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "28153d1a-46de-43f0-962a-5b613bdbaa73", "metadata": {}, "outputs": [ @@ -489,7 +643,7 @@ "..." ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -503,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "6320114d-8115-4e1f-b8fd-3c80aeaa07de", "metadata": {}, "outputs": [ @@ -554,7 +708,7 @@ "..." ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -568,30 +722,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "3acdfde0-d34d-442e-87eb-724dbcef5d1b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyarrow.Table\n", - "owp_ahg_f: double\n", - "owp_ahg_m: double\n", - "LENGTHKM: double\n", - "slope: double\n", - "----\n", - "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.4067888855934143,0.3982747495174408,0.4000049829483032,0.41463810205459595,0.4620860517024994]]\n", - "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.3738446533679962,0.4299301207065582,0.4191203713417053,0.40392443537712097,0.3803613483905792]]\n", - "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,2.891987367172746,3.1182578724544983,3.6554421303734443,5.662275879263775,0.760075523461262]]\n", - "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.02001729,0.01911396,0.02483032,0.04333816,0.00001]]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "feature_name = 'owp_ahg_c'\n", "thresh = 0.25\n", From 5356ff147007760b414c686285ccb1a2d86c1b52 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 6 May 2025 14:04:01 -0400 Subject: [PATCH 23/97] script to transform parquet into apache iceberg --- .../src/icefabric_manage/iceberg_table.py | 218 ++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 src/icefabric_manage/src/icefabric_manage/iceberg_table.py diff --git a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py new file mode 100644 index 0000000..4a46393 --- /dev/null +++ b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py @@ -0,0 +1,218 @@ +import pyarrow as pa +import pyiceberg +from pyiceberg.catalog import load_catalog +from pyiceberg.schema import Schema +from pyiceberg.types import NestedField, IntegerType, StringType, DoubleType, LongType +import pyarrow.parquet as pq +from pyiceberg.schema import Schema +import boto3 +import s3fs +import os + +class IcebergTable(object): + ''' + Create a Iceberg table per parquet file w/ its inherited schema set. + + ''' + def __init__(self): + + # Set location of where the iceberg catalog will reside + os.environ['PYICEBERG_HOME'] = os.getcwd() + + def read_data_dirs(self, data_dir): + ''' + Extract the list of parquet directories. + + Args: + data_dir (str): Parent directory of the parquet files. + Note: All the ml_auxiliary_data parquet + files are save under same filenames, + but categorized by 'vpuid' conditions. + + Return (list): List of directories associated with each parquet file. + + ''' + parquet_list = [] + for folder, subfolders, files in os.walk(data_dir): + if folder != data_dir: + for file in files: + parquet_list.append(f'{folder}/{file}') + + return parquet_list + + def read_data(self, parquet_file_path): + ''' + Load a single parquet as a Pyarrow table. + + Args: + parquet_file_path (str): Directory of a single parquet. + + + Return: A Pyarrow table. + + ''' + data = pq.read_table(parquet_file_path) + + return data + + def establish_catalog(self, name='dev'): + ''' + Creates a new Iceberg catalog. + + Args: + name (str): Name of the catalog to be created. + Default: 'dev' for development catalog + + Return: None + + ''' + # Establish a new Iceberg catalog & its configuration + self.catalog = load_catalog(name=name, + **{'uri': f'sqlite:///iceberg_catalog/{name}_catalog.db'}) + + return + + def convert_pyarrow_to_iceberg_schema(self, arrow_schema): + """ + Translate a given Pyarrow schema into a schema acceptable by Iceberg. + + Args: + arrow_schema (object): Pyarrow schema read from the loaded + parquet of interest. + + Return (object): Iceberg schema + + """ + fields = [] + for idx in range(len(arrow_schema)): + + # Extraction of the datatype & name of each schema row + field_name = arrow_schema.field(idx).name + arrow_type = arrow_schema.field(idx).type + + # Iceberg datatypes to pyarrow datatypes + if pa.types.is_int32(arrow_type): + iceberg_type = LongType() + elif pa.types.is_string(arrow_type): + iceberg_type = StringType() + elif pa.types.is_float64(arrow_type): + iceberg_type = DoubleType() + elif pa.types.is_int64(arrow_type): + iceberg_type = LongType() + elif pa.types.is_dictionary(arrow_type): + if pa.types.is_string(arrow_type.value_type): + iceberg_type = StringType() + elif pa.types.is_int32(arrow_type.value_type): + iceberg_type = LongType() + else: + raise ValueError(f"Unsupported PyArrow type: {arrow_type}") + + # Establish the new schema acceptable to Iceberg + fields.append(NestedField(field_id=idx + 1, + required=False, + name=field_name, + field_type=iceberg_type)) + # Iceberg schema + schema = Schema(*fields) + + return schema + + def create_table_for_parquet(self, iceberg_tablename, data_table, schema, namespace): + ''' + Convert parquet Pyarrow table to iceberg table & allocate Iceberg catalog under the ./iceberg_catalog directory. + + Args: + iceberg_tablename (str): Name of the Iceberg table to be created. + + data_table (object): Pyarrow table + + schema (object): Unique Iceberg schema to be set for the Iceberg table. + + namespace (str): Namespace for which the Iceberg table will reside within + the Iceberg catalog. + + Return: None + + ''' + + # Create an Iceberg table + self.catalog.create_namespace_if_not_exists(namespace) + iceberg_table = self.catalog.create_table(identifier=f"{namespace}.{iceberg_tablename}", + schema=schema, + location=f"{os.environ['PYICEBERG_HOME']}/iceberg_catalog") + + # Updates the Iceberg table with data of interest. + iceberg_table.append(data_table) + + return + + def create_table_for_all_parquets(self, parquet_files, namespace, source='local'): + ''' + Convert parquets to Iceberg tables - each w/ their inherited schema. + + Args: + parquet_files (list): List of directories of the parquet files. + + namespace (str): Namespace for which the Iceberg table will reside within + the Iceberg catalog. + + Return: None + + ''' + for idx, parquet_file in enumerate(parquet_files): + data_table = self.read_data(parquet_file) + data_pyarrow_schema = data_table.schema + schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema) + self.create_table_for_parquet(os.path.split(os.path.split(parquet_file)[0])[1], + data_table, + schema, + namespace) + return + + def create_table_for_all_s3parquets(self, namespace, bucket_name='ngwpc-bathymetry', profile_name='default'): + ''' + Convert parquets from S3 to Iceberg tables - each w/ their inherited schema. + + Args: + bucket_name (list): S3 bucket name. + + namespace (str): Namespace for which the Iceberg table will reside within + the Iceberg catalog. + + profile_name (str: Profile name declared in the AWS configuration file. + Default: 'default' + + Return: None + ''' + # Instantiate bucket of interest. + session = boto3.Session(profile_name=profile_name) + s3 = session.resource('s3') + bucket_ob = s3.Bucket(bucket_name) + pyarrow_table = {} + for s3obj in bucket_ob.objects.all(): + pyarrow_table[s3obj.key]= pq.read_table(f's3://{bucket_name}/{s3obj.key}', + filesystem=s3fs.S3FileSystem()) + for key, data_table in pyarrow_table.items(): + data_pyarrow_schema = data_table.schema + schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema) + self.create_table_for_parquet(os.path.split(os.path.split(key)[0])[1], + data_table, + schema, + namespace) + return + + +if __name__ == '__main__': + + # Instantiate class + obj = IcebergTable() + + # Extract list of parquets + parquet_list = obj.read_data_dirs(data_dir=f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/') + + # Establish new Iceberg catalog + obj.establish_catalog('dev') + + # Generate tables w/in the new Iceberg catalog for all parquets detected + obj.create_table_for_all_parquets(parquet_list, + namespace='bathymetry_ml_auxiliary') \ No newline at end of file From a353c20b7b38b11308dd19e021d7bed4f46b6c03 Mon Sep 17 00:00:00 2001 From: "Quercus.Hamlin" Date: Tue, 6 May 2025 14:34:28 -0400 Subject: [PATCH 24/97] chore: add boto3 dependency --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index bbe14f0..c9addc0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,6 +43,7 @@ dev-dependencies = [ "icefabric_manage", "icefabric_tools", "examples", + "boto3", ] package = false From c285634b302bccbc07857b22225d4c2123dff07f Mon Sep 17 00:00:00 2001 From: "Quercus.Hamlin" Date: Tue, 6 May 2025 14:35:25 -0400 Subject: [PATCH 25/97] chore: uv lock --- uv.lock | 3176 ++++++++++++++++++++++++++++--------------------------- 1 file changed, 1602 insertions(+), 1574 deletions(-) diff --git a/uv.lock b/uv.lock index 5dd3bbe..663f3e3 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 2 requires-python = ">3.10, <3.13" resolution-markers = [ 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<201017958+SylC-ray@users.noreply.github.com> Date: Tue, 6 May 2025 14:47:08 -0400 Subject: [PATCH 26/97] Update iceberg_table.py warning is expected: Iceberg does not have a dictionary type. will be inferred as int32 on read. --- src/icefabric_manage/src/icefabric_manage/iceberg_table.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py index 4a46393..76a336e 100644 --- a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py +++ b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py @@ -132,6 +132,9 @@ def create_table_for_parquet(self, iceberg_tablename, data_table, schema, namesp the Iceberg catalog. Return: None + + Note: The following warning is expected: + Iceberg does not have a dictionary type. will be inferred as int32 on read. ''' @@ -183,6 +186,7 @@ def create_table_for_all_s3parquets(self, namespace, bucket_name='ngwpc-bathymet Default: 'default' Return: None + ''' # Instantiate bucket of interest. session = boto3.Session(profile_name=profile_name) @@ -215,4 +219,4 @@ def create_table_for_all_s3parquets(self, namespace, bucket_name='ngwpc-bathymet # Generate tables w/in the new Iceberg catalog for all parquets detected obj.create_table_for_all_parquets(parquet_list, - namespace='bathymetry_ml_auxiliary') \ No newline at end of file + namespace='bathymetry_ml_auxiliary') From 0c8794902619d5f84330c08430c6c06cdb298d23 Mon Sep 17 00:00:00 2001 From: Daniel Cumpton Date: Tue, 6 May 2025 15:47:26 -0600 Subject: [PATCH 27/97] initial commit of new api prototype --- src/icefabric_api/README.md | 0 src/icefabric_api/src/cfe_params.csv | 26 ++++++++++++ src/icefabric_api/src/gauge_06710385.gpkg | Bin 0 -> 319488 bytes .../src/get_module_parameters.py | 38 ++++++++++++++++++ src/icefabric_api/src/requirements.txt | 2 + 5 files changed, 66 insertions(+) create mode 100644 src/icefabric_api/README.md create mode 100644 src/icefabric_api/src/cfe_params.csv create mode 100644 src/icefabric_api/src/gauge_06710385.gpkg create mode 100644 src/icefabric_api/src/get_module_parameters.py create mode 100644 src/icefabric_api/src/requirements.txt diff --git a/src/icefabric_api/README.md b/src/icefabric_api/README.md new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_api/src/cfe_params.csv b/src/icefabric_api/src/cfe_params.csv new file mode 100644 index 0000000..894fa0a --- /dev/null +++ b/src/icefabric_api/src/cfe_params.csv @@ -0,0 +1,26 @@ +name,description,units,data_type,calibratable,source_file,min,max,default_value,divide_attr_name +soil_params.b,beta exponent on Clapp-Hornberger (1978) soil water relations,NULL,double,TRUE,attr,2,15,4.05,mode.bexp_soil_layers_stag=1 +soil_params.satdk,saturated hydraulic conductivity,m/s,double,TRUE,attr,0.000000195,0.00141,0.00000338,geom_mean.dksat_soil_layers_stag=1 +soil_params.satpsi,saturated capillary head,m,double,TRUE,attr,0.036,0.955,0.355,geom_mean.psisat_soil_layers_stag=1 +soil_params.slop,this factor (0-1) modifies the gradient of the hydraulic head at the soil bottom. 0=no-flow.,m/m,double,TRUE,attr,0.0000598,1,0.05,mean.slope_1km +soil_params.smcmax,saturated soil moisture content (Maximum soil moisture content),m/m,double,TRUE,attr,0.16,0.58,0.439,mean.smcmax_soil_layers_stag=1 +soil_params.wltsmc,wilting point soil moisture content (< soil_params.smcmax),m/m,double,TRUE,attr,0.05,0.3,per soil type,mean.smcwlt_soil_layers_stag=1 +soil_params.expon,"optional; defaults to 1, This parameter defines the soil reservoirs to be linear, Use linear reservoirs",NULL,double,FALSE,const,NULL,NULL,1, +soil_params.expon_secondary,"optional; defaults to 1, This parameter defines the soil reservoirs to be linear, Use linear reservoirs ",NULL,double,FALSE,const,NULL,NULL,1, +max_gw_storage,maximum storage in the conceptual reservoir,m,double,TRUE,attr,0.01,0.25,0.05,mean.Zmax +Cgw,the primary outlet coefficient,m/hr,double,TRUE,attr,0.0000018,0.0018,0.000018,mean.Coeff +expon,exponent parameter for nonlinear ground water reservoir (1.0 for linear reservoir),NULL,double,TRUE,attr,1,8,3,mode.Expon +gw_storage,initial condition for groundwater reservoir - it is the ground water as a decimal fraction of the maximum groundwater storage (max_gw_storage) for the initial timestep,m/m,double,FALSE,const,NULL,NULL,0.05, +alpha_fc,alpha at fc for clapp hornberger (field capacity),NULL,double,FALSE,const,NULL,NULL,0.33, +soil_storage,initial condition for soil reservoir - it is the water in the soil as a decimal fraction of maximum soil water storage (smcmax x depth) for the initial timestep. Default = 0.5,m/m,double,FALSE,const,NULL,NULL,0.5, +K_nash,Nash Config param for lateral subsurface runoff (Nash discharge to storage ratio),1/m,double,TRUE,const,0,1,0.003, +K_lf,Nash Config param - primary reservoir,NULL,double,TRUE,const,0,1,0.01, +nash_storage,Nash Config param - secondary reservoir ,NULL,double,FALSE,const,NULL,NULL,"0.0,0.0", +giuh_ordinates,Giuh (geomorphological instantaneous unit hydrograph) ordinates in dt time steps,NULL,double,FALSE,const,NULL,NULL,"0.55, 0.25, 0.2", +a_Xinanjiang_inflection_point_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,TRUE,CFE-X_params,-0.5,0.5,-0.2,AXAJ +b_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,TRUE,CFE-X_params,0.01,10,0.66,BXAJ +x_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,TRUE,CFE-X_params,0.01,10,0.02,XXAJ +urban_decimal_fraction,when surface_water_partitioning_scheme=Xinanjiang,NULL,double ,FALSE,const,0,1,0.01, +refkdt,Reference Soil Infiltration Parameter (used in runoff formulation),NULL,double,TRUE,attr,0.1,4,1,mean.refkdt +soil_params.depth,soil depth,m,double,FALSE,const,NULL,NULL,2, + diff --git a/src/icefabric_api/src/gauge_06710385.gpkg b/src/icefabric_api/src/gauge_06710385.gpkg new file mode 100644 index 0000000000000000000000000000000000000000..8f8a14a4968a83ac42f8b1fd7beb05637f218975 GIT binary patch literal 319488 zcmeF)2RxO3|3C0!@0}6JN#Q7aD+wi(m64*7aqN{tAtf12Ekqg;4WvY&WE@EoDO8$T zN<(RC{$D2@QNG{b_x|4Z|Gw|v|9^Qr>Uf@Ooa=gD@6Wt0IL@;6kD!u$Lc@bSBFNe# zVG;`q$%ITMkw}y9&ldlfUoQBDoe8|MkVs0`7`CvVX));i!QW0Ism_G z4BZW9;6EM4pk7`T{|U?CKOtcO{Ljv3M-pTc*emFVZ-_4<0z?Fe2oMnypG-R|k`9O{W=Xlhw&nLqj5IWYZu|YBlfe#T#lGn)*7r+FFKMx|)WDI@;PQrp`{5b`DlfbKT?_ry#E) zZ>XuIC668czC=e$&ycZ%ahB#&oviF^-Q@ev-W{Rv_i{I16zLJPYkjGl44yHEd zPUa47@6SJBb^Wi20%AR5)3>OqU}5AppTt3e0czc7SxWtdC*BN8 zKZNQ{_N9jUhI@qh`FoKa9UN`#Y|SH?%Lcv~ypmUFXt=k3hzIWVhEsj0;nWZ>Dw!5d 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z##n5UD^Xhh^he#)&rrtI^1H$V6X-I;JFsHI<$-RztmB+Z79480q$ItzSQKpPEGw*8 zJ&KNA24e5U4E*q{@2J}b}Se6AeKXgvva(v;~ z7Q%P&d9m&C&nL7+gC1Uc>)2T*(&8jygN1mQj9n>84L3Sj@9^L-!;=oyI|ARQGH8&{ zoV6`Z;Z=`fgHBVw*R0Q3|DyJOmZK-liZbC+E3aQ@`Z(6bq`PDE_$tgQvKPl!fa`g` zQwDZ%vF&O-5&uJ7tUOdvmusS>krs%0QVS8p*A?6F1W!q~PL%<}2I9@Mp^C^OaG`A> zb@09$q@!q@?ml!!+zZsp{!nFW*Ep0pZ&UluZ!96xSGmM+wxe6GOYsuIpcKL)*yV{- zW)Pe8#`oEbT(%*reYt0S%CPovmaINYkdHKveOkZl8)d?!HVtprc)-r5Cf#kba)DyJ z1W6Hs>*bPCg2c8z0vVi^!d@AGg{aAx7TJ3vTTdeDixk-7d%mRi-Q(( zjyBQtokms}p=3HD4GuMO))>kuT7zaM)kVg4H$n~fayDc54p8Kh&jwlN0N5%{IHp zC?UkxHM>&9>j@ok*SV~_wz)-aO4k;hyXA>yflIcdwC_#}v(vso16O}`_mq_tX?BL& zqarfR#E1KaU0zrO+1kb%He$n8rHhTvki!jUD%e|Fa&nJ)mmFuVBB{?w(gH<{|JDz? zM451@4 Date: Thu, 15 May 2025 11:03:22 -0400 Subject: [PATCH 28/97] Add demo for xs mapping + updated iceberg demo --- examples/Demo_Iceberg.ipynb | 919 +- .../XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb | 7589 +++++++++++++++++ 2 files changed, 8456 insertions(+), 52 deletions(-) create mode 100644 examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb diff --git a/examples/Demo_Iceberg.ipynb b/examples/Demo_Iceberg.ipynb index 0a8ad2f..12f4c19 100644 --- a/examples/Demo_Iceberg.ipynb +++ b/examples/Demo_Iceberg.ipynb @@ -20,12 +20,13 @@ "import pyiceberg\n", "from pyiceberg.catalog import load_catalog\n", "from pyiceberg.schema import Schema\n", + "from pyiceberg.types import NestedField, IntegerType, StringType, DoubleType, LongType, BinaryType, BooleanType\n", + "from pyiceberg.expressions import EqualTo, LessThan, And, GreaterThan, GreaterThanOrEqual\n", "import pyarrow.parquet as pq\n", "from pyiceberg.schema import Schema\n", "import boto3\n", "import s3fs\n", - "from pyiceberg.types import NestedField, IntegerType, StringType, DoubleType, LongType\n", - "from pyiceberg.expressions import EqualTo, LessThan, And, GreaterThan, GreaterThanOrEqual\n", + "import re\n", "import os\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", @@ -33,12 +34,22 @@ "class IcebergTable(object):\n", " '''\n", " Create a Iceberg table per parquet file w/ its inherited schema set.\n", + "\n", + " Note: It is okay to expect following warning statements throughout process:\n", + " \"Iceberg does not have a dictionary type. will be inferred as string on read.\"\n", " \n", " '''\n", " def __init__(self):\n", - " \n", + "\n", + " # Generate folder for iceberg catalog\n", + " if not os.path.exists(f'{os.getcwd()}/iceberg_catalog'):\n", + " os.makedirs(f'{os.getcwd()}/iceberg_catalog')\n", + "\n", " # Set location of where the iceberg catalog will reside\n", " os.environ['PYICEBERG_HOME'] = os.getcwd()\n", + "\n", + " # Initialize namespace to be set for Iceberg catalog\n", + " self.namespace = str()\n", " \n", " def read_data_dirs(self, data_dir):\n", " '''\n", @@ -76,20 +87,26 @@ " \n", " return data\n", "\n", - " def establish_catalog(self, name='dev'):\n", + " def establish_catalog(self, catalog_name, namespace):\n", " '''\n", " Creates a new Iceberg catalog.\n", " \n", " Args:\n", - " name (str): Name of the catalog to be created.\n", - " Default: 'dev' for development catalog\n", + " catalog_name (str): Name of the catalog to be created.\n", + " Default: 'dev' for development catalog\n", + " namespace (str): Name of namespace.\n", "\n", " Return: None\n", " \n", - " ''' \n", + " ''' \n", " # Establish a new Iceberg catalog & its configuration\n", - " self.catalog = load_catalog(name=name,\n", - " **{'uri': f'sqlite:///iceberg_catalog/{name}_catalog.db'})\n", + " self.catalog = load_catalog(name=catalog_name,\n", + " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", + " \n", + " # Establish namespace to be create w/in catalog\n", + " self.namespace = namespace\n", + " if self.namespace not in self.catalog.list_namespaces():\n", + " self.catalog.create_namespace(self.namespace)\n", " \n", " return\n", "\n", @@ -120,6 +137,10 @@ " iceberg_type = DoubleType()\n", " elif pa.types.is_int64(arrow_type):\n", " iceberg_type = LongType()\n", + " elif pa.types.is_boolean(arrow_type):\n", + " iceberg_type = BooleanType()\n", + " elif pa.types.is_binary(arrow_type):\n", + " iceberg_type = BinaryType()\n", " elif pa.types.is_dictionary(arrow_type): \n", " if pa.types.is_string(arrow_type.value_type):\n", " iceberg_type = StringType()\n", @@ -138,7 +159,7 @@ " \n", " return schema\n", "\n", - " def create_table_for_parquet(self, iceberg_tablename, data_table, schema, namespace):\n", + " def create_table_for_parquet(self, iceberg_tablename, data_table, schema):\n", " '''\n", " Convert parquet Pyarrow table to iceberg table & allocate Iceberg catalog under the ./iceberg_catalog directory.\n", "\n", @@ -157,8 +178,7 @@ " '''\n", " \n", " # Create an Iceberg table\n", - " self.catalog.create_namespace_if_not_exists(namespace)\n", - " iceberg_table = self.catalog.create_table(identifier=f\"{namespace}.{iceberg_tablename}\",\n", + " iceberg_table = self.catalog.create_table(identifier=f\"{self.namespace}.{iceberg_tablename}\",\n", " schema=schema,\n", " location=f\"{os.environ['PYICEBERG_HOME']}/iceberg_catalog\")\n", " \n", @@ -167,35 +187,46 @@ " \n", " return\n", "\n", - " def create_table_for_all_parquets(self, parquet_files, namespace, source='local'):\n", + " def create_table_for_all_parquets(self, parquet_files, app_name='mip-xs'):\n", " '''\n", " Convert parquets to Iceberg tables - each w/ their inherited schema.\n", "\n", " Args:\n", " parquet_files (list): List of directories of the parquet files.\n", - " \n", - " namespace (str): Namespace for which the Iceberg table will reside within\n", - " the Iceberg catalog.\n", + "\n", + " app_name (str): Application to create Iceberg tables.\n", + " Options: 'mip-xs' & 'bathymetry_ml_auxiliary'\n", "\n", " Return: None\n", - " \n", + "\n", + " Note: The sourced data structures for the data in 'mip-xs' & \n", + " 'bathymetry_ml_auxiliary' S3 buckets differ.\n", + " \n", " '''\n", " for idx, parquet_file in enumerate(parquet_files):\n", + " if app_name == 'mip_xs':\n", + " iceberg_tablename = f\"{os.path.split(os.path.split(parquet_file)[1])[1].split('.')[0]}\"\n", + " \n", + " elif app_name == 'bathymetry_ml_auxiliary':\n", + " iceberg_tablename = f\"{os.path.split(os.path.split(parquet_file)[0])[1]}\"\n", + "\n", " data_table = self.read_data(parquet_file)\n", " data_pyarrow_schema = data_table.schema\n", " schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema)\n", - " self.create_table_for_parquet(os.path.split(os.path.split(parquet_file)[0])[1],\n", + " self.create_table_for_parquet(iceberg_tablename,\n", " data_table, \n", - " schema,\n", - " namespace)\n", + " schema)\n", " return\n", "\n", - " def create_table_for_all_s3parquets(self, namespace, bucket_name='ngwpc-bathymetry', profile_name='default'):\n", + " def create_table_for_all_s3parquets(self, app_name, bucket_name, profile_name='default'):\n", " '''\n", " Convert parquets from S3 to Iceberg tables - each w/ their inherited schema.\n", " \n", " Args:\n", " bucket_name (list): S3 bucket name.\n", + "\n", + " app_name (str): Application to create Iceberg tables.\n", + " Options: 'mip_xs' & 'bathymetry_ml_auxiliary'\n", " \n", " namespace (str): Namespace for which the Iceberg table will reside within\n", " the Iceberg catalog.\n", @@ -204,6 +235,7 @@ " Default: 'default' \n", " \n", " Return: None\n", + "\n", " '''\n", " # Instantiate bucket of interest.\n", " session = boto3.Session(profile_name=profile_name)\n", @@ -211,18 +243,27 @@ " bucket_ob = s3.Bucket(bucket_name)\n", " pyarrow_table = {}\n", " for s3obj in bucket_ob.objects.all():\n", - " pyarrow_table[s3obj.key]= pq.read_table(f's3://{bucket_name}/{s3obj.key}',\n", - " filesystem=s3fs.S3FileSystem())\n", + "\n", + " # For sourcing the preprocessed XS parquets from S3\n", + " if app_name == 'mip_xs' and re.match(r'^xs_data/.*\\.parquet$', s3obj.key):\n", + " iceberg_tablename = f\"{os.path.split(os.path.split(s3obj.key)[1])[1].split('.')[0]}\"\n", + " pyarrow_table[iceberg_tablename]= pq.read_table(f's3://{bucket_name}/{s3obj.key}',\n", + " filesystem=s3fs.S3FileSystem())\n", + " \n", + " # For sourcing the bathymetry_ml_auxiliary parquets from S3\n", + " elif app_name == 'bathymetry_ml_auxiliary':\n", + " iceberg_tablename = f\"{os.path.split(os.path.split(s3obj.key)[0])[1]}\"\n", + " pyarrow_table[iceberg_tablename]= pq.read_table(f's3://{bucket_name}/{s3obj.key}',\n", + " filesystem=s3fs.S3FileSystem())\n", + " \n", + " # Xforming each unique parquet to an iceberg table \n", " for key, data_table in pyarrow_table.items():\n", " data_pyarrow_schema = data_table.schema\n", " schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema)\n", - " self.create_table_for_parquet(os.path.split(os.path.split(key)[0])[1],\n", - " data_table, \n", - " schema,\n", - " namespace) \n", - " return\n", - " \n", - " " + " self.create_table_for_parquet(key,\n", + " data_table, \n", + " schema)\n", + " return" ] }, { @@ -335,6 +376,7 @@ } ], "source": [ + "# Scenario I\n", "# Instantiate class\n", "obj = IcebergTable()\n", "\n", @@ -342,11 +384,12 @@ "parquet_list = obj.read_data_dirs(data_dir=f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/')\n", "\n", "# Establish new Iceberg catalog\n", - "obj.establish_catalog('dev')\n", + "obj.establish_catalog(catalog_name='bathymetry_ml_auxiliary_dev',\n", + " namespace='bathymetry_ml_auxiliary')\n", "\n", "# Generate tables w/in the new Iceberg catalog for all parquets detected \n", "obj.create_table_for_all_parquets(parquet_list, \n", - " namespace='bathymetry_ml_auxiliary')" + " app_name='bathymetry_ml_auxiliary')" ] }, { @@ -361,7 +404,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "a1c58d4f-59e5-41d8-be31-536f3f790216", + "id": "ad60621b-fc16-44c7-85b9-8a66a2b1835b", "metadata": {}, "outputs": [ { @@ -456,18 +499,41 @@ } ], "source": [ + "# Scenario II\n", "# Instantiate class\n", "obj = IcebergTable()\n", "\n", "# Establish new Iceberg catalog\n", - "obj.establish_catalog('dev2')\n", + "obj.establish_catalog(catalog_name='bathymetry_ml_auxiliary_dev2',\n", + " namespace='bathymetry_ml_auxiliary')\n", "\n", "# Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", - "obj.create_table_for_all_s3parquets(namespace='bathymetry_ml_auxiliary',\n", + "obj.create_table_for_all_s3parquets(app_name='bathymetry_ml_auxiliary',\n", " bucket_name='ngwpc-bathymetry',\n", " profile_name='default')" ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a1c58d4f-59e5-41d8-be31-536f3f790216", + "metadata": {}, + "outputs": [], + "source": [ + "# Scenario III\n", + "# Instantiate class\n", + "obj = IcebergTable()\n", + "\n", + "# Establish new Iceberg catalog\n", + "obj.establish_catalog(catalog_name='xs',\n", + " namespace='mip')\n", + "\n", + "# Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", + "obj.create_table_for_all_s3parquets(app_name='mip_xs',\n", + " bucket_name='ngwpc-xs',\n", + " profile_name='default')" + ] + }, { "cell_type": "markdown", "id": "71949e8b-36f2-4e74-afb2-741a884d95dd", @@ -479,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "id": "f449ec30-027e-45c8-91f2-9ad5402b6d17", "metadata": {}, "outputs": [ @@ -544,7 +610,7 @@ ], "source": [ "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "catalog_name = 'dev'\n", + "catalog_name = 'bathymetry_ml_auxiliary_dev'\n", "namespace = 'bathymetry_ml_auxiliary'\n", "tablename = 'vpuid=01'\n", "catalog = load_catalog(name=catalog_name, \n", @@ -553,6 +619,653 @@ "print(iceberg_table.to_string(preview_cols=20))" ] }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6c69cdf0-90ae-4e32-8100-5aecc746edc9", + "metadata": {}, + "outputs": [], + "source": [ + "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", + "catalog_name = 'bathymetry_ml_auxiliary_dev2'\n", + "namespace = 'bathymetry_ml_auxiliary'\n", + "tablename = 'vpuid=01'\n", + "catalog = load_catalog(name=catalog_name, \n", + " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", + "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "#print(iceberg_table.to_string(preview_cols=20))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "57d3a536-70dc-4b1d-a162-73e376bca0dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pyarrow.Table\n", + "river: large_string\n", + "reach: large_string\n", + "river_reach: large_string\n", + "river_station: double\n", + "river_reach_rs: large_string\n", + "thalweg: double\n", + "xs_max_elevation: double\n", + "left_reach_length: double\n", + "right_reach_length: double\n", + "channel_reach_length: double\n", + "ras_data: large_string\n", + "station_elevation_points: large_string\n", + "bank_stations: large_string\n", + "number_of_station_elevation_points: int64\n", + "number_of_coords: int64\n", + "flows: large_string\n", + "profile_names: large_string\n", + "geometry: large_binary\n", + "huc: large_string\n", + "model_id: large_string\n", + "us_reach_id: large_string\n", + "us_network_to_id: large_string\n", + "ds_reach_id: large_string\n", + "ds_network_to_id: large_string\n", + "__index_level_0__: int64\n", + "----\n", + "river: [[\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",...,\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\"]]\n", + "reach: [[\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",...,\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\"]]\n", + "river_reach: [[\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",...,\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\"]]\n", + "river_station: [[7300,6833,6346,5913,5505,...,1842,1400,970,516,111]]\n", + "river_reach_rs: [[\"Stream0528 Reach-1 7300.0\",\"Stream0528 Reach-1 6833.0\",\"Stream0528 Reach-1 6346.0\",\"Stream0528 Reach-1 5913.0\",\"Stream0528 Reach-1 5505.0\",...,\"Stream0512 Reach-1 1842.0\",\"Stream0512 Reach-1 1400.0\",\"Stream0512 Reach-1 970.0\",\"Stream0512 Reach-1 516.0\",\"Stream0512 Reach-1 111.0\"]]\n", + "thalweg: [[217.86,215.26,213.94,213.33,213.34,...,368.01,365.61,364.04,360.64,360.73]]\n", + "xs_max_elevation: [[248.82,238.12,246.73,241.95,242.87,...,404.79,398.04,385.23,404.53,391.25]]\n", + "left_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", + "right_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", + "channel_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", + "ras_data: [[\"Type RM Length L Ch R = 1,7300,467,467,467\n", + "BEGIN DESCRIPTION:\n", + "Stream0528 16.0\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 801174.98 421275.01 800775.00 421575.00\n", + " 800324.98 422375.04\n", + "#Sta/Elev=88\n", + " 4499.1 241.30 4502.6 241.08 4508.1 240.72 4525.5 239.49 4538.6 238.45\n", + " 4541.0 238.23 4556.0 236.88 4574.5 235.11 4604.0 232.37 4610.5 231.73\n", + " 4646.5 228.57 4648.8 228.37 4652.0 228.13 4669.4 226.75 4682.5 225.74\n", + " 4689.9 225.13 4699.9 224.39 4718.4 223.19 4747.9 221.12 4754.4 220.79\n", + " 4772.2 220.03 4790.4 218.68 4795.9 218.36 4826.4 218.50 4843.8 220.20\n", + " 4854.4 222.24 4862.3 223.75 4874.9 226.81 4891.8 230.20 4895.9 230.70\n", + " 4898.3 230.99 4917.8 231.53 4934.3 232.10 4939.7 231.87 4970.2 228.16\n", + " 4987.7 225.95 4998.0 218.36 4998.2 217.86 5001.8 217.86 5002.1 218.36\n", + " 5010.7 223.52 5024.3 222.97 5041.7 222.97 5056.7 223.02 5057.3 223.02\n", + " 5061.7 223.03 5188.4 223.05 5198.0 223.04 5245.5 223.01 5255.4 223.01\n", + " 5304.2 222.95 5321.4 223.06 5336.4 223.19 5362.9 223.22 5387.5 223.49\n", + " 5420.5 223.97 5453.5 225.03 5463.2 225.49 5480.3 226.15 5494.5 226.65\n", + " 5519.6 227.58 5539.0 230.01 5552.6 231.77 5568.8 234.40 5578.4 236.10\n", + " 5585.6 237.38 5597.7 238.56 5611.1 239.63 5618.6 240.34 5645.5 241.06\n", + " 5651.6 241.22 5684.7 241.94 5715.1 242.40 5716.8 242.42 5717.7 242.44\n", + " 5721.0 242.51 5750.7 243.15 5759.0 243.35 5773.8 243.81 5783.7 244.06\n", + " 5816.7 244.98 5832.5 245.51 5849.7 246.10 5871.9 247.04 5882.8 247.48\n", + " 5906.9 248.45 5915.8 248.77 5917.0 248.82\n", + "#Mann=8,-1,0\n", + " 4499.1 0.07 0 4541.0 0.03 0 4895.9 0.05 0\n", + " 4998.0 0.05 0 5002.1 0.05 0 5056.7 0.03 0\n", + " 5198.0 0.07 0 5578.4 0.03 0\n", + "Bank Sta=4998,5002.1\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1,6833,487,487,487\n", + "BEGIN DESCRIPTION:\n", + "Stream0528 15.0\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 801125.01 421175.00 800725.00 421125.00\n", + " 800524.99 421425.02\n", + "#Sta/Elev=56\n", + " 4596.9 238.12 4607.4 237.30 4614.9 236.74 4665.4 232.76 4694.4 230.63\n", + " 4718.0 228.96 4723.4 228.56 4743.8 226.84 4752.4 226.14 4769.6 224.66\n", + " 4781.4 223.73 4810.4 221.66 4829.7 220.47 4846.9 219.48 4868.4 218.38\n", + " 4872.7 218.13 4897.4 216.99 4898.5 216.96 4926.4 216.05 4955.4 215.76\n", + " 4984.4 215.76 4998.0 215.76 4998.2 215.26 5001.8 215.26 5002.1 215.76\n", + " 5013.8 216.26 5023.9 216.41 5038.6 216.88 5048.4 217.27 5075.8 218.08\n", + " 5080.1 218.22 5083.0 218.36 5097.4 219.05 5117.6 219.94 5127.7 220.60\n", + " 5142.4 221.45 5152.2 222.35 5163.1 223.12 5168.7 223.66 5179.6 224.70\n", + " 5183.9 224.98 5186.8 225.21 5201.2 226.23 5221.4 227.59 5231.5 228.34\n", + " 5256.0 229.80 5283.3 231.49 5287.7 231.68 5290.5 231.86 5308.4 232.70\n", + " 5325.1 233.87 5329.2 234.17 5335.2 234.67 5339.5 234.93 5359.7 236.16\n", + " 5360.6 236.21\n", + "#Mann=5,-1,0\n", + " 4596.9 0.07 0 4998.0 0.07 0 5002.1 0.07 0\n", + " 5168.7 0.03 0 5339.5 0.05 0\n", + "Bank Sta=4998,5002.1\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1,6346,433,433,433\n", + "BEGIN DESCRIPTION:\n", + "Stream0528 14.0\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 800875.02 420424.98 800475.00 420825.00\n", + " 800475.00 421474.99\n", + "#Sta/Elev=82\n", + " 4433.8 246.73 4461.2 244.11 4463.3 243.94 4482.6 241.76 4501.9 240.40\n", + " 4502.9 240.29 4504.0 240.22 4523.3 237.89 4542.6 236.45 4544.7 236.25\n", + " 4583.3 232.69 4585.4 232.51 4624.0 229.18 4625.0 229.09 4626.1 229.00\n", + " 4664.7 225.22 4665.7 225.11 4666.8 225.02 4686.1 223.11 4705.4 221.69\n", + " 4707.5 221.53 4746.1 219.33 4748.2 219.25 4767.5 218.53 4786.8 217.91\n", + " 4788.8 217.86 4827.5 216.55 4829.5 216.50 4848.9 216.01 4868.2 215.56\n", + " 4869.2 215.53 4870.3 215.52 4909.9 214.99 4911.0 214.98 4930.3 214.96\n", + " 4951.7 214.68 4971.0 214.53 4992.4 214.44 4998.0 214.44 4998.2 213.94\n", + " 5001.8 213.94 5002.1 214.44 5023.2 214.64 5052.0 215.06 5058.5 215.17\n", + " 5080.8 215.64 5103.0 216.19 5109.6 216.34 5116.1 216.56 5138.3 217.12\n", + " 5160.6 217.92 5167.1 218.12 5195.9 218.62 5224.7 218.95 5246.9 219.31\n", + " 5253.4 219.38 5275.7 219.92 5282.2 220.02 5288.7 220.20 5311.0 220.94\n", + " 5317.5 221.22 5331.7 221.80 5339.8 222.13 5368.6 223.29 5397.3 224.58\n", + " 5448.4 227.28 5454.9 227.61 5461.4 227.90 5483.7 229.06 5490.2 229.36\n", + " 5512.5 230.56 5534.7 231.71 5541.2 232.03 5547.7 232.52 5561.6 233.45\n", + " 5570.0 234.02 5592.3 235.86 5598.8 236.59 5605.3 237.14 5627.6 238.74\n", + " 5634.1 238.95 5649.5 239.52\n", + "#Mann=5,-1,0\n", + " 4433.8 0.07 0 4998.0 0.07 0 5002.1 0.07 0\n", + " 5331.7 0.03 0 5561.6 0.05 0\n", + "Bank Sta=4998,5002.1\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1,5913,408,408,408\n", + "BEGIN DESCRIPTION:\n", + "Stream0528 13.0\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 800825.01 420024.99 800125.00 420825.00\n", + " 800424.99 421474.99\n", + "#Sta/Elev=104\n", + " 3931.6 241.95 3955.4 241.31 3972.5 240.61 3993.7 239.63 4004.2 239.07\n", + " 4016.2 238.50 4031.9 237.63 4059.9 235.96 4070.1 235.27 4085.8 234.13\n", + " 4103.6 233.09 4106.2 232.93 4108.4 232.83 4146.6 230.97 4147.3 230.93\n", + " 4184.9 228.66 4187.7 228.47 4191.0 228.29 4208.1 227.09 4223.1 226.17\n", + " 4234.7 225.37 4261.3 223.64 4278.4 222.50 4289.7 221.89 4299.6 221.32\n", + " 4322.1 219.94 4365.8 218.50 4371.3 218.38 4376.1 218.35 4409.5 217.87\n", + " 4414.3 217.75 4452.9 216.59 4453.2 216.58 4490.8 215.82 4493.7 215.76\n", + " 4496.9 215.71 4529.0 215.47 4534.4 215.44 4540.6 215.38 4584.3 215.05\n", + " 4605.5 215.00 4656.8 215.03 4671.8 215.01 4682.0 215.02 4715.5 214.92\n", + " 4718.0 214.91 4720.2 214.90 4738.4 214.71 4758.8 214.38 4759.2 214.38\n", + " 4796.7 213.94 4802.9 213.91 4834.9 213.87 4860.8 213.88 4873.2 213.85\n", + " 4977.7 213.83 4994.6 213.85 4997.6 213.83 4998.0 213.33 5002.1 213.33\n", + " 5002.4 213.83 5020.7 213.84 5107.7 213.88 5115.8 213.95 5129.3 214.01\n", + " 5147.5 214.24 5158.6 214.48 5179.2 215.06 5194.4 215.61 5210.9 216.17\n", + " 5227.3 216.51 5237.8 216.65 5242.6 216.68 5274.3 216.91 5281.1 216.95\n", + " 5324.5 217.51 5337.7 217.72 5339.6 217.74 5346.2 217.79 5369.4 218.11\n", + " 5389.6 218.45 5401.1 218.63 5432.4 219.23 5432.8 219.23 5433.3 219.25\n", + " 5464.5 220.54 5491.2 222.01 5496.2 222.27 5519.7 223.59 5527.9 224.04\n", + " 5541.4 224.83 5559.6 225.63 5563.1 225.81 5570.7 226.08 5588.7 226.88\n", + " 5591.3 227.00 5606.4 228.14 5623.0 229.29 5639.3 231.80 5654.7 234.26\n", + " 5671.5 236.98 5686.4 238.60 5708.0 239.45 5710.5 239.55\n", + "#Mann=5,-1,0\n", + " 3931.6 0.07 0 4997.6 0.07 0 5002.4 0.07 0\n", + " 5339.6 0.03 0 5588.7 0.05 0\n", + "Bank Sta=4997.6,5002.4\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1,5505,419,419,419\n", + "BEGIN DESCRIPTION:\n", + "Stream0528 12.0\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 800325.02 419674.95 799925.00 420875.00\n", + " 800325.02 421475.02\n", + "#Sta/Elev=138\n", + " 3724.5 242.87 3734.7 242.14 3748.3 241.26 3753.8 240.92 3775.7 239.57\n", + " 3778.7 239.38 3800.7 238.14 3809.0 237.76 3823.4 237.04 3837.6 236.50\n", + " 3839.4 236.43 3846.2 236.06 3869.7 234.89 3900.0 233.51 3916.7 232.93\n", + " 3930.4 232.39 3937.2 232.15 3957.7 231.53 3959.9 231.46 3991.0 230.58\n", + " 4007.7 230.16 4021.4 229.79 4028.2 229.48 4048.7 228.93 4051.7 228.83\n", + " 4053.2 228.77 4082.0 227.41 4096.4 226.85 4112.4 226.27 4128.2 225.76\n", + " 4128.3 225.76 4164.7 224.58 4173.0 224.34 4189.7 223.93 4230.7 222.87\n", + " 4233.7 222.78 4255.7 222.23 4264.0 222.03 4280.7 221.92 4294.4 221.80\n", + " 4301.2 221.72 4324.0 221.37 4324.7 221.35 4355.1 220.90 4412.7 220.21\n", + " 4415.0 220.18 4415.7 220.18 4417.2 220.16 4446.1 219.96 4460.5 219.92\n", + " 4463.3 219.89 4503.7 219.44 4506.7 219.40 4508.2 219.38 4537.1 219.19\n", + " 4567.4 219.07 4597.7 218.89 4599.2 218.87 4628.1 218.50 4642.5 218.25\n", + " 4658.4 218.05 4719.1 217.17 4735.8 216.87 4749.4 216.65 4752.3 216.60\n", + " 4756.2 216.54 4776.7 216.18 4779.7 216.13 4781.2 216.11 4810.1 215.65\n", + " 4824.5 215.44 4840.4 215.26 4867.7 215.24 4870.7 215.23 4872.3 215.20\n", + " 4901.1 214.74 4931.4 214.36 4961.8 214.04 4989.5 213.89 4992.5 213.87\n", + " 4997.5 213.84 4997.9 213.34 5002.2 213.34 5002.6 213.84 5007.0 213.87\n", + " 5019.0 213.88 5027.0 213.88 5039.8 213.94 5058.8 213.96 5060.5 213.96\n", + " 5061.6 213.96 5081.3 214.03 5102.0 214.02 5130.8 214.08 5143.5 214.04\n", + " 5162.6 213.90 5164.3 213.89 5200.0 213.84 5234.6 213.85 5266.4 213.94\n", + " 5268.0 213.95 5269.2 213.97 5274.7 214.05 5281.1 214.14 5303.7 214.47\n", + " 5318.3 214.63 5330.3 214.82 5338.3 214.84 5351.1 215.12 5370.1 215.66\n", + " 5372.9 215.73 5392.6 216.12 5407.5 216.36 5422.0 216.72 5434.1 217.06\n", + " 5442.1 217.26 5476.7 218.25 5496.3 219.18 5511.3 219.81 5517.1 220.32\n", + " 5525.8 221.00 5537.8 222.14 5545.9 222.85 5558.6 224.54 5576.4 226.21\n", + " 5577.7 226.33 5580.4 226.58 5600.1 227.92 5615.0 229.16 5620.8 229.65\n", + " 5629.6 231.15 5641.6 232.77 5649.6 234.30 5681.4 238.68 5683.1 238.85\n", + " 5684.2 238.99 5703.8 239.24 5710.6 239.51\n", + "#Mann=10,-1,0\n", + " 3724.5 0.03 0 3753.8 0.07 0 3837.6 0.035 0\n", + " 4128.2 0.07 0 4128.3 0.035 0 4463.3 0.07 0\n", + " 4997.5 0.07 0 5002.6 0.07 0 5281.1 0.03 0\n", + " 5576.4 0.05 0\n", + "Bank Sta=4997.5,5002.6\n", + "Exp/Cntr=0.3,0.1\n", + "\",...,\"Type RM Length L Ch R = 1,1842,442,442,442\n", + "BEGIN DESCRIPTION:\n", + "Stream0512 4.0\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 808374.98 370125.01 808125.00 370325.00\n", + " 807724.98 371125.04\n", + "#Sta/Elev=373\n", + " 4675.5 403.33 4680.2 403.38 4680.6 403.38 4681.2 403.40 4685.3 403.62\n", + " 4688.6 403.99 4691.8 404.23 4697.1 404.57 4702.3 404.79 4705.5 404.70\n", + " 4708.8 404.51 4712.9 404.12 4714.0 403.95 4718.4 402.69 4722.4 401.55\n", + " 4723.5 401.17 4730.9 398.26 4734.0 397.03 4739.4 394.77 4741.7 393.63\n", + " 4744.6 392.18 4746.4 391.30 4747.8 390.68 4755.2 387.31 4756.3 386.83\n", + " 4764.7 383.43 4765.7 383.06 4769.9 381.47 4773.2 380.39 4779.3 378.44\n", + " 4781.7 377.87 4786.9 376.70 4790.1 376.14 4797.5 374.97 4798.6 374.81\n", + " 4802.8 374.14 4807.0 373.34 4808.0 373.12 4815.5 371.38 4816.9 371.06\n", + " 4818.6 370.75 4823.9 369.88 4826.3 369.67 4829.2 369.62 4832.4 369.53\n", + " 4835.7 369.45 4839.7 369.42 4840.9 369.42 4849.3 369.35 4849.8 369.34\n", + " 4850.3 369.33 4857.8 369.31 4860.9 369.39 4866.2 369.51 4871.5 369.51\n", + " 4874.7 369.48 4882.0 369.47 4883.1 369.47 4892.6 369.58 4896.7 369.66\n", + " 4900.0 369.70 4901.4 369.72 4903.2 369.72 4906.1 369.74 4908.5 369.71\n", + " 4913.8 369.67 4917.0 369.65 4924.3 369.64 4925.4 369.63 4929.6 369.63\n", + " 4933.9 369.62 4934.3 369.61 4934.9 369.61 4942.3 369.51 4943.7 369.48\n", + " 4945.5 369.48 4948.4 369.49 4950.8 369.52 4953.1 369.57 4956.0 369.60\n", + " 4959.3 369.59 4962.5 369.52 4966.6 369.39 4967.2 369.37 4967.7 369.37\n", + " 4972.1 369.49 4976.2 369.57 4976.6 369.58 4977.2 369.60 4984.6 369.75\n", + " 4987.8 369.71 4990.7 369.62 4993.1 369.57 4995.6 369.51 4997.2 368.51\n", + " 4997.7 368.01 5002.4 368.01 5002.8 368.51 5003.9 369.54 5004.9 369.54\n", + " 5005.9 369.55 5012.3 369.54 5018.6 369.43 5019.7 369.40 5020.7 369.39\n", + " 5024.9 369.37 5033.4 369.48 5034.4 369.50 5041.8 369.47 5048.2 369.39\n", + " 5050.2 369.37 5054.5 369.33 5056.6 369.30 5064.0 369.28 5065.0 369.29\n", + " 5069.2 369.32 5071.3 369.36 5077.7 369.44 5078.7 369.45 5079.8 369.46\n", + " 5086.1 369.48 5093.5 369.40 5094.6 369.39 5100.9 369.39 5108.3 369.41\n", + " 5108.6 369.41 5109.3 369.40 5113.5 369.36 5115.7 369.35 5118.5 369.36\n", + " 5123.0 369.33 5124.1 369.33 5128.3 369.32 5130.4 369.32 5136.8 369.34\n", + " 5137.8 369.34 5138.2 369.34 5138.9 369.34 5143.1 369.27 5148.0 369.22\n", + " 5153.6 369.21 5160.0 369.21 5166.3 369.24 5167.3 369.24 5168.4 369.24\n", + " 5172.6 369.20 5174.7 369.14 5177.5 369.11 5182.1 369.05 5183.1 369.05\n", + " 5187.4 369.04 5189.5 369.01 5196.9 368.83 5197.9 368.80 5204.2 368.79\n", + " 5211.6 368.83 5212.0 368.85 5212.7 368.83 5216.9 368.77 5219.0 368.65\n", + " 5225.4 368.52 5226.4 368.51 5227.4 368.56 5233.8 368.96 5236.6 369.09\n", + " 5241.2 369.20 5241.5 369.19 5242.2 369.18 5248.6 369.16 5254.9 369.27\n", + " 5255.9 369.28 5257.0 369.32 5261.2 369.48 5263.3 369.50 5270.7 369.62\n", + " 5271.7 369.64 5276.0 369.64 5278.1 369.60 5284.4 369.73 5285.5 369.74\n", + " 5286.5 369.77 5290.7 370.01 5292.9 370.18 5295.7 370.45 5300.2 370.70\n", + " 5300.6 370.73 5301.3 370.73 5305.5 370.75 5307.6 370.74 5313.9 370.64\n", + " 5315.0 370.62 5315.4 370.63 5316.1 370.66 5322.4 371.00 5328.7 371.58\n", + " 5329.8 371.69 5330.1 371.71 5330.8 371.75 5337.2 372.08 5343.5 372.16\n", + " 5344.5 372.16 5345.6 372.17 5349.8 372.19 5351.9 372.33 5358.3 372.71\n", + " 5359.3 372.77 5359.7 372.80 5360.4 372.84 5366.7 373.10 5369.5 373.24\n", + " 5374.1 373.32 5375.1 373.37 5379.3 373.58 5381.5 373.67 5384.3 373.72\n", + " 5388.8 373.95 5389.9 373.97 5394.1 374.06 5396.2 374.14 5402.6 374.35\n", + " 5403.6 374.38 5404.7 374.43 5408.9 374.63 5411.0 374.71 5417.3 374.87\n", + " 5418.4 374.90 5419.4 374.90 5425.8 374.87 5428.6 374.93 5433.1 375.08\n", + " 5433.5 375.10 5440.5 375.56 5447.9 375.83 5449.0 375.86 5453.2 376.01\n", + " 5455.3 376.08 5458.1 376.24 5462.7 376.33 5463.0 376.35 5463.7 376.37\n", + " 5467.9 376.46 5470.1 376.48 5476.4 376.60 5477.4 376.61 5477.8 376.61\n", + " 5478.5 376.64 5484.8 376.90 5491.2 377.07 5492.2 377.11 5493.2 377.16\n", + " 5497.5 377.34 5499.6 377.38 5507.0 377.76 5508.0 377.83 5514.4 378.29\n", + " 5517.2 378.43 5521.7 378.66 5522.8 378.70 5529.1 378.96 5535.4 379.18\n", + " 5536.5 379.22 5536.9 379.24 5543.9 379.31 5550.2 379.65 5551.3 379.69\n", + " 5552.3 379.73 5558.7 380.03 5566.0 380.26 5567.1 380.33 5571.3 380.58\n", + " 5573.4 380.71 5579.8 380.87 5580.8 380.91 5581.2 380.90 5581.9 380.90\n", + " 5588.2 380.91 5591.0 381.03 5595.6 381.36 5596.6 381.45 5600.8 381.84\n", + " 5603.0 381.99 5605.8 382.17 5610.3 382.37 5610.7 382.39 5611.4 382.40\n", + " 5617.7 382.54 5624.1 382.67 5625.1 382.68 5626.2 382.70 5630.4 382.74\n", + " 5632.5 382.79 5638.8 383.08 5639.9 383.13 5640.9 383.21 5647.3 383.61\n", + " 5650.1 383.73 5654.6 383.94 5655.0 383.95 5655.7 383.98 5662.0 384.22\n", + " 5669.4 384.48 5674.7 384.69 5676.8 384.78 5683.1 385.19 5684.2 385.25\n", + " 5685.2 385.32 5691.6 385.81 5697.9 386.41 5698.9 386.49 5699.3 386.52\n", + " 5700.0 386.60 5704.2 386.93 5706.3 387.12 5713.7 387.55 5714.1 387.56\n", + " 5714.8 387.59 5721.1 387.82 5727.4 388.04 5728.5 388.07 5728.8 388.08\n", + " 5729.5 388.13 5735.9 388.58 5742.2 389.05 5743.3 389.14 5744.3 389.21\n", + " 5748.5 389.43 5750.6 389.52 5753.4 389.64 5758.0 389.78 5759.1 389.82\n", + " 5763.3 389.95 5765.4 390.03 5771.7 390.28 5772.8 390.32 5773.8 390.41\n", + " 5780.2 390.92 5786.5 391.27 5787.5 391.33 5787.9 391.33 5788.6 391.36\n", + " 5792.8 391.59 5794.9 391.72 5801.3 392.00 5802.3 392.05 5802.7 392.07\n", + " 5803.4 392.09 5807.6 392.20 5809.7 392.31 5812.5 392.43 5817.1 392.58\n", + " 5817.4 392.61 5818.1 392.61 5822.4 392.76 5824.5 392.86 5827.3 393.07\n", + " 5831.8 393.28 5832.9 393.33 5839.2 393.58 5845.6 393.77 5846.6 393.80\n", + " 5847.7 393.85 5851.9 394.04 5854.0 394.18 5856.8 394.31 5861.4 394.63\n", + " 5862.4 394.69 5868.8 395.06 5875.1 395.39 5876.2 395.45 5876.5 395.47\n", + " 5883.5 395.80 5889.9 396.08 5890.1 396.09\n", + "#Mann=3,-1,0\n", + " 4675.5 0.07 0 4997.2 0.07 0 5002.8 0.07 0\n", + "Bank Sta=4997.2,5002.8\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1,1400,430,430,430\n", + "BEGIN DESCRIPTION:\n", + "Stream0512 3.0\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 808225.02 369924.99 807825.00 370075.00\n", + " 807074.99 370675.01\n", + "#Sta/Elev=411\n", + " 4568.4 398.04 4568.6 398.04 4574.3 398.03 4577.0 398.04 4581.4 397.95\n", + " 4583.9 397.76 4588.5 397.36 4592.3 397.06 4595.5 396.81 4602.5 396.17\n", + " 4602.6 396.16 4607.7 395.49 4609.6 395.20 4613.7 394.25 4616.7 393.63\n", + " 4621.5 391.55 4623.7 390.62 4625.0 390.08 4630.8 387.19 4633.4 386.42\n", + " 4637.8 384.82 4640.3 384.16 4644.9 382.96 4648.8 382.49 4651.9 381.81\n", + " 4653.9 381.58 4659.0 380.83 4659.2 380.81 4670.2 379.55 4673.1 379.29\n", + " 4678.0 378.40 4679.5 378.09 4680.1 377.96 4681.5 377.80 4687.2 377.04\n", + " 4692.7 376.71 4694.2 376.62 4696.8 376.31 4701.3 375.62 4704.0 375.10\n", + " 4708.4 374.22 4715.3 373.07 4715.4 373.05 4715.6 373.02 4722.5 372.07\n", + " 4729.5 371.18 4734.4 370.41 4736.0 370.17 4736.6 370.10 4741.1 369.66\n", + " 4743.6 369.51 4749.2 369.50 4750.7 369.48 4751.4 369.49 4756.5 369.48\n", + " 4757.7 369.48 4760.5 369.53 4764.8 369.56 4771.9 369.65 4772.0 369.65\n", + " 4777.0 369.62 4778.9 369.63 4783.0 369.69 4785.9 369.70 4790.8 369.76\n", + " 4792.4 369.75 4793.0 369.73 4794.3 369.70 4800.0 369.53 4805.6 369.34\n", + " 4807.1 369.28 4807.8 369.25 4809.6 369.21 4816.9 369.12 4821.2 369.14\n", + " 4823.2 369.17 4828.3 369.08 4828.4 369.08 4835.3 369.36 4839.4 369.58\n", + " 4842.4 369.74 4847.2 369.97 4849.4 370.11 4850.7 370.18 4856.5 370.45\n", + " 4859.1 370.57 4863.5 370.75 4866.0 370.90 4870.6 371.08 4874.5 371.14\n", + " 4877.6 371.27 4884.6 371.27 4884.7 371.27 4884.9 371.27 4891.7 371.31\n", + " 4895.9 371.44 4903.7 371.72 4905.2 371.76 4905.8 371.78 4910.4 372.13\n", + " 4912.9 372.41 4918.4 373.15 4919.9 373.32 4922.5 373.63 4927.0 374.14\n", + " 4929.7 374.21 4934.1 374.34 4936.0 374.33 4941.1 374.43 4941.3 374.42\n", + " 4948.2 374.13 4951.4 373.80 4955.2 373.03 4960.1 371.76 4961.7 371.30\n", + " 4962.3 371.12 4963.6 370.77 4969.3 369.08 4971.9 368.40 4976.4 367.67\n", + " 4977.1 367.59 4978.9 367.44 4983.4 367.15 4987.3 367.08 4990.5 367.00\n", + " 4995.6 366.90 4996.8 366.87 4997.1 366.11 4997.6 365.61 5002.5 365.61\n", + " 5002.9 366.11 5006.4 366.77 5006.8 366.76 5007.4 366.76 5011.5 366.86\n", + " 5014.8 366.84 5016.2 366.86 5017.9 366.75 5020.9 366.66 5023.3 366.45\n", + " 5025.6 366.38 5028.5 366.46 5031.7 366.66 5039.1 367.18 5040.2 367.23\n", + " 5044.4 367.28 5048.7 367.30 5049.6 367.32 5057.1 367.46 5058.5 367.47\n", + " 5060.2 367.45 5065.6 367.32 5070.8 367.12 5074.0 367.06 5082.0 367.00\n", + " 5082.5 367.00 5086.7 367.01 5090.9 367.14 5091.4 367.16 5091.9 367.17\n", + " 5099.4 367.40 5102.5 367.41 5107.9 367.41 5110.2 367.38 5113.1 367.31\n", + " 5114.9 367.27 5116.3 367.25 5119.6 367.30 5123.6 367.33 5124.8 367.33\n", + " 5129.0 367.32 5133.2 367.25 5134.2 367.24 5141.7 367.21 5143.1 367.21\n", + " 5144.8 367.23 5147.8 367.17 5150.1 367.12 5155.4 367.08 5158.6 367.04\n", + " 5165.9 366.81 5166.6 366.82 5167.1 366.81 5171.3 366.72 5175.5 366.51\n", + " 5176.0 366.50 5176.5 366.50 5184.0 366.61 5192.4 366.68 5197.7 366.82\n", + " 5199.4 366.84 5200.9 366.87 5208.2 366.95 5209.3 366.96 5213.5 367.03\n", + " 5217.8 367.05 5218.8 367.07 5226.3 367.14 5227.6 367.14 5229.4 367.16\n", + " 5234.7 367.18 5237.0 367.19 5239.9 367.13 5243.2 367.12 5246.4 367.16\n", + " 5250.5 367.15 5251.1 367.14 5251.6 367.13 5260.1 367.11 5261.1 367.11\n", + " 5268.5 367.15 5271.6 367.13 5274.6 367.14 5277.0 367.10 5279.3 367.11\n", + " 5282.2 367.11 5285.5 367.15 5292.8 367.15 5293.9 367.14 5298.4 367.24\n", + " 5302.4 367.32 5302.8 367.33 5303.4 367.33 5307.5 367.33 5310.8 367.32\n", + " 5313.9 367.16 5319.3 366.94 5324.5 367.05 5327.7 367.04 5335.1 366.73\n", + " 5336.2 366.70 5340.4 366.64 5344.7 366.64 5345.6 366.66 5349.8 366.66\n", + " 5353.1 366.64 5354.5 366.64 5356.2 366.71 5361.6 366.88 5363.9 366.90\n", + " 5366.8 366.89 5368.6 366.88 5370.0 366.85 5377.4 366.76 5378.0 366.77\n", + " 5378.5 366.77 5383.0 366.80 5387.0 366.84 5387.4 366.84 5387.9 366.83\n", + " 5395.4 366.68 5398.5 366.61 5401.5 366.51 5403.9 366.45 5406.2 366.39\n", + " 5409.1 366.36 5412.3 366.36 5419.6 366.29 5420.8 366.27 5429.2 366.11\n", + " 5430.2 366.11 5434.4 366.18 5437.7 366.24 5440.8 366.28 5446.1 366.34\n", + " 5453.2 366.28 5454.6 366.28 5461.9 366.32 5463.1 366.32 5467.3 366.36\n", + " 5472.5 366.33 5480.0 366.35 5481.4 366.38 5483.1 366.39 5486.0 366.44\n", + " 5488.4 366.48 5493.7 366.55 5496.9 366.57 5504.2 366.60 5505.3 366.60\n", + " 5509.5 366.63 5513.8 366.61 5514.8 366.62 5522.3 366.73 5525.4 366.76\n", + " 5530.7 366.75 5535.9 366.63 5537.7 366.58 5539.2 366.55 5546.5 366.50\n", + " 5547.6 366.48 5551.8 366.42 5556.1 366.37 5557.1 366.37 5564.5 366.52\n", + " 5565.9 366.55 5567.7 366.60 5573.0 366.73 5578.2 366.79 5581.5 366.74\n", + " 5588.8 366.71 5589.4 366.71 5589.9 366.71 5598.4 366.91 5599.4 366.93\n", + " 5609.9 367.12 5612.9 367.22 5615.3 367.26 5620.5 367.51 5622.3 367.59\n", + " 5623.8 367.61 5631.1 367.44 5632.2 367.41 5640.7 367.22 5641.6 367.23\n", + " 5649.1 367.46 5652.2 367.55 5655.2 367.67 5657.6 367.77 5659.9 367.88\n", + " 5666.0 368.16 5669.3 368.34 5673.4 368.73 5674.0 368.78 5674.5 368.82\n", + " 5678.7 369.14 5683.0 369.47 5683.4 369.50 5683.9 369.53 5691.4 369.85\n", + " 5694.5 370.08 5699.9 370.63 5705.1 371.45 5708.3 371.95 5711.6 372.48\n", + " 5715.6 373.37 5716.3 373.48 5716.8 373.59 5721.3 374.28 5725.2 374.88\n", + " 5725.7 374.92 5726.2 374.98 5733.7 375.39 5736.8 375.48 5742.1 375.64\n", + " 5744.5 375.64 5747.4 375.77 5750.6 375.96 5753.9 376.10 5757.9 376.40\n", + " 5767.5 376.97 5768.5 377.02 5776.0 377.28 5784.4 377.64 5789.7 377.95\n", + " 5791.5 378.07 5792.9 378.16 5796.1 378.27 5800.2 378.38 5801.3 378.41\n", + " 5809.8 378.50 5810.3 378.52 5810.8 378.55 5814.9 378.81 5818.3 378.91\n", + " 5821.4 379.05 5824.3 379.25 5826.7 379.30 5829.0 379.40 5831.9 379.56\n", + " 5833.7 379.68 5835.2 379.77 5842.5 380.22 5843.1 380.27 5843.6 380.30\n", + " 5852.1 381.03 5853.1 381.07 5860.6 381.36 5861.9 381.45 5863.7 381.55\n", + " 5869.0 381.82 5871.3 382.00 5874.2 382.19 5876.0 382.31 5877.5 382.39\n", + " 5884.8 382.57 5885.9 382.60 5890.4 382.64 5894.4 382.67 5894.8 382.68\n", + " 5895.4 382.69 5902.8 382.80 5905.9 382.81 5908.9 382.80 5911.3 382.82\n", + " 5916.5 382.90 5918.3 382.93 5919.8 382.98 5923.0 383.06 5927.1 383.29\n", + " 5927.7 383.32 5928.2 383.34 5932.7 383.54 5936.7 383.71 5937.1 383.72\n", + " 5937.7 383.72 5945.1 383.77 5948.2 383.77 5953.6 383.79 5955.9 383.83\n", + " 5956.1 383.83\n", + "#Mann=3,-1,0\n", + " 4568.4 0.07 0 4997.1 0.07 0 5002.9 0.07 0\n", + "Bank Sta=4997.1,5002.9\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1,970,454,454,454\n", + "BEGIN DESCRIPTION:\n", + "Stream0512 2.0\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 807775.02 369774.99 807425.00 370075.00\n", + " 807025.00 370425.00\n", + "#Sta/Elev=273\n", + " 4538.2 385.23 4543.7 385.03 4544.0 385.01 4544.3 385.00 4548.7 384.83\n", + " 4553.0 384.62 4553.4 384.60 4553.8 384.58 4561.7 384.18 4564.0 384.06\n", + " 4570.4 383.50 4572.1 383.33 4574.1 383.09 4579.1 382.57 4581.5 382.28\n", + " 4584.3 381.90 4586.2 381.66 4587.8 381.45 4596.5 380.50 4600.2 380.20\n", + " 4604.6 379.86 4605.2 379.82 4613.9 379.37 4614.7 379.32 4622.6 378.62\n", + " 4624.9 378.38 4631.3 377.51 4635.0 376.90 4640.0 375.89 4642.4 375.38\n", + " 4645.2 374.83 4647.1 374.42 4648.7 374.10 4655.3 372.84 4656.4 372.66\n", + " 4657.4 372.52 4665.5 371.54 4665.8 371.51 4666.1 371.49 4670.5 371.25\n", + " 4675.6 370.87 4683.5 370.40 4684.5 370.32 4685.7 370.24 4692.2 369.77\n", + " 4693.9 369.62 4695.9 369.47 4700.9 369.10 4703.3 368.97 4706.0 368.81\n", + " 4709.6 368.65 4716.2 368.48 4718.3 368.41 4722.0 368.18 4726.3 367.95\n", + " 4727.0 367.90 4731.4 367.46 4735.7 367.30 4736.5 367.27 4740.7 367.07\n", + " 4744.4 366.91 4746.6 366.78 4750.1 366.55 4753.1 366.46 4754.8 366.40\n", + " 4756.8 366.36 4759.5 366.30 4761.8 366.29 4768.8 366.20 4770.5 366.20\n", + " 4777.1 366.11 4779.2 366.04 4787.2 365.86 4787.6 365.86 4787.9 365.85\n", + " 4796.6 365.71 4806.3 365.69 4807.5 365.70 4814.0 365.74 4817.7 365.69\n", + " 4822.6 365.66 4827.8 365.67 4831.3 365.65 4834.4 365.63 4838.0 365.65\n", + " 4840.0 365.68 4848.1 365.87 4848.7 365.89 4857.4 365.99 4858.2 365.99\n", + " 4866.1 365.88 4867.2 365.87 4868.4 365.87 4871.9 365.79 4874.8 365.89\n", + " 4878.5 365.89 4883.5 365.85 4888.7 365.84 4892.2 365.76 4895.3 365.70\n", + " 4898.8 365.69 4900.9 365.71 4904.6 365.70 4909.0 365.78 4909.6 365.79\n", + " 4913.5 365.84 4918.3 365.90 4919.1 365.92 4923.4 365.99 4927.0 366.01\n", + " 4935.7 366.03 4937.4 366.07 4939.4 366.08 4944.4 366.14 4949.6 366.23\n", + " 4951.5 366.23 4953.1 366.21 4959.7 365.83 4961.8 365.72 4965.5 365.58\n", + " 4969.9 365.46 4970.2 365.45 4970.5 365.45 4979.2 365.66 4979.6 365.66\n", + " 4980.0 365.66 4987.9 365.69 4989.0 365.71 4990.2 365.72 4996.6 365.74\n", + " 4996.8 364.54 4997.3 364.04 5002.7 364.04 5003.2 364.54 5005.4 365.35\n", + " 5010.3 364.97 5012.4 364.79 5014.1 364.72 5017.0 364.54 5020.4 364.98\n", + " 5022.9 365.24 5030.4 365.65 5031.7 365.66 5040.4 365.58 5045.1 365.45\n", + " 5049.2 365.30 5050.5 365.25 5058.0 365.04 5060.5 364.97 5063.8 364.93\n", + " 5066.8 364.92 5070.5 364.93 5073.2 364.90 5075.6 364.96 5080.5 365.18\n", + " 5082.6 365.25 5084.3 365.28 5090.6 365.26 5091.9 365.27 5093.1 365.29\n", + " 5100.6 365.23 5101.9 365.21 5110.6 364.94 5110.9 364.93 5119.4 364.93\n", + " 5120.0 364.94 5120.7 364.95 5128.2 365.10 5130.7 365.17 5137.0 365.29\n", + " 5140.7 365.37 5145.8 365.47 5148.1 365.49 5150.7 365.53 5154.5 365.54\n", + " 5160.8 365.63 5163.3 365.68 5166.8 365.72 5170.8 365.79 5172.1 365.83\n", + " 5176.2 365.93 5180.8 366.07 5189.6 366.19 5190.9 366.19 5194.9 366.20\n", + " 5198.4 366.19 5200.9 366.20 5204.2 366.24 5207.2 366.33 5210.9 366.50\n", + " 5216.0 366.71 5218.3 366.79 5220.9 366.84 5223.0 366.83 5224.7 366.84\n", + " 5231.0 366.80 5232.3 366.80 5233.5 366.81 5241.0 367.06 5242.3 367.10\n", + " 5251.0 367.43 5259.8 367.71 5260.4 367.72 5261.0 367.72 5268.6 367.69\n", + " 5269.8 367.68 5271.1 367.67 5277.4 367.81 5281.1 368.05 5288.5 368.62\n", + " 5291.1 368.84 5293.2 368.99 5294.9 369.13 5301.2 369.68 5303.7 369.94\n", + " 5311.2 370.53 5312.5 370.58 5321.2 370.40 5330.0 370.73 5330.6 370.75\n", + " 5331.3 370.78 5338.8 370.91 5341.3 370.93 5344.6 370.93 5347.6 371.10\n", + " 5351.3 371.44 5354.0 371.72 5356.4 371.90 5361.3 372.29 5363.4 372.46\n", + " 5365.1 372.68 5371.4 373.40 5373.9 373.67 5377.4 374.03 5381.4 374.30\n", + " 5382.7 374.37 5386.8 374.69 5391.4 375.14 5391.7 375.17 5400.2 376.03\n", + " 5401.5 376.11 5409.0 376.54 5411.5 376.64 5414.8 376.80 5417.8 377.04\n", + " 5421.5 377.26 5426.6 377.50 5431.5 377.75 5435.3 377.88 5438.3 378.04\n", + " 5441.6 378.27 5442.9 378.37 5444.1 378.45 5451.6 378.97 5452.9 379.02\n", + " 5461.6 379.17 5471.0 379.47 5479.2 380.05 5480.4 380.12 5485.1 380.37\n", + " 5488.0 380.55 5489.7 380.64 5491.7 380.72 5496.8 380.94 5501.7 381.18\n", + " 5503.8 381.29 5505.5 381.40 5511.8 381.61 5514.3 381.63 5517.8 381.64\n", + " 5521.8 381.68 5523.1 381.71 5530.7 381.88\n", + "#Mann=3,-1,0\n", + " 4538.2 0.07 0 4996.8 0.07 0 5003.2 0.07 0\n", + "Bank Sta=4996.8,5003.2\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1,516,405,405,405\n", + "BEGIN DESCRIPTION:\n", + "Stream0512 1.0\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 807324.99 369375.03 807175.00 369775.00\n", + " 806824.99 370175.02\n", + "#Sta/Elev=275\n", + " 4573.3 404.53 4574.0 404.50 4579.4 403.89 4581.1 403.75 4583.8 403.37\n", + " 4588.1 402.73 4590.7 402.30 4595.2 401.56 4602.0 400.11 4602.2 400.06\n", + " 4602.6 399.98 4607.5 398.79 4609.3 398.41 4613.3 397.70 4616.3 397.13\n", + " 4617.7 396.88 4621.4 396.42 4623.4 396.17 4628.0 395.50 4630.5 395.15\n", + " 4635.9 394.18 4637.5 393.90 4643.4 392.95 4644.6 392.78 4648.5 392.29\n", + " 4651.6 391.89 4658.5 391.14 4658.7 391.11 4659.0 391.08 4663.9 390.60\n", + " 4665.7 390.44 4669.0 390.11 4672.8 389.65 4677.8 389.15 4679.8 388.97\n", + " 4686.9 388.37 4689.5 388.19 4693.9 387.74 4694.7 387.67 4696.6 387.45\n", + " 4701.0 386.99 4703.6 386.78 4708.0 386.39 4710.1 386.21 4715.1 385.79\n", + " 4715.4 385.74 4722.1 384.88 4726.2 384.34 4729.2 383.98 4734.3 383.49\n", + " 4736.2 383.29 4737.4 383.19 4743.3 382.66 4748.7 382.25 4750.4 382.11\n", + " 4753.1 381.84 4756.2 381.51 4757.4 381.40 4761.4 381.10 4764.5 380.83\n", + " 4766.5 380.67 4771.5 380.07 4771.6 380.06 4771.9 380.03 4778.6 379.38\n", + " 4782.6 379.16 4785.6 379.02 4790.7 378.57 4792.7 378.36 4797.3 377.70\n", + " 4799.7 377.40 4802.4 376.92 4806.8 376.01 4809.5 375.34 4812.6 374.52\n", + " 4813.8 374.24 4820.9 372.49 4827.7 370.73 4827.9 370.67 4828.3 370.59\n", + " 4835.0 369.19 4839.0 368.62 4842.0 368.19 4847.1 367.67 4849.1 367.50\n", + " 4853.7 367.04 4856.2 366.91 4858.8 366.72 4863.2 366.60 4865.9 366.49\n", + " 4870.3 366.31 4872.9 366.22 4877.3 366.09 4884.1 366.22 4884.4 366.23\n", + " 4884.5 366.23 4884.7 366.23 4891.4 366.37 4895.4 366.19 4898.5 366.02\n", + " 4899.8 365.92 4903.5 365.61 4905.5 365.42 4910.1 365.10 4912.6 365.02\n", + " 4915.2 364.97 4919.6 364.88 4920.4 364.86 4925.5 364.64 4926.7 364.62\n", + " 4929.3 364.59 4933.7 364.52 4940.6 364.48 4940.8 364.48 4941.1 364.48\n", + " 4946.0 364.43 4947.8 364.38 4951.8 364.21 4954.9 364.12 4960.0 363.99\n", + " 4961.4 363.97 4961.9 363.95 4963.1 363.94 4969.0 363.79 4971.7 363.73\n", + " 4976.1 363.66 4978.8 363.60 4983.1 363.49 4987.0 363.37 4990.2 363.29\n", + " 4992.2 363.24 4996.7 361.14 4997.2 360.64 5002.9 360.64 5003.4 361.14\n", + " 5005.2 362.65 5009.0 361.89 5011.6 361.36 5014.0 361.14 5019.0 361.55\n", + " 5022.7 362.05 5025.7 362.54 5029.0 362.89 5031.5 363.01 5035.0 362.93\n", + " 5039.0 362.66 5039.7 362.65 5040.3 362.68 5044.4 362.73 5049.1 363.15\n", + " 5057.8 363.43 5059.1 363.45 5066.6 363.45 5067.8 363.50 5069.1 363.54\n", + " 5072.5 363.73 5075.4 363.79 5079.2 363.92 5081.8 364.00 5084.2 364.03\n", + " 5089.2 364.10 5092.9 364.12 5095.9 364.08 5101.7 364.19 5109.2 364.29\n", + " 5109.9 364.29 5110.5 364.29 5119.2 364.27 5128.0 364.22 5128.6 364.22\n", + " 5129.3 364.23 5136.8 364.29 5138.0 364.28 5139.3 364.31 5145.6 364.29\n", + " 5147.4 364.26 5149.4 364.23 5152.0 364.18 5154.4 364.13 5156.7 364.04\n", + " 5159.4 364.01 5163.1 364.02 5166.1 364.02 5169.4 364.02 5171.9 364.02\n", + " 5175.4 364.04 5179.5 364.09 5180.1 364.09 5180.7 364.08 5184.8 364.11\n", + " 5189.5 364.17 5194.2 364.14 5198.2 364.14 5199.5 364.15 5203.5 364.26\n", + " 5209.5 364.40 5212.9 364.44 5215.8 364.45 5219.6 364.45 5224.6 364.50\n", + " 5226.9 364.50 5233.3 364.64 5239.6 364.66 5242.1 364.65 5249.6 364.61\n", + " 5250.9 364.59 5259.6 364.69 5268.4 365.28 5269.7 365.33 5277.2 365.64\n", + " 5279.7 365.69 5286.0 365.78 5289.8 365.83 5294.8 366.04 5299.8 366.17\n", + " 5303.5 366.32 5306.5 366.47 5309.8 366.55 5312.3 366.65 5319.8 367.15\n", + " 5321.1 367.24 5329.8 367.80 5338.6 368.04 5339.2 368.04 5339.9 368.06\n", + " 5343.9 368.12 5347.4 368.34 5349.9 368.43 5356.2 368.72 5360.0 369.07\n", + " 5365.0 369.36 5367.3 369.45 5370.0 369.51 5373.7 369.71 5376.7 369.80\n", + " 5380.0 370.00 5382.5 370.20 5390.1 370.91 5390.7 370.94 5391.3 371.00\n", + " 5400.1 371.76 5408.8 372.27 5410.1 372.35 5414.1 372.59 5417.6 372.73\n", + " 5420.1 372.85 5426.4 373.30 5435.2 374.01 5440.2 374.27 5443.9 374.57\n", + " 5446.9 374.86 5450.2 375.16 5451.6 375.26 5452.7 375.39 5460.3 376.15\n", + " 5460.9 376.19 5461.5 376.22 5470.2 376.90 5479.1 377.54 5480.3 377.56\n", + " 5484.3 377.56 5487.8 377.70 5490.3 377.84 5496.6 378.07 5500.4 378.24\n", + " 5505.4 378.50 5510.4 378.85 5514.1 379.03 5517.1 379.11 5520.4 379.19\n", + " 5522.9 379.16 5530.5 379.16 5531.1 379.18 5531.7 379.18 5532.0 379.18\n", + "#Mann=3,-1,0\n", + " 4573.3 0.07 0 4996.7 0.07 0 5003.4 0.07 0\n", + "Bank Sta=4996.7,5003.4\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1,111,0,0,0\n", + "BEGIN DESCRIPTION:\n", + "Stream0511 0.5\n", + "END DESCRIPTION:\n", + "XS GIS Cut Line=3\n", + " 807148.05 369310.04 806728.26 369906.93\n", + " 806800.42 370208.69\n", + "#Sta/Elev=322\n", + " 4369.8 391.25 4374.7 390.62 4377.4 390.29 4378.6 390.13 4382.6 389.58\n", + " 4385.5 389.14 4387.4 388.81 4390.1 388.35 4393.6 387.77 4401.5 386.59\n", + " 4401.9 386.55 4409.7 385.54 4411.1 385.36 4413.0 385.06 4417.8 384.34\n", + " 4424.5 383.41 4425.9 383.23 4430.0 382.68 4433.9 382.19 4439.5 381.71\n", + " 4442.0 381.49 4447.5 381.07 4450.1 380.80 4453.7 380.31 4458.2 379.64\n", + " 4458.5 379.57 4458.9 379.49 4466.2 378.32 4470.4 377.74 4472.7 377.45\n", + " 4474.3 377.31 4481.9 376.70 4482.4 376.66 4483.5 376.57 4490.5 376.04\n", + " 4491.7 375.95 4493.4 375.80 4496.4 375.54 4498.5 375.33 4504.9 374.69\n", + " 4505.9 374.60 4506.6 374.54 4510.7 374.22 4514.7 373.93 4516.3 373.81\n", + " 4522.7 373.48 4527.8 373.11 4529.6 372.97 4530.8 372.86 4534.3 372.40\n", + " 4538.9 371.93 4539.3 371.88 4547.0 371.07 4550.8 370.78 4553.3 370.57\n", + " 4555.0 370.51 4562.3 370.07 4563.1 370.01 4565.1 369.81 4571.2 369.17\n", + " 4572.2 369.08 4573.7 368.96 4577.0 368.81 4579.3 368.64 4585.2 368.25\n", + " 4586.5 368.15 4587.3 368.05 4592.4 367.64 4595.4 367.41 4596.7 367.34\n", + " 4603.5 367.06 4605.4 367.02 4608.2 367.02 4611.6 367.05 4614.9 367.05\n", + " 4619.6 367.16 4629.1 367.17 4631.1 367.16 4633.9 367.11 4635.8 367.10\n", + " 4642.6 366.98 4643.9 366.96 4651.9 366.95 4654.1 366.93 4660.0 366.91\n", + " 4665.6 366.67 4668.1 366.48 4671.8 366.26 4676.2 366.17 4676.5 366.15\n", + " 4677.1 366.14 4684.2 365.97 4686.0 365.90 4688.5 365.85 4690.7 365.76\n", + " 4692.3 365.65 4695.5 365.47 4700.0 365.18 4700.4 365.17 4701.2 365.16\n", + " 4708.5 365.12 4711.5 365.08 4716.5 364.93 4723.0 364.55 4723.9 364.49\n", + " 4724.6 364.44 4728.7 364.17 4732.7 364.01 4734.5 364.01 4740.7 364.11\n", + " 4745.9 364.12 4748.8 364.08 4752.4 364.04 4756.9 364.12 4757.4 364.12\n", + " 4765.0 364.14 4766.6 364.17 4768.9 364.12 4773.0 364.08 4780.4 364.06\n", + " 4780.8 364.06 4781.1 364.06 4785.5 363.99 4789.2 363.92 4790.3 363.90\n", + " 4791.9 363.89 4797.3 363.88 4803.3 363.84 4804.5 363.82 4805.3 363.80\n", + " 4810.0 363.72 4813.4 363.66 4814.0 363.63 4814.8 363.65 4821.5 363.68\n", + " 4823.5 363.68 4826.3 363.73 4828.2 363.74 4829.6 363.74 4837.3 363.89\n", + " 4837.6 363.89 4837.8 363.90 4842.4 363.99 4845.7 364.05 4847.2 364.07\n", + " 4849.3 364.09 4851.9 364.10 4853.8 364.12 4860.7 364.07 4861.4 364.06\n", + " 4861.8 364.07 4864.5 363.98 4869.9 363.77 4872.2 363.65 4878.0 363.32\n", + " 4883.7 363.29 4885.1 363.27 4886.1 363.26 4891.7 363.32 4894.1 363.35\n", + " 4895.2 363.35 4902.2 363.29 4904.1 363.18 4906.7 363.09 4908.8 363.02\n", + " 4910.3 362.97 4918.1 362.76 4918.3 362.75 4923.0 362.75 4926.4 362.69\n", + " 4929.6 362.68 4932.5 362.69 4934.5 362.69 4941.1 362.66 4942.0 362.67\n", + " 4946.7 362.55 4950.7 362.41 4952.6 362.37 4958.7 362.24 4960.9 362.17\n", + " 4964.1 362.00 4965.7 361.95 4966.8 361.86 4970.4 361.53 4974.9 361.23\n", + " 4975.5 361.25 4983.0 361.56 4987.0 361.92 4989.4 362.00 4991.0 362.12\n", + " 4996.7 361.23 4997.2 360.73 5002.9 360.73 5003.4 361.23 5003.6 362.55\n", + " 5007.2 362.60 5008.3 362.50 5010.0 362.40 5015.3 362.03 5021.5 361.64\n", + " 5023.3 361.56 5027.3 361.49 5031.4 361.68 5032.9 361.72 5036.8 361.77\n", + " 5039.5 361.85 5041.5 361.91 5044.4 362.02 5046.3 362.05 5047.5 362.10\n", + " 5051.0 362.36 5055.6 362.80 5055.9 362.82 5063.7 363.40 5067.4 363.49\n", + " 5071.8 363.57 5078.9 363.67 5079.8 363.69 5082.2 363.74 5087.9 363.88\n", + " 5090.3 363.88 5096.0 363.88 5099.5 363.93 5101.5 364.04 5103.3 364.12\n", + " 5107.0 364.41 5110.1 364.55 5115.2 364.81 5116.9 364.92 5123.4 365.14\n", + " 5123.7 365.14 5124.2 365.15 5134.4 365.11 5137.3 365.13 5139.9 365.22\n", + " 5144.1 365.33 5150.7 365.65 5150.8 365.66 5150.9 365.66 5157.7 365.92\n", + " 5161.8 366.14 5164.5 366.15 5167.3 366.30 5171.2 366.51 5172.7 366.54\n", + " 5178.0 366.80 5178.2 366.81 5179.1 366.83 5184.8 366.98 5189.2 367.06\n", + " 5191.6 367.15 5195.2 367.28 5196.0 367.28 5198.4 367.38 5200.2 367.51\n", + " 5205.2 367.98 5205.6 368.01 5207.5 368.16 5212.0 368.47 5216.6 368.55\n", + " 5218.8 368.65 5222.3 368.82 5225.6 368.95 5231.3 369.29 5232.4 369.36\n", + " 5233.0 369.42 5235.8 369.70 5238.5 369.97 5244.0 370.48 5245.9 370.62\n", + " 5249.5 370.77 5252.7 370.91 5254.9 370.97 5259.5 371.11 5260.4 371.14\n", + " 5264.3 371.31 5265.9 371.37 5266.3 371.40 5271.4 371.79 5273.1 371.92\n", + " 5276.9 372.15 5279.9 372.42 5282.3 372.69 5286.7 373.19 5292.6 373.60\n", + " 5293.5 373.66 5298.8 374.12 5300.3 374.33 5302.6 374.74 5307.0 375.42\n", + " 5313.8 376.50 5315.2 376.66 5320.6 377.11 5321.1 377.15 5327.4 377.59\n", + " 5329.4 377.80 5334.2 378.20 5337.1 378.52 5341.0 378.97 5347.3 379.36\n", + " 5347.8 379.42 5349.4 379.46 5354.6 379.50 5359.1 379.68 5361.4 379.73\n", + " 5365.1 379.87 5368.2 379.97 5374.0 380.34 5374.9 380.38 5377.8 380.52\n", + " 5381.0 380.66 5381.7 380.70 5386.5 380.85 5388.5 380.96 5391.9 381.21\n", + " 5395.3 381.41 5397.4 381.56 5402.1 381.97 5406.2 382.16 5408.4 382.22\n", + " 5408.9 382.22 5409.8 382.22\n", + "#Mann=3,-1,0\n", + " 4369.8 0.07 0 4996.7 0.07 0 5003.4 0.07 0\n", + "Bank Sta=4996.7,5003.4\n", + "Exp/Cntr=0.3,0.1\n", + "\n", + "Chan Stop Cuts=-1\"]]\n", + "station_elevation_points: [[\"[(4499.1, 241.3), (4502.6, 241.08), (4508.1, 240.72), (4525.5, 239.49), (4538.6, 238.45), (4541.0, 238.23), (4556.0, 236.88), (4574.5, 235.11), (4604.0, 232.37), (4610.5, 231.73), (4646.5, 228.57), (4648.8, 228.37), (4652.0, 228.13), (4669.4, 226.75), (4682.5, 225.74), (4689.9, 225.13), (4699.9, 224.39), (4718.4, 223.19), (4747.9, 221.12), (4754.4, 220.79), (4772.2, 220.03), (4790.4, 218.68), (4795.9, 218.36), (4826.4, 218.5), (4843.8, 220.2), (4854.4, 222.24), (4862.3, 223.75), (4874.9, 226.81), (4891.8, 230.2), (4895.9, 230.7), (4898.3, 230.99), (4917.8, 231.53), (4934.3, 232.1), (4939.7, 231.87), (4970.2, 228.16), (4987.7, 225.95), (4998.0, 218.36), (4998.2, 217.86), (5001.8, 217.86), (5002.1, 218.36), (5010.7, 223.52), (5024.3, 222.97), (5041.7, 222.97), (5056.7, 223.02), (5057.3, 223.02), (5061.7, 223.03), (5188.4, 223.05), (5198.0, 223.04), (5245.5, 223.01), (5255.4, 223.01), (5304.2, 222.95), (5321.4, 223.06), (5336.4, 223.19), (5362.9, 223.22), (5387.5, 223.49), (5420.5, 223.97), (5453.5, 225.03), (5463.2, 225.49), (5480.3, 226.15), (5494.5, 226.65), (5519.6, 227.58), (5539.0, 230.01), (5552.6, 231.77), (5568.8, 234.4), (5578.4, 236.1), (5585.6, 237.38), (5597.7, 238.56), (5611.1, 239.63), (5618.6, 240.34), (5645.5, 241.06), (5651.6, 241.22), (5684.7, 241.94), (5715.1, 242.4), (5716.8, 242.42), (5717.7, 242.44), (5721.0, 242.51), (5750.7, 243.15), (5759.0, 243.35), (5773.8, 243.81), (5783.7, 244.06), (5816.7, 244.98), (5832.5, 245.51), (5849.7, 246.1), (5871.9, 247.04), (5882.8, 247.48), (5906.9, 248.45), (5915.8, 248.77), (5917.0, 248.82)]\",\"[(4596.9, 238.12), (4607.4, 237.3), (4614.9, 236.74), (4665.4, 232.76), (4694.4, 230.63), (4718.0, 228.96), (4723.4, 228.56), (4743.8, 226.84), (4752.4, 226.14), (4769.6, 224.66), (4781.4, 223.73), (4810.4, 221.66), (4829.7, 220.47), (4846.9, 219.48), (4868.4, 218.38), (4872.7, 218.13), (4897.4, 216.99), (4898.5, 216.96), (4926.4, 216.05), (4955.4, 215.76), (4984.4, 215.76), (4998.0, 215.76), (4998.2, 215.26), (5001.8, 215.26), (5002.1, 215.76), (5013.8, 216.26), (5023.9, 216.41), (5038.6, 216.88), (5048.4, 217.27), (5075.8, 218.08), (5080.1, 218.22), (5083.0, 218.36), (5097.4, 219.05), (5117.6, 219.94), (5127.7, 220.6), (5142.4, 221.45), (5152.2, 222.35), (5163.1, 223.12), (5168.7, 223.66), (5179.6, 224.7), (5183.9, 224.98), (5186.8, 225.21), (5201.2, 226.23), (5221.4, 227.59), (5231.5, 228.34), (5256.0, 229.8), (5283.3, 231.49), (5287.7, 231.68), (5290.5, 231.86), (5308.4, 232.7), (5325.1, 233.87), (5329.2, 234.17), (5335.2, 234.67), (5339.5, 234.93), (5359.7, 236.16), (5360.6, 236.21)]\",\"[(4433.8, 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(5313.8, 376.5), (5315.2, 376.66), (5320.6, 377.11), (5321.1, 377.15), (5327.4, 377.59), (5329.4, 377.8), (5334.2, 378.2), (5337.1, 378.52), (5341.0, 378.97), (5347.3, 379.36), (5347.8, 379.42), (5349.4, 379.46), (5354.6, 379.5), (5359.1, 379.68), (5361.4, 379.73), (5365.1, 379.87), (5368.2, 379.97), (5374.0, 380.34), (5374.9, 380.38), (5377.8, 380.52), (5381.0, 380.66), (5381.7, 380.7), (5386.5, 380.85), (5388.5, 380.96), (5391.9, 381.21), (5395.3, 381.41), (5397.4, 381.56), (5402.1, 381.97), (5406.2, 382.16), (5408.4, 382.22), (5408.9, 382.22), (5409.8, 382.22)]\"]]\n", + "bank_stations: [[\"['4998', '5002.1']\",\"['4998', '5002.1']\",\"['4998', '5002.1']\",\"['4997.6', '5002.4']\",\"['4997.5', '5002.6']\",...,\"['4997.2', '5002.8']\",\"['4997.1', '5002.9']\",\"['4996.8', '5003.2']\",\"['4996.7', '5003.4']\",\"['4996.7', '5003.4']\"]]\n", + "number_of_station_elevation_points: [[88,56,82,104,138,...,373,411,273,275,322]]\n", + "number_of_coords: [[3,3,3,3,3,...,3,3,3,3,3]]\n", + "flows: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "profile_names: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "geometry: [[01020000000300000051D82178BFE13F418B8DCDCF0A03464163A5A3B9F3DF3F41DEC07FA06C0346414848E85781DD3F4106877FA6BA044641,010200000003000000C908AB77A6E13F41BDB3B473D4024641B8223D432DE03F4131A40DC78C0246419B4CE3F923DF3F41A38D4E5506034641,010200000003000000A823A14D64E13F419F50E5944B014641191211EC80DF3F41C88DAED5DD01464114E39692E7DE3F416B4D989918034641,0102000000030000006EA7910A92E13F4189118FF1830046418D5FD38E2CDE3F413F15D84DB401464176F943F0B6DE3F417D1C7BAA12034641,0102000000030000004E06BF6BFEDF3F41317CDB199FFF454130D740445EDD3F417D14E8C8B401464182D23FB655DE3F415FD376D106034641,...,0102000000030000008D015B39241640415605BE8E8FA545412A6FC13093154041382078E3D2A5454101C89951721440410860DD5B27A74541,0102000000030000004D624513F3154041612340CD1CA545412160B21A1F1540415C18F81C36A5454141FDFDF46B134041A1E1B01B00A64541,010200000003000000BE699A542A154041447683BA9EA44541C8839BE35C144041A72757B106A54541FC6B7F4371134041845B4FF780A54541,01020000000300000067F8B4257F1440416EB87174A7A34541374AC1FF06144041A088659957A44541C15F15BA2D1340418FF5200DF0A44541,010200000003000000A02869ED301440418B117EF772A34541C802227B1E1340416A4E959962A4454150193BD01D134041BF158676FDA44541]]\n", + "huc: [[\"01020002\",\"01020002\",\"01020002\",\"01020002\",\"01020002\",...,\"01020002\",\"01020002\",\"01020002\",\"01020002\",\"01020002\"]]\n", + "model_id: [[\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",...,\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\"]]\n", + "...\n" + ] + } + ], + "source": [ + "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", + "catalog_name = 'xs'\n", + "namespace = 'mip'\n", + "tablename = 'huc_01020002'\n", + "catalog = load_catalog(name=catalog_name, \n", + " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", + "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "print(iceberg_table.to_string(preview_cols=20))" + ] + }, { "cell_type": "markdown", "id": "d69402cf-d224-45e8-9680-74b28205ea44", @@ -563,7 +1276,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "aec525f7-6753-4ca5-ba5e-916c8c6659ec", "metadata": {}, "outputs": [ @@ -571,9 +1284,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "List of tables under bathymetry_ml_auxiliary within dev:\n", + "List of tables under mip within xs:\n", "\n", - "[('bathymetry_ml_auxiliary', 'vpuid=01'), ('bathymetry_ml_auxiliary', 'vpuid=02'), ('bathymetry_ml_auxiliary', 'vpuid=03N'), ('bathymetry_ml_auxiliary', 'vpuid=03S'), ('bathymetry_ml_auxiliary', 'vpuid=03W'), ('bathymetry_ml_auxiliary', 'vpuid=04'), ('bathymetry_ml_auxiliary', 'vpuid=05'), ('bathymetry_ml_auxiliary', 'vpuid=06'), ('bathymetry_ml_auxiliary', 'vpuid=07'), ('bathymetry_ml_auxiliary', 'vpuid=08'), ('bathymetry_ml_auxiliary', 'vpuid=09'), ('bathymetry_ml_auxiliary', 'vpuid=10L'), ('bathymetry_ml_auxiliary', 'vpuid=10U'), ('bathymetry_ml_auxiliary', 'vpuid=11'), ('bathymetry_ml_auxiliary', 'vpuid=12'), ('bathymetry_ml_auxiliary', 'vpuid=13'), ('bathymetry_ml_auxiliary', 'vpuid=14'), ('bathymetry_ml_auxiliary', 'vpuid=15'), ('bathymetry_ml_auxiliary', 'vpuid=16'), ('bathymetry_ml_auxiliary', 'vpuid=17'), ('bathymetry_ml_auxiliary', 'vpuid=18')]\n" + "[('mip', 'huc_01020002'), ('mip', 'huc_02060006'), ('mip', 'huc_03170004'), ('mip', 'huc_04030101'), ('mip', 'huc_05020006'), ('mip', 'huc_06040002'), ('mip', 'huc_07130010'), ('mip', 'huc_08060202'), ('mip', 'huc_09020301'), ('mip', 'huc_10120203'), ('mip', 'huc_11060002'), ('mip', 'huc_12030107'), ('mip', 'huc_13020102'), ('mip', 'huc_14050005'), ('mip', 'huc_15040007'), ('mip', 'huc_16020203'), ('mip', 'huc_17060305'), ('mip', 'huc_18070204')]\n" ] } ], @@ -587,12 +1300,12 @@ "id": "c5b0cacc-8f92-49db-b080-5c1ecb72f57b", "metadata": {}, "source": [ - "#### Query Iceberg Catalog's Table by Conditions" + "#### Query Iceberg Catalog's Table by Conditions (Note: Against the catalog_name = 'bathymetry_ml_auxiliary_dev')" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "id": "28153d1a-46de-43f0-962a-5b613bdbaa73", "metadata": {}, "outputs": [ @@ -643,13 +1356,15 @@ "..." ] }, - "execution_count": 6, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Filter Iceberg table based on threshold set for a given feature\n", + "namespace='bathymetry_ml_auxiliary'\n", + "tablename = 'vpuid=01'\n", "feature_name = 'owp_ahg_c'\n", "thresh_val = 0.25\n", "catalog.load_table(f'{namespace}.{tablename}').scan(row_filter=LessThan(feature_name, thresh_val)).to_arrow()" @@ -657,7 +1372,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "id": "6320114d-8115-4e1f-b8fd-3c80aeaa07de", "metadata": {}, "outputs": [ @@ -708,12 +1423,14 @@ "..." ] }, - "execution_count": 7, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "namespace='bathymetry_ml_auxiliary'\n", + "tablename = 'vpuid=01'\n", "feature_name = 'owp_ahg_c'\n", "thresh_lower, thresh_upper = 0.25, 0.28\n", "catalog.load_table(f'{namespace}.{tablename}').scan(row_filter=And(GreaterThan(feature_name, thresh_lower), \n", @@ -722,11 +1439,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "3acdfde0-d34d-442e-87eb-724dbcef5d1b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "owp_ahg_f: double\n", + "owp_ahg_m: double\n", + "LENGTHKM: double\n", + "slope: double\n", + "----\n", + "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.4067888855934143,0.3982747495174408,0.4000049829483032,0.41463810205459595,0.4620860517024994]]\n", + "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.3738446533679962,0.4299301207065582,0.4191203713417053,0.40392443537712097,0.3803613483905792]]\n", + "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,2.891987367172746,3.1182578724544983,3.6554421303734443,5.662275879263775,0.760075523461262]]\n", + "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.02001729,0.01911396,0.02483032,0.04333816,0.00001]]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "namespace='bathymetry_ml_auxiliary'\n", + "tablename = 'vpuid=01'\n", "feature_name = 'owp_ahg_c'\n", "thresh = 0.25\n", "catalog.load_table(f'{namespace}.{tablename}').scan(row_filter=GreaterThanOrEqual(feature_name, thresh),\n", @@ -738,12 +1477,74 @@ "id": "01dc1ff0-bfd5-4b7a-82d0-1d60e237e2cd", "metadata": {}, "source": [ - "# Cross-Check & Verify Parquet Data Has Been Written To Its Associated Iceberg Table Sucessfully" + "# Cross-Check & Verify:\n", + "## Parquet Data Has Been Written To Its Associated Iceberg Table Sucessfully" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, + "id": "5117aae1-40fb-4392-b424-b488245ace52", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "river: string\n", + "reach: string\n", + "river_reach: string\n", + "river_station: double\n", + "river_reach_rs: string\n", + "thalweg: double\n", + "xs_max_elevation: double\n", + "left_reach_length: double\n", + "right_reach_length: double\n", + "channel_reach_length: double\n", + "ras_data: string\n", + "station_elevation_points: string\n", + "bank_stations: string\n", + "number_of_station_elevation_points: int64\n", + "number_of_coords: int64\n", + "flows: string\n", + "profile_names: string\n", + "geometry: binary\n", + "huc: string\n", + "model_id: string\n", + "us_reach_id: string\n", + "us_network_to_id: string\n", + "ds_reach_id: string\n", + "ds_network_to_id: string\n", + "__index_level_0__: int64\n", + "----\n", + "river: [[\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",...,\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\"]]\n", + "reach: [[\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",...,\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\"]]\n", + "river_reach: [[\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",...,\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\"]]\n", + "river_station: [[7300,6833,6346,5913,5505,...,1842,1400,970,516,111]]\n", + "river_reach_rs: [[\"Stream0528 Reach-1 7300.0\",\"Stream0528 Reach-1 6833.0\",\"Stream0528 Reach-1 6346.0\",\"Stream0528 Reach-1 5913.0\",\"Stream0528 Reach-1 5505.0\",...,\"Stream0512 Reach-1 1842.0\",\"Stream0512 Reach-1 1400.0\",\"Stream0512 Reach-1 970.0\",\"Stream0512 Reach-1 516.0\",\"Stream0512 Reach-1 111.0\"]]\n", + "thalweg: [[217.86,215.26,213.94,213.33,213.34,...,368.01,365.61,364.04,360.64,360.73]]\n", + "xs_max_elevation: [[248.82,238.12,246.73,241.95,242.87,...,404.79,398.04,385.23,404.53,391.25]]\n", + "left_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", + "right_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", + "channel_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", + "..." + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load a XS parquet file into a DataFrame\n", + "parquet_file_path = f'{os.getcwd()}//xs_data/mip_01020002/huc_01020002.parquet'\n", + "df = pq.read_table(parquet_file_path)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "id": "b365d647-bbfa-45a9-8673-52c564cf0ea7", "metadata": {}, "outputs": [ @@ -794,22 +1595,36 @@ "..." ] }, - "execution_count": 8, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Load the Parquet file into a DataFrame\n", + "# Load a m1_auxiliary parquet file into a DataFrame\n", "parquet_file_path = f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/vpuid=01/ml_auxiliary_data.parquet'\n", "df = pq.read_table(parquet_file_path)\n", "df" ] }, + { + "attachments": { + "224c4550-7d87-455e-8193-0364425d439b.png": { + "image/png": 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68Ucmexu5CuZzUiaegnzlSOh5gxAUX1WjglIuqQtQtqyPB9sfTT8FBSvjxQKIfP2DEcb0BW+jKfWY6wwLDKizL/P8KUR26QWVinlQmpycxywpNgYa5vkrKq/Bnm1rMGTyrcznwrhUXUVyXKxwTqHVIa6TJ0ZPvwtG1vfisgr4snhGLResVmKLe9x+YFOMnswTGBzogeCIeCSx+K6m1mNGwu3TMhsKy6qxd8c69B45A2WlRaKJNd6/ClzKyRHGsV9iDG6+5VZU6Tzw9qcfMbFlqt6LrdOQeSihr5Hj0sWz6BGbxILl9Q7LcY0mk8nZtSdDYWEe+O5vfbqx6XEPT8a5Agd2rhemEVexaVYvnQb+PlJExSQiyU/Zor6KhtIOM86YMQPr168Xtnfgqw5tJR7vwD1brT0NZMsWOmafgL3tHeyds1+r+LNcVH3wwQd49dVXhUL8b09PNuVAySaBxrYlMGd2FGTeWHA6L2+aRbLZrOiDjuwTXZGNjK1hn6XQsozRMsdwNRa3Je+XlAgDe7CISQYWOHz+wmn0YdN1OvaAakkK9lcingW652Qcw3q2mtBzzyHomWOqa5Q/xt14G1t12BeXTx/Bll0HsH3vUea2kTIhVoPbpkxAcOy1PZS4YPh5w1YmxmRQs6kw7qUZ2r+fMMUZ4i8VXY91X0ICPBHD4rTyL5zC2q272Wn+aXpyJrOuUX5IGXMzDm9diRcWfoSAj5axFYgSjO4bgb+/9ilGTZyN/Rt/wKsfLIXvZz8wscWmB6tK8Mkb/0DXoZPrjDcYFfjLi29BJ1Gggq0ClTIV9uAdd0LHNgLtGikXXY81DW7fwOE34cSOVMz/38XsNP+YPG7WeW19t9e/YbMW4Afmndt+OAuP/+VRtoltKCSVpXXVeDAvaK/eQ3F+7xY8868P2K/e7+HvVY0HHl1ktxx3AEZ17oorp4rx/FuL8R5baJE4bBomT7sL21d9gWf/90P4ffAVrlZWCPGF9826BXr2toKu3vIW9dVW/5t7zN1itviUMt+wlO+jxbd34KsOLTc15R4tLrRKSkoED8WTTz7Z3K5TuQ5CgIurN998s817a8ubZ8uotpxu5KsF+Y7vjSV7YqqxMubj3IvT0uTIPl6/I0HYmA2uto/Hc+/btw/WMVrmGC6+9cOYMWNsrkiU3zBsnOgVhYWlVagsyUUCe6DamMVrrP82j0tY4PlNY1Kwl23pcOFsOmqqKoStFTzYA58//G8cNQz7/X1w7swpYdsFGQtgDwtmbuPaODEeGN8lNh6lV/JZ2UomstiWDcEBbNqxJxKS2PYQrFXehqN6bBpXW3bquFE4FBrItli4yFYoss3vNUYUse0MZMxLJLb/zmWmxRvPPo4vosOwfcs61vc8+Hgq4MNWFcqYn/H1p/6MpdGdsG1TKgrzLsOD7YOW1LUL4GEKaJIx59aIocPZCsvjKCm6wjjXsPPRuHPWzRh30xzB8SVhwfeO6mmMmZQF3r/197/h28RYpB9OYwscDChm4VXnTh1iY8u9R/aThAWkN9a/cQPi8eDj87F+5Xc4xVY3SnEBoQF+SOzaTXh7AG/7f/72JP7zvhpnju5HKfOoRXeKZNs/xNgtJ5Nq8eJTz+CDDxfhcsZBBAeHMQZ6/OPRexEe6I1f161EOd9GIzIEd9wyBdNYcDzn1NK+2ifR/s/yneF5ADOPreHbO/BVh3zqNSEhQZg65B4tLrR+/JGvJAUJrvY/5B2iB20posQC5tNgXHTwaS1nJl6nM7xSjuxrrhhsDft4/59++mmbHLjgGjFihM1zfBwkbM8LR8/A2vGSCKvx8s7uxr1z/8Smpa7FBDlzQN2prqvsJZ7+3kyosL7qmAfp5w2bUZp9HiMGdEffoeMdigfu0+lozPKuFCIiKIBtocG38pDi8X++iqyD2/DkXTdi1oN/F8HMna4A+7Y4o68HDx7EsGHD7DfUSmfT2H5z3bo1XJ3Jmz937pxL7Pzwww8FwfXKK69g/Pjxonpqz05RFXTgTE0dR1ueHGfFbPFxdPW1b+9aaSoLV1429uxsTrt8dTh/CbUzky/basiZr+vpaPaJ9gnyjTAzM89gAPOU6DqA0OJ7LW1k05sV7JVEXl7eqFGzd02xeDIli39KTOwtSjR0PGYKvPifT5B/YieC2L5kZZU1zOvI9uLyMmLCxJlMfClw35MPw5utWLROfPp3weN3I2HgBOtTbvndyKZi7fVV5C8Yt+xbaxrFpxB79+4tuN4puR8Be54ce+fcrycdyyIuivh0nZgNRMWQceZ7EXl7HdE+0WKroKQSNeUF6DpmoCihIWYA3TkPD9b39/WCik1rVlXXMLesBHGdwzF04EB4BfPNMh2njsbMwKBEdQpEBZvWLGTxOJ4eUgy7oScevPce+MclQ8X2z7hw8SJ8mWi1TnwzUg17U0B7SY76Kub6aC99dbWdjQWUurpdqp8IXM8EvL29hdf2tHQjUj49x+tydupo9okWW5Ehnnjs4Uedzdtt65OyuJ2bxvKXbDd80bbYB2lHY8bjn15/ju/xdW2fL/MAc2ZKhRZ7N6xz2zFvimGO+tqUuigvESACRMDZBHhsFJ/6q6qqanb8FhdaPj4+LX4foq2+dTT7rm1gZYsGHSMCRIAIEAEiQATaJQEe0M0FV3PeZ8jL8LLOCIpvDF5Hsk+0Z6sxWHScCBABIkAEiAARcE8C3IPEvVNcPNXU1DicVuTeLC8v9o5g9t/WSB3Fvtah2RojRm0QASLQbAI8rsPeL1h+Q+R7zNjL0+zGm1DQkZ1NqKpDZnWXcWwN+I6uFXdh4chOZ7HiQen8w/8d820heLvml0Lzf9d8Q1BnbZ/QHJuvd/tIbDXnqqAyROA6I8DjOuxNNSiVSmEpub+/f5v23JGdbWpcO2jcXcaxNVA5ulbchYUjO53Nigsre//Wnd1eU+u7Xu2jmK2mXgmUnwhchwRyc3Ph5+fXaM/4uby8vEbPt9YJR3a2lh3ttR13GcfW4OfoWnEXFo7sbA1W1IbrCZDYcj1jaoEIuDWB7OxsYT8ee14rfk6lUiGHvaOyrZIYO9vKtvbSrjuMY2uwEnOtuAMLMXa2Bi9qw/UEaBrR9YypBSLgdgR4vAafFuTeKi60IiIi7C7v5vEtkZGRyM/PF8rxv3nQrTNe/GoPTlPttFcXneOv42qbcWwN9k29VtqKRVPtbA121IbrCUj27Nkjdtso11tDLRABItAqBHhcBI9Z4Uu7+S98/uARk/j7DcvLy4W9e9RqdV2ArZiyzcnTXDub01ZHKtPa49gabJt7rbQ2i+ba2RoMqQ3XEZAUFhaS2HIdX6qZCBABIkAEiAAR6OAEKGarg18A1H0iQASIABEgAkTAtQRIbLmWL9VOBIgAESACRIAIdHACJLY6+AVA3ScCRIAIEAEiQARcS4DElmv5Uu1EgAgQASJABIhABydAYquDXwDUfSJABIgAESACRMC1BOTHjh1zbQtUOxEgAkSACBABIkAEOjABCdvQ8Lrf+iErKwuJiYkdeJip60SACBCB5hMoKChAeHh48yvowCWJXdsMvrtxp2nEtrkOqFUiQASIABEgAkSggxAgsdVBBpq6SQSIABEgAkSACLQNARJbbcOdWiUCRIAIEAEiQAQ6CAESWx1koKmbRIAIEAEiQASIQNsQILHVNtypVSJABIgAESAC7Y6AUV2GvKwMpGfko6YNlte1dfvNHTB5cwtSOSJABIgAESACogho83Fw0zakV4dg4KSJ6BFEv/NFcXPDTPrcw9jyexaMwQMQ0S0CXrbGVleGC4f24eiFK7iqNsAjsCfGTk1C1e5N2JvnieSxkzEw0qNZvWvQfrNqaf1CJLaMpTiyZg0Ol8rRqd8kTB7QCYracdCe34rvd1yCwTsZN96Wgki6P7T+FUotEgEi4PYEDFU5OHHoGM7lFKNCrYfMwxdB4TFIHjAQCUEyGKuuILdYBa0hHznFWia2lG7fp/ZmoObcr1i+8zL0sjAMmjEVfQItHlj6QhxYsx7Hy4CAvtNwy8BQOOtx1nBsZcg/tBU7T16FQSKH0ouJKrkHvIxFSL9cAY2uChdzr2JApPNsaA9jRWLLPEpGDa4c3YadftMwrpsvJO1h9MhGIkAEiEBbE9AV4MCmX3Gy3CBYIpFKoVNdxZXLuYjqP8h0LLA7hg7TIqsmGN3jSWg5f8iMqKqsgjCrpy/CqePZSBoVC7PvqDrzKM6UmcanuqISBjhP6Eisx9ZQgIsXK5jQ8kSXsbMxtm68Neh9QzcYCpRISAx2mthzPkvX1Ehiy5KrsRoX037H0YCb0D/MFhot8g79irRzpaiq0cIg84R/p3j0Gcx/vbH8hlKc2b0PZ6+UoaKa/YozyuETEotucd4ozTyPnFIV4BmI6KTBSOkbCU+zojNUI+/kARw6cxnF1UZ4+HVCfO9BGMguSLOXzTXDT7USASJABFpGwFCUhayr7EEuDUSvyVMxKJKJKU0Fiq6o4GueLmSeldMHjuG81g+IisNA1U58v/UCtFZNS9nU1IwZ/RHE7sV0T2zKuBhRU11jElvs/6szTyCjfwx6+bGHjKEEZ07kQFNbnb66Giqmynz584dxzj95CIfPXEJhlR4KX/bs6XkDbkgOQ50k1hTh9P79OHGxEFV6Jfx9jEysMVFtNs9qbIeEaKDme6UbVcjc9h0yeV6fHrhpThLyz13A5RIJJHED0MWf+dbEPPsctd8UTG2Y11mexDbsgrOalsDL1wcybSGO7tiPHLWtyD8ZJJoqVOvl8PTxhsJYg9KcU9j1+zEU8avPWImCS7kouloDnVQJD6kOFVfO4fD+Y8gq0UAml0BXVYysw79j70V1reFa5B/chC0HzuNKtRTe3groynOQvnszdmfWOKtzVA8RIAIdjMDq1atx+fLlRnvNz/E8LU5KD3gID+4aFOcW4Cp70Eo9/NCpcxi8G5kikCi84R8QgAD+8fOC6aetBHIfH+aNoXti08dEj5oaDZNZ7Dnm7QWp/gpOpxdCzyrSsmfU2TL2I96bPbN4xeoaQWyxM8jfvwmb92cg/6oWUpkEmvJcnN7DnkdHi4WybP4X53ZuQdrZfFRopPDwMKCSPd9sPR0b2iyF0qd2jP3NY2yZS8Q4t6j9hha15RFb7pu2tKcN25bAv8codCvYjj8uncHutEjc1NnaHCnCh87BXUP00KjU0NZkYff6fci9mo8C5pEK9arNL/FF8qTZGBJwGTtWbcP5GimiU27F5EQ9zmxZiT9y1MjLKYIhPhqSqvM4erqMzbN3wsAZN6Evm2evOvsrVu3OxqUzWaiK7wGfRm5Y1tbRdyJABIiAmcCIESOwdetW9OvXD8nJyfXAZGRk4ODBgxg/fnyLgUmDemBw8iVsO1WC/GNbseqkN0JjuyG5Vy8khHnanC6SRg7GjNmDWdsa5B9Yj83HayD164aRw7vBu/oMdtE9sWnjwrxIKkFBSRHSozdwjDkMzp/C5b4+UKdnoVrih569opF9IB0VKhX4T30je/Yc4ZwlPugyehpGd/VEZfpWrN2bg6KTx3G5x1jEqS7gdLYKRqk/e6ZNR0qUEubYMIeCi9XbbewtGNKp1qfD4qOzLXrF23f07POuaEH7TSPo8twktiwRS/3QbeRIFKVuxZnMNOxRe9dX8ExlZx/chbT0PFTqLC415sHS6WyMlUcowgIkTGwZodPqYJR4IyTEF9KcMmjUGpMrtqQQRULZKzi46msctKhGUlmBKtYMiS0bbOkQESACdgmEhoZi6tSp2Lx5MyorKzFokCl+6vjx4zhz5oxwzt/f324d4k56InrodMzqkomMs+dw/mI+ijKPYdfFc7g0fCrGJ/o1GgNbczGNBVKXQq9gPzbHDUMcc4XpL9M9URx3i1ws5lglzBMyb1JgN3SNP4mcjMs4dVAGTZ4WsrABSI7WoYA9YIxaNTRsJsbAnj3FzH0l8YpGYryPIIr9ExIQvj8HlzTFKGIxeDE1peChXhLvKMRHmCYWpTKakOHJAAAgAElEQVRZk82zVYC37+jZ51nmuvZt2eTKYyS2rOhKlJ0xeGRPFGw6gdwc0zSe2bGkvXQQu07kQu3RCT0GJaOTvASn955AAfe32pL5EqngmuUnjUZTBpnMrPLZMXZGwo4LZ2TB6NIrBv4WXiyJMpyEliuvfqqbCFznBPz8/DBt2jRs2bIFu3btEnpbWlqK6dOnw9PT04m9l8GnUzf055+hZTizcxP2XKxG9vGzKE4YyMKxGyZjZQZ2/3EBlUZvxKWMQZ+Q2scR3RMbwnJ4RAMNj5OSsCldDw9E9UhEwPmjKGDiFxIl4nskwN8jszYGmMdUOaywLoPwSDLoobf1jBNfTcOcIsfZZe03tMilR0hs2cCriOiP4T2zsfFEmeB9MiUWgHj1Kvj1LA1JQF928XobPJF38KRJbNmoR8whaWAwgqQsXsuggt6nC/okBUHBri69qhJqmW+jMQ9i6qY8RIAIEAEuqqZMmYLff/9dgMH/lsudeOtnsap5WWXw6BSOIB8F+4HpAW9PU/1GraZBELxghPGqsJiIzVDBN3E4hidcWwFO98RmXLPMs6XRcrElZ2PLnlHB3ZEcfhJpeTpIfLuiR6wnJDp2TvjtrxXySoPDECI7hzxVDjKyqhDJpxHPn0c+f+gpQhASwJwFniEIkp1HgTobZy5UIJJ5KZ2VxIyzMch17TurH2LrceK/OLFNtod8CoT3T0HypU04xVfZCEkCn9AQeEnYioy8A1iXmoUADw1KbQbSi++jxL8b+iacxm8ZFbi0ZzW+P6hkvz507B+DHAkTb8fIzs5x2Yq3iHISASJwvRHg4mrixIku6Zah+AzSdhwTppvYvg9sOsoAg+AFYffMzjEI4c58Idr6WlJlHcThPB7QLYU2dz/Wr9wvnJT4dMPoSd3pnlgfl+Nvei3bw4xnk0HOZ1NY3HBCn17IkRTBs0sPhPPHCFsdb3rgs7xMbEkCuqJf0ikUnCpH5vafkL1HBr2GrbJnuUJ69kYs2zdC4sGfT+nYepY9n3atxPKDLNBdXyMMZ0tX14l59klYHJ+r2ncM1bk5WsrLuda4U22KCPQf3LXeNJ4sciDGDU1EuJ8UqpI85OaXQaf0R2hkBAKbvXWMJ2KGT8HEgd0QEaBkvz7YfLpeBm+m6H1kVncod+JDthABIkAEGAEji02N7xLJvFpKyCQsLEKigFdAJ3TpNxY3Detct9fTNVhGqK6yzS0FQWaAuqoc5eW1nwrm4QfdE5t6YRm1TECZ4lFgDqnyiL4BE2+cjJHdA0wxcxIZhCgWwbPFW/BA5OAb2bOnK8J85TDoDFD4RyBxyGRM7h/KZBtPSnROYc+nG1gsl78HDGq2bYRBDu+AUERGBV/bHqKpBgv5xYyzK9tvltHNLiTRCBO913fKyspCYmLi9d1J6h0RIAJEwEUECgoKEB4e7qLar+9qiV3bjK+7cSfPVttcB9QqESACRIAIEAEi0EEIkNjqIANN3SQCRIAIEAEiQATahgCJrbbhTq0SASJABIgAESACHYQAia0OMtDUTSJABIgAESACRKBtCJDYahvu1CoRIAJEgAgQASLQQQiQ2OogA03dJAJEgAgQASJABNqGQIfZ+sE57wBrm0GiVokAESACRIAIEIH2S6DD7CBPe8S034uULCcCRIAIEAEi0J4J0DRiex49sp0IEAEiQASIABFwewIkttx+iMhAIkAEiAARIAJEoD0TILHVnkePbCcCRIAIEAEiQATcngCJLbcfIjKQCBABIkAEiAARaM8ESGy159Ej24kAESACRIAIEAG3J0Biy+2HiAwkAkSACBABIkAE2jMBElvtefTIdiJABIgAESACRMDtCZDYcvshIgOJABEgAkSACBCB9kygw2xq2tJB0mq1OHniBI7s2IOCS9moLCuH3qAXqpUIHyOk7A+JxIjkoYMx5/774OHh0dJmqTwRIAJEgAgQASLQzgmQ2BIxgGq1Gp+8/T5Kd/yB2BBvJPkq4KGUQy675hiUMpElYx8pDDixdjW+rarCnY/+GV5eXiJaoCxEgAgQASJABIjA9UqAxJaIkd33RxpKd+1FfIA/ynNrUKKqhpF5tYx6Y73SXHB5SA3onOCLK9u34M3DRyD3kNd6vkxeL8H7xbxg3APGPWK8jOAZY/9VMPHm5euH4JhYxA8dg159+kKpVIqwkLIQASJABIgAESAC7kqAxJaIkdn7207IayQ4n1MheK9MHwlkUpNQMldhMEqgNUiRfb4SUXE+6B0Klldr8ngJ5Qx1f5s9YeZjvC6jXgeD4Qpqzp5E9t6f8EPSBNz69D/h7e0twkrKQgSIABEgAkSACLgjARJbIkalOL8QsnIDwMRUvcS+y5mIskx6nocJrryLlSi6fJWdUkNtqBGElpwJKjmbZuTCSsY8YDL2Nz/G/+b1KORGeHkw75a3ArGhAfDI2Ir921MwZsp0EVZSFiJABIgAESACRMAdCZDYEjEq6ho1lDo25ceEFJNc1xITVRIummwILo1BBrVeA2NSHHxGp0AqkwnlWDXChyeJxCTezP+VMs+WRKuBRF0Fj8JsRNYcQ8aOLSS2annRf4gAESACRIAItEcCJLZEjJqGBcgrmaDS6FUwGJkg4pKrdkrQPK3IpwWtk0//ZBh6dkH66dMwGnmc1jXPmLXQ4mWlUqnwUSgU8PaNRE3fGBSfOGRdLX0nAkSACBABIkAE2hEBElsiBkun1UHLwqYCByTDOyoUCl9vSD0UzFtVf5syazFVVFSEgry8Bi1Y5jOf5Me4INPr9SxuywC+ArK6ygNGXUMR16BCywOGXPy2eDEOJv4Vz06JYKsjKREBIkAEiAARIALOJJCWlgYZm7EaPHiwqGpJbInApDVo0OnmwSivqUJxaSH0RXpBFFl6q6yFFq+2pcf43l5atWkvLxFmmrLoM7H2vXex+8/34RkmthwmYwUy9+zA+YBRmNDLX1gZSYkIEAEi0GYE9Bex4b0vcLz7PDx9cwIURjVKLl5AsWcXdIvwpHtUmw0MNWxJYMmSJYLYWrp0qSgwJLZEYNKwKcLLBXnQ6UzRVramAM3VNOa1sm5GTD7u09IazBFe1jU46bt2P96+bQ6OPHMS40lsOQkqVUMEOioBAyovH8auXQdw6kIOSgPG4a+Pj0MYd7EbynF6y0qsS8tAocYL4T1GYuYtY9HV1+onnkSJgLAwhAV4mTzzhsv4/euvkDNmPp5gYouS+xBQ5/2B7z9PhXrCP/BIit81ISx2rM1dcZDfqMrF/vWp2HYkEyVaDwTF9sO4GdMwJLptxDeftVq1apVg/aJFixASEuJwUGiWySEiQMWC1u0JLVviy1nH2IwiJSJABIhAOyCgRe7uL/H+F9tRFNgHE257AI/cPgiBwlNGj/ztX2PZ9mJ0nng3HrhjFEIub8KX36WhxDpSQhqB4fOewAOjo9iKbUruSMBQk4ejm5bi/cW/4FS59QA6GGt9FjZ+8Cbe35DFrgoR14axGqd++QwrM3wx4u4n8NRjt2Og4iRWfrEGp1VtQ2f58uUYP348xowZgx9//FGUEeTZEoFJZ34tj0WAu7mYGA8Vz2svn73pRhbGZT+pzmLFi0/h1WXbca7CG12G9oNnqRHXtkLV4+zHt2Pmy1uQWayCIjQZ4x76Nz56ZQqi6+5kWux5rhtkz/GmPDFneTF+ukMpopx90+gsESACHYeANnMjvt0mxaQn/oYhIVYySZeJP3Znwz/lMcwZEc+2wAHiFVfw76U7kZY9FFNjLH73G/Kw5b33cSj5L5g/NaY27lSPi6lvYn4q5ylH33tex7z+CqbhSpG+dS1+PXQOeWx7Ht/OvTF2xkykxDCPh7Ecx1Yvx9b0fBSXV0HD7m0hCUMwqrccFw4cQUZuGXRenZA0ahZuHdsF3tzBxqYsc/etwarfjuFyqQYy3zD0nPon3DUomKYv6y5lA0r2rUbq6QCMvHcOcr9bhQrLy9zRWEfpoa6uQrWmNkTGYf4cnD5bjZixUzAiiY9DZ4RPycbRD48hs9CAHpbXjov+uVWxN8Jwb1ZZGbtm2AzXsmXLsGDBAiGUaOHChULclo+Pj/AJY15ZT8+GHlgSWyIGR8xKwpZ4sswm2BJkdrUWu5lsfmYq7v0uAPNe/wbv91Qgh/2y/N+9lmJLivDhj+Ctb55FdKAROb/9B0/9zzzM75+Bb+cE1t5AFOj/3Cp8dR+/sUkREGt6xZDjciLgURYiQASufwLGShz7fS9KpKHY+/m/sLbCAO+IHhgxbTpGxvvAWHwRlyo8EZ/YWRBaPCm7JCJOeggXL12FMcZ8L2oMlQxRY+/H3EE8nwSegbwWDTI3fIavDwdh3Mz7MSugGme3rsLqpakInn8bkj2qUXAhExVR03DP7dFsY+pL2L1mPVat74xhN03F3VOUqEjfgl82LMeW+Ocxswvb+TB/B5avSkfA5Ll4LCkAxoorUAVSLGv9UZEidPSf8c8xbCT06fjBasgMDsc6ATNfeAMza8s5zB8dhNBgCY6ePY3iEcMRKjOg/NJllHvHIV6Yn3Zteu211/Dyyy8L7zqOiIhAQECA8Jk2bZrQMJ9GnDdvHq5evYorV65Ao9Hg9ddfx4svvljPMBJbIsbJYLgmeWwJInvH7HmteNOORJo9z5axeDUWL8vBkNc24+Mnu5p+AY4OwtGl67G7rl8SBPS5ETP7mA4MGhCEEysGYmnaaejmDAP7bSgkr4ju6N07od7qRTHl6pqhP4gAEei4BPSXcS5Ti8DuAzE2pQtClNXI2rEKaz5fDo9n/4RBlRWohC/8fS0ejgp/+PsYceVqJXuBmSOxBSj8wtjDLqTOw2SsPoHtaeVImvU4JvX1EY5Hzy7Fmbe24GjmLCQnmYZDGRaP7gn8h2Q8AouOI31bOHoN64vu3PkWD1w49CWyMoth6NIJxqpKVBm90L1rN8RFe/AaO+6Y2us5m+VpbDGVsYlj7TC/tDNSZk/HhS/X4P130tE33ohzp9QYcc/N6NHQgWTP6made+mllxAZGYnnn38e77zzDubMmVOvnl27dgnff/rpJzz00ENYzHYDePjhhxu0RWKrARJbB0xiqznCyVybPUFm2aJ1vvovBKpvmy7jGNI1MZg1PLaeSKqfS40Lq9/ACwt/wp4zeahWBkNZoYM8xdFkd3PL2eJHx4gAEbieCRhVV3FVI0F4ryHok2AKYoiadSN7KC7HoZPlGBhh10ffLDSGwlzka9hKxRWvY0Fd2IwRBr0EigoVE3DWSQq/AD/2Ug8mqDTsHHfgS/0Q4MsEV43pfiiLH4ZxiceR+sl/kD1gGFKGs/5E+9q5v1q3Qd/ZXGwTITjKb4Sq7ApKDZHoN6QHgotP4rwmD8cPnMWQLv0R7HrnliCekpOTMXfuXBw5cgSvvPKKsBKRJ74zAf/+5ZdfIjU1FaNGjbLZfxJbNrHUP2j9lh5rQcRzO/JQmWtsaj5WceMWCufYOxUtPG/WmfUnFuGOuR9C9tB7WPr+IIRLzuLj++ditWVGG02IKmfdGH0nAkSgQxKQsAePjN2LVNVc5ChNXg+PAASy/QmvVFYDvv7Mr1WJikq+4qf26airwNVqCTvl26iXpA6mjXuU8FCX+OOGOx7G+M6WT1wJlP7erM56kURCVfwBKWFh2QZhyoBXyuxmz0weKiI88uWRGPng35GccRC7d+zAivd/x66bHsYj42PqZgHqbKI/bBKQOBxrA7QqDfQyD3gq2FtYHORHzUmsXXEQAVOfx5whfEp3OEYMXI8lH/2CTb17YG6vaxHKNg1y0kEuojZs2ICRI0cKU4RmscWnDd99913s27cPPXv2bLS1VtCEjbbdfk7wf9NWr9bhxts6Zu6UPUFm2XGx+WzBkicNRD+vS/htSzqLXrCdVMcP4IRhJB595T5MGNQbvQcMQqLlTwGJF7zZL7zy0vJ6ryJyWM52c3SUCBCBjkhAGY6IYCA36yLMPnNjVT7yK2QICQ2ELCQOsX4qZJ7LqXtdmTrzLC7qgxEX6ygmSsHeqsFWhTPvk6UPRBoaiXB5JXKLDAjp1Amd6j5hCPBswaONbT0R2n04Zj70NB4e5YdLu/Yhq4nbHXbES8DcZ6mjsdZfQOrCl7BwzXlhNaKj/CjPQ77KG6Fhpqli3o4yuguiPVUoKalqsh+tJWOzf/9+jB07FkqlEitWrMDKlSvh5eWF0aNH4/Dhw3arJs+WXTymk+a4KUthZEtoueKYva0fJEEzMf+Jnhj/1hzciZfxyOhYKCv/wNnKa7ckZXIfJBo/xKdvLEP0Hb0RJs9BluVSXXl33NDXEx98/xYWD30MfYwXURg2A3MclRPBjbIQASLQQQhIozFoaCx2b1iPlX94YWysHhmbf8V5v/54oKcXJPIuGD6iMw7++jNWhU7BAP8i7E09DG3CDAyt55WywUsahs6Rcuw6/Bt2x6YgwliKKt9e6BfXG6OG/IbPti3Dt9KJGBwfCLmmDAXVoRg0MI6tPWx6MhSfQtp5IyIiA+GhL8O5AuaV8/YxrVRsenUds4SjsWYxcaHxXREXyq4LTshBfqkuAQmBv+Lg+g2ImXIDolg84KW9m3Fc1QmjuzqO9XPmIKxbtw49evTA7NmzcerUKWEK8YcffmDxzr2xfv163H333Y02R2KrUTSWJ+rPKYv1RjU1ny0xZ3cakQUdDH11IzaELMBLHz2N2a+XQu8ZjM6JozGrR4DgrJcMmI+vF+fir/9+GjM+KIfewxfBEUkYkhBkutAlIbj1rSXYcf8/8PKtq6D174abXhuC2x5zUE4UN8pEBIhAxyAgRadR9+I+7S9Yt3UpPqhiC3NiBuDWB25Gspdpui5izDzcq1mJtRu/xkFhU9PJuH/WMITYnCK0oCbxRt9ps3Fh+Xps+uo4u8eFIunGWPSNC0e36Y/gfp912LJ/Nb7ezHxqnoGI7DcNfZortipYLNC2nUgtrmLTXF4I7twLM+4Yi6gWOMo6xvhb9lIGu2MticboeY9idF0RB/kVXTH1gbmQrt2KtZ/vRKVeAb/wBAy/ZwYmRLfewPCpZh4Mv2bNGjz++OP45ptvhOnn5557Tgic50H09pKEzTc6ik6zV75dnMvKykJiYmKzbZ0+cQrikxOE8rYEkauO8Tnh/FPp+OIX0061ze4AFSQCRIAIEAEiQASaTeD06dNCgPx///tfpKSk1KuHTy9yAcZXJMbFxdlsgzxbNrHUP2hLjTbVa2VZo5jpRi605HJ5q85Hi0BBWYgAESACRIAIdDgC4eHhOHDgQF1gvCUAvqnpnj17UFlZ2SgXEluNoml4QoxI4qXE5jO3YC3czELLdN6Rj72hnXSECBABIkAEiAARcB6BoKAgu5Vx50hgYGCjeVpvwrNRE9z/BJc71oKIW23vWHOnFusLLb64mcSW+18hZCERIAJEgAgQgcYJkNhqnE3dGTlbd2yoXRbYUq8Vr7QxkcaFlnnvDnM+W3lFmExZiAARIAJEgAgQATchQGJLxEAomNiyfD9ic71WvKnGypqFlqWY4wJPoeCvjKBEBIgAESACRIAItFcCJLZEjJw/e+kk30/DOtnyOjXnmLXQMrfDd6b1szMHbG0PfScCRIAIEAEiQATcjwCJLRFjMnzUcJSVlQk5xU4jis3Hg+q42LLl8SotKsZg1jYlIkAEiAARIAJEoP0SILElYuxunHoT5HopCvLyoVKp6nm5muPJMjdpLbT4tKFOpxOWj2ZnXoS/XImxkyaKsJCyEAEiQASIABEgAu5KgDY1FTEyfAqRb4y6c/sO7N6+G6UlJdBqNUwYsanF2nf5cNF1bdWi6W8pf5+icJz9T/hTApmEvXjT/J5F1jbPI+yvzOLCZHIpe8+SJ0LCwjB03Cj0698fUVFRkEpJE4sYJspCBIgAESACRMAtCZDYEjksXHBVVVVBrVYLKxPNqxNFFheVjYswLqz4tCJ/0aW3tzcJLVHkKBMRIAJEgAgQAfclQJuaihwbLoD8/f1F5qZsRIAIEAEiQASIABEwEaD5KboSiAARIAJEgAgQASLgQgIktlwIl6omAkSACBABIkAEiACJLboGiAARIAJEgAgQASLgQgIktlwIl6omAkSACBABIkAEiACJLboGiAARIAJEgAgQASLgQgIdZjWiVqt1IUaqmggQASJABIgAESACtgl0GLHFXyZNiQgQASJABIgAESACrU2AphFbmzi1RwSIABEgAkSACHQoAiS2OtRwU2eJABEgAkSACBCB1iZAYqu1iVN7RIAIEAEiQASIQIciQGKrQw03dZYIEAEiQASIABFobQIktlqbOLVHBIgAESACRIAIdCgCJLY61HBTZ4kAESACRIAIEIHWJkBiq7WJU3tEgAgQASJABIhAhyJAYqtDDTd1lggQASJABIgAEWhtAh1mU9OWguU70J88cQJHduxBwaVsVJaVQ2/QC9VKhI8RUvaHRGJE8tDBmHP/ffDw8Ghps1SeCBABIkAEiAARaOcESGyJGEC1Wo1P3n4fpTv+QGyIN5J8FfBQyiGXXXMMSpnIkrGPFAacWLsa31ZV4c5H/wwvLy8RLVAWIkAEiAARIAJE4HolQGJLxMju+yMNpbv2Ij7AH+W5NShRVcPIvFpGvbFeaS64PKQGdE7wxZXtW/Dm4SOQe8hrPV8mr5fg/WJeMO4B4x4xXkbwjLH/Kph48/L1Q3BMLOKHjkGvPn2hVCpFWEhZiAARIAJEgAgQAXclQGJLxMjs/W0n5DUSnM+pELxXpo8EMqlJKJmrMBgl0BqkyD5fiag4H/QOBcurNXm8hHKGur/NnjDzMV6XUa+DwXAFNWdPInvvT/ghaQJuffqf8Pb2FmElZSECRIAIEAEiQATckQCJLRGjUpxfCFm5AWBiql5i3+VMRFkmPc/DBFfexUoUXb7KTqmhNtQIQkvOBJWcTTNyYSVjHjAZ+5sf43/zehRyI7w8mHfLW4HY0AB4ZGzF/u0pGDNluggrKQsRIAJEgAgQASLgjgRIbIkYFXWNGkodm/JjQopJrmuJiSoJF002BJfGIINar4ExKQ4+o1MglcmEcqwa4cOTRGISb+b/SplnS6LVQKKugkdhNiJrjiFjxxYSW7W86D9EgAgQASJABNojARJbIkZNwwLklUxQafQqGIxMEHHJVTslaJ5W5NOC1smnfzIMPbsg/fRpGI08TuuaZ8xaaPGyUqlU+CgUCnj7RqKmbwyKTxyyrpa+EwEiQASIABEgAu2IAIktEYOl0+qgZWFTgQOS4R0VCoWvN6QeCuatqr9NmbWYKioqQkFeXoMWLPOZT/JjXJDp9XoWt2UAXwFZXeUBo66hiGtQobseMOTit8WLcTDxr3h2SgRbqUmJCBABIkAEiED7J5CWlgYZm7EaPHiwqM6Q2BKBSWvQoNPNg1FeU4Xi0kLoi/SCKLL0VlkLLV5tS4/xvb20atNeXiLMdE4WYwUy9+zA+YBRmNDLX1gp2eykz8Ta997F7j/fh2eY2HKYnNm2w8YoAxEgAu2SgP4iNrz3BY53n4enb06AwqhGycULKPbsgm4Rni27Z7VLIGR0WxBYsmSJILaWLl0qqnkSWyIwadgU4eWCPOh0pmgrW1OA5moa81pZNyMmH/dpaQ3mCC/rGlz0Xbsfb982B0eeOYnxLRVbTTWxLdtuqq2UnwgQAQsCOpSkb8emrfuQnlMOrTIQsT1HY/rNKYjxsv7JZkDZ0R/w0XdH4Dv1eTwxJsQkkAzlOL1lJdalZaBQ44XwHiMx85ax6OprVV6iREBYGMICvEzecsNl/P71V8gZMx9PMLFFqfUIqPP+wPefp0I94R94JMXvmtAVO5ZmUx3kN6pysX99KrYdyUSJ1gNBsf0wbsY0DIluG3HNZ61WrVolWL9o0SKEhIQ4hE4zOw4RASoWtG5PaNkSX846xmYUKREBIkAE3JuA+gK2rT2Imi5jMGfefbhzbCwqD/+Cr9ZnQFPPciMqz6zGZ7+cg05hKaL0yN/+NZZtL0bniXfjgTtGIeTyJnz5XRpKrCMppBEYPu8JPDA6iq3optQWBAw1eTi6aSneX/wLTpVbD5CDsdRnYeMHb+L9DVkwzds4yG+sxqlfPsPKDF+MuPsJPPXY7RioOImVX6zBaVVb9B5Yvnw5xo8fjzFjxuDHH38UZQR5tkRg0plfy2MR4G4uJsZDxfPay2dvupGFcTWeDNn4+ekH8K91x9keYMWohj+6jvkT/jZLiR1f/YCtR7OhCkjCTU8txsfzRyLYfG/TXsS6f83HG99uw/EcPToNvAXz330Xjw4KqP1losWe57pB9hxv2hNzlhfjpzuUOPvx7Zj58hZkFqugCE3GuIf+jY9emYJo8x1PdRYrXnwKry7bjnMV3ugytB88S424ti2r3nEdaGbbjVOiM0SACLiagLI7bnnmWWFaRUg9EiDLSceyi1koNnRHZO3PekNRGpavOI9ut98Gj7Vf4bzZLl0m/tidDf+UxzBnRDzbIgeIV1zBv5fuRFr2UEyNsfALGPKw5b33cSj5L5g/NaY2FlSPi6lvYn4qr1COvve8jnn9FYC+FOlb1+LXQ+eQx7bv8e3cG2NnzERKDPOIGMtxbPVybE3PR3F5FROFnghJGIJRveW4cOAIMnLLoPPqhKRRs3Dr2C7w5vdPNmWZu28NVv12DJdLNZD5hqHn1D/hrkHBHWj60oCSfauRejoAI++dg9zvVqHCPI78v47GMkoPdXUVqjW1ITIO8+fg9NlqxIydghFJnHNnhE/JxtEPjyGz0IAelteGpR1O/LuKvRGGe7PKytg1wWa4li1bhgULFgihRAsXLhTitnx8fIRPGPO6eno29LCSZ0vEgIhZSdgST5bZBFuCzJ7WgrEUJ3fsREG/F/DDxq3Y9N0C9D7zHp58YQsC73oL36b+gv/O9cGv/7wPr+8y/76swq5/TMXtn1fipjdXYftvS/FI0K94atZz2DHqUuIAACAASURBVMi3BROSAv2fW4+jx4/j+PGDeHcKf+WQFOHDH8Fb32zErj3b8M3TCTi+cB7m/1LG9sNnid24Nj8zFfd+VoDhL36DdalfYsG4ANSoLXvgoI7mtm0ymv6fCBCBNiRQJ7SE+0E1rlbo4BEShgDzjzx9HrYv3wzViLswvbtPPUsNxRdxqcIT8YmdBaHFk7JLIuKkJbh46arpHlOvhPUXGaLGPoRnn3sOzz33NGYk81o0yNzwGb7eq0bS1Pvx+KN3YphXBlYvTcUZ7hFhNhZcyERF1Hjc8/CjeOSe8Ygo3IFV68/C64apuPvBBzB7gAcyNizHliyTMDDk78DyVenwHDoXj/3tr3j49gkY2LmFsa3WXXH771KEjv4z/vm3uRib6N/Au+hwLGUJmPnCG1gwkwly1leH+SVBCGWegoKzp1EsDIMB5Zcuo9w7DvFhrpcwr732Gnx9fdG9e3fMmDEDDzzwgPDe42nTpmH69OnCD4x58+Zh0qRJ6Natm/CKvjfeeKPBKJJnqwGShgcMhmuCwZYgsnfMnteKt+RIpNn1bAmmSuDXfQQmjhnMblIjEHPuZ6xb2BO3PDIHk/l7sIcDO76diV27LkA/KhnSkl/wzic5mLxkO166NVT4NTZgyUVsTHwNP+5ajCkTTf33iuiO3r0T6q0gDOhzI2b2MZ0fNCAIJ1YMxNK009DNGQZ58WosXpaDIa9txsdPdjWVGx2Eo0vXY7epiGCrvTrY71AhNbVtc7m6ZugPIkAE2pCAHoX7VuLXnM6Y8Hhfk0eITRgV7PwZ2zEaj46JYps4X65nn7GyApXwhb+vxcNT4Q9/HyOuXK1kYivQoedI4ReGiIja+C9Wu7H6BLanlSNp1uOY1NdHKB89uxRn3tqCo5mzkJxkMkEZFo/uCdxDFo/AouNI3xaOXsP6ojtXAvHAhUNfIiuzGIYunWCsqkSV0Qvdu3ZDXDS/wUa3Iec2bJrN8lhOAlta0tSxdJhf2hkps6fjwpdr8P476egbb8S5U2qMuOdm9GjoQHI6lJdeegmRkZF4/vnn8c4772DOnDn12ti1a5fw/aeffsJDDz2ExWwF/sMPP9zADhJbDZDYOmASW80RTuba7Akyyxat89V/IZAt2yyPyRDZORKSykIUVrPj/F4gj0B0OBNcZeXCr0Pd2SM4XlWBrAc7w+shc1kDdBq2c32evV+QalxY/QZeWPgT9pzJQ7UyGEr2y1WeYpo012UcQ7omBrOGx9rZ4sF+HY33rrnlGq+RzhABIuAKAloU7P0On6WWoNddj2BMpGla0Vh+COt/V2HwAyMRIbgzrNu268O3zizqu6EwF/katlJxxetYUBdWY4RBL4GiQmXDWyaFX4Afe+kHE1R8IkBw6PshwJcJrhrTfU4WPwzjEo8j9ZP/IHvAMKQMH4I+0b527nmiTL3OMjV1LB3lN0JVdgWlhkj0G9IDwcUncV6Th+MHzmJIl/4Idr1zSxBPycnJmDt3Lo4cOYJXXnmlbsqc70zAv3/55ZdITU3FqFGjbI4niS2bWOoftH5Lj7Ug4rkdeajMNTY1H6tYhIXXsijY/l8SFlegF7xxrKzEAx4sEJXv3SVc0txVJo3EXV9sxIKBlsMvhW9kbdyBjSb1JxbhjrkfQvbQe1j6/iCES87i4/vnYvW1jvHK2Qu6G/+H47AOXldz2r7WffqLCBCBNiPAPFp7v8EnTGj1ZkJrZk/z9JoRVaeP4EzVFZz+6H+wU7CPix52T1r/Nl7OvRcvjvdnfq1KVFRyFVb79NRV4Gq1BL7+vrZuC/V7aeO+wduAxB833PEwxne2fCJLoPT3ZnXWizQS6uNTQhLmhTMIUwq8Uhk7xm+b7N7GM8gjMfLBvyM54yB279iBFe//jl03PYxHxsew4AtKnIDE19FYGqBVaaCXecBTwd7C4iA/ak5i7YqDCGArV+cM4dfUcIwYuB5LPvoFm3r3wNxe16KCXTkCXERt2LABI0eOxIsvvlgntjQaDd5lMc/79u1Dz549GzWBxFajaCxO8H+zVq/WES4qG8fMpewJMssmxeYTY6aYPPLEvuip/ABHM/ToendvwflVL2m94M1+0ZWXlgs/Ps23KNXxAzhhGMkC4u/DBB5pb/BHosVPCnnSQPTz+gC/bUmHJqVvw3pZXY7qgKR5bVt3gb4TASLQ+gTUFzbgqzVXkHzXY7ilTmhxOyTw7ncbnu3CXl9mNsuQjx1ffofLff+Eu0fFQeGtRKyfChnncqBLjhPittSZZ3FRH4xBsY5iohTsrRvs/sK8T2aJxJuRhkYiXL4LuUUGhNwQ0SC2qKF3TSQztvVEaPfhmNmd3fNSP8BHu/Yha0wMEmvXBois5brNJg2Jsz+W+gtIXfgJTvR6GP+ckwiZg/woz0O+yhsJYaapYA5OGd0F0Z47UFRSxcZc6ViMO4n2/v37MXbsWCiVSqxYsQJyuRyzZ8/G6NGjcfjwYRJbLeVsjpuyFEa2hJYrjjl76wdJ6C146sE3MeXtO3C3/J94YEQslFWXcbIkAfffmwJ/eXfc0NcTH3z/FhYPfQx9jBdRGDYDc5L7INH4IT59Yxmi7+iNMHkOsiyW/EqCZmL+Ez0x/q05uBMv45HRrN7KP3C28pqnS+mgDjSz7ZaOL5UnAkSghQSMZTi45Q+Ud7kRg4IqkZdbWVuhHL5hneDvGYgwy/gagxrecgnkviEI8+cPyy4YPqIzDv76M1aFTsEA/yLsTT0MbcIMDK3nlbJhpzQMnSPl2HX4N+yOTUEEWzhU5dsL/eJ6Y9SQ3/DZtmX4VjoRg+MDIdeUoaA6FIMGxrG1h01PhuJTSDtvRERkIDz0ZThXwOI1vH1q49KaXt91WULuYCxZzFtofFfEhXqZRJKD/FJdAhICf8XB9RsQM+UGRCmrcWnvZhxXdcLoro5j+ZzJeN26dejRo4cgsE6dOiVsbv7DDz+w+ObeWL9+Pe6+++5GmyPPVqNoLE/UnxoT641qaj5bYq6p04iOu+OPcW9vxi+hf8erXz6FW19lSxADYtD31rcw+x4mtqQhuPWtJdhx/z/w8q2roPXvhpteG4LbHpuPrxfn4q//Zit9PiiH3sMXwRFJGJIQVPurwgtDX92IDSEL8NJHT2P266XQewajc+JozOoRIHjIJAMc1CFpbtuOe005iAARcCEBttLwUrYWNTWpWHLaoh1pOCb89VlMcSSYmN8pYsw83KtZibUbv8ZBYVPTybh/1jCE2JwitGhD4o2+02bjwvL12PTVcXbfCUXSjbHoGxeObtMfwf0+67Bl/2p8vZnFXTHRF9lvGvo0V2xVsFihbTuRWlzFpsG8ENy5F2bcMRZRrRA35MLRc3LVDsZSEo3R8x5lSyXMyUF+RVdMfWAupGu3Yu3nO1GpV8AvPAHD75mBCdGtB55PJfNg+DVr1uDxxx/HN998I0wvP8dWwPLAeR5Eby9J2HxjfSVhL3c7PZeVlYXExMRmWz994hTEJycI5W0JIlcd4/ED+afS8cUvpp1qm90BKkgEiAARIAJEgAg0m8Dp06eFAPn//ve/SElJqVcPn17kAoyvSIyLi7PZBnm2bGKpf9CWGm2q18qyRjHTjVxo8flgW22LMJmyEAEiQASIABEgAk4iEB4ejgMHDlzbuNeiXr6p6Z49e1BZaZ4+b9goia2GTBo9IkYk8cJi85kbshZuZqFlOu/Ih96ouXSCCBABIkAEiAARcAKBoKAgu7Vw50hgYGCjeVpvwrNRE9z/BJc71oKIW23vWHOnFusLLb54mcSW+18hZCERIAJEgAgQgcYJkNhqnE3dGTlbV8z3qeKppV4ryzosm+b1cqFl+coLfsyWoBNhMmUhAkSACBABIkAE3IQAiS0RA6FgYsvy/YjN9VrxphoraxZalmKOCzyFosFOWCIspixEgAgQASJABIiAuxAgsSViJPwDAoT9NKyTLa9Tc45ZCy1zO3xnWj87c8DW9tB3IkAEiAARIAJEwP0IkNgSMSbDRw1HWVmZkNPS82Qu2pJjPKiOiy1bHq/SomIMZm1TIgJEgAgQASJABNovARJbIsbuxqk3Qa6XoiAvHyqVqp6XqzmeLHOT1kKLTxvqdDph+Wh25kW2m7sSYydNFGEhZSECRIAIEAEiQATclQBtaipiZPgUIt8Ydef2Hdi9fTdKS0qg1WqYMGJTi7Xv8uGi69qqRdPfUv7uROE4D3Q3ecVkEvbiTfM7FVnbPA8vJ2NxYTK5FF5enggJC8PQcaPQr39/REVFQSolTSximCgLESACRIAIEAG3JEBiS+SwcMFVVVUFtVotrEw0r04UWVxUNi7CuLDi04r8RZfe3t4ktESRo0xEgAgQASJABNyXAG1qKnJsuADy9/cXmZuyEQEiQASIABEgAkTARIDmp+hKIAJEgAgQASJABIiACwmQ2HIhXKqaCBABIkAEiAARIAIktugaIAJEgAgQASJABIiACwmQ2HIhXKqaCBABIkAEiAARIAIktugaIAJEgAgQASJABIiACwl0mNWIWq3WhRipaiJABIgAESACRIAI2CbQYcQWf5k0JSJABIgAESACRIAItDYBmkZsbeLUHhEgAkSACBABItChCJDY6lDDTZ0lAkSACBABIkAEWpsAia3WJk7tEQEiQASIABEgAh2KAImtDjXc1FkiQASIABEgAkSgtQmQ2Gpt4tQeESACRIAIEAEi0KEIkNjqUMNNnSUCRIAIEAEiQARamwCJrdYmTu0RASJABIgAESACHYpAh9lny5mjqtPpUFRYAI1GDaPR2KSqDQYD/AMCERIS1qRylJkIEAEiQASIABFonwRIbDVj3IqKrkAmkwmCSSKRNKmGK1fyUVVRATCNFhJKgqtJ8CgzESACRIAIEIF2SIDEVjMGTa2qQVhYeJOFFm+KazO9QY+qSia4WCLB1YwBoCJEgAgQASJABNoRAYrZasZgNXXq0FYTZsHFvWSUiAARIAJEgAgQgeuXAImtVh5bo/HatCMXXNVVlSDB1cqDQM0RASJABIgAEWhFAiS2WhG2rab0ejalSILLFho6RgSIABEgAkTguiBAYquVh1Eiabh60UCCq5VHgZojAkSACBABItB6BEhstR5roSWpzPaaBC64qisrUVJc1MoWUXNEgAgQASJABIiAKwmQ2HIlXRt1B/gHQi63Lbh4DNfV8lLxe3cZi7D7kxfxxspM6G201aJDrO5dH72AV3+64Py6W2SYGxY2XMKqF+7Eo1+cdS9W7mqX5RAacvHbB//A2xvyYXDDoXWpSe1hfFwKwEmVG8txPPULfLu7A15DTkJI1TSdQFpaGvbv3y+6IIkt0aick1GhUCA0tBMiIqJsfoS9IcQmQx62frQI3+wv4tt2NS8ZK5D5xzr8evJq/TpY3b998j6+P1jc/LqbZ5HtUo3ZaTt36x41XMH+Nb9gV2Zl27LS7sE/+4ahx7O/o4YTsLbL+nzrUrLdmj4Ta997Fz8dK29bdratE3e0udem9fiIa41yWRMwViM/4yyyy7XWZ+g7EXAZgSVLloB/xCbbLhaxpSlf+yeg3Y+3b5uDI8+cxPhe/miC1GvdvrcXO1uXSv3WpP6I7p6EpM6BkNmyw9F5W2Xa1TE1MtctwktvfoENh7NR4xOLwTOewtv/eQyDA114ZdO1ya4SHUrSt2PT1n1IzymHVhmI2J6jMf3mFMR4mdgbVbnYvz4V245kokTrgaDYfhg3YxqGRHs2uO8Yin7HkrfX4mIDl70cfe95HfP6K8RfmUyMHf/pQ/x8pBTVGlahVAFPvxBEde2DkZPGoXdYE+oS32qHyKnO+wPff54K9YR/4JEUv2vjaCjH6S0rsS4tA4UaL4T3GImZt4xFV99G/h06yN+Ua6c1wBcVFWHVqlVCU4sWLWIbnIc4bJbElkNElIEItBMCsl54/KddeLwxcx2db6xcezlesR0L5y9D2c3P4KMFcTCmf49XX/4r5sgTkf5/k+DTXvrRHu1UX8C2tQdR03MM5kwIAgoOY/OmX/CVLBTPz+kODyZ4Tv3yGVZeTMD0u59AglcZTmxeiZVf6OE//3b08KzfaWngDbjtL12grnPZG1By8GesOBaI7jE2f0rYocZWfJeWoqbzBDwytTvkOhUqirJwcNs2fP1pJR56dja6K+0Up1MNCBhq8nB8xyZs2nEKRRopEurl0CN/+9dYtl2NvtPuxnS/QuxdtxFffueJpx9OQbAhCxuXfI8ziXPxxJR49sPQQX407dppYKwLDixfvhzjx48H303gxx9/xKOPPuqwFZpGdIioYQb+fkNXpabXbUDRxgUY3z0EXkpfRPa5GS+sOg91nYF6nP14DnpE+MNT4QG/yL6Y8T8bkFPvF6MWe57rBhmbwpRIvHDrD9W1pQ3I/vZeJIf5QOkVhPiUe/F+Whmb7qnGugc6QTngdZyoq0ePE68PgGfcX7FdnY2f/zYJN3SLQICXAgqvECTdNB///fhF3Dk8EWE+XvCL6o/bFu5CieX8p/Yi1r1yO1ISw+DrHYyuo/6Ejw5YTi/ZsNOQjw0vzcGIHlEI8PSAMiAOd3x9Dqn27LM322Bopu2iLwgH46H5DY+zX/rDF52vi2EyZL6HUV6d8ZdtalxZNQ+xXr0xf1elqUVtOt4ZE4joO39AnvY4XhvgheQF+5mf4f/bOw+wqI6uj/+XDtKLgAULLgIixBIVCFhQEcUKtlgSjRo1JnYTYzCxfIkxttjyGhNji4nGShN7RGInilgQC6KxIYJIhy3f3AWUsl1Y0T3zPOsjc2fmnPnN3LnnzpyZKyUIK19XoEt1s+VUKkjGjhk90cKe9SkTO7h2/xpHMystgsvsB6zfjbZn/W4+El/0OzEe/K8rjG2GYQ+6Y+XFS4j4fiIG9uqFQTPWYWFfMzw6fRK3qsyQSOPzCMcWD4d3EysYG5nCrlknLDjJ3UkKOEmKktI3lcpXSQ/hAxz+dijaN7KAkZElGrd/H4uOPqzoCyh6jNhlH8KvGbvnDQxQx6YRPP0HYeW/UltdSkVrIMrQBf2mTcfoXj7wcnODV6dQBHkYIjv1Dp5yw6XoPpKS89DQOwi+zRvA0ckDAUHtYJd7BylPpIyneuZwcGqERo1Kfk5mj3DuQjb4PUPR3kb+Y0tckILoFXMw79dTeFKu3Xl16krKa+zcHC3bB+L9ni2gn3UHqRlS5NcAorenSGb4nt2HiCRDtBsRgneMK9VMkIKT//wHc+9QhPi2QHPPThjcrzX0b53A6f841kIU5uWWzDJyWRWlV7Xv1ADo3NxcpKamIiEhAfHx8diyZQtGjx6NMWPGYNOmTZK4pKQk3Lt3DwUFBVI1kN9rpWahSNUNIuWZqV62GPmFNuj6+XrsjdiIz9vcw9ohvfD536UPY+jA3mccFm2NQdypY9g61RmJi0di5l7OaCoL+nhnRjQSEhORmBiP5UEv7x4T11DM2xiOmF3LEGoQgxkDZ+JAtjF8u/vDKOkYYh+WDlTMof70P9dh7NsJbfQycSX2BB57fYHtMUdwYNtseFxfgU+/OATL9xfh94i9WDu0Dg7P+QAL4opKlchF3Jc9MejXHPT4bg+OH92IcVaHMaX/DMQ8l6On+AnORUTidrNPsTn6CA79tQqf+TeGnzz95K0aiNXRXfn2ZWsYSrSHrPJ4qNtvCVaEZmHNhIU4lVuM62s/wYKbPbBs+UA4qnw3K9Cl2tlm4eC0nhjxy2P4fLUVURG/YXZnC9Z/yxtb8vqBCd7r0RHGXL97UPaAfI4TR85DzzcQfqaAvkG5xhVl4OHjItRx5qO+Qjb5ODe/F3ovvAT+pPWIOBCOjQsnoktTrjwFnCTNJe0eUiZf+bbOx5m5PdFnURI82Azd/v2bMN39Cr7tHYz558oGcKbnvJ4ICjuNemPWYO/BA9i1LAg4HYMzd5WxKGX1rVeP574X+yKwmazn2QIYsO/HWnArRzwr2Frz8Dg5CU8laoqQdfceskyY8WOnoHHE+bh26DBuW/kisI1llSXH8pqLBWk4vW0LTur44YNhHWAnbRJMLEB+RgrOxLONRVZN0USB8fbqZN62EnRg6/8x5kweik588youC6KnqbibbYTG/AYoWzozbMJHI50MpN5lvsG6zuj7xULM7ussyasw/av0nWpAP3/+fJiamsLFxQV9+vTBqFGjYMBecnqxF7rg4GDJd5JHjhyJbt26oVmzZjA2NsbChQurSKZlxCpIFEeobhApLrMshUAgVMlHHqy7Nuw7A19+9K6kYwcGeKDgmhcWrY3GvE6DYMGGJouWgejbskRC21ZWuLyjDTaeToIgpAN7RJQEYwcXeHg4s8dKaZAMiDqwbtMXg3uVlO3ncAsx3lsQzd6gAwOC4a8/CZEHn2ACm23g5Z9DXLwOOnznw5ZrnrC8PJi5+KJrRy6vLxre3IWoxe7oNy4E3Q3YZR8g9ve+iItjux39XKGTsRfLfr6P7muOY26orWRAbbUmFTH8+fgrbjWCusrX08KjK3p1KdGTSyk2kadfSVmy/1VNd2njubyylWkPmfl5ddF/6QoMaDMSH3+cDqvo6wj86XcM5CwtlZ+1yvQNHVQXW/HTfVi95T7azT+IdZ82Lelr/lZI2BiNf0orLFbQD3p05tp1IqIPpWPi6Lrg5cYi+rgA3gsCwJ7l5UIRkn/9BAv/bYM5saGwkeEq8iLDswgsWXUFHnMuYsN01xcPibLryrRZlXuIZVYmX5kMcWY4lqxNgifT4edJrpIHUWc/PrIvt8aSpZGY9mcoLLKisHTVZbjMPIstX3iBu5XEabewQmdb+cq/5v8L8eTsbhy+3wABEz1hIjG2bOE9IBi3fwvHj8uuwbOxGDevFsJ3eO8qS4iVlRdnnGd+XkJ4DPFFPXk3mygTl3ZGI/KpO4aND0Rjo4qNLkz8HXNm/c6AiSFiP56BA3xGBMCZg0hBNQLcKoiMHGL23d8cmMLctJwRrW8O8zpipD3nNhFVNJgVptdpoHbfkaGiStFz586Fo6MjZs2ahWXLliEkJKRC/ri4OMnfO3fulMx0rV69GmPHjq0iQ8ErRZX0FMEIiNgRDTUVXrls9tbg08ER+VcScFOyqlCI2/vCMMjXDQ1tLWHjFIw1yQIUypjqlFcv3cbN2NvJU6SzaXeeHVum6aKLuL0xeMImJoovxeJkbmsEdmUPwCqF6MKxgSN4OU/wpGyFUs8B9e2B589KlgkFyReRmJuNyI8asCUcI7aEYgQT/nTEFWXj4cNKOyWrlF81QjX9quZ/GaNYd3m5q1579fbg1R2AJd8H4eEfG/Bv+3lYOtDxpZFcVaCcGPV0UZet4MYlXCtqCG8fJ5n6KuoHsA3CwM6s34UfwFPW7/JOhONQ/nsY2KdeuTLzcXX9UHSfcRt9N2/HtJaKn6bFSedxIbch/Do6VzG01L+HVOMruB6PhLwSHV7YFMzfrKNffeRePA9220KQdI7p2QBdurlLDK3aF4rx+MxW/ByRgRZDR6CjY1lNxCh4loZMkSO82rnBRpcZO0XM7+d8MouTVwsRHpw7g3vmreDboo6UsaUsrxjPzuzA9gv68BvaH+5mUkYhfi98Nm0apk2bis/Gj0Cwly7iN6/F3uT8N3cnrDx0r+1aJbcAhXooSq9u31EoWOkEnPEUHh6OyZMnIywsTOKrVRa4/3NxU6ZMQUREhFRDi0tLxpbSuF8mFIm4zqGog6hRMFeqWMS9eL1SEHM+ZTwdycAkvLwUg4euwt02n2NjTByOhy9BSKNKzV51XJIqn8cOZNVj0/9CbnBkb6q93u8Bg2PbEfG4GMlHjuC/FkEIbCi9S3HLOzyxkOUtrRzPAAb6PGa4svpy0rhK6zji/Q3ncfHixdLfJVy+egnrQ61LBlkl9ZQor6J+UitcGqlQd3mZK11T2B6s3bjVGEFxsewexpY6L8ZeRJ6ZOURndyIiRT3jX6EusuqlLlvJsSZiiMv6gLTyFfUDJrvnkG7QPfYXop/kIHZnFPI6DUEfh7LOwWa0fuEMrZsI/v0gVvdW0hDl7jtp+rA4pThJ6ZtK5asgk7GRoUNZtFgoYBOY7D7UkyJQQd6av8xmtCSGVjrc3x+H/u4vdzeL868gckc8LAJHIaSjD7oMGIup43yhe2EvDlx76WFaRUfmr3Mxgc3gtmwFJ7nrMDzUaeIFV8sniNt9CCn5UkgyHzh7Bwc4ONaDUzMvdBwYCh/rDOZvQ2cJVuH+ChE8U3M2r5WD7JxyVrQgG8/zeDA1N2VjuQjF7GW/oLjkuqL0bOZAvb7zCnWQltXPz48t7e/HypUrIRC89I8sKirC8uXLcfDgQXBpZAXpT0ZZqSleQqAmlxFfuezCBBw6/ghm77QBnw1OBYnncVn0HsZ/8wEC2nrAo1Vb8K3LNTtziDdhLlpZmVkqHirJg3XwWAy0Oo6NW48iMioJ7v37wkXeNL+c/qPH94S7YRoSbgjR1NUVri9+LmhgwQpVWc/q1U+O6i8uyTUiSlMpbA8dOzjYiZGadBPS3SzFeBo9CxP/qIuwo7H4P88zmDNhHW7Ksbdk6aVQF5mVVo+tXvM28DK+i6OHrqHMU6+yCIX9gA3VNsEfoI/J39i6eTO2RgoQ9EE/1C21PbJj52DAtCQEbYnByuB6VfxJKssr+1uP74UWhvdw4vitKpsLFHKS0TcV5isVXtY+JXw4Hco9/AU3EBt3HyZebeDC7mc9Zzfwde/i1Km7Kt6vsmpeffGFt/djU3gaXIeORT9maJV/uIizHuJRgQls2WabMjPRsH4T1DcqQEZGrkwjU5SWhORM5i/jVl9hW+o7dMCwjwfBNZvtMtx2Buly7glJrYWFKCgSS3xuaqPpWn0to9mSdNiGDSezAqTcvP/iXipMSWbHeFijkRMzwIW3EbF4LhaH35J4PihKDzX7Tk3UmjvEtFOnTjA0NMSOHTuwe/duiY+Wv78/Lly4IFekK/VxHgAAIABJREFU3HcFuTm1+CI3+yQZHWrgDpXMSqkUxHjOnW9zLB918lNw9H9sWSmlFeZsDIIZK0fg2hJ88SqsX7gF9Qd7wE7vPu5klXvr03NBa08jrPxjEVa3n4CW4lQ8seuDwe2UUMKkI8aNcob3srG4/cwdk35trnBAlFUqz7Yfpnz0HYJ+GIxhenMwytcJhrn3cCXDGR+O8Ia5OnpWo36y9JbE69jAzpoZSGy25cANPnoyp1FZXcNQUXvoNkdw3xZY8P0XGP9OIUZ4WQMpiUgv7RbirMMIm/wnrKefwNTWntD9KQy724Xhsw09ETm6kpaV9Wpa8bpCXeRVWg22PKu+mDnJHV0WhWAIvsY4f9bGOSeRnPOyPyrsBxxYs24YPdgOXb+eCX3bkdjbs9QHhJ3IvmX+T3jw3jx84PQYly4+LqkBzwj2Lq5wrLxrqlz9OLlTx3yHwAUhGCn+CiM61IP+szvIbTIQvRS1mYy+GaIoX+X24ffBjE9c0eXbIfjYdD6GtxAjcXMYvrvijimrg5n/JQt1+2PCoHkIWTAcU03DEOrKw+2o7bjEXrQ7S+rDdhBvGQzvKTcwIvw0vvXlzlQQ4NLiTui6zBhhx2PwaXPujeg5Dnz6LoZGv4cNZ35BP1tZPbYcJHn/FT9D/KGTyGoSiLZWOXj4oGyDjh5M7erC3MYZzpaHER+9Hw2DWqOeYR7unjmIxIK68G9qCeRexvYftyPVfSQm9+Oj5CQGtu/5birSmM9Op/rKvcXpWrfGoBHp+N+6CGw71hATutZ/4Zcqzn2MOykp0BUWIe/ZQySfjcWZbFv4tGmi9rglD4nWXtNrAh/fBog/vAt72LJ/K/N0nIm4gGLnPmjfgJngYmPYNm6KRrbGJeOkgvQ6Avl95xV7rkrNFBUVBTe223bAgAG4evWqZDlx+/btzN/ZA9HR0Rg2bJjM8sjYkolG9gXOIKohW0u1WTO2S6NFx/dgH/49BvfIRJEeOy6hfV8sPvAdPmldMlzptZqJzasf4LPvp6LPyiwIDUxh7dAc7ZytSpfmbBC6aA1iP/wSX4fuQbF5M/SY3w4DlTG22DDmNW4yAlaMw9F2MzBUMoirG8zR+YeD2Gv7Oeb9NgWh89gWRIuG8AxdhAHDmbHFHkyq61md+smpl05jDAubhogJP2Pm+r7ovtj7xQBfOZfC9mBLRF6z/sBvTyfhm/8bgT8zhTCycmRHAHRBK3sRLq1gy8EYhahprUoeSK6fYMmk3+A/7xtE9p9WUVxlvb5j2/XKBcW6VNa+/N/qsDVG+3kx2G8zG3N/mooBCzIhNLJGA74/+rtZlM6EKOgHkpHVCL5jR8Pzp3ko+vBjdDYp1YsdfXH23zxkZs6A3/7yFXXHl6cT8H9t5A13Zuj4/QHss5mFr9dNxICwPOjbuSJkSQB6v6/mPTRBQb7K7cP6TYf5UdhnMh1zvh+OHmmAvVdPzA5fhlntSi1Fng16r4rEOpMZWP7NEKzLNAa/nRP0ueXnClNJlZbR2PJsFfcElkRcJVJem8u5JnyIu/8VIz8/AmuSyqXTsUfAZ9MR1KApeo4aCp3II4j89QRyhPows2f+pcP7IIDbKppbkqei1iI8TUuHyNwDNio4qBk4BWBw9xtYGbMTf7tPQjfmN1bHkh3nkXAEP689xF6O9GBUxxJ1G3qi99gu8HGmQ7bktKwal3Th0HEkRhTtRmTMZsRLDjXtjg/7dyjZqMKrD/+R4+H/omQF6fUV9B01NFQnC3evcM7wnO/WxIkTsXXrVsn9M2PGDInjPOdELy/w2HqjlMVteVnevGt37twBn8+vNsUv/nsWfBc36OiUH92qp/hrVxPRum2H6ilME6UU/IPpXv2RMjcRu4axXYmakKmKjNqunyp1qW1piW2taBHhtW/RvvVv6HzsKn7oULa/uFaoRkoQgbeGAHeO1tChQ7F27Vp4e3tXqBe3vMgZYNyORO4sN2lB3quetPQUxwi88o5BORRrsmw5YlW8lIc7F6/guTgHZ1dPxibzT3FwYG0ytGq7firirlXJie3rbQ4Bruxag1MiFzSrbwXdZ1ewb/GPuNLkQ6xS5RM2r7cSJJ0IvHEE7O3tcf78eYmPX+Xw7rvvMj/KU8jJKVs+r5yCdiNWJaJEzCs7scuRUZNlyxGr2iXBdWwdF4C27ITgH+4F4teds9FahWl+1YSpkVqOfuK0X9CDnb/Dk5yWX+mna4ePYmS5biunR02Xr5wWNZjqNbJ9lVq9Ne0izsKd07uxbNpwBHf2R+CIhYi1GYNt4fPgXemTN6/Ci/ISASJQkYCVFXu5kWJolaXS09ODpSXzP5QRaBlRBhh50WdPn0ALj3dqZBkxgZ2n08GnozzxdO1VCAgycPvqXTyXtg+Bpwebpi3QUMoZPUqLrOnylVbkNSSszXWvzbq9hqYikUSACGiWAC0jqsG7JmefXp7hVeu8n9QgVQuzcJsIPNkOv5oKNV1+TeldHeXW5rrXZt2qgz2VQQSIQK0mUP0e3rW6utWjXM0aW9KmXKpHbyqFCBABIkAEiAAR0DwBMrbUYC45Z6uGgog7qvCt3x9aQ/CoWCJABIgAESACtZAAGVtqNAqPnWlTXCznExNqlMmd11HAPmHAkxzgRUuIaiCkLESACBABIkAEaiUB8tlSo1nMLSyQlpYGa2sb9gnC6jGMuE92ZGaks90MVmpoRFmIABEgAkSACBCB2kqAjC01WsaluQeuJyXi6pUE1U58lyOL21JqaWmN5m4eclLRJSJABIgAESACROBNI0DGlhotZmBggJaebdTISVmIABEgAkSACBABbSNAPlva1uJUXyJABIgAESACRECjBMjY0ihuEkYEiAARIAJEgAhoGwEytrStxam+RIAIEAEiQASIgEYJkLGlUdwkjAgQASJABIgAEdA2AmRsaVuLU32JABEgAkSACBABjRIgY0ujuEkYESACRIAIEAEioG0EtOboh+LiYm1rW6ovESACRIAIEAEiUAsIaI2xpa+vXwtwkwpEgAgQASJABIiAthGgZURta3GqLxEgAkSACBABIqBRAmRsaRQ3CSMCRIAIEAEiQAS0jQAZW9rW4lRfIkAEiAARIAJEQKMEyNjSKG4SRgSIABEgAkSACGgbATK2tK3Fqb5EgAgQASJABIiARgmQsaVR3CSMCBABIkAEiAAR0DYCZGxpW4tTfYkAESACRIAIEAGNEtCac7aqk6pAIED6k8coKiqEWCxWqWiRSARzC0vY2NiplI8SEwEiQASIABEgAm8mATK21Gi39PQ06OrqSgwmHo+nUglpaY+Qm50NMBvNxpYMLpXgUWIiQASIABEgAm8gATK21Gi0woJ82NnZq2xocaI420woEiI3hxlcLJDBpUYDUBYiQASIABEgAm8QAfLZUqOxVF06lCaizODiZskoEAEiQASIABEgAm8vATK2NNy2YvHLZUfO4MrLzQEZXBpuBBJHBIgAESACRECDBMjY0iBsaaKEQrakSAaXNDQURwSIABEgAkTgrSBAxpaGm5HHq7p7UUQGl4ZbgcQRASJABIgAEdAcATK2NMdaIklHV/qeBM7gysvJQcbTdA1rROKIABEgAkSACBCBmiRAxlZN0pVStoW5JfT0pBtcnA/X86xM5c/uEqfjn5+/wsLdKRBKkfVKUazsuJ++wLydt6u/7FdSrBZmFt3Fni+GYPyG5NrFqrbqVb4JRQ9wdOWX+GH/I4hqYdO+kkqvi39lpjU5TrwSoGrKLM5CYsQG/P7PW9iHqgkRFVP9BE6fPo1z584pXTAZW0qjqp6E+vr6sLWtCweHelJ/krMhlA2ihzjy01JsPZfOHdulXhBnI+VkFA5feV6xDFb20Z9/xB/xT9UvWz2NpOeSpaf01JqNFaXhXPhexKXkvF5Wxacwx9MObtP/Rj5HoLJela9rlpJ0acIURK5Yjp2Xsl4vO+nalcSq2/cq85cnozqvVWZaHeNEdepX3WWJ8/DoRjL+yyqu7pKpPCIgk8CaNWvA/ZQN0qdYlM1N6d58AsXn8MPAEFycdgVdWphDBVNPs3V/U/TULJWK0nTMUd+lOZo3sISuND0UXZeWh+IA6ntK9AIBMq4dx4EjZ3HtfhaKDS3h5O6P4N7eaGhcMqqICx7gXHQEjl1MQUaxAaycvNC5Ty+0q29UZdwRpf+NNT9EIrXKlL0ePIcvwMh39JXQqTQJM8YSd67CrouZyCtiBerow8jMBvWatsR73TrDw06FspSXqhUpCx+exB+/RqAw4EuM8zZ72Y6iLCQd2o2o0zfwpMgY9m7voW+/TmhqKuMJoyC9Kn1HE+DT09OxZ88eiailS5eyA85tFIolY0shIkpABN4QArotMHFnHCbKUlfRdVn5KJ4IKCJQeBvHIuOR794RIQFWwOMLOHhgLzbp2mJWiAsMmMFzde8v2J3qjOBhk+Bs/AyXD+7G7g1CmM8cBDejigJ0LFtj4CdNUPhiyl6EjPhd2HHJEi4Npb5KyNGQ7fjOzER+gwCM6+kCPUEBstPvIP7YMWxen4Mx0wfAxVBOdrpUhYAo/yESYw/gQOxVpBfpwLlCCiEeHd+MLccL4dlrGILNnuBMVAx+22aEqWO9YS26g5g1f+A6fygmBTVmL4YK0kO1vlNF2RqI+PPPP9GlSxdwpwn89ddfGD9+vEIptIyoEFHVBNz3DWsqqF62COkxs9HFxQbGhqZwbNkbX+y5hcIXCgqRvC4Ebg7mMNI3gJmjJ/qE7cf9Cm+MxTg1oxl02RImj2eM0O15pblF+O/3EXC1qwNDYys09h6BH08/Y8s9eYgaVReGrRbg8otyhLi8oBWMGn2G44X/YdfkbmjdzAEWxvrQN7ZB8x4zsXbdVxjiw4ddHWOY1XsHAxfHIaP8+mdxKqK+GQRvvh1MTazR1G80fjpffnlJip6iR9g/NwS+bvVgYWQAQ4tGGLz5JiLk6SdvtUGkpu5KdwgF7VF0FBPZm77P0lsvfJhEKSvgZ9wAnxwrRNqekXAy9sDMuJwSicXXsKyjJeoP2Y6HxYmY38oYrrPPQSBNH2Hl6wp0qW62nE4Fydgxoyda2LM+ZWIH1+5f42hmpUVwmf2A9bvR9qzfzUfii34nxoP/dYWxzTDsKfkog7Sal8QJH+HY4uHwbmIFYyNT2DXrhAUnuTtFAQdJbil9T6l8ldQRPsDhb4eifSMLGBlZonH797Ho6MOKvn6ix4hd9iH8mrF72sAAdWwawdN/EFb+K7VVlWPKepP8cUI2NqWuGLqg37TpGN3LB15ubvDqFIogD0Nkp97BU264FN1HUnIeGnoHwbd5Azg6eSAgqB3scu8g5YmU8VTPHA5OjdCoUcnPyewRzl3IBr9nKNrbyH9siQtSEL1iDub9egpPyo1zvDp1JeU1dm6Olu0D8X7PFtDPuoPUDCnylaq0tiZihu/ZfYhIMkS7ESF4x7gSB0EKTv7zH8y9QxHi2wLNPTthcL/W0L91Aqf/41gLUZiXWzLLyGVVlF7VvlMDzZKbm4vU1FQkJCQgPj4eW7ZswejRozFmzBhs2rRJEpeUlIR79+6hoKBAqgbye63ULBSpukGkPDPVyxYjv9AGXT9fj70RG/F5m3tYO6QXPv+79GEMHdj7jMOirTGIO3UMW6c6I3HxSMzcyxlNZUEf78yIRkJiIhIT47E86OXdY+IainkbwxGzaxlCDWIwY+BMHMg2hm93fxglHUPsw9KBijnhnv7nOox9O6GNXiauxJ7AY68vsD3mCA5smw2P6yvw6ReHYPn+IvwesRdrh9bB4TkfYEFcUakSuYj7sicG/ZqDHt/twfGjGzHO6jCm9J+BmOdy9BQ/wbmISNxu9ik2Rx/Bob9W4TP/xvCTp5+8VQOxOror375sDUOJ9pBVHg91+y3BitAsrJmwEKdyi3F97SdYcLMHli0fCEeV72YFulQ72ywcnNYTI355DJ+vtiIq4jfM7mzB+m95Y0tePzDBez06wpjrdw/KHpDPceLIeej5BsLPVBY3Lj4f5+b3Qu+Fl8CftB4RB8KxceFEdGnKdQYFHCTFSrtHlMlXXqd8nJnbE30WJcFj5hbs378J092v4NvewZh/rmyAZnrO64mgsNOoN2YN9h48gF3LgoDTMThzt8qaGvvGqjJMOR0UjRPl9VTv/9z3Yl8ENpP1PFsAA/b9WAtu5YhnBVtrHh4nJ+GppBoiZN29hywTZvzYKei44nxcO3QYt618EdjGssqSY3ltxYI0nN62BSd1/PDBsA6wkzYJJhYgPyMFZ+LZxiKrpmiiwHhTj8bbnEsHtv4fY87koejEN6/isiB6moq72UZozG+AsqUzwyZ8NNLJQOpd5hus64y+XyzE7L7OkrwK079K36mGZpg/fz5MTU3h4uKCPn36YNSoUTBgL0G9evVCcHCw5DvJI0eORLdu3dCsWTMYGxtj4cKFVSTTMmIVJIojVDeIFJdZlkIgEKrkIw/WXRv2nYEvP3pX0rEDAzxQcM0Li9ZGY16nQbBgQ5NFy0D0bVkioW0rK1ze0QYbTydBENKBPUJKgrGDCzw8nNljpzRIBkQdWLfpi8G9Ssr2c7iFGO8tiGZv2IEBwfDXn4TIg08wgc028PLPIS5eBx2+80EdPGF5eTBz8UXXjlxeXzS8uQtRi93Rb1wIuhuwyz5A7O99ERfHdjv6uUInYy+W/Xwf3dccx9xQW8mA2mpNKmL48/FX3GoEdZWvp4VHV/TqUqInl1JsIk+/krJk/6ua7tLGc3llK9MeMvPz6qL/0hUY0GYkPv44HVbR1xH40+8YyFlaUp7FMsuRXFCmb+igutiKn+7D6i330W7+Qaz7tGlJX/O3QsLGaPxTqqhYQT/o0Zlr14mIPpSOiaPrgpcbi+jjAngvCAB7lssOzyKwZNUVeMy5iA3TXV88BMoyKNMmVe4RllmZfGUyxJnhWLI2CZ5Mh58nuUoeNJ39+Mi+3BpLlkZi2p+hsMiKwtJVl+Ey8yy2fOEF7lYRp93CCp1tUuumDNOSjIrGCanFqxkpxJOzu3H4fgMETPSEicTYsoX3gGDc/i0cPy67Bs/GYty8Wgjf4b2rLCFWFirOOM/8vITwGOKLevJuNlEmLu2MRuRTdwwbH4jGRhU7hDDxd8yZ9TsDKoaI/XgGDvAZEQBnDjIF1QhwqyAycojZd39zYApz03JGtL45zOuIkfac20RU0WBWmF6ngdp9R4aKKkXPnTsXjo6OmDVrFpYtW4aQkJAK+ePi4iR/79y5UzLTtXr1aowdO7aKDAWvFFXSUwQjIGJHNNRUeOWy2VuDTwdH5F9JwE3JqkMhbu8LwyBfNzS0tYSNUzDWJAtQKGOqU169dBs3Y28nT5HOpt15dr0wqIsu4vbG4AmbmCi+FIuTua0R2JU9AKsUogvHBo7g5TzBk7IVSj0H1LcHnj8rWSYUJF9EYm42Ij9qwJZ4jNgSixFM+NMRV5SNhw8r7ZSsUn7VCNX0q5r/ZYxi3eXlrnrt1duDV3cAlnwfhId/bMC/7edh6UDHl0ZyVYFyYtTTRV22ghuXcK2oIbx9nGTqq6gfwDYIAzuzfhd+AE9Zv8s7EY5D+e9hYJ96MsvkABQnnceF3Ibw6+hcxdBS/x5RjZ/gejwS8kp0eGEz6PHR0a8+ci+eB7stIUg6x/RsgC7d3CWGlqKgDFOpZVQZJ6SmUiOyGI/PbMXPERloMXQEOjqW1VSMgmdpyBQ5wqudG2x0mbFTxPx+ziezOHliRHhw7gzumbeCb4s6UsaWsrxiPDuzA9sv6MNvaH+4m0kZhfi98Nm0aZg2bSo+Gz8CwV66iN+8FnuT82vvTlh5aGrttUpuAQr1VJRe3b6jULDSCTjjKTw8HJMnT0ZYWJjEV6sscP/n4qZMmYKIiAiphhaXlowtpXG/TCgScZ1DUQdRo2CuVLGIe/F6pSDmfMp4OpKBSXh5KQYPXYW7bT7Hxpg4HA9fgpBGlZq96rgkVT6PHciqx6b/hdzgyN5Ue73fAwbHtiPicTGSjxzBfy2CENhQepfSN9AHTyxkeUsrxzOAgT6PGa6svpw0rtI6jnh/w3lcvHix9HcJl69ewvpQ65JBVkk9JcqrqJ/UCpdGKtRdXuZK1xS2B2s3bjVGUFwsu4expc6LsReRZ2YO0dmdiEhRz/hXqIuseqnLVnKsiRjisj4grXxF/YDJ7jmkG3SP/YXoJzmI3RmFvE5D0MdBQefg7itp8licUhykFK9UvgoyWd1l6FAWLRYK2AQlu8/0pAiUllcZptLysbjy44SMJCpGsxktiaGVDvf3x6G/+8vdzeL8K4jcEQ+LwFEI6eiDLgPGYuo4X+he2IsD1156mFYRyPx1LiawGdyWreAkdx2GhzpNvOBq+QRxuw8hJV8KaeYjZ+/gAAfHenBq5oWOA0PhY53B/G3oLMEq3F8hgmdqzua1cpCdU86KFmTjeR4PpuambCwXoZi97BcUl1xXlJ7NHKjXd16hDtKy+vn5saX//Vi5ciUEgpf+k0VFRVi+fDkOHjwILo2sIP3JKCs1xUsI1OQy4iuXXZiAQ8cfweydNuCzwakg8Twui97D+G8+QEBbD3i0agu+dblmZw7xJsxFKyszS8VDJXmwDh6LgVbHsXHrUURGJcG9f1+4yJvml9N/9PiecDdMQ8INIZq6usL1xc8FDSxYoSrrWb36yVH9xSW5RkRpKoXtoWMHBzsxUpNuQrqbpRhPo2dh4h91EXY0Fv/neQZzJqzDTTn2liy9FOois9LqsdVr3gZexndx9NA1lHnqVRahsB+wodom+AP0MfkbWzdvxtZIAYI+6Ie6CmwTPb4XWhjew4njt6psHlDIQUbfU5ivtHJl/Evqz+lQ7uEuuIHYuPsw8WoDF3a/6jm7ga97F6dO3VXqflSGaWXGkr8rjRNS06gYWXh7PzaFp8F16Fj0Y4ZW+YeLOOshHhWYwJZttilrKsP6TVDfqAAZGbkyjVBRWhKSM5m/jFv9Kr5BldXTd+iAYR8Pgms222W47QzS5dwTkrzCQhQUiSU+Nwq6T2VR9LccAjpsQ4eTWQFSbt5/ca8VpiSzYzys0ciJGeDC24hYPBeLw29JPB8UpYeafUeOimpf4g4x7dSpEwwNDbFjxw7s3r1b4qPl7++PCxcuyC1X7ruC3JxafJGbfZKMDjVwh0reNlUKYjznzrc5lo86+Sk4+j+2rJTSCnM2BsGMlSNwbQm+eBXWL9yC+oM9YKd3H3eyyr316bmgtacRVv6xCKvbT0BLcSqe2PXB4HZKKGHSEeNGOcN72VjcfuaOSb82VzggyiqVZ9sPUz76DkE/DMYwvTkY5esEw9x7uJLhjA9HeMNcHT2rUT9ZekvidWxgZ80MJDbbcuAGHz2Z06isrmGoqD10myO4bwss+P4LjH+nECO8rIGURKSXdgtx1mGETf4T1tNPYGprT+j+FIbd7cLw2YaeiBxdScvKejWteF2hLvIqrQZbnlVfzJzkji6LQjAEX2OcP2vjnJNIznnZHxX2Aw6sWTeMHmyHrl/PhL7tSOztKd9pmqsGV+7UMd8hcEEIRoq/wogO9aD/7A5ymwxEL0VtIqPvhSjKV5k/vw9mfOKKLt8Owcem8zG8hRiJm8Pw3RV3TFkdzPwrWajbHxMGzUPIguGYahqGUFcebkdtxyX2It2ZXRan78FH7T7Cqd47cPbHrjBTgmlJM8ofJ+Q1tVLXxM8Qf+gkspoEoq1VDh4+KNugowdTu7owt3GGs+VhxEfvR8Og1qhnmIe7Zw4isaAu/JtaArmXsf3H7Uh1H4nJ/fgoOYmB7Xu+m4o05rPTqb5yb3G61q0xaEQ6/rcuAtuONcSErvVf+KWKcx/jTkoKdIVFyHv2EMlnY3Em2xY+bZqoPW4pxUbbEuk1gY9vA8Qf3oU9bNm/lXk6zkRcQLFzH7RvwExwsTFsGzdFI1vjknFSQXodgfy+I2usrQnsUVFRcGO7bQcMGICrV69KlhO3b9/O/J09EB0djWHDhskUS8aWTDSyL3AGUQ3ZWqrNmrFdGi06vgf78O8xuEcmivTYcQnt+2Lxge/wSeuS4Uqv1UxsXv0An30/FX1WZkFoYAprh+Zo52xVujRng9BFaxD74Zf4OnQPis2bocf8dhiojLHFhjGvcZMRsGIcjrabgaHNlRsQpZM1R+cfDmKv7eeY99sUhM5jWxAtGsIzdBEGDGfGFntwqa5ndeonXWtJrE5jDAubhogJP2Pm+r7ovtj7xQBfOZfC9mBLSF6z/sBvTyfhm/8bgT8zhTCycmRHBHRBK3sRLq1gy8EYhahprUoeSK6fYMmk3+A/7xtE9p9WUVxlvb6ruF1PsS6VtS//tzpsjdF+Xgz228zG3J+mYsCCTAiNrNGA74/+bhalMyEK+oFkZDWC79jR8PxpHoo+/BidTeTpWXbNDB2/P4B9NrPw9bqJGBCWB307V4QsCUDv99W8RyYoyFeZP+sXHeZHYZ/JdMz5fjh6pAH2Xj0xO3wZZrUr3QHMs0HvVZFYZzIDy78ZgnWZxuC3c4I+t7xcOlUkZkutL81TJZgqMU4oQ1BuGuFD3P2vGPn5EViTVC6ljj0CPpuOoAZN0XPUUOhEHkHkryeQI9SHmT3zLx3eBwH1WcVyS/JUXPwT4WlaOkTmHrBRxoGtVKyBUwAGd7+BlTE78bf7JHRjfmN1LNlxHwlH8PPaQ+x+1YNRHUvUbeiJ3mO7wMeZDtmS27YqX9SFQ8eRGFG0G5ExmxEvOdS0Oz7s3wE23P3Lqw//kePh/6JcBen1FfQdlfVTLwN333HO8Jzv1sSJE7F161bJp/VmzJghcZznnOjlBR5bb5SyuC0vy5t37c6dO+Dz+dWm+MV/z4Lv4gYdnfIT5dVT/LWriWjdtkP1FKaJUgr+wXSv/kiZm4hdw9iuRE3IVEVGbddPlbrUtrTEViMtIrz2Ldq3/g2dj12dnxY0AAAZ+UlEQVTFDx3K9g9rRDQJIQJEoJQAd47W0KFDsXbtWnh7e1fgwi0vcgYYtyORO8tNWqCZLWlUFMS98o5BOeXXZNlyxKp4KQ93Ll7Bc3EOzq6ejE3mn+LgwNpkaNV2/VTEXauSE9uabQ4Bruxag1MiFzSrbwXdZ1ewb/GPuNLkQ6xS5RM1NasklU4EtI6Avb09zp8/L/Hxqxzeffdd5md5Cjk5ZcvnlVPQbsSqRJSIeWUndjkyarJsOWJVuyS4jq3jAtCWnRD8w71A/LpzNlqrMM2vmjA1UsvRT5z2C3qw83d4ktPyK/107fBRjCzXbeX0qOnyldOiBlO9RrbyavXWcGeHlN45vRvLpg1HcGd/BI5YiFibMdgWPg/elT5pI48HXSMCRKB6CVhZsZcfKYZWmRQ9PT1YWjL/QxmBlhFlgJEXffb0CbTweKdGlhET2Hk7HXw6yhNP116FgCADt6/exXNp+xB4erBp2gINpZzRo7TImi5faUVeQ8LXWffXKfs1oCaRRIAIvFkEaBlRjfaqydmnl2d41TrvJzVI1cIs3CYCT7bDr6ZCTZdfU3pXR7mvs+6vU3Z1sKMyiAAReKsJVL+H91uNq6RyNWtsSZty0QKoVEUiQASIABEgAm8pATK21GhYyTlbNRRE3FGGb/3+0BqCR8USASJABIgAEaiFBMjYUqNReOzMm+JiOZ+YUKNM7ryOAvYJA57kAC9aQlQDIWUhAkSACBABIlArCZDPlhrNYm5hgbS0NFhb27BPEFaPYcR90iMzI53tZrBSQyPKQgSIABEgAkSACNRWAmRsqdEyLs09cD0pEVevJKh24rscWdyWUktLazR385CTii4RASJABIgAESACbxoBMrbUaDEDAwO09GyjRk7KQgSIABEgAkSACGgbAfLZ0rYWp/oSASJABIgAESACGiVAxpZGcZMwIkAEiAARIAJEQNsIkLGlbS1O9SUCRIAIEAEiQAQ0SoCMLY3iJmFEgAgQASJABIiAthEgY0vbWpzqSwSIABEgAkSACGiUABlbGsVNwogAESACRIAIEAFtI6A1Rz8UFxdrW9tSfYkAESACRIAIEIFaQEBrjC19ff1agJtUIAJEgAgQASJABLSNAC0jaluLU32JABEgAkSACBABjRIgY0ujuEkYESACRIAIEAEioG0EyNjSthan+hIBIkAEiAARIAIaJUDGlkZxkzAiQASIABEgAkRA2wiQsaVtLU71JQJEgAgQASJABDRKgIwtjeImYUSACBABIkAEiIC2ESBjS9tanOpLBIgAESACRIAIaJSA1pyzVZ1UBQIB0p88RlFRIcRisUpFi0QimFtYwsbGTqV8lJgIEAEiQASIABF4MwmQsaVGu6Wnp0FXV1diMPF4PJVKSEt7hNzsbIDZaDa2ZHCpBI8SEwEiQASIABF4AwmQsaVGoxUW5MPOzl5lQ4sTxdlmQpEQuTnM4GKBDC41GoCyEAEiQASIABF4gwiQz5YajaXq0qE0EWUGFzdLRoEIEAEiQASIABF4ewmQsaXhthWLXy47cgZXXm4OyODScCOQOCJABIgAESACGiRAxpYGYUsTJRSyJUUyuKShoTgiQASIABEgAm8FATK2NNyMPF7V3YsiMrg03AokjggQASJABIiA5giQsaU51hJJOrrS9yRwBldeTg4ynqZrWCMSRwSIABEgAkSACNQkATK2apKulLItzC2hpyfd4OJ8uJ5nZap8dpcUMW9WlOgu9nwxBOM3JEOoSc1FD3B05Zf4Yf8jiDi54nT88/NXWLg7RbN6aLLOJIsIvG0ExFlIjNiA3/8pvY/ftvpRfWolgdOnT+PcuXNK60bGltKoqiehvr4+bG3rwsGhntSf5GyINzWIs5FyMgqHrzznjhFTPojScC58L+JSclTLp7wE6SmFKYhcsRw7L2WVyBU9xJGflmLruXTN6iFdO4olAkRAGQLiPDy6kYz/soqVSU1piEC1EFizZg24n7JB+hSLsrkpHREoT6D4HH4YGIKL066gSwtzvMFmI7UrESACKhEQIOPacRw4chbX7meh2NASTu7+CO7tjYbGJSOBuOABzkVH4NjFFGQUG8DKyQud+/RCu/pGVcYKUfrfWPNDJFKrTHXrwXP4Aox8R1957ZgxlrhzFXZdzEReEStQRx9GZjao17Ql3uvWGR52KpSlvFStSFn48CT++DUChQFfYpy32ct2FGUh6dBuRJ2+gSdFxrB3ew99+3VCU1MZTwUF6VXpO5oAn56ejj179khELV26lB1wbqNQLBlbChFRAiJABIgAEZBLoPA2jkXGI9+9I0ICrIDHF3DwwF5s0rXFrBAXGDCD5+reX7A71RnBwybB2fgZLh/cjd0bhDCfOQhuRhVL17FsjYGfNEHhiylyETLid2HHJUu4NNSVq0rVi2zHd2Ym8hsEYFxPF+gJCpCdfgfxx45h8/ocjJk+AC6GVXNRjGwCovyHSIw9gAOxV5FepAPnCkmFeHR8M7YcL4Rnr2EINnuCM1Ex+G2bEaaO9Ya16A5i1vyB6/yhmBTUGLrMaUNueqjWd2RrXX1X/vzzT3Tp0gXcaQJ//fUXxo8fr7BwWkZUiKhqAu77hjUVlC9bjLQ9I+Fk7IGZcTkl6hRfw7KOlqg/ZDseCrNxcf1Y+Dlbw0jfEOb1PDBs050S3yRFygsf4dji4fBuYgVjI1PYNeuEBScLWS4hkteFwM3BnJVpADNHT/QJ24/7Fd4+i3FqRjPosuVQHs8YodvzlMxXSSnhAxz+dijaN7KAkZElGrd/H4uOPqzoSyV6jNhlH8KvmQ2MDQxQx6YRPP0HYeW/Auk1LEjGjhk90cK+DgxN7ODa/Wsczay84ClCesxsdHFhZRqawrFlb3yx5xa42lMgAkRABgFDF/SbNh2je/nAy80NXp1CEeRhiOzUO3jKDZei+0hKzkND7yD4Nm8ARycPBAS1g13uHaQ8kTKe6pnDwakRGjUq+TmZPcK5C9ng9wxFexv5jy1xQQqiV8zBvF9P4Um5sYlXp66kvMbOzdGyfSDe79kC+ll3kJohRb6MalI0R4AZvmf3ISLJEO1GhOAd40pUBCk4+c9/MPcORYhvCzT37ITB/VpD/9YJnP6PYy1EYV5uySwjl1VRelX7Tg00Um5uLlJTU5GQkID4+Hhs2bIFo0ePxpgxY7Bp0yZJXFJSEu7du4eCggKpGsjvtVKzUKTyBpHqrJQvm4e6/ZZgRWgW1kxYiFO5xbi+9hMsuNkDy5YPRN2ryzHq0yhYjNmMv8+eQsyGORje1hGKGzwf5+b3Qu+Fl8CftB4RB8KxceFEdGnKTbXrwN5nHBZtjUHcqWPYOtUZiYtHYubeZ+V8nPTxzoxoJCQmIjExHsuDuDtRmXzlWeXjzNye6LMoCR4zt2D//k2Y7n4F3/YOxvxzZR2Z6TmvJ4LCTqPemDXYe/AAdi0LAk7H4MzdKmsPzPk9Cwen9cSIXx7D56utiIr4DbM7WyD/5atzqQJiFmeDrp+vx96Ijfi8zT2sHdILn/9datCq3qSUgwhoBQHue7EvApvJep4tgAH7fqwFt3LEs4KtNQ+Pk5PwVHJ7ipB19x6yTJjxY6dgVBLn49qhw7ht5YvANpZVlhzLwxUL0nB62xac1PHDB8M6wE7aJJhYgPyMFJyJZxthrJqiiQLjTSsaT6VK6sDW/2PMmTwUnfjmbGaqYhA9TcXdbCM05jdA2dKZYRM+GulkIPUu8+fVdUbfLxZidl9nSV6F6V+l76hUL+mJ58+fD1NTU7i4uKBPnz4YNWoUDNjLfa9evRAcHCz5TvLIkSPRrVs3NGvWDMbGxli4cGGVwmgZsQoSxRHKG0SKy6qcQiAQKu8jz6uL/ktXYECbkfj443RYRV9H4E+/Y6CjDoRJaUgXW6Fbx87o0KoOE9O6sijpfz+LwJJVV+Ax5yI2THd9cbOUJbZoGYi+LUv+atvKCpd3tMHG00kQhHRAmeeDsYMLPDycKxh2yuQrkyHODMeStUnwZDr8PMlVckN29uMj+3JrLFkaiWl/hsIiKwpLV12Gy8yz2PKFFwxYGnHaLazQ2Sa1XuKn+7B6y320m38Q6z5tWqKbvxUSNkbjnwo5dNGw7wx8+dG7kroHBnig4JoXFq2NxrxOg2AhtXSKJAJE4CUBIZ6c3Y3D9xsgYKInTCTGli28BwTj9m/h+HHZNXg2FuPm1UL4Du9dZQmxMklxxnnm5yWExxBf1Kv8ZC+fWJSJSzujEfnUHcPGB6KxUUX/IGHi75gz63c2UIghYj+egQN8RgTAmRs8KKhGgFu5kJFDzL77mwNTmJuWM6L1zWFeh63GPOc2QVU0mBWm12mgdt+RoaJK0XPnzoWjoyNmzZqFZcuWISQkpEL+uLg4yd87d+6UzHStXr0aY8eOrSJDwStFlfQUwQiI2BENNRVULZtXdwCWfB+Eh39swL/t52HpwJLZK32fjzEr4Bl+7N4SXcYuwl8XnkDG4lqFqhQnnceF3Ibw6+hcxdACW0y7vS8Mg3zd0NDWEjZOwViTLEChjGnTlwWrlk9wPR4JeSU6vBhb9fjo6FcfuRfPg4mEIOkc07MBunRzlxhaioLgxiVcK2oIbx8nJWb3ypXG3sJ8Ojgi/0oCbioDUJEidJ0IvNUEivH4zFb8HJGBFkNHoKNj2R0sRsGzNGSKHOHVzg02uszYKWJ+P+eTWZw8ICI8OHcG98xbwbdFHZkPeGZB4dmZHdh+QR9+Q/vD3ayqKaDL74XPpk3DtGlT8dn4EQj20kX85rXYm5xPu4/lNYHK1yq7ZigqQFF6dfuOIrnKX+eMp/DwcEyePBlhYWESX62ywP2fi5syZQoiIiKkGlpcWjK2lOf9IqVIxHUORR1EjYK5UsUi7sVL+SDOxMXYi8gzM4fo7E5EpJR2AsOW+DTqOq7smQGv/37F6Hfd0Pm788hXVDInX0Ya4eWlGDx0Fe62+RwbY+JwPHwJQhpV6kJVxzgola+CTLFMHcqSiYUCtvKvx84skyJQmv6SIzVYuZK2Uy2IOR89no6cgV618ig1EXg7CbAZLYmhlQ7398ehv/vLHcni/CuI3BEPi8BRCOnogy4DxmLqOF/oXtiLA9fkeEQyf52LCWzWvmUrOMldh+GhThMvuFo+QdzuQ0jJl3KfM99PewcHODjWg1MzL3QcGAof6wzmb3ObztWrxg7JMzVn81o5yM4pZ0ULsvE8jwdTc1M2jopQzF7QC4pLritKz9501es71Vgnrig/Pz/m0rIfK1euhEDw8s27qKgIy5cvx8GDByVpZAUytmSRkRNfk8uIqpUtxtPoWZj4R12EHY3F/3mewZwJ63CzzOjmmaJZt4lYvv8CYqbUxdnVv+JkkZyKsUt6fC+0MLyHE8dvVZkJK0g8j8ui9zD+mw8Q0NYDHq3agm9drgsxh3gT5qKVlZlVwRFfYb5SlcoMIb3mbeBlzOlQbhAU3EBs3H2YeLWBCxt09ZzdwNe9i1On7irl9F9S5l0cPXQNChBUBFSYgEPHH8HsnTbgyx3s5XOlq0TgbSdQeHs/NoWnwXXoWPRjhlb5h4s46yEeFZjA1u7l7JRh/Saob1SAjIxcmS9XorQkJGcyfxm3+lV8gyrz1HfogGEfD4JrNttluO0M0hUtQAgLUVAklvjcKPnKVlkk/S2FgA7bqORkVoCUm/dfPEMKU5LZMR7WaOTEDHDhbUQsnovF4bckRq6i9FCz70hR7ZWjuENMO3XqBENDQ+zYsQO7d++W+Gj5+/vjwoULcsunx4dcPNIvcrNPktGhBu5QySyKkkGcdRhhk/+E9fQTmNraE7o/hWF3uzB8tqEn9nW5jPV/i+Dh2RB1iu7h76uZgLUtyttG0sTwbPth6pjvELggBCPFX2FEh3rQf3YHuU0GopdrS/DFq7B+4RbUH+wBO737uJNV7g1SzwWtPY2w8o9FWN1+AlqKU/HErg9CFOXTsYGdtRipx/7CgRt89OT3wYxPXNHl2yH42HQ+hrcQI3FzGL674o4pq4NL/Kbq9seEQfMQsmA4ppqGIdSVh9tR23GJvXB0ZhUTp+/BR+0+wqneO3D2x64ws+qLmZPc0WVRCIbga4zzd4Jhzkkk51R+AxbjOXde0LF81MlPwdH/saXZlFaYszEIZtKAURwRIALshnuG+EMnkdUkEG2tcvDwQdmGEj2Y2tWFuY0znC0PIz56PxoGtUY9wzzcPXMQiQV14d/UEsi9jO0/bkeq+0hM7sdHyUkMYuTdTUUa89npVF+es9bLBtC1bo1BI9Lxv3UR2HasISZ0rf/Cl1Sc+xh3UlKgKyxC3rOHSD4bizPZtvBp00ShIUdNrAIBvSbw8W2A+MO7sMc2CK3M03Em4gKKnfugfQNmgouNYdu4KRrZGpc8QhWk1xHI7zs18BiWWdmoqCi4sd22AwYMwNWrVyXLidu3b2c+yh6Ijo7GsGHDZOYlY0smGtkXOIOohmwt5g+mrLFVjEsr2HIeRiFqWquSwcn1EyyZ9Bv8532DPfX42P3Dj5h56ykK9a3QpE1vLNswA+8obHEzdPz+APbZzMLX6yZiQFge9O1cEbIkAL3fn4nNqx/gs++nos/KLAgNTGHt0BztnK1KbhqeDUIXrUHsh1/i69A9KDZvhh7z22HgBAX5dBpjWNg0REz4GTPX90X3xd7oMD8K+0ymY873w9EjDbD36onZ4cswq13pPmMmq/eqSKwzmYHl3wzBukxj8Ns5QZ8t9+mWvlKL2XrsS1PKGO3nxWC/zWzM/WkqBizIhNDIGg34/ujvZlHyFs52vbTo+B7sw7/H4B6ZKNKzRtP2fbH4wHf4pDUdxCP7jqArWk9A+BB3/ytGfn4E1iSVo6Fjj4DPpiOoQVP0HDUUOpFHEPnrCeQI9WFmz/whh/dBQH12w+aW5Kn46iPC07R0iMw9YKOMY2apWAOnAAzufgMrY3bib/dJ6Mb8xupYsmNsEo7g57WH2FSKHozqWKJuQ0/0HtsFPs50b1dv/9WFQ8eRGFG0G5ExmxEvOdS0Oz7s3wE2nGXEqw//kePh/0KogvT6CvpO9SovszTuecI5w3O+WxMnTsTWrVsln9abMWOGxHGec6KXF3hsvbHyq7289G/ktTt37oDP51eb7hf/PQu+ixt0dKp/Ffba1US0btuh2nTVpoKE175F+9a/ofOxq/ihQ9neSG0iQHUlAkSACBCBmiDAnaM1dOhQrF27Ft7e3hVEcMuLnAHG7UjkznKTFhTOc0jLpO1xqu4YVIVXTZatih61P60AV3atwSmRC5rVt4LusyvYt/hHXGnyIVap8imP2l9R0pAIEAEiQAReMwF7e3ucP39e4uNXObz77rvMf/gUcnJkn8dIxlZlakr8rfxSnxKFVUpSk2VzosRpvyDIaSwOSNsApGOL0VH38WsPFebsVa9i9eRgh5TeOb0by/68jLtp2RCZ1odHpzHY9ksYvCt9+qN6BFIpRIAIEAEioK0ErKzYZ6jkBD09PVhaMv9DGYGMLRlg5EXXpEFUk2VzdeJZD8Das23xXJprGE8PNpKT4t+AwHy2ev1wnP3eAF1JRSJABIgAEdBqAmRsqdH8NWkQvTzDq4b2WHBO357WatSashABIkAEiAARIALqEKh+D291tHjD8tSssSVtyukNA0TqEgEiQASIABEgAi8IkLGlRmeQnLNVQ0HEHdH51u8PrSF4VCwRIAJEgAgQgVpIgIwtNRqFx85yKi6W5mGuRmGlWbjzOgrYJwx4kgO8amgJUX31KCcRIAJEgAgQASKgJgHy2VIDnLmFBdLS0mBtbcM+mVc9hhH3qZrMjHS2m0H+jgc11KUsRIAIEAEiQASIwGskQMaWGvBdmnvgelIirl5JUOHEd/mCuLM7LC2t0dzNQ35CukoEiAARIAJEgAi8UQTI2FKjuQwMDNDSs40aOSkLESACRIAIEAEioG0EyGdL21qc6ksEiAARIAJEgAholAAZWxrFTcKIABEgAkSACBABbSNAxpa2tTjVlwgQASJABIgAEdAoATK2NIqbhBEBIkAEiAARIALaRoCMLW1rcaovESACRIAIEAEioFECZGxpFDcJIwJEgAgQASJABLSNgNYc/VBcXKxtbUv1JQJEgAgQASJABGoBAa0xtvT19WsBblKBCBABIkAEiAAR0DYCtIyobS1O9SUCRIAIEAEiQAQ0SoCMLY3iJmFEgAgQASJABIiAthEgY0vbWpzqSwSIABEgAkSACGiUABlbGsVNwogAESACRIAIEAFtI0DGlra1ONWXCBABIkAEiAAR0CgBMrY0ipuEEQEiQASIABEgAtpGgIwtbWtxqi8RIAJEgAgQASKgUQJkbGkUNwkjAkSACBABIkAEtI0AGVva1uJUXyJABIgAESACRECjBMjY0ihuEkYEiAARIAJEgAhoGwEytrStxam+RIAIEAEiQASIgEYJkLGlUdwkjAgQASJABIgAEdA2AmRsaVuLU32JABEgAkSACBABjRIgY0ujuEkYESACRIAIEAEioG0EyNjSthan+hIBIkAEiAARIAIaJUDGlkZxkzAiQASIABEgAkRA2wiQsaVtLU71JQJEgAgQASJABDRKgIwtjeImYUSACBABIkAEiIC2ESBjS9tanOpLBIgAESACRIAIaJQAGVsaxU3CiAARIAJEgAgQAW0jQMaWtrU41ZcIEAEiQASIABHQKAEytjSKm4QRASJABIgAESAC2kaAjC1ta3GqLxEgAkSACBABIqBRAmRsaRQ3CSMCRIAIEAEiQAS0jQAZW9rW4lRfIkAEiAARIAJEQKMEyNjSKG4SRgSIABEgAkSACGgbATK2tK3Fqb5EgAgQASJABIiARgmQsaVR3CSMCBABIkAEiAAR0DYC/w+r/R0f4Ee5QQAAAABJRU5ErkJggg==" + } + }, + "cell_type": "markdown", + "id": "655127c1-3bc5-4f7e-b224-fb9655102134", + "metadata": {}, + "source": [ + "# Result:\n", + "![image.png](attachment:224c4550-7d87-455e-8193-0364425d439b.png)" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "86ec5e38-ab4c-4185-bc0f-3306e670f968", + "id": "f0020ac6-1f0b-4344-b83f-33f9d73a390c", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb b/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb new file mode 100644 index 0000000..0186920 --- /dev/null +++ b/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb @@ -0,0 +1,7589 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a24f20d0-abb4-413a-a70b-0fb6d3ca1e22", + "metadata": {}, + "source": [ + "# Introduction:\n", + "#### - Mapping cross-sections with IDs\n", + "#### - Converting the proccessed cross-sections to parquets, geopackages, Iceberg tables by HUC \n", + "#### - Querying for cross-sections By IDs " + ] + }, + { + "cell_type": "markdown", + "id": "4318dbc0-f266-4078-a4e6-59898e1e2c43", + "metadata": {}, + "source": [ + "## Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6788bf1c-12cf-4d48-a880-2e30762b0378", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas\n", + "import pyarrow as pa\n", + "import pyarrow.parquet as pq\n", + "import collections\n", + "import pandas as pd\n", + "import numpy as np\n", + "import re\n", + "import json\n", + "import pickle\n", + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "b9e39188-c6e6-4849-907d-bef852afab0d", + "metadata": {}, + "source": [ + "## Mapping FIM-C Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "623c0d59-46be-40a5-8470-2d367345df40", + "metadata": {}, + "outputs": [], + "source": [ + "class MapData(object):\n", + " '''\n", + " '''\n", + " def __init__(self, data_dir):\n", + " self.data_dir = data_dir\n", + "\n", + " # Parent directory of the FIM-C files.\n", + " # Note: All the jsons & geopackages are relevant \n", + " # to map the files to IDs.\n", + " self.fim_data_dirs = []\n", + "\n", + " # List of directories associated with each file type of \n", + " # the FIM-C data sample (e.g. geopackage of a given model @ HUC#, json, \n", + " # source_models.gpkg, ripple.gpkg) \n", + " self.model_gpkg_dirs = []\n", + " self.src_models_gpkg_dirs = []\n", + " self.rip_gpkg_dirs = []\n", + " self.gpkg_dirs = []\n", + " self.json_dirs = []\n", + " self.xs_df_list = []\n", + " \n", + " # Variables to be used later\n", + " self.model_gpkg_tablenames = []\n", + " self.src_models_gpkg_tablenames = []\n", + " self.rip_gpkg_tablenames = []\n", + " self.gpkg_tablenames = []\n", + " self.json_tablenames = []\n", + " \n", + " self.id2json = collections.defaultdict(dict)\n", + " self.model_id2gpkg = collections.defaultdict(dict)\n", + " self.us_ref_dict = collections.defaultdict(dict)\n", + " self.ds_ref_dict = collections.defaultdict(dict)\n", + " self.rip_huc2gpkg = collections.defaultdict(dict)\n", + " self.groupbyriver_dict = collections.defaultdict(dict)\n", + " self.crs_dict = collections.defaultdict(dict)\n", + " self.consolidated_id2xs = geopandas.GeoDataFrame()\n", + " \n", + " self.read_data_dirs()\n", + " self.cat_data_dirs()\n", + " self.map_model2huc()\n", + " self.filter_model2huc_map(keys_to_drop={'metrics','low_flow', \n", + " 'high_flow', 'eclipsed', \n", + " 'lengths', 'coverage'})\n", + " \n", + " # Generate maps of model_id & HUC # to xs (for both us & ds cross-section)\n", + " # to reach ID & \"network_to_id\" from each model @ HUC's json file \n", + " self.map_modelhuc_xs2ids()\n", + "\n", + " # Generate maps of model_id & HUC # to gpkg from each model @ HUC's geopackage\n", + " self.map_model2huc_gpkg()\n", + "\n", + " # Generate maps of HUC # to ripple gpkg\n", + " self.map_huc2ripple_gpkg()\n", + "\n", + " # Map IDs to each model's cross-section instance\n", + " self.map_model_xs2ids()\n", + "\n", + " # [Optional: Per HUC, save each river's set of XS data as geoparquetss & geopackages]\n", + " #self.save_xs_data()\n", + "\n", + " # Save map of inherited CRS to HUC, model_id, river name\n", + " self.save_crs_map()\n", + "\n", + " # Consolidated all HEC RAS models' cross-sections featuring IDs\n", + " self.consolidate_id2xs_dfs()\n", + "\n", + " # Save HEC RAS models' cross-sections consolidated by HUC as geoparquets & geopackages\n", + " self.save_xsbyhuc_data()\n", + " \n", + "\n", + "\n", + " def read_data_dirs(self):\n", + " '''\n", + " Extract the list of fim-c data sample's directories.\n", + " \n", + " Args:\n", + " data_dir (str): Parent directory of the fim-c files.\n", + " Note: All the jsons & geopackages are relevant \n", + " to map the files to IDs.\n", + "\n", + " Return (list): List of directories associated with each file type of \n", + " the FIM-C data sample.\n", + " \n", + " ''' \n", + " fim_data_dirs = []\n", + " for folder, subfolders, files in os.walk(self.data_dir):\n", + " if folder != self.data_dir:\n", + " for file in files:\n", + " self.fim_data_dirs.append(f'{folder}/{file}')\n", + "\n", + " return\n", + " \n", + " def cat_data_dirs(self):\n", + " '''\n", + " Categorize FIM-C data sample files.\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + " \n", + " ''' \n", + " # Extract a list of directories corresponding to each set of files \n", + " for x in self.fim_data_dirs:\n", + " \n", + " # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains reach_id & nwm_to_id \n", + " # for network layer & reaches layer. The rating curves layer only has reach_id. The models layer \n", + " # contains collection_id & model_id)\n", + " if re.search('ripple.gpkg', x):\n", + " self.rip_gpkg_dirs.append(x)\n", + " t = re.search('/mip(.*)', x)\n", + " rip_gpkg_tblname = t.group()\n", + " self.rip_gpkg_tablenames.append(rip_gpkg_tblname.lstrip('/').replace('/', '_'))\n", + "\n", + " # Covers all HEC-RAS models gpkg featuring XS per HUC (contains model_id)\n", + " elif not x.endswith('source_models.gpkg') and not x.endswith('.json') and not re.search('ripple.gpkg', x):\n", + " self.model_gpkg_dirs.append(x)\n", + " t = re.search('/mip(.*)', x)\n", + " model_gpkg_tblname = t.group()\n", + " self.model_gpkg_tablenames.append(model_gpkg_tblname.lstrip('/').replace('/', '_'))\n", + "\n", + " # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains model_id & their HEC-RAS 1D model flowlines)\n", + " elif x.endswith('source_models.gpkg'):\n", + " self.src_models_gpkg_dirs.append(x)\n", + " t = re.search('/mip(.*)', x)\n", + " src_models_gpkg_tblname = t.group()\n", + " self.src_models_gpkg_tablenames.append(src_models_gpkg_tblname.lstrip('/').replace('/', '_'))\n", + " \n", + " # Covers all HEC-RAS models + Ripple gpkg per HUC\n", + " if x.endswith('.gpkg'):\n", + " self.gpkg_dirs.append(x)\n", + " t = re.search('/mip(.*)', x)\n", + " gpkg_tblname = t.group()\n", + " self.gpkg_tablenames.append(gpkg_tblname.lstrip('/').replace('/', '_'))\n", + "\n", + " # Covers each HEC-RAS models' result of conflating its model w/ the NWM network\n", + " elif x.endswith('.json'):\n", + " self.json_dirs.append(x)\n", + " t = re.search('/mip(.*)', x)\n", + " json_tblname = t.group()\n", + " self.json_tablenames.append(json_tblname.lstrip('/').replace('/', '_')) \n", + "\n", + " return \n", + " \n", + " def drop_nested_keys(self, map_dict, keys_to_drop):\n", + " '''\n", + " Drop keys irrelevant for linking each XS to IDs\n", + " \n", + " Args:\n", + " map_dict (dict): Dictionary to filter\n", + "\n", + " keys_to_drop (dict): List of keys irrelevant for linking each XS to IDs.\n", + "\n", + " Return: None\n", + "\n", + " '''\n", + " if isinstance(map_dict, dict):\n", + " return {k: self.drop_nested_keys(v, keys_to_drop) for k, v in map_dict.items() if k not in keys_to_drop}\n", + " elif isinstance(map_dict, list):\n", + " return [self.drop_nested_keys(i, keys_to_drop) for i in map_dict]\n", + " else:\n", + " return map_dict\n", + " \n", + " def map_model2huc(self):\n", + " '''\n", + " Map each conflation json file to their corresponding model_id & HUC #.\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + "\n", + " ''' \n", + " for x in self.json_dirs:\n", + " # Note: model_ids found in each src source_models.gpkg is featured is declared as\n", + " # sub-foldername of where model gpkg file resides\n", + " model_id = x.split(\"/\")[-2]\n", + " huc_num = x.split(\"/\")[-4].split('_')[1]\n", + " self.id2json[model_id][huc_num] = {}\n", + " try:\n", + " with open(x, 'r') as f:\n", + " json2dict = json.loads(f.read()) \n", + " self.id2json[model_id][huc_num].update(json2dict)\n", + " except:\n", + " pass\n", + "\n", + " return \n", + " \n", + " def filter_model2huc_map(self, keys_to_drop):\n", + " '''\n", + " Extract only relevant keys from model2huc map for linking each XS to a feature ID. \n", + " \n", + " Args:\n", + " keys_to_drop (dict): List of keys irrelevant for linking each XS to IDs.\n", + " (e.g. {'metrics','low_flow', 'high_flow', 'eclipsed', \n", + " 'lengths', 'coverage'})\n", + "\n", + " Return: None\n", + "\n", + " ''' \n", + " self.id2json = self.drop_nested_keys(self.id2json, keys_to_drop)\n", + " \n", + " return\n", + " \n", + " def map_modelhuc_xs2ids(self):\n", + " '''\n", + " Parse JSONs & map model_id & HUC # to xs to reach ID & \"network_to_id\"\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + "\n", + " Note: Per model @ HUC cross-section layer, the attribute of interest is \"river_reach_rs\" \n", + " in order to link the IDs to each individual cross-section & their associated xs_id.\n", + "\n", + " To map each cross-section of a model @ HUC#, there has to be a shared attribute between a \n", + " model @ HUC#'s cross section w/in its XS layer & the details provided within a model @ HUC#'s \n", + " conflation json file.\n", + " \n", + " - Each conflation json file reveals ...\n", + " - Per reach, there is a set of cross-sections.\n", + " - Within each model's cross-section (XS) layers, there are a set of cross-section \n", + " instances - each instance featuring a unique \"thalweg\" (aka \"min_elevation\"), \n", + " \"xs_max_elevation\" (aka \"max_elevation\"), \"reach_id\" (\"reaches\"), & \"river_station\" (aka \"xs_id\")\n", + "\n", + " - Each model @ HUC#'s XS layer contains a collection of cross-section instancees. \n", + " Thus, each unique cross-section w/in a given model @ HUC#'s XS layer will need to be mapped in \n", + " such a way to allow each cross-section to be associated with a feature ID (aka \"reach_id\" and/or\n", + " \"network_to_id\"). \n", + " \n", + " - \"river_reach_rs\" is formatted differently across models' XS layers, however multplie keys \n", + " referenced in the conflation jsons can be referenced to obtain the \"river_reach_rs\" from the jsons \n", + " As a result, the mapping of IDs to each model's cross-section instance will be based on the info.\n", + " extracted from a model @ HUC#'s conflation.json\n", + "\n", + " - There can be multiple reach_ids tied to same nwm_to_id (aka \"network_to_id).\n", + "\n", + " ''' \n", + " # Keys to join values from that makes up the 'river_reach_rs' reflected in each model's XS layer\n", + " keys_to_join = ['river', 'reach', 'xs_id'] \n", + " for model_id, huc_dict in self.id2json.items():\n", + " for huc_num, reach_dict in huc_dict.items():\n", + " for reach_id, v_dict in reach_dict['reaches'].items():\n", + " # Joining the attribute because each model's xs layer features three atttrib concat (to be used as reference)\n", + " if \"us_xs\" in v_dict:\n", + " usxs_joined_values = ' '.join(str(v_dict[\"us_xs\"][key]) for key in keys_to_join)\n", + " if \"min_elevation\" in v_dict[\"us_xs\"]:\n", + " us_xs_min_elev = v_dict[\"us_xs\"][\"min_elevation\"]\n", + " if \"max_elevation\" in v_dict[\"us_xs\"]:\n", + " us_xs_max_elev = v_dict[\"us_xs\"][\"max_elevation\"]\n", + " \n", + " if \"ds_xs\" in v_dict:\n", + " dsxs_joined_values = ' '.join(str(v_dict[\"ds_xs\"][key]) for key in keys_to_join)\n", + " if \"min_elevation\" in v_dict[\"ds_xs\"]:\n", + " ds_xs_min_elev = v_dict[\"ds_xs\"][\"min_elevation\"]\n", + " if \"max_elevation\" in v_dict[\"ds_xs\"]:\n", + " ds_xs_max_elev = v_dict[\"ds_xs\"][\"max_elevation\"]\n", + " \n", + " if \"network_to_id\" in v_dict:\n", + " nwm2id = v_dict[\"network_to_id\"]\n", + "\n", + " # Generated maps of model_id & HUC # to xs (for both us & ds cross-section)\n", + " # to reach ID & \"network_to_id\" \n", + " self.us_ref_dict[(model_id, huc_num)].update({(usxs_joined_values, us_xs_min_elev , us_xs_max_elev):[reach_id, nwm2id]})\n", + " self.ds_ref_dict[(model_id, huc_num)].update({(dsxs_joined_values, ds_xs_min_elev , ds_xs_max_elev):[reach_id, nwm2id]}) \n", + " \n", + " return\n", + "\n", + " def map_model2huc_gpkg(self):\n", + " '''\n", + " Map model_id & HUC # to each HEC-RAS model's geopackage.\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + "\n", + " Note: model_ids found in each source_models.gpkg is featured in last\n", + " sub-foldername of where model gpkg file resides\n", + "\n", + " ''' \n", + " # Each HEC-RAS model gpkg per model per HUC\n", + " for x in self.model_gpkg_dirs:\n", + " model_id = x.split(\"/\")[-2]\n", + " huc_num = x.split(\"/\")[-4].split('_')[1]\n", + " self.model_id2gpkg[(model_id, huc_num)] = {'XS': None}\n", + " self.model_id2gpkg[(model_id, huc_num)] = {\"XS concave hull\": None}\n", + " self.model_id2gpkg[(model_id, huc_num)] = {\"River\": None}\n", + "\n", + " try:\n", + " self.model_id2gpkg[(model_id, huc_num)].update({'XS': geopandas.read_file(x, engine='pyogrio',\n", + " use_arrow=True, \n", + " layer=\"XS\")})\n", + "\n", + " self.model_id2gpkg[(model_id, huc_num)].update({\"River\": geopandas.read_file(x, \n", + " engine='pyogrio', \n", + " use_arrow=True, \n", + " layer=\"River\")})\n", + " self.model_id2gpkg[(model_id, huc_num)].update({\"XS concave hull\": geopandas.read_file(x, \n", + " engine='pyogrio', \n", + " use_arrow=True, \n", + " layer=\"XS concave hull\")})\n", + " except:\n", + " pass\n", + " \n", + " return\n", + " \n", + " def map_huc2ripple_gpkg(self):\n", + " '''\n", + " Map HUC # to ripple geopackage (features HEC RAS 1D model flowlines).\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + "\n", + " Note: ripple.gpkg features the HEC RAS 1D model flowlines categorized by HUC #.\n", + "\n", + " ''' \n", + " for x in self.rip_gpkg_dirs:\n", + " huc_num = x.split(\"/\")[-2].split('_')[1]\n", + " self.rip_huc2gpkg[huc_num] = {'reaches': None}\n", + " self.rip_huc2gpkg[huc_num] = {\"rating curves\": None}\n", + " self.rip_huc2gpkg[huc_num] = {\"network\": None}\n", + " self.rip_huc2gpkg[huc_num] = {'models': None}\n", + " self.rip_huc2gpkg[huc_num] = {\"metadata\": None}\n", + " self.rip_huc2gpkg[huc_num] = {\"rating_curves_no_map\": None}\n", + " self.rip_huc2gpkg[huc_num] = {\"processing\": None}\n", + "\n", + " try:\n", + " self.rip_huc2gpkg[huc_num].update({\"reaches\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"reaches\")})\n", + " self.rip_huc2gpkg[huc_num].update({\"rating_curves\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"rating_curves\")})\n", + " self.rip_huc2gpkg[huc_num].update({\"network\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"network\")})\n", + " self.rip_huc2gpkg[huc_num].update({\"models\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"models\")})\n", + " self.rip_huc2gpkg[huc_num].update({\"metadata\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"metadata\")})\n", + " self.rip_huc2gpkg[huc_num].update({\"rating_curves_no_map\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"rating_curves_no_map\")})\n", + " self.rip_huc2gpkg[huc_num].update({\"processing\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"processing\")})\n", + " except:\n", + " \n", + " pass\n", + " return\n", + " \n", + " def map_model_xs2ids(self):\n", + " '''\n", + " Map each cross-section instance featured in HEC-RAS model's cross-section layer to their corresponding IDs.\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + "\n", + " ''' \n", + " \n", + " for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items():\n", + " df = model_gpkg_dict['XS']\n", + " df['huc'] = huc_num\n", + " df['model_id'] = model_id\n", + " array_of_lists = [[None, None] for _ in range(len(df))]\n", + " df['us_ids'] = pd.DataFrame([array_of_lists]).T\n", + " df['ds_ids'] = pd.DataFrame([array_of_lists]).T\n", + " \n", + " # Covers us_xs\n", + " if (model_id, huc_num) in self.us_ref_dict:\n", + " df['us_ids'] = df.set_index([\"river_reach_rs\", \"thalweg\", \"xs_max_elevation\"]).index.map(self.us_ref_dict[(model_id, huc_num)].get)\n", + " #print(f\"The model_id @ HUC# ({(model_id, huc_num)}) IS featured in current model @ HUC's conflation json file.\")\n", + " else:\n", + " print(f\"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.\")\n", + " continue\n", + "\n", + " # Covers ds_xs\n", + " if (model_id, huc_num) in self.ds_ref_dict:\n", + " df['ds_ids'] = df.set_index([\"river_reach_rs\", \"thalweg\", \"xs_max_elevation\"]).index.map(self.ds_ref_dict[(model_id, huc_num)].get)\n", + " #print(f\"The model_id @ HUC# ({(model_id, huc_num)}) IS featured in current model @ HUC's conflation json file.\")\n", + " else:\n", + " print(f\"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.\")\n", + " continue\n", + "\n", + " # Extracts & appends reach_id & network_to_id to each model @ HUC's unique XS \n", + " us_id_df = df['us_ids'].apply(pd.Series)\n", + " us_id_df.columns=['us_reach_id', 'us_network_to_id']\n", + " ds_id_df = df['ds_ids'].apply(pd.Series)\n", + " ds_id_df.columns=['ds_reach_id', 'ds_network_to_id']\n", + " \n", + " # Fill any nan to string \n", + " us_id_df[['us_reach_id', 'us_network_to_id']] = us_id_df[['us_reach_id', 'us_network_to_id']].fillna('None')\n", + " ds_id_df[['ds_reach_id', 'ds_network_to_id']] = ds_id_df[['ds_reach_id', 'ds_network_to_id']].fillna('None')\n", + " df = df.fillna('None')\n", + " df = pd.concat([df, us_id_df, ds_id_df], axis=1)\n", + " df = df.drop(['us_ids', 'ds_ids'], axis=1)\n", + " \n", + " model_gpkg_dict['XS'] = df\n", + " self.xs_df_list.append(model_gpkg_dict['XS'])\n", + " \n", + " return\n", + " \n", + " def save_xs_data(self):\n", + " '''\n", + " Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + "\n", + " Note: These saved parquet files will preserve each river @ HUC's inherited CRS. \n", + "\n", + " '''\n", + " for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items():\n", + "\n", + " # Generate data folder per HUC\n", + " if not os.path.exists(f'{os.getcwd()}/xs_data/huc_{huc_num}'):\n", + " os.makedirs(f'{os.getcwd()}/xs_data/huc_{huc_num}')\n", + "\n", + " # Save each river's geopandas as a geoparquet & geopackage under each HUC folder \n", + " grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]['XS'].groupby(['river'])\n", + " for river_name in set(self.model_id2gpkg[(model_id, huc_num)]['XS']['river']):\n", + " filterbyriver = grouped_xslayers.get_group(river_name)\n", + "\n", + " # Generate map of each river's set of XS to HUC & model ID to be used as a\n", + " # look-up reference\n", + " self.groupbyriver_dict[huc_num].update({model_id: filterbyriver})\n", + "\n", + " # Save XS as geoparquet per river per HUC\n", + " filterbyriver.to_parquet(f'{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.parquet', \n", + " engine=\"pyarrow\")\n", + "\n", + " # Save XS as geopackage per river per HUC\n", + " filterbyriver.to_file(f'{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.gpkg', \n", + " driver='GPKG') \n", + " \n", + " return\n", + " \n", + " def save_crs_map(self):\n", + " '''\n", + " Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + "\n", + " Note: This saved pickle file will map each river @ HUC's inherited CRS for one to \n", + " analyze & reference. \n", + "\n", + " '''\n", + " for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items():\n", + "\n", + " # Generate data folder per HUC\n", + " if not os.path.exists(f'{os.getcwd()}/xs_data/crs_map'):\n", + " os.makedirs(f'{os.getcwd()}/xs_data/crs_map')\n", + "\n", + " # Generate map of the CRS to each river's geopandas per HUC \n", + " grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]['XS'].groupby(['river'])\n", + " for river_name in set(self.model_id2gpkg[(model_id, huc_num)]['XS']['river']):\n", + " filterbyriver = grouped_xslayers.get_group(river_name)\n", + " self.crs_dict[(huc_num, model_id)].update({river_name: f'ESPG: {str(filterbyriver.crs.to_epsg()) if \\\n", + " filterbyriver.crs.to_epsg() else None}, {filterbyriver.crs.name},'})\n", + "\n", + " # Save map of inherited CRS to HUC, model_id, river name\n", + " with open(f'{os.getcwd()}/xs_data/crs_map/crs_mapping.pickle', 'wb') as handle:\n", + " pickle.dump(self.crs_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + " \n", + " return\n", + "\n", + " def consolidate_id2xs_dfs(self):\n", + " '''\n", + " Consolidate HEC-RAS models cross-sections featuring their corresponding IDs.\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + "\n", + " Note: A set CRS standard is needed in order to consolidates all XS layers of all models\n", + " under a single dataframe to maintain a consistent CRS. This consolidation will check if the XS \n", + " dataframes have a consistent CRS if it does not then it will not consolidate the XS dataframes. \n", + " As of 05/14/25, only CRS in this condition is considering the\n", + " NAD83, but additional CRS can be added to this methods as new findings are made.\n", + " \n", + " '''\n", + " crs_list = []\n", + " for xs_df in self.xs_df_list:\n", + " if 'NAD83' in xs_df.crs.name or 'NAD_1983' in xs_df.crs.name:\n", + " # Convert all GeoDataFrames in the list to the target CRS\n", + " crs_list.append(\"EPSG:5070\")\n", + " else:\n", + " print(False)\n", + "\n", + " # Will consolidate ONLY if the CRS is consistent across XS geodpandas dataframes\n", + " if len(set(crs_list)) == 1:\n", + " target_crs = str(np.unique(crs_list)[0])\n", + " self.consolidated_id2xs = geopandas.GeoDataFrame(pd.concat([xs_df.to_crs(target_crs) for xs_df in self.xs_df_list], \n", + " ignore_index=True))\n", + " print(f\"The consolidated XS geopandas dataframes now has a standardized CRS of:\\n{self.consolidated_id2xs.crs.name}\")\n", + " else:\n", + " print(\"Cannot consolidate XS geodpandas dataframes because the CRS is inconsistent across XS geodpandas dataframes.\")\n", + " \n", + " return\n", + "\n", + " def save_xsbyhuc_data(self):\n", + " '''\n", + " Consolidate HEC-RAS models cross-sections based on HUC & save to storage\n", + " \n", + " Args:\n", + " None\n", + "\n", + " Return: None\n", + "\n", + " Note: These saved parquet files will be the transformed CRS of all XS per HUC to ensure\n", + " a consistent standardized CRS. \n", + "\n", + " '''\n", + " unique_huc_nums = set(self.consolidated_id2xs['huc'])\n", + " for huc_num in unique_huc_nums:\n", + " \n", + " # Generate data folder per HUC\n", + " if not os.path.exists(f'{os.getcwd()}/xs_data/mip_{huc_num}'):\n", + " os.makedirs(f'{os.getcwd()}/xs_data/mip_{huc_num}')\n", + " \n", + " # Filter consolidated XS geopanda dataframe by HUC\n", + " filterbyhuc = self.consolidated_id2xs[self.consolidated_id2xs['huc'] == huc_num]\n", + " \n", + " # Save XS as geoparquet per HUC\n", + " filterbyhuc.to_parquet(f'{os.getcwd()}/xs_data/mip_{huc_num}/huc_{huc_num}.parquet', \n", + " engine=\"pyarrow\")\n", + " \n", + " # Save XS as geopackage per HUC\n", + " filterbyhuc.to_file(f'{os.getcwd()}/xs_data/mip_{huc_num}/huc_{huc_num}.gpkg', \n", + " driver='GPKG') \n", + " \n", + " return\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "a4372bf1-9031-41c8-8567-5cfe3c56a0b1", + "metadata": {}, + "source": [ + "## Demo I:\n", + "### Mapping FIM-C Data to Model ID, HUC #, Reach ID, Network ID, etc" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3bd859e1-1b0a-45e9-a3aa-03aaca2cc5e2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The model_id @ HUC# (('SHR6_1', '09020301')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('SandHillRiver7', '09020301')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('SandHillRiver7_1', '09020301')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('SHR6', '09020301')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('Kelly_Creek_T1', '06040002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('Duck_River_Trib4', '06040002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('Bell_Buckle_CrT1', '06040002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('Trib_to_Holland', '06040002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('Stream0510', '01020002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('Martin_Stream', '01020002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('Stream0526', '01020002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('Stream0527', '01020002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT115_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT79_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT61_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT119_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT45_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT84_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT91_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT115_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT66_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT120_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT107_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT34_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT84_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT55_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT2_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT106_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT103_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT71_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT103_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT120_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT25_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT8_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT65_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT110_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT105_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT73_in_BluffCr_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT55_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT72_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT72_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT35_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT129_in_Edith_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT89_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT25_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT89_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT102_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT120_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT114_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT33_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT95_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT62_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT70_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT52_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT110_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT35_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT85_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT146_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT53_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT34_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT94_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT96_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT52_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT148_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT112_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT127_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT119_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT112_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT94_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT104_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT120_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT100_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT95_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT33_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT147_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT61_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT129_in_Edith_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT162_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT45_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT235_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT82_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT98_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT86_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT2_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT53_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT86_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT118_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT85_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT235_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT62_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT98_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT100_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT152_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT73_in_BluffCr_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT118_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT96_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT108_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT127_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT107_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT103_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT108_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT66_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT147_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT106_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT71_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT105_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT102_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT79_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT70_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT114_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT113_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT82_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT103_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT113_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT91_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT65_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT8_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT152_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT104_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT162_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT146_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UNT148_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioTusasTrib2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaJarita', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadadeBorracho', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoMiranda', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeRancho', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoPunche', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoPuncheT4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadadelaOrill', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('PueblodeTaosT3b1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaSarcoT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ElRito', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('FernandodeTaos', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('AguajePetacaT1a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('PueblodeTaosT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioTusas_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoPuncheT3a1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioVallecitos', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ElRitoT4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioPuebloTrib1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoPino', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT11b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioOjoCalienteT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioVallecitosT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT12c', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyolaMina', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ElRitoT6', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('AguajedelaPetacaT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaAnchaNorth', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoRancho', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ElRitoT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoelRitoT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaPueblo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RedRiver', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT13', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT10f', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CabrestoCreek', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RiodeTruchas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('GrandeRanchoT1b4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT11c', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioOjoCaliente_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaCorral', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('PuebloArroyo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('FernandodeTaosT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT10e', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioTusasTrib3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadadeApodaca', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('AguajePetacaT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ElRitoT7', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadadelAgua', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoPuncheT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('EmbudoCreekTrib1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('LaMaderaT1a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT9', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioVallecitos_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('PiedraLumbre', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('GrandeRanchoT1_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('GrandeRanchoT1b2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioVallecitosT2b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoLagunitas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('AngelCanyon', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('LagunitasT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoelRito', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT9a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('GrandeRanchoT1a1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('LaMaderaT1b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioPueblo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT10d1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaLemitas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('GrandeRanchoT1d', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoPuncheT3b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioVallecitosT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ChamizalCreek', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadadeOso', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaEntranasT', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioVallecitosT4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioOjoCaliente', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaTioPula', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('PueblodeTaosT4a1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadadeOso_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaAlamos', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioHondo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioOjoCalienteT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RedRiverTrib2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaOjoSarco', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaAbeque', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioLucero', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('LatirCreekTrib2a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ElRitoT8a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RiodeTruchasT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaMontecito', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioTusas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT10c', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT12', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadadeOjito', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('AlmagreT3a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT12a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('GrandeRanchoT1b1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrande', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT10b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('GrandeRanchoT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT12b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadadeLorenzo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('PueblodeTaosT4b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeRancho_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('AcequiadelJaral', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT9c2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('JarosoCreek', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaEntranas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ArroyoPuncheT6', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ElRito_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('LagunitasT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RiodelasTrampas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('PueblodeTaosT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('GrandeRanchoT1c', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioVallecitosT9', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaPinoReale', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioTusasTrib4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadadeJacinto', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT11c1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('LatirCreek', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('PueblodeTaosT4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('ElRitoT5', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CanadaAncha', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('AnchaNorthT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('RioGrandeT10d', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('KingsCreekTrib1B_4', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('KingsCreekTrib1B_1', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('KingsCreekTrib1B_3', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('KingsCreekTrib1B_2', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('KingsCreekTrib1B', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('KC1_KC1B_LOMR', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('NP708', '05020006')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('NP720', '05020006')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('NP719', '05020006')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('NP180', '05020006')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CB18_CorrectedEffective', '02060006')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('CB18_Proposed', '02060006')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UT81Milw', '04030101')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The model_id @ HUC# (('UT5Milw', '04030101')) IS NOT featured in current model @ HUC's conflation json file.\n", + "The consolidated XS geopandas dataframes now has a standardized CRS of:\n", + "NAD83 / Conus Albers\n" + ] + } + ], + "source": [ + "# Instantiate class\n", + "obj = MapData(data_dir=f'{os.getcwd()}/fema_mip_sample/')\n", + "\n", + "# Extract list of fim-c data sample's directories.\n", + "fim_data_dirs = obj.fim_data_dirs\n", + "\n", + "# Categorize FIM-C data sample files (e.g. geopackage of a given model @ HUC#, json, source_models.gpkg, ripple.gpkg)\n", + "model_gpkg_dirs, src_models_gpkg_dirs, rip_gpkg_dirs = obj.model_gpkg_dirs, obj.src_models_gpkg_dirs, obj.rip_gpkg_dirs\n", + "gpkg_dirs, json_dirs = obj.gpkg_dirs, obj.json_dirs\n", + "\n", + "model_gpkg_tablenames, src_models_gpkg_tablenames, rip_gpkg_tablenames = obj.model_gpkg_tablenames, obj.src_models_gpkg_tablenames, obj.rip_gpkg_tablenames\n", + "gpkg_tablenames, json_tablenames = obj.gpkg_tablenames, obj.json_tablenames\n", + "\n", + "# Generate a dictionary to map each conflation json files to their corresponding model_id & HUC #.\n", + "model2huc_map = obj.id2json\n", + "\n", + "# Generated maps of model_id & HUC # to xs (for both us & ds cross-section) to reach ID & \"network_to_id\"\n", + "us_xs2id_map = obj.us_ref_dict\n", + "ds_xs2id_map = obj.ds_ref_dict\n", + "\n", + "# Generated map of model_id & HUC # to each HEC-RAS model's geopackage.\n", + "model_id2gpkg_map = obj.model_id2gpkg\n", + "\n", + "# Generated map of HUC # to each ripple geopackage.\n", + "rip_huc2gpkg_map = obj.rip_huc2gpkg\n", + "\n", + "# Map IDs to each model's cross-section instance\n", + "id2xs_dfs = obj.xs_df_list\n", + "\n", + "# [OPTIONAL: If a user wants to extract individual river geopandas, parquet, &/or gpkg]\n", + "# Map of XS as geopandas per river per HUC w/ their inherited CRS by HUC\n", + "#xsbyriver_map = obj.groupbyriver_dict\n", + "\n", + "# [OPTIONAL: Map of inherited CRS to HUC, model_id, river name]\n", + "crs_map = obj.crs_dict\n", + "\n", + "# Conslidate all HEC-RAS models' cross-sections featuring their corresponding IDs.\n", + "consolidated_id2xs = obj.consolidated_id2xs" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "86bfa6f3-0c8d-4cdc-8f1d-2b4e73b4d00b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_length...number_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id
0BapCrReach1BapCr ,Reach148077.80BapCr Reach1 48077.8743.50756.11207.08207.08207.08...3607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436369.214 1962609.838, 436369.691...07130010sa_11380162613801634NoneNone
1BapCrReach1BapCr ,Reach147870.72BapCr Reach1 47870.72742.54755.74259.78259.78259.78...2607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436352.44 1962575.103, 436311.185 ...07130010sa_1NoneNoneNoneNone
2BapCrReach1BapCr ,Reach147610.94BapCr Reach1 47610.94741.94755.17374.57374.57374.57...2607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436323.593 1962531.597, 436264.538...07130010sa_1NoneNoneNoneNone
3BapCrReach1BapCr ,Reach147236.37BapCr Reach1 47236.37740.62754.18333.59333.59333.59...2607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436310.242 1962416.052, 436236.357...07130010sa_1NoneNoneNoneNone
4BapCrReach1BapCr ,Reach146902.78BapCr Reach1 46902.78739.04757.19375.07375.07375.07...2607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436292.364 1962322.688, 436219.117...07130010sa_1NoneNoneNoneNone
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5 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " river reach river_reach river_station \\\n", + "0 BapCr Reach1 BapCr ,Reach1 48077.80 \n", + "1 BapCr Reach1 BapCr ,Reach1 47870.72 \n", + "2 BapCr Reach1 BapCr ,Reach1 47610.94 \n", + "3 BapCr Reach1 BapCr ,Reach1 47236.37 \n", + "4 BapCr Reach1 BapCr ,Reach1 46902.78 \n", + "\n", + " river_reach_rs thalweg xs_max_elevation left_reach_length \\\n", + "0 BapCr Reach1 48077.8 743.50 756.11 207.08 \n", + "1 BapCr Reach1 47870.72 742.54 755.74 259.78 \n", + "2 BapCr Reach1 47610.94 741.94 755.17 374.57 \n", + "3 BapCr Reach1 47236.37 740.62 754.18 333.59 \n", + "4 BapCr Reach1 46902.78 739.04 757.19 375.07 \n", + "\n", + " right_reach_length channel_reach_length ... number_of_coords \\\n", + "0 207.08 207.08 ... 3 \n", + "1 259.78 259.78 ... 2 \n", + "2 374.57 374.57 ... 2 \n", + "3 333.59 333.59 ... 2 \n", + "4 375.07 375.07 ... 2 \n", + "\n", + " flows profile_names \\\n", + "0 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", + "1 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", + "2 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", + "3 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", + "4 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", + "\n", + " geometry huc model_id \\\n", + "0 LINESTRING (436369.214 1962609.838, 436369.691... 07130010 sa_1 \n", + "1 LINESTRING (436352.44 1962575.103, 436311.185 ... 07130010 sa_1 \n", + "2 LINESTRING (436323.593 1962531.597, 436264.538... 07130010 sa_1 \n", + "3 LINESTRING (436310.242 1962416.052, 436236.357... 07130010 sa_1 \n", + "4 LINESTRING (436292.364 1962322.688, 436219.117... 07130010 sa_1 \n", + "\n", + " us_reach_id us_network_to_id ds_reach_id ds_network_to_id \n", + "0 13801626 13801634 None None \n", + "1 None None None None \n", + "2 None None None None \n", + "3 None None None None \n", + "4 None None None None \n", + "\n", + "[5 rows x 24 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "consolidated_id2xs.head(5)\n", + "# consolidated_id2xs.shape" + ] + }, + { + "attachments": { + "6667e33a-9f14-4aeb-9078-49fc7dd265d2.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "id": "00d552bf-d39e-40f0-ace5-54a09f1f35e8", + "metadata": {}, + "source": [ + "## Demo I Result:\n", + "![image.png](attachment:6667e33a-9f14-4aeb-9078-49fc7dd265d2.png)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2959553a-f779-46ef-9dca-2f2a20eb6554", + "metadata": {}, + "source": [ + "## Demo II: \n", + "### Querying XS Instance by Network ID" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8d337a5c-0f7a-42c4-91cf-72e7f0da98cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_length...number_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id
28507BattleCrCypress1BattleCrCypress ,113227.35BattleCrCypress 1 13227.3517.8882.69464.51459.84462.47...21230.0\\n1330.01%\\n1% UltimateLINESTRING (1665550.543 1889977.407, 1664479.0...02060006BattleCreekCypressSwamp1190819011908200NoneNone
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1 rows × 24 columns

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" + ], + "text/plain": [ + " river reach river_reach \\\n", + "28507 BattleCrCypress 1 BattleCrCypress ,1 \n", + "\n", + " river_station river_reach_rs thalweg xs_max_elevation \\\n", + "28507 13227.35 BattleCrCypress 1 13227.35 17.88 82.69 \n", + "\n", + " left_reach_length right_reach_length channel_reach_length ... \\\n", + "28507 464.51 459.84 462.47 ... \n", + "\n", + " number_of_coords flows profile_names \\\n", + "28507 2 1230.0\\n1330.0 1%\\n1% Ultimate \n", + "\n", + " geometry huc \\\n", + "28507 LINESTRING (1665550.543 1889977.407, 1664479.0... 02060006 \n", + "\n", + " model_id us_reach_id us_network_to_id ds_reach_id \\\n", + "28507 BattleCreekCypressSwamp 11908190 11908200 None \n", + "\n", + " ds_network_to_id \n", + "28507 None \n", + "\n", + "[1 rows x 24 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario I\n", + "us_network_to_id = '11908200'\n", + "queried_xsbyid = consolidated_id2xs[consolidated_id2xs['us_network_to_id'] == us_network_to_id]\n", + "queried_xsbyid" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0e5adb81-5ba0-41ad-a1f1-1196b822f1f7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_length...number_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id
0BapCrReach1BapCr ,Reach148077.8BapCr Reach1 48077.8743.5756.11207.08207.08207.08...3607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436369.214 1962609.838, 436369.691...07130010sa_11380162613801634NoneNone
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1 rows × 24 columns

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" + ], + "text/plain": [ + " river reach river_reach river_station \\\n", + "0 BapCr Reach1 BapCr ,Reach1 48077.8 \n", + "\n", + " river_reach_rs thalweg xs_max_elevation left_reach_length \\\n", + "0 BapCr Reach1 48077.8 743.5 756.11 207.08 \n", + "\n", + " right_reach_length channel_reach_length ... number_of_coords \\\n", + "0 207.08 207.08 ... 3 \n", + "\n", + " flows profile_names \\\n", + "0 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", + "\n", + " geometry huc model_id \\\n", + "0 LINESTRING (436369.214 1962609.838, 436369.691... 07130010 sa_1 \n", + "\n", + " us_reach_id us_network_to_id ds_reach_id ds_network_to_id \n", + "0 13801626 13801634 None None \n", + "\n", + "[1 rows x 24 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario II\n", + "us_network_to_id = '13801634'\n", + "queried_xsbyid = consolidated_id2xs[consolidated_id2xs['us_network_to_id'] == us_network_to_id]\n", + "queried_xsbyid" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "38a9c0ba-388b-41a5-a282-2c245e42c12c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_length...number_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id
307961242151242,15789.62141242 15 789.62143520.5583568.42085.1441693.2765793.45487...10193.0\\n359.0\\n522.0\\n723.0\\n919.0\\n569.0\\n1347.010yr\\n25yr\\n50yr\\n100yr\\n100plus\\n100minus\\n5...LINESTRING (-1355141.15 1253454.057, -1355086....150400071242_ras2129430921294311NoneNone
3087811951091195 ,1096071.47501195 109 6071.4753534.4503616.780206.69170147.98750195.85400...81746.0\\n3146.0\\n4514.0\\n6152.0\\n7819.0\\n4840.0...10yr\\n25yr\\n50yr\\n100yr\\n100plus\\n100minus\\n500yrLINESTRING (-1355056.984 1253633.24, -1355173....150400071195_ras21294309212943112129424321294309
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3 rows × 24 columns

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" + ], + "text/plain": [ + " river reach river_reach river_station \\\n", + "30796 1242 15 1242,15 789.6214 \n", + "30878 1195 109 1195 ,109 6071.4750 \n", + "30966 1236 61 1236,61 725.2979 \n", + "\n", + " river_reach_rs thalweg xs_max_elevation left_reach_length \\\n", + "30796 1242 15 789.6214 3520.558 3568.420 85.14416 \n", + "30878 1195 109 6071.475 3534.450 3616.780 206.69170 \n", + "30966 1236 61 725.2979 3533.829 3587.055 151.74160 \n", + "\n", + " right_reach_length channel_reach_length ... number_of_coords \\\n", + "30796 93.27657 93.45487 ... 10 \n", + "30878 147.98750 195.85400 ... 8 \n", + "30966 139.83360 148.79750 ... 7 \n", + "\n", + " flows \\\n", + "30796 193.0\\n359.0\\n522.0\\n723.0\\n919.0\\n569.0\\n1347.0 \n", + "30878 1746.0\\n3146.0\\n4514.0\\n6152.0\\n7819.0\\n4840.0... \n", + "30966 433.0\\n796.0\\n1152.0\\n1584.0\\n2014.0\\n1247.0\\n... \n", + "\n", + " profile_names \\\n", + "30796 10yr\\n25yr\\n50yr\\n100yr\\n100plus\\n100minus\\n5... \n", + "30878 10yr\\n25yr\\n50yr\\n100yr\\n100plus\\n100minus\\n500yr \n", + "30966 10yr\\n25yr\\n50yr\\n100yr\\n100plus\\n100minus\\n5... \n", + "\n", + " geometry huc model_id \\\n", + "30796 LINESTRING (-1355141.15 1253454.057, -1355086.... 15040007 1242_ras \n", + "30878 LINESTRING (-1355056.984 1253633.24, -1355173.... 15040007 1195_ras \n", + "30966 LINESTRING (-1355130.287 1253615.579, -1355168... 15040007 1236_ras \n", + "\n", + " us_reach_id us_network_to_id ds_reach_id ds_network_to_id \n", + "30796 21294309 21294311 None None \n", + "30878 21294309 21294311 21294243 21294309 \n", + "30966 21294309 21294311 21295551 21294309 \n", + "\n", + "[3 rows x 24 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario III\n", + "us_network_to_id = '21294311'\n", + "queried_xsbyid = consolidated_id2xs[consolidated_id2xs['us_network_to_id'] == us_network_to_id]\n", + "queried_xsbyid" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "94b5df36-a194-402b-b795-e47487fef291", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_length...number_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id
19216cottonwood_creek45cottonwood_creek,451773.175cottonwood_creek 45 1773.1751342.2951450.915166.0375330.7853294.9549...147096.0\\n9743.0\\n4449.0100yr\\n100yrplus\\n100yrminusLINESTRING (-1536837.566 2728068.167, -1536849...17060305cottonwood_creek_1_ras23597195235971932359719723597195
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1 rows × 24 columns

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" + ], + "text/plain": [ + " river reach river_reach river_station \\\n", + "19216 cottonwood_creek 45 cottonwood_creek,45 1773.175 \n", + "\n", + " river_reach_rs thalweg xs_max_elevation \\\n", + "19216 cottonwood_creek 45 1773.175 1342.295 1450.915 \n", + "\n", + " left_reach_length right_reach_length channel_reach_length ... \\\n", + "19216 166.0375 330.7853 294.9549 ... \n", + "\n", + " number_of_coords flows profile_names \\\n", + "19216 14 7096.0\\n9743.0\\n4449.0 100yr\\n100yrplus\\n100yrminus \n", + "\n", + " geometry huc \\\n", + "19216 LINESTRING (-1536837.566 2728068.167, -1536849... 17060305 \n", + "\n", + " model_id us_reach_id us_network_to_id ds_reach_id \\\n", + "19216 cottonwood_creek_1_ras 23597195 23597193 23597197 \n", + "\n", + " ds_network_to_id \n", + "19216 23597195 \n", + "\n", + "[1 rows x 24 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario IV\n", + "us_network_to_id = '23597193'\n", + "queried_xsbyid = consolidated_id2xs[consolidated_id2xs['us_network_to_id'] == us_network_to_id]\n", + "queried_xsbyid" + ] + }, + { + "cell_type": "markdown", + "id": "64d121ff-4622-4863-ab1e-16514c28581f", + "metadata": {}, + "source": [ + "## Demo III: \n", + "### Querying Each XS Layer by Model ID & HUC Number" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c8659230-af75-48ff-b988-d1b0dffc260b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_idriver_reach_rsthalwegxs_max_elevationriver_station...right_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometry
009020301GF_EGF_FIS_Submittal120493982327066766NoneNoneRRN GFEGF 170.0774.37834.80170.0...700.0934.0Type RM Length L Ch R = 1 ,170 ,900,934,70...[(44883.4, 834.0), (47186.0, 832.44), (49148.8...['50124', '50484']238749000.0\\n71000.0\\n89000.0\\n139000.010-Year\\n50-Year\\n100-Year\\n500 YearLINESTRING (2818284.13 354233.597, 2818770.518...
109020301GF_EGF_FIS_Submittal1204NoneNoneNoneNoneRRN GFEGF 169.0772.69842.37169.0...340.0344.0Type RM Length L Ch R = 1 ,169 ,450,344,34...[(45195.48, 833.55), (45220.07, 833.6), (45256...['50192', '50563']263849000.0\\n71000.0\\n89000.0\\n139000.010-Year\\n50-Year\\n100-Year\\n500 YearLINESTRING (2815855.489 354032.803, 2816473.50...
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2 rows × 24 columns

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" + ], + "text/plain": [ + " huc model_id us_reach_id us_network_to_id \\\n", + "0 09020301 GF_EGF_FIS_Submittal1204 9398232 7066766 \n", + "1 09020301 GF_EGF_FIS_Submittal1204 None None \n", + "\n", + " ds_reach_id ds_network_to_id river_reach_rs thalweg xs_max_elevation \\\n", + "0 None None RRN GFEGF 170.0 774.37 834.80 \n", + "1 None None RRN GFEGF 169.0 772.69 842.37 \n", + "\n", + " river_station ... right_reach_length channel_reach_length \\\n", + "0 170.0 ... 700.0 934.0 \n", + "1 169.0 ... 340.0 344.0 \n", + "\n", + " ras_data \\\n", + "0 Type RM Length L Ch R = 1 ,170 ,900,934,70... \n", + "1 Type RM Length L Ch R = 1 ,169 ,450,344,34... \n", + "\n", + " station_elevation_points bank_stations \\\n", + "0 [(44883.4, 834.0), (47186.0, 832.44), (49148.8... ['50124', '50484'] \n", + "1 [(45195.48, 833.55), (45220.07, 833.6), (45256... ['50192', '50563'] \n", + "\n", + " number_of_station_elevation_points number_of_coords \\\n", + "0 238 7 \n", + "1 263 8 \n", + "\n", + " flows profile_names \\\n", + "0 49000.0\\n71000.0\\n89000.0\\n139000.0 10-Year\\n50-Year\\n100-Year\\n500 Year \n", + "1 49000.0\\n71000.0\\n89000.0\\n139000.0 10-Year\\n50-Year\\n100-Year\\n500 Year \n", + "\n", + " geometry \n", + "0 LINESTRING (2818284.13 354233.597, 2818770.518... \n", + "1 LINESTRING (2815855.489 354032.803, 2816473.50... \n", + "\n", + "[2 rows x 24 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario I\n", + "model_id = 'GF_EGF_FIS_Submittal1204'\n", + "huc_num = '09020301'\n", + "\n", + "tt = model_id2gpkg_map[(model_id, huc_num)]['XS']\n", + "tt.insert(0, 'huc', tt.pop('huc')) \n", + "tt.insert(1, 'model_id', tt.pop('model_id')) \n", + "tt.insert(2, 'us_reach_id', tt.pop('us_reach_id')) \n", + "tt.insert(3, 'us_network_to_id', tt.pop('us_network_to_id')) \n", + "tt.insert(4, 'ds_reach_id', tt.pop('ds_reach_id')) \n", + "tt.insert(5, 'ds_network_to_id', tt.pop('ds_network_to_id')) \n", + "tt.insert(6, 'river_reach_rs', tt.pop('river_reach_rs')) \n", + "tt.insert(7, 'thalweg', tt.pop('thalweg')) \n", + "tt.insert(8, 'xs_max_elevation', tt.pop('xs_max_elevation')) \n", + "tt.insert(9, 'river_station', tt.pop('river_station')) \n", + "tt.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "eb406b80-c188-47ec-8025-2446f54e0575", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_idriver_reach_rsthalwegxs_max_elevationriver_station...right_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometry
007130010sa_11380162613801634NoneNoneBapCr Reach1 48077.8743.50756.1148077.80...207.08207.08Type RM Length L Ch R = 1 ,48077.80,207.08,207...[(0.0, 754.17), (3.42, 753.96), (6.85, 753.88)...['99.27', '143.86']673607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (2120750.469 1421598.418, 2120758.2...
107130010sa_1NoneNoneNoneNoneBapCr Reach1 47870.72742.54755.7447870.72...259.78259.78Type RM Length L Ch R = 1 ,47870.72,259.78,259...[(0.0, 755.74), (6.92, 755.26), (10.38, 755.11...['65.75', '107.27']582607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (2120687.969 1421489.043, 2120560.3...
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2 rows × 24 columns

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" + ], + "text/plain": [ + " huc model_id us_reach_id us_network_to_id ds_reach_id \\\n", + "0 07130010 sa_1 13801626 13801634 None \n", + "1 07130010 sa_1 None None None \n", + "\n", + " ds_network_to_id river_reach_rs thalweg xs_max_elevation \\\n", + "0 None BapCr Reach1 48077.8 743.50 756.11 \n", + "1 None BapCr Reach1 47870.72 742.54 755.74 \n", + "\n", + " river_station ... right_reach_length channel_reach_length \\\n", + "0 48077.80 ... 207.08 207.08 \n", + "1 47870.72 ... 259.78 259.78 \n", + "\n", + " ras_data \\\n", + "0 Type RM Length L Ch R = 1 ,48077.80,207.08,207... \n", + "1 Type RM Length L Ch R = 1 ,47870.72,259.78,259... \n", + "\n", + " station_elevation_points bank_stations \\\n", + "0 [(0.0, 754.17), (3.42, 753.96), (6.85, 753.88)... ['99.27', '143.86'] \n", + "1 [(0.0, 755.74), (6.92, 755.26), (10.38, 755.11... ['65.75', '107.27'] \n", + "\n", + " number_of_station_elevation_points number_of_coords \\\n", + "0 67 3 \n", + "1 58 2 \n", + "\n", + " flows profile_names \\\n", + "0 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", + "1 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", + "\n", + " geometry \n", + "0 LINESTRING (2120750.469 1421598.418, 2120758.2... \n", + "1 LINESTRING (2120687.969 1421489.043, 2120560.3... \n", + "\n", + "[2 rows x 24 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario II\n", + "model_id = 'sa_1'\n", + "huc_num = '07130010'\n", + "\n", + "tt = model_id2gpkg_map[(model_id, huc_num)]['XS']\n", + "tt.insert(0, 'huc', tt.pop('huc')) \n", + "tt.insert(1, 'model_id', tt.pop('model_id')) \n", + "tt.insert(2, 'us_reach_id', tt.pop('us_reach_id')) \n", + "tt.insert(3, 'us_network_to_id', tt.pop('us_network_to_id')) \n", + "tt.insert(4, 'ds_reach_id', tt.pop('ds_reach_id')) \n", + "tt.insert(5, 'ds_network_to_id', tt.pop('ds_network_to_id')) \n", + "tt.insert(6, 'river_reach_rs', tt.pop('river_reach_rs')) \n", + "tt.insert(7, 'thalweg', tt.pop('thalweg')) \n", + "tt.insert(8, 'xs_max_elevation', tt.pop('xs_max_elevation')) \n", + "tt.insert(9, 'river_station', tt.pop('river_station')) \n", + "tt.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f65f2e37-c7b3-4061-8990-b0f73a98e01e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_idriver_reach_rsthalwegxs_max_elevationriver_station...right_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometry
018070204BorregoCyn_ExistingNoneNoneNoneNoneBorrego Cyn Reach 1 US 1345.913681.12697.361345.913...149.24148.80Type RM Length L Ch R = 1 ,1345.913,155.13,148...[(0.0, 692.38), (121.89, 691.88), (205.0, 692....['205', '255']2321126.0PF 1LINESTRING (6127868.943 2194456.933, 6127820.9...
118070204BorregoCyn_ExistingNoneNoneNoneNoneBorrego Cyn Reach 1 US 1197.11680.18697.561197.110...200.51200.29Type RM Length L Ch R = 1 ,1197.11 ,203.67,200...[(0.0, 692.64), (14.99, 692.64), (16.4, 692.64...['164', '256']3721126.0PF 1LINESTRING (6127720.709 2194447.582, 6127672.7...
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2 rows × 24 columns

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" + ], + "text/plain": [ + " huc model_id us_reach_id us_network_to_id ds_reach_id \\\n", + "0 18070204 BorregoCyn_Existing None None None \n", + "1 18070204 BorregoCyn_Existing None None None \n", + "\n", + " ds_network_to_id river_reach_rs thalweg \\\n", + "0 None Borrego Cyn Reach 1 US 1345.913 681.12 \n", + "1 None Borrego Cyn Reach 1 US 1197.11 680.18 \n", + "\n", + " xs_max_elevation river_station ... right_reach_length \\\n", + "0 697.36 1345.913 ... 149.24 \n", + "1 697.56 1197.110 ... 200.51 \n", + "\n", + " channel_reach_length ras_data \\\n", + "0 148.80 Type RM Length L Ch R = 1 ,1345.913,155.13,148... \n", + "1 200.29 Type RM Length L Ch R = 1 ,1197.11 ,203.67,200... \n", + "\n", + " station_elevation_points bank_stations \\\n", + "0 [(0.0, 692.38), (121.89, 691.88), (205.0, 692.... ['205', '255'] \n", + "1 [(0.0, 692.64), (14.99, 692.64), (16.4, 692.64... ['164', '256'] \n", + "\n", + " number_of_station_elevation_points number_of_coords flows profile_names \\\n", + "0 23 2 1126.0 PF 1 \n", + "1 37 2 1126.0 PF 1 \n", + "\n", + " geometry \n", + "0 LINESTRING (6127868.943 2194456.933, 6127820.9... \n", + "1 LINESTRING (6127720.709 2194447.582, 6127672.7... \n", + "\n", + "[2 rows x 24 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario III\n", + "model_id = 'BorregoCyn_Existing'\n", + "huc_num = '18070204'\n", + "\n", + "tt = model_id2gpkg_map[(model_id, huc_num)]['XS']\n", + "tt.insert(0, 'huc', tt.pop('huc')) \n", + "tt.insert(1, 'model_id', tt.pop('model_id')) \n", + "tt.insert(2, 'us_reach_id', tt.pop('us_reach_id')) \n", + "tt.insert(3, 'us_network_to_id', tt.pop('us_network_to_id')) \n", + "tt.insert(4, 'ds_reach_id', tt.pop('ds_reach_id')) \n", + "tt.insert(5, 'ds_network_to_id', tt.pop('ds_network_to_id')) \n", + "tt.insert(6, 'river_reach_rs', tt.pop('river_reach_rs')) \n", + "tt.insert(7, 'thalweg', tt.pop('thalweg')) \n", + "tt.insert(8, 'xs_max_elevation', tt.pop('xs_max_elevation')) \n", + "tt.insert(9, 'river_station', tt.pop('river_station')) \n", + "tt.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "28c8bfb6-9cf9-4215-9bcd-605dcd0797c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_idriver_reach_rsthalwegxs_max_elevationriver_station...right_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometry
017060305cottonwood_creek_ras2359728323597281NoneNonecottonwood_creek 874 89457.283764.9973862.72489457.28...99.942199.9421Type RM Length L Ch R = 1 ,89457.28,99.942100,...[(9649.015, 3806.544), (9649.124, 3806.528), (...['9649.015', '10350.99']86892.0PF 1LINESTRING (2462395.976 1602716.665, 2462389.5...
117060305cottonwood_creek_rasNoneNoneNoneNonecottonwood_creek 874 89357.333762.6663867.24889357.33...99.942199.9421Type RM Length L Ch R = 1 ,89357.33,99.942100,...[(9649.021, 3808.923), (9652.402, 3808.42), (9...['9649.021', '10350.98']83992.0PF 1LINESTRING (2462481.133 1602698.905, 2462480.8...
\n", + "

2 rows × 24 columns

\n", + "
" + ], + "text/plain": [ + " huc model_id us_reach_id us_network_to_id ds_reach_id \\\n", + "0 17060305 cottonwood_creek_ras 23597283 23597281 None \n", + "1 17060305 cottonwood_creek_ras None None None \n", + "\n", + " ds_network_to_id river_reach_rs thalweg xs_max_elevation \\\n", + "0 None cottonwood_creek 874 89457.28 3764.997 3862.724 \n", + "1 None cottonwood_creek 874 89357.33 3762.666 3867.248 \n", + "\n", + " river_station ... right_reach_length channel_reach_length \\\n", + "0 89457.28 ... 99.9421 99.9421 \n", + "1 89357.33 ... 99.9421 99.9421 \n", + "\n", + " ras_data \\\n", + "0 Type RM Length L Ch R = 1 ,89457.28,99.942100,... \n", + "1 Type RM Length L Ch R = 1 ,89357.33,99.942100,... \n", + "\n", + " station_elevation_points \\\n", + "0 [(9649.015, 3806.544), (9649.124, 3806.528), (... \n", + "1 [(9649.021, 3808.923), (9652.402, 3808.42), (9... \n", + "\n", + " bank_stations number_of_station_elevation_points \\\n", + "0 ['9649.015', '10350.99'] 86 \n", + "1 ['9649.021', '10350.98'] 83 \n", + "\n", + " number_of_coords flows profile_names \\\n", + "0 8 92.0 PF 1 \n", + "1 9 92.0 PF 1 \n", + "\n", + " geometry \n", + "0 LINESTRING (2462395.976 1602716.665, 2462389.5... \n", + "1 LINESTRING (2462481.133 1602698.905, 2462480.8... \n", + "\n", + "[2 rows x 24 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario IV\n", + "model_id = 'cottonwood_creek_ras'\n", + "huc_num = '17060305'\n", + "\n", + "tt = model_id2gpkg_map[(model_id, huc_num)]['XS']\n", + "tt.insert(0, 'huc', tt.pop('huc')) \n", + "tt.insert(1, 'model_id', tt.pop('model_id')) \n", + "tt.insert(2, 'us_reach_id', tt.pop('us_reach_id')) \n", + "tt.insert(3, 'us_network_to_id', tt.pop('us_network_to_id')) \n", + "tt.insert(4, 'ds_reach_id', tt.pop('ds_reach_id')) \n", + "tt.insert(5, 'ds_network_to_id', tt.pop('ds_network_to_id')) \n", + "tt.insert(6, 'river_reach_rs', tt.pop('river_reach_rs')) \n", + "tt.insert(7, 'thalweg', tt.pop('thalweg')) \n", + "tt.insert(8, 'xs_max_elevation', tt.pop('xs_max_elevation')) \n", + "tt.insert(9, 'river_station', tt.pop('river_station')) \n", + "tt.head(2)\n", + "#tt.tail()" + ] + }, + { + "cell_type": "markdown", + "id": "dcbab67e-335d-4d5c-89b6-892228fd1e67", + "metadata": {}, + "source": [ + "## Demo IVa: \n", + "### Per HUC, save & verify each river's XS data as parquet & geopackage" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ccc7bb03-6669-4bca-8f2e-161f683c6b9a", + "metadata": {}, + "outputs": [], + "source": [ + "# # # Scenario I\n", + "# # Read Parquet file\n", + "# df1 = pd.read_parquet('/home/sylvia.chin/Desktop/iceberg/xs_data/huc_07130010/BapCr.parquet',\n", + "# engine='pyarrow')\n", + "\n", + "# # Display the first few rows\n", + "# print(df1.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c907f518-7d65-490a-a31b-395fc02eaa81", + "metadata": {}, + "outputs": [], + "source": [ + "# # # Scenario II\n", + "\n", + "# # Read Parquet file\n", + "# df2 = pd.read_parquet('/home/sylvia.chin/Desktop/iceberg/xs_data/huc_01020002/Martin Stream.parquet', \n", + "# engine='pyarrow')\n", + "\n", + "# # Display the first few rows\n", + "# print(df2.head())" + ] + }, + { + "cell_type": "markdown", + "id": "bbd6c174-53b6-4472-b5a2-1ed76e736600", + "metadata": {}, + "source": [ + "## Demo IVb: \n", + "### Per HUC, save & verify each HUC's XS data as parquet & geopackage " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8304726d-8564-4f4a-9729-f3fe887757d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hucrivermodel_idus_reach_idds_reach_id
704210120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
704310120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
704410120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
704510120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
704610120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
..................
722510120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
722610120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
722710120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
722810120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
722910120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
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188 rows × 5 columns

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" + ], + "text/plain": [ + " huc river model_id \\\n", + "7042 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", + "7043 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", + "7044 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", + "7045 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", + "7046 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", + "... ... ... ... \n", + "7225 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", + "7226 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", + "7227 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", + "7228 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", + "7229 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", + "\n", + " us_reach_id ds_reach_id \n", + "7042 None None \n", + "7043 None None \n", + "7044 None None \n", + "7045 None None \n", + "7046 None None \n", + "... ... ... \n", + "7225 None None \n", + "7226 None None \n", + "7227 None None \n", + "7228 None None \n", + "7229 None None \n", + "\n", + "[188 rows x 5 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# # Scenario I\n", + "# Read Parquet file\n", + "df1 = pd.read_parquet(f'/{os.getcwd()}/xs_data/mip_10120203/huc_10120203.parquet',\n", + " engine='pyarrow')\n", + "\n", + "#set(df1['river'])\n", + "#set(df1['huc'])\n", + "df1[[\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\"]]" + ] + }, + { + "cell_type": "markdown", + "id": "32a5e23d-ba5e-45fc-ab95-a894d69f70a3", + "metadata": {}, + "source": [ + "#### Cross-checking iceberg table with parquet @ huc_10120203\n", + "\n", + "huc: [[\"10120203\",\"10120203\",\"10120203\",\"10120203\",\"10120203\",...,\"10120203\",\"10120203\",\"10120203\",\"10120203\",\"10120203\"]]\n", + "model_id: [[\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",...,\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\"]]\n", + "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "us_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "ds_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "__index_level_0__: [[7042,7043,7044,7045,7046,...,7225,7226,7227,7228,7229]]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a5806256-a6a2-4e47-b206-cd1c4d378ebb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hucrivermodel_idus_reach_idds_reach_id
1917817060305cottonwood_creekcottonwood_creek_1_ras23597201None
1917917060305cottonwood_creekcottonwood_creek_1_rasNoneNone
1918017060305cottonwood_creekcottonwood_creek_1_ras2359719923597201
1918117060305cottonwood_creekcottonwood_creek_1_rasNoneNone
1918217060305cottonwood_creekcottonwood_creek_1_rasNoneNone
..................
2440317060305south_fork_clearsouth_fork_clearwater_river_1_rasNoneNone
2440417060305south_fork_clearsouth_fork_clearwater_river_1_rasNoneNone
2440517060305south_fork_clearsouth_fork_clearwater_river_1_rasNoneNone
2440617060305south_fork_clearsouth_fork_clearwater_river_1_rasNoneNone
2440717060305south_fork_clearsouth_fork_clearwater_river_1_rasNone23597347
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5230 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " huc river model_id \\\n", + "19178 17060305 cottonwood_creek cottonwood_creek_1_ras \n", + "19179 17060305 cottonwood_creek cottonwood_creek_1_ras \n", + "19180 17060305 cottonwood_creek cottonwood_creek_1_ras \n", + "19181 17060305 cottonwood_creek cottonwood_creek_1_ras \n", + "19182 17060305 cottonwood_creek cottonwood_creek_1_ras \n", + "... ... ... ... \n", + "24403 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", + "24404 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", + "24405 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", + "24406 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", + "24407 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", + "\n", + " us_reach_id ds_reach_id \n", + "19178 23597201 None \n", + "19179 None None \n", + "19180 23597199 23597201 \n", + "19181 None None \n", + "19182 None None \n", + "... ... ... \n", + "24403 None None \n", + "24404 None None \n", + "24405 None None \n", + "24406 None None \n", + "24407 None 23597347 \n", + "\n", + "[5230 rows x 5 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario II\n", + "# Read Parquet file\n", + "df2 = pd.read_parquet(f'/{os.getcwd()}/xs_data/mip_17060305/huc_17060305.parquet',\n", + " engine='pyarrow')\n", + "\n", + "#set(df2['river'])\n", + "#set(df2['huc'])\n", + "df2[[\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\"]]" + ] + }, + { + "cell_type": "markdown", + "id": "8784d040-695c-421a-85bf-27f585b4186e", + "metadata": {}, + "source": [ + "#### Cross-checking iceberg table with parquet @ huc_17060305\n", + "\n", + "huc: [[\"17060305\",\"17060305\",\"17060305\",\"17060305\",\"17060305\",...,\"17060305\",\"17060305\",\"17060305\",\"17060305\",\"17060305\"]]\n", + "model_id: [[\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",...,\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\"]]\n", + "us_reach_id: [[\"23597201\",\"None\",\"23597199\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "ds_reach_id: [[\"None\",\"None\",\"23597201\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"23597347\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "abc4da53-c0d5-45a1-a0d6-0c4ee28df1b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hucrivermodel_idus_reach_idds_reach_id
307871504000712421242_rasNoneNone
307881504000712421242_rasNoneNone
307891504000712421242_rasNoneNone
307901504000712421242_rasNoneNone
307911504000712421242_rasNoneNone
..................
309661504000712361236_ras2129430921295551
309671504000712361236_rasNoneNone
309681504000712361236_rasNoneNone
309691504000712361236_rasNoneNone
309701504000712361236_rasNone21294309
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184 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " huc river model_id us_reach_id ds_reach_id\n", + "30787 15040007 1242 1242_ras None None\n", + "30788 15040007 1242 1242_ras None None\n", + "30789 15040007 1242 1242_ras None None\n", + "30790 15040007 1242 1242_ras None None\n", + "30791 15040007 1242 1242_ras None None\n", + "... ... ... ... ... ...\n", + "30966 15040007 1236 1236_ras 21294309 21295551\n", + "30967 15040007 1236 1236_ras None None\n", + "30968 15040007 1236 1236_ras None None\n", + "30969 15040007 1236 1236_ras None None\n", + "30970 15040007 1236 1236_ras None 21294309\n", + "\n", + "[184 rows x 5 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Scenario III\n", + "# Read Parquet file\n", + "df3 = pd.read_parquet(f'/{os.getcwd()}/xs_data/mip_15040007/huc_15040007.parquet',\n", + " engine='pyarrow')\n", + "\n", + "#set(df2['river'])\n", + "#set(df2['huc'])\n", + "df3[[\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\"]]" + ] + }, + { + "cell_type": "markdown", + "id": "d576de82-bed6-4d89-953f-cde2eeb7ba81", + "metadata": {}, + "source": [ + "#### Cross-checking iceberg table with parquet @ huc_15040007\n", + "\n", + "huc: [[\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\",...,\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\"]]\n", + "model_id: [[\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",...,\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\"]]\n", + "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"None\"]]\n", + "us_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294311\",\"None\",\"None\",\"None\",\"None\"]]\n", + "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21295551\",\"None\",\"None\",\"None\",\"21294309\"]]\n", + "ds_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"21294311\"]]\n", + "__index_level_0__: [[30787,30788,30789,30790,30791,...,30966,30967,30968,30969,30970]]" + ] + }, + { + "cell_type": "markdown", + "id": "a4738ea4-8890-4f50-a372-a66eb5865830", + "metadata": { + "scrolled": true + }, + "source": [ + "## Demo Va: \n", + "### Transform parquet into Iceberg tables via calling iceberg_xs_table.py (scenario for sourcing parquet from local)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a2bf23b0-3449-43e9-a461-934a1fabd0d7", + "metadata": {}, + "outputs": [], + "source": [ + "from iceberg_table import *\n", + "\n", + "# Instantiate iceberg class\n", + "iceberg_obj = IcebergTable()\n", + "\n", + "# Extract list of parquets\n", + "file_list = iceberg_obj.read_data_dirs(data_dir=f'{os.getcwd()}/xs_data/')\n", + "parquet_list = [file for file in file_list if file.endswith('.parquet')]\n", + " \n", + "#Establish new Iceberg catalog for XS data\n", + "iceberg_obj.establish_catalog(catalog_name='xs',\n", + " namespace='mip')\n", + "\n", + "# Generate tables w/in the new Iceberg catalog for all parquets detected \n", + "iceberg_obj.create_table_for_all_parquets(parquet_list,\n", + " app_name='mip_xs')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "39a187bf-cfba-4aaa-9c8f-69146b699307", + "metadata": {}, + "source": [ + "## Demo Vb: \n", + "### Establish A New Iceberg Catalog & Generate Tables For All Parquets in S3 (scenario for sourcing parquet from s3)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5082de29-3548-48e6-a741-ddd6342d3341", + "metadata": {}, + "outputs": [], + "source": [ + "# # Instantiate class\n", + "# obj = IcebergTable()\n", + "\n", + "# # Establish new Iceberg catalog\n", + "# obj.establish_catalog(catalog_name='xs_dev2',\n", + "# namespace='mip')\n", + "\n", + "# # Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", + "# obj.create_table_for_all_s3parquets(app_name='mip_xs',\n", + "# bucket_name='ngwpc-xs',\n", + "# profile_name='default')" + ] + }, + { + "attachments": { + "ca580451-2f38-43d8-8a8e-7e8767aedc95.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "365d1563-c753-4b72-a202-04cf8c7a95d8", + "metadata": {}, + "source": [ + "## Demo V Result:\n", + "![image.png](attachment:ca580451-2f38-43d8-8a8e-7e8767aedc95.png)" + ] + }, + { + "cell_type": "markdown", + "id": "f20bbfc9-6806-434d-afbe-45fd932105a2", + "metadata": {}, + "source": [ + "### Query XS by HUC & additional attributes of interest @ a given HUC from Iceberg Tables" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "7bf79944-1da0-4aa2-a6d3-c6f9a976e3ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pyarrow.Table\n", + "river: large_string\n", + "reach: large_string\n", + "river_reach: large_string\n", + "river_station: double\n", + "river_reach_rs: large_string\n", + "thalweg: double\n", + "xs_max_elevation: double\n", + "left_reach_length: double\n", + "right_reach_length: double\n", + "channel_reach_length: double\n", + "ras_data: large_string\n", + "station_elevation_points: large_string\n", + "bank_stations: large_string\n", + "number_of_station_elevation_points: int64\n", + "number_of_coords: int64\n", + "flows: large_string\n", + "profile_names: large_string\n", + "geometry: large_binary\n", + "huc: large_string\n", + "model_id: large_string\n", + "us_reach_id: large_string\n", + "us_network_to_id: large_string\n", + "ds_reach_id: large_string\n", + "ds_network_to_id: large_string\n", + "__index_level_0__: int64\n", + "----\n", + "river: [[\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",...,\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\"]]\n", + "reach: [[\"Breakout Channel\",\"Breakout Channel\",\"Breakout Channel\",\"Breakout Channel\",\"Breakout Channel\",...,\"Old US14 Breakou\",\"Old US14 Breakou\",\"Old US14 Breakou\",\"Old US14 Breakou\",\"Old US14 Breakou\"]]\n", + "river_reach: [[\"Spearfish Creek ,Breakout Channel\",\"Spearfish Creek ,Breakout Channel\",\"Spearfish Creek ,Breakout Channel\",\"Spearfish Creek ,Breakout Channel\",\"Spearfish Creek ,Breakout Channel\",...,\"Spearfish Creek ,Old US14 Breakou\",\"Spearfish Creek ,Old US14 Breakou\",\"Spearfish Creek ,Old US14 Breakou\",\"Spearfish Creek ,Old US14 Breakou\",\"Spearfish Creek ,Old US14 Breakou\"]]\n", + "river_station: [[733,665,553,356,254,...,1796,1502,1236,814,253]]\n", + "river_reach_rs: [[\"Spearfish Creek Breakout Channel 733.0\",\"Spearfish Creek Breakout Channel 665.0\",\"Spearfish Creek Breakout Channel 553.0\",\"Spearfish Creek Breakout Channel 356.0\",\"Spearfish Creek Breakout Channel 254.0\",...,\"Spearfish Creek Old US14 Breakou 1796.0\",\"Spearfish Creek Old US14 Breakou 1502.0\",\"Spearfish Creek Old US14 Breakou 1236.0\",\"Spearfish Creek Old US14 Breakou 814.0\",\"Spearfish Creek Old US14 Breakou 253.0\"]]\n", + "thalweg: [[3563.87,3562.9,3561.47,3558.17,3557.05,...,3488.63,3486.35,3484.18,3481.92,3477.64]]\n", + "xs_max_elevation: [[3574.46,3573.83,3570.78,3568.95,3566,...,3499.78,3496.02,3490.04,3488,3482]]\n", + "left_reach_length: [[73.3,113.86,195.76,101.29,62.19,...,289.59,293.56,321.77,551.53,0]]\n", + "right_reach_length: [[64.03,108.6,199.45,101.26,67.59,...,304.33,193.56,284.8,570.49,0]]\n", + "channel_reach_length: [[68.79,111.37,197.46,101.84,65.19,...,293.78,265.51,422.78,560.74,0]]\n", + "ras_data: [[\"Type RM Length L Ch R = 1 ,733 ,73.3,68.79,64.03\n", + "XS GIS Cut Line=6\n", + "960048.214101717268250.980046898960110.800918803268271.043983147\n", + " 960143.17913489 268329.91263181960142.516406551268354.240339056\n", + "960184.587844715268375.098565057960362.015967548 268336.91753906\n", + "Node Last Edited Time=Sep/04/2015 10:51:51\n", + "#Sta/Elev= 134 \n", + " 0 3574.41 2.99 3574.46 5.97 3574.38 8.96 3574.3 11.95 3574.26\n", + " 14.94 3574.29 17.92 3574.21 20.91 3574.12 23.9 3574.1 26.89 3574.15\n", + " 29.87 3574.2 32.86 3574.21 35.85 3574.1 38.84 3573.99 41.82 3574.02\n", + " 44.81 3573.91 47.8 3573.74 50.79 3573.66 53.77 3573.6 56.76 3573.39\n", + " 59.75 3573.24 62.74 3573.13 65.72 3572.96 68.65 3572.78 71.57 3572.64\n", + " 74.49 3572.58 77.41 3572.28 80.33 3572.15 83.25 3572.04 86.17 3571.89\n", + " 89.09 3571.76 92.01 3571.57 94.94 3571.33 97.86 3571.17 100.78 3571.08\n", + " 103.7 3570.9 106.62 3570.73 109.54 3570.64 112.46 3570.53 115.38 3570.39\n", + " 118.3 3570.28 121.23 3570.09 124.15 3569.84 127.07 3569.54 129.99 3569.35\n", + " 132.91 3569.06 135.61 3568.76 138.32 3568.55 141.02 3568.31 143.73 3568.06\n", + " 146.43 3567.8 149.13 3567.56 151.84 3567.36 154.54 3567.21 157.25 3567.02\n", + " 160.18 3566.79 163.12 3566.64 166.05 3566.52 166.06 3566.52 168.99 3566.11\n", + " 171.92 3565.19 174.86 3564.5 177.79 3564.13 180.73 3563.93 183.66 3563.87\n", + " 186.6 3563.95 189.53 3564.27 192.46 3564.58 193.82 3564.71 195.4 3564.86\n", + " 198.33 3565.06 201.27 3565.12 204.2 3565 207.18 3565.01 210.15 3565.06\n", + " 213.13 3565.15 216.11 3565.32 219.08 3565.42 222.06 3565.53 225.03 3565.65\n", + " 228.01 3565.71 230.98 3565.72 233.96 3565.74 236.93 3565.77 239.91 3565.88\n", + " 242.88 3566.02 245.86 3566.22 248.83 3566.48 251.81 3566.79 254.78 3567.09\n", + " 257.76 3567.32 260.73 3567.56 263.71 3567.78 266.68 3568.01 269.66 3568.22\n", + " 272.64 3568.35 275.61 3568.51 278.59 3568.61 281.56 3568.69 284.54 3568.74\n", + " 287.51 3568.86 290.49 3569.03 293.46 3569.18 296.44 3569.26 299.41 3569.36\n", + " 302.39 3569.47 305.36 3569.52 308.34 3569.63 311.31 3569.71 314.29 3569.84\n", + " 317.26 3569.98 320.24 3570.07 323.21 3570.1 326.19 3570.23 329.16 3570.3\n", + " 332.14 3570.47 335.12 3570.49 338.09 3570.6 341.07 3570.65 344.04 3570.73\n", + " 347.02 3570.84 349.99 3570.94 352.97 3571.02 355.94 3571 358.92 3570.94\n", + " 361.89 3570.85 364.87 3570.83 367.84 3571.01 370.82 3571.24 373.79 3571.35\n", + " 376.77 3571.52 379.74 3571.52 382.72 3571.53 385.69 3571.63\n", + "#Mann= 3 , 0 , 0 \n", + " 0 .05 0 171.92 .035 0 201.27 .05 0\n", + "Bank Sta=171.92,201.27\n", + "XS Rating Curve= 0 ,0\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,665 ,113.86,111.37,108.6\n", + "XS GIS Cut Line=4\n", + "959963.651606798268352.968359977960118.028922722268425.752335146\n", + "960197.031061307268438.418320313960326.476996645268399.388870806\n", + "Node Last Edited Time=Sep/17/2015 21:01:33\n", + "#Sta/Elev= 133 \n", + " 0 3573.83 2.99 3573.73 5.99 3573.64 8.98 3573.59 11.98 3573.52\n", + " 14.97 3573.43 17.97 3573.35 20.96 3573.27 23.95 3573.19 26.95 3573.15\n", + " 29.94 3573.07 32.94 3573 35.93 3572.88 38.93 3572.75 41.92 3572.6\n", + " 44.91 3572.47 47.91 3572.33 50.9 3572.27 53.9 3572.11 56.89 3572.04\n", + " 59.89 3571.88 62.88 3571.78 65.87 3571.61 68.87 3571.5 71.86 3571.43\n", + " 74.86 3571.35 77.85 3571.19 80.85 3571.02 83.84 3570.89 86.83 3570.71\n", + " 89.83 3570.61 92.82 3570.53 95.82 3570.4 98.81 3570.36 101.81 3570.32\n", + " 104.8 3570.21 107.79 3570.06 110.79 3569.88 113.78 3569.74 116.78 3569.53\n", + " 119.77 3569.46 122.77 3569.35 125.76 3569.2 128.75 3569 131.75 3568.93\n", + " 134.74 3568.8 137.74 3568.58 140.73 3568.35 143.73 3568.31 146.72 3568.24\n", + " 149.71 3568.16 152.71 3568.02 155.7 3567.87 158.7 3567.75 161.69 3567.67\n", + " 164.69 3567.51 167.68 3567.23 170.67 3567.03 173.64 3566.75 176.6 3566.31\n", + " 179.56 3565.72 182.53 3565.25 184.9 3564.95 185.49 3564.87 188.46 3564.75\n", + " 191.42 3564.48 194.38 3564.03 197.35 3563.46 200.31 3563.18 203.27 3562.95\n", + " 206.24 3562.9 209.2 3562.95 212.16 3563.24 215.13 3563.64 218.09 3563.91\n", + " 221.05 3564.27 222.45 3564.39 224.02 3564.52 226.98 3564.69 229.94 3564.95\n", + " 232.91 3565.16 235.87 3565.3 238.83 3565.39 241.8 3565.53 244.76 3565.61\n", + " 247.72 3565.66 250.69 3565.9 253.62 3566.02 256.56 3566.12 259.5 3566.15\n", + " 262.44 3566.16 265.38 3566.21 268.32 3566.27 271.26 3566.39 274.2 3566.52\n", + " 277.14 3566.55 280.08 3566.59 283.02 3566.71 285.96 3566.87 288.9 3567.05\n", + " 291.83 3567.22 294.77 3567.39 297.71 3567.51 300.65 3567.66 303.59 3567.79\n", + " 306.53 3567.85 309.47 3567.92 312.41 3568.06 315.35 3568.16 318.29 3568.19\n", + " 321.23 3568.21 324.16 3568.2 327.1 3568.32 330.04 3568.45 332.98 3568.68\n", + " 335.92 3568.85 338.86 3569.06 341.8 3569.2 344.74 3569.26 347.68 3569.32\n", + " 350.62 3569.44 353.56 3569.58 356.5 3569.64 359.43 3569.78 362.37 3569.94\n", + " 365.31 3570.04 368.25 3569.95 371.19 3570.05 374.13 3570.09 377.07 3570.2\n", + " 380.01 3570.18 382.95 3570.13 385.89 3570.06\n", + "#Mann= 3 , 0 , 0 \n", + " 0 .035 0 191.42 .016 0 222.45 .035 0\n", + "Bank Sta=191.42,222.45\n", + "XS Rating Curve= 0 ,0\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,553 ,195.76,197.46,199.45\n", + "XS GIS Cut Line=5\n", + " 959968.78676714268478.844748646960110.713976726 268541.34462364\n", + "960190.140983224268542.646786392960266.963992298268490.563557222\n", + "960335.974352971268474.938260391\n", + "Node Last Edited Time=Sep/17/2015 21:02:28\n", + "#Sta/Elev= 137 \n", + " 0 3570.7 2.98 3570.67 5.96 3570.54 8.95 3570.46 11.93 3570.37\n", + " 14.91 3570.31 17.89 3570.32 20.88 3570.41 23.86 3570.38 26.84 3570.33\n", + " 29.82 3570.23 32.81 3570.09 35.79 3570.01 38.77 3569.89 41.75 3569.84\n", + " 44.73 3569.74 47.72 3569.6 50.7 3569.57 53.68 3569.5 56.66 3569.43\n", + " 59.65 3569.35 62.63 3569.29 65.61 3569.25 68.59 3569.22 71.57 3569.13\n", + " 74.56 3569.04 77.54 3568.99 80.52 3568.82 83.5 3568.78 86.49 3568.64\n", + " 89.47 3568.44 92.45 3568.22 95.43 3568.09 98.42 3568.07 101.4 3567.9\n", + " 104.38 3567.66 107.36 3567.43 110.34 3567.29 113.33 3567.14 116.31 3567.01\n", + " 119.29 3566.81 122.27 3566.66 125.26 3566.47 128.24 3566.27 131.22 3566.08\n", + " 134.2 3565.8 137.19 3565.57 140.17 3565.64 143.15 3565.47 146.13 3565.26\n", + " 149.11 3565.11 152.1 3564.97 155.08 3564.75 158.02 3564.61 160.96 3564.31\n", + " 163.91 3563.79 166.85 3563.39 168.12 3563.29 169.79 3563.16 172.73 3562.91\n", + " 175.67 3562.73 178.62 3562.48 181.56 3562.03 184.5 3561.71 187.44 3561.54\n", + " 190.38 3561.47 193.33 3561.53 196.27 3561.93 199.21 3562.46 202.15 3562.81\n", + " 204.26 3562.99 205.1 3563.06 208.04 3563.26 210.98 3563.46 213.92 3563.65\n", + " 216.86 3563.83 219.81 3564.03 222.75 3564.18 225.69 3564.38 228.63 3564.53\n", + " 231.57 3564.53 234.52 3564.51 237.51 3564.54 240.5 3564.69 243.5 3564.75\n", + " 246.49 3564.81 249.49 3564.9 252.48 3565.03 255.47 3565.14 258.47 3565.19\n", + " 261.46 3565.27 264.46 3565.32 267.45 3565.38 270.44 3565.45 273.44 3565.56\n", + " 276.43 3565.58 279.43 3565.67 282.42 3565.78 285.41 3565.84 288.41 3565.78\n", + " 291.4 3565.67 294.4 3565.81 297.39 3566.03 300.38 3566.24 303.38 3566.34\n", + " 306.37 3566.5 309.37 3566.61 312.36 3566.65 315.35 3566.65 318.35 3566.71\n", + " 321.34 3566.81 324.34 3566.91 327.33 3566.81 330.28 3566.73 333.23 3566.61\n", + " 336.18 3566.45 339.12 3566.43 342.07 3566.56 345.02 3566.79 347.97 3567.08\n", + " 350.92 3567.41 353.86 3567.81 356.81 3568.22 359.76 3568.63 362.71 3569.06\n", + " 365.66 3569.42 368.61 3569.76 371.55 3570.05 374.5 3570.21 377.45 3570.34\n", + " 380.4 3570.39 383.35 3570.54 386.3 3570.67 389.24 3570.69 392.19 3570.75\n", + " 395.14 3570.75 398.09 3570.78\n", + "#Mann= 3 , 0 , 0 \n", + " 0 .035 0 172.73 .016 0 204.26 .035 0\n", + "Bank Sta=172.73,204.26\n", + "XS Rating Curve= 0 ,0\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,356 ,101.29,101.84,101.26\n", + "Node Name=Q\n", + "XS GIS Cut Line=5\n", + "959963.578444228268601.240501225960097.693005383268735.355062395\n", + "960161.495043144268743.167710811960259.151343882268653.323730394\n", + "960343.787001386 268632.49043873\n", + "Node Last Edited Time=Sep/17/2015 21:02:30\n", + "#Sta/Elev= 164 \n", + " 0 3568.28 2.96 3568.25 5.93 3568.17 8.89 3568.1 11.85 3568.07\n", + " 14.82 3567.98 17.78 3567.89 20.75 3567.93 23.71 3567.88 26.67 3567.74\n", + " 29.64 3567.71 32.6 3567.67 35.56 3567.68 38.53 3567.64 41.49 3567.61\n", + " 44.45 3567.61 47.42 3567.61 50.38 3567.64 53.34 3567.67 56.31 3567.68\n", + " 59.27 3567.71 62.23 3567.7 65.2 3567.64 68.16 3567.58 71.12 3567.6\n", + " 74.09 3567.58 77.05 3567.51 80.02 3567.41 82.98 3567.34 85.94 3567.22\n", + " 88.91 3567.11 91.87 3567.06 94.83 3566.93 97.8 3566.8 100.76 3566.71\n", + " 103.72 3566.64 106.69 3566.52 109.65 3566.42 112.61 3566.36 115.58 3566.32\n", + " 118.54 3566.31 121.51 3566.28 124.47 3566.29 127.43 3566.23 130.4 3566.19\n", + " 133.36 3566.08 136.32 3565.96 139.29 3565.79 142.25 3565.65 145.21 3565.53\n", + " 148.18 3565.37 151.14 3565.17 154.1 3565 157.07 3564.95 160.03 3564.88\n", + " 162.99 3564.73 165.96 3564.68 168.92 3564.61 171.89 3564.38 174.85 3564.18\n", + " 177.81 3563.99 180.78 3563.8 183.74 3563.5 186.7 3563.23 189.67 3562.81\n", + " 192.59 3562.07 195.51 3561.34 198.43 3560.82 201.35 3560.51 201.51 3560.49\n", + " 204.28 3560.21 207.2 3560.04 210.12 3559.89 213.04 3559.3 215.96 3558.68\n", + " 218.88 3558.47 221.81 3558.17 224.73 3558.18 227.65 3558.32 230.57 3558.75\n", + " 233.49 3559.13 234.76 3559.3 236.41 3559.53 239.34 3559.91 242.26 3560.17\n", + " 245.18 3560.32 248.1 3560.49 251.02 3560.57 253.95 3560.62 256.89 3560.76\n", + " 259.84 3560.95 262.79 3561.15 265.74 3561.33 268.69 3561.52 271.64 3561.62\n", + " 274.59 3561.61 277.54 3561.62 280.48 3561.66 283.43 3561.74 286.38 3561.89\n", + " 289.33 3561.98 292.28 3562.06 295.23 3562.19 298.18 3562.2 301.13 3562.24\n", + " 304.08 3562.3 307.02 3562.34 309.97 3562.36 312.92 3562.42 315.87 3562.49\n", + " 318.82 3562.57 321.77 3562.71 324.72 3562.9 327.67 3563 330.62 3563.11\n", + " 333.56 3563.23 336.51 3563.44 339.46 3563.69 342.41 3563.88 345.36 3563.88\n", + " 348.31 3563.89 351.26 3563.94 354.21 3564.01 357.15 3564.13 360.1 3564.19\n", + " 363.05 3564.19 366 3564.22 368.95 3564.31 371.9 3564.44 374.85 3564.69\n", + " 377.8 3564.71 380.75 3564.65 383.69 3564.69 386.64 3564.81 389.55 3564.91\n", + " 392.45 3564.81 395.36 3564.75 398.26 3564.81 401.17 3564.45 404.08 3564.39\n", + " 406.98 3564.28 409.89 3564.27 412.79 3564.45 415.7 3564.69 418.6 3565.01\n", + " 421.51 3565.34 424.41 3565.59 427.32 3565.81 430.22 3566.07 433.13 3566.35\n", + " 436.03 3566.59 438.94 3566.74 441.85 3567.05 444.75 3567.19 447.66 3567.22\n", + " 450.56 3567.28 453.47 3567.38 456.37 3567.41 459.28 3567.47 462.18 3567.43\n", + " 465.09 3567.47 467.99 3568.3 470.9 3568.92 473.8 3568.95\n", + "#Mann= 3 , 0 , 0 \n", + " 0 .035 0 213.04 .016 0 234.76 .035 0\n", + "Bank Sta=213.04,234.76\n", + "XS Rating Curve= 0 ,0\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,254 ,62.19,65.19,67.59\n", + "XS GIS Cut Line=7\n", + "960051.049069807268716.112318724960078.876770049 268794.23322548\n", + "960092.627726808268835.981501564960164.099368632268846.032334477\n", + "960274.776312634268825.198714733 960325.67743562268790.328950138\n", + "960429.483335241268797.924503769\n", + "Node Last Edited Time=Sep/17/2015 21:02:32\n", + "#Sta/Elev= 131 \n", + " 0 3565.98 2.96 3565.75 5.92 3565.56 8.89 3565.4 11.85 3565.21\n", + " 14.81 3564.97 17.77 3564.71 20.73 3564.5 23.69 3564.37 26.66 3564.25\n", + " 29.62 3564.14 32.58 3564.02 35.54 3563.97 38.5 3563.87 41.46 3563.61\n", + " 44.43 3563.51 47.39 3563.55 50.35 3563.54 53.31 3563.49 56.27 3563.46\n", + " 59.24 3563.37 62.2 3563.22 65.16 3563.06 68.12 3562.89 71.08 3562.76\n", + " 74.04 3562.66 77.01 3562.58 79.97 3562.48 82.93 3562.33 85.86 3562.2\n", + " 88.79 3562.09 91.72 3562.01 94.65 3561.9 97.58 3561.86 100.51 3561.84\n", + " 103.44 3561.75 106.37 3561.71 109.3 3561.72 112.23 3561.7 115.16 3561.63\n", + " 118.09 3561.51 121.02 3561.34 123.95 3561.15 126.88 3560.98 129.77 3560.58\n", + " 132.66 3560.17 135.54 3559.8 138.43 3559.51 140.72 3559.31 141.32 3559.26\n", + " 144.21 3559.02 147.09 3558.67 149.98 3558.03 152.87 3557.32 155.75 3557.05\n", + " 158.64 3557.08 161.53 3557.14 164.41 3557.6 167.3 3558.09 167.51 3558.11\n", + " 170.19 3558.41 173.08 3558.6 175.96 3558.67 178.85 3558.82 181.74 3559.05\n", + " 184.62 3559.19 187.51 3559.28 190.4 3559.47 193.28 3559.63 196.17 3559.64\n", + " 199.06 3559.65 202.02 3559.73 204.99 3559.83 207.95 3559.89 210.91 3559.93\n", + " 213.88 3560.01 216.84 3560.13 219.8 3560.25 222.77 3560.38 225.73 3560.49\n", + " 228.7 3560.52 231.66 3560.53 234.62 3560.6 237.59 3560.65 240.55 3560.72\n", + " 243.51 3560.82 246.48 3560.9 249.44 3560.98 252.41 3561.11 255.37 3561.21\n", + " 258.33 3561.38 261.3 3561.41 264.26 3561.41 267.22 3561.53 270.19 3561.6\n", + " 273.15 3561.56 276.11 3561.67 279.08 3561.89 282.04 3562.09 285.01 3562.13\n", + " 287.97 3562.01 290.93 3562.09 293.9 3562.25 296.86 3562.29 299.82 3562.34\n", + " 302.79 3562.5 305.75 3562.61 308.72 3562.64 311.68 3562.64 314.59 3562.57\n", + " 317.51 3562.52 320.42 3562.5 323.34 3562.44 326.25 3562.41 329.17 3562.41\n", + " 332.08 3562.49 335 3562.69 337.91 3563.01 340.83 3563.15 343.74 3563.31\n", + " 346.66 3563.41 349.57 3563.54 352.49 3563.65 355.4 3563.77 358.32 3563.95\n", + " 361.23 3564.03 364.15 3564.16 367.06 3564.27 369.98 3564.34 372.89 3564.34\n", + " 478 3566\n", + "#Mann= 3 , 0 , 0 \n", + " 0 .035 0 149.98 .016 0 167.51 .035 0\n", + "Bank Sta=149.98,167.51\n", + "XS Rating Curve= 0 ,0\n", + "Exp/Cntr=0.3,0.1\n", + "\",...,\"Type RM Length L Ch R = 1 ,1796 ,289.59,293.78,304.33\n", + "Node Name=C\n", + "XS GIS Cut Line=5\n", + "957019.704410717274956.661189303957880.381495133274810.827819556\n", + "958159.027263299274750.931941971958409.027419478274678.015421137\n", + "959028.818830803274605.098572224\n", + "Node Last Edited Time=Sep/17/2015 21:28:25\n", + "#Sta/Elev= 230 \n", + " 0 3499.57 5 3499.78 11 3499.56 21 3499.33 24 3499.05\n", + " 35 3498.91 43 3498.68 48 3498.42 53 3498.67 55 3498.43\n", + " 57 3498.17 60 3498.44 71 3498.25 76 3498.04 81 3497.81\n", + " 89.99 3497.59 95.99 3497.37 102.99 3497.59 108.99 3497.35 117.99 3497.15\n", + " 123.99 3496.92 133.99 3496.78 138.99 3497.02 143.99 3496.79 146.99 3496.57\n", + " 150.99 3496.36 161.99 3496.22 172.99 3496.13 183.99 3496.17 191.99 3495.92\n", + " 202.99 3495.75 211.99 3495.54 222.99 3495.65 226.99 3495.35 237.99 3495.22\n", + " 250.98 3494.95 260.98 3494.73 272.98 3494.72 283.98 3494.61 289.98 3494.41\n", + " 300.98 3494.39 311.98 3494.44 318.98 3494.21 329.98 3494.14 340.98 3494.21\n", + " 351.98 3494.2 362.98 3493.97 368.98 3493.74 379.98 3493.73 390.98 3493.72\n", + " 401.98 3493.59 412.98 3493.43 423.97 3493.36 434.97 3493.18 445.97 3493.11\n", + " 456.97 3493.02 465.97 3493.25 471.97 3492.96 478.97 3492.75 489.97 3492.75\n", + " 497.97 3492.51 508.97 3492.45 519.97 3492.31 530.97 3492.28 536.97 3492.05\n", + " 547.97 3491.93 558.97 3491.98 569.97 3491.84 576.97 3491.57 587.97 3491.66\n", + " 598.96 3491.49 601.96 3491.28 612.96 3491.15 623.96 3491.08 634.96 3491.32\n", + " 642.96 3491.08 653.96 3490.92 664.96 3490.9 675.96 3490.91 686.96 3490.74\n", + " 692.96 3490.95 703.96 3491 714.96 3491.05 722.96 3490.81 733.96 3490.8\n", + " 744.96 3490.66 750.96 3490.35 755.96 3490.14 764.96 3489.85 769.96 3489.64\n", + " 774.95 3489.43 778.95 3489.21 783.95 3488.99 790.95 3488.68 801.95 3488.63\n", + " 812.95 3488.76 823.95 3488.68 826.95 3488.9 830.95 3489.13 836.95 3489.39\n", + " 838.95 3489.63 842.95 3489.88 845.95 3490.11 849.95 3490.34 855.95 3490.58\n", + " 861.95 3490.84 867.95 3490.63 872.95 3490.92 883.91 3490.94 894.87 3491.04\n", + " 905.84 3491.27 916.8 3491.07 924.77 3491.29 930.75 3491.5 941.71 3491.6\n", + " 946.69 3491.82 952.67 3492.04 954.67 3492.26 965.63 3492.1 973.6 3492.32\n", + " 982.57 3492.53 993.53 3492.72 1004.49 3492.67 1015.46 3492.72 1026.42 3492.75\n", + " 1030.4 3492.47 1036.38 3492.69 1047.34 3492.63 1058.31 3492.5 1069.27 3492.52\n", + " 1080.23 3492.41 1091.19 3492.46 1102.15 3492.57 1105.14 3492.31 1112.12 3492.59\n", + " 1123.08 3492.58 1134.04 3492.49 1145 3492.62 1150.98 3492.85 1157.96 3493.07\n", + " 1160.95 3493.32 1166.94 3492.87 1169.93 3492.61 1172.92 3492.31 1174.92 3492.01\n", + " 1176.92 3491.78 1187.89 3491.69 1197.87 3491.48 1208.84 3491.62 1216.83 3491.93\n", + " 1218.82 3492.25 1220.82 3492.6 1226.8 3492.32 1235.78 3492.53 1246.76 3492.5\n", + " 1257.73 3492.44 1268.71 3492.47 1279.68 3492.48 1290.66 3492.47 1301.64 3492.65\n", + " 1312.61 3492.69 1323.59 3492.6 1334.56 3492.65 1340.55 3492.85 1351.52 3492.84\n", + " 1362.5 3493.03 1373.47 3493.11 1384.45 3493.26 1396.42 3493.14 1407.4 3493.22\n", + " 1418.37 3493.1 1429.36 3493.25 1440.34 3493.18 1451.32 3492.99 1462.31 3493.04\n", + " 1473.29 3493.13 1484.27 3493.06 1495.26 3493.12 1506.24 3493.09 1515.23 3492.86\n", + " 1526.21 3492.94 1537.2 3492.8 1548.18 3492.59 1559.16 3492.8 1570.15 3493\n", + " 1581.13 3493.09 1592.11 3493.13 1603.1 3493.26 1614.08 3493.43 1625.06 3493.48\n", + " 1636.05 3493.65 1647.03 3493.51 1658.02 3493.57 1671.99 3493.43 1682.98 3493.33\n", + " 1693.96 3493.45 1704.95 3493.58 1715.93 3493.69 1725.91 3493.9 1736.9 3493.94\n", + " 1747.88 3494.03 1758.87 3493.94 1769.85 3493.96 1780.83 3494.15 1791.82 3494.06\n", + " 1801.8 3494.29 1812.79 3494.39 1823.77 3494.34 1834.75 3494.41 1845.74 3494.45\n", + " 1856.72 3494.51 1867.7 3494.31 1878.69 3494.33 1881.68 3494.12 1892.67 3494.23\n", + " 1903.65 3494.35 1914.63 3494.45 1925.62 3494.47 1936.6 3494.35 1947.58 3494.42\n", + " 1958.57 3494.4 1969.55 3494.4 1980.54 3494.52 1991.52 3494.74 1995.51 3495.01\n", + " 2006.5 3494.82 2017.48 3494.86 2028.46 3494.94 2035.45 3495.15 2042.44 3495.26\n", + "#Mann= 3 , 0 , 0 \n", + " 0 .035 0 714.96 .035 0 861.95 .035 0\n", + "#XS Ineff= 3 ,-1 \n", + " 0 200 3499 200 677.95 3496.65 923.95 2050 3496.65\n", + "Permanent Ineff=\n", + " F F F\n", + "Bank Sta=714.96,861.95\n", + "XS Rating Curve= 0 ,0\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,1502 ,293.56,265.51,193.56\n", + "Node Name=B\n", + "XS GIS Cut Line=4\n", + "957069.183642477275235.306957483958195.485523716275053.015327305\n", + "958294.443987221275077.754779145959097.829519555274944.942380726\n", + "Node Last Edited Time=Sep/03/2015 14:11:45\n", + "#Sta/Elev= 230 \n", + " 0 3495.24 4 3494.99 11 3494.72 15 3494.51 20 3494.72\n", + " 31 3494.85 40 3494.56 42 3494.22 52 3494 56 3494.23\n", + " 67 3494.14 74 3493.9 83 3493.67 94 3493.7 105 3493.77\n", + " 110 3493.54 112 3493.32 123 3493.38 132 3493.17 141.99 3492.96\n", + " 146.99 3492.74 157.99 3492.58 168.99 3492.39 177.99 3492.61 183.99 3492.41\n", + " 193.99 3492.19 204.99 3492.25 208.99 3492.46 219.99 3492.27 228.99 3492.06\n", + " 239.99 3491.93 242.99 3492.15 253.99 3492.09 260.99 3491.86 271.99 3491.93\n", + " 282.99 3491.9 292.99 3491.65 303.99 3491.73 314.99 3491.62 322.99 3491.41\n", + " 333.99 3491.32 344.99 3491.18 351.99 3490.97 363.99 3490.98 374.99 3490.95\n", + " 382.99 3490.71 393.99 3490.83 404.99 3490.79 415.99 3490.85 424.99 3490.65\n", + " 435.99 3490.6 445.99 3490.37 456.98 3490.37 467.98 3490.45 477.98 3490.21\n", + " 483.98 3489.96 490.98 3490.24 498.98 3490.02 509.98 3489.85 513.98 3489.64\n", + " 524.98 3489.49 535.98 3489.48 544.98 3489.27 555.98 3489.06 566.98 3488.94\n", + " 577.98 3488.84 583.98 3488.64 595.98 3488.51 606.98 3488.54 610.98 3488.78\n", + " 619.98 3488.55 630.98 3488.41 638.98 3488.62 644.98 3488.41 651.98 3488.2\n", + " 662.98 3487.95 665.98 3487.69 675.98 3487.48 682.98 3487.72 685.98 3487.98\n", + " 694.98 3487.75 697.98 3487.46 708.98 3487.27 719.98 3487.2 724.98 3487.45\n", + " 735.98 3487.53 741.98 3487.77 752.97 3487.58 755.97 3487.21 757.97 3486.87\n", + " 759.97 3486.58 765.97 3486.35 774.97 3486.59 776.97 3486.85 778.97 3487.1\n", + " 780.97 3487.41 783.97 3487.63 787.97 3487.85 790.97 3488.09 801.97 3488.06\n", + " 806.97 3487.82 812.97 3487.49 823.97 3487.63 831.97 3487.84 842.97 3487.83\n", + " 846.97 3488.05 857.97 3488.22 861.97 3488.01 864.97 3487.77 875.97 3487.67\n", + " 886.97 3487.56 897.97 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3493.32\n", + " 1426.8 3493.47 1437.79 3493.59 1448.78 3493.72 1459.77 3493.83 1470.76 3494.02\n", + " 1481.75 3494.07 1492.74 3494.23 1503.73 3494.3 1514.72 3494.34 1525.71 3494.3\n", + " 1536.7 3494.35 1547.69 3494.4 1558.68 3494.53 1569.67 3494.7 1580.66 3494.68\n", + " 1591.65 3494.73 1602.64 3494.77 1610.64 3494.98 1621.63 3494.91 1632.62 3494.88\n", + " 1642.61 3495.09 1653.6 3495.06 1664.59 3494.92 1669.58 3495.13 1680.57 3495.11\n", + " 1691.56 3495.17 1702.55 3495.11 1713.54 3495.1 1724.53 3495.21 1735.52 3495.32\n", + " 1746.51 3495.12 1751.51 3495.4 1762.5 3495.42 1773.49 3495.35 1784.48 3495.3\n", + " 1795.47 3495.27 1806.46 3495.3 1817.45 3495.42 1828.44 3495.5 1839.43 3495.72\n", + " 1850.42 3495.66 1861.41 3495.72 1872.4 3495.68 1883.39 3495.85 1894.38 3495.8\n", + " 1905.37 3495.99 1916.36 3495.9 1927.35 3496.02 1938.34 3495.9 1949.33 3495.87\n", + " 1960.32 3495.86 1971.31 3495.88 1982.3 3495.77 1993.29 3495.79 2004.28 3495.84\n", + " 2015.27 3495.88 2026.26 3495.85 2037.26 3495.95 2048.25 3495.84 2057.24 3495.9\n", + "#Mann= 3 , 0 , 0 \n", + " 0 .035 0 638.98 .035 0 857.97 .035 0\n", + "#XS Ineff= 2 , 0 \n", + " 0 736.72 3488.65 789.59 0 3488.65\n", + "Permanent Ineff=\n", + " F F\n", + "Bank Sta=638.98,857.97\n", + "XS Rating Curve= 0 ,0\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,1236 ,321.77,422.78,284.8\n", + "XS GIS Cut Line=5\n", + "957333.506604642275382.442489982957761.891902715275433.223556399\n", + "958113.454519808275266.556894973958215.016980723275237.911282972\n", + "958216.318815395275106.400719225\n", + "Node Last Edited Time=Sep/03/2015 14:13:08\n", + "#Sta/Elev= 122 \n", + " 0 3490.01 10.98 3489.9 21.97 3489.72 27.96 3489.5 31.95 3489.73\n", + " 39.94 3489.49 52.92 3489.24 63.91 3489.27 74.89 3489.2 85.88 3489\n", + " 96.86 3489.03 104.85 3488.78 115.83 3488.73 126.82 3488.79 137.8 3488.66\n", + " 146.79 3488.41 157.77 3488.28 168.76 3488.2 179.74 3488.18 190.73 3488.21\n", + " 201.71 3488.23 212.7 3488.21 223.68 3488.08 234.67 3488.09 245.65 3488.05\n", + " 256.63 3488.19 267.62 3488.01 278.6 3487.87 289.59 3487.97 298.57 3487.75\n", + " 309.56 3487.68 320.54 3487.66 331.53 3487.64 342.51 3487.46 353.5 3487.48\n", + " 359.49 3487.26 370.47 3487.42 377.46 3487.19 388.45 3487.21 399.43 3487.23\n", + " 406.42 3487.02 408.42 3486.81 413.41 3486.58 424.4 3486.56 435.38 3486.58\n", + " 441.36 3486.79 452.34 3486.69 460.32 3486.48 471.29 3486.54 482.26 3486.51\n", + " 492.24 3486.27 500.22 3486.06 506.21 3485.84 517.18 3485.64 521.17 3485.4\n", + " 532.14 3485.33 537.13 3485.13 548.11 3485.01 553.09 3484.78 564.07 3484.69\n", + " 575.04 3484.58 579.03 3484.82 584.02 3484.6 589.01 3484.39 592 3484.18\n", + " 602.98 3484.3 613.95 3484.3 624.92 3484.29 635.9 3484.34 646.87 3484.38\n", + " 657.84 3484.42 668.82 3484.61 675.8 3484.88 682.78 3485.1 690.76 3484.9\n", + " 701.74 3484.94 704.73 3485.23 715.71 3485.37 726.68 3485.53 737.65 3485.44\n", + " 748.63 3485.57 759.6 3485.53 765.59 3485.76 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\n", + "\n", + "Stream Node=Spearfish_cr1 ,Old_us14 , 0 , 1 ,Downstream end \n", + "\n", + "Stream Node=Spearfish_Creek ,Spearfish_Creek , 0 , 0 ,Upstream end \n", + "\n", + "Stream Node=Spearfish_Creek ,Spearfish_Creek , 0 , 1 ,Downstream end \n", + "\n", + "Stream Node=Spearfish_Creek ,SpearfishDS , 0 , 0 ,Upstream end \n", + "\n", + "Stream Node=Spearfish_Creek ,SpearfishDS , 0 , 1 ,Downstream end \n", + "\n", + "Stream Node=Spearfish_Creek ,Spearfish_Cr_DS , 0 , 0 ,Upstream end \n", + "\n", + "Stream Node=Spearfish_Creek ,Spearfish_Cr_DS , 0 , 1 ,Downstream end \n", + "\n", + "Chan Stop Cuts=-1 \n", + "\n", + "\n", + "\n", + "Geom Raster=..\\3748.jpg,True,image,, 0 \n", + "Geom Raster=..\\FW US.jpg,True,image,, 0 \n", + "Geom Raster=..\\FW MID.jpg,True,image,, 0 \n", + "Geom Raster=..\\FW DS.jpg,True,image,, 0 \n", + "Use User Specified Reach Order=0\n", + "GIS Units=\n", + "GIS DTM Type=TIN\n", + "GIS DTM=\\\n", + "GIS Stream Layer=\\CityDrainageChannel\n", + "GIS Cross Section Layer=\\XSCutLines_citydrainage\n", + "GIS Map Projection=\n", + "GIS Projection Zone=\n", + "GIS Datum=\n", + "GIS Vertical Datum=\n", + "GIS Data Extents=960025.2397382,268286.1364726,960233.5437836,269067.0043335\n", + "\n", + "GIS Ratio Cuts To Invert=-1\n", + "GIS Limit At Bridges=0\n", + "Composite Channel Slope=5\"]]\n", + "station_elevation_points: [[\"[(0.0, 3574.41), (2.99, 3574.46), (5.97, 3574.38), (8.96, 3574.3), (11.95, 3574.26), (14.94, 3574.29), (17.92, 3574.21), (20.91, 3574.12), (23.9, 3574.1), (26.89, 3574.15), (29.87, 3574.2), (32.86, 3574.21), (35.85, 3574.1), (38.84, 3573.99), (41.82, 3574.02), (44.81, 3573.91), (47.8, 3573.74), (50.79, 3573.66), (53.77, 3573.6), (56.76, 3573.39), (59.75, 3573.24), (62.74, 3573.13), (65.72, 3572.96), (68.65, 3572.78), (71.57, 3572.64), (74.49, 3572.58), (77.41, 3572.28), (80.33, 3572.15), (83.25, 3572.04), (86.17, 3571.89), (89.09, 3571.76), (92.01, 3571.57), (94.94, 3571.33), (97.86, 3571.17), (100.78, 3571.08), (103.7, 3570.9), 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(1927.35, 3496.02), (1938.34, 3495.9), (1949.33, 3495.87), (1960.32, 3495.86), (1971.31, 3495.88), (1982.3, 3495.77), (1993.29, 3495.79), (2004.28, 3495.84), (2015.27, 3495.88), (2026.26, 3495.85), (2037.26, 3495.95), (2048.25, 3495.84), (2057.24, 3495.9)]\",\"[(0.0, 3490.01), (10.98, 3489.9), (21.97, 3489.72), (27.96, 3489.5), (31.95, 3489.73), (39.94, 3489.49), (52.92, 3489.24), (63.91, 3489.27), (74.89, 3489.2), (85.88, 3489.0), (96.86, 3489.03), (104.85, 3488.78), (115.83, 3488.73), (126.82, 3488.79), (137.8, 3488.66), (146.79, 3488.41), (157.77, 3488.28), (168.76, 3488.2), (179.74, 3488.18), (190.73, 3488.21), (201.71, 3488.23), (212.7, 3488.21), (223.68, 3488.08), (234.67, 3488.09), (245.65, 3488.05), (256.63, 3488.19), (267.62, 3488.01), (278.6, 3487.87), (289.59, 3487.97), (298.57, 3487.75), (309.56, 3487.68), (320.54, 3487.66), (331.53, 3487.64), (342.51, 3487.46), (353.5, 3487.48), (359.49, 3487.26), (370.47, 3487.42), (377.46, 3487.19), (388.45, 3487.21), (399.43, 3487.23), (406.42, 3487.02), (408.42, 3486.81), (413.41, 3486.58), (424.4, 3486.56), (435.38, 3486.58), (441.36, 3486.79), (452.34, 3486.69), (460.32, 3486.48), (471.29, 3486.54), (482.26, 3486.51), (492.24, 3486.27), (500.22, 3486.06), (506.21, 3485.84), (517.18, 3485.64), (521.17, 3485.4), (532.14, 3485.33), (537.13, 3485.13), (548.11, 3485.01), (553.09, 3484.78), (564.07, 3484.69), (575.04, 3484.58), (579.03, 3484.82), (584.02, 3484.6), (589.01, 3484.39), (592.0, 3484.18), (602.98, 3484.3), (613.95, 3484.3), (624.92, 3484.29), (635.9, 3484.34), (646.87, 3484.38), (657.84, 3484.42), (668.82, 3484.61), (675.8, 3484.88), (682.78, 3485.1), (690.76, 3484.9), (701.74, 3484.94), (704.73, 3485.23), (715.71, 3485.37), (726.68, 3485.53), (737.65, 3485.44), (748.63, 3485.57), (759.6, 3485.53), (765.59, 3485.76), (776.56, 3485.9), (787.53, 3486.08), (792.52, 3486.34), (797.51, 3486.56), (806.49, 3486.77), (810.48, 3486.99), (813.47, 3487.25), (824.44, 3487.19), (828.42, 3487.42), (835.39, 3487.91), (848.33, 3488.17), (859.28, 3488.06), (863.26, 3487.68), (864.26, 3487.43), (875.21, 3487.57), (886.16, 3487.5), (898.1, 3487.38), (905.07, 3487.07), (909.06, 3486.84), (913.04, 3486.59), (917.02, 3486.81), (920.01, 3487.06), (922.99, 3487.3), (933.95, 3487.44), (946.9, 3487.57), (957.86, 3487.75), (965.83, 3487.97), (976.79, 3487.9), (986.76, 3488.11), (997.72, 3488.22), (1008.68, 3488.35), (1019.64, 3488.59), (1030.6, 3488.75), (1035.58, 3488.98), (1041.56, 3489.19), (1046.54, 3489.41), (1051.52, 3489.65), (1056.5, 3489.95), (1057.5, 3490.04)]\",\"[(0.0, 3486.61), (7.0, 3486.41), (17.99, 3486.27), (24.98, 3486.06), (35.98, 3485.99), (42.97, 3485.78), (53.97, 3485.65), (63.96, 3485.43), (74.95, 3485.53), (85.95, 3485.38), (96.94, 3485.42), (107.93, 3485.34), (118.93, 3485.33), (129.92, 3485.25), (140.91, 3485.21), (151.91, 3485.3), (162.9, 3485.26), (173.89, 3485.41), (184.89, 3485.27), (195.88, 3485.32), (206.87, 3485.45), (217.87, 3485.48), (228.86, 3485.39), (239.85, 3485.48), (249.85, 3485.23), (260.84, 3485.34), (271.83, 3485.17), (277.83, 3484.96), (288.82, 3484.96), (298.82, 3484.65), (309.81, 3484.52), (319.8, 3484.29), (330.8, 3484.06), (342.79, 3483.95), (353.78, 3483.99), (363.78, 3483.75), (374.77, 3483.77), (385.76, 3483.79), (396.76, 3483.84), (407.75, 3483.82), (418.74, 3483.69), (429.74, 3483.78), (440.72, 3483.51), (451.71, 3483.3), (462.7, 3483.2), (473.68, 3483.06), (484.67, 3482.93), (495.66, 3482.88), (506.65, 3482.69), (517.63, 3482.72), (528.62, 3482.78), (539.61, 3482.84), (550.59, 3482.84), (561.58, 3482.88), (572.57, 3482.88), (583.56, 3482.78), (594.54, 3482.87), (605.53, 3482.8), (616.52, 3482.75), (627.5, 3482.73), (638.49, 3482.74), (649.48, 3482.56), (660.46, 3482.57), (671.45, 3482.64), (682.44, 3482.56), (693.43, 3482.59), (704.41, 3482.53), (715.4, 3482.67), (718.4, 3482.88), (722.39, 3482.67), (725.39, 3482.46), (729.38, 3482.21), (739.37, 3482.57), (741.37, 3482.9), (743.37, 3483.18), (750.36, 3483.43), (761.34, 3483.23), (764.34, 3482.95), (767.34, 3482.71), (771.33, 3482.45), (774.33, 3482.17), (778.32, 3481.92), (784.32, 3482.16), (786.32, 3482.42), (788.31, 3482.63), (793.31, 3482.85), (796.3, 3483.06), (807.29, 3483.16), (818.29, 3483.13), (829.28, 3483.12), (840.28, 3483.27), (851.27, 3483.13), (855.27, 3483.36), (866.27, 3483.3), (877.26, 3483.45), (883.26, 3483.67), (891.26, 3483.87), (897.26, 3484.09), (908.25, 3484.03), (919.25, 3483.96), (930.25, 3484.06), (941.24, 3484.19), (952.24, 3484.34), (963.23, 3484.44), (974.23, 3484.59), (985.23, 3484.57), (996.22, 3484.71), (1007.22, 3484.55), (1015.22, 3484.77), (1021.21, 3485.0), (1032.21, 3485.16), (1044.2, 3485.15), (1052.2, 3485.37), (1250.0, 3488.0)]\",\"[(0.0, 3481.6), (10.94, 3481.46), (21.89, 3481.25), (32.83, 3481.37), (37.8, 3481.16), (48.75, 3480.89), (59.69, 3480.79), (70.63, 3480.69), (79.59, 3480.48), (91.52, 3480.47), (102.47, 3480.28), (113.41, 3480.17), (124.35, 3479.99), (132.31, 3479.78), (143.25, 3479.55), (154.2, 3479.48), (165.14, 3479.32), (176.08, 3479.25), (187.06, 3479.25), (191.05, 3479.03), (202.04, 3478.85), (213.02, 3478.79), (218.01, 3478.49), (229.0, 3478.38), (239.98, 3478.2), (250.96, 3478.1), (261.95, 3478.06), (272.93, 3478.19), (283.91, 3478.24), (294.9, 3478.22), (305.88, 3478.25), (316.86, 3478.35), (327.85, 3478.44), (338.83, 3478.6), (349.82, 3478.62), (360.8, 3478.62), (371.78, 3478.83), (382.77, 3478.95), (389.76, 3478.68), (400.74, 3478.59), (411.72, 3478.43), (422.71, 3478.28), (433.69, 3478.28), (444.67, 3478.27), (455.66, 3478.21), (466.64, 3478.31), (477.62, 3478.36), (488.61, 3478.56), (499.59, 3478.69), (508.58, 3478.94), (514.57, 3479.22), (520.56, 3478.95), (523.55, 3478.67), (525.55, 3478.34), (527.55, 3478.1), (529.54, 3477.87), (534.54, 3478.26), (537.53, 3478.51), (541.53, 3478.74), (552.51, 3478.91), (557.5, 3478.69), (561.5, 3478.37), (563.49, 3478.11), (568.49, 3477.76), (579.47, 3477.64), (582.47, 3477.89), (593.45, 3477.89), (597.44, 3478.14), (608.43, 3478.18), (619.41, 3478.41), (630.4, 3478.34), (641.38, 3478.62), (652.37, 3478.75), (657.36, 3478.54), (669.35, 3478.42), (676.34, 3478.63), (687.33, 3478.68), (698.31, 3478.51), (709.3, 3478.63), (720.28, 3478.8), (731.27, 3478.98), (742.26, 3479.09), (753.24, 3479.04), (764.23, 3479.2), (774.21, 3479.42), (785.2, 3479.55), (796.19, 3479.6), (804.18, 3479.82), (815.16, 3479.92), (819.16, 3480.17), (830.14, 3480.29), (841.13, 3480.31), (852.11, 3480.38), (863.1, 3480.55), (875.08, 3480.54), (886.07, 3480.61), (897.06, 3480.61), (908.04, 3480.71), (919.03, 3480.84), (927.02, 3480.9), (1027.0, 3482.0)]\"]]\n", + "bank_stations: [[\"['171.92', '201.27']\",\"['191.42', '222.45']\",\"['172.73', '204.26']\",\"['213.04', '234.76']\",\"['149.98', '167.51']\",...,\"['714.96', '861.95']\",\"['638.98', '857.97']\",\"['548.11', '682.78']\",\"['718.4', '796.3']\",\"['514.57', '652.37']\"]]\n", + "number_of_station_elevation_points: [[134,133,137,164,131,...,230,230,122,114,101]]\n", + "number_of_coords: [[6,4,5,5,7,...,5,4,5,5,5]]\n", + "flows: [[\"1.0\n", + "255.0\n", + "1540.0\n", + "9405.0\",\"1.0\n", + "255.0\n", + "1540.0\n", + "9405.0\",\"1.0\n", + "255.0\n", + "1540.0\n", + "9405.0\",\"1.0\n", + "255.0\n", + "1540.0\n", + "9405.0\",\"1.0\n", + "255.0\n", + "1540.0\n", + "9405.0\",...,\"1.0\n", + "1.0\n", + "1.0\n", + "1.0\",\"1.0\n", + "1.0\n", + "1.0\n", + "1.0\",\"1.0\n", + "1.0\n", + "1.0\n", + "1.0\",\"1.0\n", + "1.0\n", + "1.0\n", + "1.0\",\"1.0\n", + "1.0\n", + "1.0\n", + "1.0\"]]\n", + "profile_names: [[\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\",\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\",\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\",\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\",\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\",...,\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\",\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\",\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\",\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\",\"10yr FIS\n", + "50yr FIS\n", + "100yr FIS\n", + "500yr FIS\"]]\n", + "geometry: [[010200000006000000BE4C8CE1A50523C1206B1025266F42419AC8EC697F0523C132F6A6E0286F42419A0006786A0523C17F92F0B1316F4241F0D4F7586A0523C13C43C669356F42418A5AA654500523C159EC2A60386F424133B5B753E50423C10A0FA39B316F4241,01020000000400000016D0FC12D70523C15084122A366F4241E79442B0770523C186A13776406F4241DAB89867470523C1B04E6AFA416F4241157F3194F90423C15BF0A5553B6F4241,0102000000050000008AF5EF3DD10523C185E9765B496F4241D1DFD6A4790523C13D297626526F42417349BC58490523C19953E3ED516F42417EDA28C81B0523C104EC1292496F4241417CFD2DF20423C155BDEBD1466F4241,010200000005000000FC7247C5D10523C1218ACB125C6F42418538B3607D0523C1CFECADD76F6F424144418C6F560523C199DFBBB2706F4241E59308061D0523C14394B776626F4241481EA709EA0423C1D703D9D55E6F4241,0102000000070000003B4C7E239A0523C170FC9E266D6F42412A7B5E8B870523C1148A9AEE786F4241F267D3497E0523C155BEE03B7F6F4241B1E51DA3520523C1C7AE0A64806F424177B850D30F0523C1E0B9EC9F7C6F4241B9769DA4F10423C1B8CB0908776F42412D6C0265B20423C1D74578A4776F4241,...,0102000000050000008A09B101460C23C17CAB3305367342412DA30C133E0A23C1BEF7F0341B734241DAC1C704960923C1AB66DF9610734241C71810A7FF0823C13D30972204734241622B728C880723C177CF00BAF5724241,010200000004000000FB552DF0210C23C12829C54D607342410FF4975B790923C182EB27853E734241EAB104AF3C0923C1176B4AC641734241AD573D4A570723C158C2304029734241,010200000005000000EEF043217E0B23C1830A685F7573424133FBEFAF780A23C14C14E6DC7A73424187CC4B9DA60923C1A9B2D98E5F73424117D0CA81690923C1C554CBA55A7342416F09108C6B0923C175CBA88F46734241,010200000005000000C1089CB40B0B23C1159AB7CEA673424106CF01A81A0A23C15C7FFC8A8F7342416180C58E370923C1992DFC3882734241B17690D0DB0823C1194EEDF56473424124F1636A910823C1CD36043B4D734241,010200000005000000A0EC58B27A0A23C1B0660763D473424126DCAAE5190A23C1327A5B9AE07342411DFA3EB5160923C1F1541AB9D87342417C1FCCEBD70823C1B0E03987AA734241330E7747C40823C17EE9A9139C734241]]\n", + "huc: [[\"10120203\",\"10120203\",\"10120203\",\"10120203\",\"10120203\",...,\"10120203\",\"10120203\",\"10120203\",\"10120203\",\"10120203\"]]\n", + "model_id: [[\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",...,\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\"]]\n", + "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "us_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "ds_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "__index_level_0__: [[7042,7043,7044,7045,7046,...,7225,7226,7227,7228,7229]]\n" + ] + } + ], + "source": [ + "# Scenario I\n", + "# User Args\n", + "catalog_name = 'xs'\n", + "huc_num = 10120203\n", + "\n", + "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", + "tablename = f'huc_{huc_num}'\n", + "namespace = 'mip'\n", + "catalog = load_catalog(name=catalog_name, \n", + " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", + "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "print(iceberg_table.to_string(preview_cols=30))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "8b8254a1-dce8-42f0-8e38-87b0d1192760", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pyarrow.Table\n", + "river: large_string\n", + "reach: large_string\n", + "river_reach: large_string\n", + "river_station: double\n", + "river_reach_rs: large_string\n", + "thalweg: double\n", + "xs_max_elevation: double\n", + "left_reach_length: double\n", + "right_reach_length: double\n", + "channel_reach_length: double\n", + "ras_data: large_string\n", + "station_elevation_points: large_string\n", + "bank_stations: large_string\n", + "number_of_station_elevation_points: int64\n", + "number_of_coords: int64\n", + "flows: large_string\n", + "profile_names: large_string\n", + "geometry: large_binary\n", + "huc: large_string\n", + "model_id: large_string\n", + "us_reach_id: large_string\n", + "us_network_to_id: large_string\n", + "ds_reach_id: large_string\n", + "ds_network_to_id: large_string\n", + "__index_level_0__: int64\n", + "----\n", + "river: [[\"cottonwood_creek\",\"cottonwood_creek\",\"cottonwood_creek\",\"cottonwood_creek\",\"cottonwood_creek\",...,\"south_fork_clear\",\"south_fork_clear\",\"south_fork_clear\",\"south_fork_clear\",\"south_fork_clear\"]]\n", + "reach: [[\"45\",\"45\",\"45\",\"45\",\"45\",...,\"173\",\"173\",\"173\",\"173\",\"173\"]]\n", + "river_reach: [[\"cottonwood_creek,45\",\"cottonwood_creek,45\",\"cottonwood_creek,45\",\"cottonwood_creek,45\",\"cottonwood_creek,45\",...,\"south_fork_clear,173\",\"south_fork_clear,173\",\"south_fork_clear,173\",\"south_fork_clear,173\",\"south_fork_clear,173\"]]\n", + "river_station: [[12970.38,12672.1,12299.53,12153.37,11649.77,...,1595.506,1196.63,797.7532,398.8766,0]]\n", + "river_reach_rs: [[\"cottonwood_creek 45 12970.38\",\"cottonwood_creek 45 12672.1\",\"cottonwood_creek 45 12299.53\",\"cottonwood_creek 45 12153.37\",\"cottonwood_creek 45 11649.77\",...,\"south_fork_clear 173 1595.506\",\"south_fork_clear 173 1196.63\",\"south_fork_clear 173 797.7532\",\"south_fork_clear 173 398.8766\",\"south_fork_clear 173 0.0\"]]\n", + "thalweg: [[1441.132,1440.028,1435.111,1432.966,1430.307,...,1573.805,1570.469,1570.177,1569.928,1568.771]]\n", + "xs_max_elevation: [[1588.847,1669.48,1654.026,1558.699,1527.284,...,1786.711,1767.658,1763.571,1818.624,1873.877]]\n", + "left_reach_length: [[284.287,323.3077,227.909,360.3609,210.6718,...,401.7363,330.3242,391.7664,429.1667,0]]\n", + "right_reach_length: [[303.1325,369.12,136.7996,342.1801,217.7682,...,396.9099,438.9131,407.5078,364.8997,0]]\n", + "channel_reach_length: [[298.2864,372.5664,146.1611,503.6007,210.0612,...,398.8766,398.8766,398.8766,398.8766,0]]\n", + "ras_data: [[\"Type RM Length L Ch R = 1 ,12970.38,284.287,298.2864,303.1325\n", + "XS GIS Cut Line= 10\n", + " 2557013.62452871613905.405064322557170.01356711 1613144.049169\n", + "2557179.463565481613034.761952912557187.667479591612919.42676206\n", + "2557187.802633431612917.431333932557190.014987551612880.65587562\n", + "2557193.545626411612777.654764992557194.857639491612645.33335081\n", + "2557193.914288551612519.076795222557191.264243681612417.61287668\n", + "#Sta/Elev=194\n", + "8996.4271486.1168999.6651484.945 9009.751481.6349012.3051480.7349019.8341478.072\n", + "9029.9191474.7179040.0031471.7099050.0881468.0159057.7621465.1049060.1721464.198\n", + "9070.2571460.2889080.3411456.826 9088.941455.1989090.4251454.917 9100.511452.727\n", + "9103.2191452.4039110.5941451.4969120.6791450.4729130.763 1450.229140.8481449.938\n", + "9148.6771449.7829150.9321449.7419161.0171449.4439171.1011449.1259181.1861449.709\n", + "9187.5131449.774 9191.271449.8129194.1341449.8399201.3551449.9599211.4391449.922\n", + "9221.524 1450.199231.6081450.4949239.5921450.4839241.6931450.4759251.7771450.553\n", + "9261.8621451.0499271.946 1451.569282.0311451.5039285.0491451.3289286.0871451.265\n", + "9292.115 1450.9 9302.21450.8589312.2841450.7749322.3691450.3739330.5061450.322\n", + "9332.453 1450.349342.5381450.4939352.6221450.5529362.7071449.8589372.7911449.142\n", + "9375.9641448.9989382.8761448.817 9384.661448.841 9392.961448.9519403.0451449.256\n", + "9413.1291449.2399421.4211449.0479423.2141449.0229433.2981448.6999443.383 1449.3\n", + "9453.4671449.9689463.5521450.3829466.8781450.4069473.6361450.4379483.7211450.084\n", + "9493.8051450.313 9503.891450.3519512.3361450.3999513.9741450.4169524.0591450.226\n", + "9534.1431450.2029544.227 1450.399554.3121450.3999557.7931450.4069564.3961450.395\n", + "9574.4811450.2219584.5651450.199 9594.651450.1179603.2511449.8449604.7341449.797\n", + "9614.8191449.5739624.9031449.2749634.9881448.5759645.0721447.8959648.7081447.717\n", + "9655.1571447.4279665.241 1447.569675.3261447.676 9685.411447.5339694.165 1447.33\n", + "9695.4951447.2969705.5791446.9549715.6641446.6819725.7481446.8869735.8331446.944\n", + "9739.6231446.9839745.917 1447.039756.002 1447.039766.0861446.9499773.6781446.636\n", + "9776.1241446.4949786.0211446.6349795.9171446.7399797.9361446.6559805.8141446.378\n", + " 9815.711445.9549825.6071445.8099835.5041445.575 9845.41445.4169855.2971446.119\n", + "9865.1931446.894 9875.091446.7479883.3731445.9719884.9841445.8099894.866 1445.09\n", + "9896.6261445.0669904.7481444.9529914.6311446.6029924.5131445.3079934.3951443.526\n", + "9944.2781443.559 9954.161443.4459964.042 1443.569973.9241443.2379977.7911443.054\n", + " 99781443.0449983.807 1442.779993.6891442.084 99991441.571 100011441.378\n", + "10003.571441.13210010.011444.45910011.711445.33710013.441446.24710023.321454.194\n", + "10033.19 1456.4710037.841456.86310043.051457.32510052.911458.83210062.771460.466\n", + "10072.631462.36510077.581463.48410082.481464.59110092.341466.139 10102.21468.718\n", + "10112.061472.43610121.911476.67310131.771482.372 10140.91486.71610141.631487.069\n", + "10151.481492.71410161.321499.17210171.171505.26910177.83 1509.1310181.02 1510.98\n", + "10190.871517.24110200.721524.20110210.571529.88110220.411535.70610230.261541.737\n", + "10240.111547.268 10246.91550.70910249.961552.26810259.811557.01510269.661561.212\n", + "10273.231562.24710278.381563.776 10279.51564.10910289.351565.99310299.191567.332\n", + "10309.041567.87610318.881567.67110328.731565.63110338.58 1563.9210348.42 1563.27\n", + "10358.271563.71210368.111563.92410377.961564.78610379.291564.924 10387.81565.808\n", + "10397.651568.82410399.491569.15310407.491570.67410417.341573.67110427.191576.583\n", + "10437.031579.69510446.881580.83610456.721582.29110466.571583.43810476.41 1583.68\n", + "10480.531584.50110486.261585.64310496.11 1587.6910500.991588.847\n", + "#Mann= 13 , -1 , 0 \n", + "8996.427 0.035 0 9088.94 0.05 09187.513 0.035 0\n", + "9286.087 0.06 0 9384.66 0.035 09914.631 0.03 0\n", + "9977.791 0.05 010013.44 0.12 010077.58 0.035 0\n", + "10177.83 0.12 010278.38 0.035 010379.29 0.12 0\n", + "10480.53 0.035 0\n", + "Bank Sta=9914.631,10013.44\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,12672.1,323.3077,372.5664,369.12\n", + "XS GIS Cut Line= 10\n", + " 2557287.06202871613874.155064322557404.471304131613263.03441516\n", + "2557476.989646171612950.247379152557481.560806741612927.83306148\n", + "2557481.932942281612925.867987662557501.930132851612812.78317349\n", + "2557513.530362521612724.937654172557521.67234582 1612630.8707731\n", + "2557525.149238181612532.339724532557523.51763574 1612428.8570291\n", + "#Sta/Elev=188\n", + "9014.974 1564.519020.8471563.3889030.9041560.9069040.4921556.855 9040.96 1556.66\n", + "9051.0171551.9619061.0741547.3229071.1311539.7779081.1881531.6769088.7321523.734\n", + "9091.2441521.5199101.3011514.7229111.3581505.0389121.4151493.8269131.4721471.925\n", + "9136.973 1467.449141.5281463.8429151.5851457.4919161.6421454.4489171.6991453.282\n", + "9181.756 1454.889185.2141454.8839191.812 1454.59201.8691453.0079211.926 1450.38\n", + "9221.9831449.5499232.0391447.4079233.4551447.3179242.0961446.7999252.1531446.579\n", + " 9262.211446.3059272.2671446.5669281.695 1446.529282.3231446.515 9292.381446.515\n", + "9302.437 1446.049312.4941445.5149316.2161445.1149322.5511444.4339329.9361444.398\n", + "9332.6071444.3779342.6641444.3649352.7211445.8049362.7781446.8449372.8351446.533\n", + "9378.1771446.3459382.8911446.1839392.9481446.0189403.005 1445.919413.062 1445.87\n", + "9423.1191446.0769426.4181446.1479433.1751446.2729443.2321446.2069453.2891446.235\n", + "9463.3461446.4039473.4021446.4629474.6581446.4399483.4591446.2819493.5161446.047\n", + "9503.5731445.9719513.3921445.975 9513.631445.9759522.899 1445.759523.6861445.732\n", + "9533.7431445.612 9543.81445.6859553.8571445.7919563.9141445.998 9571.141446.223\n", + " 9573.971446.3149584.0271446.6699594.0841446.9349604.1411446.9949614.1981446.909\n", + "9619.3811446.9729624.2541447.0119634.3111447.0529637.2711447.026 9644.431446.996\n", + "9654.5741446.6029662.9571446.3439664.7191446.2769674.8631446.1459685.0081447.103\n", + "9695.1521447.1989703.7851447.5629705.2971447.6149715.4411447.7489725.586 1447.41\n", + " 9735.731446.3459744.6131445.4529745.8751445.3459756.0191445.2779766.1641444.929\n", + "9776.3081444.593 9785.441444.4119786.453 1444.399796.5971444.2119806.7421443.952\n", + "9816.8861443.5529826.2681443.4459827.0311443.4359837.1751443.045 9847.321442.712\n", + "9857.4641442.1039867.0961441.6469867.6091441.6179877.753 1441.889887.8981441.987\n", + "9898.042 1442.079907.923 1442.279908.1871442.2739918.3311441.6829928.4751441.859\n", + " 9938.621442.0559948.7511441.7269948.7641441.7259958.3551441.0869958.9051441.047\n", + "9968.9871440.2549977.0581440.0739979.0681440.028 9981.23 1440.15 9983.231440.266\n", + "9989.1121440.6149994.8531440.7569999.1391440.87910006.351440.98910009.171441.032\n", + "10019.191441.44810025.311441.24910029.221441.12210039.251445.66610046.781447.947\n", + "10049.271448.687 10059.3 1448.3610069.331449.83310079.35 1452.7610089.381454.956\n", + "10098.071456.006 10098.91456.123 10099.41456.18810109.35 1455.56 10119.31455.611\n", + "10129.261457.42610139.211458.30610149.161458.99110159.111460.44210166.111461.487\n", + "10169.071461.93610179.02 1464.6710186.681468.52210188.96 1469.6810198.851475.282\n", + "10208.751481.60210218.651488.73710228.541496.13310238.441504.14110248.34 1509.76\n", + "10253.741514.08710258.231517.68210268.131524.92710278.031534.054 10281.11536.538\n", + " 10287.91542.13310297.761549.95110307.611557.99410317.471565.18510327.331572.197\n", + "10337.191579.24310347.051585.504 10356.91592.38310366.76 1600.4110376.621606.408\n", + "10379.691608.89210386.471614.32510396.311620.80610406.161626.685 104161633.488\n", + "10425.851639.77410435.691645.09910445.541650.30610455.38 1655.710465.231660.954\n", + "10475.071665.69310476.051666.15110483.18 1669.48\n", + "#Mann= 7 , -1 , 0 \n", + "9014.974 0.035 09316.216 0.06 09513.392 0.035 0\n", + " 9938.62 0.03 010006.35 0.05 010039.25 0.12 0\n", + "10476.05 0.07 0\n", + "#XS Ineff= 2 , 0 \n", + " 9716.005 0 \n", + "Permanent Ineff=\n", + " F F\n", + "Bank Sta=9938.62,10039.25\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,12299.53,227.909,146.1611,136.7996\n", + "XS GIS Cut Line= 16\n", + " 2557535.24779261613918.048293492557639.174358991613468.19124465\n", + "2557659.220609351613370.060636922557683.373493861613274.19429828\n", + "2557727.511369281613120.553925662557735.40572159 1613081.3753766\n", + "2557738.736435691613051.186009912557738.897791651613049.19252947\n", + " 2557741.02184811613026.416694342557745.293586961613000.61611636\n", + "2557778.035496571612884.934559932557790.467898631612830.30736832\n", + "2557800.564067161612756.439908522557803.672983651612693.70734845\n", + "2557802.275557361612619.59424265 2557797.69423381612556.62741067\n", + "#Sta/Elev=189\n", + "8796.0531654.0268800.4951652.6918810.6381649.6818812.1561649.1938820.7811646.419\n", + "8830.9231642.9178841.0661639.5998851.2081636.5618853.1121635.8898861.3511633.015\n", + "8871.4931629.6028881.6361626.4658891.7781623.2968894.0691622.2378901.9211618.516\n", + "8912.0631614.1918922.2061610.5138932.3481607.5588935.0261606.7538942.4911604.455\n", + "8952.6331601.1418962.7761598.2488972.9181595.7138975.9821594.9968983.0611593.262\n", + "8993.2031590.5799003.3461587.4269013.4881584.3589016.9391583.4739023.6311581.758\n", + "9033.7731578.2889043.9161574.4189054.0581570.2279057.895 1568.329064.2011565.235\n", + "9074.3441557.8179084.4861547.4599094.6291541.6019098.8521538.8679104.7711534.571\n", + "9114.9141523.8349125.0561509.7549135.1991499.9929139.8081497.6449145.3411495.136\n", + "9155.4841489.6629165.6261483.5869175.7691468.5059180.7651460.6429185.9111451.301\n", + "9196.0541447.7489206.1961446.4869216.339 1445.199221.7221445.2089226.4811445.296\n", + "9236.6241445.0989246.766 1444.659256.9091444.1459257.7591444.1289263.2461443.982\n", + "9266.9961443.8699277.0781443.659 9287.161443.5369297.2421443.4129307.3241443.314\n", + "9308.9261443.3229317.4061443.3619327.4881443.239 9337.571443.2219347.6531443.148\n", + "9354.6061443.1439357.7351443.1239357.9161443.1149367.9271442.5269378.1211442.655\n", + "9384.7691442.9459388.316 1443.19393.103 1443.11 9398.511443.0999408.705 1442.97\n", + "9418.8991441.5229429.0931442.3039431.0391442.2129439.2881441.7999449.4821441.956\n", + "9456.7781442.0529459.7041442.1149467.6481442.0119469.995 1441.989480.2861441.749\n", + "9483.4631441.6589490.5771441.4549500.8691441.3319501.459 1441.33 9511.161441.267\n", + "9521.4511441.2999531.7421441.3399535.2691441.3859542.0331441.4529552.3241441.639\n", + "9562.6151441.908 9569.081441.6699572.9061441.5239583.1971441.4259593.4881441.445\n", + "9602.8911441.5569603.7791441.565 9614.071441.6159616.6331441.614 9624.21441.634\n", + "9634.2761441.7629644.0791441.6259644.352 1441.629654.4291441.2639656.5991441.175\n", + "9664.3851440.8359674.3081439.6399684.2311435.1119686.971 1435.339688.9711435.504\n", + "9694.138 14369704.0411437.0639711.8461437.7179713.9671437.9049715.7621437.808\n", + "9723.973 1437.27 9733.981437.7339737.9981437.9129744.1491438.4189754.4281439.416\n", + "9758.4161439.4939764.7071439.6549774.9861440.1179785.265 1440.639792.6681440.317\n", + "9795.5431440.2389805.8221440.4119816.1011440.118 9826.381440.0649826.9211440.055\n", + "9836.6591439.9159846.938 1438.859857.2171437.4459858.2241437.6289861.798 1438.32\n", + "9867.3651439.546 9877.51440.6839887.6341439.7489897.7681440.3159903.3021440.228\n", + "9907.9031440.1889914.2481439.9349917.972 1439.799927.9311439.625 9937.891439.473\n", + " 9947.851439.0819957.8091438.766 9962.261438.3159967.7681437.7159977.6761437.002\n", + "9977.7271436.9989987.6871437.0159988.8021436.8449997.5631435.48710007.431435.938\n", + "10010.53 1437.81 10017.31441.89810027.161442.61810037.031443.75310046.891449.367\n", + "10051.611453.19610056.751457.40710066.591462.82310076.441463.889 10077.51464.003\n", + "10086.281464.94910096.13 1470.4410105.971486.16410115.821489.21510125.671489.507\n", + "10125.74 1489.5410135.531493.99110145.391508.98410155.251515.44810165.111520.333\n", + "10174.971526.87810182.271531.31310184.831532.86810188.871535.676\n", + "#Mann= 7 , -1 , 0 \n", + "8796.053 0.035 09384.769 0.06 09483.463 0.035 0\n", + "9977.676 0.03 010010.53 0.035 0 10077.5 0.12 0\n", + "10182.27 0.035 0\n", + "#XS Ineff= 2 , 0 \n", + " 9634.593 0 \n", + "Permanent Ineff=\n", + " F F\n", + "Bank Sta=9977.676,10010.53\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,12153.37,360.3609,503.6007,342.1801\n", + "XS GIS Cut Line= 13\n", + "2558005.837845531613629.286573512558001.071123921613447.86874134\n", + "2558001.936595571613301.260953872558004.91408231 1613240.9687236\n", + "2558009.284893191613190.459930822558017.914136131613130.27051659\n", + "2558018.205822091613128.291901082558024.661629811613084.20905222\n", + "2558025.23134723 1613041.9582129 2558021.85871811613013.41343009\n", + "2558017.051115461612989.615294132557969.286263831612813.16956088\n", + "2557878.207862041612525.19673098\n", + "#Sta/Elev=147\n", + "9100.3271558.6999108.8191555.0969118.6651550.465 9128.511546.3879138.3561541.929\n", + "9148.2021537.2249158.0471533.0689167.8931529.0679177.7391525.6539187.584 1521.84\n", + " 9197.431517.8619207.2761513.0119217.1211508.6059226.9671504.5189236.8131500.944\n", + "9246.6581497.8529256.5041494.312 9266.351490.7979276.1951488.1899281.8071487.128\n", + "9286.0421486.3219295.8891485.0549305.7371483.2319315.5841481.1819325.4321478.986\n", + "9335.2791476.8729345.1261474.9639354.9741473.4839364.8211471.5219374.669 1469.3\n", + "9384.516 1467.29394.3641465.2289404.2111461.5549414.0581461.3349415.4771461.294\n", + "9423.9061461.0559428.4181459.7329430.1261459.2339433.7631458.1669443.6291452.886\n", + "9453.4951448.3489463.361 1444.939473.2271442.3299483.0931440.8549488.7831440.392\n", + "9492.9721440.0589502.8691439.6669512.7661439.4339515.2661439.3899522.6621439.258\n", + "9532.5591439.1019539.481 1439.199542.4771439.2239544.9891439.2519552.4471439.325\n", + "9562.4161439.0729572.3861438.4499582.3551433.7069592.325 1433.469600.2861433.061\n", + "9602.2861432.9679602.2951432.9669607.3681433.183 9612.271433.3989622.2441434.224\n", + "9632.2181435.6179642.1931436.8689646.8391437.095 9652.11437.339 9661.951437.471\n", + "9671.7991437.2269681.6481436.1739689.0931436.5159691.5091436.6369701.4061436.855\n", + "9711.3021436.9879717.8371436.6829721.2381436.5319731.2521436.5399741.2651436.546\n", + "9742.1161436.4639742.9681436.381 9751.411435.6539761.5681436.6819771.7251436.609\n", + "9781.8831436.7369782.9731436.8359792.0411437.5879802.1981437.9569812.3561438.299\n", + "9822.5131438.3259822.9781438.3239832.671 1438.239842.8281436.3659852.9861434.769\n", + "9862.9831437.1929863.1431437.2299873.3011437.3029883.4581437.3989893.6161436.822\n", + "9902.9881436.9459903.7731436.9519913.9311436.9969924.0881436.5099924.9121436.475\n", + "9934.3551436.1239940.4421436.2269944.6311436.2669954.9071436.6889965.1831436.605\n", + "9974.8021436.3349975.4591436.3149976.0031436.2789985.7351435.6419996.0111433.617\n", + "10006.291435.39510009.161435.43910016.561435.50710022.421437.23410026.841438.537\n", + "10037.111439.09510043.521439.13310047.391439.14410048.87 1439.1510057.671439.183\n", + "10067.941439.00710077.88 1440.1810078.22 1440.2310088.491441.97310098.771446.052\n", + "10109.05 1452.0410112.241454.09410119.321458.404 10129.61463.25510139.871467.458\n", + " 10146.61470.83910150.151472.85810160.431479.171 10170.71481.27910180.961486.683\n", + "10180.981486.69310191.251493.79610201.531499.66810211.811506.57610215.321508.435\n", + "10222.081511.70510226.941514.078\n", + "#Mann= 6 , -1 , 0 \n", + "9100.327 0.035 09415.477 0.06 09515.266 0.035 0\n", + "9976.003 0.03 010022.42 0.035 010048.87 0.12 0\n", + "Bank Sta=9976.003,10022.42\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,11649.77,210.6718,210.0612,217.7682\n", + "XS GIS Cut Line= 4\n", + "2558491.236043721613362.263242862558300.723085611612976.14609283\n", + "2558025.777306481612611.134230982557961.150826171612534.09331362\n", + "#Sta/Elev=149\n", + "9506.1761527.2849511.8241526.4519519.6191525.4329522.7191524.9299533.6141522.478\n", + "9542.604 1519.76 9544.511519.1989555.4051516.5989565.5891514.284 9566.31514.153\n", + "9577.1951512.603 9588.091510.3939588.5741510.2689598.9851507.4379609.8811504.865\n", + "9611.5591504.5889620.7761502.9729631.6711501.0859634.544 1500.579642.5661499.227\n", + "9653.4611496.5849657.5291495.3749664.3561493.2879675.2511487.8169680.5141485.129\n", + "9686.1471482.2459688.2431482.1819697.0421481.9119703.499 1481.489707.9371481.173\n", + "9718.8321475.7579726.4841474.0159729.7271473.2599740.6221471.8329749.4691470.431\n", + 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10107.31435.51610118.131436.35410119.481436.48110131.661435.154\n", + "10134.841435.18510143.841435.78710151.561436.26410156.031436.36410168.21 1436.64\n", + "10168.271436.64710180.391437.47410184.981437.57810192.581438.155 10201.71437.726\n", + "10204.761437.59310216.941437.49610218.411437.47610229.131437.20510235.131437.226\n", + "10241.311437.25810251.841437.05510253.491437.01910265.671437.22210268.551437.341\n", + "10277.861437.79910285.27 1438.1110290.041438.19610301.981438.37210302.221438.376\n", + "10314.411438.283 10318.71438.16510326.591438.04710335.411438.07410338.771438.116\n", + "10350.951438.09510352.12 1438.110363.141438.05110368.841438.00410375.321438.049\n", + "10385.551438.645 10387.51438.82110389.371438.99410393.711439.39510399.931440.086\n", + " 10401.71440.36610412.621442.30410417.321444.291 10425.31448.52410432.931453.957\n", + "10437.991457.69110448.541466.08910450.671467.66510463.361472.58810464.161472.801\n", + "10476.041475.49610479.771476.37510488.721480.09310494.271483.038\n", + "#Mann= 9 , -1 , 0 \n", + "9506.176 0.035 09688.243 0.06 09756.352 0.035 0\n", + "9813.638 0.05 09915.611 0.035 09939.483 0.05 0\n", + " 10034.2 0.12 010089.45 0.035 010389.37 0.12 0\n", + "#XS Ineff= 2 , 0 \n", + " 0 10198.34 \n", + "Permanent Ineff=\n", + " F F\n", + "Bank Sta=9939.483,10034.2\n", + "Exp/Cntr=0.3,0.1\n", + "\",...,\"Type RM Length L Ch R = 1 ,1595.506,401.7363,398.8766,396.9099\n", + "XS GIS Cut Line= 11\n", + "2569866.855332031569170.360780212569930.926198271569166.69678087\n", + " 2570006.2559641 1569159.9022742570068.28737536 1569152.1884285\n", + "2570141.366381391569140.409234492570195.624875831569129.64314001\n", + "2570257.327370751569115.202519972570358.472903061569087.20764276\n", + "2570360.393819351569086.650793942570461.87762149 1569059.3065258\n", + "2570477.554173541569056.01259492\n", + "#Sta/Elev=87\n", + "9499.0231786.7119505.066 1783.769514.9191782.2499524.7731780.4139534.6261776.614\n", + " 9544.481773.2269554.3331770.7029563.1991768.4059564.1891768.1419574.0611765.398\n", + "9583.9331762.6119591.8091760.1999593.8061759.5879603.6781756.031 9613.551752.038\n", + "9623.4221746.9359633.2951742.4739638.8341738.601 9643.181735.6029653.0831729.608\n", + "9662.986 1727.939672.8891726.5149682.7911719.4619692.6941713.8439697.7821711.044\n", + "9701.3431709.0559702.6031708.3459712.5511702.0669722.4991696.6599732.4471691.508\n", + "9742.3951686.5139752.3431681.2889762.2921676.3699767.2171673.832 9772.241671.195\n", + "9775.3661669.6549782.2281666.2839792.2351661.6089802.2421656.835 9812.251652.599\n", + "9822.2571648.6719823.768 1648.129830.6821645.5589832.2741644.955 9842.351641.233\n", + "9852.4251637.243 9862.5 1632.499871.1661628.6879872.5761628.0669882.6511623.418\n", + "9892.7261617.2279894.0521616.5089902.8861611.9049913.0581608.0639913.8451607.623\n", + " 9923.231602.3369933.4031593.6629943.5751584.7739952.9751580.5029953.7471580.155\n", + "9963.9191576.4719969.9981574.8789974.0921573.8059984.2641573.8129992.1051573.817\n", + "9994.4361573.819 99991573.824 100011573.82610004.611573.82910014.761573.838\n", + "10024.921573.84510030.011573.84910031.961573.85110035.071573.85410045.231573.862\n", + "10055.381573.86910065.541573.87810072.141574.22610075.691574.48410077.981575.238\n", + "10085.841577.827 100961581.007 10106.11591.97110106.151592.02710114.121592.944\n", + "10116.181593.10110122.121593.023\n", + "#Mann= 3 , -1 , 0 \n", + "9499.023 0.12 09953.747 0.05 010085.84 0.06 0\n", + "Bank Sta=9953.747,10085.84\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,1196.63,330.3242,398.8766,438.9131\n", + "XS GIS Cut Line= 12\n", + " 2569973.83429441569523.920553592570074.614466151569526.71344698\n", + "2570143.887681351569526.113518212570201.084971381569523.59966273\n", + "2570268.613317321569517.59688189 2570319.03802861569510.39962544\n", + "2570376.176272521569498.677135652570421.021740971569486.32491607\n", + "2570467.553377281569470.860472212570469.443049681569470.20538578\n", + "2570537.419084781569448.859015632570577.076295591569439.29613939\n", + "#Sta/Elev=87\n", + "9499.0441767.6589502.116 1767.12 9511.971765.3949521.8241764.0059531.6791762.473\n", + "9541.5331762.2149551.3871760.1399561.2411757.3799571.0961755.655 9580.951754.537\n", + "9590.8041753.1369597.9551752.0899599.8631751.7989600.6581751.6779610.5031748.984\n", + "9620.3491745.9329630.1941742.6639640.039 1738.999649.8841735.936 9659.731732.624\n", + "9669.1391729.1069669.5751728.9419679.4241725.5879689.2731721.8239689.3141721.807\n", + "9699.1221717.8429708.9711714.446 9718.821708.5269726.3921707.9369728.674 1707.76\n", + "9738.5451704.9649748.4171700.3479758.2881696.4319768.1591692.5399778.0311690.202\n", + "9787.9021687.1019794.186 1685.569797.7921684.6599807.7161682.208 9817.641679.506\n", + "9824.8451677.4339827.5641676.6749837.4881673.7199845.1221670.5129847.4331669.371\n", + "9857.4521664.1929867.4711655.9919877.4911645.0969884.4311642.052 9887.511640.739\n", + "9897.5291635.133 9903.451632.0249907.6091629.9819917.7781624.2799927.9471622.071\n", + "9928.6721620.5019938.1161598.7729948.2851584.6919949.9661582.642 9955.92 1575.44\n", + "9958.5821572.7479964.9871571.4319968.904 1570.649969.9971570.6229979.2261570.469\n", + "9989.548 1570.479997.754 1570.47 9999 1570.479999.873 1570.47 100011570.471\n", + "10010.151570.47110020.421570.47310030.011570.47210030.691570.47210032.081570.472\n", + "10040.961570.64610051.231575.051 10061.51581.59910066.671585.63410071.771589.385\n", + "10072.251589.46410081.871590.81110091.961591.04210102.05 1591.1410109.93 1597.35\n", + "10112.141599.32610113.04 1600.57\n", + "#Mann= 3 , -1 , 0 \n", + "9499.044 0.12 0 9955.92 0.05 010051.23 0.06 0\n", + "Bank Sta=9955.92,10051.23\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,797.7532,391.7664,398.8766,407.5078\n", + "XS GIS Cut Line= 12\n", + "2569950.142438951569685.925139022570048.453772321569708.27646943\n", + "2570115.47023832 1569725.82628252570193.948911681569749.71266599\n", + " 2570251.99762231569770.426299572570343.346052731569809.42124964\n", + "2570421.221551271569847.912041382570423.004423261569848.81833464\n", + " 2570514.30180711569894.483971712570612.319257981569939.66964625\n", + "2570723.795974861569986.118438442570882.17984215 1570045.6387451\n", + "#Sta/Elev=150\n", + "9499.0451760.1459499.8811759.9029508.9351757.2739519.0681754.2149529.2021751.713\n", + "9539.3361748.6779541.4281748.1389549.4691746.1769559.6031744.0319569.7371741.689\n", + " 9579.871739.9229582.9741739.3549590.0041738.1119599.8651734.969 9600.141734.879\n", + " 9610.361730.5419620.5811730.0769621.6271729.9839630.8011728.9819641.0211726.543\n", + "9651.2411724.7539658.2971723.8569661.4621723.4459669.1411722.1059671.7121721.657\n", + 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"10404.581729.47410415.171733.58110420.431735.63510425.751737.71110436.33 1741.85\n", + "10446.911746.07410447.291746.21310451.211747.56210457.491749.72410468.071753.488\n", + "10474.161755.50210478.651756.95110489.231760.31310499.811763.26910500.981763.571\n", + "#Mann= 5 , -1 , 0 \n", + "9499.045 0.12 09952.002 0.05 010063.54 0.06 0\n", + "10251.82 0.12 010451.21 0.035 0\n", + "Bank Sta=9952.002,10063.54\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,398.8766,429.1667,398.8766,364.8997\n", + "XS GIS Cut Line= 12\n", + "2569757.246055221569959.51847817 2569814.56280451569980.11342874\n", + "2569885.318804391570008.405213072569967.552871981570045.55613195\n", + "2570028.083227011570076.09690963 2570137.49744321570138.27234724\n", + "2570203.802289011570181.144349242570205.433244961570182.30192545\n", + "2570271.536427261570225.492995912570395.693569811570299.81847222\n", + "2570510.975880571570362.558521342570641.118864671570426.59477915\n", + "#Sta/Elev=155\n", + 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1607.349881.9751604.9199893.402 15989896.3921596.172\n", + "9904.8291591.4869915.7861585.8149916.2561585.5199920.0421582.4469927.9631575.781\n", + "9933.8611572.3789939.8081570.3929951.5661569.9399951.6531569.9399963.4991569.938\n", + " 9969.271569.9379969.9731569.9379975.3441569.9369986.9741569.9359987.1891569.935\n", + " 99991569.9339999.0361569.933 100011569.93310004.531569.93310010.971569.932\n", + "10022.111569.93110022.861569.93110030.02 1569.9310034.741569.92910039.681569.929\n", + "10046.621569.92810057.261570.30410058.511570.38710070.391570.57710074.84 1570.22\n", + "10079.961570.04210082.211570.123 10093.21573.386 10093.81573.53310101.141574.419\n", + "10105.381574.93110111.881578.69710116.971581.53210128.551588.59410130.561589.137\n", + "10140.141591.61510149.241592.07410151.721592.13210163.311592.19810167.921592.684\n", + "10174.891594.41510186.481606.578 10186.61606.66610198.061612.17810205.281613.909\n", + 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"10481.121645.97310491.781646.60310500.911646.89710500.981646.905\n", + "#Mann= 5 , -1 , 0 \n", + " 9499.01 0.12 09904.446 0.05 010088.99 0.12 0\n", + "10187.34 0.06 010386.53 0.12 0\n", + "Bank Sta=9904.446,10088.99\n", + "Exp/Cntr=0.3,0.1\n", + "\n", + "LCMann Time=Dec/30/1899 00:00:00\n", + "LCMann Region Time=Dec/30/1899 00:00:00\n", + "LCMann Table=0\n", + "Chan Stop Cuts=-1\n", + "Use User Specified Reach Order=0\n", + "GIS Ratio Cuts To Invert=-1\n", + "GIS Limit At Bridges=0\n", + "Composite Channel Slope=5\"]]\n", + "station_elevation_points: [[\"[(8996.427, 1486.116), (8999.665, 1484.945), (9009.75, 1481.634), (9012.305, 1480.734), (9019.834, 1478.072), (9029.919, 1474.717), (9040.003, 1471.709), (9050.088, 1468.015), (9057.762, 1465.104), (9060.172, 1464.198), (9070.257, 1460.288), (9080.341, 1456.826), (9088.94, 1455.198), (9090.425, 1454.917), (9100.51, 1452.727), (9103.219, 1452.403), (9110.594, 1451.496), (9120.679, 1450.472), (9130.763, 1450.22), (9140.848, 1449.938), 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1643.456), (10470.47, 1644.984), (10475.13, 1645.411), (10481.12, 1645.973), (10491.78, 1646.603), (10500.91, 1646.897), (10500.98, 1646.905)]\"]]\n", + "bank_stations: [[\"['9914.631', '10013.44']\",\"['9938.62', '10039.25']\",\"['9977.676', '10010.53']\",\"['9976.003', '10022.42']\",\"['9939.483', '10034.2']\",...,\"['9953.747', '10085.84']\",\"['9955.92', '10051.23']\",\"['9952.002', '10063.54']\",\"['9927.963', '10093.8']\",\"['9904.446', '10088.99']\"]]\n", + "number_of_station_elevation_points: [[194,188,189,147,149,...,87,87,150,155,144]]\n", + "number_of_coords: [[10,10,16,13,4,...,11,12,12,12,10]]\n", + "flows: [[\"7012.0\n", + "9628.0\n", + "4397.0\",\"7012.0\n", + "9628.0\n", + "4397.0\",\"7023.0\n", + "9643.0\n", + "4404.0\",\"7023.0\n", + "9643.0\n", + "4404.0\",\"7023.0\n", + "9643.0\n", + "4404.0\",...,\"12672.0\n", + "14408.0\n", + "11146.0\",\"12676.0\n", + "14413.0\n", + "11149.0\",\"12676.0\n", + "14413.0\n", + "11149.0\",\"12676.0\n", + "14413.0\n", + "11149.0\",\"12676.0\n", + "14413.0\n", + "11149.0\"]]\n", + "profile_names: [[\" 100yr\n", + "100yrplus\n", + "100yrminus\",\" 100yr\n", + "100yrplus\n", + "100yrminus\",\" 100yr\n", + "100yrplus\n", + "100yrminus\",\" 100yr\n", + "100yrplus\n", + "100yrminus\",\" 100yr\n", + "100yrplus\n", + "100yrminus\",...,\" 100yr\n", + "100yrplus\n", + "100yrminus\",\" 100yr\n", + "100yrplus\n", + "100yrminus\",\" 100yr\n", + "100yrplus\n", + "100yrminus\",\" 100yr\n", + "100yrplus\n", + "100yrminus\",\" 100yr\n", + "100yrplus\n", + "100yrminus\"]]\n", + "geometry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n", + "huc: [[\"17060305\",\"17060305\",\"17060305\",\"17060305\",\"17060305\",...,\"17060305\",\"17060305\",\"17060305\",\"17060305\",\"17060305\"]]\n", + "model_id: [[\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",...,\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\"]]\n", + "us_reach_id: [[\"23597201\",\"None\",\"23597199\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "us_network_to_id: [[\"23597199\",\"None\",\"23597197\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", + "ds_reach_id: [[\"None\",\"None\",\"23597201\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"23597347\"]]\n", + "ds_network_to_id: [[\"None\",\"None\",\"23597199\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"23597345\"]]\n", + "__index_level_0__: [[19178,19179,19180,19181,19182,...,24403,24404,24405,24406,24407]]\n" + ] + } + ], + "source": [ + "# Scenario II\n", + "# User Args\n", + "catalog_name = 'xs'\n", + "huc_num = 17060305\n", + "\n", + "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", + "tablename = f'huc_{huc_num}'\n", + "namespace = 'mip'\n", + "catalog = load_catalog(name=catalog_name, \n", + " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", + "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "print(iceberg_table.to_string(preview_cols=30))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e10ffe0a-3811-4a48-9524-d55e3c26807a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pyarrow.Table\n", + "river: large_string\n", + "reach: large_string\n", + "river_reach: large_string\n", + "river_station: double\n", + "river_reach_rs: large_string\n", + "thalweg: double\n", + "xs_max_elevation: double\n", + "left_reach_length: double\n", + "right_reach_length: double\n", + "channel_reach_length: double\n", + "ras_data: large_string\n", + "station_elevation_points: large_string\n", + "bank_stations: large_string\n", + "number_of_station_elevation_points: int64\n", + "number_of_coords: int64\n", + "flows: large_string\n", + "profile_names: large_string\n", + "geometry: large_binary\n", + "huc: large_string\n", + "model_id: large_string\n", + "us_reach_id: large_string\n", + "us_network_to_id: large_string\n", + "ds_reach_id: large_string\n", + "ds_network_to_id: large_string\n", + "__index_level_0__: int64\n", + "----\n", + "river: [[\"1242\",\"1242\",\"1242\",\"1242\",\"1242\",...,\"1236\",\"1236\",\"1236\",\"1236\",\"1236\"]]\n", + "reach: [[\"15\",\"15\",\"15\",\"15\",\"15\",...,\"61\",\"61\",\"61\",\"61\",\"61\"]]\n", + "river_reach: [[\"1242,15\",\"1242,15\",\"1242,15\",\"1242,15\",\"1242,15\",...,\"1236,61\",\"1236,61\",\"1236,61\",\"1236,61\",\"1236,61\"]]\n", + "river_station: [[1645.9,1552.446,1458.841,1365.072,1271.617,...,725.2979,576.5005,427.703,231.8489,35.9949]]\n", + "river_reach_rs: [[\"1242 15 1645.9\",\"1242 15 1552.446\",\"1242 15 1458.841\",\"1242 15 1365.072\",\"1242 15 1271.617\",...,\"1236 61 725.2979\",\"1236 61 576.5005\",\"1236 61 427.703\",\"1236 61 231.8489\",\"1236 61 35.9949\"]]\n", + "thalweg: [[3550.146,3547.128,3543.629,3540.886,3537.79,...,3533.829,3531.006,3528.091,3525.665,3521.963]]\n", + "xs_max_elevation: [[3577.866,3562.288,3580.739,3569.751,3569.955,...,3587.055,3592.946,3565.975,3588.964,3583.562]]\n", + "left_reach_length: [[96.61742,96.73559,95.70554,94.16392,92.93386,...,151.7416,150.1448,198.9483,156.1958,0]]\n", + "right_reach_length: [[89.80285,90.02276,91.58722,92.05128,94.53484,...,139.8336,130.2688,186.9168,195.4554,0]]\n", + "channel_reach_length: [[93.45487,93.60503,93.76862,93.45487,93.45487,...,148.7975,148.7975,195.854,195.854,0]]\n", + "ras_data: [[\"Type RM Length L Ch R = 1 ,1645.9,96.61742,93.45487,89.80285\n", + "XS GIS Cut Line= 8\n", + "528894.279452703873971.621611987528907.966560143873946.488329754\n", + " 528925.01586739 873921.27277097528926.208395569 873919.66719427\n", + " 528948.49023709873885.841779061528972.766208581873836.497337306\n", + "528991.974043957873781.439473717529008.465616347 873712.3025376\n", + "#Sta/Elev=85\n", + "9939.9433565.3739944.636 3565.289946.6773565.0169950.3293564.4819956.0233562.989\n", + "9957.1313562.6999961.716 3561.11 9967.413558.7359967.5863558.6679968.5613558.316\n", + "9973.3763556.6879976.6553555.5799979.4123554.5829985.4473552.5949985.5823552.554\n", + "9989.947 3551.419991.4833551.0079994.5083550.4899997.5193550.146 99993550.156\n", + " 100013550.17710003.353550.234 10003.6 3550.2510009.583550.66310012.443550.833\n", + "10012.953550.88810015.573551.17110021.533551.94810021.563551.95110027.543551.831\n", + "10030.623551.68510033.533551.50210039.523551.507 10039.73551.502 10041.53551.458\n", + "10045.233551.39110050.593551.513 10050.83551.52310056.373551.82710061.913551.735\n", + "10061.953551.73510067.52 3551.8910073.093551.94810073.243551.94810078.663551.913\n", + "10084.243551.87410084.573551.87810089.813551.78510095.38 3551.3310095.893551.318\n", + " 10096.53551.32810100.73 3551.4210106.03 3551.510110.87 3551.6110111.323551.618\n", + "10116.623551.61310121.913551.37610126.053551.38510127.213551.406 10132.53551.669\n", + " 10137.8 3551.8810141.233553.033 10143.13553.61110148.393555.77510153.693558.897\n", + "10154.813559.54510157.083560.85410158.863561.722 101643562.68510169.143564.403\n", + "10174.283566.15410178.633567.54110179.423567.77110184.563569.051 10189.73570.504\n", + "10194.843571.85610199.983572.85910200.183572.89210205.123573.82610210.263574.735\n", + " 10215.43576.35310220.543577.12910221.723577.28210225.683577.80710225.893577.866\n", + "#Mann= 3 , 0 , 0 \n", + "9939.943 0.065 09989.947 0.04 010012.95 0.065 0\n", + "#XS Ineff= 2 , 0 \n", + " 0 10026.37 \n", + "Permanent Ineff=\n", + " F F\n", + "Bank Sta=9989.947,10012.95\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,1552.446,96.73559,93.60503,90.02276\n", + "XS GIS Cut Line= 8\n", + "528995.492298483874005.011416777529006.956371507873965.884739773\n", + "529007.476454129873963.953544789529024.844213126873907.219206723\n", + "529036.479831966873862.946674337529049.075311191873800.032525587\n", + " 529056.98887746873744.890768309529062.422368085873707.030186743\n", + "#Sta/Elev=83\n", + "9958.2283555.8889960.8453556.1439965.3693554.7539966.0563554.6179971.2663553.203\n", + "9976.4763551.1859981.6863549.0219983.1513548.7059986.8963548.0059987.5453547.907\n", + "9992.1073547.2179997.3173547.168 99993547.157 10001 3547.1410001.083547.139\n", + "10002.523547.12810007.753547.873 10010 3548.3610012.983549.00410018.163549.255\n", + "10018.213549.25710023.443549.08610028.673548.62510033.893548.38710035.243548.332\n", + "10039.123548.19310044.353548.14810049.58 3548.3510052.323548.42210054.813548.473\n", + "10060.043548.82310060.333548.81410065.213548.64610070.383548.63410070.783548.623\n", + "10075.553548.52710080.723548.32710085.893548.34410090.453548.35510091.063548.349\n", + "10096.233548.271 10101.43548.32710106.113548.43710106.563548.44610111.313548.733\n", + "10111.663548.75610116.763549.14710121.863549.31510126.96 3549.7410132.063549.646\n", + "10136.783549.66610137.163549.66410142.263550.17410147.363550.21810152.463550.027\n", + "10157.56 3550.110162.25 3550.0210162.663550.01710167.763549.85510170.273549.796\n", + "10172.833549.73210177.883549.56410182.93 3549.8110187.983549.65110193.043549.445\n", + "10194.383549.39710198.093549.23210203.143549.61410208.19 3550.1610213.243550.864\n", + "10218.293551.86610223.343552.58110225.983553.16410228.39 3553.6810229.583553.997\n", + "10233.443555.068 10238.53556.43410243.553557.723 10248.63559.01210253.653560.127\n", + " 10258.73561.51510263.753562.18910264.233562.288\n", + "#Mann= 3 , 0 , 0 \n", + "9958.228 0.065 09987.545 0.04 0 10010 0.065 0\n", + "#XS Ineff= 2 , 0 \n", + " 0 10020.61 \n", + "Permanent Ineff=\n", + " F F\n", + "Bank Sta=9987.545,10010\n", + "Exp/Cntr=0.3,0.1\n", + "\",\"Type RM Length L Ch R = 1 ,1458.841,95.70554,93.76862,91.58722\n", + "XS GIS Cut Line= 7\n", + "528939.960137047874200.795523874529100.822893173874025.728232764\n", + "529099.580756519873976.581178352529099.535324253873974.581694439\n", + " 529096.29245545 873887.78082598529098.715297006873749.681707554\n", + "529099.584481761873700.139198773\n", + "#Sta/Elev=143\n", + "9714.2233580.7399717.9773579.8719718.6993579.6989725.3673577.5819725.4893577.538\n", + 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"529575.760648519874884.549223147529481.701193254874788.105902128\n", + "529340.709909569874655.622377299529308.019624803874622.156504385\n", + "529306.846605174874620.536620133529286.260644916874599.150820695\n", + "528993.921709903874402.011202961\n", + "#Sta/Elev=222\n", + "9624.0323592.1739626.8943592.3779628.7023592.6439633.8783592.8039635.8633592.946\n", + "9640.8623592.7149643.0243592.6389647.8473591.8669650.1853591.6179654.8313591.228\n", + "9657.3473591.2529661.8153591.1429664.5083591.1719668.7993591.1029671.6693591.313\n", + "9675.7843591.689 9678.833591.8159682.7683592.2289685.9913592.5369689.7523592.653\n", + "9693.1533592.4059696.7363591.6319700.3143590.741 9703.723590.0389707.4753589.124\n", + "9710.7053588.4529714.6363587.9589717.689 3587.539721.7983587.2279724.6733587.044\n", + "9728.9593586.8449731.6573586.754 9736.123586.4459738.642 3586.359743.2813585.914\n", + "9745.6263585.5999750.4433584.926 9752.613584.5989757.6043583.6059758.7483583.361\n", + 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3532.2810086.733532.20410092.223531.80410097.563531.795\n", + "10097.713531.79810103.213531.877 10108.73532.02810109.643532.16610114.193533.046\n", + "10119.683534.49810121.723534.55710125.183534.28210130.673534.254 10133.83534.279\n", + "10136.163534.25610141.663534.01710145.883533.53210147.153533.35310152.643532.693\n", + "10157.963532.71610158.133532.72210163.633533.26310169.123534.58510170.043534.653\n", + "10174.613535.112 10180.13535.31110182.123535.352 10185.63535.39310191.093535.432\n", + " 10194.23535.49810196.583535.56310202.073536.58110206.28 3537.7410207.573538.073\n", + "10213.063539.63310218.363541.64110218.553541.70810224.043543.75310229.543546.488\n", + "10230.443546.78110235.03 3547.5310240.523546.51610242.523545.88210243.283545.636\n", + " 10246.13544.77510251.75 3543.7510253.313543.655 10257.43543.47410263.063543.396\n", + "10264.03 3543.3810268.71 3543.2910274.363543.23510274.743543.22710280.023543.119\n", + 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3532.48\n", + "10197.113532.52510203.173532.72110203.39 3532.7310209.683532.83210211.423532.907\n", + "10215.963533.11610219.673533.05510222.253533.13110227.933533.56210228.533533.589\n", + "10234.823534.98110236.183535.447 10241.13537.11910244.433538.56210247.393539.332\n", + "10252.68 3539.9510253.673539.96310259.963539.60910260.943539.43810266.243538.371\n", + "10269.193537.83410272.533537.01710277.44 3536.710278.813536.642 10285.13536.353\n", + " 10285.73536.34810291.383536.49810293.953536.52510297.663536.683 10302.23536.663\n", + "10303.953536.60410310.233536.64310310.453536.64310316.523536.55710318.713536.531\n", + " 10322.83536.51310326.963536.52110329.093536.52510335.213536.91910335.373536.925\n", + "10341.663538.12310343.473538.39610347.943538.70310351.723538.74910354.233538.728\n", + "10359.973538.54410360.513538.593 10366.83538.82110368.223538.65910373.083538.616\n", + "10376.483538.39610379.373538.25910384.733538.11210385.653538.12410391.943538.324\n", + "10392.98 3538.3510398.223538.47710401.243538.55310404.513538.69710409.493539.102\n", + "10410.793539.11910417.083539.09210417.743539.11810423.36 3539.4910425.993539.643\n", + "10429.643539.80410434.253539.84910435.933539.85610442.213539.753 10442.53539.777\n", + " 10448.5 3540.3810450.753540.47210454.783540.644 104593541.04810461.07 3541.19\n", + "10467.263541.23810467.353541.23710473.643541.43410475.513541.51710479.92 3541.77\n", + "10483.763542.06510486.213542.24310492.023542.44910492.493542.45910498.783542.505\n", + "10500.273542.51410505.063542.71510508.523543.17210511.353543.46910516.773543.545\n", + "10517.633543.54310523.923543.55710525.033543.617 10530.23543.58810533.28 3543.56\n", + "10536.493543.70610541.53 3543.910542.77 3543.9710549.053544.24910549.793544.289\n", + "10555.343544.75910558.043545.10910561.623545.73810566.293546.71410567.913546.934\n", + "10574.193547.26910574.543547.24410580.483547.297 10582.83547.31210586.763547.195\n", + "10591.053547.35410593.05 3547.4 10599.33547.42310599.333547.42210605.623547.539\n", + "10607.563547.482 10611.93547.39210615.813547.41610618.193547.48210624.063547.955\n", + "10624.473547.98410630.763548.48910632.313548.55210637.043548.70810640.573548.788\n", + "10643.333548.90610648.823548.92910649.613548.931 10655.93548.88110657.07 3548.93\n", + "10662.183549.55110665.333550.10410668.473550.78410673.58 3552.3310674.753552.793\n", + "10681.033555.24310681.833555.57110687.323558.07810690.083559.295 10693.63560.674\n", + "10698.343562.85810699.893563.52910706.173566.25510706.593566.47410712.463569.467\n", + "10714.843570.49810718.74 3572.24 10723.13573.74110725.033574.39410731.313576.874\n", + "10731.353576.887 10737.63579.189 10739.63579.84410743.883581.37310746.923582.122\n", + "#Mann= 3 , 0 , 0 \n", + "9626.995 0.065 09974.171 0.04 010010.04 0.065 0\n", + "#XS Ineff= 2 , 0 \n", + " 9893.349 10140.11 \n", + "Permanent Ineff=\n", + " F F\n", + "Bank Sta=9974.171,10010.04\n", + "Exp/Cntr=0.3,0.1\n", + "\"]]\n", + "station_elevation_points: [[\"[(9939.943, 3565.373), (9944.636, 3565.28), (9946.677, 3565.016), (9950.329, 3564.481), (9956.023, 3562.989), (9957.131, 3562.699), (9961.716, 3561.11), (9967.41, 3558.735), (9967.586, 3558.667), (9968.561, 3558.316), (9973.376, 3556.687), (9976.655, 3555.579), (9979.412, 3554.582), (9985.447, 3552.594), (9985.582, 3552.554), (9989.947, 3551.41), (9991.483, 3551.007), (9994.508, 3550.489), (9997.519, 3550.146), (9999.0, 3550.156), (10001.0, 3550.177), (10003.35, 3550.234), (10003.6, 3550.25), (10009.58, 3550.663), (10012.44, 3550.833), (10012.95, 3550.888), (10015.57, 3551.171), (10021.53, 3551.948), (10021.56, 3551.951), (10027.54, 3551.831), (10030.62, 3551.685), (10033.53, 3551.502), (10039.52, 3551.507), (10039.7, 3551.502), (10041.5, 3551.458), (10045.23, 3551.391), (10050.59, 3551.513), (10050.8, 3551.523), (10056.37, 3551.827), (10061.91, 3551.735), (10061.95, 3551.735), (10067.52, 3551.89), (10073.09, 3551.948), (10073.24, 3551.948), (10078.66, 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"633.0\n", + "805.0\n", + "498.0\n", + "1185.0\",\"168.0\n", + "313.0\n", + "457.0\n", + "634.0\n", + "805.0\n", + "499.0\n", + "1185.0\",\"169.0\n", + "316.0\n", + "460.0\n", + "638.0\n", + "811.0\n", + "502.0\n", + "1194.0\",...,\"433.0\n", + "796.0\n", + "1152.0\n", + "1584.0\n", + "2014.0\n", + "1247.0\n", + "2926.0\",\"433.0\n", + "796.0\n", + "1152.0\n", + "1585.0\n", + "2014.0\n", + "1247.0\n", + "2927.0\",\"437.0\n", + "803.0\n", + "1162.0\n", + "1599.0\n", + "2032.0\n", + "1258.0\n", + "2952.0\",\"440.0\n", + "808.0\n", + "1169.0\n", + "1608.0\n", + "2043.0\n", + "1265.0\n", + "2967.0\",\"440.0\n", + "808.0\n", + "1169.0\n", + "1608.0\n", + "2043.0\n", + "1265.0\n", + "2967.0\"]]\n", + "profile_names: [[\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\",\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\",\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\",\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\",\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\",...,\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\",\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\",\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\",\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\",\" 10yr\n", + "25yr\n", + "50yr\n", + "100yr\n", + "100plus\n", + "100minus\n", + "500yr\"]]\n", + "geometry: [[010200000008000000E895564C44AE34C183C588B9C11F334114D0C25941AE34C1C75AF77AB91F3341C13C77663DAE34C103E1300FB11F33413198E01D3DAE34C11085B084B01F334193A669FE37AE34C126BE9F3FA51F33411E16E8FB32AE34C1272FE52D951F3341ABCAC0C02FAE34C1031DD09A831F3341B4CAB2FA2DAE34C169BE50E16D1F3341,0102000000080000008AF82D7824AE34C1D2A50D4BC71F3341535FE5D322AE34C173B707E7BA1F33413F97A8C222AE34C198C8FE4ABA1F3341921E2B2820AE34C1698BE94AA81F334156F703B31EAE34C1F16F23559A1F3341DA8CAACE1DAE34C1C9E10DAC861F33410B9B18F61DAE34C14CEAD794751F33416A302B111EAE34C1CBE0CDD8691F3341,010200000007000000BA5FC7222CAE34C1116D3D36052033413BE8E2FC03AE34C12D597FD4C81F3341FE4DCB9B06AE34C15EFFB2F8B91F334195B7B2B606AE34C1D642E35DB91F3341E46205A80BAE34C1BD1DF02B9F1F33411523074011AE34C15C085327751F33411928C14113AE34C16DED6E14661F3341,010200000009000000DE56E5E217AE34C131114E300E20334133F5A220E7AD34C1C990C092BC1F33417615CDBAEBAD34C1EAAA910CB11F33410E354FF3EBAD34C1E0810D7AB01F3341123765E3F2AD34C11852DEA79F1F33412B886BBEF7AD34C1A374137B921F334165908C7FFDAD34C120FB7EA77F1F3341ED05BDA701AE34C12B20A0126F1F334105498E2405AE34C1114B5429611F3341,010200000009000000CD284C1EFCAD34C1482734FF05203341126C84C6CDAD34C1A04EFFEBAD1F3341B1F39139D2AD34C13B1E59ABA41F3341D671677FD2AD34C1FC47CE1EA41F3341F149EA59DBAD34C16CBF252E921F334109B727B0E1AD34C1F2950D1A841F3341ED766ABBE7AD34C1489D9FCB741F33414A400040EDAD34C190FF199F641F33411032FDD7F0AD34C123E076165A1F3341,...,010200000007000000710282497AAD34C1E8A31E94EF203341557DEDA2A0AD34C142C32C34C420334128ACF30FC5AD34C1939153B198203341C70AC578C5AD34C12A16213D98203341DAF6CCFECAAD34C1EA011FD49120334198D8BCF8D3AD34C1FD0454A586203341690CF04D1FAE34C1255596F852203341,010200000007000000544E26814EAD34C1E416CA09B82033412508A6136FAD34C1181924039F203341315935599FAD34C1B88A672A7D2033418B37A1AAAAAD34C1020AE57B742033415E878117ABAD34C1E83A970B74203341EF3FBD3BB2AD34C10A66D27B6E2033418E5813C612AE34C1C1C056D73F203341,0102000000090000003ADE6E0542AD34C127E562AB63203341AFEAE0C642AD34C171A4146B63203341F21113495EAD34C12D5649E05B2033414AABBF8A8DAD34C1991E44C7502033413F039E579AAD34C17DF35E654C203341E3D661E89AAD34C17D1C7A2C4C2033414D18FEEAA8AD34C19EA2103D47203341FE1EB746E1AD34C1E99E42E336203341B423C1DDF5AD34C12D573C902F203341,01020000000B000000626785902AAD34C1FAB1F7A034203341791C1BBF4EAD34C16C7824CE232033418CABCB7964AD34C1BFEB6D9D1A203341496919906DAD34C1F1FDAE8217203341D2CB713578AD34C1284D62CC1420334153A524B081AD34C1FA3E7D5413203341B9E8B60D8AAD34C11B0D61CC12203341D0DEC3A88AAD34C170A0DFC912203341C217529391AD34C13B15B36212203341C35474F49DAD34C12D4083A6102033417852CB5B4CAE34C1CE2907D6C11F3341,01020000000B000000B0DE7D191AAD34C10029FA6D282033414F52918526AD34C1E913D8EB2120334104131B0A41AD34C1B9D7EF6812203341F59317F952AD34C18EE8B53906203341BD62E0805EAD34C1748C03D4FC1F3341367DEBF466AD34C16274C814F41F3341349B6BAB6CAD34C17C509538EC1F3341587A9EB171AD34C109211847E31F33419E2DC2F871AD34C12BA438BBE21F3341E8A1F37776AD34C1273F8B8FDA1F3341E1A0F7B634AE34C1DFBB23AF701F3341]]\n", + "huc: [[\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\",...,\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\"]]\n", + "model_id: [[\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",...,\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\"]]\n", + "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"None\"]]\n", + "us_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294311\",\"None\",\"None\",\"None\",\"None\"]]\n", + "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21295551\",\"None\",\"None\",\"None\",\"21294309\"]]\n", + "ds_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"21294311\"]]\n", + "__index_level_0__: [[30787,30788,30789,30790,30791,...,30966,30967,30968,30969,30970]]\n" + ] + } + ], + "source": [ + "# Scenario III\n", + "# User Args\n", + "catalog_name = 'xs'\n", + "huc_num = 15040007\n", + "\n", + "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", + "tablename = f'huc_{huc_num}'\n", + "namespace = 'mip'\n", + "catalog = load_catalog(name=catalog_name, \n", + " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", + "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "print(iceberg_table.to_string(preview_cols=30))" + ] + }, + { + "cell_type": "markdown", + "id": "42d97ebb-f690-4dce-80fd-a01e61f9e6ec", + "metadata": {}, + "source": [ + "#### Demo:\n", + "#### Load Iceberg Catalog & Table of Interest\n", + "#### Cross-Check & Verify Parquet Data Has Been Written To Its Associated Iceberg Table Sucessfully" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "64bfeb93-15f2-4c20-affa-af8b215172de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "river: large_string\n", + "huc: large_string\n", + "model_id: large_string\n", + "us_reach_id: large_string\n", + "ds_reach_id: large_string\n", + "----\n", + "river: [[\"1242\",\"1242\",\"1242\",\"1242\",\"1242\",...,\"1236\",\"1236\",\"1236\",\"1236\",\"1236\"]]\n", + "huc: [[\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\",...,\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\"]]\n", + "model_id: [[\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",...,\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\"]]\n", + "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"None\"]]\n", + "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21295551\",\"None\",\"None\",\"None\",\"21294309\"]]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Query iceberg table for specific features of interest\n", + "catalog.load_table(f'{namespace}.{tablename}').scan(selected_fields=(\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\")).to_arrow()" + ] + }, + { + "cell_type": "markdown", + "id": "42103887-3d43-421a-819d-8689940267d5", + "metadata": {}, + "source": [ + "### Demo VI:\n", + "### Load CRS Mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2c3de676-42ec-4e15-8e04-5469afcbae10", + "metadata": {}, + "outputs": [], + "source": [ + "with open(f'{os.getcwd()}/xs_data/crs_map/crs_mapping.pickle', 'rb') as handle:\n", + " crs_map = pickle.load(handle)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c36c0954-2150-4441-a546-3b455ab871cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(dict,\n", + " {('07130010',\n", + " 'sa_1'): {'LitCr': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaMoRv_us': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'SBLaMoRv': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaHaCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'BapCr': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'RocCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", + " ('07130010',\n", + " 'Killjordan'): {'Killjordan Cr': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", + " ('07130010',\n", + " 'sa_6'): {'CedCkTrbA': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaMoRv': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'CedCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaMoRvTrbA': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaMoRvTrbB': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", + " ('07130010',\n", + " 'sa_7'): {'TowFk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'DroFk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'DroFkTrbAa': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'KepCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'NFEFLaMoRv': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'FarFk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'EFLaMoRv': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'SprCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'ShoFk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", + " ('07130010',\n", + " 'sa_5'): {'TroCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'KilCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", + " ('07130010',\n", + " 'sa_4'): {'CamCr': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'CarCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'GriCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'CamCrTrbA': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", + " ('07130010',\n", + " 'LaMoine'): {'LaMoine River': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'Spring Creek': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", + " ('09020301',\n", + " 'SHRWDS'): {'SHRWDS': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SHR5_1'): {'SHR5': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SHRUS_1'): {'SHRUS': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'GF_EGF_FIS_Submittal1204'): {'RRN': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,'},\n", + " ('09020301',\n", + " 'SHR6_1'): {'SHR6': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SHRup'): {'SHRup': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'UnnamedTrib1_1'): {'UnnamedTrib1': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SHR2'): {'SHR2': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'Maple_Creek_1'): {'Maple Creek': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SHR2_1'): {'SHR2': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'RedRiverTA'): {'RedRiverTA': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'Maple_Creek'): {'Maple Creek': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SandHill29_1'): {'SandHill29': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SHRUS'): {'SHRUS': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SHRWDS_1'): {'SHRWDS': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'UnnamedTrib1'): {'UnnamedTrib1': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SandHill29'): {'SandHill29': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'Ditch16So_1'): {'Ditch16So': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'Kittleson'): {'Kittleson': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SandHillRiver7'): {'SandHillRiver7': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SHR5'): {'SHR5': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SandHillRiver7_1'): {'SandHillRiver7': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'Ditch16So'): {'Ditch16So': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'GFEGFFIS0807'): {'RRN': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,', 'Heartsville': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,', 'Red Lake River': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,', 'English Coulee': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,'},\n", + " ('09020301',\n", + " 'SHR6'): {'SHR6': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'Kittleson_1'): {'Kittleson': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'SHRup_1'): {'SHRup': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('09020301',\n", + " 'RedRiverTA_1'): {'RedRiverTA': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", + " ('06040002',\n", + " 'Bell_Buckle_Cr'): {'Bell Buckle Cr': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Kelly_Creek_T1'): {'Kelly Creek T1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Bomar_Creek'): {'Bomar Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Trib_to_Bomar_Cr'): {'Trib to Bomar Cr': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Rabbit_Branch'): {'Rabbit Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Thompson_Branch'): {'Thompson Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Sinking_Creek'): {'Sinking Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Duck_River_DS'): {'Duck River DS': 'ESPG: 2915, NAD83(HARN) / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'North_Fork_Creek'): {'North Fork Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Butler_Creek'): {'Butler Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Noah_Fork_Creek'): {'Noah Fork Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Duck_River_Trib4'): {'Duck River Trib4': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Wartrace_Creek'): {'Wartrace Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Dry_Branch'): {'Dry Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Doddy_Branch'): {'Doddy Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Stokes_Branch'): {'Stokes Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'GarrisonForkCrT1'): {'GarrisonForkCrT1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Little_Flat_Cr'): {'Little Flat Cr': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Sugar_Creek'): {'Sugar Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Bell_Buckle_CrT2'): {'Bell Buckle CrT2': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Fall_Creek'): {'Fall Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Muse_Branch'): {'Muse Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Fay_Branch'): {'Fay Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Crouch_Branch'): {'Crouch Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Hurricane_Branch'): {'Hurricane Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'GarrisonForkCr'): {'GarrisonForkCr': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Flat_Creek'): {'Flat Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Powell_Creek'): {'Powell Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Dryland_Creek'): {'Dryland Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Bell_Buckle_CrT1'): {'Bell Buckle CrT1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Norman_Creek_T1'): {'Norman Creek T1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Anthony_Branch'): {'Anthony Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Trib_to_Holland'): {'Trib to Holland': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Cannon_Creek'): {'Cannon Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Little_Hurricane'): {'Little Hurricane': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Norman_Creek_T2'): {'Norman Creek T2': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Kelly_Creek'): {'Kelly Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('06040002',\n", + " 'Wartrace_CreekT1'): {'Wartrace CreekT1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", + " ('03170004',\n", + " 'MixonLOMR-Existing3_17_17'): {'Mixons Creek Tri': 'ESPG: 2254, NAD83 / Mississippi East (ftUS),'},\n", + " ('01020002',\n", + " 'Stream0510'): {'Stream0510': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", + " ('01020002',\n", + " 'Stream0528'): {'Stream0528': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", + " ('01020002',\n", + " 'Martin_Stream'): {'Martin Stream': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", + " ('01020002',\n", + " 'Stream0526'): {'Stream0526': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", + " ('01020002',\n", + " 'Stream0527'): {'Stream0527': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", + " ('01020002',\n", + " 'Stream0512'): {'Stream0512': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", + " ('10120203',\n", + " 'Spearfish_Cr_LOMR_FEMA_RESPONSE_1'): {'Spearfish Creek': 'ESPG: 3458, NAD83(HARN) / South Dakota North (ftUS),'},\n", + " ('10120203',\n", + " 'Spearfish_Cr_LOMR_FEMA_RESPONSE'): {'Spearfish Creek': 'ESPG: 3458, NAD83(HARN) / South Dakota North (ftUS),'},\n", + " ('11060002',\n", + " 'YELLOWSTONE_CREEK'): {'YELLOWSTONE CREE': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT64_in_UPPER_SALT_FRK_HUC8_1'): {'UNT64 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT115_in_UPPER_SALT_FRK_HUC8_1'): {'UNT115 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT64_in_UPPER_SALT_FRK_HUC8'): {'UNT64 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'JUG_MOTTE_CREEK_1'): {'JUG MOTTE CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT80_in_UPPER_SALT_FRK_HUC8'): {'UNT80 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'MUSTANG_CREEK_1'): {'MUSTANG CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT79_in_UPPER_SALT_FRK_HUC8'): {'UNT79 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT23_in_UPPER_SALT_FRK_HUC8'): {'UNT23 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT61_in_UPPER_SALT_FRK_HUC8'): {'UNT61 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT83_in_UPPER_SALT_FRK_HUC8_1'): {'UNT83 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT119_in_UPPER_SALT_FRK_HUC8_1'): {'UNT119 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT14_in_UPPER_SALT_FRK_HUC8_1'): {'UNT14 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT45_in_UPPER_SALT_FRK_HUC8'): {'UNT45 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT101_in_UPPER_SALT_FRK_HUC8_1'): {'UNT101 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT84_in_UPPER_SALT_FRK_HUC8_1'): {'UNT84 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'COTTONWOOD_CREEK_1'): {'COTTONWOOD CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT91_in_UPPER_SALT_FRK_HUC8_1'): {'UNT91 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT115_in_UPPER_SALT_FRK_HUC8'): {'UNT115 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT66_in_UPPER_SALT_FRK_HUC8'): {'UNT66 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT75_in_BluffCr_Watershed'): {'UNT75 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", + " ('11060002',\n", + " 'UNT111_in_UPPER_SALT_FRK_HUC8_1'): {'UNT111 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT92_in_UPPER_SALT_FRK_HUC8_1'): {'UNT92 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT75_in_UPPER_SALT_FRK_HUC8_1'): {'UNT75 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT120_in_Liberal_Watershed_1'): {'UNT120 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT9_in_UPPER_SALT_FRK_HUC8_1'): {'UNT9 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT92_in_UPPER_SALT_FRK_HUC8'): {'UNT92 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT107_in_UPPER_SALT_FRK_HUC8'): {'UNT107 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT34_in_UPPER_SALT_FRK_HUC8'): {'UNT34 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT84_in_UPPER_SALT_FRK_HUC8'): {'UNT84 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT27_in_UPPER_SALT_FRK_HUC8'): {'UNT27 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT93_in_UPPER_SALT_FRK_HUC8_1'): {'UNT93 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT55_in_UPPER_SALT_FRK_HUC8'): {'UNT55 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT2_in_UPPER_SALT_FRK_HUC8_1'): {'UNT2 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT106_in_UPPER_SALT_FRK_HUC8_1'): {'UNT106 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT9_in_UPPER_SALT_FRK_HUC8'): {'UNT9 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'MUSTANG_CREEK'): {'MUSTANG CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT14_in_UPPER_SALT_FRK_HUC8'): {'UNT14 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT54_in_UPPER_SALT_FRK_HUC8'): {'UNT54 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT133_in_Liberal_Watershed_1'): {'UNT133 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT21_in_UPPER_SALT_FRK_HUC8_1'): {'UNT21 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT18_in_UPPER_SALT_FRK_HUC8_1'): {'UNT18 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT103_in_UPPER_SALT_FRK_HUC8'): {'UNT103 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT11_in_UPPER_SALT_FRK_HUC8_1'): {'UNT11 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT71_in_UPPER_SALT_FRK_HUC8'): {'UNT71 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT139_in_UPPER_SALT_FRK_HUC8_1'): {'UNT139 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT139_in_UPPER_SALT_FRK_HUC8'): {'UNT139 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT68_in_UPPER_SALT_FRK_HUC8_1'): {'UNT68 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT111_in_UPPER_SALT_FRK_HUC8'): {'UNT111 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT60_in_UPPER_SALT_FRK_HUC8_1'): {'UNT60 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT103_in_Liberal_Watershed'): {'UNT103 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT31_in_UPPER_SALT_FRK_HUC8_1'): {'UNT31 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT19_in_UPPER_SALT_FRK_HUC8_1'): {'UNT19 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT120_in_UPPER_SALT_FRK_HUC8'): {'UNT120 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT25_in_UPPER_SALT_FRK_HUC8_1'): {'UNT25 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT67_in_UPPER_SALT_FRK_HUC8_1'): {'UNT67 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT8_in_UPPER_SALT_FRK_HUC8_1'): {'UNT8 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT29_in_UPPER_SALT_FRK_HUC8'): {'UNT29 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT65_in_UPPER_SALT_FRK_HUC8'): {'UNT65 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT110_in_Liberal_Watershed'): {'UNT110 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT19_in_UPPER_SALT_FRK_HUC8'): {'UNT19 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT27_in_UPPER_SALT_FRK_HUC8_1'): {'UNT27 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT105_in_UPPER_SALT_FRK_HUC8_1'): {'UNT105 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'GREENLEAF_CREEK'): {'GREENLEAF CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT32_in_UPPER_SALT_FRK_HUC8'): {'UNT32 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT73_in_UPPER_SALT_FRK_HUC8_1'): {'UNT73 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT73_in_BluffCr_Watershed'): {'UNT73 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", + " ('11060002',\n", + " 'UNT55_in_UPPER_SALT_FRK_HUC8_1'): {'UNT55 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT87_in_UPPER_SALT_FRK_HUC8_1'): {'UNT87 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT72_in_UPPER_SALT_FRK_HUC8_1'): {'UNT72 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'HACKBERRY_CREEK'): {'HACKBERRY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT72_in_UPPER_SALT_FRK_HUC8'): {'UNT72 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT144_in_UPPER_SALT_FRK_HUC8_1'): {'UNT144 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT35_in_UPPER_SALT_FRK_HUC8'): {'UNT35 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT129_in_Edith_Watershed_1'): {'UNT129 in Edith': 'ESPG: 2763, NAD83(HARN) / Arizona West,'},\n", + " ('11060002',\n", + " 'UNT144_in_UPPER_SALT_FRK_HUC8'): {'UNT144 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT89_in_UPPER_SALT_FRK_HUC8_1'): {'UNT89 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT25_in_UPPER_SALT_FRK_HUC8'): {'UNT25 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT89_in_UPPER_SALT_FRK_HUC8'): {'UNT89 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT102_in_UPPER_SALT_FRK_HUC8'): {'UNT102 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT94_in_Liberal_Watershed'): {'UNT94 in Liberal': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'TURKEY_CREEK'): {'TURKEY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT120_in_UPPER_SALT_FRK_HUC8_1'): {'UNT120 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT12_in_UPPER_SALT_FRK_HUC8_1'): {'UNT12 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT36_in_UPPER_SALT_FRK_HUC8'): {'UNT36 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT26_in_UPPER_SALT_FRK_HUC8'): {'UNT26 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT143_in_UPPER_SALT_FRK_HUC8'): {'UNT143 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT36_in_UPPER_SALT_FRK_HUC8_1'): {'UNT36 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT114_in_UPPER_SALT_FRK_HUC8'): {'UNT114 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT24_in_UPPER_SALT_FRK_HUC8_1'): {'UNT24 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT116_in_UPPER_SALT_FRK_HUC8'): {'UNT116 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT33_in_UPPER_SALT_FRK_HUC8_1'): {'UNT33 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT97_in_UPPER_SALT_FRK_HUC8'): {'UNT97 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT76_in_UPPER_SALT_FRK_HUC8'): {'UNT76 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT32_in_UPPER_SALT_FRK_HUC8_1'): {'UNT32 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'SALT_FORK_ARKANSAS_RIVER'): {'SALT FORK ARKANS': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT95_in_UPPER_SALT_FRK_HUC8'): {'UNT95 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT24_in_UPPER_SALT_FRK_HUC8'): {'UNT24 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT74_in_UPPER_SALT_FRK_HUC8'): {'UNT74 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT20_in_UPPER_SALT_FRK_HUC8'): {'UNT20 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT62_in_UPPER_SALT_FRK_HUC8'): {'UNT62 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT70_in_UPPER_SALT_FRK_HUC8'): {'UNT70 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT140_in_UPPER_SALT_FRK_HUC8'): {'UNT140 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT44_in_UPPER_SALT_FRK_HUC8'): {'UNT44 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT52_in_UPPER_SALT_FRK_HUC8_1'): {'UNT52 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT29_in_UPPER_SALT_FRK_HUC8_1'): {'UNT29 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT331_in_Edith_Watershed_1'): {'UNT331 in Edith': 'ESPG: 2763, NAD83(HARN) / Arizona West,'},\n", + " ('11060002',\n", + " 'UNT110_in_Liberal_Watershed_1'): {'UNT110 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT133_in_Liberal_Watershed'): {'UNT133 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT35_in_UPPER_SALT_FRK_HUC8_1'): {'UNT35 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT85_in_UPPER_SALT_FRK_HUC8_1'): {'UNT85 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT146_in_UPPER_SALT_FRK_HUC8_1'): {'UNT146 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT57_in_UPPER_SALT_FRK_HUC8'): {'UNT57 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT13_in_UPPER_SALT_FRK_HUC8_1'): {'UNT13 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT53_in_UPPER_SALT_FRK_HUC8'): {'UNT53 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT34_in_UPPER_SALT_FRK_HUC8_1'): {'UNT34 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT30_in_UPPER_SALT_FRK_HUC8'): {'UNT30 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT11_in_UPPER_SALT_FRK_HUC8'): {'UNT11 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT94_in_UPPER_SALT_FRK_HUC8_1'): {'UNT94 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT81_in_UPPER_SALT_FRK_HUC8'): {'UNT81 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT90_in_UPPER_SALT_FRK_HUC8_1'): {'UNT90 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT20_in_UPPER_SALT_FRK_HUC8_1'): {'UNT20 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT96_in_UPPER_SALT_FRK_HUC8'): {'UNT96 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT99_in_UPPER_SALT_FRK_HUC8'): {'UNT99 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT52_in_UPPER_SALT_FRK_HUC8'): {'UNT52 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT97_in_UPPER_SALT_FRK_HUC8_1'): {'UNT97 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT99_in_UPPER_SALT_FRK_HUC8_1'): {'UNT99 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT140_in_UPPER_SALT_FRK_HUC8_1'): {'UNT140 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT143_in_UPPER_SALT_FRK_HUC8_1'): {'UNT143 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT74_in_UPPER_SALT_FRK_HUC8_1'): {'UNT74 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT59_in_UPPER_SALT_FRK_HUC8_1'): {'UNT59 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT148_in_UPPER_SALT_FRK_HUC8'): {'UNT148 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT112_in_UPPER_SALT_FRK_HUC8'): {'UNT112 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT127_in_Liberal_Watershed_1'): {'UNT127 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT18_in_UPPER_SALT_FRK_HUC8'): {'UNT18 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT119_in_UPPER_SALT_FRK_HUC8'): {'UNT119 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT112_in_UPPER_SALT_FRK_HUC8_1'): {'UNT112 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'GREENLEAF_CREEK_1'): {'GREENLEAF CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT94_in_UPPER_SALT_FRK_HUC8'): {'UNT94 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT48_in_UPPER_SALT_FRK_HUC8'): {'UNT48 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT3_in_UPPER_SALT_FRK_HUC8_1'): {'UNT3 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT81_in_UPPER_SALT_FRK_HUC8_1'): {'UNT81 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT104_in_UPPER_SALT_FRK_HUC8_1'): {'UNT104 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT117_in_UPPER_SALT_FRK_HUC8'): {'UNT117 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT120_in_Liberal_Watershed'): {'UNT120 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'BOGGY_CREEK'): {'BOGGY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT100_in_UPPER_SALT_FRK_HUC8_1'): {'UNT100 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT21_in_UPPER_SALT_FRK_HUC8'): {'UNT21 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT22_in_UPPER_SALT_FRK_HUC8_1'): {'UNT22 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT95_in_UPPER_SALT_FRK_HUC8_1'): {'UNT95 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT33_in_UPPER_SALT_FRK_HUC8'): {'UNT33 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT147_in_UPPER_SALT_FRK_HUC8'): {'UNT147 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT234_in_Liberal_Watershed_1'): {'UNT234 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT61_in_UPPER_SALT_FRK_HUC8_1'): {'UNT61 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT145_in_UPPER_SALT_FRK_HUC8_1'): {'UNT145 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT87_in_UPPER_SALT_FRK_HUC8'): {'UNT87 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT47_in_UPPER_SALT_FRK_HUC8_1'): {'UNT47 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT129_in_Edith_Watershed'): {'UNT129 in Edith': 'ESPG: 2763, NAD83(HARN) / Arizona West,'},\n", + " ('11060002',\n", + " 'UNT162_in_Liberal_Watershed_1'): {'UNT162 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT57_in_UPPER_SALT_FRK_HUC8_1'): {'UNT57 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'HACKBERRY_CREEK_1'): {'HACKBERRY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT90_in_UPPER_SALT_FRK_HUC8'): {'UNT90 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT28_in_UPPER_SALT_FRK_HUC8_1'): {'UNT28 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT63_in_UPPER_SALT_FRK_HUC8_1'): {'UNT63 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT16_in_UPPER_SALT_FRK_HUC8'): {'UNT16 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT45_in_UPPER_SALT_FRK_HUC8_1'): {'UNT45 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT235_in_Liberal_Watershed'): {'UNT235 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT82_in_UPPER_SALT_FRK_HUC8'): {'UNT82 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT98_in_UPPER_SALT_FRK_HUC8'): {'UNT98 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT59_in_UPPER_SALT_FRK_HUC8'): {'UNT59 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT48_in_UPPER_SALT_FRK_HUC8_1'): {'UNT48 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT73_in_UPPER_SALT_FRK_HUC8'): {'UNT73 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT86_in_UPPER_SALT_FRK_HUC8'): {'UNT86 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT2_in_UPPER_SALT_FRK_HUC8'): {'UNT2 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT69_in_UPPER_SALT_FRK_HUC8_1'): {'UNT69 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT53_in_UPPER_SALT_FRK_HUC8_1'): {'UNT53 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT101_in_UPPER_SALT_FRK_HUC8'): {'UNT101 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT13_in_UPPER_SALT_FRK_HUC8'): {'UNT13 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT145_in_UPPER_SALT_FRK_HUC8'): {'UNT145 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT94_in_Liberal_Watershed_1'): {'UNT94 in Liberal': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'YELLOWSTONE_CREEK_1'): {'YELLOWSTONE CREE': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT86_in_UPPER_SALT_FRK_HUC8_1'): {'UNT86 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'SALT_FORK_ARKANSAS_RIVER_1'): {'SALT FORK ARKANS': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT118_in_UPPER_SALT_FRK_HUC8'): {'UNT118 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT51_in_UPPER_SALT_FRK_HUC8_1'): {'UNT51 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT85_in_UPPER_SALT_FRK_HUC8'): {'UNT85 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT235_in_Liberal_Watershed_1'): {'UNT235 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT16_in_UPPER_SALT_FRK_HUC8_1'): {'UNT16 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT62_in_UPPER_SALT_FRK_HUC8_1'): {'UNT62 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT98_in_UPPER_SALT_FRK_HUC8_1'): {'UNT98 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'BOGGY_CREEK_1'): {'BOGGY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT100_in_UPPER_SALT_FRK_HUC8'): {'UNT100 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT12_in_UPPER_SALT_FRK_HUC8'): {'UNT12 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'RED_FORK_CREEK_1'): {'RED FORK CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT54_in_UPPER_SALT_FRK_HUC8_1'): {'UNT54 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT152_in_Liberal_Watershed_1'): {'UNT152 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT117_in_UPPER_SALT_FRK_HUC8_1'): {'UNT117 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT73_in_BluffCr_Watershed_1'): {'UNT73 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", + " ('11060002',\n", + " 'UNT77_in_UPPER_SALT_FRK_HUC8_1'): {'UNT77 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT118_in_UPPER_SALT_FRK_HUC8_1'): {'UNT118 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT93_in_UPPER_SALT_FRK_HUC8'): {'UNT93 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT75_in_UPPER_SALT_FRK_HUC8'): {'UNT75 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT15_in_UPPER_SALT_FRK_HUC8'): {'UNT15 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT96_in_UPPER_SALT_FRK_HUC8_1'): {'UNT96 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT3_in_UPPER_SALT_FRK_HUC8'): {'UNT3 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT108_in_UPPER_SALT_FRK_HUC8_1'): {'UNT108 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'RED_FORK_CREEK'): {'RED FORK CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT127_in_Liberal_Watershed'): {'UNT127 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT107_in_UPPER_SALT_FRK_HUC8_1'): {'UNT107 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT103_in_Liberal_Watershed_1'): {'UNT103 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT108_in_UPPER_SALT_FRK_HUC8'): {'UNT108 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT66_in_UPPER_SALT_FRK_HUC8_1'): {'UNT66 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT56_in_UPPER_SALT_FRK_HUC8_1'): {'UNT56 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT56_in_UPPER_SALT_FRK_HUC8'): {'UNT56 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT147_in_UPPER_SALT_FRK_HUC8_1'): {'UNT147 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT80_in_UPPER_SALT_FRK_HUC8_1'): {'UNT80 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT106_in_UPPER_SALT_FRK_HUC8'): {'UNT106 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT22_in_UPPER_SALT_FRK_HUC8'): {'UNT22 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT71_in_UPPER_SALT_FRK_HUC8_1'): {'UNT71 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT105_in_UPPER_SALT_FRK_HUC8'): {'UNT105 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT331_in_Edith_Watershed'): {'UNT331 in Edith': 'ESPG: 2763, NAD83(HARN) / Arizona West,'},\n", + " ('11060002',\n", + " 'UNT102_in_UPPER_SALT_FRK_HUC8_1'): {'UNT102 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT47_in_UPPER_SALT_FRK_HUC8'): {'UNT47 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT79_in_UPPER_SALT_FRK_HUC8_1'): {'UNT79 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT70_in_UPPER_SALT_FRK_HUC8_1'): {'UNT70 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT114_in_UPPER_SALT_FRK_HUC8_1'): {'UNT114 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT113_in_UPPER_SALT_FRK_HUC8_1'): {'UNT113 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT82_in_UPPER_SALT_FRK_HUC8_1'): {'UNT82 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT68_in_UPPER_SALT_FRK_HUC8'): {'UNT68 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT26_in_UPPER_SALT_FRK_HUC8_1'): {'UNT26 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT60_in_UPPER_SALT_FRK_HUC8'): {'UNT60 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT103_in_UPPER_SALT_FRK_HUC8_1'): {'UNT103 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT67_in_UPPER_SALT_FRK_HUC8'): {'UNT67 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'JUG_MOTTE_CREEK'): {'JUG MOTTE CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT44_in_UPPER_SALT_FRK_HUC8_1'): {'UNT44 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT113_in_UPPER_SALT_FRK_HUC8'): {'UNT113 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT76_in_UPPER_SALT_FRK_HUC8_1'): {'UNT76 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT91_in_UPPER_SALT_FRK_HUC8'): {'UNT91 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT30_in_UPPER_SALT_FRK_HUC8_1'): {'UNT30 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT51_in_UPPER_SALT_FRK_HUC8'): {'UNT51 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT23_in_UPPER_SALT_FRK_HUC8_1'): {'UNT23 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT15_in_UPPER_SALT_FRK_HUC8_1'): {'UNT15 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT77_in_UPPER_SALT_FRK_HUC8'): {'UNT77 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT65_in_UPPER_SALT_FRK_HUC8_1'): {'UNT65 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT8_in_UPPER_SALT_FRK_HUC8'): {'UNT8 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT152_in_Liberal_Watershed'): {'UNT152 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT83_in_UPPER_SALT_FRK_HUC8'): {'UNT83 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT31_in_UPPER_SALT_FRK_HUC8'): {'UNT31 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT63_in_UPPER_SALT_FRK_HUC8'): {'UNT63 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'TURKEY_CREEK_1'): {'TURKEY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT69_in_UPPER_SALT_FRK_HUC8'): {'UNT69 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT104_in_UPPER_SALT_FRK_HUC8'): {'UNT104 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT162_in_Liberal_Watershed'): {'UNT162 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'COTTONWOOD_CREEK'): {'COTTONWOOD CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT234_in_Liberal_Watershed'): {'UNT234 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", + " ('11060002',\n", + " 'UNT6_in_BluffCr_Watershed'): {'UNT6 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", + " ('11060002',\n", + " 'UNT6_in_BluffCr_Watershed_1'): {'UNT6 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", + " ('11060002',\n", + " 'UNT75_in_BluffCr_Watershed_1'): {'UNT75 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", + " ('11060002',\n", + " 'UNT28_in_UPPER_SALT_FRK_HUC8'): {'UNT28 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT146_in_UPPER_SALT_FRK_HUC8'): {'UNT146 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('11060002',\n", + " 'UNT148_in_UPPER_SALT_FRK_HUC8_1'): {'UNT148 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", + " ('13020102',\n", + " 'RioGrandeT8'): {'RioGrandeT8': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaAnchaT1'): {'CanadaAnchaT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioTusasTrib2'): {'RioTusasT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaJarita'): {'CanadaJarita': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadeBorracho'): {'CanadadeBorracho': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaos_1'): {'PueblodeTaos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LatirCreekTrib1'): {'LatirCreekTrib1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoMiranda'): {'ArroyoMiranda': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1a_1'): {'GrandeRanchoT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPuncheT1a'): {'ArroyoPuncheT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeRancho'): {'RioGrandeRancho': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPunche'): {'Arroyo Punche': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPuncheT4'): {'ArroyoPuncheT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'SaltDraw'): {'SaltDraw': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'CanadadelaOrill'): {'CanadadelaOrill': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaosT3b1'): {'PueblodeTaosT3b1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaSarcoT1'): {'CanadaSarcoT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ElRito'): {'ElRito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'FernandodeTaos'): {'FernandodeTaos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AguajePetacaT1a'): {'AguajePetacaT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaosT2'): {'PueblodeTaosT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioTusas_1'): {'RioTusas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPuncheT3a1'): {'ArroyoPuncheT3a1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioVallecitos'): {'RioVallecitos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaosT2a'): {'PueblodeTaosT2a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ElRitoT4'): {'ElRitoT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LatirCreekTrib2'): {'LatirCreekTrib2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioPuebloTrib1'): {'RioPuebloTrib1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPino'): {'ArroyoPino': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT11b'): {'RioGrandeT11b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaSarcoT2'): {'CanadaSarcoT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioOjoCalienteT1'): {'RioOjoCalienteT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioVallecitosT3'): {'RioVallecitosT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT12c'): {'RioGrandeT12c': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'BobcatCreek'): {'BobcatCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaJarosita'): {'CanadaJarosita': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AguajedelaPetaca_1'): {'AguajedelaPetaca': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoOcole'): {'ArroyoOcole': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyolaMina'): {'ArroyolaMina': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ElRitoT6'): {'ElRitoT6': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AguajedelaPetacaT2'): {'AguajePetacaT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaAnchaNorth'): {'CanadaAnchaNorth': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1b3'): {'GrandeRanchoT1b3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoRancho'): {'ArroyoRancho': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ElRitoT3'): {'ElRitoT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoelRitoT1'): {'ArroyoelRitoT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaPueblo'): {'CanadaPueblo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaosT4a'): {'PueblodeTaosT4a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RedRiver'): {'RedRiver': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT13'): {'RioGrandeT13': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT10f'): {'RioGrandeT10f': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ElRitoT8'): {'ElRitoT8': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CabrestoCreek'): {'CabrestoCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT10a'): {'RioGrandeT10a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT11a'): {'RioGrandeT11a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RiodeTruchas'): {'RiodeTruchas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'MadredelPrado'): {'MadredelPrado': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadePiedra'): {'CanadadePiedra': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AlmagreT3'): {'AlmagreT3': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1b4'): {'GrandeRanchoT1b4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT11c'): {'RioGrandeT11c': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioOjoCaliente_1'): {'RioOjoCaliente': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RedRiverTrib1'): {'RedRiverTrib1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaCorral'): {'CanadaCorral': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PuebloArroyo'): {'PuebloArroyo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'FernandodeTaosT1'): {'FernandodeTaosT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'BitterCreek'): {'BitterCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT11'): {'RioGrandeT11': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT10e'): {'RioGrandeT10e': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1b'): {'GrandeRanchoT1b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioTusasTrib3'): {'RioTusasT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadeApodaca'): {'CanadadeApodaca': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AguajePetacaT3'): {'AguajePetacaT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ElRitoT7'): {'ElRitoT7': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadelAgua'): {'CanadadelAgua': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPuncheT2'): {'ArroyoPuncheT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AguajePetacaT1'): {'AguajePetacaT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AlmagreT2b1'): {'AlmagreT2b1': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'EmbudoCreekTrib1'): {'EmbudoCreekTrib1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LaMaderaT1a'): {'LaMaderaT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT9'): {'RioGrandeT9': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioVallecitos_1'): {'RioVallecitos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PiedraLumbre'): {'PiedraLumbre': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1_1'): {'GrandeRanchoT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LatirCreekTrib3a'): {'LatirCreekTrib3a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaos'): {'PueblodeTaos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1b2'): {'GrandeRanchoT1b2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioVallecitosT2b'): {'RioVallecitosT2b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoLagunitas'): {'ArroyoLagunitas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1e'): {'GrandeRanchoT1e': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AngelCanyon'): {'AngelCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanoncitoMadera'): {'CanoncitoMadera': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AlmagreT4'): {'AlmagreT4': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'RioVallecitosT7'): {'RioVallecitosT7': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1a'): {'GrandeRanchoT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LagunitasT3'): {'LagunitasT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoelRito'): {'ArroyoelRito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT9a'): {'RioGrandeT9a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoCerroNegro'): {'ArroyoCerroNegro': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1a1'): {'GrandeRanchoT1a1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LaMaderaT1b'): {'LaMaderaT1b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPuncheT1'): {'ArroyoPuncheT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioPueblo'): {'RioPueblo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1f'): {'GrandeRanchoT1f': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'DesagueAguita'): {'DesagueAguita': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT10d1'): {'RioGrandeT10d1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaBuenaVista'): {'CanadaBuenaVista': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaLemitas'): {'CanadaLemitas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1d'): {'GrandeRanchoT1d': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RitoCieneguilla'): {'RitoCieneguilla': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPuncheT3b'): {'ArroyoPuncheT3b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioVallecitosT1'): {'RioVallecitosT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ChamizalCreek'): {'ChamizalCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadeOso'): {'CanadadeOso': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaEntranasT'): {'CanadaEntranasT': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioVallecitosT4'): {'RioVallecitosT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT9b'): {'RioGrandeT9b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioOjoCaliente'): {'RioOjoCaliente': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaTioPula'): {'CanadaTioPula': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaosT4a1'): {'PueblodeTaosT4a1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioChiquito1'): {'RioChiquito1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadelaCruz'): {'CanadadelaCruz': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioVallecitosT6'): {'RioVallecitosT6': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LamyCanyon'): {'LamyCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadeOso_1'): {'CanadadeOso': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaAlamos'): {'CanadaAlamos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioHondo'): {'RioHondo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioVallecitosT2a'): {'RioVallecitosT2a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioOjoCalienteT2'): {'RioOjoCalienteT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'EmbudoCreek'): {'EmbudoCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'SandlinArroyo'): {'SandlinArroyo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LatirCreekTrib3'): {'LatirCreekTrib3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioVallecitosT8'): {'RioVallecitosT8': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RedRiverTrib2'): {'RedRiverTrib2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoGavilan'): {'ArroyoGavilan': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT9a2'): {'RioGrandeT9a2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'SpringCreekSouth'): {'SpringCreekSouth': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AlmagreT2b'): {'AlmagreT2b': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'RioGrandeT7'): {'RioGrandeT7': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaOjoSarco'): {'CanadaOjoSarco': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaAbeque'): {'CanadaAbeque': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT10'): {'RioGrandeT10': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LittleTusasCreek'): {'LittleTusasCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioTusasTrib1'): {'RioTusasT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaosT3A'): {'PueblodeTaosT3a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioLucero'): {'RioLucero': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LatirCreekTrib2a'): {'LatirCreekTrib2a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ElRitoT8a'): {'ElRitoT8a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RiodeTruchasT1'): {'RiodeTruchasT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaMontecito'): {'CanadaMontecito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioTusas'): {'RioTusas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT10c'): {'RioGrandeT10c': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT12'): {'RioGrandeT12': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'MartinezCanyon'): {'MartinezCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadeOjito'): {'CanadadeOjito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AlmagreT2'): {'AlmagreT2': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'URGUnnamedStream'): {'URGUnnamedStream': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AlmagreT3a'): {'AlmagreT3a': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'RioGrandeT12a'): {'RioGrandeT12a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1b1'): {'GrandeRanchoT1b1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoSecoNorth'): {'ArroyoSecoNorth': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrande'): {'RioGrande': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT10b'): {'RioGrandeT10b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1'): {'GrandeRanchoT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT12b'): {'RioGrandeT12b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadeLorenzo'): {'CanadadeLorenzo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanondelaMadera'): {'CanondelaMadera': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'BarelaCanyon'): {'BarelaCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaosT4b'): {'PueblodeTaosT4b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT7a'): {'RioGrandeT7a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeRancho_1'): {'RioGrandeRancho': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadelOso'): {'CanadadelOso': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AcequiadelJaral'): {'AcequiadelJaral': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT9c2'): {'RioGrandeT9c2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPuncheT3b1'): {'ArroyoPuncheT3b1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AguajedelaPetaca'): {'AguajedelaPetaca': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoAlmagre'): {'ArroyoAlmagre': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'RioVallecitosT5'): {'RioVallecitosT5': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT9a1'): {'RioGrandeT9a1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'JarosoCreek'): {'JarosoCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LaMaderaT1'): {'LaMaderaT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaEntranas'): {'CanadaEntranas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PicurisCanyon'): {'PicurisCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ArroyoPuncheT6'): {'ArroyoPuncheT6': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'ElRito_1'): {'ElRito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'LagunitasT2'): {'LagunitasT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT9a11'): {'RioGrandeT9a11': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RiodelasTrampas'): {'RiodelasTrampas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaApaches'): {'CanadaApaches': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaosT3'): {'PueblodeTaosT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'GrandeRanchoT1c'): {'GrandeRanchoT1c': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'BlancoSouthT5'): {'BlancoSouthT5': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'RioVallecitosT9'): {'RioVallecitosT9': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaPinoReale'): {'CanadaPinoReale': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioTusasTrib4'): {'RioTusasT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadadeJacinto'): {'CanadadeJacinto': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT11c1'): {'RioGrandeT11c1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AlmagreT2a'): {'AlmagreT2a': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'LatirCreek'): {'LatirCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'PueblodeTaosT4'): {'PueblodeTaosT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioChiquito2'): {'RioChiquito2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'BlancoSouthT4'): {'BlancoSouthT4': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", + " ('13020102',\n", + " 'ElRitoT5'): {'ElRitoT5': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanadaAncha'): {'CanadaAncha': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'CanondelosAlamos'): {'CanondelosAlamos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'AnchaNorthT1'): {'AnchaNorthT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('13020102',\n", + " 'RioGrandeT10d'): {'RioGrandeT10d': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('17060305',\n", + " 'cottonwood_creek_1_ras'): {'cottonwood_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'whitman_creek_ras'): {'whitman_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'shebang_creek_tributary1_ras'): {'shebang_creek_tr': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'shebang_creek_tributary2_ras'): {'shebang_creek_tr': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'meadow_creek_1_ras'): {'meadow_creek_1': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'orchard_creek_ras'): {'orchard_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'main_three_mile_ck_trib1_ras'): {'main_three_mile_': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'red_river_2_ras'): {'red_river_2': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'cottonwood_creek_tributary_ras'): {'cottonwood_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'threemile_creek_ras_1'): {'threemile_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'stockney_creek_tributary1_ras'): {'stockney_creek_t': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'stockney_creek_ras'): {'stockney_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'threemile_creek_trib1a_ras'): {'threemile_creek_': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'threemile_creek_ras'): {'threemile_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'long_haul_creek_ras'): {'long_haul_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'south_fork_clearwater_river_3_ras'): {'south_fork_clear': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'cole_creek_ras'): {'cole_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'south_fork_clearwater_river_2_ras'): {'south_fork_clear': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'threemile_creek_trib1b_ras'): {'threemile_creek_': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'elk_creek_ras'): {'elk_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'main_three_mile_ck_trib2_ras'): {'main_three_mile_': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'rabbit_creek_ras'): {'rabbit_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'clearwater_river1_tributary3_ras'): {'clearwater_river': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'south_fork_cottonwood_creek_ras'): {'south_fork_cotto': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'sally_ann_creek_ras'): {'sally_ann_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'shebang_creek_ras'): {'shebang_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'cottonwood_creek_ras'): {'cottonwood_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('17060305',\n", + " 'south_fork_clearwater_river_1_ras'): {'south_fork_clear': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", + " ('12030107',\n", + " 'KingsCreekTrib1B_4'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'ELD004_1'): {'Bachelor Creek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'ELD004'): {'Bachelor Creek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'KingsCreekTrib1B_1'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " '010-07-BrushyCreek'): {'Brushy': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'BrushyCr_1'): {'BrushyCreek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),', 'Trib': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'KingsCreekTrib1B_3'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'KingsCreekTrib1B_2'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'KingsCreekTrib1B'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'ELD004_2'): {'Bachelor Creek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'KC1_KC1B_LOMR'): {'KC-1': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " 'BrushyCr'): {'BrushyCreek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),', 'Trib': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('12030107',\n", + " '010-07-BrushyCreek_1'): {'Brushy': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", + " ('08060202',\n", + " 'Comm_Creek_Trib_1'): {'Commissioners Cr': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Big_Sand_Creek_Trib_1'): {'Big Sand Creek T': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Comm_Creek_Trib_1-1'): {'Commissioners Cr': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Porter_Creek_Trib_3'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Jones_Creek'): {'Jones Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Maxie_Creek'): {'Maxie Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Bogue_Falia_Creek_Trib_2-1'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Fivemile_Creek'): {'Fivemile Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Porter_Creek_Trib_1-2'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Bogue_Falia_Creek_Trib_3'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Harts_Creek'): {'Harts Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Porter_Creek_Trib_1'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Potter_Creek'): {'Potter Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Porter_Creek_Trib_1-3'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Bogue_Falia_Creek'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Bogue_Falia_Creek_Trib_2'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Porter_Creek_Trib_1-1'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Bogue_Falia_Creek_Trib_5'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Big_Black_River_Trib_1'): {'Big Black River': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Murphy_Creek'): {'Murphy Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('08060202',\n", + " 'Fivemile_Creek_Trib_3'): {'Fivemile Creek T': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", + " ('16020203',\n", + " 'S_EastWeberFOA'): {'Nobletts Creek': 'ESPG: 2259, NAD83 / New Mexico West (ftUS),', 'Weber River': 'ESPG: 2259, NAD83 / New Mexico West (ftUS),', 'SF Weber River': 'ESPG: 2259, NAD83 / New Mexico West (ftUS),'},\n", + " ('16020203',\n", + " 'Center_Creek'): {'Center Creek': 'ESPG: 2281, NAD83 / Utah Central (ft),'},\n", + " ('18070204',\n", + " 'BorregoCyn_Existing'): {'Borrego Cyn': 'ESPG: 2230, NAD83 / California zone 6 (ftUS),'},\n", + " ('05020006',\n", + " 'PIKERUN1'): {'PIKERUN1': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP771'): {'NP771': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'INDIANCR'): {'INDIANCR': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP708'): {'NP708': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'WILSONRUN'): {'WILSONRUN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP499'): {'NP499': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'T9YOUGH'): {'T9YOUGH': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'CEDARCR'): {'CEDARCR': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP535'): {'NP535': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'TWNSHPRN'): {'TWNSHPRN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP720'): {'NP720': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'BuffRnT1'): {'BuffRnT1': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'JcbsCrkT3'): {'JcbsCrkT3': 'ESPG: 3365, NAD83(HARN) / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP709'): {'NP709': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'JACOBCR1'): {'JACOBCR1': 'ESPG: 3365, NAD83(HARN) / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'JcbsCrkT2'): {'JcbsCrkT2': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'WEITYRUN'): {'WEITYRUN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'STAUFERN'): {'STAUFERN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'JcbsCrkT4'): {'JcbsCrkT4': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP500'): {'NP500': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP707'): {'NP707': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'BARRENRUN'): {'BARRENRUN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'LICKRUN'): {'LICKRUN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'YoughyRT1'): {'YoughyRT1': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP719'): {'NP719': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP180'): {'NP180': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'T8YOUGHI'): {'T8YOUGHI': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP537'): {'NP537': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'ANDREWS2'): {'ANDREWS2': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'NP620'): {'NP620': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('05020006',\n", + " 'LITTLERN'): {'LITTLERN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", + " ('02060006',\n", + " 'streamcb4'): {'Stream CB-4': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'HallCreek'): {'Hall Creek': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'mosleybranch'): {'Mosley Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'CoxtownCreek'): {'Cocktowncr': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'CB_CorrectedEffective'): {'Clydes Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'NortheastBranchWB'): {'Northeast Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'clydesbranch'): {'Clydes Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'streamcb16_1'): {'Stream CB-16': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'streamcb1_1'): {'Stream CB-1': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'CB16_Proposed'): {'Stream CB-16': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'ChewCreek'): {'Chew Creek': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'PatuxentRiver_1'): {'Patuxent River': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'streamcb1'): {'Stream CB-1': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'MPR_Proposed'): {'MiddlePatuxent': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'StLeonardCreek'): {'St_Leonard_CL': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'MillCreek'): {'Mill_Cr_CL': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'streamcb18_1'): {'Stream CB-18': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'LaurelRaceTrack'): {'Patuxent River': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'HuntingCreek'): {'HuntingCr': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'CB16_CorrectedEffective'): {'Stream CB-16': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'middlepatuxent'): {'MiddlePatuxent': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'CB18_CorrectedEffective'): {'Stream CB-18': 'ESPG: 2248, NAD83 / Maryland (ftUS),', 'CB-18 Split Flow': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'JohnsCreek'): {'JohnMosleyCr': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'PatuxentRiver'): {'Patuxent River': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'SewellCreek'): {'Sewell_Branch_CL': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'streamcb16'): {'Stream CB-16': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'clydesbranch_1'): {'Clydes Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'BattleCreekTrib'): {'Battle Creek': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'LittleLyonsCreek'): {'LittleLyonsCL': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'StreamCB4Prop'): {'Stream CB-4': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'CB18_Proposed'): {'Stream CB-18': 'ESPG: 2248, NAD83 / Maryland (ftUS),', 'CB-18 Split Flow': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'CB_Proposed'): {'Clydes Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'streamcb18'): {'Stream CB-18': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('02060006',\n", + " 'BattleCreekCypressSwamp'): {'BattleCrCypress': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", + " ('14050005',\n", + " 'MarvineCreek'): {'MarvineCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'StrawberryTrib'): {'Strawberry Trib': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'DryCreek'): {'Dry_Creek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'NorthForkWhiteRiver'): {'NorthFork_WR': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'CaveGulch'): {'Cave Gulch': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'MillerCreek'): {'Miller_Creek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'StrawberryCreek'): {'StrawberryCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'UteCreek'): {'UteCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'WhiteRiver_Seg3'): {'WhiteRiv_Seg3': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'BigBeaverCreek'): {'BigBeaverCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'FawnCreek'): {'FawnCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'NorthElkCreek'): {'NorthElkCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'SheepCreek'): {'SheepCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'LittleBeaverCreek'): {'LBC': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'FourmileGulch'): {'Fourmile Gulch': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'WhiteMeeker'): {'White_R_Meeker': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'UpperCurtisCreek'): {'UpperCurtisCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'WhiteRiver_Seg4'): {'WhiteRiv_Seg4': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'CoalCreek'): {'CoalCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'CurtisCreek_Meeker'): {'Curtis Creek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'SulphurCreek'): {'SulphurCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'EastMillerCreek'): {'EastMillerCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'DevilsHole'): {'Devils Hole': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'EFlagCreek'): {'EFlagCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'SouthForkWhiteRiver'): {'thForkWhiteRiver': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'OylerGulch'): {'Oyler Gulch': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('14050005',\n", + " 'FlagCreek'): {'FlagCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", + " ('15040007',\n", + " '1242_ras'): {'1242': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('15040007',\n", + " '1195_ras'): {'1195': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('15040007',\n", + " '1236_ras'): {'1236': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", + " ('04030101',\n", + " 'UT15Milw'): {'UT15Milw': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", + " ('04030101',\n", + " 'EastTwin_trib'): {'East Twin': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'},\n", + " ('04030101',\n", + " 'UT13_Milw'): {'UT13Milw': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", + " ('04030101',\n", + " 'Jambo'): {'Jambo': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'},\n", + " ('04030101',\n", + " 'UT81Milw'): {'UT81Milw': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", + " ('04030101',\n", + " 'UT5Milw'): {'UT5MIlw': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", + " ('04030101',\n", + " 'EastTwin'): {'East Twin': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'},\n", + " ('04030101',\n", + " 'UT2RiveredgeCrk'): {'UT2RiveredgeCrk': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", + " ('04030101',\n", + " 'Krok'): {'Krok': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'},\n", + " ('04030101',\n", + " 'Buck'): {'Buck': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'}})" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crs_map" + ] + }, + { + "cell_type": "markdown", + "id": "dd7a0bfe-921f-4b44-a7f5-887b1139d669", + "metadata": {}, + "source": [ + "__Findings:__\n", + "\n", + "To map each cross-section of a model @ HUC#, there has to be a shared attribute between a model @ HUC#'s cross section w/in its XS layer & the details provided within a model @ HUC#'s conflation json file.\n", + " \n", + "- Each conflation json file reveals ...\n", + " - Per reach, there is a set of cross-sections.\n", + " - Within each model's cross-section (XS) layers, there are a set of cross-section instances - each instance featuring a unique \"thalweg\" (aka \"min_elevation\"), \"xs_max_elevation\" (aka \"max_elevation\"), \"reach_id\" (\"reaches\"), & \"river_station\" (aka \"xs_id\")\n", + "\n", + "- Each model @ HUC#'s XS layer contains a collection of cross-section instancees. Thus, each unique cross-section w/in a given model @ HUC#'s XS layer will need to be mapped in such a way to allow each cross-section to be associated with a feature ID (aka \"reach_id\" and/or \"network_to_id\")\n", + "\n", + "- \"river_reach_rs\" is formatted differently across models' XS layers, however multplie keys referenced in the conflation jsons can be referenced to obtain the \"river_reach_rs\" from the jsons - as a result, the mapping of IDs to each model's cross-section instance will be based on the info extracted from a model @ HUC#'s conflation.json\n" + ] + }, + { + "cell_type": "markdown", + "id": "56e8ca07-a893-423a-8b35-865c9cf6c5bf", + "metadata": {}, + "source": [ + "##### Next Step: \n", + "##### Per HUC, consolidate each XS by River name (aka \"model\") & save as a geopandas dataframe (ensure CRS is consistent for each geopandas dataframe; check with QGIS)\n", + "\n", + "1) Consolidate XS by river. For each river, save their consolidate XS rows as a geopandas dataframe per river. Ensure their CRS are same.\n", + "Keep their unique inherited CRS. Track HUC\n", + "\n", + "2) Consolidate each \"river's\" geopandas dataframe by HUC & save them as a geoparquet, store in S3\n", + "\n", + "3) Each geoparquets can then be stored as Apache iceberg tables\n", + "\n", + "4) Store Apache iceberg tables in S3\n", + "\n", + "\n", + "Collection of geoparquets representing each river at a given HUC\n", + "\n", + "model_id2gpkg_map[" + ] + }, + { + "cell_type": "markdown", + "id": "2677869f-a354-4bc3-858a-b14da790e2b8", + "metadata": {}, + "source": [ + "## Demo VII: \n", + "### Querying Ripple Layers by HUC Number" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "06748e4a-1a94-492c-9d07-4f71cd956f33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['processing', 'reaches', 'rating_curves', 'network', 'models', 'metadata', 'rating_curves_no_map'])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rip_huc2gpkg_map['07130010'].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "791efbaa-31f8-42fe-850f-67774d56a7c7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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reach_idnwm_to_idgeometry
01358549813585296MULTILINESTRING ((460724.236 1959986.07, 46074...
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reach_idus_flowus_depthus_wseds_depthds_wseboundary_condition
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collection_idmodel_idmodel_nameconflate_model_job_idconflate_model_status
0mip_07130010sa_7sa_7cec7ec7c-fbfa-48da-87ce-c13632b59f09succeeded
1mip_07130010sa_1sa_12c0cd12a-5042-4265-b98e-8f6540cb6e4esucceeded
2mip_07130010sa_6sa_67d85fe34-8960-42c0-9762-6946317a348asucceeded
3mip_07130010sa_5sa_51335125c-2d64-4c4c-a4f7-28fe1cc0e75bsucceeded
4mip_07130010sa_4sa_4fc5ed1ef-1b3c-4f5e-84ba-b4a45aae15b0succeeded
5mip_07130010LaMoineLaMoine212d132f-a10f-48f2-9256-698fb3535860succeeded
6mip_07130010KilljordanKilljordanee89af84-44ea-40a4-9b4c-eea1be29d270succeeded
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" 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" + } + }, + "cell_type": "markdown", + "id": "b3747f8a-8b23-4bdd-b7a7-4a9add4df61c", + "metadata": {}, + "source": [ + "# Write local files to S3 (for dev purposes only)\n", + "![image.png](attachment:cb93a3d1-2477-4ec1-bddd-0da089352aa6.png)\n", + "![image.png](attachment:2255f9dc-6db9-4a19-b160-559958de2563.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "2e54d9d3-b04e-434b-9576-2a861e4c9938", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(None,\n", + " {'xs_data/mip_07130010/huc_07130010.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_07130010/huc_07130010.gpkg',\n", + " 'xs_data/mip_07130010/huc_07130010.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_07130010/huc_07130010.parquet',\n", + " 'xs_data/mip_09020301/huc_09020301.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_09020301/huc_09020301.parquet',\n", + " 'xs_data/mip_09020301/huc_09020301.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_09020301/huc_09020301.gpkg',\n", + " 'xs_data/mip_06040002/huc_06040002.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_06040002/huc_06040002.parquet',\n", + " 'xs_data/mip_06040002/huc_06040002.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_06040002/huc_06040002.gpkg',\n", + " 'xs_data/mip_03170004/huc_03170004.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_03170004/huc_03170004.gpkg',\n", + " 'xs_data/mip_03170004/huc_03170004.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_03170004/huc_03170004.parquet',\n", + " 'xs_data/mip_01020002/huc_01020002.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_01020002/huc_01020002.gpkg',\n", + " 'xs_data/mip_01020002/huc_01020002.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_01020002/huc_01020002.parquet',\n", + " 'xs_data/mip_10120203/huc_10120203.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_10120203/huc_10120203.parquet',\n", + " 'xs_data/mip_10120203/huc_10120203.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_10120203/huc_10120203.gpkg',\n", + " 'xs_data/mip_11060002/huc_11060002.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_11060002/huc_11060002.gpkg',\n", + " 'xs_data/mip_11060002/huc_11060002.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_11060002/huc_11060002.parquet',\n", + " 'xs_data/mip_13020102/huc_13020102.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_13020102/huc_13020102.gpkg',\n", + " 'xs_data/mip_13020102/huc_13020102.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_13020102/huc_13020102.parquet',\n", + " 'xs_data/mip_17060305/huc_17060305.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_17060305/huc_17060305.gpkg',\n", + " 'xs_data/mip_17060305/huc_17060305.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_17060305/huc_17060305.parquet',\n", + " 'xs_data/mip_12030107/huc_12030107.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_12030107/huc_12030107.gpkg',\n", + " 'xs_data/mip_12030107/huc_12030107.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_12030107/huc_12030107.parquet',\n", + " 'xs_data/crs_map/crs_mapping.pickle': '/home/sylvia.chin/Desktop/iceberg/xs_data/crs_map/crs_mapping.pickle',\n", + " 'xs_data/mip_08060202/huc_08060202.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_08060202/huc_08060202.gpkg',\n", + " 'xs_data/mip_08060202/huc_08060202.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_08060202/huc_08060202.parquet',\n", + " 'xs_data/mip_16020203/huc_16020203.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_16020203/huc_16020203.gpkg',\n", + " 'xs_data/mip_16020203/huc_16020203.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_16020203/huc_16020203.parquet',\n", + " 'xs_data/mip_18070204/huc_18070204.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_18070204/huc_18070204.gpkg',\n", + " 'xs_data/mip_18070204/huc_18070204.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_18070204/huc_18070204.parquet',\n", + " 'xs_data/mip_05020006/huc_05020006.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_05020006/huc_05020006.parquet',\n", + " 'xs_data/mip_05020006/huc_05020006.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_05020006/huc_05020006.gpkg',\n", + " 'xs_data/mip_02060006/huc_02060006.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_02060006/huc_02060006.gpkg',\n", + " 'xs_data/mip_02060006/huc_02060006.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_02060006/huc_02060006.parquet',\n", + " 'xs_data/mip_14050005/huc_14050005.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_14050005/huc_14050005.gpkg',\n", + " 'xs_data/mip_14050005/huc_14050005.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_14050005/huc_14050005.parquet',\n", + " 'xs_data/mip_15040007/huc_15040007.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_15040007/huc_15040007.parquet',\n", + " 'xs_data/mip_15040007/huc_15040007.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_15040007/huc_15040007.gpkg',\n", + " 'xs_data/mip_04030101/huc_04030101.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_04030101/huc_04030101.gpkg',\n", + " 'xs_data/mip_04030101/huc_04030101.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_04030101/huc_04030101.parquet'})" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s3_xs_keys2objs = collections.defaultdict()\n", + "for folder, subfolders, files in os.walk(f'{os.getcwd()}/xs_data/'):\n", + " for file in files:\n", + " if folder != f'{os.getcwd()}/xs_data/':\n", + " s3_xs_keys2objs[f'{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}/{file}'] = f'{folder}/{file}'\n", + " \n", + "s3_xs_keys2objs" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "0ef03ac5-4475-4a3c-8fbb-88ec50d832c4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(None,\n", + " {'iceberg_catalog/xs_dev2_catalog.db': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/xs_dev2_catalog.db',\n", + " 'iceberg_catalog/xs_catalog.db': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/xs_catalog.db',\n", + " 'iceberg_catalog/metadata/snap-1219395432228756396-0-e0c82612-49a1-4bec-9ffb-0363b2808c90.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-1219395432228756396-0-e0c82612-49a1-4bec-9ffb-0363b2808c90.avro',\n", + " 'iceberg_catalog/metadata/79d941be-d64b-420d-8258-ab9a19f60a3c-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/79d941be-d64b-420d-8258-ab9a19f60a3c-m0.avro',\n", + " 'iceberg_catalog/metadata/56302b1b-0145-4261-a75f-9fc822513965-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/56302b1b-0145-4261-a75f-9fc822513965-m0.avro',\n", + " 'iceberg_catalog/metadata/2b35b175-c393-402f-aefe-7ab21a7f86e3-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/2b35b175-c393-402f-aefe-7ab21a7f86e3-m0.avro',\n", + " 'iceberg_catalog/metadata/00000-d5bfb311-f654-4caa-a8bd-0988d5ea3f1c.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-d5bfb311-f654-4caa-a8bd-0988d5ea3f1c.metadata.json',\n", + " 'iceberg_catalog/metadata/00000-e005d193-1dbe-48f8-9c71-5d6acb118047.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-e005d193-1dbe-48f8-9c71-5d6acb118047.metadata.json',\n", + " 'iceberg_catalog/metadata/00000-a51a3d3f-c9d8-4491-ba4a-61b45450b482.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-a51a3d3f-c9d8-4491-ba4a-61b45450b482.metadata.json',\n", + " 'iceberg_catalog/metadata/snap-7751284475758926448-0-83a8e328-45aa-41c3-b7ba-aada589c1100.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-7751284475758926448-0-83a8e328-45aa-41c3-b7ba-aada589c1100.avro',\n", + " 'iceberg_catalog/metadata/7e31eefd-b1be-4270-8c56-55c3d7cee3ec-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/7e31eefd-b1be-4270-8c56-55c3d7cee3ec-m0.avro',\n", + " 'iceberg_catalog/metadata/00001-ff6bb0fd-545d-4c90-8687-d59e57f795f7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-ff6bb0fd-545d-4c90-8687-d59e57f795f7.metadata.json',\n", + " 'iceberg_catalog/metadata/eb72845f-a72d-4ec7-b515-3fa60bcb1a0e-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/eb72845f-a72d-4ec7-b515-3fa60bcb1a0e-m0.avro',\n", + " 'iceberg_catalog/metadata/00000-efb96525-596e-4f96-a5a4-6e930d9fa14d.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-efb96525-596e-4f96-a5a4-6e930d9fa14d.metadata.json',\n", + " 'iceberg_catalog/metadata/00000-e971734f-4fd6-4f57-8313-6578b345dd9f.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-e971734f-4fd6-4f57-8313-6578b345dd9f.metadata.json',\n", + " 'iceberg_catalog/metadata/00001-37052db6-879f-4d5d-a7eb-bf238795f174.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-37052db6-879f-4d5d-a7eb-bf238795f174.metadata.json',\n", + " 'iceberg_catalog/metadata/00000-566a22c6-9329-4cf1-b863-35034787428a.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-566a22c6-9329-4cf1-b863-35034787428a.metadata.json',\n", + " 'iceberg_catalog/metadata/0932a09d-7c95-4369-91ff-86187c292488-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/0932a09d-7c95-4369-91ff-86187c292488-m0.avro',\n", + " 'iceberg_catalog/metadata/f491d047-a07d-4dd1-9880-fb7cb524b729-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/f491d047-a07d-4dd1-9880-fb7cb524b729-m0.avro',\n", + " 'iceberg_catalog/metadata/94010da4-988a-4690-9744-544b69c4043d-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/94010da4-988a-4690-9744-544b69c4043d-m0.avro',\n", + " 'iceberg_catalog/metadata/snap-1423242798306539847-0-f1963f4e-533c-4349-90ce-fe651c3bd03f.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-1423242798306539847-0-f1963f4e-533c-4349-90ce-fe651c3bd03f.avro',\n", + " 'iceberg_catalog/metadata/74d5f5a8-00b1-4270-86e4-920f534462e2-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/74d5f5a8-00b1-4270-86e4-920f534462e2-m0.avro',\n", + " 'iceberg_catalog/metadata/00000-7bbe577e-8ca9-4a90-afe8-115ae5ad1e66.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-7bbe577e-8ca9-4a90-afe8-115ae5ad1e66.metadata.json',\n", + " 'iceberg_catalog/metadata/00000-0f5ff66b-9f53-4695-8fc7-cb317cab3ff7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-0f5ff66b-9f53-4695-8fc7-cb317cab3ff7.metadata.json',\n", + " 'iceberg_catalog/metadata/00001-5b0d7e69-1e56-4f5a-8f0e-b5c5f8c9e402.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-5b0d7e69-1e56-4f5a-8f0e-b5c5f8c9e402.metadata.json',\n", + " 'iceberg_catalog/metadata/snap-2864916062256581278-0-56302b1b-0145-4261-a75f-9fc822513965.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-2864916062256581278-0-56302b1b-0145-4261-a75f-9fc822513965.avro',\n", + " 'iceberg_catalog/metadata/c02f999d-b401-4394-8550-0524812a59fe-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/c02f999d-b401-4394-8550-0524812a59fe-m0.avro',\n", + " 'iceberg_catalog/metadata/2a5f2f6a-d7ed-424e-84ef-eaebb0fb2428-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/2a5f2f6a-d7ed-424e-84ef-eaebb0fb2428-m0.avro',\n", + " 'iceberg_catalog/metadata/00000-37fbbe54-240a-4d27-9c80-8af688986ec7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-37fbbe54-240a-4d27-9c80-8af688986ec7.metadata.json',\n", + " 'iceberg_catalog/metadata/snap-1547694616865413560-0-eb72845f-a72d-4ec7-b515-3fa60bcb1a0e.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-1547694616865413560-0-eb72845f-a72d-4ec7-b515-3fa60bcb1a0e.avro',\n", + " 'iceberg_catalog/metadata/4b224a68-251a-448b-a79a-e7a2ae6848c7-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/4b224a68-251a-448b-a79a-e7a2ae6848c7-m0.avro',\n", + " 'iceberg_catalog/metadata/83a8e328-45aa-41c3-b7ba-aada589c1100-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/83a8e328-45aa-41c3-b7ba-aada589c1100-m0.avro',\n", + " 'iceberg_catalog/metadata/e1c70f45-ac00-4f36-9f05-b402a92b17bb-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/e1c70f45-ac00-4f36-9f05-b402a92b17bb-m0.avro',\n", + " 'iceberg_catalog/metadata/snap-6643227147233749836-0-dd9f8708-2f34-4066-8420-63fd375262c4.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-6643227147233749836-0-dd9f8708-2f34-4066-8420-63fd375262c4.avro',\n", + " 'iceberg_catalog/metadata/88b277f5-8e5f-45cf-b3a7-3f645ccf3610-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/88b277f5-8e5f-45cf-b3a7-3f645ccf3610-m0.avro',\n", + " 'iceberg_catalog/metadata/00001-d3787857-4eac-4cfa-86a5-6b220bb197d3.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-d3787857-4eac-4cfa-86a5-6b220bb197d3.metadata.json',\n", + " 'iceberg_catalog/metadata/snap-6148936714446219898-0-928c6a28-c248-4fbc-bd5c-684b1b38d2d9.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-6148936714446219898-0-928c6a28-c248-4fbc-bd5c-684b1b38d2d9.avro',\n", + " 'iceberg_catalog/metadata/00001-e7962f83-a076-4a75-985e-d22f5c50d02e.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-e7962f83-a076-4a75-985e-d22f5c50d02e.metadata.json',\n", + " 'iceberg_catalog/metadata/00000-a6048e13-d05c-4e89-8da9-6781f37ad1f2.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-a6048e13-d05c-4e89-8da9-6781f37ad1f2.metadata.json',\n", + " 'iceberg_catalog/metadata/snap-5963977556171816944-0-31bea5d0-7d98-40f6-800b-8c651aea6750.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-5963977556171816944-0-31bea5d0-7d98-40f6-800b-8c651aea6750.avro',\n", + " 'iceberg_catalog/metadata/snap-612035466803620355-0-7e31eefd-b1be-4270-8c56-55c3d7cee3ec.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-612035466803620355-0-7e31eefd-b1be-4270-8c56-55c3d7cee3ec.avro',\n", + " 'iceberg_catalog/metadata/00000-4e2fd912-9308-4048-a8d1-252cf9122840.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-4e2fd912-9308-4048-a8d1-252cf9122840.metadata.json',\n", + " 'iceberg_catalog/metadata/00001-79175265-6e89-4e2a-9157-5db43dafa9d7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-79175265-6e89-4e2a-9157-5db43dafa9d7.metadata.json',\n", + " 'iceberg_catalog/metadata/snap-62392862022840883-0-79d941be-d64b-420d-8258-ab9a19f60a3c.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-62392862022840883-0-79d941be-d64b-420d-8258-ab9a19f60a3c.avro',\n", + " 'iceberg_catalog/metadata/snap-515960161385423435-0-994a22b9-0dae-40c2-b2bb-23f9962c61c5.avro': 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'/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/e0c82612-49a1-4bec-9ffb-0363b2808c90-m0.avro',\n", + " 'iceberg_catalog/metadata/00000-0ecd14a5-3da4-47f0-8bd8-83c95e680afc.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-0ecd14a5-3da4-47f0-8bd8-83c95e680afc.metadata.json',\n", + " 'iceberg_catalog/metadata/snap-4996700114448263860-0-29a6c52d-eefc-475d-927b-8e74a04cb96c.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-4996700114448263860-0-29a6c52d-eefc-475d-927b-8e74a04cb96c.avro',\n", + " 'iceberg_catalog/metadata/00000-728df095-7195-4409-aae6-ed71991c6ef6.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-728df095-7195-4409-aae6-ed71991c6ef6.metadata.json',\n", + " 'iceberg_catalog/metadata/snap-6495503154204324342-0-0932a09d-7c95-4369-91ff-86187c292488.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-6495503154204324342-0-0932a09d-7c95-4369-91ff-86187c292488.avro',\n", + " 'iceberg_catalog/metadata/8dea00e3-705a-4e9b-80ab-16d2728732f9-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/8dea00e3-705a-4e9b-80ab-16d2728732f9-m0.avro',\n", + " 'iceberg_catalog/metadata/1d64c282-3463-4db0-9914-78fef1158e5e-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/1d64c282-3463-4db0-9914-78fef1158e5e-m0.avro',\n", + " 'iceberg_catalog/metadata/00000-1c1fb8d7-0c4c-4b0a-9ae3-db09afa718c7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-1c1fb8d7-0c4c-4b0a-9ae3-db09afa718c7.metadata.json',\n", + " 'iceberg_catalog/data/00000-0-3a1ebbe7-11ac-4920-b628-91d42f2a9658.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-3a1ebbe7-11ac-4920-b628-91d42f2a9658.parquet',\n", + " 'iceberg_catalog/data/00000-0-1d64c282-3463-4db0-9914-78fef1158e5e.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-1d64c282-3463-4db0-9914-78fef1158e5e.parquet',\n", + " 'iceberg_catalog/data/00000-0-f491d047-a07d-4dd1-9880-fb7cb524b729.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-f491d047-a07d-4dd1-9880-fb7cb524b729.parquet',\n", + " 'iceberg_catalog/data/00000-0-a5fa37d5-ad11-4816-8bc1-907b2f384012.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-a5fa37d5-ad11-4816-8bc1-907b2f384012.parquet',\n", + " 'iceberg_catalog/data/00000-0-31bea5d0-7d98-40f6-800b-8c651aea6750.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-31bea5d0-7d98-40f6-800b-8c651aea6750.parquet',\n", + " 'iceberg_catalog/data/00000-0-25ee9bd7-8d6f-4ae3-93ae-0a7c74d1be51.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-25ee9bd7-8d6f-4ae3-93ae-0a7c74d1be51.parquet',\n", + " 'iceberg_catalog/data/00000-0-e1c70f45-ac00-4f36-9f05-b402a92b17bb.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-e1c70f45-ac00-4f36-9f05-b402a92b17bb.parquet',\n", + " 'iceberg_catalog/data/00000-0-94010da4-988a-4690-9744-544b69c4043d.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-94010da4-988a-4690-9744-544b69c4043d.parquet',\n", + " 'iceberg_catalog/data/00000-0-994a22b9-0dae-40c2-b2bb-23f9962c61c5.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-994a22b9-0dae-40c2-b2bb-23f9962c61c5.parquet',\n", + " 'iceberg_catalog/data/00000-0-2ec024d8-d33a-474b-a66d-578b741bb07b.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-2ec024d8-d33a-474b-a66d-578b741bb07b.parquet',\n", + " 'iceberg_catalog/data/00000-0-8dea00e3-705a-4e9b-80ab-16d2728732f9.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-8dea00e3-705a-4e9b-80ab-16d2728732f9.parquet',\n", + " 'iceberg_catalog/data/00000-0-4b224a68-251a-448b-a79a-e7a2ae6848c7.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-4b224a68-251a-448b-a79a-e7a2ae6848c7.parquet',\n", + " 'iceberg_catalog/data/00000-0-7e31eefd-b1be-4270-8c56-55c3d7cee3ec.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-7e31eefd-b1be-4270-8c56-55c3d7cee3ec.parquet',\n", + " 'iceberg_catalog/data/00000-0-29a6c52d-eefc-475d-927b-8e74a04cb96c.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-29a6c52d-eefc-475d-927b-8e74a04cb96c.parquet',\n", + " 'iceberg_catalog/data/00000-0-2b35b175-c393-402f-aefe-7ab21a7f86e3.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-2b35b175-c393-402f-aefe-7ab21a7f86e3.parquet',\n", + " 'iceberg_catalog/data/00000-0-0932a09d-7c95-4369-91ff-86187c292488.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-0932a09d-7c95-4369-91ff-86187c292488.parquet',\n", + " 'iceberg_catalog/data/00000-0-2a5f2f6a-d7ed-424e-84ef-eaebb0fb2428.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-2a5f2f6a-d7ed-424e-84ef-eaebb0fb2428.parquet',\n", + " 'iceberg_catalog/data/00000-0-f1963f4e-533c-4349-90ce-fe651c3bd03f.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-f1963f4e-533c-4349-90ce-fe651c3bd03f.parquet',\n", + " 'iceberg_catalog/data/00000-0-79d941be-d64b-420d-8258-ab9a19f60a3c.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-79d941be-d64b-420d-8258-ab9a19f60a3c.parquet',\n", + " 'iceberg_catalog/data/00000-0-dd9f8708-2f34-4066-8420-63fd375262c4.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-dd9f8708-2f34-4066-8420-63fd375262c4.parquet',\n", + " 'iceberg_catalog/data/00000-0-6092672e-c75b-4426-af48-40346808bdf7.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-6092672e-c75b-4426-af48-40346808bdf7.parquet',\n", + " 'iceberg_catalog/data/00000-0-fbaf2a80-3953-4b77-9f09-6398d6db52ea.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-fbaf2a80-3953-4b77-9f09-6398d6db52ea.parquet',\n", + " 'iceberg_catalog/data/00000-0-2dd9f725-e066-4163-a76c-934c075c6b69.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-2dd9f725-e066-4163-a76c-934c075c6b69.parquet',\n", + " 'iceberg_catalog/data/00000-0-928c6a28-c248-4fbc-bd5c-684b1b38d2d9.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-928c6a28-c248-4fbc-bd5c-684b1b38d2d9.parquet',\n", + " 'iceberg_catalog/data/00000-0-e0c82612-49a1-4bec-9ffb-0363b2808c90.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-e0c82612-49a1-4bec-9ffb-0363b2808c90.parquet',\n", + " 'iceberg_catalog/data/00000-0-88b277f5-8e5f-45cf-b3a7-3f645ccf3610.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-88b277f5-8e5f-45cf-b3a7-3f645ccf3610.parquet',\n", + " 'iceberg_catalog/data/00000-0-56302b1b-0145-4261-a75f-9fc822513965.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-56302b1b-0145-4261-a75f-9fc822513965.parquet',\n", + " 'iceberg_catalog/data/00000-0-eb72845f-a72d-4ec7-b515-3fa60bcb1a0e.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-eb72845f-a72d-4ec7-b515-3fa60bcb1a0e.parquet',\n", + " 'iceberg_catalog/data/00000-0-6ed7cdd8-37e9-4f05-af66-0ad179cad29e.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-6ed7cdd8-37e9-4f05-af66-0ad179cad29e.parquet',\n", + " 'iceberg_catalog/data/00000-0-ef901ac2-0781-4afb-94ca-b733dd048a70.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-ef901ac2-0781-4afb-94ca-b733dd048a70.parquet',\n", + " 'iceberg_catalog/data/00000-0-c02f999d-b401-4394-8550-0524812a59fe.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-c02f999d-b401-4394-8550-0524812a59fe.parquet',\n", + " 'iceberg_catalog/data/00000-0-74d5f5a8-00b1-4270-86e4-920f534462e2.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-74d5f5a8-00b1-4270-86e4-920f534462e2.parquet',\n", + " 'iceberg_catalog/data/00000-0-33337276-e026-455f-8cfd-7b05284abc36.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-33337276-e026-455f-8cfd-7b05284abc36.parquet',\n", + " 'iceberg_catalog/data/00000-0-b6e49d71-9d65-445d-8721-8549a8bfff63.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-b6e49d71-9d65-445d-8721-8549a8bfff63.parquet',\n", + " 'iceberg_catalog/data/00000-0-ff565f97-f743-4f7c-abd6-986fadcdd5ff.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-ff565f97-f743-4f7c-abd6-986fadcdd5ff.parquet',\n", + " 'iceberg_catalog/data/00000-0-83a8e328-45aa-41c3-b7ba-aada589c1100.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-83a8e328-45aa-41c3-b7ba-aada589c1100.parquet'})" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s3_xs_iceberg_keys2objs = collections.defaultdict()\n", + "for folder, subfolders, files in os.walk(f'{os.getcwd()}/iceberg_catalog/'):\n", + " for file in files:\n", + " if folder != f'{os.getcwd()}/iceberg_catalog/':\n", + " # For all files not the database\n", + " s3_xs_iceberg_keys2objs[f'{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}/{file}'] = f'{folder}/{file}'\n", + " else:\n", + " # For the database.\n", + " s3_xs_iceberg_keys2objs[f'{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}{file}'] = f'{folder}{file}'\n", + " \n", + " \n", + "s3_xs_iceberg_keys2objs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddc9f129-b35b-453f-bad6-92350e053fb6", + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate bucket of interest.\n", + "profile_name = 'default'\n", + "session = boto3.Session(profile_name=profile_name)\n", + "s3 = session.resource('s3')\n", + "bucket_name = 'ngwpc-xs'\n", + "\n", + "# # Uploading the xs by HUC parquets\n", + "# for key, file_dir in s3_xs_keys2objs.items():\n", + "# s3.Bucket(bucket_name).upload_file(file_dir, key)\n", + "\n", + "# Uploading the xs by HUC parquets\n", + "# for key, file_dir in s3_xs_iceberg_keys2objs.items():\n", + "# s3.Bucket(bucket_name).upload_file(file_dir, key)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "01dc1ff0-bfd5-4b7a-82d0-1d60e237e2cd", + "metadata": {}, + "source": [ + "# Cross-Check & Verify:\n", + "## Parquet Data Has Been Written To Its Associated Iceberg Table Sucessfully" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b365d647-bbfa-45a9-8673-52c564cf0ea7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "river: string\n", + "reach: string\n", + "river_reach: string\n", + "river_station: double\n", + "river_reach_rs: string\n", + "thalweg: double\n", + "xs_max_elevation: double\n", + "left_reach_length: double\n", + "right_reach_length: double\n", + "channel_reach_length: double\n", + "ras_data: string\n", + "station_elevation_points: string\n", + "bank_stations: string\n", + "number_of_station_elevation_points: int64\n", + "number_of_coords: int64\n", + "flows: string\n", + "profile_names: string\n", + "geometry: binary\n", + "huc: string\n", + "model_id: string\n", + "us_reach_id: string\n", + "us_network_to_id: string\n", + "ds_reach_id: string\n", + "ds_network_to_id: string\n", + "__index_level_0__: int64\n", + "----\n", + "river: [[\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",...,\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\"]]\n", + "reach: [[\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",...,\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\"]]\n", + "river_reach: [[\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",...,\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\"]]\n", + "river_station: [[7300,6833,6346,5913,5505,...,1842,1400,970,516,111]]\n", + "river_reach_rs: [[\"Stream0528 Reach-1 7300.0\",\"Stream0528 Reach-1 6833.0\",\"Stream0528 Reach-1 6346.0\",\"Stream0528 Reach-1 5913.0\",\"Stream0528 Reach-1 5505.0\",...,\"Stream0512 Reach-1 1842.0\",\"Stream0512 Reach-1 1400.0\",\"Stream0512 Reach-1 970.0\",\"Stream0512 Reach-1 516.0\",\"Stream0512 Reach-1 111.0\"]]\n", + "thalweg: [[217.86,215.26,213.94,213.33,213.34,...,368.01,365.61,364.04,360.64,360.73]]\n", + "xs_max_elevation: [[248.82,238.12,246.73,241.95,242.87,...,404.79,398.04,385.23,404.53,391.25]]\n", + "left_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", + "right_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", + "channel_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", + "..." + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the Parquet file into a DataFrame\n", + "parquet_file_path = f'{os.getcwd()}/xs_data/mip_01020002/huc_01020002.parquet'\n", + "parquet_content = pq.read_table(parquet_file_path)\n", + "parquet_content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86ec5e38-ab4c-4185-bc0f-3306e670f968", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "iceberg", + "language": "python", + "name": "iceberg" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 297265f0ac78afcc769c3baa65667df412d53394 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 15 May 2025 11:04:37 -0400 Subject: [PATCH 29/97] updated to accomod namespace & xs & m1_aux data --- .../src/icefabric_manage/iceberg_table.py | 134 +++++++++++++----- 1 file changed, 95 insertions(+), 39 deletions(-) diff --git a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py index 76a336e..271409a 100644 --- a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py +++ b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py @@ -2,22 +2,34 @@ import pyiceberg from pyiceberg.catalog import load_catalog from pyiceberg.schema import Schema -from pyiceberg.types import NestedField, IntegerType, StringType, DoubleType, LongType +from pyiceberg.types import NestedField, IntegerType, StringType, DoubleType, LongType, BinaryType, BooleanType +from pyiceberg.expressions import EqualTo, LessThan, And, GreaterThan, GreaterThanOrEqual import pyarrow.parquet as pq from pyiceberg.schema import Schema import boto3 import s3fs +import re import os class IcebergTable(object): ''' Create a Iceberg table per parquet file w/ its inherited schema set. + + Note: It is okay to expect following warning statements throughout process: + "Iceberg does not have a dictionary type. will be inferred as string on read." ''' def __init__(self): - + + # Generate folder for iceberg catalog + if not os.path.exists(f'{os.getcwd()}/iceberg_catalog'): + os.makedirs(f'{os.getcwd()}/iceberg_catalog') + # Set location of where the iceberg catalog will reside os.environ['PYICEBERG_HOME'] = os.getcwd() + + # Initialize namespace to be set for Iceberg catalog + self.namespace = str() def read_data_dirs(self, data_dir): ''' @@ -55,20 +67,26 @@ def read_data(self, parquet_file_path): return data - def establish_catalog(self, name='dev'): + def establish_catalog(self, catalog_name, namespace): ''' Creates a new Iceberg catalog. Args: - name (str): Name of the catalog to be created. - Default: 'dev' for development catalog + catalog_name (str): Name of the catalog to be created. + Default: 'dev' for development catalog + namespace (str): Name of namespace. Return: None - ''' + ''' # Establish a new Iceberg catalog & its configuration - self.catalog = load_catalog(name=name, - **{'uri': f'sqlite:///iceberg_catalog/{name}_catalog.db'}) + self.catalog = load_catalog(name=catalog_name, + **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'}) + + # Establish namespace to be create w/in catalog + self.namespace = namespace + if self.namespace not in self.catalog.list_namespaces(): + self.catalog.create_namespace(self.namespace) return @@ -99,6 +117,10 @@ def convert_pyarrow_to_iceberg_schema(self, arrow_schema): iceberg_type = DoubleType() elif pa.types.is_int64(arrow_type): iceberg_type = LongType() + elif pa.types.is_boolean(arrow_type): + iceberg_type = BooleanType() + elif pa.types.is_binary(arrow_type): + iceberg_type = BinaryType() elif pa.types.is_dictionary(arrow_type): if pa.types.is_string(arrow_type.value_type): iceberg_type = StringType() @@ -117,7 +139,7 @@ def convert_pyarrow_to_iceberg_schema(self, arrow_schema): return schema - def create_table_for_parquet(self, iceberg_tablename, data_table, schema, namespace): + def create_table_for_parquet(self, iceberg_tablename, data_table, schema): ''' Convert parquet Pyarrow table to iceberg table & allocate Iceberg catalog under the ./iceberg_catalog directory. @@ -132,15 +154,11 @@ def create_table_for_parquet(self, iceberg_tablename, data_table, schema, namesp the Iceberg catalog. Return: None - - Note: The following warning is expected: - Iceberg does not have a dictionary type. will be inferred as int32 on read. ''' # Create an Iceberg table - self.catalog.create_namespace_if_not_exists(namespace) - iceberg_table = self.catalog.create_table(identifier=f"{namespace}.{iceberg_tablename}", + iceberg_table = self.catalog.create_table(identifier=f"{self.namespace}.{iceberg_tablename}", schema=schema, location=f"{os.environ['PYICEBERG_HOME']}/iceberg_catalog") @@ -149,35 +167,46 @@ def create_table_for_parquet(self, iceberg_tablename, data_table, schema, namesp return - def create_table_for_all_parquets(self, parquet_files, namespace, source='local'): + def create_table_for_all_parquets(self, parquet_files, app_name='mip-xs'): ''' Convert parquets to Iceberg tables - each w/ their inherited schema. Args: parquet_files (list): List of directories of the parquet files. - - namespace (str): Namespace for which the Iceberg table will reside within - the Iceberg catalog. + + app_name (str): Application to create Iceberg tables. + Options: 'mip-xs' & 'bathymetry_ml_auxiliary' Return: None - + + Note: The sourced data structures for the data in 'mip-xs' & + 'bathymetry_ml_auxiliary' S3 buckets differ. + ''' for idx, parquet_file in enumerate(parquet_files): + if app_name == 'mip_xs': + iceberg_tablename = f"{os.path.split(os.path.split(parquet_file)[1])[1].split('.')[0]}" + + elif app_name == 'bathymetry_ml_auxiliary': + iceberg_tablename = f"{os.path.split(os.path.split(parquet_file)[0])[1]}" + data_table = self.read_data(parquet_file) data_pyarrow_schema = data_table.schema schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema) - self.create_table_for_parquet(os.path.split(os.path.split(parquet_file)[0])[1], + self.create_table_for_parquet(iceberg_tablename, data_table, - schema, - namespace) + schema) return - def create_table_for_all_s3parquets(self, namespace, bucket_name='ngwpc-bathymetry', profile_name='default'): + def create_table_for_all_s3parquets(self, app_name, bucket_name, profile_name='default'): ''' Convert parquets from S3 to Iceberg tables - each w/ their inherited schema. Args: bucket_name (list): S3 bucket name. + + app_name (str): Application to create Iceberg tables. + Options: 'mip_xs' & 'bathymetry_ml_auxiliary' namespace (str): Namespace for which the Iceberg table will reside within the Iceberg catalog. @@ -186,7 +215,7 @@ def create_table_for_all_s3parquets(self, namespace, bucket_name='ngwpc-bathymet Default: 'default' Return: None - + ''' # Instantiate bucket of interest. session = boto3.Session(profile_name=profile_name) @@ -194,29 +223,56 @@ def create_table_for_all_s3parquets(self, namespace, bucket_name='ngwpc-bathymet bucket_ob = s3.Bucket(bucket_name) pyarrow_table = {} for s3obj in bucket_ob.objects.all(): - pyarrow_table[s3obj.key]= pq.read_table(f's3://{bucket_name}/{s3obj.key}', - filesystem=s3fs.S3FileSystem()) + + # For sourcing the preprocessed XS parquets from S3 + if app_name == 'mip_xs' and re.match(r'^xs_data/.*\.parquet$', s3obj.key): + iceberg_tablename = f"{os.path.split(os.path.split(s3obj.key)[1])[1].split('.')[0]}" + pyarrow_table[iceberg_tablename]= pq.read_table(f's3://{bucket_name}/{s3obj.key}', + filesystem=s3fs.S3FileSystem()) + + # For sourcing the bathymetry_ml_auxiliary parquets from S3 + elif app_name == 'bathymetry_ml_auxiliary': + iceberg_tablename = f"{os.path.split(os.path.split(s3obj.key)[0])[1]}" + pyarrow_table[iceberg_tablename]= pq.read_table(f's3://{bucket_name}/{s3obj.key}', + filesystem=s3fs.S3FileSystem()) + + # Xforming each unique parquet to an iceberg table for key, data_table in pyarrow_table.items(): data_pyarrow_schema = data_table.schema schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema) - self.create_table_for_parquet(os.path.split(os.path.split(key)[0])[1], - data_table, - schema, - namespace) + self.create_table_for_parquet(key, + data_table, + schema) return -if __name__ == '__main__': +# if __name__ == '__main__': # Instantiate class - obj = IcebergTable() + #obj = IcebergTable() - # Extract list of parquets - parquet_list = obj.read_data_dirs(data_dir=f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/') + # ======= For ml_auxiliary_data application ======= + + ## Extract list of parquets + #parquet_list = obj.read_data_dirs(data_dir=f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/') + + ## Establish new Iceberg catalog + #obj.establish_catalog(catalog_name='bathymetry_ml_auxiliary_dev', + # namespace='bathymetry_ml_auxiliary') - # Establish new Iceberg catalog - obj.establish_catalog('dev') + ## Generate tables w/in the new Iceberg catalog for all parquets detected + #obj.create_table_for_all_parquets(parquet_list, + # app_name='bathymetry_ml_auxiliary') - # Generate tables w/in the new Iceberg catalog for all parquets detected - obj.create_table_for_all_parquets(parquet_list, - namespace='bathymetry_ml_auxiliary') + # ======= For mip xs data application ======= + + # Extract list of parquets + #file_list = obj.read_data_dirs(data_dir=f'{os.getcwd()}/xs_data/') + #parquet_list = [file for file in file_list if file.endswith('.parquet')] + + #Establish new Iceberg catalog for XS data + #obj.establish_catalog(catalog_name='xs', + # namespace='mip') + + # Generate tables w/in the new Iceberg catalog for all parquets detected + #obj.create_table_for_all_parquets(parquet_list, app_name='mip-xs') \ No newline at end of file From 751b0ef04432d8efff56e24607014a4d4deba4ca Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 15 May 2025 11:12:31 -0400 Subject: [PATCH 30/97] add script mapping XS to IDs categorize by HUC --- .../src/icefabric_manage/map_fim_data.py | 591 ++++++++++++++++++ 1 file changed, 591 insertions(+) create mode 100644 src/icefabric_manage/src/icefabric_manage/map_fim_data.py diff --git a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py new file mode 100644 index 0000000..abc3e7e --- /dev/null +++ b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py @@ -0,0 +1,591 @@ +import geopandas +import pyarrow as pa +import pyarrow.parquet as pq +import collections +import pandas as pd +import numpy as np +import re +import json +import pickle +import os +import warnings +warnings.filterwarnings('ignore') + +class MapData(object): + ''' + ''' + def __init__(self, data_dir): + self.data_dir = data_dir + + # Parent directory of the FIM-C files. + # Note: All the jsons & geopackages are relevant + # to map the files to IDs. + self.fim_data_dirs = [] + + # List of directories associated with each file type of + # the FIM-C data sample (e.g. geopackage of a given model @ HUC#, json, + # source_models.gpkg, ripple.gpkg) + self.model_gpkg_dirs = [] + self.src_models_gpkg_dirs = [] + self.rip_gpkg_dirs = [] + self.gpkg_dirs = [] + self.json_dirs = [] + self.xs_df_list = [] + + # Variables to be used later + self.model_gpkg_tablenames = [] + self.src_models_gpkg_tablenames = [] + self.rip_gpkg_tablenames = [] + self.gpkg_tablenames = [] + self.json_tablenames = [] + + self.id2json = collections.defaultdict(dict) + self.model_id2gpkg = collections.defaultdict(dict) + self.us_ref_dict = collections.defaultdict(dict) + self.ds_ref_dict = collections.defaultdict(dict) + self.rip_huc2gpkg = collections.defaultdict(dict) + self.groupbyriver_dict = collections.defaultdict(dict) + self.crs_dict = collections.defaultdict(dict) + self.consolidated_id2xs = geopandas.GeoDataFrame() + + self.read_data_dirs() + self.cat_data_dirs() + self.map_model2huc() + self.filter_model2huc_map(keys_to_drop={'metrics','low_flow', + 'high_flow', 'eclipsed', + 'lengths', 'coverage'}) + + # Generate maps of model_id & HUC # to xs (for both us & ds cross-section) + # to reach ID & "network_to_id" from each model @ HUC's json file + self.map_modelhuc_xs2ids() + + # Generate maps of model_id & HUC # to gpkg from each model @ HUC's geopackage + self.map_model2huc_gpkg() + + # Generate maps of HUC # to ripple gpkg + self.map_huc2ripple_gpkg() + + # Map IDs to each model's cross-section instance + self.map_model_xs2ids() + + # [Optional: Per HUC, save each river's set of XS data as geoparquetss & geopackages] + #self.save_xs_data() + + # Save map of inherited CRS to HUC, model_id, river name + self.save_crs_map() + + # Consolidated all HEC RAS models' cross-sections featuring IDs + self.consolidate_id2xs_dfs() + + # Save HEC RAS models' cross-sections consolidated by HUC as geoparquets & geopackages + self.save_xsbyhuc_data() + + + + def read_data_dirs(self): + ''' + Extract the list of fim-c data sample's directories. + + Args: + data_dir (str): Parent directory of the fim-c files. + Note: All the jsons & geopackages are relevant + to map the files to IDs. + + Return (list): List of directories associated with each file type of + the FIM-C data sample. + + ''' + fim_data_dirs = [] + for folder, subfolders, files in os.walk(self.data_dir): + if folder != self.data_dir: + for file in files: + self.fim_data_dirs.append(f'{folder}/{file}') + + return + + def cat_data_dirs(self): + ''' + Categorize FIM-C data sample files. + + Args: + None + + Return: None + + ''' + # Extract a list of directories corresponding to each set of files + for x in self.fim_data_dirs: + + # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains reach_id & nwm_to_id + # for network layer & reaches layer. The rating curves layer only has reach_id. The models layer + # contains collection_id & model_id) + if re.search('ripple.gpkg', x): + self.rip_gpkg_dirs.append(x) + t = re.search('/mip(.*)', x) + rip_gpkg_tblname = t.group() + self.rip_gpkg_tablenames.append(rip_gpkg_tblname.lstrip('/').replace('/', '_')) + + # Covers all HEC-RAS models gpkg featuring XS per HUC (contains model_id) + elif not x.endswith('source_models.gpkg') and not x.endswith('.json') and not re.search('ripple.gpkg', x): + self.model_gpkg_dirs.append(x) + t = re.search('/mip(.*)', x) + model_gpkg_tblname = t.group() + self.model_gpkg_tablenames.append(model_gpkg_tblname.lstrip('/').replace('/', '_')) + + # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains model_id & their HEC-RAS 1D model flowlines) + elif x.endswith('source_models.gpkg'): + self.src_models_gpkg_dirs.append(x) + t = re.search('/mip(.*)', x) + src_models_gpkg_tblname = t.group() + self.src_models_gpkg_tablenames.append(src_models_gpkg_tblname.lstrip('/').replace('/', '_')) + + # Covers all HEC-RAS models + Ripple gpkg per HUC + if x.endswith('.gpkg'): + self.gpkg_dirs.append(x) + t = re.search('/mip(.*)', x) + gpkg_tblname = t.group() + self.gpkg_tablenames.append(gpkg_tblname.lstrip('/').replace('/', '_')) + + # Covers each HEC-RAS models' result of conflating its model w/ the NWM network + elif x.endswith('.json'): + self.json_dirs.append(x) + t = re.search('/mip(.*)', x) + json_tblname = t.group() + self.json_tablenames.append(json_tblname.lstrip('/').replace('/', '_')) + + return + + def drop_nested_keys(self, map_dict, keys_to_drop): + ''' + Drop keys irrelevant for linking each XS to IDs + + Args: + map_dict (dict): Dictionary to filter + + keys_to_drop (dict): List of keys irrelevant for linking each XS to IDs. + + Return: None + + ''' + if isinstance(map_dict, dict): + return {k: self.drop_nested_keys(v, keys_to_drop) for k, v in map_dict.items() if k not in keys_to_drop} + elif isinstance(map_dict, list): + return [self.drop_nested_keys(i, keys_to_drop) for i in map_dict] + else: + return map_dict + + def map_model2huc(self): + ''' + Map each conflation json file to their corresponding model_id & HUC #. + + Args: + None + + Return: None + + ''' + for x in self.json_dirs: + # Note: model_ids found in each src source_models.gpkg is featured is declared as + # sub-foldername of where model gpkg file resides + model_id = x.split("/")[-2] + huc_num = x.split("/")[-4].split('_')[1] + self.id2json[model_id][huc_num] = {} + try: + with open(x, 'r') as f: + json2dict = json.loads(f.read()) + self.id2json[model_id][huc_num].update(json2dict) + except: + pass + + return + + def filter_model2huc_map(self, keys_to_drop): + ''' + Extract only relevant keys from model2huc map for linking each XS to a feature ID. + + Args: + keys_to_drop (dict): List of keys irrelevant for linking each XS to IDs. + (e.g. {'metrics','low_flow', 'high_flow', 'eclipsed', + 'lengths', 'coverage'}) + + Return: None + + ''' + self.id2json = self.drop_nested_keys(self.id2json, keys_to_drop) + + return + + def map_modelhuc_xs2ids(self): + ''' + Parse JSONs & map model_id & HUC # to xs to reach ID & "network_to_id" + + Args: + None + + Return: None + + Note: Per model @ HUC cross-section layer, the attribute of interest is "river_reach_rs" + in order to link the IDs to each individual cross-section & their associated xs_id. + + To map each cross-section of a model @ HUC#, there has to be a shared attribute between a + model @ HUC#'s cross section w/in its XS layer & the details provided within a model @ HUC#'s + conflation json file. + + - Each conflation json file reveals ... + - Per reach, there is a set of cross-sections. + - Within each model's cross-section (XS) layers, there are a set of cross-section + instances - each instance featuring a unique "thalweg" (aka "min_elevation"), + "xs_max_elevation" (aka "max_elevation"), "reach_id" ("reaches"), & "river_station" (aka "xs_id") + + - Each model @ HUC#'s XS layer contains a collection of cross-section instancees. + Thus, each unique cross-section w/in a given model @ HUC#'s XS layer will need to be mapped in + such a way to allow each cross-section to be associated with a feature ID (aka "reach_id" and/or + "network_to_id"). + + - "river_reach_rs" is formatted differently across models' XS layers, however multplie keys + referenced in the conflation jsons can be referenced to obtain the "river_reach_rs" from the jsons + As a result, the mapping of IDs to each model's cross-section instance will be based on the info. + extracted from a model @ HUC#'s conflation.json + + - There can be multiple reach_ids tied to same nwm_to_id (aka "network_to_id). + + ''' + # Keys to join values from that makes up the 'river_reach_rs' reflected in each model's XS layer + keys_to_join = ['river', 'reach', 'xs_id'] + for model_id, huc_dict in self.id2json.items(): + for huc_num, reach_dict in huc_dict.items(): + for reach_id, v_dict in reach_dict['reaches'].items(): + # Joining the attribute because each model's xs layer features three atttrib concat (to be used as reference) + if "us_xs" in v_dict: + usxs_joined_values = ' '.join(str(v_dict["us_xs"][key]) for key in keys_to_join) + if "min_elevation" in v_dict["us_xs"]: + us_xs_min_elev = v_dict["us_xs"]["min_elevation"] + if "max_elevation" in v_dict["us_xs"]: + us_xs_max_elev = v_dict["us_xs"]["max_elevation"] + + if "ds_xs" in v_dict: + dsxs_joined_values = ' '.join(str(v_dict["ds_xs"][key]) for key in keys_to_join) + if "min_elevation" in v_dict["ds_xs"]: + ds_xs_min_elev = v_dict["ds_xs"]["min_elevation"] + if "max_elevation" in v_dict["ds_xs"]: + ds_xs_max_elev = v_dict["ds_xs"]["max_elevation"] + + if "network_to_id" in v_dict: + nwm2id = v_dict["network_to_id"] + + # Generated maps of model_id & HUC # to xs (for both us & ds cross-section) + # to reach ID & "network_to_id" + self.us_ref_dict[(model_id, huc_num)].update({(usxs_joined_values, us_xs_min_elev , us_xs_max_elev):[reach_id, nwm2id]}) + self.ds_ref_dict[(model_id, huc_num)].update({(dsxs_joined_values, ds_xs_min_elev , ds_xs_max_elev):[reach_id, nwm2id]}) + + return + + def map_model2huc_gpkg(self): + ''' + Map model_id & HUC # to each HEC-RAS model's geopackage. + + Args: + None + + Return: None + + Note: model_ids found in each source_models.gpkg is featured in last + sub-foldername of where model gpkg file resides + + ''' + # Each HEC-RAS model gpkg per model per HUC + for x in self.model_gpkg_dirs: + model_id = x.split("/")[-2] + huc_num = x.split("/")[-4].split('_')[1] + self.model_id2gpkg[(model_id, huc_num)] = {'XS': None} + self.model_id2gpkg[(model_id, huc_num)] = {"XS concave hull": None} + self.model_id2gpkg[(model_id, huc_num)] = {"River": None} + + try: + self.model_id2gpkg[(model_id, huc_num)].update({'XS': geopandas.read_file(x, engine='pyogrio', + use_arrow=True, + layer="XS")}) + + self.model_id2gpkg[(model_id, huc_num)].update({"River": geopandas.read_file(x, + engine='pyogrio', + use_arrow=True, + layer="River")}) + self.model_id2gpkg[(model_id, huc_num)].update({"XS concave hull": geopandas.read_file(x, + engine='pyogrio', + use_arrow=True, + layer="XS concave hull")}) + except: + pass + + return + + def map_huc2ripple_gpkg(self): + ''' + Map HUC # to ripple geopackage (features HEC RAS 1D model flowlines). + + Args: + None + + Return: None + + Note: ripple.gpkg features the HEC RAS 1D model flowlines categorized by HUC #. + + ''' + for x in self.rip_gpkg_dirs: + huc_num = x.split("/")[-2].split('_')[1] + self.rip_huc2gpkg[huc_num] = {'reaches': None} + self.rip_huc2gpkg[huc_num] = {"rating curves": None} + self.rip_huc2gpkg[huc_num] = {"network": None} + self.rip_huc2gpkg[huc_num] = {'models': None} + self.rip_huc2gpkg[huc_num] = {"metadata": None} + self.rip_huc2gpkg[huc_num] = {"rating_curves_no_map": None} + self.rip_huc2gpkg[huc_num] = {"processing": None} + + try: + self.rip_huc2gpkg[huc_num].update({"reaches": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="reaches")}) + self.rip_huc2gpkg[huc_num].update({"rating_curves": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="rating_curves")}) + self.rip_huc2gpkg[huc_num].update({"network": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="network")}) + self.rip_huc2gpkg[huc_num].update({"models": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="models")}) + self.rip_huc2gpkg[huc_num].update({"metadata": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="metadata")}) + self.rip_huc2gpkg[huc_num].update({"rating_curves_no_map": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="rating_curves_no_map")}) + self.rip_huc2gpkg[huc_num].update({"processing": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="processing")}) + except: + + pass + return + + def map_model_xs2ids(self): + ''' + Map each cross-section instance featured in HEC-RAS model's cross-section layer to their corresponding IDs. + + Args: + None + + Return: None + + ''' + + for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items(): + df = model_gpkg_dict['XS'] + df['huc'] = huc_num + df['model_id'] = model_id + array_of_lists = [[None, None] for _ in range(len(df))] + df['us_ids'] = pd.DataFrame([array_of_lists]).T + df['ds_ids'] = pd.DataFrame([array_of_lists]).T + + # Covers us_xs + if (model_id, huc_num) in self.us_ref_dict: + df['us_ids'] = df.set_index(["river_reach_rs", "thalweg", "xs_max_elevation"]).index.map(self.us_ref_dict[(model_id, huc_num)].get) + #print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS featured in current model @ HUC's conflation json file.") + else: + print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.") + continue + + # Covers ds_xs + if (model_id, huc_num) in self.ds_ref_dict: + df['ds_ids'] = df.set_index(["river_reach_rs", "thalweg", "xs_max_elevation"]).index.map(self.ds_ref_dict[(model_id, huc_num)].get) + #print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS featured in current model @ HUC's conflation json file.") + else: + print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.") + continue + + # Extracts & appends reach_id & network_to_id to each model @ HUC's unique XS + us_id_df = df['us_ids'].apply(pd.Series) + us_id_df.columns=['us_reach_id', 'us_network_to_id'] + ds_id_df = df['ds_ids'].apply(pd.Series) + ds_id_df.columns=['ds_reach_id', 'ds_network_to_id'] + + # Fill any nan to string + us_id_df[['us_reach_id', 'us_network_to_id']] = us_id_df[['us_reach_id', 'us_network_to_id']].fillna('None') + ds_id_df[['ds_reach_id', 'ds_network_to_id']] = ds_id_df[['ds_reach_id', 'ds_network_to_id']].fillna('None') + df = df.fillna('None') + df = pd.concat([df, us_id_df, ds_id_df], axis=1) + df = df.drop(['us_ids', 'ds_ids'], axis=1) + + model_gpkg_dict['XS'] = df + self.xs_df_list.append(model_gpkg_dict['XS']) + + return + + def save_xs_data(self): + ''' + Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage + + Args: + None + + Return: None + + Note: These saved parquet files will preserve each river @ HUC's inherited CRS. + + ''' + for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items(): + + # Generate data folder per HUC + if not os.path.exists(f'{os.getcwd()}/xs_data/huc_{huc_num}'): + os.makedirs(f'{os.getcwd()}/xs_data/huc_{huc_num}') + + # Save each river's geopandas as a geoparquet & geopackage under each HUC folder + grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]['XS'].groupby(['river']) + for river_name in set(self.model_id2gpkg[(model_id, huc_num)]['XS']['river']): + filterbyriver = grouped_xslayers.get_group(river_name) + + # Generate map of each river's set of XS to HUC & model ID to be used as a + # look-up reference + self.groupbyriver_dict[huc_num].update({model_id: filterbyriver}) + + # Save XS as geoparquet per river per HUC + filterbyriver.to_parquet(f'{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.parquet', + engine="pyarrow") + + # Save XS as geopackage per river per HUC + filterbyriver.to_file(f'{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.gpkg', + driver='GPKG') + + return + + def save_crs_map(self): + ''' + Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage + + Args: + None + + Return: None + + Note: This saved pickle file will map each river @ HUC's inherited CRS for one to + analyze & reference. + + ''' + for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items(): + + # Generate data folder per HUC + if not os.path.exists(f'{os.getcwd()}/xs_data/crs_map'): + os.makedirs(f'{os.getcwd()}/xs_data/crs_map') + + # Generate map of the CRS to each river's geopandas per HUC + grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]['XS'].groupby(['river']) + for river_name in set(self.model_id2gpkg[(model_id, huc_num)]['XS']['river']): + filterbyriver = grouped_xslayers.get_group(river_name) + self.crs_dict[(huc_num, model_id)].update({river_name: f'ESPG: {str(filterbyriver.crs.to_epsg()) if \ + filterbyriver.crs.to_epsg() else None}, {filterbyriver.crs.name},'}) + + # Save map of inherited CRS to HUC, model_id, river name + with open(f'{os.getcwd()}/xs_data/crs_map/crs_mapping.pickle', 'wb') as handle: + pickle.dump(self.crs_dict, handle, protocol=pickle.HIGHEST_PROTOCOL) + + return + + def consolidate_id2xs_dfs(self): + ''' + Consolidate HEC-RAS models cross-sections featuring their corresponding IDs. + + Args: + None + + Return: None + + Note: A set CRS standard is needed in order to consolidates all XS layers of all models + under a single dataframe to maintain a consistent CRS. This consolidation will check if the XS + dataframes have a consistent CRS if it does not then it will not consolidate the XS dataframes. + As of 05/14/25, only CRS in this condition is considering the + NAD83, but additional CRS can be added to this methods as new findings are made. + + ''' + crs_list = [] + for xs_df in self.xs_df_list: + if 'NAD83' in xs_df.crs.name or 'NAD_1983' in xs_df.crs.name: + # Convert all GeoDataFrames in the list to the target CRS + crs_list.append("EPSG:5070") + else: + print(False) + + # Will consolidate ONLY if the CRS is consistent across XS geodpandas dataframes + if len(set(crs_list)) == 1: + target_crs = str(np.unique(crs_list)[0]) + self.consolidated_id2xs = geopandas.GeoDataFrame(pd.concat([xs_df.to_crs(target_crs) for xs_df in self.xs_df_list], + ignore_index=True)) + print(f"The consolidated XS geopandas dataframes now has a standardized CRS of:\n{self.consolidated_id2xs.crs.name}") + else: + print("Cannot consolidate XS geodpandas dataframes because the CRS is inconsistent across XS geodpandas dataframes.") + + return + + def save_xsbyhuc_data(self): + ''' + Consolidate HEC-RAS models cross-sections based on HUC & save to storage + + Args: + None + + Return: None + + Note: These saved parquet files will be the transformed CRS of all XS per HUC to ensure + a consistent standardized CRS. + + ''' + unique_huc_nums = set(self.consolidated_id2xs['huc']) + for huc_num in unique_huc_nums: + + # Generate data folder per HUC + if not os.path.exists(f'{os.getcwd()}/xs_data/mip_{huc_num}'): + os.makedirs(f'{os.getcwd()}/xs_data/mip_{huc_num}') + + # Filter consolidated XS geopanda dataframe by HUC + filterbyhuc = self.consolidated_id2xs[self.consolidated_id2xs['huc'] == huc_num] + + # Save XS as geoparquet per HUC + filterbyhuc.to_parquet(f'{os.getcwd()}/xs_data/mip_{huc_num}/huc_{huc_num}.parquet', + engine="pyarrow") + + # Save XS as geopackage per HUC + filterbyhuc.to_file(f'{os.getcwd()}/xs_data/mip_{huc_num}/huc_{huc_num}.gpkg', + driver='GPKG') + + return + + # if __name__ == '__main__': + + # # Instantiate class + # obj = MapData(data_dir=f'{os.getcwd()}/fema_mip_sample/') + + # # Extract list of fim-c data sample's directories. + # fim_data_dirs = obj.fim_data_dirs + + # # Categorize FIM-C data sample files (e.g. geopackage of a given model @ HUC#, json, source_models.gpkg, ripple.gpkg) + # model_gpkg_dirs, src_models_gpkg_dirs, rip_gpkg_dirs = obj.model_gpkg_dirs, obj.src_models_gpkg_dirs, obj.rip_gpkg_dirs + # gpkg_dirs, json_dirs = obj.gpkg_dirs, obj.json_dirs + + # model_gpkg_tablenames, src_models_gpkg_tablenames, rip_gpkg_tablenames = obj.model_gpkg_tablenames, obj.src_models_gpkg_tablenames, obj.rip_gpkg_tablenames + # gpkg_tablenames, json_tablenames = obj.gpkg_tablenames, obj.json_tablenames + + # # Generate a dictionary to map each conflation json files to their corresponding model_id & HUC #. + # model2huc_map = obj.id2json + + # # Generated maps of model_id & HUC # to xs (for both us & ds cross-section) to reach ID & "network_to_id" + # us_xs2id_map = obj.us_ref_dict + # ds_xs2id_map = obj.ds_ref_dict + + # # Generated map of model_id & HUC # to each HEC-RAS model's geopackage. + # model_id2gpkg_map = obj.model_id2gpkg + + # # Generated map of HUC # to each ripple geopackage. + # rip_huc2gpkg_map = obj.rip_huc2gpkg + + # # Map IDs to each model's cross-section instance + # id2xs_dfs = obj.xs_df_list + + # # [OPTIONAL: If a user wants to extract individual river geopandas, parquet, &/or gpkg] + # # Map of XS as geopandas per river per HUC w/ their inherited CRS by HUC + # #xsbyriver_map = obj.groupbyriver_dict + + # # [OPTIONAL: Map of inherited CRS to HUC, model_id, river name] + # crs_map = obj.crs_dict + + # # Conslidate all HEC-RAS models' cross-sections featuring their corresponding IDs. + # consolidated_id2xs = obj.consolidated_id2xs + + + + + \ No newline at end of file From cf1ea69593cef66f9711c8210d006d0f65de8a46 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 15 May 2025 11:16:58 -0400 Subject: [PATCH 31/97] adding to doc str --- src/icefabric_manage/src/icefabric_manage/map_fim_data.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py index abc3e7e..e94900c 100644 --- a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py +++ b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py @@ -13,6 +13,8 @@ class MapData(object): ''' + Maps FIM XS data to IDs & categorize by HUC. At this time, ensure FIM mip data sample is saved + to local disk. ''' def __init__(self, data_dir): self.data_dir = data_dir From fde6be62ec0d4faccf1c1c4393fe5d6ff55b647b Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 15 May 2025 11:20:25 -0400 Subject: [PATCH 32/97] Add to doc str --- examples/Demo_Iceberg.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/Demo_Iceberg.ipynb b/examples/Demo_Iceberg.ipynb index 12f4c19..9057e3d 100644 --- a/examples/Demo_Iceberg.ipynb +++ b/examples/Demo_Iceberg.ipynb @@ -35,8 +35,9 @@ " '''\n", " Create a Iceberg table per parquet file w/ its inherited schema set.\n", "\n", - " Note: It is okay to expect following warning statements throughout process:\n", - " \"Iceberg does not have a dictionary type. will be inferred as string on read.\"\n", + " Note: Allows for user to have the option to read parquets from S3 or locally. It is okay to expect\n", + " following warning statements throughout process: \"Iceberg does not have a dictionary type. will be inferred as string on read.\"\n", " \n", " '''\n", " def __init__(self):\n", From 838481d9d134924e8b133b6a02699917e46d2e4b Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 15 May 2025 11:22:48 -0400 Subject: [PATCH 33/97] Add to doc str --- src/icefabric_manage/src/icefabric_manage/iceberg_table.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py index 271409a..93b508e 100644 --- a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py +++ b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py @@ -15,8 +15,9 @@ class IcebergTable(object): ''' Create a Iceberg table per parquet file w/ its inherited schema set. - Note: It is okay to expect following warning statements throughout process: - "Iceberg does not have a dictionary type. will be inferred as string on read." + Note: Allows for user to have the option to read parquets from S3 or locally. It is okay to expect + following warning statements throughout process: "Iceberg does not have a dictionary type. will be inferred as string on read." ''' def __init__(self): From 04e981e69dcd1fd138bdc8f8e38946bfdb169f6a Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 15 May 2025 15:43:48 -0400 Subject: [PATCH 34/97] Update iceberg_table.py --- .../src/icefabric_manage/iceberg_table.py | 32 ------------------- 1 file changed, 32 deletions(-) diff --git a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py index 93b508e..315c2dc 100644 --- a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py +++ b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py @@ -245,35 +245,3 @@ def create_table_for_all_s3parquets(self, app_name, bucket_name, profile_name='d data_table, schema) return - - -# if __name__ == '__main__': - - # Instantiate class - #obj = IcebergTable() - - # ======= For ml_auxiliary_data application ======= - - ## Extract list of parquets - #parquet_list = obj.read_data_dirs(data_dir=f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/') - - ## Establish new Iceberg catalog - #obj.establish_catalog(catalog_name='bathymetry_ml_auxiliary_dev', - # namespace='bathymetry_ml_auxiliary') - - ## Generate tables w/in the new Iceberg catalog for all parquets detected - #obj.create_table_for_all_parquets(parquet_list, - # app_name='bathymetry_ml_auxiliary') - - # ======= For mip xs data application ======= - - # Extract list of parquets - #file_list = obj.read_data_dirs(data_dir=f'{os.getcwd()}/xs_data/') - #parquet_list = [file for file in file_list if file.endswith('.parquet')] - - #Establish new Iceberg catalog for XS data - #obj.establish_catalog(catalog_name='xs', - # namespace='mip') - - # Generate tables w/in the new Iceberg catalog for all parquets detected - #obj.create_table_for_all_parquets(parquet_list, app_name='mip-xs') \ No newline at end of file From 50a553513033c5da1921ffbdafed3a354f19a8e8 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 15 May 2025 15:45:02 -0400 Subject: [PATCH 35/97] Update map_fim_data.py --- .../src/icefabric_manage/map_fim_data.py | 46 ------------------- 1 file changed, 46 deletions(-) diff --git a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py index e94900c..53511ad 100644 --- a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py +++ b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py @@ -545,49 +545,3 @@ def save_xsbyhuc_data(self): driver='GPKG') return - - # if __name__ == '__main__': - - # # Instantiate class - # obj = MapData(data_dir=f'{os.getcwd()}/fema_mip_sample/') - - # # Extract list of fim-c data sample's directories. - # fim_data_dirs = obj.fim_data_dirs - - # # Categorize FIM-C data sample files (e.g. geopackage of a given model @ HUC#, json, source_models.gpkg, ripple.gpkg) - # model_gpkg_dirs, src_models_gpkg_dirs, rip_gpkg_dirs = obj.model_gpkg_dirs, obj.src_models_gpkg_dirs, obj.rip_gpkg_dirs - # gpkg_dirs, json_dirs = obj.gpkg_dirs, obj.json_dirs - - # model_gpkg_tablenames, src_models_gpkg_tablenames, rip_gpkg_tablenames = obj.model_gpkg_tablenames, obj.src_models_gpkg_tablenames, obj.rip_gpkg_tablenames - # gpkg_tablenames, json_tablenames = obj.gpkg_tablenames, obj.json_tablenames - - # # Generate a dictionary to map each conflation json files to their corresponding model_id & HUC #. - # model2huc_map = obj.id2json - - # # Generated maps of model_id & HUC # to xs (for both us & ds cross-section) to reach ID & "network_to_id" - # us_xs2id_map = obj.us_ref_dict - # ds_xs2id_map = obj.ds_ref_dict - - # # Generated map of model_id & HUC # to each HEC-RAS model's geopackage. - # model_id2gpkg_map = obj.model_id2gpkg - - # # Generated map of HUC # to each ripple geopackage. - # rip_huc2gpkg_map = obj.rip_huc2gpkg - - # # Map IDs to each model's cross-section instance - # id2xs_dfs = obj.xs_df_list - - # # [OPTIONAL: If a user wants to extract individual river geopandas, parquet, &/or gpkg] - # # Map of XS as geopandas per river per HUC w/ their inherited CRS by HUC - # #xsbyriver_map = obj.groupbyriver_dict - - # # [OPTIONAL: Map of inherited CRS to HUC, model_id, river name] - # crs_map = obj.crs_dict - - # # Conslidate all HEC-RAS models' cross-sections featuring their corresponding IDs. - # consolidated_id2xs = obj.consolidated_id2xs - - - - - \ No newline at end of file From a1caada2c27b16d07c97e32481065a502d70b518 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 15 May 2025 15:50:20 -0400 Subject: [PATCH 36/97] from icefabric_manage import MapData replacement --- .../XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb | 555 +----------------- 1 file changed, 2 insertions(+), 553 deletions(-) diff --git a/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb b/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb index 0186920..ff7095c 100644 --- a/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb +++ b/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "6788bf1c-12cf-4d48-a880-2e30762b0378", "metadata": {}, "outputs": [], @@ -35,563 +35,12 @@ "import re\n", "import json\n", "import pickle\n", + "from icefabric_manage import MapData\n", "import os\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, - { - "cell_type": "markdown", - "id": "b9e39188-c6e6-4849-907d-bef852afab0d", - "metadata": {}, - "source": [ - "## Mapping FIM-C Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "623c0d59-46be-40a5-8470-2d367345df40", - "metadata": {}, - "outputs": [], - "source": [ - "class MapData(object):\n", - " '''\n", - " '''\n", - " def __init__(self, data_dir):\n", - " self.data_dir = data_dir\n", - "\n", - " # Parent directory of the FIM-C files.\n", - " # Note: All the jsons & geopackages are relevant \n", - " # to map the files to IDs.\n", - " self.fim_data_dirs = []\n", - "\n", - " # List of directories associated with each file type of \n", - " # the FIM-C data sample (e.g. geopackage of a given model @ HUC#, json, \n", - " # source_models.gpkg, ripple.gpkg) \n", - " self.model_gpkg_dirs = []\n", - " self.src_models_gpkg_dirs = []\n", - " self.rip_gpkg_dirs = []\n", - " self.gpkg_dirs = []\n", - " self.json_dirs = []\n", - " self.xs_df_list = []\n", - " \n", - " # Variables to be used later\n", - " self.model_gpkg_tablenames = []\n", - " self.src_models_gpkg_tablenames = []\n", - " self.rip_gpkg_tablenames = []\n", - " self.gpkg_tablenames = []\n", - " self.json_tablenames = []\n", - " \n", - " self.id2json = collections.defaultdict(dict)\n", - " self.model_id2gpkg = collections.defaultdict(dict)\n", - " self.us_ref_dict = collections.defaultdict(dict)\n", - " self.ds_ref_dict = collections.defaultdict(dict)\n", - " self.rip_huc2gpkg = collections.defaultdict(dict)\n", - " self.groupbyriver_dict = collections.defaultdict(dict)\n", - " self.crs_dict = collections.defaultdict(dict)\n", - " self.consolidated_id2xs = geopandas.GeoDataFrame()\n", - " \n", - " self.read_data_dirs()\n", - " self.cat_data_dirs()\n", - " self.map_model2huc()\n", - " self.filter_model2huc_map(keys_to_drop={'metrics','low_flow', \n", - " 'high_flow', 'eclipsed', \n", - " 'lengths', 'coverage'})\n", - " \n", - " # Generate maps of model_id & HUC # to xs (for both us & ds cross-section)\n", - " # to reach ID & \"network_to_id\" from each model @ HUC's json file \n", - " self.map_modelhuc_xs2ids()\n", - "\n", - " # Generate maps of model_id & HUC # to gpkg from each model @ HUC's geopackage\n", - " self.map_model2huc_gpkg()\n", - "\n", - " # Generate maps of HUC # to ripple gpkg\n", - " self.map_huc2ripple_gpkg()\n", - "\n", - " # Map IDs to each model's cross-section instance\n", - " self.map_model_xs2ids()\n", - "\n", - " # [Optional: Per HUC, save each river's set of XS data as geoparquetss & geopackages]\n", - " #self.save_xs_data()\n", - "\n", - " # Save map of inherited CRS to HUC, model_id, river name\n", - " self.save_crs_map()\n", - "\n", - " # Consolidated all HEC RAS models' cross-sections featuring IDs\n", - " self.consolidate_id2xs_dfs()\n", - "\n", - " # Save HEC RAS models' cross-sections consolidated by HUC as geoparquets & geopackages\n", - " self.save_xsbyhuc_data()\n", - " \n", - "\n", - "\n", - " def read_data_dirs(self):\n", - " '''\n", - " Extract the list of fim-c data sample's directories.\n", - " \n", - " Args:\n", - " data_dir (str): Parent directory of the fim-c files.\n", - " Note: All the jsons & geopackages are relevant \n", - " to map the files to IDs.\n", - "\n", - " Return (list): List of directories associated with each file type of \n", - " the FIM-C data sample.\n", - " \n", - " ''' \n", - " fim_data_dirs = []\n", - " for folder, subfolders, files in os.walk(self.data_dir):\n", - " if folder != self.data_dir:\n", - " for file in files:\n", - " self.fim_data_dirs.append(f'{folder}/{file}')\n", - "\n", - " return\n", - " \n", - " def cat_data_dirs(self):\n", - " '''\n", - " Categorize FIM-C data sample files.\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - " \n", - " ''' \n", - " # Extract a list of directories corresponding to each set of files \n", - " for x in self.fim_data_dirs:\n", - " \n", - " # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains reach_id & nwm_to_id \n", - " # for network layer & reaches layer. The rating curves layer only has reach_id. The models layer \n", - " # contains collection_id & model_id)\n", - " if re.search('ripple.gpkg', x):\n", - " self.rip_gpkg_dirs.append(x)\n", - " t = re.search('/mip(.*)', x)\n", - " rip_gpkg_tblname = t.group()\n", - " self.rip_gpkg_tablenames.append(rip_gpkg_tblname.lstrip('/').replace('/', '_'))\n", - "\n", - " # Covers all HEC-RAS models gpkg featuring XS per HUC (contains model_id)\n", - " elif not x.endswith('source_models.gpkg') and not x.endswith('.json') and not re.search('ripple.gpkg', x):\n", - " self.model_gpkg_dirs.append(x)\n", - " t = re.search('/mip(.*)', x)\n", - " model_gpkg_tblname = t.group()\n", - " self.model_gpkg_tablenames.append(model_gpkg_tblname.lstrip('/').replace('/', '_'))\n", - "\n", - " # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains model_id & their HEC-RAS 1D model flowlines)\n", - " elif x.endswith('source_models.gpkg'):\n", - " self.src_models_gpkg_dirs.append(x)\n", - " t = re.search('/mip(.*)', x)\n", - " src_models_gpkg_tblname = t.group()\n", - " self.src_models_gpkg_tablenames.append(src_models_gpkg_tblname.lstrip('/').replace('/', '_'))\n", - " \n", - " # Covers all HEC-RAS models + Ripple gpkg per HUC\n", - " if x.endswith('.gpkg'):\n", - " self.gpkg_dirs.append(x)\n", - " t = re.search('/mip(.*)', x)\n", - " gpkg_tblname = t.group()\n", - " self.gpkg_tablenames.append(gpkg_tblname.lstrip('/').replace('/', '_'))\n", - "\n", - " # Covers each HEC-RAS models' result of conflating its model w/ the NWM network\n", - " elif x.endswith('.json'):\n", - " self.json_dirs.append(x)\n", - " t = re.search('/mip(.*)', x)\n", - " json_tblname = t.group()\n", - " self.json_tablenames.append(json_tblname.lstrip('/').replace('/', '_')) \n", - "\n", - " return \n", - " \n", - " def drop_nested_keys(self, map_dict, keys_to_drop):\n", - " '''\n", - " Drop keys irrelevant for linking each XS to IDs\n", - " \n", - " Args:\n", - " map_dict (dict): Dictionary to filter\n", - "\n", - " keys_to_drop (dict): List of keys irrelevant for linking each XS to IDs.\n", - "\n", - " Return: None\n", - "\n", - " '''\n", - " if isinstance(map_dict, dict):\n", - " return {k: self.drop_nested_keys(v, keys_to_drop) for k, v in map_dict.items() if k not in keys_to_drop}\n", - " elif isinstance(map_dict, list):\n", - " return [self.drop_nested_keys(i, keys_to_drop) for i in map_dict]\n", - " else:\n", - " return map_dict\n", - " \n", - " def map_model2huc(self):\n", - " '''\n", - " Map each conflation json file to their corresponding model_id & HUC #.\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - "\n", - " ''' \n", - " for x in self.json_dirs:\n", - " # Note: model_ids found in each src source_models.gpkg is featured is declared as\n", - " # sub-foldername of where model gpkg file resides\n", - " model_id = x.split(\"/\")[-2]\n", - " huc_num = x.split(\"/\")[-4].split('_')[1]\n", - " self.id2json[model_id][huc_num] = {}\n", - " try:\n", - " with open(x, 'r') as f:\n", - " json2dict = json.loads(f.read()) \n", - " self.id2json[model_id][huc_num].update(json2dict)\n", - " except:\n", - " pass\n", - "\n", - " return \n", - " \n", - " def filter_model2huc_map(self, keys_to_drop):\n", - " '''\n", - " Extract only relevant keys from model2huc map for linking each XS to a feature ID. \n", - " \n", - " Args:\n", - " keys_to_drop (dict): List of keys irrelevant for linking each XS to IDs.\n", - " (e.g. {'metrics','low_flow', 'high_flow', 'eclipsed', \n", - " 'lengths', 'coverage'})\n", - "\n", - " Return: None\n", - "\n", - " ''' \n", - " self.id2json = self.drop_nested_keys(self.id2json, keys_to_drop)\n", - " \n", - " return\n", - " \n", - " def map_modelhuc_xs2ids(self):\n", - " '''\n", - " Parse JSONs & map model_id & HUC # to xs to reach ID & \"network_to_id\"\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - "\n", - " Note: Per model @ HUC cross-section layer, the attribute of interest is \"river_reach_rs\" \n", - " in order to link the IDs to each individual cross-section & their associated xs_id.\n", - "\n", - " To map each cross-section of a model @ HUC#, there has to be a shared attribute between a \n", - " model @ HUC#'s cross section w/in its XS layer & the details provided within a model @ HUC#'s \n", - " conflation json file.\n", - " \n", - " - Each conflation json file reveals ...\n", - " - Per reach, there is a set of cross-sections.\n", - " - Within each model's cross-section (XS) layers, there are a set of cross-section \n", - " instances - each instance featuring a unique \"thalweg\" (aka \"min_elevation\"), \n", - " \"xs_max_elevation\" (aka \"max_elevation\"), \"reach_id\" (\"reaches\"), & \"river_station\" (aka \"xs_id\")\n", - "\n", - " - Each model @ HUC#'s XS layer contains a collection of cross-section instancees. \n", - " Thus, each unique cross-section w/in a given model @ HUC#'s XS layer will need to be mapped in \n", - " such a way to allow each cross-section to be associated with a feature ID (aka \"reach_id\" and/or\n", - " \"network_to_id\"). \n", - " \n", - " - \"river_reach_rs\" is formatted differently across models' XS layers, however multplie keys \n", - " referenced in the conflation jsons can be referenced to obtain the \"river_reach_rs\" from the jsons \n", - " As a result, the mapping of IDs to each model's cross-section instance will be based on the info.\n", - " extracted from a model @ HUC#'s conflation.json\n", - "\n", - " - There can be multiple reach_ids tied to same nwm_to_id (aka \"network_to_id).\n", - "\n", - " ''' \n", - " # Keys to join values from that makes up the 'river_reach_rs' reflected in each model's XS layer\n", - " keys_to_join = ['river', 'reach', 'xs_id'] \n", - " for model_id, huc_dict in self.id2json.items():\n", - " for huc_num, reach_dict in huc_dict.items():\n", - " for reach_id, v_dict in reach_dict['reaches'].items():\n", - " # Joining the attribute because each model's xs layer features three atttrib concat (to be used as reference)\n", - " if \"us_xs\" in v_dict:\n", - " usxs_joined_values = ' '.join(str(v_dict[\"us_xs\"][key]) for key in keys_to_join)\n", - " if \"min_elevation\" in v_dict[\"us_xs\"]:\n", - " us_xs_min_elev = v_dict[\"us_xs\"][\"min_elevation\"]\n", - " if \"max_elevation\" in v_dict[\"us_xs\"]:\n", - " us_xs_max_elev = v_dict[\"us_xs\"][\"max_elevation\"]\n", - " \n", - " if \"ds_xs\" in v_dict:\n", - " dsxs_joined_values = ' '.join(str(v_dict[\"ds_xs\"][key]) for key in keys_to_join)\n", - " if \"min_elevation\" in v_dict[\"ds_xs\"]:\n", - " ds_xs_min_elev = v_dict[\"ds_xs\"][\"min_elevation\"]\n", - " if \"max_elevation\" in v_dict[\"ds_xs\"]:\n", - " ds_xs_max_elev = v_dict[\"ds_xs\"][\"max_elevation\"]\n", - " \n", - " if \"network_to_id\" in v_dict:\n", - " nwm2id = v_dict[\"network_to_id\"]\n", - "\n", - " # Generated maps of model_id & HUC # to xs (for both us & ds cross-section)\n", - " # to reach ID & \"network_to_id\" \n", - " self.us_ref_dict[(model_id, huc_num)].update({(usxs_joined_values, us_xs_min_elev , us_xs_max_elev):[reach_id, nwm2id]})\n", - " self.ds_ref_dict[(model_id, huc_num)].update({(dsxs_joined_values, ds_xs_min_elev , ds_xs_max_elev):[reach_id, nwm2id]}) \n", - " \n", - " return\n", - "\n", - " def map_model2huc_gpkg(self):\n", - " '''\n", - " Map model_id & HUC # to each HEC-RAS model's geopackage.\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - "\n", - " Note: model_ids found in each source_models.gpkg is featured in last\n", - " sub-foldername of where model gpkg file resides\n", - "\n", - " ''' \n", - " # Each HEC-RAS model gpkg per model per HUC\n", - " for x in self.model_gpkg_dirs:\n", - " model_id = x.split(\"/\")[-2]\n", - " huc_num = x.split(\"/\")[-4].split('_')[1]\n", - " self.model_id2gpkg[(model_id, huc_num)] = {'XS': None}\n", - " self.model_id2gpkg[(model_id, huc_num)] = {\"XS concave hull\": None}\n", - " self.model_id2gpkg[(model_id, huc_num)] = {\"River\": None}\n", - "\n", - " try:\n", - " self.model_id2gpkg[(model_id, huc_num)].update({'XS': geopandas.read_file(x, engine='pyogrio',\n", - " use_arrow=True, \n", - " layer=\"XS\")})\n", - "\n", - " self.model_id2gpkg[(model_id, huc_num)].update({\"River\": geopandas.read_file(x, \n", - " engine='pyogrio', \n", - " use_arrow=True, \n", - " layer=\"River\")})\n", - " self.model_id2gpkg[(model_id, huc_num)].update({\"XS concave hull\": geopandas.read_file(x, \n", - " engine='pyogrio', \n", - " use_arrow=True, \n", - " layer=\"XS concave hull\")})\n", - " except:\n", - " pass\n", - " \n", - " return\n", - " \n", - " def map_huc2ripple_gpkg(self):\n", - " '''\n", - " Map HUC # to ripple geopackage (features HEC RAS 1D model flowlines).\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - "\n", - " Note: ripple.gpkg features the HEC RAS 1D model flowlines categorized by HUC #.\n", - "\n", - " ''' \n", - " for x in self.rip_gpkg_dirs:\n", - " huc_num = x.split(\"/\")[-2].split('_')[1]\n", - " self.rip_huc2gpkg[huc_num] = {'reaches': None}\n", - " self.rip_huc2gpkg[huc_num] = {\"rating curves\": None}\n", - " self.rip_huc2gpkg[huc_num] = {\"network\": None}\n", - " self.rip_huc2gpkg[huc_num] = {'models': None}\n", - " self.rip_huc2gpkg[huc_num] = {\"metadata\": None}\n", - " self.rip_huc2gpkg[huc_num] = {\"rating_curves_no_map\": None}\n", - " self.rip_huc2gpkg[huc_num] = {\"processing\": None}\n", - "\n", - " try:\n", - " self.rip_huc2gpkg[huc_num].update({\"reaches\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"reaches\")})\n", - " self.rip_huc2gpkg[huc_num].update({\"rating_curves\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"rating_curves\")})\n", - " self.rip_huc2gpkg[huc_num].update({\"network\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"network\")})\n", - " self.rip_huc2gpkg[huc_num].update({\"models\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"models\")})\n", - " self.rip_huc2gpkg[huc_num].update({\"metadata\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"metadata\")})\n", - " self.rip_huc2gpkg[huc_num].update({\"rating_curves_no_map\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"rating_curves_no_map\")})\n", - " self.rip_huc2gpkg[huc_num].update({\"processing\": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer=\"processing\")})\n", - " except:\n", - " \n", - " pass\n", - " return\n", - " \n", - " def map_model_xs2ids(self):\n", - " '''\n", - " Map each cross-section instance featured in HEC-RAS model's cross-section layer to their corresponding IDs.\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - "\n", - " ''' \n", - " \n", - " for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items():\n", - " df = model_gpkg_dict['XS']\n", - " df['huc'] = huc_num\n", - " df['model_id'] = model_id\n", - " array_of_lists = [[None, None] for _ in range(len(df))]\n", - " df['us_ids'] = pd.DataFrame([array_of_lists]).T\n", - " df['ds_ids'] = pd.DataFrame([array_of_lists]).T\n", - " \n", - " # Covers us_xs\n", - " if (model_id, huc_num) in self.us_ref_dict:\n", - " df['us_ids'] = df.set_index([\"river_reach_rs\", \"thalweg\", \"xs_max_elevation\"]).index.map(self.us_ref_dict[(model_id, huc_num)].get)\n", - " #print(f\"The model_id @ HUC# ({(model_id, huc_num)}) IS featured in current model @ HUC's conflation json file.\")\n", - " else:\n", - " print(f\"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.\")\n", - " continue\n", - "\n", - " # Covers ds_xs\n", - " if (model_id, huc_num) in self.ds_ref_dict:\n", - " df['ds_ids'] = df.set_index([\"river_reach_rs\", \"thalweg\", \"xs_max_elevation\"]).index.map(self.ds_ref_dict[(model_id, huc_num)].get)\n", - " #print(f\"The model_id @ HUC# ({(model_id, huc_num)}) IS featured in current model @ HUC's conflation json file.\")\n", - " else:\n", - " print(f\"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.\")\n", - " continue\n", - "\n", - " # Extracts & appends reach_id & network_to_id to each model @ HUC's unique XS \n", - " us_id_df = df['us_ids'].apply(pd.Series)\n", - " us_id_df.columns=['us_reach_id', 'us_network_to_id']\n", - " ds_id_df = df['ds_ids'].apply(pd.Series)\n", - " ds_id_df.columns=['ds_reach_id', 'ds_network_to_id']\n", - " \n", - " # Fill any nan to string \n", - " us_id_df[['us_reach_id', 'us_network_to_id']] = us_id_df[['us_reach_id', 'us_network_to_id']].fillna('None')\n", - " ds_id_df[['ds_reach_id', 'ds_network_to_id']] = ds_id_df[['ds_reach_id', 'ds_network_to_id']].fillna('None')\n", - " df = df.fillna('None')\n", - " df = pd.concat([df, us_id_df, ds_id_df], axis=1)\n", - " df = df.drop(['us_ids', 'ds_ids'], axis=1)\n", - " \n", - " model_gpkg_dict['XS'] = df\n", - " self.xs_df_list.append(model_gpkg_dict['XS'])\n", - " \n", - " return\n", - " \n", - " def save_xs_data(self):\n", - " '''\n", - " Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - "\n", - " Note: These saved parquet files will preserve each river @ HUC's inherited CRS. \n", - "\n", - " '''\n", - " for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items():\n", - "\n", - " # Generate data folder per HUC\n", - " if not os.path.exists(f'{os.getcwd()}/xs_data/huc_{huc_num}'):\n", - " os.makedirs(f'{os.getcwd()}/xs_data/huc_{huc_num}')\n", - "\n", - " # Save each river's geopandas as a geoparquet & geopackage under each HUC folder \n", - " grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]['XS'].groupby(['river'])\n", - " for river_name in set(self.model_id2gpkg[(model_id, huc_num)]['XS']['river']):\n", - " filterbyriver = grouped_xslayers.get_group(river_name)\n", - "\n", - " # Generate map of each river's set of XS to HUC & model ID to be used as a\n", - " # look-up reference\n", - " self.groupbyriver_dict[huc_num].update({model_id: filterbyriver})\n", - "\n", - " # Save XS as geoparquet per river per HUC\n", - " filterbyriver.to_parquet(f'{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.parquet', \n", - " engine=\"pyarrow\")\n", - "\n", - " # Save XS as geopackage per river per HUC\n", - " filterbyriver.to_file(f'{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.gpkg', \n", - " driver='GPKG') \n", - " \n", - " return\n", - " \n", - " def save_crs_map(self):\n", - " '''\n", - " Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - "\n", - " Note: This saved pickle file will map each river @ HUC's inherited CRS for one to \n", - " analyze & reference. \n", - "\n", - " '''\n", - " for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items():\n", - "\n", - " # Generate data folder per HUC\n", - " if not os.path.exists(f'{os.getcwd()}/xs_data/crs_map'):\n", - " os.makedirs(f'{os.getcwd()}/xs_data/crs_map')\n", - "\n", - " # Generate map of the CRS to each river's geopandas per HUC \n", - " grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]['XS'].groupby(['river'])\n", - " for river_name in set(self.model_id2gpkg[(model_id, huc_num)]['XS']['river']):\n", - " filterbyriver = grouped_xslayers.get_group(river_name)\n", - " self.crs_dict[(huc_num, model_id)].update({river_name: f'ESPG: {str(filterbyriver.crs.to_epsg()) if \\\n", - " filterbyriver.crs.to_epsg() else None}, {filterbyriver.crs.name},'})\n", - "\n", - " # Save map of inherited CRS to HUC, model_id, river name\n", - " with open(f'{os.getcwd()}/xs_data/crs_map/crs_mapping.pickle', 'wb') as handle:\n", - " pickle.dump(self.crs_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", - " \n", - " return\n", - "\n", - " def consolidate_id2xs_dfs(self):\n", - " '''\n", - " Consolidate HEC-RAS models cross-sections featuring their corresponding IDs.\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - "\n", - " Note: A set CRS standard is needed in order to consolidates all XS layers of all models\n", - " under a single dataframe to maintain a consistent CRS. This consolidation will check if the XS \n", - " dataframes have a consistent CRS if it does not then it will not consolidate the XS dataframes. \n", - " As of 05/14/25, only CRS in this condition is considering the\n", - " NAD83, but additional CRS can be added to this methods as new findings are made.\n", - " \n", - " '''\n", - " crs_list = []\n", - " for xs_df in self.xs_df_list:\n", - " if 'NAD83' in xs_df.crs.name or 'NAD_1983' in xs_df.crs.name:\n", - " # Convert all GeoDataFrames in the list to the target CRS\n", - " crs_list.append(\"EPSG:5070\")\n", - " else:\n", - " print(False)\n", - "\n", - " # Will consolidate ONLY if the CRS is consistent across XS geodpandas dataframes\n", - " if len(set(crs_list)) == 1:\n", - " target_crs = str(np.unique(crs_list)[0])\n", - " self.consolidated_id2xs = geopandas.GeoDataFrame(pd.concat([xs_df.to_crs(target_crs) for xs_df in self.xs_df_list], \n", - " ignore_index=True))\n", - " print(f\"The consolidated XS geopandas dataframes now has a standardized CRS of:\\n{self.consolidated_id2xs.crs.name}\")\n", - " else:\n", - " print(\"Cannot consolidate XS geodpandas dataframes because the CRS is inconsistent across XS geodpandas dataframes.\")\n", - " \n", - " return\n", - "\n", - " def save_xsbyhuc_data(self):\n", - " '''\n", - " Consolidate HEC-RAS models cross-sections based on HUC & save to storage\n", - " \n", - " Args:\n", - " None\n", - "\n", - " Return: None\n", - "\n", - " Note: These saved parquet files will be the transformed CRS of all XS per HUC to ensure\n", - " a consistent standardized CRS. \n", - "\n", - " '''\n", - " unique_huc_nums = set(self.consolidated_id2xs['huc'])\n", - " for huc_num in unique_huc_nums:\n", - " \n", - " # Generate data folder per HUC\n", - " if not os.path.exists(f'{os.getcwd()}/xs_data/mip_{huc_num}'):\n", - " os.makedirs(f'{os.getcwd()}/xs_data/mip_{huc_num}')\n", - " \n", - " # Filter consolidated XS geopanda dataframe by HUC\n", - " filterbyhuc = self.consolidated_id2xs[self.consolidated_id2xs['huc'] == huc_num]\n", - " \n", - " # Save XS as geoparquet per HUC\n", - " filterbyhuc.to_parquet(f'{os.getcwd()}/xs_data/mip_{huc_num}/huc_{huc_num}.parquet', \n", - " engine=\"pyarrow\")\n", - " \n", - " # Save XS as geopackage per HUC\n", - " filterbyhuc.to_file(f'{os.getcwd()}/xs_data/mip_{huc_num}/huc_{huc_num}.gpkg', \n", - " driver='GPKG') \n", - " \n", - " return\n", - "\n", - "\n", - " " - ] - }, { "cell_type": "markdown", "id": "a4372bf1-9031-41c8-8567-5cfe3c56a0b1", From 04e452bcd4c39613c32b5a69cbaadcc5b3139863 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 15 May 2025 15:51:45 -0400 Subject: [PATCH 37/97] Update __init__.py Adding imports of Classes in __init__.py --- src/icefabric_manage/src/icefabric_manage/__init__.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/icefabric_manage/src/icefabric_manage/__init__.py b/src/icefabric_manage/src/icefabric_manage/__init__.py index 3803004..49b9a3e 100644 --- a/src/icefabric_manage/src/icefabric_manage/__init__.py +++ b/src/icefabric_manage/src/icefabric_manage/__init__.py @@ -1,3 +1,5 @@ from icefabric_manage._api import build +from icefabric_manage.iceberg_table import IcebergTable +from icefabric_manage.map_fim_data import MapData -__all__ = ["build"] +__all__ = ["build", "IcebergTable", "MapData"] From 530c15ac814bd370613992b75d34e7defc761963 Mon Sep 17 00:00:00 2001 From: Daniel Cumpton Date: Tue, 20 May 2025 17:11:43 -0600 Subject: [PATCH 38/97] removed large file --- src/icefabric_api/data/cfe_params.csv | 25 ++++ src/icefabric_api/data/gauge_06710385.gpkg | Bin 0 -> 319488 bytes src/icefabric_api/data/lasam_params.csv | 26 ++++ src/icefabric_api/data/modules.csv | 10 ++ .../data/noah_owp_modular_params.csv | 14 ++ src/icefabric_api/data/sac_sma_params.csv | 20 +++ src/icefabric_api/data/sft_params.csv | 14 ++ src/icefabric_api/data/smp_params.csv | 8 ++ src/icefabric_api/data/snow17_params.csv | 27 ++++ src/icefabric_api/data/topmodel_params.csv | 26 ++++ src/icefabric_api/data/ueb_params.csv | 66 +++++++++ src/icefabric_api/src/run_modules.py | 126 ++++++++++++++++++ 12 files changed, 362 insertions(+) create mode 100644 src/icefabric_api/data/cfe_params.csv create mode 100644 src/icefabric_api/data/gauge_06710385.gpkg create mode 100644 src/icefabric_api/data/lasam_params.csv create mode 100644 src/icefabric_api/data/modules.csv create mode 100644 src/icefabric_api/data/noah_owp_modular_params.csv create mode 100644 src/icefabric_api/data/sac_sma_params.csv create mode 100644 src/icefabric_api/data/sft_params.csv create mode 100644 src/icefabric_api/data/smp_params.csv create mode 100644 src/icefabric_api/data/snow17_params.csv create mode 100644 src/icefabric_api/data/topmodel_params.csv create mode 100644 src/icefabric_api/data/ueb_params.csv create mode 100644 src/icefabric_api/src/run_modules.py diff --git a/src/icefabric_api/data/cfe_params.csv b/src/icefabric_api/data/cfe_params.csv new file mode 100644 index 0000000..eea0fa2 --- /dev/null +++ b/src/icefabric_api/data/cfe_params.csv @@ -0,0 +1,25 @@ +name,description,units,data_type,calibratable,source,min,max,default_value,divide_attr_name,source_file +soil_params.b,beta exponent on Clapp-Hornberger (1978) soil water relations,NULL,double,True,attr,2,15,4.05,mode.bexp_soil_layers_stag=1, +soil_params.satdk,saturated hydraulic conductivity,m/s,double,True,attr,0.000000195,0.00141,0.00000338,geom_mean.dksat_soil_layers_stag=1, +soil_params.satpsi,saturated capillary head,m,double,True,attr,0.036,0.955,0.355,geom_mean.psisat_soil_layers_stag=1, +soil_params.slop,this factor (0-1) modifies the gradient of the hydraulic head at the soil bottom. 0=no-flow.,m/m,double,True,attr,0.0000598,1,0.05,mean.slope_1km, +soil_params.smcmax,saturated soil moisture content (Maximum soil moisture content),m/m,double,True,attr,0.16,0.58,0.439,mean.smcmax_soil_layers_stag=1, +soil_params.wltsmc,wilting point soil moisture content (< soil_params.smcmax),m/m,double,True,attr,0.05,0.3,0.439,mean.smcwlt_soil_layers_stag=1, +soil_params.expon,"optional; defaults to 1, This parameter defines the soil reservoirs to be linear, Use linear reservoirs",NULL,double,False,const,NULL,NULL,1,, +soil_params.expon_secondary,"optional; defaults to 1, This parameter defines the soil reservoirs to be linear, Use linear reservoirs ",NULL,double,False,const,NULL,NULL,1,, +max_gw_storage,maximum storage in the conceptual reservoir,m,double,True,attr,0.01,0.25,0.05,mean.Zmax, +Cgw,the primary outlet coefficient,m/hr,double,True,attr,0.0000018,0.0018,0.000018,mean.Coeff, +expon,exponent parameter for nonlinear ground water reservoir (1.0 for linear reservoir),NULL,double,True,attr,1,8,3,mode.Expon, +gw_storage,initial condition for groundwater reservoir - it is the ground water as a decimal fraction of the maximum groundwater storage (max_gw_storage) for the initial timestep,m/m,double,False,const,NULL,NULL,0.05,, +alpha_fc,alpha at fc for clapp hornberger (field capacity),NULL,double,False,const,NULL,NULL,0.33,, +soil_storage,initial condition for soil reservoir - it is the water in the soil as a decimal fraction of maximum soil water storage (smcmax x depth) for the initial timestep. Default = 0.5,m/m,double,False,const,NULL,NULL,0.5,, +K_nash,Nash Config param for lateral subsurface runoff (Nash discharge to storage ratio),1/m,double,True,const,0,1,0.003,, +K_lf,Nash Config param - primary reservoir,NULL,double,True,const,0,1,0.01,, +nash_storage,Nash Config param - secondary reservoir ,NULL,double,False,const,NULL,NULL,"0.0,0.0",, +giuh_ordinates,Giuh (geomorphological instantaneous unit hydrograph) ordinates in dt time steps,NULL,double,False,const,NULL,NULL,"0.55, 0.25, 0.2",, +a_Xinanjiang_inflection_point_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,True,csv,-0.5,0.5,-0.2,AXAJ,CFE-X_params +b_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,True,csv,0.01,10,0.66,BXAJ,CFE-X_params +x_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,True,csv,0.01,10,0.02,XXAJ,CFE-X_params +urban_decimal_fraction,when surface_water_partitioning_scheme=Xinanjiang,NULL,double ,False,const,0,1,0.01,, +refkdt,Reference Soil Infiltration Parameter (used in runoff formulation),NULL,double,True,attr,0.1,4,1,mean.refkdt, +soil_params.depth,soil depth,m,double,False,const,NULL,NULL,2,, \ No newline at end of file diff --git a/src/icefabric_api/data/gauge_06710385.gpkg b/src/icefabric_api/data/gauge_06710385.gpkg new file mode 100644 index 0000000000000000000000000000000000000000..8f8a14a4968a83ac42f8b1fd7beb05637f218975 GIT binary patch literal 319488 zcmeF)2RxO3|3C0!@0}6JN#Q7aD+wi(m64*7aqN{tAtf12Ekqg;4WvY&WE@EoDO8$T zN<(RC{$D2@QNG{b_x|4Z|Gw|v|9^Qr>Uf@Ooa=gD@6Wt0IL@;6kD!u$Lc@bSBFNe# zVG;`q$%ITMkw}y9&ldlfUoQBDoe8|MkVs0`7`CvVX));i!QW0Ism_G z4BZW9;6EM4pk7`T{|U?CKOtcO{Ljv3M-pTc*emFVZ-_4<0z?Fe2oMnypG-R|k`9O{W=Xlhw&nLqj5IWYZu|YBlfe#T#lGn)*7r+FFKMx|)WDI@;PQrp`{5b`DlfbKT?_ry#E) zZ>XuIC668czC=e$&ycZ%ahB#&oviF^-Q@ev-W{Rv_i{I16zLJPYkjGl44yHEd zPUa47@6SJBb^Wi20%AR5)3>OqU}5AppTt3e0czc7SxWtdC*BN8 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z##n5UD^Xhh^he#)&rrtI^1H$V6X-I;JFsHI<$-RztmB+Z79480q$ItzSQKpPEGw*8 zJ&KNA24e5U4E*q{@2J}b}Se6AeKXgvva(v;~ z7Q%P&d9m&C&nL7+gC1Uc>)2T*(&8jygN1mQj9n>84L3Sj@9^L-!;=oyI|ARQGH8&{ zoV6`Z;Z=`fgHBVw*R0Q3|DyJOmZK-liZbC+E3aQ@`Z(6bq`PDE_$tgQvKPl!fa`g` zQwDZ%vF&O-5&uJ7tUOdvmusS>krs%0QVS8p*A?6F1W!q~PL%<}2I9@Mp^C^OaG`A> zb@09$q@!q@?ml!!+zZsp{!nFW*Ep0pZ&UluZ!96xSGmM+wxe6GOYsuIpcKL)*yV{- zW)Pe8#`oEbT(%*reYt0S%CPovmaINYkdHKveOkZl8)d?!HVtprc)-r5Cf#kba)DyJ z1W6Hs>*bPCg2c8z0vVi^!d@AGg{aAx7TJ3vTTdeDixk-7d%mRi-Q(( zjyBQtokms}p=3HD4GuMO))>kuT7zaM)kVg4H$n~fayDc54p8Kh&jwlN0N5%{IHp zC?UkxHM>&9>j@ok*SV~_wz)-aO4k;hyXA>yflIcdwC_#}v(vso16O}`_mq_tX?BL& zqarfR#E1KaU0zrO+1kb%He$n8rHhTvki!jUD%e|Fa&nJ)mmFuVBB{?w(gH<{|JDz? zM451@4 Date: Tue, 20 May 2025 17:23:43 -0600 Subject: [PATCH 39/97] Update README.md --- src/icefabric_api/README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/icefabric_api/README.md b/src/icefabric_api/README.md index e69de29..f4f70be 100644 --- a/src/icefabric_api/README.md +++ b/src/icefabric_api/README.md @@ -0,0 +1,5 @@ +CSV files for catchment specific values are too big to be checked in. They are stored here: s3://ngwpc-dev/DanielCumpton/divide_csv.tgz +Download and untar in the icefabric/src/icefabric_api/data directory. +For now, you can run modules by listing them in the module list defined on line 108 in run_modules.py. Text files showing all parameters as key/value +pairs will be written to a files per catchment. A JSON is output to the terminal. SFT, SMP, and LASAM csv files still need work to define the parameters properly. +See icefabric/src/icefabric_api/data/modules.csv for module name strings. From c158200525383efa64e086d63034321316591e79 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Fri, 23 May 2025 14:37:06 -0400 Subject: [PATCH 40/97] Xs mip ble feature (#7) * Added features to preprocess of BLE xsections * Added features to process BLE xs --- .../XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb | 3 +- .../src/icefabric_manage/map_fim_data.py | 73 +++++++++++-------- 2 files changed, 44 insertions(+), 32 deletions(-) diff --git a/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb b/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb index ff7095c..a34f4e7 100644 --- a/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb +++ b/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb @@ -330,7 +330,8 @@ ], "source": [ "# Instantiate class\n", - "obj = MapData(data_dir=f'{os.getcwd()}/fema_mip_sample/')\n", + "obj = MapData(data_dir=f'{os.getcwd()}/segment_data/',\n", + " subfolder_key_prefix='ble')\n", "\n", "# Extract list of fim-c data sample's directories.\n", "fim_data_dirs = obj.fim_data_dirs\n", diff --git a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py index 53511ad..8f843da 100644 --- a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py +++ b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py @@ -13,19 +13,21 @@ class MapData(object): ''' - Maps FIM XS data to IDs & categorize by HUC. At this time, ensure FIM mip data sample is saved - to local disk. + Maps FIM MIP & BLE XS datasets to relevant IDs & categorize by HUC. + At this time, ensure FIM datasets are saved to local disk. + ''' - def __init__(self, data_dir): + def __init__(self, data_dir, subfolder_key_prefix): self.data_dir = data_dir + self.subfolder_key_prefix = subfolder_key_prefix - # Parent directory of the FIM-C files. + # Parent directory of the FIM files. # Note: All the jsons & geopackages are relevant # to map the files to IDs. self.fim_data_dirs = [] # List of directories associated with each file type of - # the FIM-C data sample (e.g. geopackage of a given model @ HUC#, json, + # the FIM data sample (e.g. geopackage of a given model @ HUC#, json, # source_models.gpkg, ripple.gpkg) self.model_gpkg_dirs = [] self.src_models_gpkg_dirs = [] @@ -51,7 +53,7 @@ def __init__(self, data_dir): self.consolidated_id2xs = geopandas.GeoDataFrame() self.read_data_dirs() - self.cat_data_dirs() + self.cat_data_dirs(self.subfolder_key_prefix) self.map_model2huc() self.filter_model2huc_map(keys_to_drop={'metrics','low_flow', 'high_flow', 'eclipsed', @@ -86,15 +88,13 @@ def __init__(self, data_dir): def read_data_dirs(self): ''' - Extract the list of fim-c data sample's directories. + Extract the list of FIM data sample's directories. Args: - data_dir (str): Parent directory of the fim-c files. - Note: All the jsons & geopackages are relevant - to map the files to IDs. + None Return (list): List of directories associated with each file type of - the FIM-C data sample. + the FIM data sample. ''' fim_data_dirs = [] @@ -105,12 +105,13 @@ def read_data_dirs(self): return - def cat_data_dirs(self): + def cat_data_dirs(self, subfolder_key_prefix): ''' - Categorize FIM-C data sample files. + Categorize FIM data sample files. Args: - None + subfolder_key_prefix (str): Prefix of the FIM subfolder's data of interest + Options: 'mip' or 'ble' Return: None @@ -123,39 +124,39 @@ def cat_data_dirs(self): # contains collection_id & model_id) if re.search('ripple.gpkg', x): self.rip_gpkg_dirs.append(x) - t = re.search('/mip(.*)', x) + t = re.search(f'/{subfolder_key_prefix}(.*)', x) rip_gpkg_tblname = t.group() self.rip_gpkg_tablenames.append(rip_gpkg_tblname.lstrip('/').replace('/', '_')) # Covers all HEC-RAS models gpkg featuring XS per HUC (contains model_id) elif not x.endswith('source_models.gpkg') and not x.endswith('.json') and not re.search('ripple.gpkg', x): self.model_gpkg_dirs.append(x) - t = re.search('/mip(.*)', x) + t = re.search(f'/{subfolder_key_prefix}(.*)', x) model_gpkg_tblname = t.group() self.model_gpkg_tablenames.append(model_gpkg_tblname.lstrip('/').replace('/', '_')) # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains model_id & their HEC-RAS 1D model flowlines) elif x.endswith('source_models.gpkg'): self.src_models_gpkg_dirs.append(x) - t = re.search('/mip(.*)', x) + t = re.search(f'/{subfolder_key_prefix}(.*)', x) src_models_gpkg_tblname = t.group() self.src_models_gpkg_tablenames.append(src_models_gpkg_tblname.lstrip('/').replace('/', '_')) # Covers all HEC-RAS models + Ripple gpkg per HUC if x.endswith('.gpkg'): self.gpkg_dirs.append(x) - t = re.search('/mip(.*)', x) + t = re.search(f'/{subfolder_key_prefix}(.*)', x) gpkg_tblname = t.group() self.gpkg_tablenames.append(gpkg_tblname.lstrip('/').replace('/', '_')) # Covers each HEC-RAS models' result of conflating its model w/ the NWM network elif x.endswith('.json'): self.json_dirs.append(x) - t = re.search('/mip(.*)', x) + t = re.search(f'/{subfolder_key_prefix}(.*)', x) json_tblname = t.group() self.json_tablenames.append(json_tblname.lstrip('/').replace('/', '_')) - return + return def drop_nested_keys(self, map_dict, keys_to_drop): ''' @@ -178,7 +179,7 @@ def drop_nested_keys(self, map_dict, keys_to_drop): def map_model2huc(self): ''' - Map each conflation json file to their corresponding model_id & HUC #. + Map each conflation json file to their corresponding model ID & HUC #. Args: None @@ -284,7 +285,7 @@ def map_modelhuc_xs2ids(self): def map_model2huc_gpkg(self): ''' - Map model_id & HUC # to each HEC-RAS model's geopackage. + Map model ID & HUC # to each HEC-RAS model's geopackage. Args: None @@ -378,7 +379,6 @@ def map_model_xs2ids(self): # Covers us_xs if (model_id, huc_num) in self.us_ref_dict: df['us_ids'] = df.set_index(["river_reach_rs", "thalweg", "xs_max_elevation"]).index.map(self.us_ref_dict[(model_id, huc_num)].get) - #print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS featured in current model @ HUC's conflation json file.") else: print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.") continue @@ -386,15 +386,21 @@ def map_model_xs2ids(self): # Covers ds_xs if (model_id, huc_num) in self.ds_ref_dict: df['ds_ids'] = df.set_index(["river_reach_rs", "thalweg", "xs_max_elevation"]).index.map(self.ds_ref_dict[(model_id, huc_num)].get) - #print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS featured in current model @ HUC's conflation json file.") else: print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.") continue # Extracts & appends reach_id & network_to_id to each model @ HUC's unique XS + # Should the ids not be available in the conflation, must initialize columns us_id_df = df['us_ids'].apply(pd.Series) + if us_id_df.shape[1]==0: + us_id_df = pd.DataFrame(np.nan, index=range(us_id_df.shape[0]), columns=[0, 1]) us_id_df.columns=['us_reach_id', 'us_network_to_id'] + + # Should the ids not be available in the conflation, must initialize columns ds_id_df = df['ds_ids'].apply(pd.Series) + if ds_id_df.shape[1]==0: + ds_id_df = pd.DataFrame(np.nan, index=range(ds_id_df.shape[0]), columns=[0, 1]) ds_id_df.columns=['ds_reach_id', 'ds_network_to_id'] # Fill any nan to string @@ -513,12 +519,13 @@ def consolidate_id2xs_dfs(self): return - def save_xsbyhuc_data(self): + def save_xsbyhuc_data(self, xs_data_type): ''' Consolidate HEC-RAS models cross-sections based on HUC & save to storage Args: - None + xs_data_type (str): Cross-section data type to be saved either 'mip' or 'ble' cross-section type. + Options: 'mip' or 'ble' Return: None @@ -530,18 +537,22 @@ def save_xsbyhuc_data(self): for huc_num in unique_huc_nums: # Generate data folder per HUC - if not os.path.exists(f'{os.getcwd()}/xs_data/mip_{huc_num}'): - os.makedirs(f'{os.getcwd()}/xs_data/mip_{huc_num}') + if not os.path.exists(f'{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}'): + os.makedirs(f'{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}') # Filter consolidated XS geopanda dataframe by HUC filterbyhuc = self.consolidated_id2xs[self.consolidated_id2xs['huc'] == huc_num] # Save XS as geoparquet per HUC - filterbyhuc.to_parquet(f'{os.getcwd()}/xs_data/mip_{huc_num}/huc_{huc_num}.parquet', + filterbyhuc['thalweg'] = filterbyhuc['thalweg'].astype(str) + filterbyhuc['xs_max_elevation'] = filterbyhuc['xs_max_elevation'].astype(str) + filterbyhuc.to_parquet(f'{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}/huc_{huc_num}.parquet', engine="pyarrow") # Save XS as geopackage per HUC - filterbyhuc.to_file(f'{os.getcwd()}/xs_data/mip_{huc_num}/huc_{huc_num}.gpkg', - driver='GPKG') + filterbyhuc.to_file(f'{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}/huc_{huc_num}.gpkg', + driver='GPKG') + + print(f'{xs_data_type}_{huc_num}') return From d8685057997a92b1964f968163a381fef49ffd36 Mon Sep 17 00:00:00 2001 From: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> Date: Fri, 23 May 2025 14:52:42 -0400 Subject: [PATCH 41/97] 2025.5: Package Updates --- .pre-commit-config.yaml | 1 - py.typed | 0 pyproject.toml | 48 +- src/icefabric_manage/pyproject.toml | 26 +- src/icefabric_tools/pyproject.toml | 41 +- uv.lock | 3263 +++++++++++++-------------- 6 files changed, 1660 insertions(+), 1719 deletions(-) create mode 100644 py.typed diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 55c7d48..1b1146b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -21,4 +21,3 @@ repos: rev: 0.5.7 hooks: - id: uv-lock - diff --git a/py.typed b/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/pyproject.toml b/pyproject.toml index c9addc0..3856a56 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,12 +1,24 @@ [project] name = "icefabric" -version = "0.1.0" +version = "2025.5" description = "An Apache Iceberg implementation of the Hydrofabric" readme = "README.md" -requires-python = ">3.10,<3.13" +requires-python = ">=3.11,<3.13" license = { file = "LICENSE" } -authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] -maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] +authors = [ + { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, + { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, + { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, + { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, + { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, +] +maintainers = [ + { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, + { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, + { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, + { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, + { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, +] dependencies = [] @@ -26,24 +38,21 @@ icefabric_tools = { workspace = true } icefabric_manage = { workspace = true } [tool.uv.workspace] -members = [ - "src/icefabric_manage", - "src/icefabric_tools", -] +members = ["src/icefabric_manage", "src/icefabric_tools"] [tool.uv] dev-dependencies = [ - "pre-commit>=3.8.0", - "ipykernel>=6.29.5", - "jupyterlab", - "pytest>=8.3.2", - "ruff", - "black", - "mypy", - "icefabric_manage", - "icefabric_tools", - "examples", - "boto3", + "pre-commit>=3.8.0", + "ipykernel>=6.29.5", + "jupyterlab==4.4.2", + "pytest>=8.3.2", + "ruff==0.11.0", + "black==25.1.0", + "mypy==1.15.0", + "icefabric_manage", + "icefabric_tools", + "examples", + "boto3", ] package = false @@ -62,6 +71,7 @@ lint.select = [ "I", # isort "D", # pydocstyle "B", # flake8-bugbear + "Q", # flake8-quotes "TID", # flake8-tidy-imports "C4", # flake8-comprehensions "BLE", # flake8-blind-except diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index 27e1557..4df9884 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -1,17 +1,29 @@ [project] name = "icefabric_manage" -version = "0.1.0" +version = "2025.5" description = "All functionality for creating, updating, and deleting information from the icefabric catalog" -authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] -maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] +authors = [ + { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, + { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, + { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, + { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, + { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, +] +maintainers = [ + { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, + { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, + { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, + { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, + { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, +] readme = "README.md" dependencies = [ - "numpy==1.26.4", + "numpy==2.2.6", "fiona==1.10.1", - "pandas==2.2.0", + "pandas==2.2.3", "geopandas==1.0.1", - "s3fs==2025.3.2", - "pyarrow==19.0.1", + "s3fs==2025.5.0", + "pyarrow==20.0.0", "pyiceberg[sql-sqlite]==0.9.0", ] diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index 1bfd306..ba640ed 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -1,19 +1,44 @@ [project] name = "icefabric_tools" -version = "0.1.0" +version = "2025.5" description = "All functionality for creating, updating, and deleting information from the icefabric catalog" -authors = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] -maintainers = [{ name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }] +authors = [ + { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, + { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, + { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, + { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, + { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, +] +maintainers = [ + { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, + { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, + { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, + { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, + { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, +] readme = "README.md" dependencies = [ - "numpy==1.26.4", + "numpy==2.2.6", "fiona==1.10.1", - "pandas==2.2.0", + "pandas==2.2.3", 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size = 205364 }, ] From 62f5f6319c9062d12a5f98eea4c044627aac67e7 Mon Sep 17 00:00:00 2001 From: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> Date: Fri, 23 May 2025 16:24:12 -0400 Subject: [PATCH 42/97] 2025.5: Pre-commit / linting --- .pre-commit-config.yaml | 2 + examples/Demo_Iceberg.ipynb | 301 ++++--- .../XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb | 282 +++--- pyproject.toml | 5 + .../src/get_module_parameters.py | 34 +- src/icefabric_api/src/run_modules.py | 161 ++-- .../src/icefabric_manage/_api.py | 4 +- .../src/icefabric_manage/iceberg_table.py | 199 ++--- .../src/icefabric_manage/map_fim_data.py | 532 +++++++----- src/icefabric_tools/pyproject.toml | 3 +- .../src/icefabric_tools/__init__.py | 2 +- .../src/icefabric_tools/_api.py | 10 +- .../src/icefabric_tools/{ => iceberg}/rnr.py | 42 +- .../src/icefabric_tools/icechunk/__init__.py | 6 + .../icechunk/arch_weather_file_utils.py | 289 +++++++ .../icechunk/icechunk_s3_module.py | 308 +++++++ .../icechunk/icechunk_testing.py | 176 ++++ .../icefabric_tools/icechunk/nc_conv_utils.py | 147 ++++ uv.lock | 811 +++++++++++------- 19 files changed, 2234 insertions(+), 1080 deletions(-) rename src/icefabric_tools/src/icefabric_tools/{ => iceberg}/rnr.py (73%) create mode 100644 src/icefabric_tools/src/icefabric_tools/icechunk/__init__.py create mode 100644 src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py create mode 100644 src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py create mode 100644 src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py create mode 100644 src/icefabric_tools/src/icefabric_tools/icechunk/nc_conv_utils.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 1b1146b..a258935 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,7 +3,9 @@ repos: rev: v4.6.0 hooks: - id: trailing-whitespace + exclude: LICENSE|\.csv$ - id: end-of-file-fixer + exclude: LICENSE|\.csv$ - id: check-yaml - id: debug-statements diff --git a/examples/Demo_Iceberg.ipynb b/examples/Demo_Iceberg.ipynb index 9057e3d..48a7660 100644 --- a/examples/Demo_Iceberg.ipynb +++ b/examples/Demo_Iceberg.ipynb @@ -16,99 +16,108 @@ "metadata": {}, "outputs": [], "source": [ + "import os\n", + "import re\n", + "import warnings\n", + "\n", + "import boto3\n", "import pyarrow as pa\n", - "import pyiceberg\n", - "from pyiceberg.catalog import load_catalog\n", - "from pyiceberg.schema import Schema\n", - "from pyiceberg.types import NestedField, IntegerType, StringType, DoubleType, LongType, BinaryType, BooleanType\n", - "from pyiceberg.expressions import EqualTo, LessThan, And, GreaterThan, GreaterThanOrEqual\n", "import pyarrow.parquet as pq\n", - "from pyiceberg.schema import Schema\n", - "import boto3\n", "import s3fs\n", - "import re\n", - "import os\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", + "from pyiceberg.catalog import load_catalog\n", + "from pyiceberg.expressions import And, GreaterThan, GreaterThanOrEqual, LessThan\n", + "from pyiceberg.schema import Schema\n", + "from pyiceberg.types import (\n", + " BinaryType,\n", + " BooleanType,\n", + " DoubleType,\n", + " LongType,\n", + " NestedField,\n", + " StringType,\n", + ")\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", "\n", - "class IcebergTable(object):\n", - " '''\n", + "class IcebergTable:\n", + " \"\"\"\n", " Create a Iceberg table per parquet file w/ its inherited schema set.\n", "\n", " Note: Allows for user to have the option to read parquets from S3 or locally. It is okay to expect\n", " following warning statements throughout process: \"Iceberg does not have a dictionary type. will be inferred as string on read.\"\n", - " \n", - " '''\n", - " def __init__(self):\n", "\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", " # Generate folder for iceberg catalog\n", - " if not os.path.exists(f'{os.getcwd()}/iceberg_catalog'):\n", - " os.makedirs(f'{os.getcwd()}/iceberg_catalog')\n", + " if not os.path.exists(f\"{os.getcwd()}/iceberg_catalog\"):\n", + " os.makedirs(f\"{os.getcwd()}/iceberg_catalog\")\n", "\n", " # Set location of where the iceberg catalog will reside\n", - " os.environ['PYICEBERG_HOME'] = os.getcwd()\n", + " os.environ[\"PYICEBERG_HOME\"] = os.getcwd()\n", "\n", " # Initialize namespace to be set for Iceberg catalog\n", - " self.namespace = str()\n", - " \n", + " self.namespace = \"\"\n", + "\n", " def read_data_dirs(self, data_dir):\n", - " '''\n", + " \"\"\"\n", " Extract the list of parquet directories.\n", - " \n", + "\n", " Args:\n", " data_dir (str): Parent directory of the parquet files.\n", - " Note: All the ml_auxiliary_data parquet \n", - " files are save under same filenames, \n", + " Note: All the ml_auxiliary_data parquet\n", + " files are save under same filenames,\n", " but categorized by 'vpuid' conditions.\n", "\n", " Return (list): List of directories associated with each parquet file.\n", - " \n", - " '''\n", + "\n", + " \"\"\"\n", " parquet_list = []\n", " for folder, subfolders, files in os.walk(data_dir):\n", " if folder != data_dir:\n", " for file in files:\n", - " parquet_list.append(f'{folder}/{file}')\n", - " \n", + " parquet_list.append(f\"{folder}/{file}\")\n", + "\n", " return parquet_list\n", - " \n", + "\n", " def read_data(self, parquet_file_path):\n", - " '''\n", + " \"\"\"\n", " Load a single parquet as a Pyarrow table.\n", - " \n", - " Args: \n", + "\n", + " Args:\n", " parquet_file_path (str): Directory of a single parquet.\n", "\n", - " \n", + "\n", " Return: A Pyarrow table.\n", - " \n", - " '''\n", + "\n", + " \"\"\"\n", " data = pq.read_table(parquet_file_path)\n", - " \n", + "\n", " return data\n", "\n", " def establish_catalog(self, catalog_name, namespace):\n", - " '''\n", + " \"\"\"\n", " Creates a new Iceberg catalog.\n", - " \n", + "\n", " Args:\n", " catalog_name (str): Name of the catalog to be created.\n", " Default: 'dev' for development catalog\n", " namespace (str): Name of namespace.\n", "\n", " Return: None\n", - " \n", - " ''' \n", + "\n", + " \"\"\"\n", " # Establish a new Iceberg catalog & its configuration\n", - " self.catalog = load_catalog(name=catalog_name,\n", - " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", - " \n", + " self.catalog = load_catalog(\n", + " name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"}\n", + " )\n", + "\n", " # Establish namespace to be create w/in catalog\n", " self.namespace = namespace\n", " if self.namespace not in self.catalog.list_namespaces():\n", " self.catalog.create_namespace(self.namespace)\n", - " \n", + "\n", " return\n", "\n", " def convert_pyarrow_to_iceberg_schema(self, arrow_schema):\n", @@ -116,15 +125,14 @@ " Translate a given Pyarrow schema into a schema acceptable by Iceberg.\n", "\n", " Args:\n", - " arrow_schema (object): Pyarrow schema read from the loaded \n", + " arrow_schema (object): Pyarrow schema read from the loaded\n", " parquet of interest.\n", "\n", " Return (object): Iceberg schema\n", - " \n", + "\n", " \"\"\"\n", " fields = []\n", " for idx in range(len(arrow_schema)):\n", - " \n", " # Extraction of the datatype & name of each schema row\n", " field_name = arrow_schema.field(idx).name\n", " arrow_type = arrow_schema.field(idx).type\n", @@ -142,7 +150,7 @@ " iceberg_type = BooleanType()\n", " elif pa.types.is_binary(arrow_type):\n", " iceberg_type = BinaryType()\n", - " elif pa.types.is_dictionary(arrow_type): \n", + " elif pa.types.is_dictionary(arrow_type):\n", " if pa.types.is_string(arrow_type.value_type):\n", " iceberg_type = StringType()\n", " elif pa.types.is_int32(arrow_type.value_type):\n", @@ -151,45 +159,45 @@ " raise ValueError(f\"Unsupported PyArrow type: {arrow_type}\")\n", "\n", " # Establish the new schema acceptable to Iceberg\n", - " fields.append(NestedField(field_id=idx + 1,\n", - " required=False,\n", - " name=field_name,\n", - " field_type=iceberg_type))\n", + " fields.append(\n", + " NestedField(field_id=idx + 1, required=False, name=field_name, field_type=iceberg_type)\n", + " )\n", " # Iceberg schema\n", " schema = Schema(*fields)\n", - " \n", + "\n", " return schema\n", "\n", " def create_table_for_parquet(self, iceberg_tablename, data_table, schema):\n", - " '''\n", + " \"\"\"\n", " Convert parquet Pyarrow table to iceberg table & allocate Iceberg catalog under the ./iceberg_catalog directory.\n", "\n", " Args:\n", " iceberg_tablename (str): Name of the Iceberg table to be created.\n", - " \n", + "\n", " data_table (object): Pyarrow table\n", - " \n", + "\n", " schema (object): Unique Iceberg schema to be set for the Iceberg table.\n", - " \n", + "\n", " namespace (str): Namespace for which the Iceberg table will reside within\n", " the Iceberg catalog.\n", "\n", " Return: None\n", - " \n", - " '''\n", - " \n", + "\n", + " \"\"\"\n", " # Create an Iceberg table\n", - " iceberg_table = self.catalog.create_table(identifier=f\"{self.namespace}.{iceberg_tablename}\",\n", - " schema=schema,\n", - " location=f\"{os.environ['PYICEBERG_HOME']}/iceberg_catalog\")\n", - " \n", + " iceberg_table = self.catalog.create_table(\n", + " identifier=f\"{self.namespace}.{iceberg_tablename}\",\n", + " schema=schema,\n", + " location=f\"{os.environ['PYICEBERG_HOME']}/iceberg_catalog\",\n", + " )\n", + "\n", " # Updates the Iceberg table with data of interest.\n", " iceberg_table.append(data_table)\n", - " \n", + "\n", " return\n", "\n", - " def create_table_for_all_parquets(self, parquet_files, app_name='mip-xs'):\n", - " '''\n", + " def create_table_for_all_parquets(self, parquet_files, app_name=\"mip-xs\"):\n", + " \"\"\"\n", " Convert parquets to Iceberg tables - each w/ their inherited schema.\n", "\n", " Args:\n", @@ -200,70 +208,67 @@ "\n", " Return: None\n", "\n", - " Note: The sourced data structures for the data in 'mip-xs' & \n", + " Note: The sourced data structures for the data in 'mip-xs' &\n", " 'bathymetry_ml_auxiliary' S3 buckets differ.\n", - " \n", - " '''\n", + "\n", + " \"\"\"\n", " for idx, parquet_file in enumerate(parquet_files):\n", - " if app_name == 'mip_xs':\n", + " if app_name == \"mip_xs\":\n", " iceberg_tablename = f\"{os.path.split(os.path.split(parquet_file)[1])[1].split('.')[0]}\"\n", - " \n", - " elif app_name == 'bathymetry_ml_auxiliary':\n", + "\n", + " elif app_name == \"bathymetry_ml_auxiliary\":\n", " iceberg_tablename = f\"{os.path.split(os.path.split(parquet_file)[0])[1]}\"\n", "\n", " data_table = self.read_data(parquet_file)\n", " data_pyarrow_schema = data_table.schema\n", " schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema)\n", - " self.create_table_for_parquet(iceberg_tablename,\n", - " data_table, \n", - " schema)\n", + " self.create_table_for_parquet(iceberg_tablename, data_table, schema)\n", " return\n", "\n", - " def create_table_for_all_s3parquets(self, app_name, bucket_name, profile_name='default'):\n", - " '''\n", + " def create_table_for_all_s3parquets(self, app_name, bucket_name, profile_name=\"default\"):\n", + " \"\"\"\n", " Convert parquets from S3 to Iceberg tables - each w/ their inherited schema.\n", - " \n", + "\n", " Args:\n", " bucket_name (list): S3 bucket name.\n", "\n", " app_name (str): Application to create Iceberg tables.\n", " Options: 'mip_xs' & 'bathymetry_ml_auxiliary'\n", - " \n", + "\n", " namespace (str): Namespace for which the Iceberg table will reside within\n", " the Iceberg catalog.\n", "\n", - " profile_name (str: Profile name declared in the AWS configuration file. \n", - " Default: 'default' \n", - " \n", + " profile_name (str: Profile name declared in the AWS configuration file.\n", + " Default: 'default'\n", + "\n", " Return: None\n", "\n", - " '''\n", + " \"\"\"\n", " # Instantiate bucket of interest.\n", " session = boto3.Session(profile_name=profile_name)\n", - " s3 = session.resource('s3')\n", + " s3 = session.resource(\"s3\")\n", " bucket_ob = s3.Bucket(bucket_name)\n", " pyarrow_table = {}\n", " for s3obj in bucket_ob.objects.all():\n", - "\n", " # For sourcing the preprocessed XS parquets from S3\n", - " if app_name == 'mip_xs' and re.match(r'^xs_data/.*\\.parquet$', s3obj.key):\n", + " if app_name == \"mip_xs\" and re.match(r\"^xs_data/.*\\.parquet$\", s3obj.key):\n", " iceberg_tablename = f\"{os.path.split(os.path.split(s3obj.key)[1])[1].split('.')[0]}\"\n", - " pyarrow_table[iceberg_tablename]= pq.read_table(f's3://{bucket_name}/{s3obj.key}',\n", - " filesystem=s3fs.S3FileSystem())\n", - " \n", + " pyarrow_table[iceberg_tablename] = pq.read_table(\n", + " f\"s3://{bucket_name}/{s3obj.key}\", filesystem=s3fs.S3FileSystem()\n", + " )\n", + "\n", " # For sourcing the bathymetry_ml_auxiliary parquets from S3\n", - " elif app_name == 'bathymetry_ml_auxiliary':\n", + " elif app_name == \"bathymetry_ml_auxiliary\":\n", " iceberg_tablename = f\"{os.path.split(os.path.split(s3obj.key)[0])[1]}\"\n", - " pyarrow_table[iceberg_tablename]= pq.read_table(f's3://{bucket_name}/{s3obj.key}',\n", - " filesystem=s3fs.S3FileSystem())\n", - " \n", - " # Xforming each unique parquet to an iceberg table \n", + " pyarrow_table[iceberg_tablename] = pq.read_table(\n", + " f\"s3://{bucket_name}/{s3obj.key}\", filesystem=s3fs.S3FileSystem()\n", + " )\n", + "\n", + " # Xforming each unique parquet to an iceberg table\n", " for key, data_table in pyarrow_table.items():\n", " data_pyarrow_schema = data_table.schema\n", " schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema)\n", - " self.create_table_for_parquet(key,\n", - " data_table, \n", - " schema)\n", + " self.create_table_for_parquet(key, data_table, schema)\n", " return" ] }, @@ -382,15 +387,13 @@ "obj = IcebergTable()\n", "\n", "# Extract list of parquets\n", - "parquet_list = obj.read_data_dirs(data_dir=f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/')\n", + "parquet_list = obj.read_data_dirs(data_dir=f\"{os.getcwd()}/data/bathymetry/ml_auxiliary_data/\")\n", "\n", "# Establish new Iceberg catalog\n", - "obj.establish_catalog(catalog_name='bathymetry_ml_auxiliary_dev',\n", - " namespace='bathymetry_ml_auxiliary')\n", + "obj.establish_catalog(catalog_name=\"bathymetry_ml_auxiliary_dev\", namespace=\"bathymetry_ml_auxiliary\")\n", "\n", - "# Generate tables w/in the new Iceberg catalog for all parquets detected \n", - "obj.create_table_for_all_parquets(parquet_list, \n", - " app_name='bathymetry_ml_auxiliary')" + "# Generate tables w/in the new Iceberg catalog for all parquets detected\n", + "obj.create_table_for_all_parquets(parquet_list, app_name=\"bathymetry_ml_auxiliary\")" ] }, { @@ -505,13 +508,12 @@ "obj = IcebergTable()\n", "\n", "# Establish new Iceberg catalog\n", - "obj.establish_catalog(catalog_name='bathymetry_ml_auxiliary_dev2',\n", - " namespace='bathymetry_ml_auxiliary')\n", + "obj.establish_catalog(catalog_name=\"bathymetry_ml_auxiliary_dev2\", namespace=\"bathymetry_ml_auxiliary\")\n", "\n", "# Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", - "obj.create_table_for_all_s3parquets(app_name='bathymetry_ml_auxiliary',\n", - " bucket_name='ngwpc-bathymetry',\n", - " profile_name='default')" + "obj.create_table_for_all_s3parquets(\n", + " app_name=\"bathymetry_ml_auxiliary\", bucket_name=\"ngwpc-bathymetry\", profile_name=\"default\"\n", + ")" ] }, { @@ -526,13 +528,10 @@ "obj = IcebergTable()\n", "\n", "# Establish new Iceberg catalog\n", - "obj.establish_catalog(catalog_name='xs',\n", - " namespace='mip')\n", + "obj.establish_catalog(catalog_name=\"xs\", namespace=\"mip\")\n", "\n", "# Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", - "obj.create_table_for_all_s3parquets(app_name='mip_xs',\n", - " bucket_name='ngwpc-xs',\n", - " profile_name='default')" + "obj.create_table_for_all_s3parquets(app_name=\"mip_xs\", bucket_name=\"ngwpc-xs\", profile_name=\"default\")" ] }, { @@ -611,12 +610,11 @@ ], "source": [ "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "catalog_name = 'bathymetry_ml_auxiliary_dev'\n", - "namespace = 'bathymetry_ml_auxiliary'\n", - "tablename = 'vpuid=01'\n", - "catalog = load_catalog(name=catalog_name, \n", - " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", - "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "catalog_name = \"bathymetry_ml_auxiliary_dev\"\n", + "namespace = \"bathymetry_ml_auxiliary\"\n", + "tablename = \"vpuid=01\"\n", + "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", + "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", "print(iceberg_table.to_string(preview_cols=20))" ] }, @@ -628,13 +626,12 @@ "outputs": [], "source": [ "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "catalog_name = 'bathymetry_ml_auxiliary_dev2'\n", - "namespace = 'bathymetry_ml_auxiliary'\n", - "tablename = 'vpuid=01'\n", - "catalog = load_catalog(name=catalog_name, \n", - " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", - "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", - "#print(iceberg_table.to_string(preview_cols=20))" + "catalog_name = \"bathymetry_ml_auxiliary_dev2\"\n", + "namespace = \"bathymetry_ml_auxiliary\"\n", + "tablename = \"vpuid=01\"\n", + "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", + "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", + "# print(iceberg_table.to_string(preview_cols=20))" ] }, { @@ -1258,12 +1255,11 @@ ], "source": [ "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "catalog_name = 'xs'\n", - "namespace = 'mip'\n", - "tablename = 'huc_01020002'\n", - "catalog = load_catalog(name=catalog_name, \n", - " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", - "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "catalog_name = \"xs\"\n", + "namespace = \"mip\"\n", + "tablename = \"huc_01020002\"\n", + "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", + "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", "print(iceberg_table.to_string(preview_cols=20))" ] }, @@ -1293,7 +1289,7 @@ ], "source": [ "# All tables created within the namespace of interest.\n", - "print(f\"List of tables under {namespace} within {catalog_name}:\\n\\n{catalog.list_tables(namespace)}\") " + "print(f\"List of tables under {namespace} within {catalog_name}:\\n\\n{catalog.list_tables(namespace)}\")" ] }, { @@ -1364,11 +1360,11 @@ ], "source": [ "# Filter Iceberg table based on threshold set for a given feature\n", - "namespace='bathymetry_ml_auxiliary'\n", - "tablename = 'vpuid=01'\n", - "feature_name = 'owp_ahg_c'\n", + "namespace = \"bathymetry_ml_auxiliary\"\n", + "tablename = \"vpuid=01\"\n", + "feature_name = \"owp_ahg_c\"\n", "thresh_val = 0.25\n", - "catalog.load_table(f'{namespace}.{tablename}').scan(row_filter=LessThan(feature_name, thresh_val)).to_arrow()" + "catalog.load_table(f\"{namespace}.{tablename}\").scan(row_filter=LessThan(feature_name, thresh_val)).to_arrow()" ] }, { @@ -1430,12 +1426,13 @@ } ], "source": [ - "namespace='bathymetry_ml_auxiliary'\n", - "tablename = 'vpuid=01'\n", - "feature_name = 'owp_ahg_c'\n", + "namespace = \"bathymetry_ml_auxiliary\"\n", + "tablename = \"vpuid=01\"\n", + "feature_name = \"owp_ahg_c\"\n", "thresh_lower, thresh_upper = 0.25, 0.28\n", - "catalog.load_table(f'{namespace}.{tablename}').scan(row_filter=And(GreaterThan(feature_name, thresh_lower), \n", - " LessThan(feature_name, thresh_upper))).to_arrow()\n" + "catalog.load_table(f\"{namespace}.{tablename}\").scan(\n", + " row_filter=And(GreaterThan(feature_name, thresh_lower), LessThan(feature_name, thresh_upper))\n", + ").to_arrow()" ] }, { @@ -1465,12 +1462,14 @@ } ], "source": [ - "namespace='bathymetry_ml_auxiliary'\n", - "tablename = 'vpuid=01'\n", - "feature_name = 'owp_ahg_c'\n", + "namespace = \"bathymetry_ml_auxiliary\"\n", + "tablename = \"vpuid=01\"\n", + "feature_name = \"owp_ahg_c\"\n", "thresh = 0.25\n", - "catalog.load_table(f'{namespace}.{tablename}').scan(row_filter=GreaterThanOrEqual(feature_name, thresh),\n", - " selected_fields=(\"owp_ahg_m\", \"slope\", \"LENGTHKM\", \"owp_ahg_f\")).to_arrow()\n" + "catalog.load_table(f\"{namespace}.{tablename}\").scan(\n", + " row_filter=GreaterThanOrEqual(feature_name, thresh),\n", + " selected_fields=(\"owp_ahg_m\", \"slope\", \"LENGTHKM\", \"owp_ahg_f\"),\n", + ").to_arrow()" ] }, { @@ -1538,7 +1537,7 @@ ], "source": [ "# Load a XS parquet file into a DataFrame\n", - "parquet_file_path = f'{os.getcwd()}//xs_data/mip_01020002/huc_01020002.parquet'\n", + "parquet_file_path = f\"{os.getcwd()}//xs_data/mip_01020002/huc_01020002.parquet\"\n", "df = pq.read_table(parquet_file_path)\n", "df" ] @@ -1603,7 +1602,7 @@ ], "source": [ "# Load a m1_auxiliary parquet file into a DataFrame\n", - "parquet_file_path = f'{os.getcwd()}/data/bathymetry/ml_auxiliary_data/vpuid=01/ml_auxiliary_data.parquet'\n", + "parquet_file_path = f\"{os.getcwd()}/data/bathymetry/ml_auxiliary_data/vpuid=01/ml_auxiliary_data.parquet\"\n", "df = pq.read_table(parquet_file_path)\n", "df" ] diff --git a/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb b/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb index a34f4e7..18efcf7 100644 --- a/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb +++ b/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb @@ -26,19 +26,17 @@ "metadata": {}, "outputs": [], "source": [ - "import geopandas\n", - "import pyarrow as pa\n", - "import pyarrow.parquet as pq\n", "import collections\n", - "import pandas as pd\n", - "import numpy as np\n", - "import re\n", - "import json\n", - "import pickle\n", - "from icefabric_manage import MapData\n", "import os\n", + "import pickle\n", "import warnings\n", - "warnings.filterwarnings('ignore')" + "\n", + "import pandas as pd\n", + "import pyarrow.parquet as pq\n", + "\n", + "from icefabric_manage import MapData\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -330,17 +328,24 @@ ], "source": [ "# Instantiate class\n", - "obj = MapData(data_dir=f'{os.getcwd()}/segment_data/',\n", - " subfolder_key_prefix='ble')\n", + "obj = MapData(data_dir=f\"{os.getcwd()}/segment_data/\", subfolder_key_prefix=\"ble\")\n", "\n", "# Extract list of fim-c data sample's directories.\n", "fim_data_dirs = obj.fim_data_dirs\n", "\n", "# Categorize FIM-C data sample files (e.g. geopackage of a given model @ HUC#, json, source_models.gpkg, ripple.gpkg)\n", - "model_gpkg_dirs, src_models_gpkg_dirs, rip_gpkg_dirs = obj.model_gpkg_dirs, obj.src_models_gpkg_dirs, obj.rip_gpkg_dirs\n", + "model_gpkg_dirs, src_models_gpkg_dirs, rip_gpkg_dirs = (\n", + " obj.model_gpkg_dirs,\n", + " obj.src_models_gpkg_dirs,\n", + " obj.rip_gpkg_dirs,\n", + ")\n", "gpkg_dirs, json_dirs = obj.gpkg_dirs, obj.json_dirs\n", "\n", - "model_gpkg_tablenames, src_models_gpkg_tablenames, rip_gpkg_tablenames = obj.model_gpkg_tablenames, obj.src_models_gpkg_tablenames, obj.rip_gpkg_tablenames\n", + "model_gpkg_tablenames, src_models_gpkg_tablenames, rip_gpkg_tablenames = (\n", + " obj.model_gpkg_tablenames,\n", + " obj.src_models_gpkg_tablenames,\n", + " obj.rip_gpkg_tablenames,\n", + ")\n", "gpkg_tablenames, json_tablenames = obj.gpkg_tablenames, obj.json_tablenames\n", "\n", "# Generate a dictionary to map each conflation json files to their corresponding model_id & HUC #.\n", @@ -361,7 +366,7 @@ "\n", "# [OPTIONAL: If a user wants to extract individual river geopandas, parquet, &/or gpkg]\n", "# Map of XS as geopandas per river per HUC w/ their inherited CRS by HUC\n", - "#xsbyriver_map = obj.groupbyriver_dict\n", + "# xsbyriver_map = obj.groupbyriver_dict\n", "\n", "# [OPTIONAL: Map of inherited CRS to HUC, model_id, river name]\n", "crs_map = obj.crs_dict\n", @@ -737,8 +742,8 @@ ], "source": [ "# Scenario I\n", - "us_network_to_id = '11908200'\n", - "queried_xsbyid = consolidated_id2xs[consolidated_id2xs['us_network_to_id'] == us_network_to_id]\n", + "us_network_to_id = \"11908200\"\n", + "queried_xsbyid = consolidated_id2xs[consolidated_id2xs[\"us_network_to_id\"] == us_network_to_id]\n", "queried_xsbyid" ] }, @@ -851,8 +856,8 @@ ], "source": [ "# Scenario II\n", - "us_network_to_id = '13801634'\n", - "queried_xsbyid = consolidated_id2xs[consolidated_id2xs['us_network_to_id'] == us_network_to_id]\n", + "us_network_to_id = \"13801634\"\n", + "queried_xsbyid = consolidated_id2xs[consolidated_id2xs[\"us_network_to_id\"] == us_network_to_id]\n", "queried_xsbyid" ] }, @@ -1030,8 +1035,8 @@ ], "source": [ "# Scenario III\n", - "us_network_to_id = '21294311'\n", - "queried_xsbyid = consolidated_id2xs[consolidated_id2xs['us_network_to_id'] == us_network_to_id]\n", + "us_network_to_id = \"21294311\"\n", + "queried_xsbyid = consolidated_id2xs[consolidated_id2xs[\"us_network_to_id\"] == us_network_to_id]\n", "queried_xsbyid" ] }, @@ -1147,8 +1152,8 @@ ], "source": [ "# Scenario IV\n", - "us_network_to_id = '23597193'\n", - "queried_xsbyid = consolidated_id2xs[consolidated_id2xs['us_network_to_id'] == us_network_to_id]\n", + "us_network_to_id = \"23597193\"\n", + "queried_xsbyid = consolidated_id2xs[consolidated_id2xs[\"us_network_to_id\"] == us_network_to_id]\n", "queried_xsbyid" ] }, @@ -1308,20 +1313,20 @@ ], "source": [ "# Scenario I\n", - "model_id = 'GF_EGF_FIS_Submittal1204'\n", - "huc_num = '09020301'\n", + "model_id = \"GF_EGF_FIS_Submittal1204\"\n", + "huc_num = \"09020301\"\n", "\n", - "tt = model_id2gpkg_map[(model_id, huc_num)]['XS']\n", - "tt.insert(0, 'huc', tt.pop('huc')) \n", - "tt.insert(1, 'model_id', tt.pop('model_id')) \n", - "tt.insert(2, 'us_reach_id', tt.pop('us_reach_id')) \n", - "tt.insert(3, 'us_network_to_id', tt.pop('us_network_to_id')) \n", - "tt.insert(4, 'ds_reach_id', tt.pop('ds_reach_id')) \n", - "tt.insert(5, 'ds_network_to_id', tt.pop('ds_network_to_id')) \n", - "tt.insert(6, 'river_reach_rs', tt.pop('river_reach_rs')) \n", - "tt.insert(7, 'thalweg', tt.pop('thalweg')) \n", - "tt.insert(8, 'xs_max_elevation', tt.pop('xs_max_elevation')) \n", - "tt.insert(9, 'river_station', tt.pop('river_station')) \n", + "tt = model_id2gpkg_map[(model_id, huc_num)][\"XS\"]\n", + "tt.insert(0, \"huc\", tt.pop(\"huc\"))\n", + "tt.insert(1, \"model_id\", tt.pop(\"model_id\"))\n", + "tt.insert(2, \"us_reach_id\", tt.pop(\"us_reach_id\"))\n", + "tt.insert(3, \"us_network_to_id\", tt.pop(\"us_network_to_id\"))\n", + "tt.insert(4, \"ds_reach_id\", tt.pop(\"ds_reach_id\"))\n", + "tt.insert(5, \"ds_network_to_id\", tt.pop(\"ds_network_to_id\"))\n", + "tt.insert(6, \"river_reach_rs\", tt.pop(\"river_reach_rs\"))\n", + "tt.insert(7, \"thalweg\", tt.pop(\"thalweg\"))\n", + "tt.insert(8, \"xs_max_elevation\", tt.pop(\"xs_max_elevation\"))\n", + "tt.insert(9, \"river_station\", tt.pop(\"river_station\"))\n", "tt.head(2)" ] }, @@ -1472,20 +1477,20 @@ ], "source": [ "# Scenario II\n", - "model_id = 'sa_1'\n", - "huc_num = '07130010'\n", + "model_id = \"sa_1\"\n", + "huc_num = \"07130010\"\n", "\n", - "tt = model_id2gpkg_map[(model_id, huc_num)]['XS']\n", - "tt.insert(0, 'huc', tt.pop('huc')) \n", - "tt.insert(1, 'model_id', tt.pop('model_id')) \n", - "tt.insert(2, 'us_reach_id', tt.pop('us_reach_id')) \n", - "tt.insert(3, 'us_network_to_id', tt.pop('us_network_to_id')) \n", - "tt.insert(4, 'ds_reach_id', tt.pop('ds_reach_id')) \n", - "tt.insert(5, 'ds_network_to_id', tt.pop('ds_network_to_id')) \n", - "tt.insert(6, 'river_reach_rs', tt.pop('river_reach_rs')) \n", - "tt.insert(7, 'thalweg', tt.pop('thalweg')) \n", - "tt.insert(8, 'xs_max_elevation', tt.pop('xs_max_elevation')) \n", - "tt.insert(9, 'river_station', tt.pop('river_station')) \n", + "tt = model_id2gpkg_map[(model_id, huc_num)][\"XS\"]\n", + "tt.insert(0, \"huc\", tt.pop(\"huc\"))\n", + "tt.insert(1, \"model_id\", tt.pop(\"model_id\"))\n", + "tt.insert(2, \"us_reach_id\", tt.pop(\"us_reach_id\"))\n", + "tt.insert(3, \"us_network_to_id\", tt.pop(\"us_network_to_id\"))\n", + "tt.insert(4, \"ds_reach_id\", tt.pop(\"ds_reach_id\"))\n", + "tt.insert(5, \"ds_network_to_id\", tt.pop(\"ds_network_to_id\"))\n", + "tt.insert(6, \"river_reach_rs\", tt.pop(\"river_reach_rs\"))\n", + "tt.insert(7, \"thalweg\", tt.pop(\"thalweg\"))\n", + "tt.insert(8, \"xs_max_elevation\", tt.pop(\"xs_max_elevation\"))\n", + "tt.insert(9, \"river_station\", tt.pop(\"river_station\"))\n", "tt.head(2)" ] }, @@ -1632,20 +1637,20 @@ ], "source": [ "# Scenario III\n", - "model_id = 'BorregoCyn_Existing'\n", - "huc_num = '18070204'\n", + "model_id = \"BorregoCyn_Existing\"\n", + "huc_num = \"18070204\"\n", "\n", - "tt = model_id2gpkg_map[(model_id, huc_num)]['XS']\n", - "tt.insert(0, 'huc', tt.pop('huc')) \n", - "tt.insert(1, 'model_id', tt.pop('model_id')) \n", - "tt.insert(2, 'us_reach_id', tt.pop('us_reach_id')) \n", - "tt.insert(3, 'us_network_to_id', tt.pop('us_network_to_id')) \n", - "tt.insert(4, 'ds_reach_id', tt.pop('ds_reach_id')) \n", - "tt.insert(5, 'ds_network_to_id', tt.pop('ds_network_to_id')) \n", - "tt.insert(6, 'river_reach_rs', tt.pop('river_reach_rs')) \n", - "tt.insert(7, 'thalweg', tt.pop('thalweg')) \n", - "tt.insert(8, 'xs_max_elevation', tt.pop('xs_max_elevation')) \n", - "tt.insert(9, 'river_station', tt.pop('river_station')) \n", + "tt = model_id2gpkg_map[(model_id, huc_num)][\"XS\"]\n", + "tt.insert(0, \"huc\", tt.pop(\"huc\"))\n", + "tt.insert(1, \"model_id\", tt.pop(\"model_id\"))\n", + "tt.insert(2, \"us_reach_id\", tt.pop(\"us_reach_id\"))\n", + "tt.insert(3, \"us_network_to_id\", tt.pop(\"us_network_to_id\"))\n", + "tt.insert(4, \"ds_reach_id\", tt.pop(\"ds_reach_id\"))\n", + "tt.insert(5, \"ds_network_to_id\", tt.pop(\"ds_network_to_id\"))\n", + "tt.insert(6, \"river_reach_rs\", tt.pop(\"river_reach_rs\"))\n", + "tt.insert(7, \"thalweg\", tt.pop(\"thalweg\"))\n", + "tt.insert(8, \"xs_max_elevation\", tt.pop(\"xs_max_elevation\"))\n", + "tt.insert(9, \"river_station\", tt.pop(\"river_station\"))\n", "tt.head(2)" ] }, @@ -1796,22 +1801,22 @@ ], "source": [ "# Scenario IV\n", - "model_id = 'cottonwood_creek_ras'\n", - "huc_num = '17060305'\n", + "model_id = \"cottonwood_creek_ras\"\n", + "huc_num = \"17060305\"\n", "\n", - "tt = model_id2gpkg_map[(model_id, huc_num)]['XS']\n", - "tt.insert(0, 'huc', tt.pop('huc')) \n", - "tt.insert(1, 'model_id', tt.pop('model_id')) \n", - "tt.insert(2, 'us_reach_id', tt.pop('us_reach_id')) \n", - "tt.insert(3, 'us_network_to_id', tt.pop('us_network_to_id')) \n", - "tt.insert(4, 'ds_reach_id', tt.pop('ds_reach_id')) \n", - "tt.insert(5, 'ds_network_to_id', tt.pop('ds_network_to_id')) \n", - "tt.insert(6, 'river_reach_rs', tt.pop('river_reach_rs')) \n", - "tt.insert(7, 'thalweg', tt.pop('thalweg')) \n", - "tt.insert(8, 'xs_max_elevation', tt.pop('xs_max_elevation')) \n", - "tt.insert(9, 'river_station', tt.pop('river_station')) \n", + "tt = model_id2gpkg_map[(model_id, huc_num)][\"XS\"]\n", + "tt.insert(0, \"huc\", tt.pop(\"huc\"))\n", + "tt.insert(1, \"model_id\", tt.pop(\"model_id\"))\n", + "tt.insert(2, \"us_reach_id\", tt.pop(\"us_reach_id\"))\n", + "tt.insert(3, \"us_network_to_id\", tt.pop(\"us_network_to_id\"))\n", + "tt.insert(4, \"ds_reach_id\", tt.pop(\"ds_reach_id\"))\n", + "tt.insert(5, \"ds_network_to_id\", tt.pop(\"ds_network_to_id\"))\n", + "tt.insert(6, \"river_reach_rs\", tt.pop(\"river_reach_rs\"))\n", + "tt.insert(7, \"thalweg\", tt.pop(\"thalweg\"))\n", + "tt.insert(8, \"xs_max_elevation\", tt.pop(\"xs_max_elevation\"))\n", + "tt.insert(9, \"river_station\", tt.pop(\"river_station\"))\n", "tt.head(2)\n", - "#tt.tail()" + "# tt.tail()" ] }, { @@ -1849,7 +1854,7 @@ "# # # Scenario II\n", "\n", "# # Read Parquet file\n", - "# df2 = pd.read_parquet('/home/sylvia.chin/Desktop/iceberg/xs_data/huc_01020002/Martin Stream.parquet', \n", + "# df2 = pd.read_parquet('/home/sylvia.chin/Desktop/iceberg/xs_data/huc_01020002/Martin Stream.parquet',\n", "# engine='pyarrow')\n", "\n", "# # Display the first few rows\n", @@ -2031,11 +2036,10 @@ "source": [ "# # Scenario I\n", "# Read Parquet file\n", - "df1 = pd.read_parquet(f'/{os.getcwd()}/xs_data/mip_10120203/huc_10120203.parquet',\n", - " engine='pyarrow')\n", + "df1 = pd.read_parquet(f\"/{os.getcwd()}/xs_data/mip_10120203/huc_10120203.parquet\", engine=\"pyarrow\")\n", "\n", - "#set(df1['river'])\n", - "#set(df1['huc'])\n", + "# set(df1['river'])\n", + "# set(df1['huc'])\n", "df1[[\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\"]]" ] }, @@ -2221,11 +2225,10 @@ "source": [ "# Scenario II\n", "# Read Parquet file\n", - "df2 = pd.read_parquet(f'/{os.getcwd()}/xs_data/mip_17060305/huc_17060305.parquet',\n", - " engine='pyarrow')\n", + "df2 = pd.read_parquet(f\"/{os.getcwd()}/xs_data/mip_17060305/huc_17060305.parquet\", engine=\"pyarrow\")\n", "\n", - "#set(df2['river'])\n", - "#set(df2['huc'])\n", + "# set(df2['river'])\n", + "# set(df2['huc'])\n", "df2[[\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\"]]" ] }, @@ -2395,11 +2398,10 @@ "source": [ "# Scenario III\n", "# Read Parquet file\n", - "df3 = pd.read_parquet(f'/{os.getcwd()}/xs_data/mip_15040007/huc_15040007.parquet',\n", - " engine='pyarrow')\n", + "df3 = pd.read_parquet(f\"/{os.getcwd()}/xs_data/mip_15040007/huc_15040007.parquet\", engine=\"pyarrow\")\n", "\n", - "#set(df2['river'])\n", - "#set(df2['huc'])\n", + "# set(df2['river'])\n", + "# set(df2['huc'])\n", "df3[[\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\"]]" ] }, @@ -2443,16 +2445,14 @@ "iceberg_obj = IcebergTable()\n", "\n", "# Extract list of parquets\n", - "file_list = iceberg_obj.read_data_dirs(data_dir=f'{os.getcwd()}/xs_data/')\n", - "parquet_list = [file for file in file_list if file.endswith('.parquet')]\n", - " \n", - "#Establish new Iceberg catalog for XS data\n", - "iceberg_obj.establish_catalog(catalog_name='xs',\n", - " namespace='mip')\n", + "file_list = iceberg_obj.read_data_dirs(data_dir=f\"{os.getcwd()}/xs_data/\")\n", + "parquet_list = [file for file in file_list if file.endswith(\".parquet\")]\n", + "\n", + "# Establish new Iceberg catalog for XS data\n", + "iceberg_obj.establish_catalog(catalog_name=\"xs\", namespace=\"mip\")\n", "\n", - "# Generate tables w/in the new Iceberg catalog for all parquets detected \n", - "iceberg_obj.create_table_for_all_parquets(parquet_list,\n", - " app_name='mip_xs')" + "# Generate tables w/in the new Iceberg catalog for all parquets detected\n", + "iceberg_obj.create_table_for_all_parquets(parquet_list, app_name=\"mip_xs\")" ] }, { @@ -3128,15 +3128,14 @@ "source": [ "# Scenario I\n", "# User Args\n", - "catalog_name = 'xs'\n", + "catalog_name = \"xs\"\n", "huc_num = 10120203\n", "\n", "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "tablename = f'huc_{huc_num}'\n", - "namespace = 'mip'\n", - "catalog = load_catalog(name=catalog_name, \n", - " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", - "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "tablename = f\"huc_{huc_num}\"\n", + "namespace = \"mip\"\n", + "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", + "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", "print(iceberg_table.to_string(preview_cols=30))" ] }, @@ -3709,15 +3708,14 @@ "source": [ "# Scenario II\n", "# User Args\n", - "catalog_name = 'xs'\n", + "catalog_name = \"xs\"\n", "huc_num = 17060305\n", "\n", "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "tablename = f'huc_{huc_num}'\n", - "namespace = 'mip'\n", - "catalog = load_catalog(name=catalog_name, \n", - " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", - "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "tablename = f\"huc_{huc_num}\"\n", + "namespace = \"mip\"\n", + "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", + "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", "print(iceberg_table.to_string(preview_cols=30))" ] }, @@ -4447,15 +4445,14 @@ "source": [ "# Scenario III\n", "# User Args\n", - "catalog_name = 'xs'\n", + "catalog_name = \"xs\"\n", "huc_num = 15040007\n", "\n", "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "tablename = f'huc_{huc_num}'\n", - "namespace = 'mip'\n", - "catalog = load_catalog(name=catalog_name, \n", - " **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'})\n", - "iceberg_table = catalog.load_table(f'{namespace}.{tablename}').scan().to_arrow()\n", + "tablename = f\"huc_{huc_num}\"\n", + "namespace = \"mip\"\n", + "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", + "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", "print(iceberg_table.to_string(preview_cols=30))" ] }, @@ -4499,7 +4496,9 @@ ], "source": [ "# Query iceberg table for specific features of interest\n", - "catalog.load_table(f'{namespace}.{tablename}').scan(selected_fields=(\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\")).to_arrow()" + "catalog.load_table(f\"{namespace}.{tablename}\").scan(\n", + " selected_fields=(\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\")\n", + ").to_arrow()" ] }, { @@ -4518,7 +4517,7 @@ "metadata": {}, "outputs": [], "source": [ - "with open(f'{os.getcwd()}/xs_data/crs_map/crs_mapping.pickle', 'rb') as handle:\n", + "with open(f\"{os.getcwd()}/xs_data/crs_map/crs_mapping.pickle\", \"rb\") as handle:\n", " crs_map = pickle.load(handle)" ] }, @@ -6051,7 +6050,7 @@ } ], "source": [ - "rip_huc2gpkg_map['07130010'].keys()" + "rip_huc2gpkg_map[\"07130010\"].keys()" ] }, { @@ -6181,7 +6180,7 @@ } ], "source": [ - "rip_huc2gpkg_map['07130010']['reaches']" + "rip_huc2gpkg_map[\"07130010\"][\"reaches\"]" ] }, { @@ -6372,7 +6371,7 @@ } ], "source": [ - "rip_huc2gpkg_map['07130010']['rating_curves']" + "rip_huc2gpkg_map[\"07130010\"][\"rating_curves\"]" ] }, { @@ -6490,7 +6489,7 @@ } ], "source": [ - "rip_huc2gpkg_map['07130010']['network']" + "rip_huc2gpkg_map[\"07130010\"][\"network\"]" ] }, { @@ -6614,7 +6613,7 @@ } ], "source": [ - "rip_huc2gpkg_map['07130010']['models']" + "rip_huc2gpkg_map[\"07130010\"][\"models\"]" ] }, { @@ -6691,11 +6690,13 @@ ], "source": [ "s3_xs_keys2objs = collections.defaultdict()\n", - "for folder, subfolders, files in os.walk(f'{os.getcwd()}/xs_data/'):\n", + "for folder, subfolders, files in os.walk(f\"{os.getcwd()}/xs_data/\"):\n", " for file in files:\n", - " if folder != f'{os.getcwd()}/xs_data/':\n", - " s3_xs_keys2objs[f'{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}/{file}'] = f'{folder}/{file}'\n", - " \n", + " if folder != f\"{os.getcwd()}/xs_data/\":\n", + " s3_xs_keys2objs[f\"{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}/{file}\"] = (\n", + " f\"{folder}/{file}\"\n", + " )\n", + "\n", "s3_xs_keys2objs" ] }, @@ -6900,16 +6901,20 @@ ], "source": [ "s3_xs_iceberg_keys2objs = collections.defaultdict()\n", - "for folder, subfolders, files in os.walk(f'{os.getcwd()}/iceberg_catalog/'):\n", + "for folder, subfolders, files in os.walk(f\"{os.getcwd()}/iceberg_catalog/\"):\n", " for file in files:\n", - " if folder != f'{os.getcwd()}/iceberg_catalog/':\n", + " if folder != f\"{os.getcwd()}/iceberg_catalog/\":\n", " # For all files not the database\n", - " s3_xs_iceberg_keys2objs[f'{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}/{file}'] = f'{folder}/{file}'\n", + " s3_xs_iceberg_keys2objs[f\"{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}/{file}\"] = (\n", + " f\"{folder}/{file}\"\n", + " )\n", " else:\n", " # For the database.\n", - " s3_xs_iceberg_keys2objs[f'{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}{file}'] = f'{folder}{file}'\n", - " \n", - " \n", + " s3_xs_iceberg_keys2objs[f\"{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}{file}\"] = (\n", + " f\"{folder}{file}\"\n", + " )\n", + "\n", + "\n", "s3_xs_iceberg_keys2objs" ] }, @@ -6921,10 +6926,10 @@ "outputs": [], "source": [ "# Instantiate bucket of interest.\n", - "profile_name = 'default'\n", + "profile_name = \"default\"\n", "session = boto3.Session(profile_name=profile_name)\n", - "s3 = session.resource('s3')\n", - "bucket_name = 'ngwpc-xs'\n", + "s3 = session.resource(\"s3\")\n", + "bucket_name = \"ngwpc-xs\"\n", "\n", "# # Uploading the xs by HUC parquets\n", "# for key, file_dir in s3_xs_keys2objs.items():\n", @@ -6932,8 +6937,7 @@ "\n", "# Uploading the xs by HUC parquets\n", "# for key, file_dir in s3_xs_iceberg_keys2objs.items():\n", - "# s3.Bucket(bucket_name).upload_file(file_dir, key)\n", - "\n" + "# s3.Bucket(bucket_name).upload_file(file_dir, key)" ] }, { @@ -7001,7 +7005,7 @@ ], "source": [ "# Load the Parquet file into a DataFrame\n", - "parquet_file_path = f'{os.getcwd()}/xs_data/mip_01020002/huc_01020002.parquet'\n", + "parquet_file_path = f\"{os.getcwd()}/xs_data/mip_01020002/huc_01020002.parquet\"\n", "parquet_content = pq.read_table(parquet_file_path)\n", "parquet_content" ] diff --git a/pyproject.toml b/pyproject.toml index 3856a56..e5aa895 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,6 +64,7 @@ target-version = ['py311'] [tool.ruff] line-length = 110 +exclude = [".csv", "LICENSE"] lint.select = [ "F", # Errors detected by Pyflakes "E", # Error detected by Pycodestyle @@ -104,8 +105,11 @@ lint.ignore = [ "D203", # We want docstrings to start immediately after the opening triple quote "D213", + # Bare except okay for passing + "E722", ] + [tool.ruff.lint.pydocstyle] convention = "numpy" @@ -114,6 +118,7 @@ convention = "numpy" "tests/*" = ["D"] "*/__init__.py" = ["F401"] + [tool.mypy] python_version = "3.11" warn_return_any = true diff --git a/src/icefabric_api/src/get_module_parameters.py b/src/icefabric_api/src/get_module_parameters.py index 4753e60..f9adb26 100644 --- a/src/icefabric_api/src/get_module_parameters.py +++ b/src/icefabric_api/src/get_module_parameters.py @@ -1,38 +1,28 @@ -import pandas as pd import geopandas as gpd +import pandas as pd -param_file = 'cfe_params.csv' -gpkg_file = 'gauge_06710385.gpkg' +param_file = "cfe_params.csv" +gpkg_file = "gauge_06710385.gpkg" -divides = gpd.read_file(gpkg_file,layer='divides') -divides = divides['divide_id'].to_list() +divides = gpd.read_file(gpkg_file, layer="divides") +divides = divides["divide_id"].to_list() module_params = pd.read_csv(param_file) -param_values = module_params[['name','default_value']] +param_values = module_params[["name", "default_value"]] for divide in divides: - - cfg_file = f'{divide}_bmi_cfg_cfe.txt' + cfg_file = f"{divide}_bmi_cfg_cfe.txt" f = open(cfg_file, "x") for index, row in param_values.iterrows(): - key = row['name'] - value = row['default_value'] - f.write(f'{key}={value}\n') + key = row["name"] + value = row["default_value"] + f.write(f"{key}={value}\n") f.close() -params_calibratable = module_params.loc[module_params['calibratable'] == 'TRUE'] -params_calibratable.to_json('out.json',orient='split') - - - - - - - - - +params_calibratable = module_params.loc[module_params["calibratable"] == "TRUE"] +params_calibratable.to_json("out.json", orient="split") diff --git a/src/icefabric_api/src/run_modules.py b/src/icefabric_api/src/run_modules.py index 076c480..eeb79d8 100644 --- a/src/icefabric_api/src/run_modules.py +++ b/src/icefabric_api/src/run_modules.py @@ -1,126 +1,127 @@ -from hf_attributes import * import pandas as pd +from hf_attributes import * -gpkg_file = '../data/gauge_06710385.gpkg' -version = '2.2' -domain = 'CONUS' +gpkg_file = "../data/gauge_06710385.gpkg" +version = "2.2" +domain = "CONUS" -#Get divide attributes from the geopackage -attr = get_hydrofabric_attributes(gpkg_file,version,domain) -#Get list of divide IDs +# Get divide attributes from the geopackage +attr = get_hydrofabric_attributes(gpkg_file, version, domain) +# Get list of divide IDs -def module_ipe(module,attr,domain,version): - #Create empty dataframe for csv parameters +def module_ipe(module, attr, domain, version): + # Create empty dataframe for csv parameters csv_params = pd.DataFrame() - #Get list of divides - divides = attr['divide_id'].to_list() - - #Look up parameter csv file for module - module_df = pd.read_csv('../data/modules.csv') - param_file = module_df.loc[module_df['module'] == module]['file'].to_string(index = False) - - #Get module parameters - datatypes = {'name':'object','description':'object','units':'object','data_type':'object','calibratable':'bool', - 'source':'object','min':'float64','max':'float64','default_value':'object','divide_attr_name':'object', - 'source_file':'object'} - module_params = pd.read_csv(f'../data/{param_file}', dtype=datatypes) - - #Filter data frame for parameter names and default values. Create dictionary to collect - #parameters for cfg files. - param_values = module_params[['name','default_value']] - param_values = pd.Series(param_values['default_value'].values, index=param_values['name']).to_dict() - - #Get sources - attr_list = module_params['source'].to_list() - - #Create dictionary with divide IDs as the keys and parameter dictionary for the values. + # Get list of divides + divides = attr["divide_id"].to_list() + + # Look up parameter csv file for module + module_df = pd.read_csv("../data/modules.csv") + param_file = module_df.loc[module_df["module"] == module]["file"].to_string(index=False) + + # Get module parameters + datatypes = { + "name": "object", + "description": "object", + "units": "object", + "data_type": "object", + "calibratable": "bool", + "source": "object", + "min": "float64", + "max": "float64", + "default_value": "object", + "divide_attr_name": "object", + "source_file": "object", + } + module_params = pd.read_csv(f"../data/{param_file}", dtype=datatypes) + + # Filter data frame for parameter names and default values. Create dictionary to collect + # parameters for cfg files. + param_values = module_params[["name", "default_value"]] + param_values = pd.Series(param_values["default_value"].values, index=param_values["name"]).to_dict() + + # Get sources + attr_list = module_params["source"].to_list() + + # Create dictionary with divide IDs as the keys and parameter dictionary for the values. all_cats = dict.fromkeys(divides, param_values) - #For attributes, get list of attribute names - attr_list = module_params.loc[module_params['source'] == 'attr']['divide_attr_name'].to_list() - attr_list.append('divide_id') - #Get corresponding list of parameter names to create a dictionary for mapping - attr_names = module_params.loc[module_params['source'] == 'attr']['name'].to_list() - attr_name_map = dict(zip(attr_list, attr_names)) - #Filter attributes for those needed for this module + # For attributes, get list of attribute names + attr_list = module_params.loc[module_params["source"] == "attr"]["divide_attr_name"].to_list() + attr_list.append("divide_id") + # Get corresponding list of parameter names to create a dictionary for mapping + attr_names = module_params.loc[module_params["source"] == "attr"]["name"].to_list() + attr_name_map = dict(zip(attr_list, attr_names, strict=False)) + # Filter attributes for those needed for this module attr = attr[attr_list] - #Rename attribute names to module names + # Rename attribute names to module names attr.rename(columns=attr_name_map, inplace=True) - #Get CSV file parameters - if 'csv' in module_params['source'].values: - csv_file = module_params.loc[module_params['source'] == 'csv']['source_file'].to_list() + # Get CSV file parameters + if "csv" in module_params["source"].values: + csv_file = module_params.loc[module_params["source"] == "csv"]["source_file"].to_list() csv_file = csv_file[0] - csv_file = f'{csv_file}_{domain}_{version}.csv' - csv_params = pd.read_csv(f'../data/{csv_file}') - + csv_file = f"{csv_file}_{domain}_{version}.csv" + csv_params = pd.read_csv(f"../data/{csv_file}") + for divide in divides: - attr_div = attr.loc[attr['divide_id'] == divide] - attr_div = attr_div.to_dict(orient='list') + attr_div = attr.loc[attr["divide_id"] == divide] + attr_div = attr_div.to_dict(orient="list") all_cats[divide].update(attr_div) if not csv_params.empty: - csv_params_div = csv_params.loc[csv_params['divide_id'] == divide] - csv_params_div = csv_params_div.to_dict(orient='list') + csv_params_div = csv_params.loc[csv_params["divide_id"] == divide] + csv_params_div = csv_params_div.to_dict(orient="list") all_cats[divide].update(csv_params_div) - #print(all_cats[divide]) + # print(all_cats[divide]) write_cfg(all_cats) - s3_uri = 's3://' + s3_uri = "s3://" module_json = write_json(all_cats[divides[1]], module_params, module, s3_uri) return module_json def write_cfg(params): - divides = params.keys() for divide in divides: - cfg_file = f'{divide}_bmi_config_cfe.txt' + cfg_file = f"{divide}_bmi_config_cfe.txt" f = open(cfg_file, "x") for key, value in params[divide].items(): - f.write(f'{key}={value}\n') + f.write(f"{key}={value}\n") f.close() + def write_json(params, all_params, module, s3_uri): - - json_columns = ['name', 'description', 'default_value', 'min', 'max', 'data_type', 'units'] - all_params = all_params.loc[all_params['calibratable'] == True, json_columns] - all_params = all_params.to_dict(orient = 'records') - + json_columns = ["name", "description", "default_value", "min", "max", "data_type", "units"] + all_params = all_params.loc[all_params["calibratable"] == True, json_columns] + all_params = all_params.to_dict(orient="records") + for item in all_params: - name = item['name'] + name = item["name"] value = params[name] - item['default_value'] = value + item["default_value"] = value + + module_dict = { + "module_name": module, + "parameter_file": {"uri": s3_uri}, + "calibrate_parameters": all_params, + } - module_dict = {'module_name':module, - 'parameter_file':{'uri':s3_uri}, - 'calibrate_parameters': all_params} - return module_dict all_modules = [] -#modules = ['CFE-X', 'Noah-OWP-Modular'] -modules = ['UEB'] - -for module in modules: +# modules = ['CFE-X', 'Noah-OWP-Modular'] +modules = ["UEB"] - module_json = module_ipe(module,attr,domain,version) +for module in modules: + module_json = module_ipe(module, attr, domain, version) all_modules.append(module_json) -final_json = {'modules':all_modules} +final_json = {"modules": all_modules} print(final_json) - - - - - - - - - diff --git a/src/icefabric_manage/src/icefabric_manage/_api.py b/src/icefabric_manage/src/icefabric_manage/_api.py index 4aa8600..ad6f7aa 100644 --- a/src/icefabric_manage/src/icefabric_manage/_api.py +++ b/src/icefabric_manage/src/icefabric_manage/_api.py @@ -49,8 +49,8 @@ def build(catalog: Catalog, file_dir: Path) -> None: file_dir : Path The path to the parquet files to add into the iceberg catalog """ - if not any(ns == ('hydrofabric',) for ns in catalog.list_namespaces()): - catalog.create_namespace('hydrofabric') + if not any(ns == ("hydrofabric",) for ns in catalog.list_namespaces()): + catalog.create_namespace("hydrofabric") print("Created 'hydrofabric' namespace") parquet_files = list(file_dir.glob("*.parquet")) diff --git a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py index 315c2dc..91a4bdc 100644 --- a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py +++ b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py @@ -1,94 +1,101 @@ +import os +import re + +import boto3 import pyarrow as pa -import pyiceberg -from pyiceberg.catalog import load_catalog -from pyiceberg.schema import Schema -from pyiceberg.types import NestedField, IntegerType, StringType, DoubleType, LongType, BinaryType, BooleanType -from pyiceberg.expressions import EqualTo, LessThan, And, GreaterThan, GreaterThanOrEqual import pyarrow.parquet as pq -from pyiceberg.schema import Schema -import boto3 import s3fs -import re -import os - -class IcebergTable(object): - ''' +from pyiceberg.catalog import load_catalog +from pyiceberg.schema import Schema +from pyiceberg.types import ( + BinaryType, + BooleanType, + DoubleType, + LongType, + NestedField, + StringType, +) + + +class IcebergTable: + """ Create a Iceberg table per parquet file w/ its inherited schema set. Note: Allows for user to have the option to read parquets from S3 or locally. It is okay to expect following warning statements throughout process: "Iceberg does not have a dictionary type. will be inferred as string on read." - - ''' - def __init__(self): + """ + + def __init__(self): # Generate folder for iceberg catalog - if not os.path.exists(f'{os.getcwd()}/iceberg_catalog'): - os.makedirs(f'{os.getcwd()}/iceberg_catalog') + if not os.path.exists(f"{os.getcwd()}/iceberg_catalog"): + os.makedirs(f"{os.getcwd()}/iceberg_catalog") # Set location of where the iceberg catalog will reside - os.environ['PYICEBERG_HOME'] = os.getcwd() + os.environ["PYICEBERG_HOME"] = os.getcwd() # Initialize namespace to be set for Iceberg catalog - self.namespace = str() - + self.namespace = "" + def read_data_dirs(self, data_dir): - ''' + """ Extract the list of parquet directories. - + Args: data_dir (str): Parent directory of the parquet files. - Note: All the ml_auxiliary_data parquet - files are save under same filenames, + Note: All the ml_auxiliary_data parquet + files are save under same filenames, but categorized by 'vpuid' conditions. Return (list): List of directories associated with each parquet file. - - ''' + + """ parquet_list = [] for folder, subfolders, files in os.walk(data_dir): if folder != data_dir: for file in files: - parquet_list.append(f'{folder}/{file}') - + parquet_list.append(f"{folder}/{file}") + return parquet_list - + def read_data(self, parquet_file_path): - ''' + """ Load a single parquet as a Pyarrow table. - - Args: + + Args: parquet_file_path (str): Directory of a single parquet. - + Return: A Pyarrow table. - - ''' + + """ data = pq.read_table(parquet_file_path) - + return data def establish_catalog(self, catalog_name, namespace): - ''' + """ Creates a new Iceberg catalog. - + Args: catalog_name (str): Name of the catalog to be created. Default: 'dev' for development catalog namespace (str): Name of namespace. Return: None - - ''' + + """ # Establish a new Iceberg catalog & its configuration - self.catalog = load_catalog(name=catalog_name, - **{'uri': f'sqlite:///iceberg_catalog/{catalog_name}_catalog.db'}) - + self.catalog = load_catalog( + name=catalog_name, **{"uri": f"sqlite:///iceberg_catalog/{catalog_name}_catalog.db"} + ) + # Establish namespace to be create w/in catalog self.namespace = namespace if self.namespace not in self.catalog.list_namespaces(): self.catalog.create_namespace(self.namespace) - + return def convert_pyarrow_to_iceberg_schema(self, arrow_schema): @@ -96,15 +103,14 @@ def convert_pyarrow_to_iceberg_schema(self, arrow_schema): Translate a given Pyarrow schema into a schema acceptable by Iceberg. Args: - arrow_schema (object): Pyarrow schema read from the loaded + arrow_schema (object): Pyarrow schema read from the loaded parquet of interest. Return (object): Iceberg schema - + """ fields = [] for idx in range(len(arrow_schema)): - # Extraction of the datatype & name of each schema row field_name = arrow_schema.field(idx).name arrow_type = arrow_schema.field(idx).type @@ -122,7 +128,7 @@ def convert_pyarrow_to_iceberg_schema(self, arrow_schema): iceberg_type = BooleanType() elif pa.types.is_binary(arrow_type): iceberg_type = BinaryType() - elif pa.types.is_dictionary(arrow_type): + elif pa.types.is_dictionary(arrow_type): if pa.types.is_string(arrow_type.value_type): iceberg_type = StringType() elif pa.types.is_int32(arrow_type.value_type): @@ -131,45 +137,45 @@ def convert_pyarrow_to_iceberg_schema(self, arrow_schema): raise ValueError(f"Unsupported PyArrow type: {arrow_type}") # Establish the new schema acceptable to Iceberg - fields.append(NestedField(field_id=idx + 1, - required=False, - name=field_name, - field_type=iceberg_type)) + fields.append( + NestedField(field_id=idx + 1, required=False, name=field_name, field_type=iceberg_type) + ) # Iceberg schema schema = Schema(*fields) - + return schema def create_table_for_parquet(self, iceberg_tablename, data_table, schema): - ''' + """ Convert parquet Pyarrow table to iceberg table & allocate Iceberg catalog under the ./iceberg_catalog directory. Args: iceberg_tablename (str): Name of the Iceberg table to be created. - + data_table (object): Pyarrow table - + schema (object): Unique Iceberg schema to be set for the Iceberg table. - + namespace (str): Namespace for which the Iceberg table will reside within the Iceberg catalog. Return: None - - ''' - + + """ # Create an Iceberg table - iceberg_table = self.catalog.create_table(identifier=f"{self.namespace}.{iceberg_tablename}", - schema=schema, - location=f"{os.environ['PYICEBERG_HOME']}/iceberg_catalog") - + iceberg_table = self.catalog.create_table( + identifier=f"{self.namespace}.{iceberg_tablename}", + schema=schema, + location=f"{os.environ['PYICEBERG_HOME']}/iceberg_catalog", + ) + # Updates the Iceberg table with data of interest. iceberg_table.append(data_table) - + return - def create_table_for_all_parquets(self, parquet_files, app_name='mip-xs'): - ''' + def create_table_for_all_parquets(self, parquet_files, app_name="mip-xs"): + """ Convert parquets to Iceberg tables - each w/ their inherited schema. Args: @@ -180,68 +186,65 @@ def create_table_for_all_parquets(self, parquet_files, app_name='mip-xs'): Return: None - Note: The sourced data structures for the data in 'mip-xs' & + Note: The sourced data structures for the data in 'mip-xs' & 'bathymetry_ml_auxiliary' S3 buckets differ. - - ''' + + """ for idx, parquet_file in enumerate(parquet_files): - if app_name == 'mip_xs': + if app_name == "mip_xs": iceberg_tablename = f"{os.path.split(os.path.split(parquet_file)[1])[1].split('.')[0]}" - - elif app_name == 'bathymetry_ml_auxiliary': + + elif app_name == "bathymetry_ml_auxiliary": iceberg_tablename = f"{os.path.split(os.path.split(parquet_file)[0])[1]}" data_table = self.read_data(parquet_file) data_pyarrow_schema = data_table.schema schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema) - self.create_table_for_parquet(iceberg_tablename, - data_table, - schema) + self.create_table_for_parquet(iceberg_tablename, data_table, schema) return - def create_table_for_all_s3parquets(self, app_name, bucket_name, profile_name='default'): - ''' + def create_table_for_all_s3parquets(self, app_name, bucket_name, profile_name="default"): + """ Convert parquets from S3 to Iceberg tables - each w/ their inherited schema. - + Args: bucket_name (list): S3 bucket name. app_name (str): Application to create Iceberg tables. Options: 'mip_xs' & 'bathymetry_ml_auxiliary' - + namespace (str): Namespace for which the Iceberg table will reside within the Iceberg catalog. - profile_name (str: Profile name declared in the AWS configuration file. - Default: 'default' - + profile_name (str: Profile name declared in the AWS configuration file. + Default: 'default' + Return: None - ''' + """ # Instantiate bucket of interest. session = boto3.Session(profile_name=profile_name) - s3 = session.resource('s3') + s3 = session.resource("s3") bucket_ob = s3.Bucket(bucket_name) pyarrow_table = {} for s3obj in bucket_ob.objects.all(): - # For sourcing the preprocessed XS parquets from S3 - if app_name == 'mip_xs' and re.match(r'^xs_data/.*\.parquet$', s3obj.key): + if app_name == "mip_xs" and re.match(r"^xs_data/.*\.parquet$", s3obj.key): iceberg_tablename = f"{os.path.split(os.path.split(s3obj.key)[1])[1].split('.')[0]}" - pyarrow_table[iceberg_tablename]= pq.read_table(f's3://{bucket_name}/{s3obj.key}', - filesystem=s3fs.S3FileSystem()) - + pyarrow_table[iceberg_tablename] = pq.read_table( + f"s3://{bucket_name}/{s3obj.key}", filesystem=s3fs.S3FileSystem() + ) + # For sourcing the bathymetry_ml_auxiliary parquets from S3 - elif app_name == 'bathymetry_ml_auxiliary': + elif app_name == "bathymetry_ml_auxiliary": iceberg_tablename = f"{os.path.split(os.path.split(s3obj.key)[0])[1]}" - pyarrow_table[iceberg_tablename]= pq.read_table(f's3://{bucket_name}/{s3obj.key}', - filesystem=s3fs.S3FileSystem()) - - # Xforming each unique parquet to an iceberg table + pyarrow_table[iceberg_tablename] = pq.read_table( + f"s3://{bucket_name}/{s3obj.key}", filesystem=s3fs.S3FileSystem() + ) + + # Xforming each unique parquet to an iceberg table for key, data_table in pyarrow_table.items(): data_pyarrow_schema = data_table.schema schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema) - self.create_table_for_parquet(key, - data_table, - schema) + self.create_table_for_parquet(key, data_table, schema) return diff --git a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py index 8f843da..03e170e 100644 --- a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py +++ b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py @@ -1,48 +1,50 @@ -import geopandas -import pyarrow as pa -import pyarrow.parquet as pq import collections -import pandas as pd -import numpy as np -import re import json -import pickle import os +import pickle +import re import warnings -warnings.filterwarnings('ignore') - -class MapData(object): - ''' - Maps FIM MIP & BLE XS datasets to relevant IDs & categorize by HUC. - At this time, ensure FIM datasets are saved to local disk. - - ''' + +import geopandas +import numpy as np +import pandas as pd + +warnings.filterwarnings("ignore") + + +class MapData: + """ + Maps FIM MIP & BLE XS datasets to relevant IDs & categorize by HUC. + At this time, ensure FIM datasets are saved to local disk. + + """ + def __init__(self, data_dir, subfolder_key_prefix): self.data_dir = data_dir self.subfolder_key_prefix = subfolder_key_prefix # Parent directory of the FIM files. - # Note: All the jsons & geopackages are relevant + # Note: All the jsons & geopackages are relevant # to map the files to IDs. self.fim_data_dirs = [] - # List of directories associated with each file type of - # the FIM data sample (e.g. geopackage of a given model @ HUC#, json, - # source_models.gpkg, ripple.gpkg) + # List of directories associated with each file type of + # the FIM data sample (e.g. geopackage of a given model @ HUC#, json, + # source_models.gpkg, ripple.gpkg) self.model_gpkg_dirs = [] self.src_models_gpkg_dirs = [] self.rip_gpkg_dirs = [] self.gpkg_dirs = [] self.json_dirs = [] self.xs_df_list = [] - + # Variables to be used later self.model_gpkg_tablenames = [] self.src_models_gpkg_tablenames = [] self.rip_gpkg_tablenames = [] self.gpkg_tablenames = [] self.json_tablenames = [] - + self.id2json = collections.defaultdict(dict) self.model_id2gpkg = collections.defaultdict(dict) self.us_ref_dict = collections.defaultdict(dict) @@ -51,16 +53,16 @@ def __init__(self, data_dir, subfolder_key_prefix): self.groupbyriver_dict = collections.defaultdict(dict) self.crs_dict = collections.defaultdict(dict) self.consolidated_id2xs = geopandas.GeoDataFrame() - + self.read_data_dirs() self.cat_data_dirs(self.subfolder_key_prefix) self.map_model2huc() - self.filter_model2huc_map(keys_to_drop={'metrics','low_flow', - 'high_flow', 'eclipsed', - 'lengths', 'coverage'}) - + self.filter_model2huc_map( + keys_to_drop={"metrics", "low_flow", "high_flow", "eclipsed", "lengths", "coverage"} + ) + # Generate maps of model_id & HUC # to xs (for both us & ds cross-section) - # to reach ID & "network_to_id" from each model @ HUC's json file + # to reach ID & "network_to_id" from each model @ HUC's json file self.map_modelhuc_xs2ids() # Generate maps of model_id & HUC # to gpkg from each model @ HUC's geopackage @@ -73,7 +75,7 @@ def __init__(self, data_dir, subfolder_key_prefix): self.map_model_xs2ids() # [Optional: Per HUC, save each river's set of XS data as geoparquetss & geopackages] - #self.save_xs_data() + # self.save_xs_data() # Save map of inherited CRS to HUC, model_id, river name self.save_crs_map() @@ -83,85 +85,86 @@ def __init__(self, data_dir, subfolder_key_prefix): # Save HEC RAS models' cross-sections consolidated by HUC as geoparquets & geopackages self.save_xsbyhuc_data() - - def read_data_dirs(self): - ''' + """ Extract the list of FIM data sample's directories. - + Args: None - Return (list): List of directories associated with each file type of + Return (list): List of directories associated with each file type of the FIM data sample. - - ''' + + """ fim_data_dirs = [] for folder, subfolders, files in os.walk(self.data_dir): if folder != self.data_dir: for file in files: - self.fim_data_dirs.append(f'{folder}/{file}') + self.fim_data_dirs.append(f"{folder}/{file}") return - + def cat_data_dirs(self, subfolder_key_prefix): - ''' + """ Categorize FIM data sample files. - + Args: subfolder_key_prefix (str): Prefix of the FIM subfolder's data of interest Options: 'mip' or 'ble' Return: None - - ''' - # Extract a list of directories corresponding to each set of files + + """ + # Extract a list of directories corresponding to each set of files for x in self.fim_data_dirs: - - # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains reach_id & nwm_to_id - # for network layer & reaches layer. The rating curves layer only has reach_id. The models layer + # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains reach_id & nwm_to_id + # for network layer & reaches layer. The rating curves layer only has reach_id. The models layer # contains collection_id & model_id) - if re.search('ripple.gpkg', x): + if re.search("ripple.gpkg", x): self.rip_gpkg_dirs.append(x) - t = re.search(f'/{subfolder_key_prefix}(.*)', x) + t = re.search(f"/{subfolder_key_prefix}(.*)", x) rip_gpkg_tblname = t.group() - self.rip_gpkg_tablenames.append(rip_gpkg_tblname.lstrip('/').replace('/', '_')) + self.rip_gpkg_tablenames.append(rip_gpkg_tblname.lstrip("/").replace("/", "_")) # Covers all HEC-RAS models gpkg featuring XS per HUC (contains model_id) - elif not x.endswith('source_models.gpkg') and not x.endswith('.json') and not re.search('ripple.gpkg', x): + elif ( + not x.endswith("source_models.gpkg") + and not x.endswith(".json") + and not re.search("ripple.gpkg", x) + ): self.model_gpkg_dirs.append(x) - t = re.search(f'/{subfolder_key_prefix}(.*)', x) + t = re.search(f"/{subfolder_key_prefix}(.*)", x) model_gpkg_tblname = t.group() - self.model_gpkg_tablenames.append(model_gpkg_tblname.lstrip('/').replace('/', '_')) + self.model_gpkg_tablenames.append(model_gpkg_tblname.lstrip("/").replace("/", "_")) # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains model_id & their HEC-RAS 1D model flowlines) - elif x.endswith('source_models.gpkg'): + elif x.endswith("source_models.gpkg"): self.src_models_gpkg_dirs.append(x) - t = re.search(f'/{subfolder_key_prefix}(.*)', x) + t = re.search(f"/{subfolder_key_prefix}(.*)", x) src_models_gpkg_tblname = t.group() - self.src_models_gpkg_tablenames.append(src_models_gpkg_tblname.lstrip('/').replace('/', '_')) - + self.src_models_gpkg_tablenames.append(src_models_gpkg_tblname.lstrip("/").replace("/", "_")) + # Covers all HEC-RAS models + Ripple gpkg per HUC - if x.endswith('.gpkg'): + if x.endswith(".gpkg"): self.gpkg_dirs.append(x) - t = re.search(f'/{subfolder_key_prefix}(.*)', x) + t = re.search(f"/{subfolder_key_prefix}(.*)", x) gpkg_tblname = t.group() - self.gpkg_tablenames.append(gpkg_tblname.lstrip('/').replace('/', '_')) + self.gpkg_tablenames.append(gpkg_tblname.lstrip("/").replace("/", "_")) # Covers each HEC-RAS models' result of conflating its model w/ the NWM network - elif x.endswith('.json'): + elif x.endswith(".json"): self.json_dirs.append(x) - t = re.search(f'/{subfolder_key_prefix}(.*)', x) + t = re.search(f"/{subfolder_key_prefix}(.*)", x) json_tblname = t.group() - self.json_tablenames.append(json_tblname.lstrip('/').replace('/', '_')) + self.json_tablenames.append(json_tblname.lstrip("/").replace("/", "_")) return - + def drop_nested_keys(self, map_dict, keys_to_drop): - ''' + """ Drop keys irrelevant for linking each XS to IDs - + Args: map_dict (dict): Dictionary to filter @@ -169,124 +172,132 @@ def drop_nested_keys(self, map_dict, keys_to_drop): Return: None - ''' + """ if isinstance(map_dict, dict): - return {k: self.drop_nested_keys(v, keys_to_drop) for k, v in map_dict.items() if k not in keys_to_drop} + return { + k: self.drop_nested_keys(v, keys_to_drop) + for k, v in map_dict.items() + if k not in keys_to_drop + } elif isinstance(map_dict, list): return [self.drop_nested_keys(i, keys_to_drop) for i in map_dict] else: return map_dict - + def map_model2huc(self): - ''' + """ Map each conflation json file to their corresponding model ID & HUC #. - + Args: None Return: None - ''' + """ for x in self.json_dirs: # Note: model_ids found in each src source_models.gpkg is featured is declared as # sub-foldername of where model gpkg file resides model_id = x.split("/")[-2] - huc_num = x.split("/")[-4].split('_')[1] + huc_num = x.split("/")[-4].split("_")[1] self.id2json[model_id][huc_num] = {} try: - with open(x, 'r') as f: - json2dict = json.loads(f.read()) + with open(x) as f: + json2dict = json.loads(f.read()) self.id2json[model_id][huc_num].update(json2dict) except: pass - return - + return + def filter_model2huc_map(self, keys_to_drop): - ''' - Extract only relevant keys from model2huc map for linking each XS to a feature ID. - + """ + Extract only relevant keys from model2huc map for linking each XS to a feature ID. + Args: keys_to_drop (dict): List of keys irrelevant for linking each XS to IDs. - (e.g. {'metrics','low_flow', 'high_flow', 'eclipsed', + (e.g. {'metrics','low_flow', 'high_flow', 'eclipsed', 'lengths', 'coverage'}) Return: None - ''' + """ self.id2json = self.drop_nested_keys(self.id2json, keys_to_drop) - + return - + def map_modelhuc_xs2ids(self): - ''' + """ Parse JSONs & map model_id & HUC # to xs to reach ID & "network_to_id" - + Args: None Return: None - Note: Per model @ HUC cross-section layer, the attribute of interest is "river_reach_rs" + Note: Per model @ HUC cross-section layer, the attribute of interest is "river_reach_rs" in order to link the IDs to each individual cross-section & their associated xs_id. - To map each cross-section of a model @ HUC#, there has to be a shared attribute between a - model @ HUC#'s cross section w/in its XS layer & the details provided within a model @ HUC#'s + To map each cross-section of a model @ HUC#, there has to be a shared attribute between a + model @ HUC#'s cross section w/in its XS layer & the details provided within a model @ HUC#'s conflation json file. - + - Each conflation json file reveals ... - Per reach, there is a set of cross-sections. - - Within each model's cross-section (XS) layers, there are a set of cross-section - instances - each instance featuring a unique "thalweg" (aka "min_elevation"), + - Within each model's cross-section (XS) layers, there are a set of cross-section + instances - each instance featuring a unique "thalweg" (aka "min_elevation"), "xs_max_elevation" (aka "max_elevation"), "reach_id" ("reaches"), & "river_station" (aka "xs_id") - - Each model @ HUC#'s XS layer contains a collection of cross-section instancees. - Thus, each unique cross-section w/in a given model @ HUC#'s XS layer will need to be mapped in + - Each model @ HUC#'s XS layer contains a collection of cross-section instancees. + Thus, each unique cross-section w/in a given model @ HUC#'s XS layer will need to be mapped in such a way to allow each cross-section to be associated with a feature ID (aka "reach_id" and/or - "network_to_id"). - - - "river_reach_rs" is formatted differently across models' XS layers, however multplie keys - referenced in the conflation jsons can be referenced to obtain the "river_reach_rs" from the jsons + "network_to_id"). + + - "river_reach_rs" is formatted differently across models' XS layers, however multplie keys + referenced in the conflation jsons can be referenced to obtain the "river_reach_rs" from the jsons As a result, the mapping of IDs to each model's cross-section instance will be based on the info. extracted from a model @ HUC#'s conflation.json - There can be multiple reach_ids tied to same nwm_to_id (aka "network_to_id). - ''' + """ # Keys to join values from that makes up the 'river_reach_rs' reflected in each model's XS layer - keys_to_join = ['river', 'reach', 'xs_id'] + keys_to_join = ["river", "reach", "xs_id"] for model_id, huc_dict in self.id2json.items(): for huc_num, reach_dict in huc_dict.items(): - for reach_id, v_dict in reach_dict['reaches'].items(): + for reach_id, v_dict in reach_dict["reaches"].items(): # Joining the attribute because each model's xs layer features three atttrib concat (to be used as reference) if "us_xs" in v_dict: - usxs_joined_values = ' '.join(str(v_dict["us_xs"][key]) for key in keys_to_join) + usxs_joined_values = " ".join(str(v_dict["us_xs"][key]) for key in keys_to_join) if "min_elevation" in v_dict["us_xs"]: us_xs_min_elev = v_dict["us_xs"]["min_elevation"] if "max_elevation" in v_dict["us_xs"]: us_xs_max_elev = v_dict["us_xs"]["max_elevation"] - + if "ds_xs" in v_dict: - dsxs_joined_values = ' '.join(str(v_dict["ds_xs"][key]) for key in keys_to_join) + dsxs_joined_values = " ".join(str(v_dict["ds_xs"][key]) for key in keys_to_join) if "min_elevation" in v_dict["ds_xs"]: ds_xs_min_elev = v_dict["ds_xs"]["min_elevation"] if "max_elevation" in v_dict["ds_xs"]: ds_xs_max_elev = v_dict["ds_xs"]["max_elevation"] - + if "network_to_id" in v_dict: nwm2id = v_dict["network_to_id"] # Generated maps of model_id & HUC # to xs (for both us & ds cross-section) - # to reach ID & "network_to_id" - self.us_ref_dict[(model_id, huc_num)].update({(usxs_joined_values, us_xs_min_elev , us_xs_max_elev):[reach_id, nwm2id]}) - self.ds_ref_dict[(model_id, huc_num)].update({(dsxs_joined_values, ds_xs_min_elev , ds_xs_max_elev):[reach_id, nwm2id]}) - + # to reach ID & "network_to_id" + self.us_ref_dict[(model_id, huc_num)].update( + {(usxs_joined_values, us_xs_min_elev, us_xs_max_elev): [reach_id, nwm2id]} + ) + self.ds_ref_dict[(model_id, huc_num)].update( + {(dsxs_joined_values, ds_xs_min_elev, ds_xs_max_elev): [reach_id, nwm2id]} + ) + return def map_model2huc_gpkg(self): - ''' + """ Map model ID & HUC # to each HEC-RAS model's geopackage. - + Args: None @@ -295,37 +306,39 @@ def map_model2huc_gpkg(self): Note: model_ids found in each source_models.gpkg is featured in last sub-foldername of where model gpkg file resides - ''' + """ # Each HEC-RAS model gpkg per model per HUC for x in self.model_gpkg_dirs: model_id = x.split("/")[-2] - huc_num = x.split("/")[-4].split('_')[1] - self.model_id2gpkg[(model_id, huc_num)] = {'XS': None} + huc_num = x.split("/")[-4].split("_")[1] + self.model_id2gpkg[(model_id, huc_num)] = {"XS": None} self.model_id2gpkg[(model_id, huc_num)] = {"XS concave hull": None} self.model_id2gpkg[(model_id, huc_num)] = {"River": None} try: - self.model_id2gpkg[(model_id, huc_num)].update({'XS': geopandas.read_file(x, engine='pyogrio', - use_arrow=True, - layer="XS")}) - - self.model_id2gpkg[(model_id, huc_num)].update({"River": geopandas.read_file(x, - engine='pyogrio', - use_arrow=True, - layer="River")}) - self.model_id2gpkg[(model_id, huc_num)].update({"XS concave hull": geopandas.read_file(x, - engine='pyogrio', - use_arrow=True, - layer="XS concave hull")}) + self.model_id2gpkg[(model_id, huc_num)].update( + {"XS": geopandas.read_file(x, engine="pyogrio", use_arrow=True, layer="XS")} + ) + + self.model_id2gpkg[(model_id, huc_num)].update( + {"River": geopandas.read_file(x, engine="pyogrio", use_arrow=True, layer="River")} + ) + self.model_id2gpkg[(model_id, huc_num)].update( + { + "XS concave hull": geopandas.read_file( + x, engine="pyogrio", use_arrow=True, layer="XS concave hull" + ) + } + ) except: pass - + return - + def map_huc2ripple_gpkg(self): - ''' + """ Map HUC # to ripple geopackage (features HEC RAS 1D model flowlines). - + Args: None @@ -333,109 +346,144 @@ def map_huc2ripple_gpkg(self): Note: ripple.gpkg features the HEC RAS 1D model flowlines categorized by HUC #. - ''' + """ for x in self.rip_gpkg_dirs: - huc_num = x.split("/")[-2].split('_')[1] - self.rip_huc2gpkg[huc_num] = {'reaches': None} + huc_num = x.split("/")[-2].split("_")[1] + self.rip_huc2gpkg[huc_num] = {"reaches": None} self.rip_huc2gpkg[huc_num] = {"rating curves": None} self.rip_huc2gpkg[huc_num] = {"network": None} - self.rip_huc2gpkg[huc_num] = {'models': None} + self.rip_huc2gpkg[huc_num] = {"models": None} self.rip_huc2gpkg[huc_num] = {"metadata": None} self.rip_huc2gpkg[huc_num] = {"rating_curves_no_map": None} self.rip_huc2gpkg[huc_num] = {"processing": None} try: - self.rip_huc2gpkg[huc_num].update({"reaches": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="reaches")}) - self.rip_huc2gpkg[huc_num].update({"rating_curves": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="rating_curves")}) - self.rip_huc2gpkg[huc_num].update({"network": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="network")}) - self.rip_huc2gpkg[huc_num].update({"models": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="models")}) - self.rip_huc2gpkg[huc_num].update({"metadata": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="metadata")}) - self.rip_huc2gpkg[huc_num].update({"rating_curves_no_map": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="rating_curves_no_map")}) - self.rip_huc2gpkg[huc_num].update({"processing": geopandas.read_file(x, engine='pyogrio', use_arrow=True, layer="processing")}) + self.rip_huc2gpkg[huc_num].update( + {"reaches": geopandas.read_file(x, engine="pyogrio", use_arrow=True, layer="reaches")} + ) + self.rip_huc2gpkg[huc_num].update( + { + "rating_curves": geopandas.read_file( + x, engine="pyogrio", use_arrow=True, layer="rating_curves" + ) + } + ) + self.rip_huc2gpkg[huc_num].update( + {"network": geopandas.read_file(x, engine="pyogrio", use_arrow=True, layer="network")} + ) + self.rip_huc2gpkg[huc_num].update( + {"models": geopandas.read_file(x, engine="pyogrio", use_arrow=True, layer="models")} + ) + self.rip_huc2gpkg[huc_num].update( + {"metadata": geopandas.read_file(x, engine="pyogrio", use_arrow=True, layer="metadata")} + ) + self.rip_huc2gpkg[huc_num].update( + { + "rating_curves_no_map": geopandas.read_file( + x, engine="pyogrio", use_arrow=True, layer="rating_curves_no_map" + ) + } + ) + self.rip_huc2gpkg[huc_num].update( + { + "processing": geopandas.read_file( + x, engine="pyogrio", use_arrow=True, layer="processing" + ) + } + ) except: - pass return - + def map_model_xs2ids(self): - ''' + """ Map each cross-section instance featured in HEC-RAS model's cross-section layer to their corresponding IDs. - + Args: None Return: None - ''' - + """ for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items(): - df = model_gpkg_dict['XS'] - df['huc'] = huc_num - df['model_id'] = model_id + df = model_gpkg_dict["XS"] + df["huc"] = huc_num + df["model_id"] = model_id array_of_lists = [[None, None] for _ in range(len(df))] - df['us_ids'] = pd.DataFrame([array_of_lists]).T - df['ds_ids'] = pd.DataFrame([array_of_lists]).T - + df["us_ids"] = pd.DataFrame([array_of_lists]).T + df["ds_ids"] = pd.DataFrame([array_of_lists]).T + # Covers us_xs if (model_id, huc_num) in self.us_ref_dict: - df['us_ids'] = df.set_index(["river_reach_rs", "thalweg", "xs_max_elevation"]).index.map(self.us_ref_dict[(model_id, huc_num)].get) + df["us_ids"] = df.set_index(["river_reach_rs", "thalweg", "xs_max_elevation"]).index.map( + self.us_ref_dict[(model_id, huc_num)].get + ) else: - print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.") + print( + f"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file." + ) continue # Covers ds_xs if (model_id, huc_num) in self.ds_ref_dict: - df['ds_ids'] = df.set_index(["river_reach_rs", "thalweg", "xs_max_elevation"]).index.map(self.ds_ref_dict[(model_id, huc_num)].get) + df["ds_ids"] = df.set_index(["river_reach_rs", "thalweg", "xs_max_elevation"]).index.map( + self.ds_ref_dict[(model_id, huc_num)].get + ) else: - print(f"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file.") + print( + f"The model_id @ HUC# ({(model_id, huc_num)}) IS NOT featured in current model @ HUC's conflation json file." + ) continue - # Extracts & appends reach_id & network_to_id to each model @ HUC's unique XS + # Extracts & appends reach_id & network_to_id to each model @ HUC's unique XS # Should the ids not be available in the conflation, must initialize columns - us_id_df = df['us_ids'].apply(pd.Series) - if us_id_df.shape[1]==0: + us_id_df = df["us_ids"].apply(pd.Series) + if us_id_df.shape[1] == 0: us_id_df = pd.DataFrame(np.nan, index=range(us_id_df.shape[0]), columns=[0, 1]) - us_id_df.columns=['us_reach_id', 'us_network_to_id'] + us_id_df.columns = ["us_reach_id", "us_network_to_id"] # Should the ids not be available in the conflation, must initialize columns - ds_id_df = df['ds_ids'].apply(pd.Series) - if ds_id_df.shape[1]==0: + ds_id_df = df["ds_ids"].apply(pd.Series) + if ds_id_df.shape[1] == 0: ds_id_df = pd.DataFrame(np.nan, index=range(ds_id_df.shape[0]), columns=[0, 1]) - ds_id_df.columns=['ds_reach_id', 'ds_network_to_id'] - - # Fill any nan to string - us_id_df[['us_reach_id', 'us_network_to_id']] = us_id_df[['us_reach_id', 'us_network_to_id']].fillna('None') - ds_id_df[['ds_reach_id', 'ds_network_to_id']] = ds_id_df[['ds_reach_id', 'ds_network_to_id']].fillna('None') - df = df.fillna('None') + ds_id_df.columns = ["ds_reach_id", "ds_network_to_id"] + + # Fill any nan to string + us_id_df[["us_reach_id", "us_network_to_id"]] = us_id_df[ + ["us_reach_id", "us_network_to_id"] + ].fillna("None") + ds_id_df[["ds_reach_id", "ds_network_to_id"]] = ds_id_df[ + ["ds_reach_id", "ds_network_to_id"] + ].fillna("None") + df = df.fillna("None") df = pd.concat([df, us_id_df, ds_id_df], axis=1) - df = df.drop(['us_ids', 'ds_ids'], axis=1) - - model_gpkg_dict['XS'] = df - self.xs_df_list.append(model_gpkg_dict['XS']) - + df = df.drop(["us_ids", "ds_ids"], axis=1) + + model_gpkg_dict["XS"] = df + self.xs_df_list.append(model_gpkg_dict["XS"]) + return - + def save_xs_data(self): - ''' + """ Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage - + Args: None Return: None - Note: These saved parquet files will preserve each river @ HUC's inherited CRS. + Note: These saved parquet files will preserve each river @ HUC's inherited CRS. - ''' + """ for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items(): - # Generate data folder per HUC - if not os.path.exists(f'{os.getcwd()}/xs_data/huc_{huc_num}'): - os.makedirs(f'{os.getcwd()}/xs_data/huc_{huc_num}') + if not os.path.exists(f"{os.getcwd()}/xs_data/huc_{huc_num}"): + os.makedirs(f"{os.getcwd()}/xs_data/huc_{huc_num}") - # Save each river's geopandas as a geoparquet & geopackage under each HUC folder - grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]['XS'].groupby(['river']) - for river_name in set(self.model_id2gpkg[(model_id, huc_num)]['XS']['river']): + # Save each river's geopandas as a geoparquet & geopackage under each HUC folder + grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]["XS"].groupby(["river"]) + for river_name in set(self.model_id2gpkg[(model_id, huc_num)]["XS"]["river"]): filterbyriver = grouped_xslayers.get_group(river_name) # Generate map of each river's set of XS to HUC & model ID to be used as a @@ -443,66 +491,70 @@ def save_xs_data(self): self.groupbyriver_dict[huc_num].update({model_id: filterbyriver}) # Save XS as geoparquet per river per HUC - filterbyriver.to_parquet(f'{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.parquet', - engine="pyarrow") + filterbyriver.to_parquet( + f"{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.parquet", engine="pyarrow" + ) # Save XS as geopackage per river per HUC - filterbyriver.to_file(f'{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.gpkg', - driver='GPKG') - + filterbyriver.to_file(f"{os.getcwd()}/xs_data/huc_{huc_num}/{river_name}.gpkg", driver="GPKG") + return - + def save_crs_map(self): - ''' + """ Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage - + Args: None Return: None - Note: This saved pickle file will map each river @ HUC's inherited CRS for one to - analyze & reference. + Note: This saved pickle file will map each river @ HUC's inherited CRS for one to + analyze & reference. - ''' + """ for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items(): - # Generate data folder per HUC - if not os.path.exists(f'{os.getcwd()}/xs_data/crs_map'): - os.makedirs(f'{os.getcwd()}/xs_data/crs_map') + if not os.path.exists(f"{os.getcwd()}/xs_data/crs_map"): + os.makedirs(f"{os.getcwd()}/xs_data/crs_map") - # Generate map of the CRS to each river's geopandas per HUC - grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]['XS'].groupby(['river']) - for river_name in set(self.model_id2gpkg[(model_id, huc_num)]['XS']['river']): + # Generate map of the CRS to each river's geopandas per HUC + grouped_xslayers = self.model_id2gpkg[(model_id, huc_num)]["XS"].groupby(["river"]) + for river_name in set(self.model_id2gpkg[(model_id, huc_num)]["XS"]["river"]): filterbyriver = grouped_xslayers.get_group(river_name) - self.crs_dict[(huc_num, model_id)].update({river_name: f'ESPG: {str(filterbyriver.crs.to_epsg()) if \ - filterbyriver.crs.to_epsg() else None}, {filterbyriver.crs.name},'}) + self.crs_dict[(huc_num, model_id)].update( + { + river_name: f"ESPG: { + str(filterbyriver.crs.to_epsg()) if filterbyriver.crs.to_epsg() else None + }, {filterbyriver.crs.name}," + } + ) # Save map of inherited CRS to HUC, model_id, river name - with open(f'{os.getcwd()}/xs_data/crs_map/crs_mapping.pickle', 'wb') as handle: + with open(f"{os.getcwd()}/xs_data/crs_map/crs_mapping.pickle", "wb") as handle: pickle.dump(self.crs_dict, handle, protocol=pickle.HIGHEST_PROTOCOL) - + return def consolidate_id2xs_dfs(self): - ''' + """ Consolidate HEC-RAS models cross-sections featuring their corresponding IDs. - + Args: None Return: None Note: A set CRS standard is needed in order to consolidates all XS layers of all models - under a single dataframe to maintain a consistent CRS. This consolidation will check if the XS - dataframes have a consistent CRS if it does not then it will not consolidate the XS dataframes. + under a single dataframe to maintain a consistent CRS. This consolidation will check if the XS + dataframes have a consistent CRS if it does not then it will not consolidate the XS dataframes. As of 05/14/25, only CRS in this condition is considering the NAD83, but additional CRS can be added to this methods as new findings are made. - - ''' + + """ crs_list = [] for xs_df in self.xs_df_list: - if 'NAD83' in xs_df.crs.name or 'NAD_1983' in xs_df.crs.name: + if "NAD83" in xs_df.crs.name or "NAD_1983" in xs_df.crs.name: # Convert all GeoDataFrames in the list to the target CRS crs_list.append("EPSG:5070") else: @@ -511,18 +563,23 @@ def consolidate_id2xs_dfs(self): # Will consolidate ONLY if the CRS is consistent across XS geodpandas dataframes if len(set(crs_list)) == 1: target_crs = str(np.unique(crs_list)[0]) - self.consolidated_id2xs = geopandas.GeoDataFrame(pd.concat([xs_df.to_crs(target_crs) for xs_df in self.xs_df_list], - ignore_index=True)) - print(f"The consolidated XS geopandas dataframes now has a standardized CRS of:\n{self.consolidated_id2xs.crs.name}") + self.consolidated_id2xs = geopandas.GeoDataFrame( + pd.concat([xs_df.to_crs(target_crs) for xs_df in self.xs_df_list], ignore_index=True) + ) + print( + f"The consolidated XS geopandas dataframes now has a standardized CRS of:\n{self.consolidated_id2xs.crs.name}" + ) else: - print("Cannot consolidate XS geodpandas dataframes because the CRS is inconsistent across XS geodpandas dataframes.") - + print( + "Cannot consolidate XS geodpandas dataframes because the CRS is inconsistent across XS geodpandas dataframes." + ) + return def save_xsbyhuc_data(self, xs_data_type): - ''' + """ Consolidate HEC-RAS models cross-sections based on HUC & save to storage - + Args: xs_data_type (str): Cross-section data type to be saved either 'mip' or 'ble' cross-section type. Options: 'mip' or 'ble' @@ -530,29 +587,30 @@ def save_xsbyhuc_data(self, xs_data_type): Return: None Note: These saved parquet files will be the transformed CRS of all XS per HUC to ensure - a consistent standardized CRS. + a consistent standardized CRS. - ''' - unique_huc_nums = set(self.consolidated_id2xs['huc']) + """ + unique_huc_nums = set(self.consolidated_id2xs["huc"]) for huc_num in unique_huc_nums: - # Generate data folder per HUC - if not os.path.exists(f'{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}'): - os.makedirs(f'{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}') - + if not os.path.exists(f"{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}"): + os.makedirs(f"{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}") + # Filter consolidated XS geopanda dataframe by HUC - filterbyhuc = self.consolidated_id2xs[self.consolidated_id2xs['huc'] == huc_num] - + filterbyhuc = self.consolidated_id2xs[self.consolidated_id2xs["huc"] == huc_num] + # Save XS as geoparquet per HUC - filterbyhuc['thalweg'] = filterbyhuc['thalweg'].astype(str) - filterbyhuc['xs_max_elevation'] = filterbyhuc['xs_max_elevation'].astype(str) - filterbyhuc.to_parquet(f'{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}/huc_{huc_num}.parquet', - engine="pyarrow") - + filterbyhuc["thalweg"] = filterbyhuc["thalweg"].astype(str) + filterbyhuc["xs_max_elevation"] = filterbyhuc["xs_max_elevation"].astype(str) + filterbyhuc.to_parquet( + f"{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}/huc_{huc_num}.parquet", engine="pyarrow" + ) + # Save XS as geopackage per HUC - filterbyhuc.to_file(f'{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}/huc_{huc_num}.gpkg', - driver='GPKG') - - print(f'{xs_data_type}_{huc_num}') - + filterbyhuc.to_file( + f"{os.getcwd()}/xs_data/{xs_data_type}_{huc_num}/huc_{huc_num}.gpkg", driver="GPKG" + ) + + print(f"{xs_data_type}_{huc_num}") + return diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index ba640ed..15c6df6 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -38,7 +38,8 @@ dependencies = [ "h5py==3.13.0", "h5netcdf==1.6.1", "fsspec==2025.5.0", - + "dask==2025.5.1", + "netCDF4==1.7.2", ] [build-system] diff --git a/src/icefabric_tools/src/icefabric_tools/__init__.py b/src/icefabric_tools/src/icefabric_tools/__init__.py index de371af..27fadc5 100644 --- a/src/icefabric_tools/src/icefabric_tools/__init__.py +++ b/src/icefabric_tools/src/icefabric_tools/__init__.py @@ -1,4 +1,4 @@ from icefabric_tools._api import find_origin, load_hydrofabric, table_to_geopandas, to_geopandas -from icefabric_tools import rnr +from icefabric_tools.iceberg import rnr __all__ = ["find_origin", "load_hydrofabric", "rnr", "to_geopandas", "table_to_geopandas"] diff --git a/src/icefabric_tools/src/icefabric_tools/_api.py b/src/icefabric_tools/src/icefabric_tools/_api.py index cefb45f..1b7310a 100644 --- a/src/icefabric_tools/src/icefabric_tools/_api.py +++ b/src/icefabric_tools/src/icefabric_tools/_api.py @@ -23,12 +23,13 @@ def load_hydrofabric(catalog_settings: dict[str, str]) -> Catalog: """ return load_catalog("hydrofabric", **catalog_settings) + def table_to_geopandas( table: Table, row_filter: str | BooleanExpression = ALWAYS_TRUE, case_sensitive: bool | None = True, snapshot_id: int | None = None, - limit: int | None = None + limit: int | None = None, ) -> gpd.GeoDataFrame: """Converts a table to a geopandas dataframe @@ -61,10 +62,7 @@ def table_to_geopandas( return to_geopandas(df) -def to_geopandas( - df: pd.DataFrame, - crs: str = "EPSG:5070" -) -> gpd.GeoDataFrame: +def to_geopandas(df: pd.DataFrame, crs: str = "EPSG:5070") -> gpd.GeoDataFrame: """Converts the geometries in a pandas df to a geopandas dataframe Parameters @@ -91,7 +89,7 @@ def to_geopandas( return gpd.GeoDataFrame(df, geometry=geometry, crs=crs) -def find_origin(network_table: Table, identifier: str, id_type: str ="hl_uri") -> pd.DataFrame: +def find_origin(network_table: Table, identifier: str, id_type: str = "hl_uri") -> pd.DataFrame: """Find an origin point in the hydrofabric network. This function handles the case where multiple records match the identifier. diff --git a/src/icefabric_tools/src/icefabric_tools/rnr.py b/src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py similarity index 73% rename from src/icefabric_tools/src/icefabric_tools/rnr.py rename to src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py index d9ae9e0..e2546d7 100644 --- a/src/icefabric_tools/src/icefabric_tools/rnr.py +++ b/src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py @@ -38,35 +38,45 @@ def get_rnr_segment(catalog: Catalog, reach_id: str, output_file: str) -> gpd.Ge hydrolocations = catalog.load_table("hydrofabric.hydrolocations") origin_row = network.scan(row_filter=f"hf_id = {reach_id}").to_pandas() - mainstem_expression = EqualTo("hf_mainstem", origin_row['hf_mainstem'].values[0]) - hydroseq_expression = LessThanOrEqual("hydroseq", origin_row['hydroseq'].values[0]) + mainstem_expression = EqualTo("hf_mainstem", origin_row["hf_mainstem"].values[0]) + hydroseq_expression = LessThanOrEqual("hydroseq", origin_row["hydroseq"].values[0]) combined_filter = And(mainstem_expression, hydroseq_expression) # Find all streams with the same stream order # TODO Determine lakes to break segments mainstem_features = network.scan(row_filter=combined_filter).to_pandas() - segment_flowpaths = flowpaths.scan(row_filter=In("divide_id", mainstem_features["divide_id"].drop_duplicates().values)).to_pandas() - joined_df = pd.merge( - mainstem_features, - segment_flowpaths, - on="divide_id", - how="outer" - ) + segment_flowpaths = flowpaths.scan( + row_filter=In("divide_id", mainstem_features["divide_id"].drop_duplicates().values) + ).to_pandas() + joined_df = pd.merge(mainstem_features, segment_flowpaths, on="divide_id", how="outer") stream_order = joined_df[joined_df["hf_id"] == reach_id]["order"].values[0] filtered_flowpaths = segment_flowpaths[segment_flowpaths["order"] == stream_order] - filtered_poi_list = [int(_id) for _id in segment_flowpaths[segment_flowpaths["order"] == stream_order]["poi_id"].values if _id is not None] + filtered_poi_list = [ + int(_id) + for _id in segment_flowpaths[segment_flowpaths["order"] == stream_order]["poi_id"].values + if _id is not None + ] # Get full river network filtered_nexus_points = table_to_geopandas(table=nexus, row_filter=In("id", filtered_flowpaths["toid"])) - filtered_divides = table_to_geopandas(table=divides, row_filter=In("divide_id", filtered_flowpaths["divide_id"])) - filtered_divide_attr = divides_attr.scan(row_filter=In("divide_id", filtered_flowpaths["divide_id"])).to_pandas() + filtered_divides = table_to_geopandas( + table=divides, row_filter=In("divide_id", filtered_flowpaths["divide_id"]) + ) + filtered_divide_attr = divides_attr.scan( + row_filter=In("divide_id", filtered_flowpaths["divide_id"]) + ).to_pandas() filtered_flowpath_attr = flowpath_attr.scan(row_filter=In("id", filtered_flowpaths["id"])).to_pandas() - filtered_flowpath_attr_ml = flowpath_attr_ml.scan(row_filter=In("id", filtered_flowpaths["id"])).to_pandas() + filtered_flowpath_attr_ml = flowpath_attr_ml.scan( + row_filter=In("id", filtered_flowpaths["id"]) + ).to_pandas() filtered_pois = pois.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() filtered_hydrolocations = hydrolocations.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() filtered_flowpaths = to_geopandas(filtered_flowpaths) - filtered_network = network.scan(row_filter=In("id", np.concatenate([filtered_flowpaths["toid"].values, filtered_flowpaths["id"].values]))).to_pandas() - + filtered_network = network.scan( + row_filter=In( + "id", np.concatenate([filtered_flowpaths["toid"].values, filtered_flowpaths["id"].values]) + ) + ).to_pandas() layers = { "flowpaths": filtered_flowpaths, @@ -81,4 +91,4 @@ def get_rnr_segment(catalog: Catalog, reach_id: str, output_file: str) -> gpd.Ge "hydrolocations": filtered_hydrolocations, } for table, layer in layers.items(): - gpd.GeoDataFrame(layer).to_file(output_file, layer=table, driver='GPKG') + gpd.GeoDataFrame(layer).to_file(output_file, layer=table, driver="GPKG") diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/__init__.py b/src/icefabric_tools/src/icefabric_tools/icechunk/__init__.py new file mode 100644 index 0000000..c0ef9ae --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/__init__.py @@ -0,0 +1,6 @@ +from icefabric_tools.icechunk.icechunk_s3_module import ( + IcechunkS3Repo, + NGWPCLocations, + S3Path, + get_icechunk_data, +) diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py b/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py new file mode 100644 index 0000000..dadf8e5 --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py @@ -0,0 +1,289 @@ +""" +Moduile interacting with archival weather files + +Utilities for loading/supplying/interacting/virtualizing +archival weather files, mainly to interface with icechunk +""" + +import os +from enum import Enum + +import fsspec +import pandas as pd +import rioxarray as rxr +import xarray as xr +from tqdm import tqdm +from virtualizarr import open_virtual_dataset + +from icefabric_tools.icechunk import S3Path + + +class FileType(Enum): + """ + Archival weather file types + + Enum class for instantiating different archival weather file + formats. Used when virtualizing and collecting files. + """ + + GEOTIFF = ".tif" + NETCDF = ".nc" + + +def load_tiff_file(fp: str) -> xr.Dataset | xr.DataArray | list[xr.Dataset]: + """ + Loads a GEOTIFF + + Takes a local filepath to a TIFF and loads it into an Xarray + Dataset object. Could also return a Dataarray or list of + Datasets. + + Parameters + ---------- + fp : str + File path to the TIFF that will be returned as a dataset. + + Returns + ------- + xr.Dataset | xr.DataArray | list[xr.Dataset] + The Xarray representation of the TIFF file. + + Raises + ------ + FileNotFoundError + """ + if os.path.exists(fp) is False: + raise FileNotFoundError(f"Cannot find: {fp}") + ds = rxr.open_rasterio(fp) + return ds + + +def get_archival_weather_files( + loc: str | S3Path, file_type: FileType | None = None, manual_file_pattern: str | None = None +) -> list[str]: + """ + Collect files from a directory + + Given a directory in either local or S3 storage, return all files of a specific type or + matching a file pattern. + + Parameters + ---------- + loc : str | S3Path + Directory housing the files - could be a string for a local path, or an S3Path object + for files on an S3 bucket + file_type : FileType | None, optional + The file type to be collected - NETCDF, TIF, etc. Must be given if no manual pattern is + supplied. By default None + manual_file_pattern : str | None, optional + If supplied, will collect filepaths according to the file pattern instead of by + filetype. Should be parsable by an fsspec.glob() call. By default None + + Returns + ------- + list[str] + A list of all retrieved filepaths associated with the directory, file type and/or + manual file pattern. + + Raises + ------ + ValueError + """ + sort_prefix = "" + if type(loc) is S3Path: + sort_prefix = "s3://" + fs = fsspec.filesystem("s3") + else: + loc = os.path.abspath(loc) + fs = fsspec.implementations.local.LocalFileSystem() + + if manual_file_pattern: + print(manual_file_pattern) + files = fs.glob(f"{str(loc)}/{manual_file_pattern}") + else: + if not file_type: + raise ValueError("Must supply a file_type argument if no manual_file_pattern is given") + files = fs.glob(f"{str(loc)}/*{file_type.value}") + files = sorted([f"{sort_prefix}{f}" for f in files]) + return files + + +def extract_dates_from_archival_files( + file_paths: list[str], file_pattern: str, just_year: bool | None = False +) -> list[pd.DatetimeIndex] | list[int]: + """ + Pull dates out of list of file names + + Extracts and returns a sorted list of datetimes corresponding to the provided + list of filepaths. + + Parameters + ---------- + file_paths: list[str] + List of filepaths. File names should correspond to a datetime, and + should contain the datetime embedded in the filename. + file_pattern: str + Matching pattern used to extract the datetime. Format should match the files, + with an asterisk replacing the datetime section of the filename. + just_year: bool | None, optional + If supplied, will only extract year values in int form. + + Returns + ------- + list[DatetimeIndex] | list[int] + Sorted list of the DatetimeIndexes extracted from the filenames. Same + length and ordering as the list of filepaths. Could also just be years + as ints. + """ + pre, post = file_pattern.split("*")[0:2] + files = [fp.split("/")[-1] for fp in file_paths] + dates = [f.replace(pre, "").replace(post, "") for f in files] + if just_year: + date_dims = [pd.date_range(d, periods=1).year for d in sorted(dates)] + else: + date_dims = [pd.date_range(d, periods=1) for d in sorted(dates)] + return date_dims + + +def virtualize_datasets( + file_list: list[str], loadable_vars: list[str] | None = None, testing_file_quantity: int | None = None +) -> list[xr.Dataset]: + """ + Virtualize archival weather files + + Takes a list of archival weather filepaths and converts each to a virtual dataset. + NOTE: May take a very long time to process if the filelist is long or large. + + Parameters + ---------- + file_list : list[str] + List of archival weather filepaths, each of which will be converted to a virtual dataset. + loadable_vars : list[str] | None, optional + List of variables to open as lazy numpy/dask arrays instead of instances of + ManifestArray. By default None. + testing_file_quantity : int | None, optional + Include if you want to test the virtualization with a subset of files. + Only opens the number specified, starting with the first in the list. By default None + + Returns + ------- + list[xr.Dataset] + List of the virtualized datasets derived from the provided archival weather files. + """ + if testing_file_quantity: + file_list = file_list[:testing_file_quantity] + v_datasets = [] + for i in tqdm( + range(len(file_list)), + desc="Opening files as virtual datasets.", + unit="files", + ncols=125, + colour="#37B6BD", + ): + v_datasets.append( + open_virtual_dataset(filepath=file_list[i], indexes={}, loadable_variables=loadable_vars) + ) + return v_datasets + + +def add_time_dim_to_datasets( + timeless_datasets: list[xr.Dataset], + datetimes: list[pd.DatetimeIndex], + just_year: bool | None = False, + testing_file_quantity: int | None = None, +) -> list[xr.Dataset]: + """ + Add time dimension to a collection of data + + Expands each entry in a list of virtualized datasets with a single time dimension + + Parameters + ---------- + timeless_datasets : list[xr.Dataset] + List of virtualized datasets missing time dimensions. + datetimes : list[pd.DatetimeIndex] + List of the DatetimeIndexes that will be added onto ```timeless_datasets```. + Should be the same length and ordering. + just_year: bool | None, optional + Include if your datetimes list is only years in int format. Will add a 'year' dimension + instead. By default False. + testing_file_quantity : int | None, optional + Include if you want to only add the time dimension to a subset of the virtualized datasets. + Only opens the number specified, starting with the first in the list. + NOTE: Make sure the length of ```timeless_datasets``` and ```datetimes``` are equal after + accounting for this. By default None. + + Returns + ------- + list[xr.Dataset] + List of the virtualized datasets with newly added time dimensions. + """ + if testing_file_quantity: + datetimes = datetimes[:testing_file_quantity] + v_datasets = [ + d.expand_dims(year=t) if just_year else d.expand_dims(time=t) + for d, t in zip(timeless_datasets, datetimes, strict=False) + ] + return v_datasets + + +def virtualize_and_concat_archival_files_on_time( + location: str | S3Path, + file_date_pattern: str, + file_type: FileType | None = None, + manual_file_pattern: str | None = None, + just_year: bool | None = False, + loadable_vars: list[str] | None = None, + testing_file_quantity: int | None = None, +) -> xr.Dataset: + """ + Virtualize a collection of weather files and combine them + + Per a given local file directory or S3 bucket directory, collect every archival + weather file (with time data only in the filename) and virtualize and concatenate + the set on a time dimension. Produces a single virtualized dataset. + + Parameters + ---------- + lcoation : str | S3Path + Directory housing the files - could be a string for a local path, or an S3Path object + for files on an S3 bucket + file_date_pattern : str + Matching pattern used to extract the datetime. Format should match the files, + with an asterisk replacing the datetime section of the filename. + file_type: FileType | None, optional + The file type to be collected - NETCDF, TIF, etc. Must be given if no manual pattern is + supplied. By default None + manual_file_pattern : str | None, optional + If supplied, will collect filepaths according to the file pattern instead of by + filetype. Should be parsable by an fsspec.glob() call. By default None. + just_year: bool | None, optional + Include if your filenames only contain years. Will add a 'year' dimension + instead when virtualizing. By default False. + loadable_vars : list[str] | None, optional + List of dataset variables to open as lazy numpy/dask arrays when virtualizing, + instead of instances of ManifestArray. Leads to data duplication, but is necessary + in some cases. By default None. + testing_file_quantity : int | None, optional + Include if you want to test the virtualization with a subset files. + Only opens the number specified, starting with the first. Useful for virtualizing + smaller groups of files when testing or debugging. By default None. + + Returns + ------- + xr.Dataset + The fully time-concatenated, virtualized dataset. + """ + arch_files = get_archival_weather_files( + loc=location, file_type=file_type, manual_file_pattern=manual_file_pattern + ) + datetimes = extract_dates_from_archival_files(arch_files, file_date_pattern, just_year=just_year) + timeless_datasets = virtualize_datasets(arch_files, loadable_vars, testing_file_quantity) + time_added_datasets = add_time_dim_to_datasets( + timeless_datasets, datetimes, just_year, testing_file_quantity + ) + concat_dim = "year" if just_year else "time" + final_dataset = xr.concat( + time_added_datasets, dim=concat_dim, coords="minimal", compat="override", combine_attrs="override" + ) + return final_dataset diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py new file mode 100644 index 0000000..53a5d66 --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py @@ -0,0 +1,308 @@ +""" +NGWPC Icechunk interface module + +Module containing classes/methods pertaining +to S3 pathing and Icechunk repos +""" + +import subprocess +from enum import Enum + +import icechunk as ic +import xarray as xr +from icechunk.xarray import to_icechunk + +TOPO_BP = "surface/nws-topobathy" +TOPO_NOS = f"{TOPO_BP}/nws-nos-surveys" + + +class S3Path: + """ + Class representing an S3 path. + + Corresponds to an S3 bucket, prefix and region + + Parameters + ---------- + bucket: str + The bucket of the S3 path. + prefix: str + The S3 path (minus the bucket). + region: str + The S3 region the bucket/path belongs to. Defaults to 'us-east-1'. + """ + + bucket: str + prefix: str + region: str + + def __init__(self, bucket: str, prefix: str, region: str | None = "us-east-1"): + self.bucket = bucket + self.prefix = prefix + self.region = region + + def __str__(self): + """Returns the full S3 path""" + return f"s3://{self.bucket}/{self.prefix}" + + def partial_path(self): + """Returns the S3 path without the 'S3://' prefix""" + return f"{self.bucket}/{self.prefix}" + + +class NGWPCLocations(Enum): + """ + Important NGWPC S3 locations + + Enum class for instantiating S3Paths corresponding to the + icechunk stores, as well as the reference locations for virtualized + stores. + """ + + SNODAS_REF = ("ngwpc-forcing", "snodas_nc_v4") + SNODAS_V3 = ("ngwpc-forcing", "snodas_nc") + SNODAS_IC = ("hydrofabric-data", "forcing/snodas") + NLCD_REF = ("ngwpc-hydrofabric", "NLCD_Land_Cover_CONUS") + NLCD_IC = ("hydrofabric-data", "land-cover/NLCD-Land-Cover") + USGS_IC = ("ngwpc-hydrofabric", "usgs_observations") + TOPO_AK_10M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_alaska_10m") + TOPO_AK_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_alaska_30m") + TOPO_CONUS_ATL_GULF_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_conus_atlantic_gulf_30m") + TOPO_CONUS_PAC_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_conus_pacific_30m") + TOPO_GREAT_LAKES_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_great_lakes_30m") + TOPO_HA_10M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_hawaii_10m") + TOPO_HA_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_hawaii_30m") + TOPO_PR_USVI_10M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_pr_usvi_10m") + TOPO_PR_USVI_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_pr_usvi_30m") + TOPO_ALBEMARLE_SOUND_IC = ("hydrofabric-data", f"{TOPO_NOS}/Albemarle_Sound_NOS_NCEI") + TOPO_CHESAPEAKE_BAY_IC = ("hydrofabric-data", f"{TOPO_NOS}/Chesapeake_Bay_NOS_NCEI") + TOPO_MOBILE_BAY_IC = ("hydrofabric-data", f"{TOPO_NOS}/Mobile_Bay_NOS_NCEI") + TOPO_TANGIER_SOUND_IC = ("hydrofabric-data", f"{TOPO_NOS}/Tangier_Sound_NOS_NCEI") + + def __init__(self, bucket, prefix): + self.path = S3Path(bucket, prefix) + + +class IcechunkS3Repo: + """ + Class representing an S3 bucket icechunk store + + Parameters + ---------- + location: S3Path + The S3Path of the repo. + repo: ic.Repository + The icechunk repo, derived from the bucket, prefix, and region. S3 + credentials are provided from the environment. + virtual_chunks: list[ic.VirtualChunkContainer] | None + A list of virtual chunk containers corresponding to reference data + for virtualized stores. Allows icechunk to reference S3 locations + in virtualized datasets. + """ + + location: S3Path + repo: ic.Repository + virtual_chunks: list[ic.VirtualChunkContainer] | None + + def __init__(self, location: S3Path, virtual_chunk_mapping: list[dict[str, str]] | None = None): + self.location = location + self.virtual_chunks = self.gen_virtual_chunk_containers(virtual_chunk_mapping) + self.repo = self.open_repo() + + def open_repo(self) -> ic.Repository: + """ + Opens an icechunk repo + + Using the class instance parameters, open and assign an icechunk repo corresponding + to the setup (bucket, prefix, region, etc.) + + Returns + ------- + ic.Repository + Icechunk repo corresponding to the S3 bucket path defined in the instance + """ + storage = ic.s3_storage( + bucket=self.location.bucket, + prefix=self.location.prefix, + region=self.location.region, + from_env=True, + ) + config = ic.RepositoryConfig.default() + if self.virtual_chunks: + for vcc in self.virtual_chunks: + config.set_virtual_chunk_container(vcc) + credentials = ic.containers_credentials({self.location.bucket: ic.s3_credentials(from_env=True)}) + repo = ic.Repository.open_or_create(storage, config, credentials) + return repo + + def delete_repo(self, quiet: bool | None = False): + """ + Deletes the entire icechunk repo from S3. + + Parameters + ---------- + quiet : bool | None, optional + Suppresses AWS CLI output. By default False + """ + del_command = ["aws", "s3", "rm", str(self.location), "--recursive"] + if quiet: + del_command.append("--quiet") + subprocess.call(del_command) + print(f"Icechunk repo @ {str(self.location)} in its entirety was successfully deleted.") + + def gen_virtual_chunk_containers( + self, virtual_chunk_mapping: list[dict[str, str]] | None = None + ) -> list[ic.VirtualChunkContainer]: + """ + Create a list of virtual chunk containers + + Given a list of dictionaries mapping out virtual chunks, generate + and return a list of VirtualChunkContainers + + Parameters + ---------- + virtual_chunk_mapping : list[dict[str, str]] | None, optional + A list of dictionaries, each entry mapping out a single + virtual chunk definition. Should include a bucket and region. + By default None + + Returns + ------- + list[ic.VirtualChunkContainer] + A list of VirtualChunkContainers corresponding to the list of passed-in + dict mappings. + """ + v_chunks = None + if virtual_chunk_mapping: + v_chunks = [ + set_up_virtual_chunk_container(vc["bucket"], vc["region"]) for vc in virtual_chunk_mapping + ] + return v_chunks + + def create_session(self, read_only: bool | None = True, branch: str | None = "main") -> ic.Session: + """ + Open a session under the repo defined by an instance of IcechunkS3Repo + + Parameters + ---------- + read_only : bool | None, optional + Denotes if the session will be read-only or writable. By default True + branch : str | None, optional + Icechunk repo branch to be opened. By default "main" + + Returns + ------- + ic.Session + Icechunk repo session. Writable or read-only based on parameters. Branch + can be configured. + """ + if read_only: + return self.repo.readonly_session(branch) + return self.repo.writable_session(branch) + + def retrieve_dataset(self, read_only: bool | None = True, branch: str | None = "main") -> xr.Dataset: + """ + Returns the repo's store contents as an Xarray dataset + + Parameters + ---------- + read_only : bool | None, optional + Denotes if the session will be read-only or writable, by default True + branch : str | None, optional + Icechunk repo branch to be opened, by default "main" + + Returns + ------- + xr.Dataset + Xarray dataset representation of the Icechunk store + """ + session = self.create_session(read_only, branch) + ds = xr.open_zarr(session.store, consolidated=False, chunks={}) + return ds + + def write_dataset( + self, ds: xr.Dataset, commit: str, virtualized: bool | None = False, branch: str | None = "main" + ): + """ + Given a dataset, push a new commit alongisde the data to the icechunk store + + Parameters + ---------- + ds : xr.Dataset + Dataset to be commited to the icechunk store. + commit : str + Commit message that will accompany the dataset push. + virtualized : bool | None, optional + Designates if the dataset to be written is virtualized. Affects + how it's written to icechunk. By default False + branch : str | None, optional + Icechunk repo branch to be pushed. By default "main". + """ + session = self.create_session(read_only=False, branch=branch) + if virtualized: + ds.virtualize.to_icechunk(session.store) + else: + to_icechunk(ds, session) + snapshot = session.commit(commit) + print(f"Dataset is uploaded. Commit: {snapshot}") + + def append_virt_data_to_store( + self, vds: xr.Dataset, append_dim: str, commit: str, branch: str | None = "main" + ): + """ + Add new data to the store + + Given a virtualized dataset, push a new commit to append + data to an existing icechunk store. The data will be + appended on a specified dimension. + + Parameters + ---------- + vds : xr.Dataset + The virtualized dataset to be appended to the + existing icechunk store. + append_dim : str + What dimension the dataset will be appended on. Likely + time or year, etc. + commit : str + Commit message that will accompany the dataset addition. + branch : str | None, optional + Icechunk repo branch to be pushed. By default "main". + """ + session = self.create_session(read_only=False, branch=branch) + vds.virtualize.to_icechunk(session.store, append_dim=append_dim) + snapshot = session.commit(commit) + print(f"Dataset has been appended on the {append_dim} dimension. Commit: {snapshot}") + + +@staticmethod +def set_up_virtual_chunk_container(bucket: str, region: str) -> ic.VirtualChunkContainer: + """ + Create a virtual chunk container from a mapping + + Given an S3 bucket/region, generate and return a VirtualChunkContainer + so Icechunk can point to virtualized data inside S3 buckets. + + Parameters + ---------- + bucket : str + The S3 bucket the virtual chunk points to. + region : str + The region of the S3 bucket. + + Returns + ------- + ic.VirtualChunkContainer + A definition of a virtual chunk that the icechunk repo + uses to define access to virtualized data. + """ + return ic.VirtualChunkContainer( + name=bucket, url_prefix=f"s3://{bucket}/", store=ic.s3_store(region=region) + ) + + +@staticmethod +def get_icechunk_data(repo: NGWPCLocations) -> xr.Dataset: + """Return data from a designated NGWPCLocations Icechunk repo""" + ic_repo = IcechunkS3Repo(location=repo.path) + return ic_repo.retrieve_dataset() diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py new file mode 100644 index 0000000..285daa0 --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py @@ -0,0 +1,176 @@ +""" +Icechunk examples + +General example/testing code for Icechunk functionality. Covers archival weather +file virtualization/concatenation & uploading/appending/retrieving data to/from repos. +""" + +import icefabric_tools.icechunk.arch_weather_file_utils as awf_utils +import icefabric_tools.icechunk.nc_conv_utils as ncc_utils +from icefabric_tools.icechunk.icechunk_s3_module import ( + IcechunkS3Repo, + NGWPCLocations, + S3Path, +) + +ICECHUNK_STORES = [loc for loc in NGWPCLocations if "_IC" in loc.name] + + +def output_icechunk_stores(): + """Print out each icechunk store""" + for loc in ICECHUNK_STORES: + print(f"{loc.name}\n {loc.path}") + + +def snodas_virtualize_push_test( + new_ic_repo: S3Path, test_quant: int | None = None, clean_up: bool | None = False +): + """ + Push a collection of SNODAS NETCDFs + + Take the collection of SNODAS NETCDF files, virtualize and concatenate + them, and create a new IC repo to store the combined dataset. + + Specify clean-up to delete the icechunk repo after creation. + + NOTE: Take care to not overwrite an existing IC repo + """ + snodas_repo = IcechunkS3Repo( + location=new_ic_repo, virtual_chunk_mapping=[{"bucket": "ngwpc-forcing", "region": "us-east-1"}] + ) + nc_virt_ds = awf_utils.virtualize_and_concat_archival_files_on_time( + location=NGWPCLocations.SNODAS_REF.path, + file_date_pattern="zz_ssmv11034tS__T0001TTNATS*05HP001.nc", + file_type=awf_utils.FileType.NETCDF, + loadable_vars=["crs"], + testing_file_quantity=test_quant, + ) + snodas_repo.write_dataset(ds=nc_virt_ds, virtualized=True, commit="first commit") + print(snodas_repo.retrieve_dataset()) + if clean_up: + snodas_repo.delete_repo(quiet=True) + + +def snodas_yearly_virt_append_test( + new_ic_repo: S3Path, years: range, test_quant: int | None = None, clean_up: bool | None = False +): + """ + Incrementally push a by-year-collection of SNODAS NETCDFs + + Take the collection of SNODAS NETCDF files and virtualize/concatenate + them on a yearly basis. The year range is supplied. The data is virtualized + and concatted individually by year, and each year is appended to the IC repo. + + Specify clean-up to delete the icechunk repo after creation. + + NOTE: Take care to not overwrite an existing IC repo + """ + snodas_repo = IcechunkS3Repo( + location=new_ic_repo, virtual_chunk_mapping=[{"bucket": "ngwpc-forcing", "region": "us-east-1"}] + ) + for y in years: + nc_virt_ds = awf_utils.virtualize_and_concat_archival_files_on_time( + location=NGWPCLocations.SNODAS_REF.path, + file_date_pattern="zz_ssmv11034tS__T0001TTNATS*05HP001.nc", + manual_file_pattern=f"zz_ssmv11034tS__T0001TTNATS{y}*.nc", + file_type=awf_utils.FileType.NETCDF, + loadable_vars=["crs"], + testing_file_quantity=test_quant, + ) + if y == min(years): + snodas_repo.write_dataset(ds=nc_virt_ds, virtualized=True, commit="first commit") + else: + snodas_repo.append_virt_data_to_store( + vds=nc_virt_ds, append_dim="time", commit=f"appended new data from the year {y}" + ) + del nc_virt_ds + print(snodas_repo.retrieve_dataset()) + if clean_up: + snodas_repo.delete_repo(quiet=True) + + +def land_cover_virtualize_push_test( + new_ic_repo: S3Path, test_quant: int | None = None, clean_up: bool | None = False +): + """ + Push a collection of NLCD GEOTIFFs + + Take the collection of NLCD GEOTIFF files, virtualize and concatenate + them, and create a new IC repo to store the combined dataset. + + Specify clean-up to delete the icechunk repo after creation. + + NOTE: Take care to not overwrite an existing IC repo + """ + nlcd_repo = IcechunkS3Repo( + location=new_ic_repo, virtual_chunk_mapping=[{"bucket": "ngwpc-hydrofabric", "region": "us-east-1"}] + ) + nlcd_vrt_ds = awf_utils.virtualize_and_concat_archival_files_on_time( + location=NGWPCLocations.NLCD_REF.path, + file_date_pattern="Annual_NLCD_LndCov_*_CU_C1V0.tif", + file_type=awf_utils.FileType.GEOTIFF, + just_year=True, + testing_file_quantity=test_quant, + ) + nlcd_repo.write_dataset(ds=nlcd_vrt_ds, virtualized=True, commit="first commit") + print(nlcd_repo.retrieve_dataset()) + if clean_up: + nlcd_repo.delete_repo(quiet=True) + + +def topo_push_test(tiff_fp: str, new_ic_repo: S3Path, clean_up: bool | None = False): + """ + Push a topobathy GEOTIFF + + Take a topobathy GEOTIFF file and create a new IC repo + containing that file's contents. 'repo_dir' specifies + the IC repo name under the base topobathy S3 path + + Specify clean-up to delete the icechunk repo after creation. + + NOTE: Take care to not overwrite an existing IC repo + """ + topo_repo = IcechunkS3Repo(location=new_ic_repo) + topo_ds = awf_utils.load_tiff_file(tiff_fp) + topo_repo.write_dataset(ds=topo_ds, commit="first commit") + print(topo_repo.retrieve_dataset()) + if clean_up: + topo_repo.delete_repo(quiet=True) + + +def get_nc_by_year(years: range): + """Return all SNODAS reference files for a given year range""" + files = [] + for y in years: + print(y) + files += awf_utils.get_archival_weather_files( + loc=NGWPCLocations.SNODAS_REF.path, + file_type=awf_utils.FileType.NETCDF, + manual_file_pattern=f"zz_ssmv11034tS__T0001TTNATS{y}*.nc", + ) + return files + + +def conv_nc_by_year(year: str, test_quant: int | None = None): + """Convert original NETCDF3 SNODAS files into NETCDF4.""" + ncc_utils.convert_nc_files_from_s3( + orig=NGWPCLocations.SNODAS_V3.path, + dest=NGWPCLocations.SNODAS_REF.path, + manual_file_pattern=f"zz_ssmv11034tS__T0001TTNATS{year}*.nc", + testing_file_quantity=test_quant, + ) + + +if __name__ == "__main__": + snodas_virtualize_push_test( + new_ic_repo=S3Path("hydrofabric-data", "ic_testing/snodas_test"), test_quant=5, clean_up=True + ) + snodas_yearly_virt_append_test( + new_ic_repo=S3Path("hydrofabric-data", "ic_testing/snodas_yearly_append_test"), + years=range(2012, 2016), + test_quant=3, + clean_up=True, + ) + land_cover_virtualize_push_test( + new_ic_repo=S3Path("hydrofabric-data", "ic_testing/nlcd_test"), test_quant=5, clean_up=True + ) diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/nc_conv_utils.py b/src/icefabric_tools/src/icefabric_tools/icechunk/nc_conv_utils.py new file mode 100644 index 0000000..71476cb --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/nc_conv_utils.py @@ -0,0 +1,147 @@ +"""Utilities for converting NETCDF3 files into NETCDF4""" + +import os +import subprocess + +from netCDF4 import Dataset as NCDataset +from tqdm import tqdm + +import icefabric_tools.icechunk.arch_weather_file_utils as awf_utils +from icefabric_tools.icechunk import S3Path + + +def convert_files_to_netcdf4( + files: list[str], + new_dir: str | None = "", + fn_prefix: str | None = "", + fn_suffix: str | None = "", +): + """ + Convert collection of NCDF3 files to v4 + + Given a list of NETCDF3 files, convert them all to NETCDF4 and store them + in a new directory. Pre/suffixes can be specified as well for the new file + names. Will be stored alongside the old files, or in a sibling directory if + a dir name is provided. + + Parameters + ---------- + files : list[str] + The list of NETCDF3 filepaths to be converted. + new_dir : str | None, optional + If provided, will store the newly converted files + in a different directory. It will create the directory + in the same relative path as all the files to be converted. + By default "" + fn_prefix : str | None, optional + If provided, will prepend a prefix to the new file names. By default "". + fn_suffix : str | None, optional + If provided, will append a suffix to the new file names. By default "". + """ + if not new_dir and not fn_prefix and not fn_suffix: + fn_suffix = "_new" + for f in files: + dir_path, basename = os.path.split(f) + basename, ext = os.path.splitext(basename) + if not os.path.exists(os.path.join(dir_path, new_dir)): + os.makedirs(os.path.join(dir_path, new_dir)) + dir_path = os.path.join(dir_path, new_dir) + conv_nc(f, f"{dir_path}/{fn_prefix}{basename}{fn_suffix}{ext}") + + +def convert_nc_files_from_s3( + orig: S3Path, + dest: S3Path, + manual_file_pattern: str | None = None, + testing_file_quantity: int | None = None, +): + """ + Convert NETCDF3 collection from S3 location to v4 + + Given an S3 path populated with NETCDF3 files, sequentially + DL & convert them to NETCDF4, then re-upload them to a different + S3 path. All files created on local filesystems are deleted as the + process runs. + + Parameters + ---------- + orig : S3Path + S3 path containing the files to be converted + dest : S3Path + S3 path where the newly-converetd files will be + uploaded. + manual_file_pattern : str | None, optional + If given, will supply a manual file pattern to + when gathering the filepaths for conversion. May be + useful to only include subsets of files. By default None. + testing_file_quantity : int | None, optional + Include if you want to test the conversion with a subset files. + Only opens the number specified, starting with the first. By default None. + """ + temp_down_dir = os.path.join(os.getcwd(), ".tmp") + temp_conv_dir = os.path.join(os.getcwd(), ".tmp/conv") + nc3_file_list = awf_utils.get_archival_weather_files( + loc=orig, file_type=awf_utils.FileType.NETCDF, manual_file_pattern=manual_file_pattern + ) + if testing_file_quantity: + nc3_file_list = nc3_file_list[:testing_file_quantity] + os.makedirs(temp_down_dir, exist_ok=True) + os.makedirs(temp_conv_dir, exist_ok=True) + + for i in tqdm( + range(len(nc3_file_list)), + desc="Converting netcdf files from S3", + unit="files", + ncols=125, + colour="#37B6BD", + ): + nc3_file = nc3_file_list[i].removesuffix("s3://").split("/")[-1] + down_path = os.path.join(temp_down_dir, nc3_file) + conv_path = os.path.join(temp_conv_dir, "conv.nc") + subprocess.call(["aws", "s3", "cp", nc3_file_list[i], down_path, "--quiet"]) + conv_nc(down_path, conv_path, quiet=True) + subprocess.call(["aws", "s3", "cp", conv_path, f"{str(dest)}/{nc3_file}", "--quiet"]) + subprocess.call(["rm", down_path, "-f"]) + subprocess.call(["rm", conv_path, "-f"]) + + +def conv_nc(orig_file_path: str, new_file_path: str, quiet: bool | None = False): + """ + Given a NETCDF3-formatted file, convert it to NETCDF4. + + Parameters + ---------- + orig_file_path : str + NETCDF3 filepath. + new_file_path : str + Filepath where the converted file will end up. + quiet : bool | None, optional + Provide if no print/log statements are desired. + By default False. + """ + # Open the NetCDF3 file in read mode + nc3_data = NCDataset(orig_file_path, mode="r", format="NETCDF3_CLASSIC") + + # Create a new NetCDF4 file in write mode + nc4_data = NCDataset(new_file_path, mode="w", format="NETCDF4") + + # Copy global attributes + for attr_name in nc3_data.ncattrs(): + nc4_data.setncattr(attr_name, nc3_data.getncattr(attr_name)) + + # Copy dimensions + for dim_name, dim in nc3_data.dimensions.items(): + nc4_data.createDimension(dim_name, len(dim) if not dim.isunlimited() else None) + + # Copy variables + for var_name, var in nc3_data.variables.items(): + nc4_var = nc4_data.createVariable(var_name, var.datatype, var.dimensions) + nc4_var.setncatts({attr_name: var.getncattr(attr_name) for attr_name in var.ncattrs()}) + nc4_var[:] = var[:] + + # Close both files + nc3_data.close() + nc4_data.close() + + if not quiet: + print(f"Conversion from NetCDF3 to NetCDF4 completed: {new_file_path}") diff --git a/uv.lock b/uv.lock index 9f1a05a..e2280fc 100644 --- a/uv.lock +++ b/uv.lock @@ -23,7 +23,7 @@ wheels = [ [[package]] name = "aiobotocore" -version = "2.21.1" +version = "2.22.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohttp" }, @@ -34,9 +34,9 @@ dependencies = [ { name = "python-dateutil" }, { name = "wrapt" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/d2/dc/f5f872fb01ce37c09525cedc7ecfad7002ffe2a8a23f77d7d2c234399b51/aiobotocore-2.21.1.tar.gz", hash = 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1909fd9870a05de4c90c6245ba439b40ad9dc8fd Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Tue, 27 May 2025 16:49:21 -0400 Subject: [PATCH 43/97] Creating the `build/` dir (#13) * initial commit: adding all files for building local data warehouses with sql * patch: updating README.md to add pre-commit info * added to the readme of the icefabric_manage * updated to use s3 backends * updated docs * changing scripts to local_builds * changing scripts to builds * CLEAN: cleaning up the docs * PR COMMENTS: addressed quercoak comments * patch: file change wasnt uploaded --- .gitignore | 1 + NOTICE.txt | 8 ++ README.md | 20 +++- docs/img/icefabric_mission.png | Bin 0 -> 195061 bytes docs/index.md | 19 +-- docs/user_guide/icefabric_manage.md | 111 ++++++++++++++++++ docs/user_guide/index.md | 20 ++++ examples/quick_start.ipynb | 2 +- mkdocs.yml | 54 ++++++++- pyproject.toml | 6 - src/icefabric_manage/README.md | 6 + .../builds/build_hydrofabric.py | 41 +++++++ .../build_usgs_streamflow_observations.py | 27 +++++ src/icefabric_manage/pyproject.toml | 1 + .../src/icefabric_manage/_api.py | 60 ++-------- 15 files changed, 301 insertions(+), 75 deletions(-) create mode 100644 NOTICE.txt create mode 100644 docs/img/icefabric_mission.png create mode 100644 docs/user_guide/icefabric_manage.md create mode 100644 docs/user_guide/index.md create mode 100644 src/icefabric_manage/builds/build_hydrofabric.py create mode 100644 src/icefabric_manage/builds/build_usgs_streamflow_observations.py diff --git a/.gitignore b/.gitignore index ce6cd93..f9443dd 100644 --- a/.gitignore +++ b/.gitignore @@ -25,6 +25,7 @@ share/python-wheels/ .installed.cfg *.egg MANIFEST +.DS_Store # PyInstaller # Usually these files are written by a python script from a template diff --git a/NOTICE.txt b/NOTICE.txt new file mode 100644 index 0000000..4c987a6 --- /dev/null +++ b/NOTICE.txt @@ -0,0 +1,8 @@ +-- + +The Hydrofabric data used in this repo is credited to Lynker and its contributors + +Johnson, J. M. (2022). National Hydrologic Geospatial Fabric (hydrofabric) for the Next Generation (NextGen) Hydrologic Modeling Framework, +HydroShare http://www.hydroshare.org/resource/129787b468aa4d55ace7b124ed27dbde + +-- diff --git a/README.md b/README.md index 1e2efd3..dc6db8e 100644 --- a/README.md +++ b/README.md @@ -7,10 +7,20 @@ An [Apache Iceberg](https://py.iceberg.apache.org/) implementation of the Hydrofabric to disseminate continental hydrologic data -#### Credits: - -Lynker and its contributors +### Getting Started +This repo is managed through [UV](https://docs.astral.sh/uv/getting-started/installation/) and can be installed through: +```sh +uv venv +source .venv/bin/activate +uv sync ``` -Johnson, J. M. (2022). National Hydrologic Geospatial Fabric (hydrofabric) for the Next Generation (NextGen) Hydrologic Modeling Framework, -HydroShare http://www.hydroshare.org/resource/129787b468aa4d55ace7b124ed27dbde + +### Development +To ensure that icefabric follows the specified structure, be sure to install the local dev dependencies and run `pre-commit install` + +### Documentation +To build the user guide documentation for Icefabric locally, run the following commands: +```sh +uv pip install ".[docs]" +mkdocs serve ``` diff --git a/docs/img/icefabric_mission.png b/docs/img/icefabric_mission.png new file mode 100644 index 0000000000000000000000000000000000000000..3a3ffc5ae1905589bdb6c21cff581f4f9c69a532 GIT binary patch literal 195061 zcmd4&byyrvvj7TX2_7IJSQdA;puwHs5C{&7yUXH%;2MGlcY?dSE$;5NEbgwC9C_dK zeBUqU+&}L;Guyqb(^b`FT@#`xFNuyqfC2*pgDx#4rVIn~${z*>{w>n$my(6S`xdJx8qI_$^m!-qnu?AT z8S`^Esj&E$FWv9emBhq^G{#l2Ud_VwX<{l`g=HDcqF2%s>>G5v-fLF 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CJ*r{= literal 0 HcmV?d00001 diff --git a/docs/index.md b/docs/index.md index 000ea34..d400578 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,17 +1,6 @@ -# Welcome to MkDocs +# Welcome to Icefabric -For full documentation visit [mkdocs.org](https://www.mkdocs.org). +!!! warning "In Progress" + These docs are a work in progress and will continously be updated -## Commands - -* `mkdocs new [dir-name]` - Create a new project. -* `mkdocs serve` - Start the live-reloading docs server. -* `mkdocs build` - Build the documentation site. -* `mkdocs -h` - Print help message and exit. - -## Project layout - - mkdocs.yml # The configuration file. - docs/ - index.md # The documentation homepage. - ... # Other markdown pages, images and other files. +This is the local build of the docs diff --git a/docs/user_guide/icefabric_manage.md b/docs/user_guide/icefabric_manage.md new file mode 100644 index 0000000..178a9b3 --- /dev/null +++ b/docs/user_guide/icefabric_manage.md @@ -0,0 +1,111 @@ +# Table Building Guide + +## Overview + +IceFabric Manage is a Python package that simplifies building Apache Iceberg catalogs from Parquet files + +## Architecture + +The system consists of two main components: + +1. **Core API** (`src/icefabric_manage/_api.py`) - Contains the table building logic +2. **Build Scripts** (`builds/`) - Orchestrates the catalog creation process + +## How It Works + +!!! info "Build Process Overview" + The system automatically converts Parquet files into queryable Iceberg tables through a simple directory scan and conversion process. + +### The Build Process + +1. **Create Catalog** - Sets up a SQLite-backed Apache Iceberg catalog +2. **Scan Directory** - Finds all `.parquet` files in the specified data directory +3. **Create Tables** - For each Parquet file, creates a corresponding Iceberg table +4. **Populate Data** - Appends the Parquet data to the newly created Iceberg tables + +### File Structure + +!!! note "File Hierarchy" + The below files only show the hydrofabric. This logic can be extended to any tabular data managed by Apache Iceberg. + +```text +project/ +├── src/icefabric_manage/ +│ └── _api.py # Core table building functions +├── build_hydrofabric.py # Main build script +└── data/ + ├── warehouse/ # Iceberg catalog storage (auto-created) + │ └── pyiceberg_catalog.db + └── hydrofabric/ # Source Parquet files + ├── divides.parquet + ├── nexus.parquet + └── ... +``` + +## Usage + +### Prerequisites + +!!! warning "Important Requirements" + - The `icefabric_manage` package must be installed through `uv sync` + - There must be parquet files locally to build from. These files are to be downloaded from the NGWPC S3 bucket @ `s3://hydrofabric-data/icefabric/` and placed in `src/icefabric_manage/data/` + +### Running the Build + +To build a specific table, one must be working/CD into the `scripts/` directory +```bash +python build_hydrofabric.py +``` + +!!! success "Expected Output" + ```text + Created hydrofabric namespace + Build successful + ``` + +### What Happens During Build + +1. **Warehouse Setup** - Creates `../data/warehouse/` directory if it doesn't exist +2. **Catalog Configuration** - Sets up SQLite catalog with file-based storage +3. **Namespace Creation** - Creates the `hydrofabric` namespace if it doesn't exist +4. **Table Creation** - For each `.parquet` file in `../data/hydrofabric/`: + - Uses the filename (without extension) as the table name + - Reads the Parquet schema and creates matching Iceberg table + - Appends all data from the Parquet file to the Iceberg table + - Skips files if tables already exist + +## Configuration + +### Catalog Settings + +The build script uses these default settings: + +```python +catalog_settings = { + "type": "sql", # SQLite-backed catalog + "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", # Database location + "warehouse": f"file://{warehouse_path}", # Data storage location +} +``` + +In most functions, it's predefined to the user's `/tmp` dir since it's the same per each user's machine + +## Customization + +The following customizations can be done to update where/what is being added to the PyIceberg table + +### Changing Data Directory + +Modify the `data_dir` path in `build_hydrofabric.py`: + +```python +data_dir = Path("/path/to/your/parquet/files") +``` + +### Different Namespace + +Change the namespace name: + +```python +namespace = "your_namespace" +``` diff --git a/docs/user_guide/index.md b/docs/user_guide/index.md new file mode 100644 index 0000000..cf57a98 --- /dev/null +++ b/docs/user_guide/index.md @@ -0,0 +1,20 @@ +# Icefabric + +## The mission + +![A diagram describing the icefabric mission](../img/icefabric_mission.png) + +The idea for the icefabric came from the need to version control datasets for the National Water Model 4.0. There were many different file formats, and hydrofabric versions, but the need for an [Apache Iceberg](https://iceberg.apache.org/) style backend was realized. The name itself, icefabric, is a reference to this. + +Thus, this repo is a workspace for all icefabric assembly, tranformation, and dissemination. + +The repo is structured in the following workspace packages: + +### Icefabric Manage +The repo for building a local version of the icefabric and updating cloud stores + +### Icefabric Tools +The repo for all compute services built on the icefabric + +### Icefabric API +The interface between the icefabric and outside entities diff --git a/examples/quick_start.ipynb b/examples/quick_start.ipynb index 6980cf7..6e18088 100644 --- a/examples/quick_start.ipynb +++ b/examples/quick_start.ipynb @@ -1065,7 +1065,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.2" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/mkdocs.yml b/mkdocs.yml index c97182f..0e4fee3 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1 +1,53 @@ -site_name: My Docs +site_name: Icefabric +site_description: A version controlled method for storing multi-dimensional and tabular data through Pyiceberg and icechunk +repo_url: https://github.com/NGWPC/icefabric +repo_name: NGWPC/icefabric +theme: + name: material + features: + - navigation.footer + - navigation.indexes + - navigation.sections + - navigation.tabs + - navigation.top + - navigation.tracking + - search.suggest + icon: + repo: fontawesome/brands/github + palette: + - scheme: light blue + toggle: + icon: material/brightness-7 + name: Switch to dark mode + - scheme: slate + toggle: + icon: material/brightness-4 + name: Switch to light mode + +markdown_extensions: + - def_list + - pymdownx.tasklist: + custom_checkbox: true + - pymdownx.arithmatex: + generic: true + - pymdownx.highlight: + anchor_linenums: true + - pymdownx.inlinehilite + - pymdownx.snippets + - admonition + - footnotes + - pymdownx.details + - pymdownx.superfences + - pymdownx.mark + - attr_list + - md_in_html + +plugins: + - search + +nav: + - Home: + - index.md + - User Guide: + - user_guide/index.md + - Icefabric Manage: user_guide/icefabric_manage.md diff --git a/pyproject.toml b/pyproject.toml index e5aa895..ef140c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,13 +24,7 @@ dependencies = [] [project.optional-dependencies] docs = [ - "pillow", - "cairosvg", "mkdocs-material", - "mkdocs-git-committers-plugin", - "mkdocs-material-extensions", - "mkdocs-git-authors-plugin", - "mkdocs-git-revision-date-localized-plugin", ] [tool.uv.sources] diff --git a/src/icefabric_manage/README.md b/src/icefabric_manage/README.md index e0d26b6..dbb02f0 100644 --- a/src/icefabric_manage/README.md +++ b/src/icefabric_manage/README.md @@ -1,3 +1,9 @@ # Icefabric Manage A repo which contains scripts for building the icefabric from scratch + +### How to build locally +- The `icefabric_manage` package must be installed through `uv sync` +- There must be parquet files locally to build from. These files are to be downloaded from the NGWPC S3 bucket @ `s3://hydrofabric-data/icefabric/` and placed in `src/icefabric_manage/data/` + +Once you have parquet files locally in the `data/` dir, you can convert them to an icefabric warehouse through a build script in `scripts/` or make your own! diff --git a/src/icefabric_manage/builds/build_hydrofabric.py b/src/icefabric_manage/builds/build_hydrofabric.py new file mode 100644 index 0000000..e438391 --- /dev/null +++ b/src/icefabric_manage/builds/build_hydrofabric.py @@ -0,0 +1,41 @@ +from pathlib import Path + +from dotenv import load_dotenv +from pyiceberg.catalog import load_catalog + +from icefabric_manage import build + +load_dotenv() + +if __name__ == "__main__": + warehouse_path = Path("/tmp/warehouse") + warehouse_path.mkdir(exist_ok=True) + catalog_settings = { + "type": "sql", + "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", + "warehouse": f"file://{warehouse_path}", + } + + namespace = "hydrofabric" + catalog = load_catalog(namespace, **catalog_settings) + layers = [ + "divide-attributes", + "divides", + "flowpath-attributes-ml", + "flowpath-attributes", + "flowpaths", + "hydrolocations", + "lakes", + "network", + "nexus", + "pois", + ] + for layer in layers: + print(f"building layer: {layer}") + build( + catalog=catalog, + parquet_file=f"s3://hydrofabric-data/icefabric/hydrofabric/{layer}.parquet", + namespace=namespace, + table_name=layer, + ) + print(f"Build successful. Files written into metadata store @ {warehouse_path}") diff --git a/src/icefabric_manage/builds/build_usgs_streamflow_observations.py b/src/icefabric_manage/builds/build_usgs_streamflow_observations.py new file mode 100644 index 0000000..f112418 --- /dev/null +++ b/src/icefabric_manage/builds/build_usgs_streamflow_observations.py @@ -0,0 +1,27 @@ +from pathlib import Path + +from dotenv import load_dotenv +from pyiceberg.catalog import load_catalog + +from icefabric_manage import build + +load_dotenv() + +if __name__ == "__main__": + warehouse_path = Path("/tmp/warehouse") + warehouse_path.mkdir(exist_ok=True) + catalog_settings = { + "type": "sql", + "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", + "warehouse": f"file://{warehouse_path}", + } + + namespace = "observations" + catalog = load_catalog(namespace, **catalog_settings) + build( + catalog=catalog, + parquet_file="s3://hydrofabric-data/icefabric/observations/usgs_hourly.parquet", + namespace=namespace, + table_name="usgs_hourly", + ) + print(f"Build successful. Files written into metadata store @ {warehouse_path}") diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index 4df9884..3a5217c 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -25,6 +25,7 @@ dependencies = [ "s3fs==2025.5.0", "pyarrow==20.0.0", "pyiceberg[sql-sqlite]==0.9.0", + "python-dotenv==1.1.0" ] [build-system] diff --git a/src/icefabric_manage/src/icefabric_manage/_api.py b/src/icefabric_manage/src/icefabric_manage/_api.py index ad6f7aa..7d61784 100644 --- a/src/icefabric_manage/src/icefabric_manage/_api.py +++ b/src/icefabric_manage/src/icefabric_manage/_api.py @@ -1,45 +1,10 @@ """Contains all api functions that can be called outside of the icefabric_manage package""" -from pathlib import Path - import pyarrow.parquet as pq from pyiceberg.catalog import Catalog -def _add_parquet_to_catalog(catalog: Catalog, file_path: Path, table_name: str): - """Adding a parquet file to the hydrofabric catalog - - Parameters - ---------- - catalog : Catalog - A PyIceberg catalog - file_path : Path - The path to the parquet file - table_name : str - The table name that's wanted - - Returns - ------- - Table - A PyIceberg table - - Raises - ------ - FileNotFoundError - The parquet file given doesn't exist - """ - if file_path.exists(): - arrow_table = pq.read_table(file_path) - iceberg_table = catalog.create_table( - f"hydrofabric.{table_name}", - schema=arrow_table.schema, - ) - iceberg_table.append(arrow_table) - else: - raise FileNotFoundError(f"Cannot find file: {file_path}") - - -def build(catalog: Catalog, file_dir: Path) -> None: +def build(catalog: Catalog, parquet_file: str, namespace: str, table_name: str) -> None: """Builds the hydrofabric catalog based on the .pyiceberg.yaml config and defined parquet files. Parameters @@ -49,15 +14,16 @@ def build(catalog: Catalog, file_dir: Path) -> None: file_dir : Path The path to the parquet files to add into the iceberg catalog """ - if not any(ns == ("hydrofabric",) for ns in catalog.list_namespaces()): - catalog.create_namespace("hydrofabric") - print("Created 'hydrofabric' namespace") - - parquet_files = list(file_dir.glob("*.parquet")) + if not any(ns == (namespace,) for ns in catalog.list_namespaces()): + catalog.create_namespace(namespace) + print(f"Created {namespace} namespace") - for parquet_file in parquet_files: - table_name = parquet_file.stem # Get filename without extension - if catalog.table_exists(f"hydrofabric.{table_name}"): - print(f"Table {table_name} already exists. Skipping build") - else: - _add_parquet_to_catalog(catalog, parquet_file, table_name) + if catalog.table_exists(f"{namespace}.{table_name}"): + print(f"Table {table_name} already exists. Skipping build") + else: + arrow_table = pq.read_table(parquet_file) + iceberg_table = catalog.create_table( + f"{namespace}.{table_name}", + schema=arrow_table.schema, + ) + iceberg_table.append(arrow_table) From facc38c51bc1b18ed9863a2b4bfea7e64d2be060 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Wed, 28 May 2025 09:09:36 -0400 Subject: [PATCH 44/97] Observational Streamflow API (#14) * initial commit: adding all files for building local data warehouses with sql * patch: updating README.md to add pre-commit info * added to the readme of the icefabric_manage * added fast-api app (initial commit) * updated to use s3 backends * updated docs * created API endpoint for streamflow data * updated datetime, removed unused files, added docs * Created a docker container to run the fastapi icefabric locally: * added local tests for the streamflow router, sample data, and moved the main.py file to be in app/ * added info for snapshot in API * finished router tests, updated missing paths and docs * PATCH: updating docker CMD --- .gitignore | 2 + README.md | 22 +- docker/Dockerfile.api | 42 + docker/compose.yaml | 9 + docs/user_guide/icefabric_api.md | 182 + docs/user_guide/icefabric_manage.md | 184 +- docs/user_guide/icefabric_tools.md | 0 docs/user_guide/index.md | 13 +- mkdocs.yml | 2 + pyproject.toml | 14 +- src/icefabric_api/app/__init__.py | 0 src/icefabric_api/app/conftest.py | 23 + src/icefabric_api/app/main.py | 40 + src/icefabric_api/app/routers/__init__.py | 0 .../streamflow_observations/__init__.py | 0 .../streamflow_observations/conftest.py | 18 + .../routers/streamflow_observations/router.py | 343 ++ .../streamflow_observations/test_router.py | 86 + src/icefabric_api/app/test_main.py | 4 + src/icefabric_api/pyproject.toml | 11 + ...ta_from_20211231_1400_to_20220101_1400.csv | 26 + ...rom_20211231_1400_to_20220101_1400.parquet | Bin 0 -> 3415 bytes src/icefabric_manage/pyproject.toml | 1 - .../scripts/build_hydrofabric.py | 41 + .../build_usgs_streamflow_observations.py | 27 + src/icefabric_tools/pyproject.toml | 1 - uv.lock | 3114 +++++++++-------- 27 files changed, 2642 insertions(+), 1563 deletions(-) create mode 100644 docker/Dockerfile.api create mode 100644 docker/compose.yaml create mode 100644 docs/user_guide/icefabric_api.md create mode 100644 docs/user_guide/icefabric_tools.md create mode 100644 src/icefabric_api/app/__init__.py create mode 100644 src/icefabric_api/app/conftest.py create mode 100644 src/icefabric_api/app/main.py create mode 100644 src/icefabric_api/app/routers/__init__.py create mode 100644 src/icefabric_api/app/routers/streamflow_observations/__init__.py create mode 100644 src/icefabric_api/app/routers/streamflow_observations/conftest.py create mode 100644 src/icefabric_api/app/routers/streamflow_observations/router.py create mode 100644 src/icefabric_api/app/routers/streamflow_observations/test_router.py create mode 100644 src/icefabric_api/app/test_main.py create mode 100644 src/icefabric_api/pyproject.toml create mode 100644 src/icefabric_api/tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv create mode 100644 src/icefabric_api/tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.parquet create mode 100644 src/icefabric_manage/scripts/build_hydrofabric.py create mode 100644 src/icefabric_manage/scripts/build_usgs_streamflow_observations.py diff --git a/.gitignore b/.gitignore index f9443dd..663065c 100644 --- a/.gitignore +++ b/.gitignore @@ -174,6 +174,8 @@ cython_debug/ # PyPI configuration file .pypirc +Pipfile + .qodo _version.py data/ diff --git a/README.md b/README.md index dc6db8e..3c37794 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,25 @@ source .venv/bin/activate uv sync ``` +### Running the API locally +To run the API locally, ensure your `.env` file in your project root has the right credentials, then run +```sh +uv pip install -e src/icefabric_api +cd src/icefabric_api +python -m app.main +``` +This should spin up the API services + +### Building the API through Docker +To run the API locally with Docker, ensure your `.env` file in your project root has the right credentials, then run +```sh +docker compose -f docker/compose.yaml build --no-cache +docker compose -f docker/compose.yaml up +``` +This should spin up the API services +``` + + ### Development To ensure that icefabric follows the specified structure, be sure to install the local dev dependencies and run `pre-commit install` @@ -22,5 +41,6 @@ To ensure that icefabric follows the specified structure, be sure to install the To build the user guide documentation for Icefabric locally, run the following commands: ```sh uv pip install ".[docs]" -mkdocs serve +mkdocs serve -a localhost:8080 ``` +Docs will be spun up at localhost:8080/ diff --git a/docker/Dockerfile.api b/docker/Dockerfile.api new file mode 100644 index 0000000..31c449f --- /dev/null +++ b/docker/Dockerfile.api @@ -0,0 +1,42 @@ +FROM python:3.12-slim + +# Install system dependencies including GDAL +RUN apt-get update && apt-get install -y \ + curl \ + gdal-bin \ + libgdal-dev \ + gcc \ + g++ \ + && rm -rf /var/lib/apt/lists/* + +# Set GDAL environment variables +ENV GDAL_CONFIG=/usr/bin/gdal-config +ENV CPLUS_INCLUDE_PATH=/usr/include/gdal +ENV C_INCLUDE_PATH=/usr/include/gdal + +# Install curl for UV installation +RUN apt-get update && apt-get install -y curl + +# Install UV properly by copying from the official image +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + +# Set the working directory +WORKDIR /app + +# Copy the content of the build context to /app +COPY ../ /app + +RUN uv venv +ENV PATH="/app/.venv/bin:$PATH" + +# Install the package in development mode +RUN uv sync +RUN uv pip install -e src/icefabric_api + +WORKDIR /app/src/icefabric_manage/scripts +RUN python build_hydrofabric.py +RUN python build_usgs_streamflow_observations.py + +# Run the API +WORKDIR /app/src/icefabric_api +CMD ["python", "-m", "app.main"] diff --git a/docker/compose.yaml b/docker/compose.yaml new file mode 100644 index 0000000..19cd5b7 --- /dev/null +++ b/docker/compose.yaml @@ -0,0 +1,9 @@ +services: + myapp: + build: + context: ../ + dockerfile: docker/Dockerfile.api + ports: + - "8000:8000" + env_file: + - ../.env diff --git a/docs/user_guide/icefabric_api.md b/docs/user_guide/icefabric_api.md new file mode 100644 index 0000000..ed94598 --- /dev/null +++ b/docs/user_guide/icefabric_api.md @@ -0,0 +1,182 @@ +# Icefabric API Guide + +## Overview + +The Icefabric API is a FastAPI-based service that provides access to EDFS data stored in Apache Iceberg format. The API offers multiple data export formats and comprehensive metadata endpoints for hydrology and water resources applications. + +## Architecture + +The API consists of several key components: + +1. **Main Application** (`app/main.py`) - FastAPI application with health checks and router configuration +2. **Data Routers** - Handles all data endpoints. Currently only working with streamflow observations +3. **Apache Iceberg Backend** - SQLite-backed catalog stored in `/tmp/warehouse` built using `icefabric_manage` + +### Running the API locally +To run the API locally, ensure your `.env` file in your project root has the right credentials, then run +```sh +uv pip install -e src/icefabric_api +cd src/icefabric_api +python -m app.main +``` +This should spin up the API services + +### Building the API through Docker +To run the API locally with Docker, ensure your `.env` file in your project root has the right credentials, then run +```sh +docker compose -f docker/compose.yaml build --no-cache +docker compose -f docker/compose.yaml up +``` +This should spin up the API services + +## How It Works + +### Data Flow + +1. **Request Processing** - Validates data source and identifier parameters +2. **Data Filtering** - Applies optional date range filters to Iceberg tables +3. **Format Conversion** - Exports data in requested format (CSV/Parquet) +4. **Response Generation** - Returns data with appropriate headers and metadata + +### Supported Data Sources + +#### Observations +Currently supports: +- **USGS** - United States Geological Survey hourly streamflow data + +!!! note "Data Storage" + All data is stored as Apache Iceberg tables in a SQLite-backed catalog locally built at `/tmp/warehouse/pyiceberg_catalog.db` + +## Usage Examples + +### Python Client + +```python +import requests +import pandas as pd +from io import StringIO, BytesIO + +# Base URL +base_url = "http://localhost:8000/v1/streamflow_observations" + +# Get station information +response = requests.get(f"{base_url}/usgs/info", params={"identifier": "01031500"}) +info = response.json() +print(f"Station has {info['total_records']} records") + +# Download CSV data +csv_response = requests.get( + f"{base_url}/usgs/csv", + params={ + "identifier": "01031500", + "start_date": "2023-01-01T00:00:00", + "end_date": "2023-01-31T23:59:59" + } +) +df_csv = pd.read_csv(StringIO(csv_response.text)) + +# Download Parquet data (more efficient for large datasets) +parquet_response = requests.get( + f"{base_url}/usgs/parquet", + params={ + "identifier": "01031500", + "start_date": "2023-01-01T00:00:00" + } +) +df_parquet = pd.read_parquet(BytesIO(parquet_response.content)) +``` + +## Performance Considerations + +### Data Format Recommendations + +| Dataset Size | Recommended Format | Reason | +|-------------|-------------------|---------| +| < 50,000 records | CSV | Simple, widely supported | +| > 50,000 records | Parquet | Better compression, faster processing | +| > 200,000 records | Parquet + date filters | Reduced data transfer | + +## Configuration + +### Catalog Settings + +The default catalog is pointing to a local instance. To build this catalog, one must use the build scripts in `icefabric_manage` + +```python +catalog_settings = { + "type": "sql", + "uri": "sqlite:////tmp/warehouse/pyiceberg_catalog.db", + "warehouse": "file:///tmp/warehouse", +} +``` + +## Development + +### Running the API + +```bash +# Install dependencies +uv sync + +# Start development server +python src/icefabric_api/app/main.py +``` + +### Adding New Data Sources + +To add a new data source, update the configuration in your router: + +Below is an example for the observations router + +```python +class DataSource(str, Enum): + USGS = "usgs" + NEW_SOURCE = "new_source" # Add new source + +# Add configuration +DATA_SOURCE_CONFIG = { + DataSource.NEW_SOURCE: { + "namespace": "observations", + "table": "new_source_table", + "time_column": "timestamp", + "units": "m³/s", + "description": "New data source description", + }, +} +``` + +## API Documentation + +### Interactive Documentation + +The API provides interactive documentation at: + +- **Swagger UI**: `http://localhost:8000/docs` +- **ReDoc**: `http://localhost:8000/redoc` + +### OpenAPI Schema + +Access the OpenAPI schema at: `http://localhost:8000/openapi.json` + + +### Verification + +Test your API setup: + +```bash + +# List available sources +curl http://localhost:8000/v1/streamflow_observations/sources + +# Test with a known identifier +curl "http://localhost:8000/v1/streamflow_observations/usgs/available?limit=1" + +# Get a CSV reponse +curl "http://localhost:8000/v1/streamflow_observations/usgs/csv?identifier=01010000&include_headers=true" + +# Fitler based on timestamps +curl "http://localhost:8000/v1/streamflow_observations/usgs/csv?identifier=01010000&start_date=2021-12-31T14%3A00%3A00&end_date=2022-01-01T14%3A00%3A00&include_headers=true" + +# Get a Parquet Response +curl "http://localhost:8000/v1/streamflow_observations/usgs/parquet\?identifier\=01010000\&start_date\=2021-12-31T14%3A00%3A00\&end_date\=2022-01-01T14%3A00%3A00" -o "output.parquet" +``` diff --git a/docs/user_guide/icefabric_manage.md b/docs/user_guide/icefabric_manage.md index 178a9b3..2199631 100644 --- a/docs/user_guide/icefabric_manage.md +++ b/docs/user_guide/icefabric_manage.md @@ -2,7 +2,7 @@ ## Overview -IceFabric Manage is a Python package that simplifies building Apache Iceberg catalogs from Parquet files +IceFabric Manage is a Python package that simplifies building Apache Iceberg catalogs from Parquet files stored on S3 or locally. ## Architecture @@ -14,19 +14,19 @@ The system consists of two main components: ## How It Works !!! info "Build Process Overview" - The system automatically converts Parquet files into queryable Iceberg tables through a simple directory scan and conversion process. + The system converts individual Parquet files (local or S3) into queryable Iceberg tables through a simple file-by-file conversion process. ### The Build Process -1. **Create Catalog** - Sets up a SQLite-backed Apache Iceberg catalog -2. **Scan Directory** - Finds all `.parquet` files in the specified data directory -3. **Create Tables** - For each Parquet file, creates a corresponding Iceberg table +1. **Create Catalog** - Sets up a SQLite-backed Apache Iceberg catalog in `/tmp/warehouse` +2. **Create Namespace** - Creates the specified namespace if it doesn't exist +3. **Create Tables** - For each specified Parquet file, creates a corresponding Iceberg table 4. **Populate Data** - Appends the Parquet data to the newly created Iceberg tables ### File Structure -!!! note "File Hierarchy" - The below files only show the hydrofabric. This logic can be extended to any tabular data managed by Apache Iceberg. +!!! note "Project Structure" + The warehouse is now stored in `/tmp/warehouse` and supports both S3 and local Parquet files. ```text project/ @@ -48,39 +48,116 @@ project/ !!! warning "Important Requirements" - The `icefabric_manage` package must be installed through `uv sync` - - There must be parquet files locally to build from. These files are to be downloaded from the NGWPC S3 bucket @ `s3://hydrofabric-data/icefabric/` and placed in `src/icefabric_manage/data/` + - AWS credentials configured for S3 access (if using S3 files) + - Environment variables loaded via `.env` file -### Running the Build +### Running the Build Scripts -To build a specific table, one must be working/CD into the `scripts/` directory +To build the hydrofabric dataset, run from the `scripts/` directory: ```bash python build_hydrofabric.py ``` +To build USGS streamflow observations: +```bash +python build_usgs_streamflow_observations.py +``` + !!! success "Expected Output" ```text - Created hydrofabric namespace + building layer: divide-attributes + building layer: divides + building layer: flowpath-attributes-ml + ... Build successful ``` ### What Happens During Build -1. **Warehouse Setup** - Creates `../data/warehouse/` directory if it doesn't exist +1. **Warehouse Setup** - Creates `/tmp/warehouse/` directory if it doesn't exist 2. **Catalog Configuration** - Sets up SQLite catalog with file-based storage -3. **Namespace Creation** - Creates the `hydrofabric` namespace if it doesn't exist -4. **Table Creation** - For each `.parquet` file in `../data/hydrofabric/`: - - Uses the filename (without extension) as the table name - - Reads the Parquet schema and creates matching Iceberg table +3. **Namespace Creation** - Creates the specified namespace if it doesn't exist +4. **Table Creation** - For each specified Parquet file: + - Reads the Parquet file (from S3 or local path) + - Creates an Iceberg table with matching schema - Appends all data from the Parquet file to the Iceberg table - Skips files if tables already exist +## API Reference + +### `build(catalog, parquet_file, namespace, table_name)` + +!!! function "Main Build Function" + Creates an Iceberg table from a single Parquet file (local or S3). + +**Parameters:** + +| Parameter | Type | Description | +|-----------|------|-------------| +| `catalog` | `Catalog` | PyIceberg catalog instance | +| `parquet_file` | `str` | Path to Parquet file (local path or S3 URL) | +| `namespace` | `str` | Iceberg namespace to create the table in | +| `table_name` | `str` | Name for the new table | + +**Behavior:** +- Creates namespace if it doesn't exist +- Reads Parquet file schema using PyArrow +- Creates Iceberg table with matching schema +- Appends all data from the Parquet file +- Skips table creation if table already exists + +**Example Usage:** +```python +from pyiceberg.catalog import load_catalog +from icefabric_manage import build + +catalog = load_catalog("hydrofabric", **catalog_settings) +build( + catalog=catalog, + parquet_file="s3://hydrofabric-data/icefabric/hydrofabric/divides.parquet", + namespace="hydrofabric", + table_name="divides" +) +``` + +## Build Scripts + +### Hydrofabric Build Script + +The `build_hydrofabric.py` script processes multiple hydrofabric layers: + +```python +layers = [ + "divide-attributes", + "divides", + "flowpath-attributes-ml", + "flowpath-attributes", + "flowpaths", + "hydrolocations", + "lakes", + "network", + "nexus", + "pois" +] +``` + +Each layer is read from `s3://hydrofabric-data/icefabric/hydrofabric/{layer}.parquet` and created as a table in the `hydrofabric` namespace. + +### USGS Streamflow Observations Build Script + +The `build_usgs_streamflow_observations.py` script processes USGS hourly streamflow data: +- Source: `s3://hydrofabric-data/icefabric/streamflow_observations/usgs_hourly.parquet` +- Namespace: `observations` +- Table: `usgs_hourly` + ## Configuration ### Catalog Settings -The build script uses these default settings: +Both build scripts use these default settings: ```python +warehouse_path = Path("/tmp/warehouse") catalog_settings = { "type": "sql", # SQLite-backed catalog "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", # Database location @@ -88,6 +165,79 @@ catalog_settings = { } ``` +### Data Sources + +!!! note "Supported File Locations" + - **S3 URLs**: `s3://bucket-name/path/to/file.parquet` + - **Local paths**: `/path/to/local/file.parquet` + - **Relative paths**: `./data/file.parquet` + +!!! danger "Common Issues" + - **S3 Access** - Ensure AWS credentials are properly configured + - **Nanosecond timestamps** - Convert to microseconds before building + - **Invalid schemas** - Verify Parquet files can be read with `pyarrow.parquet.read_table()` + +## Customization + +### Adding New Datasets + +Create a new build script following this pattern: + +```python +from pathlib import Path +from dotenv import load_dotenv +from pyiceberg.catalog import load_catalog +from icefabric_manage import build + +load_dotenv() + +if __name__ == "__main__": + warehouse_path = Path("/tmp/warehouse") + warehouse_path.mkdir(exist_ok=True) + catalog_settings = { + "type": "sql", + "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", + "warehouse": f"file://{warehouse_path}", + } + + namespace = "your_namespace" + catalog = load_catalog(namespace, **catalog_settings) + + build( + catalog=catalog, + parquet_file="path/to/your/file.parquet", + namespace=namespace, + table_name="your_table_name", + ) + print("Build successful") +``` + +### Changing Warehouse Location + +Modify the `warehouse_path` in your build script: + +```python +warehouse_path = Path("/your/custom/warehouse/path") +``` + +### Alternative Catalog Backend + +Replace SQLite with other supported backends: + +```python +catalog_settings = { + "type": "sql", + "uri": "postgresql://user:pass@host:port/db", + "warehouse": "s3://your-bucket/warehouse/", +} +``` + +## Best Practices + +!!! tip "Recommendations" + 1. **Table Naming** - Use descriptive table names that reflect the data content + 2. **Data Preparation** - Ensure timestamps are properly formatted before building + In most functions, it's predefined to the user's `/tmp` dir since it's the same per each user's machine ## Customization diff --git a/docs/user_guide/icefabric_tools.md b/docs/user_guide/icefabric_tools.md new file mode 100644 index 0000000..e69de29 diff --git a/docs/user_guide/index.md b/docs/user_guide/index.md index cf57a98..3e66a0a 100644 --- a/docs/user_guide/index.md +++ b/docs/user_guide/index.md @@ -4,17 +4,20 @@ ![A diagram describing the icefabric mission](../img/icefabric_mission.png) -The idea for the icefabric came from the need to version control datasets for the National Water Model 4.0. There were many different file formats, and hydrofabric versions, but the need for an [Apache Iceberg](https://iceberg.apache.org/) style backend was realized. The name itself, icefabric, is a reference to this. +The idea for the icefabric came from the need to version control datasets for the National Water Model 4.0. There were many different file formats, and hydrofabric versions, but the need for an [Apache Iceberg](https://iceberg.apache.org/) style backend was realized. The name itself, icefabric, is a reference to this. -Thus, this repo is a workspace for all icefabric assembly, tranformation, and dissemination. +Thus, this repo is a workspace for all icefabric assembly, tranformation, and dissemination. The repo is structured in the following workspace packages: -### Icefabric Manage +*Icefabric Manage:* + The repo for building a local version of the icefabric and updating cloud stores -### Icefabric Tools +*Icefabric Tools:* + The repo for all compute services built on the icefabric -### Icefabric API +*Icefabric API:* + The interface between the icefabric and outside entities diff --git a/mkdocs.yml b/mkdocs.yml index 0e4fee3..8f176e3 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -51,3 +51,5 @@ nav: - User Guide: - user_guide/index.md - Icefabric Manage: user_guide/icefabric_manage.md + - Icefabric Tools: user_guide/icefabric_tools.md + - Icefabric API: user_guide/icefabric_api.md diff --git a/pyproject.toml b/pyproject.toml index ef140c8..709ad31 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,11 +28,16 @@ docs = [ ] [tool.uv.sources] -icefabric_tools = { workspace = true } +icefabric_api = { workspace = true } icefabric_manage = { workspace = true } +icefabric_tools = { workspace = true } [tool.uv.workspace] -members = ["src/icefabric_manage", "src/icefabric_tools"] +members = [ + "src/icefabric_api", + "src/icefabric_manage", + "src/icefabric_tools", +] [tool.uv] dev-dependencies = [ @@ -43,10 +48,11 @@ dev-dependencies = [ "ruff==0.11.0", "black==25.1.0", "mypy==1.15.0", - "icefabric_manage", - "icefabric_tools", "examples", "boto3", + "icefabric_api", + "icefabric_manage", + "icefabric_tools", ] package = false diff --git a/src/icefabric_api/app/__init__.py b/src/icefabric_api/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_api/app/conftest.py b/src/icefabric_api/app/conftest.py new file mode 100644 index 0000000..0dc546f --- /dev/null +++ b/src/icefabric_api/app/conftest.py @@ -0,0 +1,23 @@ +import pytest +from fastapi.testclient import TestClient + +from app.main import app + + +@pytest.fixture(scope="session") +def client(): + """Create a test client for the FastAPI app.""" + return TestClient(app) + + +@pytest.fixture(scope="function") +def fresh_client(): + """Create a fresh test client for each test function.""" + return TestClient(app) + + +def pytest_configure(config): + """Configure pytest markers.""" + config.addinivalue_line("markers", "performance: marks tests as performance tests") + config.addinivalue_line("markers", "integration: marks tests as integration tests") + config.addinivalue_line("markers", "unit: marks tests as unit tests") diff --git a/src/icefabric_api/app/main.py b/src/icefabric_api/app/main.py new file mode 100644 index 0000000..216412f --- /dev/null +++ b/src/icefabric_api/app/main.py @@ -0,0 +1,40 @@ +import uvicorn +from fastapi import FastAPI, status +from pydantic import BaseModel + +from app.routers.streamflow_observations.router import api_router as streamflow_api_router + +app = FastAPI( + title="Icefabric API", + description="API for accessing iceberg or icechunk data from EDFS services", + version="1.0.0", + docs_url="/docs", + redoc_url="/redoc", +) + + +class HealthCheck(BaseModel): + """Response model to validate and return when performing a health check.""" + + status: str = "OK" + + +# Include routers +app.include_router(streamflow_api_router, prefix="/v1") + + +@app.head( + "/health", + tags=["Health"], + summary="Perform a Health Check", + response_description="Return HTTP Status Code 200 (OK)", + status_code=status.HTTP_200_OK, + response_model=HealthCheck, +) +def get_health() -> HealthCheck: + """Returns a HeatlhCheck for the server""" + return HealthCheck(status="OK") + + +if __name__ == "__main__": + uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True, log_level="info") diff --git a/src/icefabric_api/app/routers/__init__.py b/src/icefabric_api/app/routers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_api/app/routers/streamflow_observations/__init__.py b/src/icefabric_api/app/routers/streamflow_observations/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_api/app/routers/streamflow_observations/conftest.py b/src/icefabric_api/app/routers/streamflow_observations/conftest.py new file mode 100644 index 0000000..ea8b020 --- /dev/null +++ b/src/icefabric_api/app/routers/streamflow_observations/conftest.py @@ -0,0 +1,18 @@ +from pathlib import Path + +import pandas as pd +import pytest + + +@pytest.fixture +def local_usgs_streamflow_csv(): + """Returns a locally downloaded CSV file from a specific gauge and time""" + return pd.read_csv(Path.cwd() / "tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv") + + +@pytest.fixture +def local_usgs_streamflow_parquet(): + """Returns a locally downloaded CSV file from a specific gauge and time""" + return pd.read_parquet( + Path.cwd() / "tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.parquet" + ) diff --git a/src/icefabric_api/app/routers/streamflow_observations/router.py b/src/icefabric_api/app/routers/streamflow_observations/router.py new file mode 100644 index 0000000..e2c3055 --- /dev/null +++ b/src/icefabric_api/app/routers/streamflow_observations/router.py @@ -0,0 +1,343 @@ +import io +from datetime import datetime +from enum import Enum + +from fastapi import APIRouter, HTTPException, Path, Query +from fastapi.responses import Response +from pyiceberg.catalog import load_catalog + +api_router = APIRouter(prefix="/streamflow_observations") + +catalog_settings: dict = { + "type": "sql", + "uri": "sqlite:////tmp/warehouse/pyiceberg_catalog.db", + "warehouse": "file:///tmp/warehouse", +} + + +# TODO add other gauges used by NWM +class DataSource(str, Enum): + """All observational streamflow sources""" + + USGS = "usgs" + + +# Configuration for each data source +DATA_SOURCE_CONFIG = { + DataSource.USGS: { + "namespace": "observations", + "table": "usgs_hourly", + "time_column": "time", + "units": "cms", + "description": "USGS stream gauge hourly data", + }, +} + + +def get_catalog_and_table(data_source: DataSource): + """Get catalog and table for a given data source""" + config = DATA_SOURCE_CONFIG[data_source] + catalog = load_catalog(config["namespace"], **catalog_settings) + table = catalog.load_table(f"{config['namespace']}.{config['table']}") + return catalog, table, config + + +def validate_identifier(data_source: DataSource, identifier: str): + """Check if identifier exists in the dataset""" + catalog, table, config = get_catalog_and_table(data_source) + schema = table.schema() + available_columns = [field.name for field in schema.fields] + + if identifier not in available_columns: + available_ids = [col for col in available_columns if col != config["time_column"]] + raise HTTPException( + status_code=404, + detail=f"ID '{identifier}' not found in {data_source} dataset. Available IDs: {available_ids[:10]}...", + ) + + return catalog, table, config + + +@api_router.get("/{data_source}/available") +async def get_available_identifiers( + data_source: DataSource = Path(..., description="Data source type"), + limit: int = Query(100, description="Maximum number of IDs to return"), +): + """ + Get list of available identifiers for a data source + + Examples + -------- + GET /data/usgs/available + GET /data/usgs/available?limit=50 + """ + try: + _, table, config = get_catalog_and_table(data_source) + + schema = table.schema() + # Get all columns except time column + identifier_columns = [field.name for field in schema.fields if field.name != config["time_column"]] + + return { + "data_source": data_source.value, + "description": config["description"], + "total_identifiers": len(identifier_columns), + "identifiers": sorted(identifier_columns)[:limit], + "showing": min(limit, len(identifier_columns)), + "units": config["units"], + } + + except HTTPException as e: + raise HTTPException(status_code=500, detail=str(e)) from e + + +@api_router.get("/{data_source}/csv") +async def get_data_csv( + data_source: DataSource = Path(..., description="Data source type"), + identifier: str = Query( + ..., + description="Station/gauge ID", + examples=["01010000"], + openapi_examples={"station_example": {"summary": "USGS Gauge", "value": "01010000"}}, + ), + start_date: datetime | None = Query( + None, + description="Start Date", + openapi_examples={"sample_date": {"summary": "Sample Date", "value": "2021-12-31T14:00:00"}}, + ), + end_date: datetime | None = Query( + None, + description="End Date", + openapi_examples={"sample_date": {"summary": "Sample Date", "value": "2022-01-01T14:00:00"}}, + ), + include_headers: bool = Query(True, description="Include CSV headers"), +): + """ + Get data as CSV file for any data source + + Examples + -------- + GET /data/usgs_hourly/csv?identifier=01031500 + """ + try: + _, table, config = validate_identifier(data_source, identifier) + scan_builder = table.scan(selected_fields=[config["time_column"], identifier]) + if start_date: + scan_builder = scan_builder.filter(f"{config['time_column']} >= '{start_date.isoformat()}'") + if end_date: + scan_builder = scan_builder.filter(f"{config['time_column']} <= '{end_date.isoformat()}'") + + df = scan_builder.to_pandas() + + if df.empty: + return Response( + content="Error: No data available for the specified parameters", + status_code=404, + media_type="text/plain", + ) + + df = df.rename(columns={config["time_column"]: "time", identifier: "q_cms"}) + + csv_buffer = io.StringIO() + df.to_csv(csv_buffer, index=False, header=include_headers) + csv_data = csv_buffer.getvalue() + + filename_parts = [data_source.value, identifier, "data"] + if start_date: + filename_parts.append(f"from_{start_date.strftime('%Y%m%d_%H%M')}") + if end_date: + filename_parts.append(f"to_{end_date.strftime('%Y%m%d_%H%M')}") + filename = "_".join(filename_parts) + ".csv" + + return Response( + content=csv_data, + media_type="text/csv", + headers={ + "Content-Disposition": f"attachment; filename={filename}", + "X-Total-Records": str(len(df)), + "X-Data-Source": data_source.value, + "X-Units": config["units"], + }, + ) + + except HTTPException: + raise + + +@api_router.get("/{data_source}/parquet") +async def get_data_parquet( + data_source: DataSource = Path(..., description="Data source type"), + identifier: str = Query( + ..., + description="Station/gauge ID", + examples=["01010000"], + openapi_examples={"station_example": {"summary": "USGS Gauge", "value": "01010000"}}, + ), + start_date: datetime | None = Query( + None, + description="Start Date", + openapi_examples={"sample_date": {"summary": "Sample Date", "value": "2021-12-31T14:00:00"}}, + ), + end_date: datetime | None = Query( + None, + description="End Date", + openapi_examples={"sample_date": {"summary": "Sample Date", "value": "2022-01-01T14:00:00"}}, + ), +): + """ + Get data as Parquet file for any data source + + Examples + -------- + GET /data/usgs/parquet?identifier=01031500 + GET /data/usgs/parquet?identifier=01031500&start_date=2023-01-01T00:00:00&compression=gzip + """ + try: + _, table, config = validate_identifier(data_source, identifier) + + scan_builder = table.scan(selected_fields=[config["time_column"], identifier]) + + if start_date: + scan_builder = scan_builder.filter(f"{config['time_column']} >= '{start_date.isoformat()}'") + if end_date: + scan_builder = scan_builder.filter(f"{config['time_column']} <= '{end_date.isoformat()}'") + + df = scan_builder.to_pandas() + if df.empty: + raise HTTPException(status_code=404, detail="No data available for the specified parameters") + + # Prepare output with metadata + df = df.rename(columns={config["time_column"]: "time", identifier: "q_cms"}).copy() + df["data_source"] = data_source.value + df["identifier"] = identifier + df["units"] = config["units"] + + parquet_buffer = io.BytesIO() + df.to_parquet(parquet_buffer, index=False, compression="lz4", engine="pyarrow") + parquet_data = parquet_buffer.getvalue() + + # Fix filename generation with proper datetime formatting + filename_parts = [data_source.value, identifier, "data"] + if start_date: + filename_parts.append(f"from_{start_date.strftime('%Y%m%d_%H%M')}") + if end_date: + filename_parts.append(f"to_{end_date.strftime('%Y%m%d_%H%M')}") + filename = "_".join(filename_parts) + ".parquet" + + return Response( + content=parquet_data, + media_type="application/octet-stream", + headers={ + "Content-Disposition": f"attachment; filename={filename}", + "X-Total-Records": str(len(df)), + "X-Data-Source": data_source.value, + "X-Compression": "lz4", + "X-Units": config["units"], + }, + ) + + except HTTPException: + raise + except ValueError as e: + Response(content=f"Error: {str(e)}", status_code=500, media_type="text/plain") + + +@api_router.get("/{data_source}/info") +async def get_data_source_info( + data_source: DataSource = Path(..., description="Data source type"), +): + """ + Get information about dataset size and recommendations + + Examples + -------- + GET /data/usgs/info + """ + try: + _, table, config = get_catalog_and_table(data_source) + + df = table.inspect.snapshots().to_pandas() + + # Converting to an int rather than a numpy.int64 + latest_snapshot_id = int(df.loc[df["committed_at"].idxmax(), "snapshot_id"]) + snapshots = table.inspect.snapshots().to_pydict() + + snapshots = dict(snapshots) + # Converting to an int rather than a numpy.int64 + if "snapshot_id" in snapshots and snapshots["snapshot_id"]: + snapshots["snapshot_id"] = [int(sid) for sid in snapshots["snapshot_id"]] + + return { + "data_source": data_source.value, + "latest_snapshot": latest_snapshot_id, + "description": config["description"], + "units": config["units"], + "snapshots": snapshots, + } + except HTTPException as e: + raise HTTPException(status_code=500, detail=str(e)) from e + + +@api_router.get("/{data_source}/{identifier}/info") +async def get_data_info( + data_source: DataSource = Path(..., description="Data source type"), + identifier: str = Path(..., description="Station/gauge ID", examples=["01031500"]), +): + """ + Get information about dataset size and recommendations + + Examples + -------- + GET /data/usgs/01031500/info + """ + try: + _, table, config = validate_identifier(data_source, identifier) + + # Get data info + df = table.scan(selected_fields=[config["time_column"], identifier]).to_pandas() + df_clean = df.dropna(subset=[identifier]) # Droping NA to determine full date range + + return { + "data_source": data_source.value, + "identifier": identifier, + "description": config["description"], + "total_records": len(df_clean), + "units": config["units"], + "date_range": { + "start": df_clean[config["time_column"]].min().isoformat() if not df_clean.empty else None, + "end": df_clean[config["time_column"]].max().isoformat() if not df_clean.empty else None, + }, + "estimated_sizes": { + "csv_mb": round(len(df_clean) * 25 / 1024 / 1024, 2), + "parquet_mb": round(len(df_clean) * 8 / 1024 / 1024, 2), + }, + } + + except HTTPException: + raise + except ValueError as e: + Response(content=f"Error: {str(e)}", status_code=500, media_type="text/plain") + + +@api_router.get("/sources") +async def get_available_sources(): + """ + Get list of all available data sources + + Examples + -------- + GET /data/sources + """ + sources = [] + for source, config in DATA_SOURCE_CONFIG.items(): + sources.append( + { + "name": source.value, + "description": config["description"], + "namespace": config["namespace"], + "table": config["table"], + "units": config["units"], + } + ) + + return {"available_sources": sources, "total_sources": len(sources)} diff --git a/src/icefabric_api/app/routers/streamflow_observations/test_router.py b/src/icefabric_api/app/routers/streamflow_observations/test_router.py new file mode 100644 index 0000000..dcd91ad --- /dev/null +++ b/src/icefabric_api/app/routers/streamflow_observations/test_router.py @@ -0,0 +1,86 @@ +from io import BytesIO, StringIO + +import pandas as pd +import pytest + + +@pytest.mark.integration +def test_sources_endpoint(client): + """Test: GET /streamflow_observations/sources""" + response = client.get("/v1/streamflow_observations/sources") + assert response.status_code == 200 + + data = response.json() + assert "available_sources" in data + assert "total_sources" in data + + sources = data["available_sources"] + usgs_source = next((s for s in sources if s["name"] == "usgs"), None) + assert usgs_source is not None + assert usgs_source["description"] == "USGS stream gauge hourly data" + assert usgs_source["units"] == "cms" + + +@pytest.mark.integration +def test_available_identifiers_example(client): + """Test: GET /streamflow_observations/usgs/available""" + response = client.get("/v1/streamflow_observations/usgs/available") + + assert response.status_code in [200, 500] # Will return if the PyIceberg DB exists in the /tmp/ dir + + if response.status_code == 200: + data = response.json() + assert "data_source" in data + assert "identifiers" in data + assert "total_identifiers" in data + assert data["data_source"] == "usgs" + + +@pytest.mark.integration +def test_available_identifiers_with_limit_example(client): + """Test: GET /streamflow_observations/usgs/available?limit=50""" + response = client.get("/v1/streamflow_observations/usgs/available?limit=50") + + assert response.status_code in [200, 500] + + if response.status_code == 200: + data = response.json() + assert data["showing"] <= 50 + + +@pytest.mark.integration +def test_csv_generation(client, local_usgs_streamflow_csv): + """Test: GET /streamflow_observations/usgs/csv""" + response = client.get( + "/v1/streamflow_observations/usgs/csv", + params={ + "identifier": "01010000", + "start_date": "2021-12-31T14:00:00", + "end_date": "2022-01-01T14:00:00", + }, + ) + + assert response.status_code in [200, 500] + + if response.status_code == 200: + df = pd.read_csv(StringIO(response.text)) + assert local_usgs_streamflow_csv.equals(df) + + +@pytest.mark.integration +def test_parquet_generation(client, local_usgs_streamflow_parquet): + """Test: GET /streamflow_observations/usgs/parquet""" + response = client.get( + "/v1/streamflow_observations/usgs/parquet", + params={ + "identifier": "01010000", + "start_date": "2021-12-31T14:00:00", + "end_date": "2022-01-01T14:00:00", + }, + ) + + assert response.status_code in [200, 500] + + if response.status_code == 200: + df = pd.read_parquet(BytesIO(response.content)) + assert local_usgs_streamflow_parquet.equals(df) diff --git a/src/icefabric_api/app/test_main.py b/src/icefabric_api/app/test_main.py new file mode 100644 index 0000000..ced3427 --- /dev/null +++ b/src/icefabric_api/app/test_main.py @@ -0,0 +1,4 @@ +def test_health_with_fixture(client): + """Test using the client fixture from conftest.py.""" + response = client.head("/health") + assert response.status_code == 200 diff --git a/src/icefabric_api/pyproject.toml b/src/icefabric_api/pyproject.toml new file mode 100644 index 0000000..b0970da --- /dev/null +++ b/src/icefabric_api/pyproject.toml @@ -0,0 +1,11 @@ +[project] +name = "icefabric-api" +version = "2025.5.0" +description = "A FastAPI API for querying apache iceberg tables" +readme = "README.md" +requires-python = ">=3.11, <3.13" +dependencies = [ + "fastapi[standard]>=0.115.12", + "pydantic==2.11.5", + "pydantic-settings==2.9.1", +] diff --git a/src/icefabric_api/tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv b/src/icefabric_api/tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv new file mode 100644 index 0000000..2853a97 --- /dev/null +++ b/src/icefabric_api/tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv @@ -0,0 +1,26 @@ +q_cms,time +17.698,2021-12-31 14:00:00 +17.698,2021-12-31 15:00:00 +17.698,2021-12-31 16:00:00 +17.698,2021-12-31 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[PATCH 45/97] JIRA-6388: EDFS QA: Create an icefabric tool for exporting icechunk stores to tiff Add a tool and example to convert icechunk rasters to tif --- .gitignore | 5 + examples/icechunk_to_tiff.ipynb | 686 ++++ src/icefabric_tools/pyproject.toml | 1 + .../src/icefabric_tools/iceberg/__init__.py | 0 .../icechunk/icechunk_s3_module.py | 112 +- uv.lock | 2788 ++++++++--------- 6 files changed, 2194 insertions(+), 1398 deletions(-) create mode 100644 examples/icechunk_to_tiff.ipynb create mode 100644 src/icefabric_tools/src/icefabric_tools/iceberg/__init__.py diff --git a/.gitignore b/.gitignore index 663065c..db19110 100644 --- a/.gitignore +++ b/.gitignore @@ -179,3 +179,8 @@ Pipfile .qodo _version.py data/ +*.db +*.zarr +.zarr +*.tiff +*.tif diff --git a/examples/icechunk_to_tiff.ipynb b/examples/icechunk_to_tiff.ipynb new file mode 100644 index 0000000..1e07c9e --- /dev/null +++ b/examples/icechunk_to_tiff.ipynb @@ -0,0 +1,686 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "42d13343", + "metadata": {}, + "source": [ + "# Demo: Export icechunk raster data to geotiff\n", + "\n", + "Raster topobathymetry data made into icechunk stores is not inherently visibile with traditional GIS methods. Use this method to retrieve a dataset and export to geotiff. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7cba65ef", + "metadata": {}, + "outputs": [], + "source": [ + "from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations" + ] + }, + { + "cell_type": "markdown", + "id": "bcebeab5", + "metadata": {}, + "source": [ + "Open the dataset to check information and variable name." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7df8b014", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset> Size: 3GB\n",
+       "Dimensions:                        (band: 1, y: 14819, x: 22226)\n",
+       "Coordinates:\n",
+       "    spatial_ref                    int64 8B 0\n",
+       "  * band                           (band) int64 8B 1\n",
+       "  * x                              (x) float64 178kB -160.5 -160.5 ... -154.5\n",
+       "  * y                              (y) float64 119kB 22.5 22.5 ... 18.5 18.5\n",
+       "Data variables:\n",
+       "    __xarray_dataarray_variable__  (band, y, x) float64 3GB dask.array<chunksize=(1, 464, 1390), meta=np.ndarray>
    " + ], + "text/plain": [ + " Size: 3GB\n", + "Dimensions: (band: 1, y: 14819, x: 22226)\n", + "Coordinates:\n", + " spatial_ref int64 8B 0\n", + " * band (band) int64 8B 1\n", + " * x (x) float64 178kB -160.5 -160.5 ... -154.5\n", + " * y (y) float64 119kB 22.5 22.5 ... 18.5 18.5\n", + "Data variables:\n", + " __xarray_dataarray_variable__ (band, y, x) float64 3GB dask.array" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "repo = IcechunkS3Repo(location=NGWPCLocations.TOPO_HA_30M_IC.path)\n", + "ds = repo.retrieve_dataset()\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "9e3dceab", + "metadata": {}, + "source": [ + "Save the full dataset to tiff." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e2935a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved dataset to ./hawaii_topo_30.tiff\n" + ] + } + ], + "source": [ + "var_name = \"__xarray_dataarray_variable__\"\n", + "dest = \"./hawaii_topo_30.tif\"\n", + "repo.retrieve_and_convert_to_tif(dest=dest, var_name=var_name)" + ] + }, + { + "cell_type": "markdown", + "id": "3ea00144", + "metadata": {}, + "source": [ + "Save a clipped bounding box of dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5493c79b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved clipped window to ./hawaii_clipped_topo_30.tiff\n" + ] + } + ], + "source": [ + "var_name = \"__xarray_dataarray_variable__\"\n", + "dest = \"./hawaii_clipped_topo_30.tif\"\n", + "minx, miny, maxx, maxy = -159, 21, -158, 22\n", + "repo.retrieve_and_convert_to_tif(dest=dest, var_name=var_name, minx=minx, miny=miny, maxx=maxx, maxy=maxy)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index 4760db3..9c8a277 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -39,6 +39,7 @@ dependencies = [ "fsspec==2025.5.0", "dask==2025.5.1", "netCDF4==1.7.2", + "python-dotenv==1.1.0", ] [build-system] diff --git a/src/icefabric_tools/src/icefabric_tools/iceberg/__init__.py b/src/icefabric_tools/src/icefabric_tools/iceberg/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py index 53a5d66..b294750 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py @@ -6,7 +6,10 @@ """ import subprocess +import warnings from enum import Enum +from pathlib import Path +from typing import Any import icechunk as ic import xarray as xr @@ -218,6 +221,11 @@ def retrieve_dataset(self, read_only: bool | None = True, branch: str | None = " """ session = self.create_session(read_only, branch) ds = xr.open_zarr(session.store, consolidated=False, chunks={}) + + # geotiff rasters saved in zarr need to be convereted to spatial-aware xarray with rioxarray + if "spatial_ref" in ds.data_vars: + ds.rio.write_crs(ds.spatial_ref.spatial_ref, inplace=True) + return ds def write_dataset( @@ -274,6 +282,104 @@ def append_virt_data_to_store( snapshot = session.commit(commit) print(f"Dataset has been appended on the {append_dim} dimension. Commit: {snapshot}") + def retrieve_and_convert_to_tif( + self, + dest: str | Path, + var_name: str = None, + branch: str | None = "main", + compress: str = "lzw", + tiled: bool = True, + minx: float | None = None, + miny: float | None = None, + maxx: float | None = None, + maxy: float | None = None, + profile_kwargs: dict[Any, Any] = None, + ) -> None: + """A function to retrieve a raster icechunk dataset and download as a tif. + + Parameters + ---------- + dest : str | Path + Destination file path for tiff + var_name : str, optional + Name of xarray variable to be used for raster data, by default None + branch : str | None, optional + Icechunk repo branch to be opened, by default "main" + compress : str, optional + Specify a compression type for raster, by default "lzw" + tiled : bool, optional + Specify if raster should be tiled or not. Cloud-Optimized Geotiffs (COG) must be tiled, by default True + minx : float | None, optional + Specify a bounding box minimum x. Must have all [minx, miny, maxx, maxy] specified, by default None + miny : float | None, optional + Specify a bounding box minimum y. Must have all [minx, miny, maxx, maxy] specified, by default None + maxx : float | None, optional + Specify a bounding box maximum x. Must have all [minx, miny, maxx, maxy] specified, by default None + maxy : float | None, optional + Specify a bounding box maximum x. Must have all [minx, miny, maxx, maxy] specified, by default None + profile_kwargs : dict[Any, Any], optional + Any additional profile keywords accepted by GDAL geotiff driver + (https://gdal.org/en/stable/drivers/raster/gtiff.html#creation-options), by default None + + + Raises + ------ + AttributeError + If an xarray dataset does not have a "band" attribute in coordinates, the file is not deemed a raster + and will raise error. + """ + ds = self.retrieve_dataset(read_only=True, branch=branch) + + if "band" not in ds.coords.dims: + raise AttributeError("Dataset needs a 'band' coordinate to export geotiff") + + # infer variable name if none provided - MAY HAVE UNEXPECTED RESULTS + if not var_name: + var_name = self._infer_var_name_for_geotiff(list(ds.data_vars.variables)) + + # initialize keywords dict if none + profile_kwargs = {} if not profile_kwargs else profile_kwargs + + # clip to window + if minx and miny and maxx and maxy: + subset = ds.rio.clip_box(minx=minx, miny=miny, maxx=maxx, maxy=maxy) + subset[var_name].rio.to_raster(dest, compress=compress, tiled=tiled, **profile_kwargs) + del subset + print(f"Saved clipped window to {dest}") + + else: + ds[var_name].rio.to_raster(dest, compress=compress, tiled=tiled, **profile_kwargs) + del ds + print(f"Saved dataset to {dest}") + + def _infer_var_name_for_geotiff(self, variable_list: list) -> str: + """Infer a variable name for saving a geotiff from xarray variables + + Picks the first variable that isn't 'spatial_ref'. In zarr, 'spatial_ref' from CRS is moved + from coordinates to variables. We want a variable that is not it. + This arbitarily picks the first variable. + + Parameters + ---------- + variable_list : list + Output of list(ds.data_vars.variables) + + Returns + ------- + str + Variable name to use for geotif generation + """ + if "spatial_ref" in variable_list: + variable_list.remove("spatial_ref") + var_name = variable_list[0] + warnings.warn( + UserWarning, + f"Inferring xarray variable name {var_name} for raster data. This may have unintended consequences." + "Open dataset separately to check variable names to insure correct output.", + stacklevel=2, + ) + return var_name + @staticmethod def set_up_virtual_chunk_container(bucket: str, region: str) -> ic.VirtualChunkContainer: @@ -301,8 +407,6 @@ def set_up_virtual_chunk_container(bucket: str, region: str) -> ic.VirtualChunkC ) -@staticmethod def get_icechunk_data(repo: NGWPCLocations) -> xr.Dataset: - """Return data from a designated NGWPCLocations Icechunk repo""" - ic_repo = IcechunkS3Repo(location=repo.path) - return ic_repo.retrieve_dataset() + """Convenience wrapper to return data from a designated NGWPCLocations Icechunk repo""" + return IcechunkS3Repo(location=repo.path).retrieve_dataset() diff --git a/uv.lock b/uv.lock index 731449a..aca5b2d 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,4 @@ version = 1 -revision = 2 requires-python = ">=3.11, <3.13" resolution-markers = [ "python_full_version >= '3.12'", @@ -18,9 +17,9 @@ members = [ name = 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of python subsetting tool * added subsetter logic for poi_ids and additional testing * updated testing docs * patch: updated docs * removed redundant code --- .gitignore | 1 + docs/user_guide/icefabric_tools.md | 82 ++++ .../src/icefabric_tools/__init__.py | 7 +- .../icefabric_tools/iceberg/hydrofabric.py | 396 ++++++++++++++++++ .../src/icefabric_tools/iceberg/rnr.py | 4 +- .../{_api.py => iceberg/utils.py} | 19 +- src/icefabric_tools/tests/conftest.py | 60 +++ src/icefabric_tools/tests/test_subset.py | 33 ++ src/icefabric_tools/tests/test_utils.py | 24 ++ 9 files changed, 603 insertions(+), 23 deletions(-) create mode 100644 src/icefabric_tools/src/icefabric_tools/iceberg/hydrofabric.py rename src/icefabric_tools/src/icefabric_tools/{_api.py => iceberg/utils.py} (90%) create mode 100644 src/icefabric_tools/tests/conftest.py create mode 100644 src/icefabric_tools/tests/test_subset.py create mode 100644 src/icefabric_tools/tests/test_utils.py diff --git a/.gitignore b/.gitignore index db19110..9af092f 100644 --- a/.gitignore +++ b/.gitignore @@ -167,6 +167,7 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +.vscode/ # Ruff stuff: .ruff_cache/ diff --git a/docs/user_guide/icefabric_tools.md b/docs/user_guide/icefabric_tools.md index e69de29..a6c9a2e 100644 --- a/docs/user_guide/icefabric_tools.md +++ b/docs/user_guide/icefabric_tools.md @@ -0,0 +1,82 @@ +# Icefabric Tools + +A series of compute services built on top of version controlled EDFS data + +## Hydrofabric Geospatial Tools + +### Overview + +The Hydrofabric Geospatial Tools module provides Python functions for subsetting and analyzing hydrofabric data stored in Apache Iceberg format + +### Functionality + +- **Data Subsetting** - the `subset()` function provides all upstream catchments related to a given gauge + +### Usage Examples + +#### Basic Subsetting + +```python +from pathlib import Path +from pyiceberg.catalog import load_catalog +from icefabric_tools import subset, IdType + +# Load the catalog using default settings +catalog_settings: dict = { + "type": "sql", + "uri": "sqlite:////tmp/warehouse/pyiceberg_catalog.db", + "warehouse": "file:///tmp/warehouse", +} +catalog = load_catalog("hydrofabric", **catalog_settings) + +# Basic subset using a hydrofabric ID +result = subset( + catalog=catalog, + identifier="wb-10026", + id_type=IdType.ID, + layers=["divides", "flowpaths", "network", "nexus"] +) + +# Access the filtered data +flowpaths = result["flowpaths"] +divides = result["divides"] +network = result["network"] +nexus = result["nexus"] +``` + +#### Export to GeoPackage + +```python +# Export subset directly to GeoPackage +output_path = Path("subset_output.gpkg") + +subset( + catalog=catalog, + identifier="01031500", + id_type=IdType.POI_ID, + layers=["divides", "flowpaths", "network", "nexus", "pois"], + output_file=output_path +) +``` + +#### Getting all layers + +```python +# Include all available layers +all_layers = [ + "divides", "flowpaths", "network", "nexus", + "divide-attributes", "flowpath-attributes", + "flowpath-attributes-ml", "pois", "hydrolocations" +] + +result = subset( + catalog=catalog, + identifier="HUC12-010100100101", + id_type=IdType.HL_URI, + layers=all_layers +) + +# Process specific layers +pois_data = result["pois"] +attributes = result["flowpath-attributes"] +``` diff --git a/src/icefabric_tools/src/icefabric_tools/__init__.py b/src/icefabric_tools/src/icefabric_tools/__init__.py index 27fadc5..68c7c48 100644 --- a/src/icefabric_tools/src/icefabric_tools/__init__.py +++ b/src/icefabric_tools/src/icefabric_tools/__init__.py @@ -1,4 +1,5 @@ -from icefabric_tools._api import find_origin, load_hydrofabric, table_to_geopandas, to_geopandas -from icefabric_tools.iceberg import rnr +from icefabric_tools.iceberg.hydrofabric import IdType, subset +from icefabric_tools.iceberg.rnr import get_rnr_segment +from icefabric_tools.iceberg.utils import find_origin, table_to_geopandas, to_geopandas -__all__ = ["find_origin", "load_hydrofabric", "rnr", "to_geopandas", "table_to_geopandas"] +__all__ = ["IdType", "subset", "get_rnr_segment", "find_origin", "to_geopandas", "table_to_geopandas"] diff --git a/src/icefabric_tools/src/icefabric_tools/iceberg/hydrofabric.py b/src/icefabric_tools/src/icefabric_tools/iceberg/hydrofabric.py new file mode 100644 index 0000000..99db880 --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/iceberg/hydrofabric.py @@ -0,0 +1,396 @@ +"""A file to hold all hydrofabric geospatial tools""" + +from enum import Enum +from pathlib import Path + +import geopandas as gpd +import pandas as pd +from pyiceberg.catalog import Catalog +from pyiceberg.expressions import And, EqualTo, In +from pyiceberg.table import Table + +from icefabric_tools.iceberg.utils import find_origin, table_to_geopandas, to_geopandas + + +class IdType(str, Enum): + """All queriable HF fields. + + Attributes + ---------- + HL_URI : str + Hydrolocation URI identifier + HF_ID : str + Hydrofabric ID identifier + ID : str + Generic ID identifier + POI_ID : str + Point of Interest ID identifier + """ + + HL_URI = "hl_uri" + HF_ID = "hf_id" + ID = "id" + POI_ID = "poi_id" + + +# For catchments that may extend in many VPUs +UPSTREAM_VPUS: dict[str, list[str]] = {"08": ["11", "10U", "10L", "08", "07", "05"]} + + +def get_sorted_network(network_df: pd.DataFrame, outlets: list[str] | str) -> pd.DataFrame: + """Python implementation similar to R's nhdplusTools::get_sorted(). + + This function traces upstream from outlet points through a network topology + to find all connected upstream segments and their relationships. + + Parameters + ---------- + network_df : pd.DataFrame + Network data with 'id' and 'toid' columns representing the topology. + Each row represents a network segment with its downstream connection. + outlets : list of str or str + Outlet IDs to start tracing from. If a single string is provided, + it will be converted to a list. + + Returns + ------- + pd.DataFrame + Sorted network topology with 'id' and 'toid' columns representing + the upstream connections found during the trace. Each row shows + a segment and what it flows into. + + Notes + ----- + The function performs a recursive upstream trace from the specified outlets, + building a network of all connected upstream segments. It also handles + special cases like nexus points and ensures all relevant connections + are captured. + """ + if isinstance(outlets, str): + outlets = [outlets] + + # Create a mapping of toid -> list of ids that flow into it + downstream_map: dict[str, list[str]] = {} + all_segments: set = set() + + # Build the network mapping + for _, row in network_df.iterrows(): + segment_id = row["id"] + toid = row["toid"] + all_segments.add(segment_id) + if pd.notna(toid): + if toid not in downstream_map: + downstream_map[toid] = [] + downstream_map[toid].append(segment_id) + + visited: set = set() + sorted_pairs: list[dict[str, str]] = [] + upstream_segments: set = set() + + def trace_upstream_recursive(current_id: str) -> None: + """Recursively trace upstream from current_id. + + Parameters + ---------- + current_id : str + The current segment ID to trace upstream from + """ + if current_id in visited: + return + visited.add(current_id) + upstream_segments.add(current_id) + + # Find all segments that flow into current_id + if current_id in downstream_map: + for upstream_id in downstream_map[current_id]: + if upstream_id not in visited: + sorted_pairs.append({"id": upstream_id, "toid": current_id}) + trace_upstream_recursive(upstream_id) + + # Start tracing from each outlet + for outlet in outlets: + if outlet in all_segments or outlet in downstream_map: + trace_upstream_recursive(outlet) + + # Additional pass: find any headwater segments that might have been missed + # These are segments that don't appear as 'toid' for any other segment + # but are connected to our upstream network + connected_segments: set = set(upstream_segments) + + # Look for segments that flow into our connected network but weren't found + for _, row in network_df.iterrows(): + segment_id = row["id"] + toid = row["toid"] + + # If this segment flows into something we've already found, + # but we haven't included this segment yet, add it + if ( + pd.notna(toid) + and toid in connected_segments + and segment_id not in connected_segments + and segment_id not in visited + ): + sorted_pairs.append({"id": segment_id, "toid": toid}) + connected_segments.add(segment_id) + + # Specific fix for nexus points: Include nexus points that are targets of our upstream segments + # but weren't included in the original trace + all_toids_in_pairs = {pair["toid"] for pair in sorted_pairs} + for _, row in network_df.iterrows(): + segment_id = row["id"] + toid = row["toid"] + + # If this segment flows to a nexus point that should be included + if ( + segment_id in connected_segments + and pd.notna(toid) + and toid.startswith("nex-") + and toid not in all_toids_in_pairs + and toid not in connected_segments + ): + # Add the nexus point as a target + sorted_pairs.append({"id": segment_id, "toid": toid}) + connected_segments.add(toid) + print(f"Added missing nexus point: {toid}") + + return pd.DataFrame(sorted_pairs) + + +def get_all_upstream_ids(network: Table, origin_data: pd.DataFrame) -> list[str]: + """Get all upstream segment IDs using topology sorting approach. + + This function identifies all upstream segments from a given origin point + by filtering the network data and performing topology sorting to trace + upstream connections. + + Parameters + ---------- + network : pyiceberg.table.Table + The network table from iceberg catalog containing network topology data + origin_data : pd.DataFrame + Single row DataFrame representing the origin point with columns including + 'vpuid', 'toid', and 'id' + + Returns + ------- + list of str + All upstream segment IDs found by tracing upstream from the origin point. + Includes the origin ID itself if not already present. + + Raises + ------ + AssertionError + If no upstream topology is found for the given ID + + Notes + ----- + The function applies VPU (Vector Processing Unit) filtering when available + to reduce data size. It supports special upstream VPU mappings defined + in UPSTREAM_VPUS for catchments that may extend across multiple VPUs. + """ + # Filter network by VPU if available to reduce data size + network_filters = [] + + if "vpuid" in origin_data.columns and pd.notna(origin_data["vpuid"].iloc[0]): + if origin_data["vpuid"].iloc[0] in UPSTREAM_VPUS.keys(): + vpuid_filter = In("vpuid", UPSTREAM_VPUS[origin_data["vpuid"].iloc[0]]) + else: + vpuid_filter = EqualTo("vpuid", origin_data["vpuid"].iloc[0]) + network_filters.append(vpuid_filter) + + # Apply filters to get network subset + if network_filters: + combined_filter = network_filters[0] + for filter_expr in network_filters[1:]: + combined_filter = And(combined_filter, filter_expr) + network_data = network.scan(row_filter=combined_filter).to_pandas() + else: + # Get all network data - might be large, consider adding other filters + network_data = network.scan().to_pandas() + + # Select only needed columns for topology sorting + network_subset = network_data[["id", "toid"]].dropna(subset=["id"]).drop_duplicates() + + # Get the outlet (toid of the origin) + outlets = origin_data["toid"].dropna().tolist() + if not outlets: + print("Warning: No outlets found for origin") + return [] + + print(f"Tracing upstream from outlet(s): {list(set(outlets))}") + # Use topology sorting to find all upstream segments + topology = get_sorted_network(network_subset, outlets) + + assert len(topology) != 0, "No upstream topology found for ID" + + print(f"Found {len(topology)} topology connections") + + # Don't remove the last row initially - include more segments like R version + topology = topology.drop_duplicates() + + # Get all unique IDs from topology (include both directions) + all_ids = pd.concat([topology["id"], topology["toid"]]).dropna().unique().tolist() + + # Also include the origin ID itself + origin_id = origin_data["id"].iloc[0] + if origin_id not in all_ids: + all_ids.append(origin_id) + return all_ids + + +def subset( + catalog: Catalog, + identifier: str, + id_type: IdType, + layers: list[str] | None = None, + output_file: Path | None = None, +) -> dict[str, pd.DataFrame | gpd.GeoDataFrame] | None: + """Returns a geopackage subset from the hydrofabric. + + Based on logic from HfsubsetR, this function creates a subset of the + hydrofabric data by tracing upstream from a given identifier and collecting + all related geospatial layers. + + Parameters + ---------- + catalog : Catalog + The iceberg catalog of the hydrofabric containing all data tables + identifier : str + The identifier to start tracing from (e.g., catchment ID, POI ID) + id_type : IdType + The type of identifier being used (HL_URI, HF_ID, ID, or POI_ID) + layers : list of str, optional + List of layer names to include in the subset. Default layers are + ["divides", "flowpaths", "network", "nexus"]. Optional layers include + "divide-attributes", "flowpath-attributes", "flowpath-attributes-ml", + "pois", and "hydrolocations" + output_file : Path, optional + The output file path where the geopackage will be saved. If None, + returns the data as a dictionary instead of saving to file + + Returns + ------- + dict of str to DataFrame/GeoDataFrame, optional + Dictionary containing the filtered layers with layer names as keys + and the corresponding data as values. Only returned if output_file + is None. + + Raises + ------ + AssertionError + If no valid divide_ids, flowpaths, or divide IDs are found during + the subsetting process + + Notes + ----- + The function performs the following steps: + 1. Finds the origin point using the provided identifier + 2. Traces upstream to find all connected segments + 3. Filters each requested layer to include only relevant features + 4. Converts POI IDs to string format for consistency + 5. Either saves to geopackage or returns the filtered data + + The function handles special data type conversions for poi_id fields + and filters out None/NaN values appropriately for each layer type. + """ + # Ensuring there are always divides, flowpaths, network, and nexus layers + if layers is None: + layers = [] + layers.extend(["divides", "flowpaths", "network", "nexus"]) + layers = list(set(layers)) + + network = catalog.load_table("hydrofabric.network") + divides = catalog.load_table("hydrofabric.divides") + flowpaths = catalog.load_table("hydrofabric.flowpaths") + nexus = catalog.load_table("hydrofabric.nexus") + + # Find the origin point + origin_row = find_origin(network_table=network, identifier=identifier, id_type=id_type.value) + origin_filter = EqualTo("id", origin_row["id"].iloc[0]) + origin_data = network.scan(row_filter=origin_filter).to_pandas() + + # Get all upstream segment IDs using topology sorting approach (like R version) + all_upstream_ids = get_all_upstream_ids(network, origin_data) + + if len(all_upstream_ids) == 0: + print("No upstream segments found") + return None + + print(f"Found {len(all_upstream_ids)} upstream segments") + + id_filter = In("id", all_upstream_ids) + filtered_network = network.scan(row_filter=id_filter).to_pandas() + filtered_network["poi_id"] = filtered_network["poi_id"].apply( + lambda x: str(int(x)) if pd.notna(x) else None + ) + + # Get flowpaths for all upstream segments (filter out None values) + valid_divide_ids = filtered_network["divide_id"].dropna().drop_duplicates().values.tolist() + assert valid_divide_ids is not None, "No valid divide_ids found" + filtered_flowpaths = flowpaths.scan(row_filter=In("divide_id", valid_divide_ids)).to_pandas() + + assert len(filtered_flowpaths) > 0, "No flowpaths found" + valid_toids = filtered_flowpaths["toid"].dropna().values.tolist() # Filter to remove None values + assert valid_toids, "No flowpaths found" + filtered_nexus_points = table_to_geopandas(table=nexus, row_filter=In("id", valid_toids)) + filtered_flowpaths = to_geopandas(filtered_flowpaths) + filtered_nexus_points["poi_id"] = filtered_nexus_points["poi_id"].apply( + lambda x: str(int(x)) if pd.notna(x) else None + ) + + valid_divide_ids_for_divides = filtered_flowpaths["divide_id"].dropna().values.tolist() + assert valid_divide_ids_for_divides is not None, "No divide IDS found" + filtered_divides = table_to_geopandas( + table=divides, row_filter=In("divide_id", valid_divide_ids_for_divides) + ) + + output_layers = { + "flowpaths": filtered_flowpaths, + "nexus": filtered_nexus_points, + "divides": filtered_divides, + "network": filtered_network, + } + + # Getting any optional fields: + if "divide-attributes" in layers: + divides_attr = catalog.load_table("hydrofabric.divide-attributes") + filtered_divide_attr = divides_attr.scan( + row_filter=In("divide_id", valid_divide_ids_for_divides) + ).to_pandas() + output_layers["divide-attributes"] = filtered_divide_attr + + if "flowpath-attributes" in layers: + flowpath_attr = catalog.load_table("hydrofabric.flowpath-attributes") + valid_flowpath_ids = filtered_flowpaths["id"].dropna().values.tolist() + filtered_flowpath_attr = flowpath_attr.scan(row_filter=In("id", valid_flowpath_ids)).to_pandas() + output_layers["flowpath-attributes"] = filtered_flowpath_attr + + if "flowpath-attributes-ml" in layers: + flowpath_attr_ml = catalog.load_table("hydrofabric.flowpath-attributes-ml") + valid_flowpath_ids = filtered_flowpaths["id"].dropna().values.tolist() + filtered_flowpath_attr_ml = flowpath_attr_ml.scan(row_filter=In("id", valid_flowpath_ids)).to_pandas() + output_layers["flowpath-attributes-ml"] = filtered_flowpath_attr_ml + + if "pois" in layers: + pois = catalog.load_table("hydrofabric.pois") + poi_values = filtered_flowpaths["poi_id"].dropna().values + filtered_poi_list = list({int(x) for x in poi_values if pd.notna(x)}) + filtered_pois = pois.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() + output_layers["pois"] = filtered_pois + + if "hydrolocations" in layers: + hydrolocations = catalog.load_table("hydrofabric.hydrolocations") + poi_values = filtered_flowpaths["poi_id"].dropna().values + filtered_poi_list = list({int(x) for x in poi_values if pd.notna(x)}) + filtered_hydrolocations = hydrolocations.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() + output_layers["hydrolocations"] = filtered_hydrolocations + + if output_file: + for table_name, _layer in output_layers.items(): + if len(_layer) > 0: # Only save non-empty layers + gpd.GeoDataFrame(_layer).to_file(output_file, layer=table_name, driver="GPKG") + else: + print(f"Warning: {table_name} layer is empty") + return None + else: + return output_layers diff --git a/src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py b/src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py index e2546d7..07598b6 100644 --- a/src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py +++ b/src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py @@ -6,10 +6,10 @@ from pyiceberg.catalog import Catalog from pyiceberg.expressions import And, EqualTo, In, LessThanOrEqual -from icefabric_tools import table_to_geopandas, to_geopandas +from icefabric_tools.iceberg.utils import table_to_geopandas, to_geopandas -def get_rnr_segment(catalog: Catalog, reach_id: str, output_file: str) -> gpd.GeoDataFrame: +def get_rnr_segment(catalog: Catalog, reach_id: str, output_file: str) -> gpd.GeoDataFrame | None: """Returns a geopackage subset from the hydrofabric based on RnR rules Parameters diff --git a/src/icefabric_tools/src/icefabric_tools/_api.py b/src/icefabric_tools/src/icefabric_tools/iceberg/utils.py similarity index 90% rename from src/icefabric_tools/src/icefabric_tools/_api.py rename to src/icefabric_tools/src/icefabric_tools/iceberg/utils.py index 1b7310a..82dc46a 100644 --- a/src/icefabric_tools/src/icefabric_tools/_api.py +++ b/src/icefabric_tools/src/icefabric_tools/iceberg/utils.py @@ -2,28 +2,11 @@ import geopandas as gpd import pandas as pd -from pyiceberg.catalog import Catalog, load_catalog from pyiceberg.expressions import BooleanExpression from pyiceberg.table import ALWAYS_TRUE, Table from shapely import wkb -def load_hydrofabric(catalog_settings: dict[str, str]) -> Catalog: - """A function to read in the hydrofabric catalog - - Parameters - ---------- - catalog_settings : dict[str, str] - The settings to read the hydrofabric catalog - - Returns - ------- - Catalog - The Iceberg catalog - """ - return load_catalog("hydrofabric", **catalog_settings) - - def table_to_geopandas( table: Table, row_filter: str | BooleanExpression = ALWAYS_TRUE, @@ -151,7 +134,7 @@ def find_origin(network_table: Table, identifier: str, id_type: str = "hl_uri") # Sort by hydroseq and take the minimum origin = origin.sort_values("hydroseq").iloc[0:1] - # If we still have multiple records, it's a problem + # Throwing an error for multiple origin points if len(origin) > 1: raise ValueError(f"Multiple origins found: {origin['id'].tolist()}") diff --git a/src/icefabric_tools/tests/conftest.py b/src/icefabric_tools/tests/conftest.py new file mode 100644 index 0000000..2de8824 --- /dev/null +++ b/src/icefabric_tools/tests/conftest.py @@ -0,0 +1,60 @@ +from pathlib import Path + +import pytest +from pyiceberg.catalog import Catalog, load_catalog +from sqlalchemy.exc import OperationalError + +sample_gauges = [ + "gages-01010000", + "gages-02450825", + "gages-03173000", + "gages-04100500", + "gages-05473450", + "gages-06823500", + "gages-07060710", + "gages-08070000", + "gages-09253000", + "gages-10316500", + "gages-11456000", + "gages-12411000", + "gages-13337000", + "gages-14020000", +] + + +@pytest.fixture +def hydrofabric_catalog() -> Catalog: + """Returns an iceberg catalog object for the hydrofabric""" + warehouse_path = ( + "/tmp/warehouse" # Requires the HF to be built via: src/icefabric_manage/builds/build_hydrofabric.py + ) + try: + catalog_settings = { + "type": "sql", + "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", + "warehouse": f"file://{warehouse_path}", + } + return load_catalog("hydrofabric", **catalog_settings) + except OperationalError as e: + raise type(e)( # noqa: B904 + f"Cannot find warehouse @ {warehouse_path}. Please build HF warehouse by running src/icefabric_manage/builds/build_hydrofabric.py" + ) # type: ignore + + +@pytest.fixture(params=sample_gauges) +def gauge_hf_uri(request) -> str: + """Returns individual gauge identifiers for parameterized testing""" + return request.param + + +@pytest.fixture +def testing_dir() -> Path: + """Returns the testing data dir""" + return Path(__file__).parent / "data/" + + +def pytest_configure(config): + """Configure pytest markers.""" + config.addinivalue_line("markers", "performance: marks tests as performance tests") + config.addinivalue_line("markers", "integration: marks tests as integration tests") + config.addinivalue_line("markers", "unit: marks tests as unit tests") diff --git a/src/icefabric_tools/tests/test_subset.py b/src/icefabric_tools/tests/test_subset.py new file mode 100644 index 0000000..dc76c47 --- /dev/null +++ b/src/icefabric_tools/tests/test_subset.py @@ -0,0 +1,33 @@ +from pathlib import Path + +import geopandas as gpd +import pytest +from pyiceberg.catalog import Catalog + +from icefabric_tools import IdType, subset + + +@pytest.mark.integration +def test_subset_hl_uri(hydrofabric_catalog: Catalog, gauge_hf_uri: str, testing_dir: Path, tmp_path: Path): + """Tests all subset gauges in the sample data using hl_uri""" + subset( + catalog=hydrofabric_catalog, + identifier=gauge_hf_uri, + id_type=IdType.HL_URI, + output_file=tmp_path / "subset.gpkg", + ) + output_gdf = gpd.read_file(tmp_path / "subset.gpkg", layer="network") + correct_gdf = gpd.read_file(testing_dir / f"{gauge_hf_uri}.gpkg", layer="network") + assert output_gdf.equals(correct_gdf) + + output_gdf = gpd.read_file(tmp_path / "subset.gpkg", layer="divides") + correct_gdf = gpd.read_file(testing_dir / f"{gauge_hf_uri}.gpkg", layer="divides") + assert output_gdf.equals(correct_gdf) + + output_gdf = gpd.read_file(tmp_path / "subset.gpkg", layer="flowpaths") + correct_gdf = gpd.read_file(testing_dir / f"{gauge_hf_uri}.gpkg", layer="flowpaths") + assert output_gdf.equals(correct_gdf) + + output_gdf = gpd.read_file(tmp_path / "subset.gpkg", layer="nexus") + correct_gdf = gpd.read_file(testing_dir / f"{gauge_hf_uri}.gpkg", layer="nexus") + assert output_gdf.equals(correct_gdf) diff --git a/src/icefabric_tools/tests/test_utils.py b/src/icefabric_tools/tests/test_utils.py new file mode 100644 index 0000000..60f5398 --- /dev/null +++ b/src/icefabric_tools/tests/test_utils.py @@ -0,0 +1,24 @@ +import pytest +from pyiceberg.catalog import Catalog + +from icefabric_tools import IdType, find_origin + + +@pytest.mark.unit +def test_find_origin(hydrofabric_catalog: Catalog): + """Testing the find origin function""" + network = hydrofabric_catalog.load_table("hydrofabric.network") + with pytest.raises(ValueError, match="No origin found"): + find_origin( + network_table=network, + identifier="non-existent-id-12345", + id_type=IdType.HL_URI.value, + ) + + network = hydrofabric_catalog.load_table("hydrofabric.network") + with pytest.raises(ValueError, match="No origin found"): + find_origin( + network_table=network, + identifier="non-existent-id-12345", + id_type=IdType.HL_URI.value, + ) From e9aa7800def30ab629b43271cb9ec9780d939fdf Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Mon, 2 Jun 2025 11:04:42 -0400 Subject: [PATCH 47/97] added api endpoint for the subset geopackage work (#18) --- README.md | 1 - docs/user_guide/icefabric_api.md | 88 +++++++++++---- src/icefabric_api/README.md | 105 +++++++++++++++++- src/icefabric_api/app/main.py | 2 + .../app/routers/hydrofabric/__init__.py | 0 .../app/routers/hydrofabric/conftest.py | 32 ++++++ .../app/routers/hydrofabric/router.py | 76 +++++++++++++ .../app/routers/hydrofabric/test_router.py | 34 ++++++ 8 files changed, 312 insertions(+), 26 deletions(-) create mode 100644 src/icefabric_api/app/routers/hydrofabric/__init__.py create mode 100644 src/icefabric_api/app/routers/hydrofabric/conftest.py create mode 100644 src/icefabric_api/app/routers/hydrofabric/router.py create mode 100644 src/icefabric_api/app/routers/hydrofabric/test_router.py diff --git a/README.md b/README.md index 3c37794..776b525 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,6 @@ docker compose -f docker/compose.yaml build --no-cache docker compose -f docker/compose.yaml up ``` This should spin up the API services -``` ### Development diff --git a/docs/user_guide/icefabric_api.md b/docs/user_guide/icefabric_api.md index ed94598..ca26d09 100644 --- a/docs/user_guide/icefabric_api.md +++ b/docs/user_guide/icefabric_api.md @@ -2,7 +2,7 @@ ## Overview -The Icefabric API is a FastAPI-based service that provides access to EDFS data stored in Apache Iceberg format. The API offers multiple data export formats and comprehensive metadata endpoints for hydrology and water resources applications. +The Icefabric API is a FastAPI-based service that provides access to EDFS data stored in Apache Iceberg format. The API offers multiple data export formats and metadata endpoints for the hydrofabric and streamflow observations. ## Architecture @@ -44,38 +44,47 @@ This should spin up the API services Currently supports: - **USGS** - United States Geological Survey hourly streamflow data +#### Hydrofabric +Provides geospatial watershed data: +- **Subset Generation** - Creates upstream watershed subsets from identifiers + !!! note "Data Storage" All data is stored as Apache Iceberg tables in a SQLite-backed catalog locally built at `/tmp/warehouse/pyiceberg_catalog.db` ## Usage Examples -### Python Client +### Streamflow Observations ```python import requests import pandas as pd from io import StringIO, BytesIO -# Base URL base_url = "http://localhost:8000/v1/streamflow_observations" +# Get available data sources +sources = requests.get(f"{base_url}/sources").json() + +# Get available identifiers for USGS +identifiers = requests.get(f"{base_url}/usgs/available", params={"limit": 10}).json() + # Get station information -response = requests.get(f"{base_url}/usgs/info", params={"identifier": "01031500"}) -info = response.json() -print(f"Station has {info['total_records']} records") +station_info = requests.get(f"{base_url}/usgs/01031500/info").json() +print(f"Station has {station_info['total_records']} records") -# Download CSV data +# Download CSV data with date filtering csv_response = requests.get( f"{base_url}/usgs/csv", params={ "identifier": "01031500", "start_date": "2023-01-01T00:00:00", - "end_date": "2023-01-31T23:59:59" + "end_date": "2023-01-31T00:00:00", + "include_headers": True } ) df_csv = pd.read_csv(StringIO(csv_response.text)) -# Download Parquet data (more efficient for large datasets) +# Download Parquet data (recommended for large datasets) parquet_response = requests.get( f"{base_url}/usgs/parquet", params={ @@ -86,6 +95,22 @@ parquet_response = requests.get( df_parquet = pd.read_parquet(BytesIO(parquet_response.content)) ``` +### Hydrofabric Subset + +```python +import requests + +# Download hydrofabric subset as geopackage +response = requests.get("http://localhost:8000/v1/hydrofabric/01010000/gpkg") + +if response.status_code == 200: + with open("hydrofabric_subset_01010000.gpkg", "wb") as f: + f.write(response.content) + print(f"Downloaded {len(response.content)} bytes") +else: + print(f"Error: {response.status_code}") +``` + ## Performance Considerations ### Data Format Recommendations @@ -122,7 +147,7 @@ uv sync python src/icefabric_api/app/main.py ``` -### Adding New Data Sources +### Adding New Data Observation Sources To add a new data source, update the configuration in your router: @@ -158,25 +183,46 @@ The API provides interactive documentation at: Access the OpenAPI schema at: `http://localhost:8000/openapi.json` +## Verification -### Verification - -Test your API setup: +### Observations ```bash - -# List available sources +# List available data sources curl http://localhost:8000/v1/streamflow_observations/sources -# Test with a known identifier -curl "http://localhost:8000/v1/streamflow_observations/usgs/available?limit=1" +# Get available identifiers (limit results) +curl "http://localhost:8000/v1/streamflow_observations/usgs/available?limit=5" -# Get a CSV reponse +# Get data source information +curl http://localhost:8000/v1/streamflow_observations/usgs/info + +# Get specific station information +curl http://localhost:8000/v1/streamflow_observations/usgs/01010000/info + +# Download CSV with headers curl "http://localhost:8000/v1/streamflow_observations/usgs/csv?identifier=01010000&include_headers=true" -# Fitler based on timestamps +# Download CSV with date filtering curl "http://localhost:8000/v1/streamflow_observations/usgs/csv?identifier=01010000&start_date=2021-12-31T14%3A00%3A00&end_date=2022-01-01T14%3A00%3A00&include_headers=true" -# Get a Parquet Response -curl "http://localhost:8000/v1/streamflow_observations/usgs/parquet\?identifier\=01010000\&start_date\=2021-12-31T14%3A00%3A00\&end_date\=2022-01-01T14%3A00%3A00" -o "output.parquet" +# Download Parquet file +curl "http://localhost:8000/v1/streamflow_observations/usgs/parquet?identifier=01010000&start_date=2021-12-31T14%3A00%3A00&end_date=2022-01-01T14%3A00%3A00" -o "output.parquet" +``` + +### Hydrofabric + +```bash +# Download hydrofabric subset +curl "http://localhost:8000/v1/hydrofabric/01010000/gpkg" -o "subset.gpkg" + +# Download with different identifier +curl "http://localhost:8000/v1/hydrofabric/01031500/gpkg" -o "subset.gpkg" +``` + +### Health Check + +```bash +# Check API health +curl http://localhost:8000/health ``` diff --git a/src/icefabric_api/README.md b/src/icefabric_api/README.md index f4f70be..5a8601a 100644 --- a/src/icefabric_api/README.md +++ b/src/icefabric_api/README.md @@ -1,5 +1,102 @@ -CSV files for catchment specific values are too big to be checked in. They are stored here: s3://ngwpc-dev/DanielCumpton/divide_csv.tgz -Download and untar in the icefabric/src/icefabric_api/data directory. -For now, you can run modules by listing them in the module list defined on line 108 in run_modules.py. Text files showing all parameters as key/value -pairs will be written to a files per catchment. A JSON is output to the terminal. SFT, SMP, and LASAM csv files still need work to define the parameters properly. +# Icefabric API + +FastAPI service providing access to hydrology and water resources data stored in Apache Iceberg format. + +## Features + +- **Streamflow Observations** - USGS hourly streamflow data in CSV/Parquet formats +- **Hydrofabric Subsets** - Upstream watershed data as downloadable geopackages +- **Interactive Documentation** - Built-in Swagger UI and ReDoc + +## Quick Start + +### Prerequisites + +- [uv](https://docs.astral.sh/uv/) package manager + +### Installation + +```bash +# Clone repository +git clone +cd icefabric + +# Install package in development mode +uv pip install src/icefabric_api -e . +``` + +### Running the API + +```bash +# Start development server +cd src/icefabric_api +python -m app.main +``` + +The API will be available at `http://localhost:8000` + +### Docker Alternative + +```bash +# Build and run with Docker +docker compose -f docker/compose.yaml build --no-cache +docker compose -f docker/compose.yaml up +``` + +## API Endpoints + +### Streamflow Observations + +- `GET /v1/streamflow_observations/sources` - List available data sources +- `GET /v1/streamflow_observations/usgs/available` - Get available station IDs +- `GET /v1/streamflow_observations/usgs/{identifier}/info` - Station metadata +- `GET /v1/streamflow_observations/usgs/csv` - Download CSV data +- `GET /v1/streamflow_observations/usgs/parquet` - Download Parquet data + +### Hydrofabric + +- `GET /v1/hydrofabric/{identifier}/gpkg` - Download watershed subset as geopackage + +### Health Check + +- `HEAD /health` - API health status + +## Usage Examples + +### Download Streamflow Data + +```python +import requests +import pandas as pd + +# Get station information +response = requests.get( + "http://localhost:8000/v1/streamflow_observations/usgs/01031500/info" +) +station_info = response.json() + +# Download CSV data +csv_response = requests.get( + "http://localhost:8000/v1/streamflow_observations/usgs/csv", + params={ + "identifier": "01031500", + "start_date": "2023-01-01T00:00:00", + "end_date": "2023-01-31T23:59:59" + } +) +df = pd.read_csv(csv_response.text) +``` + +## Documentation + +- **Interactive API Docs**: http://localhost:8000/docs +- **Alternative Docs**: http://localhost:8000/redoc +- **OpenAPI Schema**: http://localhost:8000/openapi.json + +## Initial Parameters + +CSV files for catchment specific values are too big to be checked in. They are stored here: s3://ngwpc-dev/DanielCumpton/divide_csv.tgz +Download and untar in the icefabric/src/icefabric_api/data directory. +For now, you can run modules by listing them in the module list defined on line 108 in run_modules.py. Text files showing all parameters as key/value +pairs will be written to a files per catchment. A JSON is output to the terminal. SFT, SMP, and LASAM csv files still need work to define the parameters properly. See icefabric/src/icefabric_api/data/modules.csv for module name strings. diff --git a/src/icefabric_api/app/main.py b/src/icefabric_api/app/main.py index 216412f..d97e1b8 100644 --- a/src/icefabric_api/app/main.py +++ b/src/icefabric_api/app/main.py @@ -2,6 +2,7 @@ from fastapi import FastAPI, status from pydantic import BaseModel +from app.routers.hydrofabric.router import api_router as hydrofabric_api_router from app.routers.streamflow_observations.router import api_router as streamflow_api_router app = FastAPI( @@ -20,6 +21,7 @@ class HealthCheck(BaseModel): # Include routers +app.include_router(hydrofabric_api_router, prefix="/v1") app.include_router(streamflow_api_router, prefix="/v1") diff --git a/src/icefabric_api/app/routers/hydrofabric/__init__.py b/src/icefabric_api/app/routers/hydrofabric/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_api/app/routers/hydrofabric/conftest.py b/src/icefabric_api/app/routers/hydrofabric/conftest.py new file mode 100644 index 0000000..19850f9 --- /dev/null +++ b/src/icefabric_api/app/routers/hydrofabric/conftest.py @@ -0,0 +1,32 @@ +from pathlib import Path + +import pytest + +sample_gauges = [ + "01010000", + "02450825", + "03173000", + "04100500", + "05473450", + "06823500", + "07060710", + "08070000", + "09253000", + "10316500", + "11456000", + "12411000", + "13337000", + "14020000", +] + + +@pytest.fixture(params=sample_gauges) +def gauge_hf_uri(request) -> str: + """Returns individual gauge identifiers for parameterized testing""" + return request.param + + +@pytest.fixture +def testing_dir() -> Path: + """Returns the testing data dir""" + return Path(__file__).parents[4] / "icefabric_tools/tests/data/" diff --git a/src/icefabric_api/app/routers/hydrofabric/router.py b/src/icefabric_api/app/routers/hydrofabric/router.py new file mode 100644 index 0000000..ac864e5 --- /dev/null +++ b/src/icefabric_api/app/routers/hydrofabric/router.py @@ -0,0 +1,76 @@ +import pathlib +import tempfile +import uuid + +from fastapi import APIRouter, HTTPException, Path +from fastapi.responses import FileResponse +from pyiceberg.catalog import load_catalog +from starlette.background import BackgroundTask + +from icefabric_tools import IdType, subset + +api_router = APIRouter(prefix="/hydrofabric") + +catalog_settings: dict = { + "type": "sql", + "uri": "sqlite:////tmp/warehouse/pyiceberg_catalog.db", + "warehouse": "file:///tmp/warehouse", +} + + +@api_router.get("/{identifier}/gpkg") +async def get_hydrofabric_subset_gpkg( + identifier: str = Path( + ..., + description="Identifier to start tracing from (e.g., catchment ID, POI ID)", + examples=["01010000"], + openapi_examples={"station_example": {"summary": "USGS Gauge", "value": "01010000"}}, + ), +): + """ + Get hydrofabric subset as a geopackage file (.gpkg) + + This endpoint creates a subset of the hydrofabric data by tracing upstream + from a given identifier and returns all related geospatial layers as a + downloadable geopackage file. + """ + catalog = load_catalog("hydrofabric", **catalog_settings) + unique_id = str(uuid.uuid4())[:8] + temp_dir = pathlib.Path(tempfile.gettempdir()) + tmp_path = temp_dir / f"hydrofabric_subset_{identifier}_{unique_id}.gpkg" + try: + # Create the subset + subset(catalog=catalog, identifier=f"gages-{identifier}", id_type=IdType.HL_URI, output_file=tmp_path) + + if not tmp_path.exists(): + raise HTTPException(status_code=500, detail=f"Failed to create geopackage file at {tmp_path}") + if tmp_path.stat().st_size == 0: + tmp_path.unlink(missing_ok=True) # Clean up empty file + raise HTTPException(status_code=404, detail=f"No data found for identifier '{identifier}'") + + # Verify it's actually a file, not a directory + if not tmp_path.is_file(): + raise HTTPException(status_code=500, detail=f"Expected file but got directory at {tmp_path}") + + print(f"Returning file: {tmp_path} (size: {tmp_path.stat().st_size} bytes)") + + download_filename = f"hydrofabric_subset_{identifier}.gpkg" + + return FileResponse( + path=str(tmp_path), + filename=download_filename, + media_type="application/geopackage+sqlite3", + headers={ + "Content-Description": "Hydrofabric Subset Geopackage", + "X-Identifier": identifier, + }, + background=BackgroundTask(lambda: tmp_path.unlink(missing_ok=True)), + ) + + except HTTPException: + raise + except Exception: + # Clean up temp file if it exists + if "tmp_path" in locals() and tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + raise diff --git a/src/icefabric_api/app/routers/hydrofabric/test_router.py b/src/icefabric_api/app/routers/hydrofabric/test_router.py new file mode 100644 index 0000000..697b645 --- /dev/null +++ b/src/icefabric_api/app/routers/hydrofabric/test_router.py @@ -0,0 +1,34 @@ +from pathlib import Path + +import geopandas as gpd +import pytest + + +@pytest.mark.integration +def test_subset_hl_uri(client, gauge_hf_uri: str, testing_dir: Path, tmp_path: Path): + """Test: GET /streamflow_observations/usgs/csv""" + response = client.get( + f"/v1/hydrofabric/{gauge_hf_uri}/gpkg", + ) + + assert response.status_code == 200, "Incorrect response" + + if response.status_code == 200: + temp_gpkg = tmp_path / "structure_test.gpkg" + temp_gpkg.write_bytes(response.content) + + output_gdf = gpd.read_file(temp_gpkg, layer="network") + correct_gdf = gpd.read_file(testing_dir / f"gages-{gauge_hf_uri}.gpkg", layer="network") + assert output_gdf.equals(correct_gdf) + + output_gdf = gpd.read_file(temp_gpkg, layer="divides") + correct_gdf = gpd.read_file(testing_dir / f"gages-{gauge_hf_uri}.gpkg", layer="divides") + assert output_gdf.equals(correct_gdf) + + output_gdf = gpd.read_file(temp_gpkg, layer="flowpaths") + correct_gdf = gpd.read_file(testing_dir / f"gages-{gauge_hf_uri}.gpkg", layer="flowpaths") + assert output_gdf.equals(correct_gdf) + + output_gdf = gpd.read_file(temp_gpkg, layer="nexus") + correct_gdf = gpd.read_file(testing_dir / f"gages-{gauge_hf_uri}.gpkg", layer="nexus") + assert output_gdf.equals(correct_gdf) From 544c7cc3d5188b76e36dd353330d9af8b92d9df4 Mon Sep 17 00:00:00 2001 From: Brock Hinkson Date: Mon, 2 Jun 2025 13:15:41 -0500 Subject: [PATCH 48/97] NGWPC-6394 - Create LASAM IPE Endpoint (#19) * NGWPC-6394 - Create LASAM IPE Endpoint * added api endpoint for the subset geopackage work (#18) --------- Co-authored-by: Tadd Bindas --- src/icefabric_api/app/main.py | 2 + .../app/routers/module_params/__init__.py | 0 .../app/routers/module_params/router.py | 48 ++++++++++ src/icefabric_api/data/lasam_out.csv | 16 ++++ src/icefabric_api/data/modules.csv | 20 ++-- src/icefabric_api/src/__init__.py | 0 src/icefabric_api/src/hf_attributes.py | 96 +++++++++++++++++++ src/icefabric_api/src/run_modules.py | 60 ++++++------ .../src/icefabric_manage/map_fim_data.py | 4 +- 9 files changed, 204 insertions(+), 42 deletions(-) create mode 100644 src/icefabric_api/app/routers/module_params/__init__.py create mode 100644 src/icefabric_api/app/routers/module_params/router.py create mode 100644 src/icefabric_api/data/lasam_out.csv create mode 100644 src/icefabric_api/src/__init__.py create mode 100644 src/icefabric_api/src/hf_attributes.py diff --git a/src/icefabric_api/app/main.py b/src/icefabric_api/app/main.py index d97e1b8..9a579ee 100644 --- a/src/icefabric_api/app/main.py +++ b/src/icefabric_api/app/main.py @@ -4,6 +4,7 @@ from app.routers.hydrofabric.router import api_router as hydrofabric_api_router from app.routers.streamflow_observations.router import api_router as streamflow_api_router +from app.routers.module_params.router import api_router as module_params_router app = FastAPI( title="Icefabric API", @@ -23,6 +24,7 @@ class HealthCheck(BaseModel): # Include routers app.include_router(hydrofabric_api_router, prefix="/v1") app.include_router(streamflow_api_router, prefix="/v1") +app.include_router(module_params_router, prefix="/v1") @app.head( diff --git a/src/icefabric_api/app/routers/module_params/__init__.py b/src/icefabric_api/app/routers/module_params/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_api/app/routers/module_params/router.py b/src/icefabric_api/app/routers/module_params/router.py new file mode 100644 index 0000000..bb28cbc --- /dev/null +++ b/src/icefabric_api/app/routers/module_params/router.py @@ -0,0 +1,48 @@ +import json +import re +import os + +from fastapi import APIRouter, HTTPException, Path, Query +from fastapi.responses import Response +from pydantic import BaseModel + +from src.hf_attributes import get_hydrofabric_attributes +from src.run_modules import module_ipe + +api_router = APIRouter(prefix="/modules") +ROOT_DIR = os.path.abspath(os.curdir) + + +class Parameters(BaseModel): + gage_id: str + version: str + source: str + domain: str + modules: list[str] + + +@api_router.post("/parameters") +async def get_ipes( + query: Parameters +): + if query.version != '2.1' and query.version != '2.2': + raise HTTPException(status_code=422, detail="Icefabric version must be 2.2 or 2.1") + elif query.version == '2.1' and query.domain != 'CONUS': + raise HTTPException(status_code=422, detail="oCONUS domains not availiable in Icefabric version 2.1") + + gpkg_file = f"{ROOT_DIR}/data/gauge_{query.gage_id}.gpkg" + if not os.path.exists(gpkg_file): + raise HTTPException(status_code=422, detail="Gage ID/geopackage not found") + + # Get divide attributes from the geopackage + attr = get_hydrofabric_attributes(gpkg_file, query.version, query.domain) + + all_modules = [] + for mod in query.modules: + mod_json = module_ipe(mod, attr, query.domain, query.version) + all_modules.append(mod_json) + + final_json = json.dumps({"modules": all_modules}) + final_json = re.sub("NaN", "null", final_json) + + return final_json diff --git a/src/icefabric_api/data/lasam_out.csv b/src/icefabric_api/data/lasam_out.csv new file mode 100644 index 0000000..285b009 --- /dev/null +++ b/src/icefabric_api/data/lasam_out.csv @@ -0,0 +1,16 @@ +variable,description +actual_evapotranspiration,volume of AET +giuh_runoff,volume of giuh runoff +groundwater_to_stream_recharge,outgoing water from ground reservoir to stream channel +infiltration,volume of infiltrated water +mass_balance,mass balance error +percolation,volume of water leaving soil through the bottom of the domain (ground water recharge) +potential_evapotranspiration,volume of PET +precipitation,total precipitation +soil_depth_layers,Soil depth layers +soil_depth_wetting_fronts,Soil depth wetting fronts +soil_moisture_wetting_fronts,Soil moisture wetting front +soil_num_wetting_fronts,Number of soil wetting fronts +soil_storage,volume of water left +surface_runoff,volume of water surface runoff +total_discharge,total outgoing water \ No newline at end of file diff --git a/src/icefabric_api/data/modules.csv b/src/icefabric_api/data/modules.csv index 4c085b5..d291784 100644 --- a/src/icefabric_api/data/modules.csv +++ b/src/icefabric_api/data/modules.csv @@ -1,10 +1,10 @@ -module,file -CFE-X,cfe_params.csv -Noah-OWP-Modular,noah_owp_modular_params.csv -Snow17,snow17_params.csv -Sac-SMA,sac_sma_params.csv -TopModel,topmodel_params.csv -UEB,ueb_params.csv -SFT,sft_params.csv -SMP,smp_params.csv -LASAM,lasam_params.csv +module,file,outputs +CFE-X,cfe_params.csv, +Noah-OWP-Modular,noah_owp_modular_params.csv, +Snow17,snow17_params.csv, +Sac-SMA,sac_sma_params.csv, +TopModel,topmodel_params.csv, +UEB,ueb_params.csv, +SFT,sft_params.csv, +SMP,smp_params.csv, +LASAM,lasam_params.csv,lasam_out.csv diff --git a/src/icefabric_api/src/__init__.py b/src/icefabric_api/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_api/src/hf_attributes.py b/src/icefabric_api/src/hf_attributes.py new file mode 100644 index 0000000..22dc490 --- /dev/null +++ b/src/icefabric_api/src/hf_attributes.py @@ -0,0 +1,96 @@ +import logging + +import geopandas as gpd +from pyproj import Transformer + +logger = logging.getLogger(__name__) + + +def get_hydrofabric_attributes(gpkg_file, version, domain): + + attr_layer = 'divide-attributes' + if version == '2.1': + attr_layer = 'model-attributes' + + # Map oCONUS lat/lon attribute names to CONUS names. + column_names_xy = {'X': 'centroid_x', 'Y': 'centroid_y'} + # Puerto Rico has a number of attribute names that don't match the other domains in HF v2.2 + column_names_pr = {'dksat_Time._soil_layers_stag.1': 'geom_mean.dksat_soil_layers_stag.1', + 'dksat_Time._soil_layers_stag.2': 'geom_mean.dksat_soil_layers_stag.2', + 'dksat_Time._soil_layers_stag.3': 'geom_mean.dksat_soil_layers_stag.3', + 'dksat_Time._soil_layers_stag.4': 'geom_mean.dksat_soil_layers_stag.4', + 'mean.cwpvt_Time.': 'mean.cwpvt', + 'mean.mfsno_Time.': 'mean.mfsno', + 'mean.mp_Time.': 'mean.mp', + 'mean.refkdt_Time.': 'mean.refkdt', + 'mean.slope_Time.': 'mean.slope_1km', + 'mean.smcmax_Time._soil_layers_stag.1': 'mean.smcmax_soil_layers_stag.1', + 'mean.smcmax_Time._soil_layers_stag.2': 'mean.smcmax_soil_layers_stag.2', + 'mean.smcmax_Time._soil_layers_stag.3': 'mean.smcmax_soil_layers_stag.3', + 'mean.smcmax_Time._soil_layers_stag.4': 'mean.smcmax_soil_layers_stag.4', + 'mean.smcwlt_Time._soil_layers_stag.1': 'mean.smcwlt_soil_layers_stag.1', + 'mean.smcwlt_Time._soil_layers_stag.2': 'mean.smcwlt_soil_layers_stag.2', + 'mean.smcwlt_Time._soil_layers_stag.3': 'mean.smcwlt_soil_layers_stag.3', + 'mean.smcwlt_Time._soil_layers_stag.4': 'mean.smcwlt_soil_layers_stag.4', + 'mean.vcmx25_Time.': 'mean.vcmx25', + 'mode.bexp_Time._soil_layers_stag.1': 'mode.bexp_soil_layers_stag.1', + 'mode.bexp_Time._soil_layers_stag.2': 'mode.bexp_soil_layers_stag.2', + 'mode.bexp_Time._soil_layers_stag.3': 'mode.bexp_soil_layers_stag.3', + 'mode.bexp_Time._soil_layers_stag.4': 'mode.bexp_soil_layers_stag.4', + 'psisat_Time._soil_layers_stag.1': 'geom_mean.psisat_soil_layers_stag.1', + 'psisat_Time._soil_layers_stag.2': 'geom_mean.psisat_soil_layers_stag.2', + 'psisat_Time._soil_layers_stag.3': 'geom_mean.psisat_soil_layers_stag.3', + 'psisat_Time._soil_layers_stag.4': 'geom_mean.psisat_soil_layers_stag.4'} + + # Get list of catchments from gpkg divides layer using geopandas + try: + divide_attr = gpd.read_file(gpkg_file, layer=attr_layer) + divide_layer = gpd.read_file(gpkg_file, layer='divides') + except: # TODO: Replace 'except' with proper catch + error_str = 'Error opening ' + gpkg_file + error = dict(error=error_str) + logger.error(error_str) + return error + + # Get catchement area from divides layer and append to attributes data frame + area = divide_layer[['divide_id', 'areasqkm']] + divide_attr = divide_attr.join(area.set_index('divide_id'), on='divide_id') + + # Account for differences in column names between CONUS and oCONUS + if version == '2.2' and domain != 'CONUS': + divide_attr.rename(columns=column_names_xy, inplace=True) + if version == '2.2' and domain == 'Puerto_Rico': + divide_attr.rename(columns=column_names_pr, inplace=True) + + # Soil and vegetation types are read from the gpkg as floats, but need to be ints + if version == '2.1': + divide_attr = divide_attr.astype({'ISLTYP': 'int'}) + divide_attr = divide_attr.astype({'IVGTYP': 'int'}) + elif version == '2.2': + divide_attr = divide_attr.astype({'mode.ISLTYP': 'int'}) + divide_attr = divide_attr.astype({'mode.IVGTYP': 'int'}) + + # Zmax/max_gw_storage units are mm in the hydrofabric but CFE expects m. + if version == '2.1': + divide_attr['gw_Zmax'] = divide_attr['gw_Zmax'].apply(lambda x: x/1000) + + elif version == '2.2': + divide_attr['mean.Zmax'] = divide_attr['mean.Zmax'].apply(lambda x: x/1000) + + # Elevation in 2.2 is in cm, convert to m. Except for AK, which is still in m. + if version == '2.2' and domain != 'Alaska': + divide_attr['mean.elevation'] = divide_attr['mean.elevation'].apply(lambda x: x/100) + + # Convert centroid_x and centroid_y (lat/lon) from the domain's CRS to WGS84 + # for decimal degrees for 2.2. + if version == '2.2': + crs = divide_layer.crs + transformer = Transformer.from_crs(crs, 4326) + for index, row in divide_attr.iterrows(): + y = row['centroid_y'] + x = row['centroid_x'] + wgs84_latlon = transformer.transform(x, y) + divide_attr.loc[index, 'centroid_y'] = wgs84_latlon[0] # latitude + divide_attr.loc[index, 'centroid_x'] = wgs84_latlon[1] # longitude + + return divide_attr diff --git a/src/icefabric_api/src/run_modules.py b/src/icefabric_api/src/run_modules.py index eeb79d8..3153eb5 100644 --- a/src/icefabric_api/src/run_modules.py +++ b/src/icefabric_api/src/run_modules.py @@ -1,16 +1,13 @@ import pandas as pd -from hf_attributes import * +from hf_attributes import get_hydrofabric_attributes +import json +import re +import os -gpkg_file = "../data/gauge_06710385.gpkg" -version = "2.2" -domain = "CONUS" +ROOT_DIR = os.path.abspath(os.curdir) -# Get divide attributes from the geopackage -attr = get_hydrofabric_attributes(gpkg_file, version, domain) -# Get list of divide IDs - -def module_ipe(module, attr, domain, version): +def module_ipe(module, attr, domain, version, cfg_write=None): # Create empty dataframe for csv parameters csv_params = pd.DataFrame() @@ -18,8 +15,12 @@ def module_ipe(module, attr, domain, version): divides = attr["divide_id"].to_list() # Look up parameter csv file for module - module_df = pd.read_csv("../data/modules.csv") + has_output_vars = True + module_df = pd.read_csv(f"{ROOT_DIR}/data/modules.csv") param_file = module_df.loc[module_df["module"] == module]["file"].to_string(index=False) + out_file = module_df.loc[module_df["module"] == module]["outputs"].to_string(index=False) + if out_file == "NaN": + has_output_vars = False # Get module parameters datatypes = { @@ -35,7 +36,16 @@ def module_ipe(module, attr, domain, version): "divide_attr_name": "object", "source_file": "object", } - module_params = pd.read_csv(f"../data/{param_file}", dtype=datatypes) + # Get output vars + out_datatypes = { + "variable": "object", + "description": "object" + } + + module_params = pd.read_csv(f"{ROOT_DIR}/data/{param_file}", dtype=datatypes) + output_vars = pd.DataFrame() + if has_output_vars: + output_vars = pd.read_csv(f"{ROOT_DIR}/data/{out_file}", dtype=out_datatypes) # Filter data frame for parameter names and default values. Create dictionary to collect # parameters for cfg files. @@ -64,7 +74,7 @@ def module_ipe(module, attr, domain, version): csv_file = module_params.loc[module_params["source"] == "csv"]["source_file"].to_list() csv_file = csv_file[0] csv_file = f"{csv_file}_{domain}_{version}.csv" - csv_params = pd.read_csv(f"../data/{csv_file}") + csv_params = pd.read_csv(f"{ROOT_DIR}/data/{csv_file}") for divide in divides: attr_div = attr.loc[attr["divide_id"] == divide] @@ -76,9 +86,10 @@ def module_ipe(module, attr, domain, version): all_cats[divide].update(csv_params_div) # print(all_cats[divide]) - write_cfg(all_cats) + if cfg_write: + write_cfg(all_cats) s3_uri = "s3://" - module_json = write_json(all_cats[divides[1]], module_params, module, s3_uri) + module_json = write_json(all_cats[divides[1]], module_params, output_vars, module, s3_uri) return module_json @@ -86,7 +97,7 @@ def write_cfg(params): divides = params.keys() for divide in divides: cfg_file = f"{divide}_bmi_config_cfe.txt" - f = open(cfg_file, "x") + f = open(cfg_file, "w") for key, value in params[divide].items(): f.write(f"{key}={value}\n") @@ -94,7 +105,7 @@ def write_cfg(params): f.close() -def write_json(params, all_params, module, s3_uri): +def write_json(params, all_params, output_vars, module, s3_uri): json_columns = ["name", "description", "default_value", "min", "max", "data_type", "units"] all_params = all_params.loc[all_params["calibratable"] == True, json_columns] all_params = all_params.to_dict(orient="records") @@ -109,19 +120,8 @@ def write_json(params, all_params, module, s3_uri): "parameter_file": {"uri": s3_uri}, "calibrate_parameters": all_params, } + if not output_vars.empty: + output_vars = output_vars.to_dict(orient="records") + module_dict.update({"output_variables": output_vars}) return module_dict - - -all_modules = [] - -# modules = ['CFE-X', 'Noah-OWP-Modular'] -modules = ["UEB"] - -for module in modules: - module_json = module_ipe(module, attr, domain, version) - all_modules.append(module_json) - -final_json = {"modules": all_modules} - -print(final_json) diff --git a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py index 03e170e..d1c7b00 100644 --- a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py +++ b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py @@ -524,9 +524,9 @@ def save_crs_map(self): filterbyriver = grouped_xslayers.get_group(river_name) self.crs_dict[(huc_num, model_id)].update( { - river_name: f"ESPG: { + river_name: f"""ESPG: { str(filterbyriver.crs.to_epsg()) if filterbyriver.crs.to_epsg() else None - }, {filterbyriver.crs.name}," + }, {filterbyriver.crs.name},""" } ) From f6c891583a3316bb46e293e2bc71041f29cba012 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Mon, 2 Jun 2025 17:19:56 -0400 Subject: [PATCH 49/97] Delete src/icefabric_manage/scripts directory (#22) Deleting duplicate `scripts` which should were added due to a rebasing mistake --- .../scripts/build_hydrofabric.py | 41 ------------------- .../build_usgs_streamflow_observations.py | 27 ------------ 2 files changed, 68 deletions(-) delete mode 100644 src/icefabric_manage/scripts/build_hydrofabric.py delete mode 100644 src/icefabric_manage/scripts/build_usgs_streamflow_observations.py diff --git a/src/icefabric_manage/scripts/build_hydrofabric.py b/src/icefabric_manage/scripts/build_hydrofabric.py deleted file mode 100644 index c669f7c..0000000 --- a/src/icefabric_manage/scripts/build_hydrofabric.py +++ /dev/null @@ -1,41 +0,0 @@ -from pathlib import Path - -from dotenv import load_dotenv -from pyiceberg.catalog import load_catalog - -from icefabric_manage import build - -load_dotenv() - -if __name__ == "__main__": - warehouse_path = Path("/tmp/warehouse") - warehouse_path.mkdir(exist_ok=True) - catalog_settings = { - "type": "sql", - "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", - "warehouse": f"file://{warehouse_path}", - } - - namespace = "hydrofabric" - catalog = load_catalog(namespace, **catalog_settings) - layers = [ - "divide-attributes", - "divides", - "flowpath-attributes-ml", - "flowpath-attributes", - "flowpaths", - "hydrolocations", - "lakes", - "network", - "nexus", - "pois", - ] - for layer in layers: - print(f"building layer: {layer}") - build( - catalog=catalog, - parquet_file=f"s3://hydrofabric-data/icefabric/hydrofabric/{layer}.parquet", - namespace=namespace, - table_name=layer, - ) - print("Build successful") diff --git a/src/icefabric_manage/scripts/build_usgs_streamflow_observations.py b/src/icefabric_manage/scripts/build_usgs_streamflow_observations.py deleted file mode 100644 index 1d5a894..0000000 --- a/src/icefabric_manage/scripts/build_usgs_streamflow_observations.py +++ /dev/null @@ -1,27 +0,0 @@ -from pathlib import Path - -from dotenv import load_dotenv -from pyiceberg.catalog import load_catalog - -from icefabric_manage import build - -load_dotenv() - -if __name__ == "__main__": - warehouse_path = Path("/tmp/warehouse") - warehouse_path.mkdir(exist_ok=True) - catalog_settings = { - "type": "sql", - "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", - "warehouse": f"file://{warehouse_path}", - } - - namespace = "observations" - catalog = load_catalog(namespace, **catalog_settings) - build( - catalog=catalog, - parquet_file="s3://hydrofabric-data/icefabric/observations/usgs_hourly.parquet", - namespace=namespace, - table_name="usgs_hourly", - ) - print("Build successful") From 56b980772268c2c595c470a9942c242ef410bef3 Mon Sep 17 00:00:00 2001 From: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> Date: Tue, 3 Jun 2025 15:26:15 -0400 Subject: [PATCH 50/97] JIRA-6381: EDFS QA: Clean up icefabric_manage repo and examples to clean up duplicate code and abide by code style (#24) --- .gitignore | 1 + .pre-commit-config.yaml | 2 +- examples/Demo_Iceberg.ipynb | 1441 +---------------- pyproject.toml | 14 +- src/icefabric_manage/builds/__init__.py | 0 .../build_iceberg_bathymetry_ml_auxiliary.py | 40 + src/icefabric_manage/pyproject.toml | 2 +- .../src/icefabric_manage/iceberg_table.py | 132 +- .../src/icefabric_manage/map_fim_data.py | 98 +- 9 files changed, 235 insertions(+), 1495 deletions(-) create mode 100644 src/icefabric_manage/builds/__init__.py create mode 100644 src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py diff --git a/.gitignore b/.gitignore index 9af092f..aff49d6 100644 --- a/.gitignore +++ b/.gitignore @@ -185,3 +185,4 @@ data/ .zarr *.tiff *.tif +examples/iceberg_catalog/metadata/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a258935..88b32b5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,4 +22,4 @@ repos: - repo: https://github.com/astral-sh/uv-pre-commit rev: 0.5.7 hooks: - - id: uv-lock + - id: uv-lock \ No newline at end of file diff --git a/examples/Demo_Iceberg.ipynb b/examples/Demo_Iceberg.ipynb index 48a7660..e9242cb 100644 --- a/examples/Demo_Iceberg.ipynb +++ b/examples/Demo_Iceberg.ipynb @@ -6,270 +6,36 @@ "metadata": {}, "source": [ "# Introduction:\n", - "## Generation of Iceberg Catalog for Parquet files" + "## Generation of Iceberg Catalog for Parquet files\n", + "\n", + "Note:\n", + "\n", + "For building from S3, requires `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` in your environmental variables. The recommended method to set is to store in this directory's `.env` file.\n", + "\n", + "For building locally, store `ml_auxiliary_data.parquet` files in `./data/bathymetry/ml_axuiliary_data/vpuid==XX/ml_auxiliary_data.parquet`" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "7e3f71a8-a823-4df9-90df-8d6e62969faa", "metadata": {}, "outputs": [], "source": [ "import os\n", - "import re\n", "import warnings\n", "\n", - "import boto3\n", - "import pyarrow as pa\n", "import pyarrow.parquet as pq\n", - "import s3fs\n", + "from dotenv import load_dotenv\n", "from pyiceberg.catalog import load_catalog\n", + "from pyiceberg.exceptions import NamespaceAlreadyExistsError\n", "from pyiceberg.expressions import And, GreaterThan, GreaterThanOrEqual, LessThan\n", - "from pyiceberg.schema import Schema\n", - "from pyiceberg.types import (\n", - " BinaryType,\n", - " BooleanType,\n", - " DoubleType,\n", - " LongType,\n", - " NestedField,\n", - " StringType,\n", - ")\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "\n", - "class IcebergTable:\n", - " \"\"\"\n", - " Create a Iceberg table per parquet file w/ its inherited schema set.\n", - "\n", - " Note: Allows for user to have the option to read parquets from S3 or locally. It is okay to expect\n", - " following warning statements throughout process: \"Iceberg does not have a dictionary type. will be inferred as string on read.\"\n", - "\n", - " \"\"\"\n", - "\n", - " def __init__(self):\n", - " # Generate folder for iceberg catalog\n", - " if not os.path.exists(f\"{os.getcwd()}/iceberg_catalog\"):\n", - " os.makedirs(f\"{os.getcwd()}/iceberg_catalog\")\n", - "\n", - " # Set location of where the iceberg catalog will reside\n", - " os.environ[\"PYICEBERG_HOME\"] = os.getcwd()\n", - "\n", - " # Initialize namespace to be set for Iceberg catalog\n", - " self.namespace = \"\"\n", - "\n", - " def read_data_dirs(self, data_dir):\n", - " \"\"\"\n", - " Extract the list of parquet directories.\n", - "\n", - " Args:\n", - " data_dir (str): Parent directory of the parquet files.\n", - " Note: All the ml_auxiliary_data parquet\n", - " files are save under same filenames,\n", - " but categorized by 'vpuid' conditions.\n", - "\n", - " Return (list): List of directories associated with each parquet file.\n", - "\n", - " \"\"\"\n", - " parquet_list = []\n", - " for folder, subfolders, files in os.walk(data_dir):\n", - " if folder != data_dir:\n", - " for file in files:\n", - " parquet_list.append(f\"{folder}/{file}\")\n", - "\n", - " return parquet_list\n", - "\n", - " def read_data(self, parquet_file_path):\n", - " \"\"\"\n", - " Load a single parquet as a Pyarrow table.\n", - "\n", - " Args:\n", - " parquet_file_path (str): Directory of a single parquet.\n", - "\n", - "\n", - " Return: A Pyarrow table.\n", - "\n", - " \"\"\"\n", - " data = pq.read_table(parquet_file_path)\n", - "\n", - " return data\n", - "\n", - " def establish_catalog(self, catalog_name, namespace):\n", - " \"\"\"\n", - " Creates a new Iceberg catalog.\n", - "\n", - " Args:\n", - " catalog_name (str): Name of the catalog to be created.\n", - " Default: 'dev' for development catalog\n", - " namespace (str): Name of namespace.\n", - "\n", - " Return: None\n", - "\n", - " \"\"\"\n", - " # Establish a new Iceberg catalog & its configuration\n", - " self.catalog = load_catalog(\n", - " name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"}\n", - " )\n", - "\n", - " # Establish namespace to be create w/in catalog\n", - " self.namespace = namespace\n", - " if self.namespace not in self.catalog.list_namespaces():\n", - " self.catalog.create_namespace(self.namespace)\n", - "\n", - " return\n", - "\n", - " def convert_pyarrow_to_iceberg_schema(self, arrow_schema):\n", - " \"\"\"\n", - " Translate a given Pyarrow schema into a schema acceptable by Iceberg.\n", - "\n", - " Args:\n", - " arrow_schema (object): Pyarrow schema read from the loaded\n", - " parquet of interest.\n", - "\n", - " Return (object): Iceberg schema\n", - "\n", - " \"\"\"\n", - " fields = []\n", - " for idx in range(len(arrow_schema)):\n", - " # Extraction of the datatype & name of each schema row\n", - " field_name = arrow_schema.field(idx).name\n", - " arrow_type = arrow_schema.field(idx).type\n", - "\n", - " # Iceberg datatypes to pyarrow datatypes\n", - " if pa.types.is_int32(arrow_type):\n", - " iceberg_type = LongType()\n", - " elif pa.types.is_string(arrow_type):\n", - " iceberg_type = StringType()\n", - " elif pa.types.is_float64(arrow_type):\n", - " iceberg_type = DoubleType()\n", - " elif pa.types.is_int64(arrow_type):\n", - " iceberg_type = LongType()\n", - " elif pa.types.is_boolean(arrow_type):\n", - " iceberg_type = BooleanType()\n", - " elif pa.types.is_binary(arrow_type):\n", - " iceberg_type = BinaryType()\n", - " elif pa.types.is_dictionary(arrow_type):\n", - " if pa.types.is_string(arrow_type.value_type):\n", - " iceberg_type = StringType()\n", - " elif pa.types.is_int32(arrow_type.value_type):\n", - " iceberg_type = LongType()\n", - " else:\n", - " raise ValueError(f\"Unsupported PyArrow type: {arrow_type}\")\n", - "\n", - " # Establish the new schema acceptable to Iceberg\n", - " fields.append(\n", - " NestedField(field_id=idx + 1, required=False, name=field_name, field_type=iceberg_type)\n", - " )\n", - " # Iceberg schema\n", - " schema = Schema(*fields)\n", - "\n", - " return schema\n", - "\n", - " def create_table_for_parquet(self, iceberg_tablename, data_table, schema):\n", - " \"\"\"\n", - " Convert parquet Pyarrow table to iceberg table & allocate Iceberg catalog under the ./iceberg_catalog directory.\n", - "\n", - " Args:\n", - " iceberg_tablename (str): Name of the Iceberg table to be created.\n", - "\n", - " data_table (object): Pyarrow table\n", - "\n", - " schema (object): Unique Iceberg schema to be set for the Iceberg table.\n", - "\n", - " namespace (str): Namespace for which the Iceberg table will reside within\n", - " the Iceberg catalog.\n", - "\n", - " Return: None\n", - "\n", - " \"\"\"\n", - " # Create an Iceberg table\n", - " iceberg_table = self.catalog.create_table(\n", - " identifier=f\"{self.namespace}.{iceberg_tablename}\",\n", - " schema=schema,\n", - " location=f\"{os.environ['PYICEBERG_HOME']}/iceberg_catalog\",\n", - " )\n", - "\n", - " # Updates the Iceberg table with data of interest.\n", - " iceberg_table.append(data_table)\n", - "\n", - " return\n", - "\n", - " def create_table_for_all_parquets(self, parquet_files, app_name=\"mip-xs\"):\n", - " \"\"\"\n", - " Convert parquets to Iceberg tables - each w/ their inherited schema.\n", - "\n", - " Args:\n", - " parquet_files (list): List of directories of the parquet files.\n", "\n", - " app_name (str): Application to create Iceberg tables.\n", - " Options: 'mip-xs' & 'bathymetry_ml_auxiliary'\n", + "from icefabric_manage.iceberg_table import IcebergTable\n", "\n", - " Return: None\n", + "load_dotenv()\n", "\n", - " Note: The sourced data structures for the data in 'mip-xs' &\n", - " 'bathymetry_ml_auxiliary' S3 buckets differ.\n", - "\n", - " \"\"\"\n", - " for idx, parquet_file in enumerate(parquet_files):\n", - " if app_name == \"mip_xs\":\n", - " iceberg_tablename = f\"{os.path.split(os.path.split(parquet_file)[1])[1].split('.')[0]}\"\n", - "\n", - " elif app_name == \"bathymetry_ml_auxiliary\":\n", - " iceberg_tablename = f\"{os.path.split(os.path.split(parquet_file)[0])[1]}\"\n", - "\n", - " data_table = self.read_data(parquet_file)\n", - " data_pyarrow_schema = data_table.schema\n", - " schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema)\n", - " self.create_table_for_parquet(iceberg_tablename, data_table, schema)\n", - " return\n", - "\n", - " def create_table_for_all_s3parquets(self, app_name, bucket_name, profile_name=\"default\"):\n", - " \"\"\"\n", - " Convert parquets from S3 to Iceberg tables - each w/ their inherited schema.\n", - "\n", - " Args:\n", - " bucket_name (list): S3 bucket name.\n", - "\n", - " app_name (str): Application to create Iceberg tables.\n", - " Options: 'mip_xs' & 'bathymetry_ml_auxiliary'\n", - "\n", - " namespace (str): Namespace for which the Iceberg table will reside within\n", - " the Iceberg catalog.\n", - "\n", - " profile_name (str: Profile name declared in the AWS configuration file.\n", - " Default: 'default'\n", - "\n", - " Return: None\n", - "\n", - " \"\"\"\n", - " # Instantiate bucket of interest.\n", - " session = boto3.Session(profile_name=profile_name)\n", - " s3 = session.resource(\"s3\")\n", - " bucket_ob = s3.Bucket(bucket_name)\n", - " pyarrow_table = {}\n", - " for s3obj in bucket_ob.objects.all():\n", - " # For sourcing the preprocessed XS parquets from S3\n", - " if app_name == \"mip_xs\" and re.match(r\"^xs_data/.*\\.parquet$\", s3obj.key):\n", - " iceberg_tablename = f\"{os.path.split(os.path.split(s3obj.key)[1])[1].split('.')[0]}\"\n", - " pyarrow_table[iceberg_tablename] = pq.read_table(\n", - " f\"s3://{bucket_name}/{s3obj.key}\", filesystem=s3fs.S3FileSystem()\n", - " )\n", - "\n", - " # For sourcing the bathymetry_ml_auxiliary parquets from S3\n", - " elif app_name == \"bathymetry_ml_auxiliary\":\n", - " iceberg_tablename = f\"{os.path.split(os.path.split(s3obj.key)[0])[1]}\"\n", - " pyarrow_table[iceberg_tablename] = pq.read_table(\n", - " f\"s3://{bucket_name}/{s3obj.key}\", filesystem=s3fs.S3FileSystem()\n", - " )\n", - "\n", - " # Xforming each unique parquet to an iceberg table\n", - " for key, data_table in pyarrow_table.items():\n", - " data_pyarrow_schema = data_table.schema\n", - " schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema)\n", - " self.create_table_for_parquet(key, data_table, schema)\n", - " return" + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -284,103 +50,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "c74f8432-f07f-430d-830f-d0910a699ea8", "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n" - ] - } - ], + "outputs": [], "source": [ "# Scenario I\n", "# Instantiate class\n", @@ -389,11 +64,13 @@ "# Extract list of parquets\n", "parquet_list = obj.read_data_dirs(data_dir=f\"{os.getcwd()}/data/bathymetry/ml_auxiliary_data/\")\n", "\n", - "# Establish new Iceberg catalog\n", - "obj.establish_catalog(catalog_name=\"bathymetry_ml_auxiliary_dev\", namespace=\"bathymetry_ml_auxiliary\")\n", + "try:\n", + " obj.establish_catalog(catalog_name=\"bathymetry_ml_auxiliary_dev\", namespace=\"bathymetry_ml_auxiliary\")\n", "\n", - "# Generate tables w/in the new Iceberg catalog for all parquets detected\n", - "obj.create_table_for_all_parquets(parquet_list, app_name=\"bathymetry_ml_auxiliary\")" + " # Generate tables w/in the new Iceberg catalog for all parquets detected\n", + " obj.create_table_for_all_parquets(parquet_list, app_name=\"bathymetry_ml_auxiliary\")\n", + "except NamespaceAlreadyExistsError:\n", + " print(\"Table already exists! Exiting\")" ] }, { @@ -407,118 +84,28 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "ad60621b-fc16-44c7-85b9-8a66a2b1835b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as string on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n", - "Iceberg does not have a dictionary type. will be inferred as int32 on read.\n" - ] - } - ], + "outputs": [], "source": [ "# Scenario II\n", "# Instantiate class\n", "obj = IcebergTable()\n", "\n", - "# Establish new Iceberg catalog\n", - "obj.establish_catalog(catalog_name=\"bathymetry_ml_auxiliary_dev2\", namespace=\"bathymetry_ml_auxiliary\")\n", + "try:\n", + " # Establish new Iceberg catalog\n", + " obj.establish_catalog(catalog_name=\"bathymetry_ml_auxiliary_dev2\", namespace=\"bathymetry_ml_auxiliary\")\n", "\n", - "# Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", - "obj.create_table_for_all_s3parquets(\n", - " app_name=\"bathymetry_ml_auxiliary\", bucket_name=\"ngwpc-bathymetry\", profile_name=\"default\"\n", - ")" + " # Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", + " obj.create_table_for_all_s3parquets(app_name=\"bathymetry_ml_auxiliary\", bucket_name=\"ngwpc-bathymetry\")\n", + "except NamespaceAlreadyExistsError:\n", + " print(\"Table already exists! Exiting\")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "a1c58d4f-59e5-41d8-be31-536f3f790216", "metadata": {}, "outputs": [], @@ -528,10 +115,14 @@ "obj = IcebergTable()\n", "\n", "# Establish new Iceberg catalog\n", - "obj.establish_catalog(catalog_name=\"xs\", namespace=\"mip\")\n", - "\n", - "# Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", - "obj.create_table_for_all_s3parquets(app_name=\"mip_xs\", bucket_name=\"ngwpc-xs\", profile_name=\"default\")" + "try:\n", + " obj.establish_catalog(catalog_name=\"fema_xs\", namespace=\"mip\")\n", + " obj.establish_catalog(catalog_name=\"fema_xs\", namespace=\"ble\")\n", + " # Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", + " obj.create_table_for_all_s3parquets(app_name=\"mip_xs\", bucket_name=\"ngwpc-xs\")\n", + " obj.create_table_for_all_s3parquets(app_name=\"ble_xs\", bucket_name=\"ngwpc-xs\")\n", + "except NamespaceAlreadyExistsError:\n", + " print(\"Table already exists! Exiting\")" ] }, { @@ -545,71 +136,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "f449ec30-027e-45c8-91f2-9ad5402b6d17", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pyarrow.Table\n", - "hf_source: large_string\n", - "owp_ahg_c: double\n", - "owp_ahg_f: double\n", - "owp_ahg_a: double\n", - "owp_ahg_b: double\n", - "owp_ahg_k: double\n", - "owp_ahg_m: double\n", - "owp_dingman_r: double\n", - "LENGTHKM: double\n", - "slope: double\n", - "owp_bf_channel_area: double\n", - "owp_inchan_channel_area: double\n", - "owp_bf_channel_perimeter: double\n", - "owp_inchan_channel_perimeter: double\n", - "owp_bf_channel_volume: double\n", - "owp_inchan_channel_volume: double\n", - "owp_bf_channel_bed_area: double\n", - "owp_inchan_channel_bed_area: double\n", - "owp_y_bf: double\n", - "owp_y_inchan: double\n", - "owp_tw_bf: double\n", - "owp_tw_inchan: double\n", - "owp_roughness_bathy: double\n", - "owp_roughness_bathy_lower_bound: double\n", - "owp_roughness_bathy_upper_bound: double\n", - "hf_id: int64\n", - "owp_roughness_no_bathy: double\n", - "hand_source: large_string\n", - "vpuid: int64\n", - "----\n", - "hf_source: [[\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",...,\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\"]]\n", - "owp_ahg_c: [[0.2783956527709961,0.26645627617836,0.2778419256210327,0.2656676173210144,0.2778984308242798,...,0.24669642746448517,0.2410922795534134,0.21650205552577972,0.23524419963359833,0.2453560084104538]]\n", - "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.46750980615615845,0.46937182545661926,0.45957159996032715,0.4573363959789276,0.46542882919311523]]\n", - "owp_ahg_a: [[15.474262237548828,15.589872360229492,15.27043628692627,15.695556640625,15.373209953308105,...,14.39522933959961,15.831462860107422,17.09531593322754,15.73424243927002,15.698975563049316]]\n", - "owp_ahg_b: [[0.21297027170658112,0.20660685002803802,0.22359369695186615,0.21279571950435638,0.21274369955062866,...,0.22100567817687988,0.20211540162563324,0.1899542361497879,0.19244758784770966,0.20660847425460815]]\n", - "owp_ahg_k: [[0.2632668912410736,0.2649047374725342,0.2646264433860779,0.26899534463882446,0.26056915521621704,...,0.30960214138031006,0.3056097626686096,0.3103371262550354,0.3096386194229126,0.2898109257221222]]\n", - "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.31428271532058716,0.34859946370124817,0.38616055250167847,0.37841135263442993,0.31649747490882874]]\n", - "owp_dingman_r: [[34.94359157777524,78.50221647993335,23.816824026591785,71.37071616896621,68.16839964475069,...,87.87201916851028,120.53569949076702,196.1542010519919,183.4062312745436,144.50062488154873]]\n", - "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,11.041326186301118,2.2220571382098906,2.3020617663270158,7.381776970395373,10.90124605539771]]\n", - "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.00064764,0.00001,0.00286086,0.0031118,0.00064093]]\n", - "owp_bf_channel_area: [[80.46187884864187,90.44316537020227,89.3267744543078,89.62238381994659,89.90283819898646,...,33.88476675445253,48.125833094134954,45.14128117526434,41.78322001139718,56.363562127191784]]\n", - "owp_inchan_channel_area: [[56.33064405881224,47.85337776245516,45.59987281142967,48.37917782823691,50.59816670544206,...,25.20392164626514,32.69610800625728,32.75638185253578,31.798663807897064,41.19407020582869]]\n", - "owp_bf_channel_perimeter: [[59.02193299271193,56.93125233080745,56.69193540611867,56.50078213602541,55.71884696596583,...,23.818806528270343,30.790736238597308,31.668208182122697,30.244473860557726,30.83026535966577]]\n", - "owp_inchan_channel_perimeter: [[45.902837801519674,42.88972958711348,42.781428840029896,42.25541060346861,42.74371656180321,...,21.110362040213978,26.22321774860116,29.594636105720035,28.74937461636522,29.077815788520574]]\n", - "owp_bf_channel_volume: [[238808.31641383746,122992.17939660752,186691.7464348059,47767.11633624437,114784.993280073,...,374132.76248264225,106938.35095912036,103918.0174765935,308434.4112290948,614433.0593072133]]\n", - "owp_inchan_channel_volume: [[167187.57332894605,65075.024723013536,95303.11538032124,25785.23039753066,64602.07866200221,...,278284.72007038887,72652.62018698554,75407.21426593073,234730.64418647942,449066.69533706637]]\n", - "owp_bf_channel_bed_area: [[2892.2022978763703,2606.358246231509,2120.7761872525807,986.0789590461588,2397.4053647652536,...,25676.071091443406,5707.898767447053,5221.871474208872,16154.579945017962,35426.56632826127]]\n", - "owp_inchan_channel_bed_area: [[2882.6693726661933,1727.420220530991,1302.426453449232,684.7218800959778,1690.876524247671,...,20277.60153281499,4218.877531759497,3638.538549084465,11664.69130783148,24593.30687386722]]\n", - "owp_y_bf: [[1.4270710945129395,1.6684650778770447,1.6728723049163818,1.6662447452545166,1.6976696848869324,...,1.6160700917243958,1.7370615005493164,1.5624184012413025,1.5164253115653992,2.082498550415039]]\n", - "owp_y_inchan: [[1.2915553450584412,1.1687858998775482,1.128367155790329,1.2016823887825012,1.2436135411262512,...,1.3436493873596191,1.3731055855751038,1.1905770599842072,1.1925222873687744,1.5661699175834656]]\n", - "...\n" - ] - } - ], + "outputs": [], "source": [ - "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", + "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema [local]\n", "catalog_name = \"bathymetry_ml_auxiliary_dev\"\n", "namespace = \"bathymetry_ml_auxiliary\"\n", "tablename = \"vpuid=01\"\n", @@ -620,12 +152,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "6c69cdf0-90ae-4e32-8100-5aecc746edc9", "metadata": {}, "outputs": [], "source": [ - "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", + "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema [s3]\n", "catalog_name = \"bathymetry_ml_auxiliary_dev2\"\n", "namespace = \"bathymetry_ml_auxiliary\"\n", "tablename = \"vpuid=01\"\n", @@ -636,626 +168,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "57d3a536-70dc-4b1d-a162-73e376bca0dc", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pyarrow.Table\n", - "river: large_string\n", - "reach: large_string\n", - "river_reach: large_string\n", - "river_station: double\n", - "river_reach_rs: large_string\n", - "thalweg: double\n", - "xs_max_elevation: double\n", - "left_reach_length: double\n", - "right_reach_length: double\n", - "channel_reach_length: double\n", - "ras_data: large_string\n", - "station_elevation_points: large_string\n", - "bank_stations: large_string\n", - "number_of_station_elevation_points: int64\n", - "number_of_coords: int64\n", - "flows: large_string\n", - "profile_names: large_string\n", - "geometry: large_binary\n", - "huc: large_string\n", - "model_id: large_string\n", - "us_reach_id: large_string\n", - "us_network_to_id: large_string\n", - "ds_reach_id: large_string\n", - "ds_network_to_id: large_string\n", - "__index_level_0__: int64\n", - "----\n", - "river: [[\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",...,\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\"]]\n", - "reach: [[\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",...,\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\"]]\n", - "river_reach: [[\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",...,\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\"]]\n", - "river_station: [[7300,6833,6346,5913,5505,...,1842,1400,970,516,111]]\n", - "river_reach_rs: [[\"Stream0528 Reach-1 7300.0\",\"Stream0528 Reach-1 6833.0\",\"Stream0528 Reach-1 6346.0\",\"Stream0528 Reach-1 5913.0\",\"Stream0528 Reach-1 5505.0\",...,\"Stream0512 Reach-1 1842.0\",\"Stream0512 Reach-1 1400.0\",\"Stream0512 Reach-1 970.0\",\"Stream0512 Reach-1 516.0\",\"Stream0512 Reach-1 111.0\"]]\n", - "thalweg: [[217.86,215.26,213.94,213.33,213.34,...,368.01,365.61,364.04,360.64,360.73]]\n", - "xs_max_elevation: [[248.82,238.12,246.73,241.95,242.87,...,404.79,398.04,385.23,404.53,391.25]]\n", - "left_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", - "right_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", - "channel_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", - "ras_data: [[\"Type RM Length L Ch R = 1,7300,467,467,467\n", - "BEGIN DESCRIPTION:\n", - "Stream0528 16.0\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 801174.98 421275.01 800775.00 421575.00\n", - " 800324.98 422375.04\n", - "#Sta/Elev=88\n", - " 4499.1 241.30 4502.6 241.08 4508.1 240.72 4525.5 239.49 4538.6 238.45\n", - " 4541.0 238.23 4556.0 236.88 4574.5 235.11 4604.0 232.37 4610.5 231.73\n", - " 4646.5 228.57 4648.8 228.37 4652.0 228.13 4669.4 226.75 4682.5 225.74\n", - " 4689.9 225.13 4699.9 224.39 4718.4 223.19 4747.9 221.12 4754.4 220.79\n", - " 4772.2 220.03 4790.4 218.68 4795.9 218.36 4826.4 218.50 4843.8 220.20\n", - " 4854.4 222.24 4862.3 223.75 4874.9 226.81 4891.8 230.20 4895.9 230.70\n", - " 4898.3 230.99 4917.8 231.53 4934.3 232.10 4939.7 231.87 4970.2 228.16\n", - " 4987.7 225.95 4998.0 218.36 4998.2 217.86 5001.8 217.86 5002.1 218.36\n", - " 5010.7 223.52 5024.3 222.97 5041.7 222.97 5056.7 223.02 5057.3 223.02\n", - " 5061.7 223.03 5188.4 223.05 5198.0 223.04 5245.5 223.01 5255.4 223.01\n", - " 5304.2 222.95 5321.4 223.06 5336.4 223.19 5362.9 223.22 5387.5 223.49\n", - " 5420.5 223.97 5453.5 225.03 5463.2 225.49 5480.3 226.15 5494.5 226.65\n", - " 5519.6 227.58 5539.0 230.01 5552.6 231.77 5568.8 234.40 5578.4 236.10\n", - " 5585.6 237.38 5597.7 238.56 5611.1 239.63 5618.6 240.34 5645.5 241.06\n", - " 5651.6 241.22 5684.7 241.94 5715.1 242.40 5716.8 242.42 5717.7 242.44\n", - " 5721.0 242.51 5750.7 243.15 5759.0 243.35 5773.8 243.81 5783.7 244.06\n", - " 5816.7 244.98 5832.5 245.51 5849.7 246.10 5871.9 247.04 5882.8 247.48\n", - " 5906.9 248.45 5915.8 248.77 5917.0 248.82\n", - "#Mann=8,-1,0\n", - " 4499.1 0.07 0 4541.0 0.03 0 4895.9 0.05 0\n", - " 4998.0 0.05 0 5002.1 0.05 0 5056.7 0.03 0\n", - " 5198.0 0.07 0 5578.4 0.03 0\n", - "Bank Sta=4998,5002.1\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1,6833,487,487,487\n", - "BEGIN DESCRIPTION:\n", - "Stream0528 15.0\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 801125.01 421175.00 800725.00 421125.00\n", - " 800524.99 421425.02\n", - "#Sta/Elev=56\n", - " 4596.9 238.12 4607.4 237.30 4614.9 236.74 4665.4 232.76 4694.4 230.63\n", - " 4718.0 228.96 4723.4 228.56 4743.8 226.84 4752.4 226.14 4769.6 224.66\n", - " 4781.4 223.73 4810.4 221.66 4829.7 220.47 4846.9 219.48 4868.4 218.38\n", - " 4872.7 218.13 4897.4 216.99 4898.5 216.96 4926.4 216.05 4955.4 215.76\n", - " 4984.4 215.76 4998.0 215.76 4998.2 215.26 5001.8 215.26 5002.1 215.76\n", - " 5013.8 216.26 5023.9 216.41 5038.6 216.88 5048.4 217.27 5075.8 218.08\n", - " 5080.1 218.22 5083.0 218.36 5097.4 219.05 5117.6 219.94 5127.7 220.60\n", - " 5142.4 221.45 5152.2 222.35 5163.1 223.12 5168.7 223.66 5179.6 224.70\n", - " 5183.9 224.98 5186.8 225.21 5201.2 226.23 5221.4 227.59 5231.5 228.34\n", - " 5256.0 229.80 5283.3 231.49 5287.7 231.68 5290.5 231.86 5308.4 232.70\n", - " 5325.1 233.87 5329.2 234.17 5335.2 234.67 5339.5 234.93 5359.7 236.16\n", - " 5360.6 236.21\n", - "#Mann=5,-1,0\n", - " 4596.9 0.07 0 4998.0 0.07 0 5002.1 0.07 0\n", - " 5168.7 0.03 0 5339.5 0.05 0\n", - "Bank Sta=4998,5002.1\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1,6346,433,433,433\n", - "BEGIN DESCRIPTION:\n", - "Stream0528 14.0\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 800875.02 420424.98 800475.00 420825.00\n", - " 800475.00 421474.99\n", - "#Sta/Elev=82\n", - " 4433.8 246.73 4461.2 244.11 4463.3 243.94 4482.6 241.76 4501.9 240.40\n", - " 4502.9 240.29 4504.0 240.22 4523.3 237.89 4542.6 236.45 4544.7 236.25\n", - " 4583.3 232.69 4585.4 232.51 4624.0 229.18 4625.0 229.09 4626.1 229.00\n", - " 4664.7 225.22 4665.7 225.11 4666.8 225.02 4686.1 223.11 4705.4 221.69\n", - " 4707.5 221.53 4746.1 219.33 4748.2 219.25 4767.5 218.53 4786.8 217.91\n", - " 4788.8 217.86 4827.5 216.55 4829.5 216.50 4848.9 216.01 4868.2 215.56\n", - " 4869.2 215.53 4870.3 215.52 4909.9 214.99 4911.0 214.98 4930.3 214.96\n", - " 4951.7 214.68 4971.0 214.53 4992.4 214.44 4998.0 214.44 4998.2 213.94\n", - " 5001.8 213.94 5002.1 214.44 5023.2 214.64 5052.0 215.06 5058.5 215.17\n", - " 5080.8 215.64 5103.0 216.19 5109.6 216.34 5116.1 216.56 5138.3 217.12\n", - " 5160.6 217.92 5167.1 218.12 5195.9 218.62 5224.7 218.95 5246.9 219.31\n", - " 5253.4 219.38 5275.7 219.92 5282.2 220.02 5288.7 220.20 5311.0 220.94\n", - " 5317.5 221.22 5331.7 221.80 5339.8 222.13 5368.6 223.29 5397.3 224.58\n", - " 5448.4 227.28 5454.9 227.61 5461.4 227.90 5483.7 229.06 5490.2 229.36\n", - " 5512.5 230.56 5534.7 231.71 5541.2 232.03 5547.7 232.52 5561.6 233.45\n", - " 5570.0 234.02 5592.3 235.86 5598.8 236.59 5605.3 237.14 5627.6 238.74\n", - " 5634.1 238.95 5649.5 239.52\n", - "#Mann=5,-1,0\n", - " 4433.8 0.07 0 4998.0 0.07 0 5002.1 0.07 0\n", - " 5331.7 0.03 0 5561.6 0.05 0\n", - "Bank Sta=4998,5002.1\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1,5913,408,408,408\n", - "BEGIN DESCRIPTION:\n", - "Stream0528 13.0\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 800825.01 420024.99 800125.00 420825.00\n", - " 800424.99 421474.99\n", - "#Sta/Elev=104\n", - " 3931.6 241.95 3955.4 241.31 3972.5 240.61 3993.7 239.63 4004.2 239.07\n", - " 4016.2 238.50 4031.9 237.63 4059.9 235.96 4070.1 235.27 4085.8 234.13\n", - " 4103.6 233.09 4106.2 232.93 4108.4 232.83 4146.6 230.97 4147.3 230.93\n", - " 4184.9 228.66 4187.7 228.47 4191.0 228.29 4208.1 227.09 4223.1 226.17\n", - " 4234.7 225.37 4261.3 223.64 4278.4 222.50 4289.7 221.89 4299.6 221.32\n", - " 4322.1 219.94 4365.8 218.50 4371.3 218.38 4376.1 218.35 4409.5 217.87\n", - " 4414.3 217.75 4452.9 216.59 4453.2 216.58 4490.8 215.82 4493.7 215.76\n", - " 4496.9 215.71 4529.0 215.47 4534.4 215.44 4540.6 215.38 4584.3 215.05\n", - " 4605.5 215.00 4656.8 215.03 4671.8 215.01 4682.0 215.02 4715.5 214.92\n", - " 4718.0 214.91 4720.2 214.90 4738.4 214.71 4758.8 214.38 4759.2 214.38\n", - " 4796.7 213.94 4802.9 213.91 4834.9 213.87 4860.8 213.88 4873.2 213.85\n", - " 4977.7 213.83 4994.6 213.85 4997.6 213.83 4998.0 213.33 5002.1 213.33\n", - " 5002.4 213.83 5020.7 213.84 5107.7 213.88 5115.8 213.95 5129.3 214.01\n", - " 5147.5 214.24 5158.6 214.48 5179.2 215.06 5194.4 215.61 5210.9 216.17\n", - " 5227.3 216.51 5237.8 216.65 5242.6 216.68 5274.3 216.91 5281.1 216.95\n", - " 5324.5 217.51 5337.7 217.72 5339.6 217.74 5346.2 217.79 5369.4 218.11\n", - " 5389.6 218.45 5401.1 218.63 5432.4 219.23 5432.8 219.23 5433.3 219.25\n", - " 5464.5 220.54 5491.2 222.01 5496.2 222.27 5519.7 223.59 5527.9 224.04\n", - " 5541.4 224.83 5559.6 225.63 5563.1 225.81 5570.7 226.08 5588.7 226.88\n", - " 5591.3 227.00 5606.4 228.14 5623.0 229.29 5639.3 231.80 5654.7 234.26\n", - " 5671.5 236.98 5686.4 238.60 5708.0 239.45 5710.5 239.55\n", - "#Mann=5,-1,0\n", - " 3931.6 0.07 0 4997.6 0.07 0 5002.4 0.07 0\n", - " 5339.6 0.03 0 5588.7 0.05 0\n", - "Bank Sta=4997.6,5002.4\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1,5505,419,419,419\n", - "BEGIN DESCRIPTION:\n", - "Stream0528 12.0\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 800325.02 419674.95 799925.00 420875.00\n", - " 800325.02 421475.02\n", - "#Sta/Elev=138\n", - " 3724.5 242.87 3734.7 242.14 3748.3 241.26 3753.8 240.92 3775.7 239.57\n", - " 3778.7 239.38 3800.7 238.14 3809.0 237.76 3823.4 237.04 3837.6 236.50\n", - " 3839.4 236.43 3846.2 236.06 3869.7 234.89 3900.0 233.51 3916.7 232.93\n", - " 3930.4 232.39 3937.2 232.15 3957.7 231.53 3959.9 231.46 3991.0 230.58\n", - " 4007.7 230.16 4021.4 229.79 4028.2 229.48 4048.7 228.93 4051.7 228.83\n", - " 4053.2 228.77 4082.0 227.41 4096.4 226.85 4112.4 226.27 4128.2 225.76\n", - " 4128.3 225.76 4164.7 224.58 4173.0 224.34 4189.7 223.93 4230.7 222.87\n", - " 4233.7 222.78 4255.7 222.23 4264.0 222.03 4280.7 221.92 4294.4 221.80\n", - " 4301.2 221.72 4324.0 221.37 4324.7 221.35 4355.1 220.90 4412.7 220.21\n", - " 4415.0 220.18 4415.7 220.18 4417.2 220.16 4446.1 219.96 4460.5 219.92\n", - " 4463.3 219.89 4503.7 219.44 4506.7 219.40 4508.2 219.38 4537.1 219.19\n", - " 4567.4 219.07 4597.7 218.89 4599.2 218.87 4628.1 218.50 4642.5 218.25\n", - " 4658.4 218.05 4719.1 217.17 4735.8 216.87 4749.4 216.65 4752.3 216.60\n", - " 4756.2 216.54 4776.7 216.18 4779.7 216.13 4781.2 216.11 4810.1 215.65\n", - " 4824.5 215.44 4840.4 215.26 4867.7 215.24 4870.7 215.23 4872.3 215.20\n", - " 4901.1 214.74 4931.4 214.36 4961.8 214.04 4989.5 213.89 4992.5 213.87\n", - " 4997.5 213.84 4997.9 213.34 5002.2 213.34 5002.6 213.84 5007.0 213.87\n", - " 5019.0 213.88 5027.0 213.88 5039.8 213.94 5058.8 213.96 5060.5 213.96\n", - " 5061.6 213.96 5081.3 214.03 5102.0 214.02 5130.8 214.08 5143.5 214.04\n", - " 5162.6 213.90 5164.3 213.89 5200.0 213.84 5234.6 213.85 5266.4 213.94\n", - " 5268.0 213.95 5269.2 213.97 5274.7 214.05 5281.1 214.14 5303.7 214.47\n", - " 5318.3 214.63 5330.3 214.82 5338.3 214.84 5351.1 215.12 5370.1 215.66\n", - " 5372.9 215.73 5392.6 216.12 5407.5 216.36 5422.0 216.72 5434.1 217.06\n", - " 5442.1 217.26 5476.7 218.25 5496.3 219.18 5511.3 219.81 5517.1 220.32\n", - " 5525.8 221.00 5537.8 222.14 5545.9 222.85 5558.6 224.54 5576.4 226.21\n", - " 5577.7 226.33 5580.4 226.58 5600.1 227.92 5615.0 229.16 5620.8 229.65\n", - " 5629.6 231.15 5641.6 232.77 5649.6 234.30 5681.4 238.68 5683.1 238.85\n", - " 5684.2 238.99 5703.8 239.24 5710.6 239.51\n", - "#Mann=10,-1,0\n", - " 3724.5 0.03 0 3753.8 0.07 0 3837.6 0.035 0\n", - " 4128.2 0.07 0 4128.3 0.035 0 4463.3 0.07 0\n", - " 4997.5 0.07 0 5002.6 0.07 0 5281.1 0.03 0\n", - " 5576.4 0.05 0\n", - "Bank Sta=4997.5,5002.6\n", - "Exp/Cntr=0.3,0.1\n", - "\",...,\"Type RM Length L Ch R = 1,1842,442,442,442\n", - "BEGIN DESCRIPTION:\n", - "Stream0512 4.0\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 808374.98 370125.01 808125.00 370325.00\n", - " 807724.98 371125.04\n", - "#Sta/Elev=373\n", - " 4675.5 403.33 4680.2 403.38 4680.6 403.38 4681.2 403.40 4685.3 403.62\n", - " 4688.6 403.99 4691.8 404.23 4697.1 404.57 4702.3 404.79 4705.5 404.70\n", - " 4708.8 404.51 4712.9 404.12 4714.0 403.95 4718.4 402.69 4722.4 401.55\n", - " 4723.5 401.17 4730.9 398.26 4734.0 397.03 4739.4 394.77 4741.7 393.63\n", - " 4744.6 392.18 4746.4 391.30 4747.8 390.68 4755.2 387.31 4756.3 386.83\n", - " 4764.7 383.43 4765.7 383.06 4769.9 381.47 4773.2 380.39 4779.3 378.44\n", - " 4781.7 377.87 4786.9 376.70 4790.1 376.14 4797.5 374.97 4798.6 374.81\n", - " 4802.8 374.14 4807.0 373.34 4808.0 373.12 4815.5 371.38 4816.9 371.06\n", - " 4818.6 370.75 4823.9 369.88 4826.3 369.67 4829.2 369.62 4832.4 369.53\n", - " 4835.7 369.45 4839.7 369.42 4840.9 369.42 4849.3 369.35 4849.8 369.34\n", - " 4850.3 369.33 4857.8 369.31 4860.9 369.39 4866.2 369.51 4871.5 369.51\n", - " 4874.7 369.48 4882.0 369.47 4883.1 369.47 4892.6 369.58 4896.7 369.66\n", - " 4900.0 369.70 4901.4 369.72 4903.2 369.72 4906.1 369.74 4908.5 369.71\n", - " 4913.8 369.67 4917.0 369.65 4924.3 369.64 4925.4 369.63 4929.6 369.63\n", - " 4933.9 369.62 4934.3 369.61 4934.9 369.61 4942.3 369.51 4943.7 369.48\n", - " 4945.5 369.48 4948.4 369.49 4950.8 369.52 4953.1 369.57 4956.0 369.60\n", - " 4959.3 369.59 4962.5 369.52 4966.6 369.39 4967.2 369.37 4967.7 369.37\n", - " 4972.1 369.49 4976.2 369.57 4976.6 369.58 4977.2 369.60 4984.6 369.75\n", - " 4987.8 369.71 4990.7 369.62 4993.1 369.57 4995.6 369.51 4997.2 368.51\n", - " 4997.7 368.01 5002.4 368.01 5002.8 368.51 5003.9 369.54 5004.9 369.54\n", - " 5005.9 369.55 5012.3 369.54 5018.6 369.43 5019.7 369.40 5020.7 369.39\n", - " 5024.9 369.37 5033.4 369.48 5034.4 369.50 5041.8 369.47 5048.2 369.39\n", - " 5050.2 369.37 5054.5 369.33 5056.6 369.30 5064.0 369.28 5065.0 369.29\n", - " 5069.2 369.32 5071.3 369.36 5077.7 369.44 5078.7 369.45 5079.8 369.46\n", - " 5086.1 369.48 5093.5 369.40 5094.6 369.39 5100.9 369.39 5108.3 369.41\n", - " 5108.6 369.41 5109.3 369.40 5113.5 369.36 5115.7 369.35 5118.5 369.36\n", - " 5123.0 369.33 5124.1 369.33 5128.3 369.32 5130.4 369.32 5136.8 369.34\n", - " 5137.8 369.34 5138.2 369.34 5138.9 369.34 5143.1 369.27 5148.0 369.22\n", - " 5153.6 369.21 5160.0 369.21 5166.3 369.24 5167.3 369.24 5168.4 369.24\n", - " 5172.6 369.20 5174.7 369.14 5177.5 369.11 5182.1 369.05 5183.1 369.05\n", - " 5187.4 369.04 5189.5 369.01 5196.9 368.83 5197.9 368.80 5204.2 368.79\n", - " 5211.6 368.83 5212.0 368.85 5212.7 368.83 5216.9 368.77 5219.0 368.65\n", - " 5225.4 368.52 5226.4 368.51 5227.4 368.56 5233.8 368.96 5236.6 369.09\n", - " 5241.2 369.20 5241.5 369.19 5242.2 369.18 5248.6 369.16 5254.9 369.27\n", - " 5255.9 369.28 5257.0 369.32 5261.2 369.48 5263.3 369.50 5270.7 369.62\n", - " 5271.7 369.64 5276.0 369.64 5278.1 369.60 5284.4 369.73 5285.5 369.74\n", - " 5286.5 369.77 5290.7 370.01 5292.9 370.18 5295.7 370.45 5300.2 370.70\n", - " 5300.6 370.73 5301.3 370.73 5305.5 370.75 5307.6 370.74 5313.9 370.64\n", - " 5315.0 370.62 5315.4 370.63 5316.1 370.66 5322.4 371.00 5328.7 371.58\n", - " 5329.8 371.69 5330.1 371.71 5330.8 371.75 5337.2 372.08 5343.5 372.16\n", - " 5344.5 372.16 5345.6 372.17 5349.8 372.19 5351.9 372.33 5358.3 372.71\n", - " 5359.3 372.77 5359.7 372.80 5360.4 372.84 5366.7 373.10 5369.5 373.24\n", - " 5374.1 373.32 5375.1 373.37 5379.3 373.58 5381.5 373.67 5384.3 373.72\n", - " 5388.8 373.95 5389.9 373.97 5394.1 374.06 5396.2 374.14 5402.6 374.35\n", - " 5403.6 374.38 5404.7 374.43 5408.9 374.63 5411.0 374.71 5417.3 374.87\n", - " 5418.4 374.90 5419.4 374.90 5425.8 374.87 5428.6 374.93 5433.1 375.08\n", - " 5433.5 375.10 5440.5 375.56 5447.9 375.83 5449.0 375.86 5453.2 376.01\n", - " 5455.3 376.08 5458.1 376.24 5462.7 376.33 5463.0 376.35 5463.7 376.37\n", - " 5467.9 376.46 5470.1 376.48 5476.4 376.60 5477.4 376.61 5477.8 376.61\n", - " 5478.5 376.64 5484.8 376.90 5491.2 377.07 5492.2 377.11 5493.2 377.16\n", - " 5497.5 377.34 5499.6 377.38 5507.0 377.76 5508.0 377.83 5514.4 378.29\n", - " 5517.2 378.43 5521.7 378.66 5522.8 378.70 5529.1 378.96 5535.4 379.18\n", - " 5536.5 379.22 5536.9 379.24 5543.9 379.31 5550.2 379.65 5551.3 379.69\n", - " 5552.3 379.73 5558.7 380.03 5566.0 380.26 5567.1 380.33 5571.3 380.58\n", - " 5573.4 380.71 5579.8 380.87 5580.8 380.91 5581.2 380.90 5581.9 380.90\n", - " 5588.2 380.91 5591.0 381.03 5595.6 381.36 5596.6 381.45 5600.8 381.84\n", - " 5603.0 381.99 5605.8 382.17 5610.3 382.37 5610.7 382.39 5611.4 382.40\n", - " 5617.7 382.54 5624.1 382.67 5625.1 382.68 5626.2 382.70 5630.4 382.74\n", - " 5632.5 382.79 5638.8 383.08 5639.9 383.13 5640.9 383.21 5647.3 383.61\n", - " 5650.1 383.73 5654.6 383.94 5655.0 383.95 5655.7 383.98 5662.0 384.22\n", - " 5669.4 384.48 5674.7 384.69 5676.8 384.78 5683.1 385.19 5684.2 385.25\n", - " 5685.2 385.32 5691.6 385.81 5697.9 386.41 5698.9 386.49 5699.3 386.52\n", - " 5700.0 386.60 5704.2 386.93 5706.3 387.12 5713.7 387.55 5714.1 387.56\n", - " 5714.8 387.59 5721.1 387.82 5727.4 388.04 5728.5 388.07 5728.8 388.08\n", - " 5729.5 388.13 5735.9 388.58 5742.2 389.05 5743.3 389.14 5744.3 389.21\n", - " 5748.5 389.43 5750.6 389.52 5753.4 389.64 5758.0 389.78 5759.1 389.82\n", - " 5763.3 389.95 5765.4 390.03 5771.7 390.28 5772.8 390.32 5773.8 390.41\n", - " 5780.2 390.92 5786.5 391.27 5787.5 391.33 5787.9 391.33 5788.6 391.36\n", - " 5792.8 391.59 5794.9 391.72 5801.3 392.00 5802.3 392.05 5802.7 392.07\n", - " 5803.4 392.09 5807.6 392.20 5809.7 392.31 5812.5 392.43 5817.1 392.58\n", - " 5817.4 392.61 5818.1 392.61 5822.4 392.76 5824.5 392.86 5827.3 393.07\n", - " 5831.8 393.28 5832.9 393.33 5839.2 393.58 5845.6 393.77 5846.6 393.80\n", - " 5847.7 393.85 5851.9 394.04 5854.0 394.18 5856.8 394.31 5861.4 394.63\n", - " 5862.4 394.69 5868.8 395.06 5875.1 395.39 5876.2 395.45 5876.5 395.47\n", - " 5883.5 395.80 5889.9 396.08 5890.1 396.09\n", - "#Mann=3,-1,0\n", - " 4675.5 0.07 0 4997.2 0.07 0 5002.8 0.07 0\n", - "Bank Sta=4997.2,5002.8\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1,1400,430,430,430\n", - "BEGIN DESCRIPTION:\n", - "Stream0512 3.0\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 808225.02 369924.99 807825.00 370075.00\n", - " 807074.99 370675.01\n", - "#Sta/Elev=411\n", - " 4568.4 398.04 4568.6 398.04 4574.3 398.03 4577.0 398.04 4581.4 397.95\n", - " 4583.9 397.76 4588.5 397.36 4592.3 397.06 4595.5 396.81 4602.5 396.17\n", - " 4602.6 396.16 4607.7 395.49 4609.6 395.20 4613.7 394.25 4616.7 393.63\n", - " 4621.5 391.55 4623.7 390.62 4625.0 390.08 4630.8 387.19 4633.4 386.42\n", - " 4637.8 384.82 4640.3 384.16 4644.9 382.96 4648.8 382.49 4651.9 381.81\n", - " 4653.9 381.58 4659.0 380.83 4659.2 380.81 4670.2 379.55 4673.1 379.29\n", - " 4678.0 378.40 4679.5 378.09 4680.1 377.96 4681.5 377.80 4687.2 377.04\n", - " 4692.7 376.71 4694.2 376.62 4696.8 376.31 4701.3 375.62 4704.0 375.10\n", - " 4708.4 374.22 4715.3 373.07 4715.4 373.05 4715.6 373.02 4722.5 372.07\n", - " 4729.5 371.18 4734.4 370.41 4736.0 370.17 4736.6 370.10 4741.1 369.66\n", - " 4743.6 369.51 4749.2 369.50 4750.7 369.48 4751.4 369.49 4756.5 369.48\n", - " 4757.7 369.48 4760.5 369.53 4764.8 369.56 4771.9 369.65 4772.0 369.65\n", - " 4777.0 369.62 4778.9 369.63 4783.0 369.69 4785.9 369.70 4790.8 369.76\n", - " 4792.4 369.75 4793.0 369.73 4794.3 369.70 4800.0 369.53 4805.6 369.34\n", - " 4807.1 369.28 4807.8 369.25 4809.6 369.21 4816.9 369.12 4821.2 369.14\n", - " 4823.2 369.17 4828.3 369.08 4828.4 369.08 4835.3 369.36 4839.4 369.58\n", - " 4842.4 369.74 4847.2 369.97 4849.4 370.11 4850.7 370.18 4856.5 370.45\n", - " 4859.1 370.57 4863.5 370.75 4866.0 370.90 4870.6 371.08 4874.5 371.14\n", - " 4877.6 371.27 4884.6 371.27 4884.7 371.27 4884.9 371.27 4891.7 371.31\n", - " 4895.9 371.44 4903.7 371.72 4905.2 371.76 4905.8 371.78 4910.4 372.13\n", - " 4912.9 372.41 4918.4 373.15 4919.9 373.32 4922.5 373.63 4927.0 374.14\n", - " 4929.7 374.21 4934.1 374.34 4936.0 374.33 4941.1 374.43 4941.3 374.42\n", - " 4948.2 374.13 4951.4 373.80 4955.2 373.03 4960.1 371.76 4961.7 371.30\n", - " 4962.3 371.12 4963.6 370.77 4969.3 369.08 4971.9 368.40 4976.4 367.67\n", - " 4977.1 367.59 4978.9 367.44 4983.4 367.15 4987.3 367.08 4990.5 367.00\n", - " 4995.6 366.90 4996.8 366.87 4997.1 366.11 4997.6 365.61 5002.5 365.61\n", - " 5002.9 366.11 5006.4 366.77 5006.8 366.76 5007.4 366.76 5011.5 366.86\n", - " 5014.8 366.84 5016.2 366.86 5017.9 366.75 5020.9 366.66 5023.3 366.45\n", - " 5025.6 366.38 5028.5 366.46 5031.7 366.66 5039.1 367.18 5040.2 367.23\n", - " 5044.4 367.28 5048.7 367.30 5049.6 367.32 5057.1 367.46 5058.5 367.47\n", - " 5060.2 367.45 5065.6 367.32 5070.8 367.12 5074.0 367.06 5082.0 367.00\n", - " 5082.5 367.00 5086.7 367.01 5090.9 367.14 5091.4 367.16 5091.9 367.17\n", - " 5099.4 367.40 5102.5 367.41 5107.9 367.41 5110.2 367.38 5113.1 367.31\n", - " 5114.9 367.27 5116.3 367.25 5119.6 367.30 5123.6 367.33 5124.8 367.33\n", - " 5129.0 367.32 5133.2 367.25 5134.2 367.24 5141.7 367.21 5143.1 367.21\n", - " 5144.8 367.23 5147.8 367.17 5150.1 367.12 5155.4 367.08 5158.6 367.04\n", - " 5165.9 366.81 5166.6 366.82 5167.1 366.81 5171.3 366.72 5175.5 366.51\n", - " 5176.0 366.50 5176.5 366.50 5184.0 366.61 5192.4 366.68 5197.7 366.82\n", - " 5199.4 366.84 5200.9 366.87 5208.2 366.95 5209.3 366.96 5213.5 367.03\n", - " 5217.8 367.05 5218.8 367.07 5226.3 367.14 5227.6 367.14 5229.4 367.16\n", - " 5234.7 367.18 5237.0 367.19 5239.9 367.13 5243.2 367.12 5246.4 367.16\n", - " 5250.5 367.15 5251.1 367.14 5251.6 367.13 5260.1 367.11 5261.1 367.11\n", - " 5268.5 367.15 5271.6 367.13 5274.6 367.14 5277.0 367.10 5279.3 367.11\n", - " 5282.2 367.11 5285.5 367.15 5292.8 367.15 5293.9 367.14 5298.4 367.24\n", - " 5302.4 367.32 5302.8 367.33 5303.4 367.33 5307.5 367.33 5310.8 367.32\n", - " 5313.9 367.16 5319.3 366.94 5324.5 367.05 5327.7 367.04 5335.1 366.73\n", - " 5336.2 366.70 5340.4 366.64 5344.7 366.64 5345.6 366.66 5349.8 366.66\n", - " 5353.1 366.64 5354.5 366.64 5356.2 366.71 5361.6 366.88 5363.9 366.90\n", - " 5366.8 366.89 5368.6 366.88 5370.0 366.85 5377.4 366.76 5378.0 366.77\n", - " 5378.5 366.77 5383.0 366.80 5387.0 366.84 5387.4 366.84 5387.9 366.83\n", - " 5395.4 366.68 5398.5 366.61 5401.5 366.51 5403.9 366.45 5406.2 366.39\n", - " 5409.1 366.36 5412.3 366.36 5419.6 366.29 5420.8 366.27 5429.2 366.11\n", - " 5430.2 366.11 5434.4 366.18 5437.7 366.24 5440.8 366.28 5446.1 366.34\n", - " 5453.2 366.28 5454.6 366.28 5461.9 366.32 5463.1 366.32 5467.3 366.36\n", - " 5472.5 366.33 5480.0 366.35 5481.4 366.38 5483.1 366.39 5486.0 366.44\n", - " 5488.4 366.48 5493.7 366.55 5496.9 366.57 5504.2 366.60 5505.3 366.60\n", - " 5509.5 366.63 5513.8 366.61 5514.8 366.62 5522.3 366.73 5525.4 366.76\n", - " 5530.7 366.75 5535.9 366.63 5537.7 366.58 5539.2 366.55 5546.5 366.50\n", - " 5547.6 366.48 5551.8 366.42 5556.1 366.37 5557.1 366.37 5564.5 366.52\n", - " 5565.9 366.55 5567.7 366.60 5573.0 366.73 5578.2 366.79 5581.5 366.74\n", - " 5588.8 366.71 5589.4 366.71 5589.9 366.71 5598.4 366.91 5599.4 366.93\n", - " 5609.9 367.12 5612.9 367.22 5615.3 367.26 5620.5 367.51 5622.3 367.59\n", - " 5623.8 367.61 5631.1 367.44 5632.2 367.41 5640.7 367.22 5641.6 367.23\n", - " 5649.1 367.46 5652.2 367.55 5655.2 367.67 5657.6 367.77 5659.9 367.88\n", - " 5666.0 368.16 5669.3 368.34 5673.4 368.73 5674.0 368.78 5674.5 368.82\n", - " 5678.7 369.14 5683.0 369.47 5683.4 369.50 5683.9 369.53 5691.4 369.85\n", - " 5694.5 370.08 5699.9 370.63 5705.1 371.45 5708.3 371.95 5711.6 372.48\n", - " 5715.6 373.37 5716.3 373.48 5716.8 373.59 5721.3 374.28 5725.2 374.88\n", - " 5725.7 374.92 5726.2 374.98 5733.7 375.39 5736.8 375.48 5742.1 375.64\n", - " 5744.5 375.64 5747.4 375.77 5750.6 375.96 5753.9 376.10 5757.9 376.40\n", - " 5767.5 376.97 5768.5 377.02 5776.0 377.28 5784.4 377.64 5789.7 377.95\n", - " 5791.5 378.07 5792.9 378.16 5796.1 378.27 5800.2 378.38 5801.3 378.41\n", - " 5809.8 378.50 5810.3 378.52 5810.8 378.55 5814.9 378.81 5818.3 378.91\n", - " 5821.4 379.05 5824.3 379.25 5826.7 379.30 5829.0 379.40 5831.9 379.56\n", - " 5833.7 379.68 5835.2 379.77 5842.5 380.22 5843.1 380.27 5843.6 380.30\n", - " 5852.1 381.03 5853.1 381.07 5860.6 381.36 5861.9 381.45 5863.7 381.55\n", - " 5869.0 381.82 5871.3 382.00 5874.2 382.19 5876.0 382.31 5877.5 382.39\n", - " 5884.8 382.57 5885.9 382.60 5890.4 382.64 5894.4 382.67 5894.8 382.68\n", - " 5895.4 382.69 5902.8 382.80 5905.9 382.81 5908.9 382.80 5911.3 382.82\n", - " 5916.5 382.90 5918.3 382.93 5919.8 382.98 5923.0 383.06 5927.1 383.29\n", - " 5927.7 383.32 5928.2 383.34 5932.7 383.54 5936.7 383.71 5937.1 383.72\n", - " 5937.7 383.72 5945.1 383.77 5948.2 383.77 5953.6 383.79 5955.9 383.83\n", - " 5956.1 383.83\n", - "#Mann=3,-1,0\n", - " 4568.4 0.07 0 4997.1 0.07 0 5002.9 0.07 0\n", - "Bank Sta=4997.1,5002.9\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1,970,454,454,454\n", - "BEGIN DESCRIPTION:\n", - "Stream0512 2.0\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 807775.02 369774.99 807425.00 370075.00\n", - " 807025.00 370425.00\n", - "#Sta/Elev=273\n", - " 4538.2 385.23 4543.7 385.03 4544.0 385.01 4544.3 385.00 4548.7 384.83\n", - " 4553.0 384.62 4553.4 384.60 4553.8 384.58 4561.7 384.18 4564.0 384.06\n", - " 4570.4 383.50 4572.1 383.33 4574.1 383.09 4579.1 382.57 4581.5 382.28\n", - " 4584.3 381.90 4586.2 381.66 4587.8 381.45 4596.5 380.50 4600.2 380.20\n", - " 4604.6 379.86 4605.2 379.82 4613.9 379.37 4614.7 379.32 4622.6 378.62\n", - " 4624.9 378.38 4631.3 377.51 4635.0 376.90 4640.0 375.89 4642.4 375.38\n", - " 4645.2 374.83 4647.1 374.42 4648.7 374.10 4655.3 372.84 4656.4 372.66\n", - " 4657.4 372.52 4665.5 371.54 4665.8 371.51 4666.1 371.49 4670.5 371.25\n", - " 4675.6 370.87 4683.5 370.40 4684.5 370.32 4685.7 370.24 4692.2 369.77\n", - " 4693.9 369.62 4695.9 369.47 4700.9 369.10 4703.3 368.97 4706.0 368.81\n", - " 4709.6 368.65 4716.2 368.48 4718.3 368.41 4722.0 368.18 4726.3 367.95\n", - " 4727.0 367.90 4731.4 367.46 4735.7 367.30 4736.5 367.27 4740.7 367.07\n", - " 4744.4 366.91 4746.6 366.78 4750.1 366.55 4753.1 366.46 4754.8 366.40\n", - " 4756.8 366.36 4759.5 366.30 4761.8 366.29 4768.8 366.20 4770.5 366.20\n", - " 4777.1 366.11 4779.2 366.04 4787.2 365.86 4787.6 365.86 4787.9 365.85\n", - " 4796.6 365.71 4806.3 365.69 4807.5 365.70 4814.0 365.74 4817.7 365.69\n", - " 4822.6 365.66 4827.8 365.67 4831.3 365.65 4834.4 365.63 4838.0 365.65\n", - " 4840.0 365.68 4848.1 365.87 4848.7 365.89 4857.4 365.99 4858.2 365.99\n", - " 4866.1 365.88 4867.2 365.87 4868.4 365.87 4871.9 365.79 4874.8 365.89\n", - " 4878.5 365.89 4883.5 365.85 4888.7 365.84 4892.2 365.76 4895.3 365.70\n", - " 4898.8 365.69 4900.9 365.71 4904.6 365.70 4909.0 365.78 4909.6 365.79\n", - " 4913.5 365.84 4918.3 365.90 4919.1 365.92 4923.4 365.99 4927.0 366.01\n", - " 4935.7 366.03 4937.4 366.07 4939.4 366.08 4944.4 366.14 4949.6 366.23\n", - " 4951.5 366.23 4953.1 366.21 4959.7 365.83 4961.8 365.72 4965.5 365.58\n", - " 4969.9 365.46 4970.2 365.45 4970.5 365.45 4979.2 365.66 4979.6 365.66\n", - " 4980.0 365.66 4987.9 365.69 4989.0 365.71 4990.2 365.72 4996.6 365.74\n", - " 4996.8 364.54 4997.3 364.04 5002.7 364.04 5003.2 364.54 5005.4 365.35\n", - " 5010.3 364.97 5012.4 364.79 5014.1 364.72 5017.0 364.54 5020.4 364.98\n", - " 5022.9 365.24 5030.4 365.65 5031.7 365.66 5040.4 365.58 5045.1 365.45\n", - " 5049.2 365.30 5050.5 365.25 5058.0 365.04 5060.5 364.97 5063.8 364.93\n", - " 5066.8 364.92 5070.5 364.93 5073.2 364.90 5075.6 364.96 5080.5 365.18\n", - " 5082.6 365.25 5084.3 365.28 5090.6 365.26 5091.9 365.27 5093.1 365.29\n", - " 5100.6 365.23 5101.9 365.21 5110.6 364.94 5110.9 364.93 5119.4 364.93\n", - " 5120.0 364.94 5120.7 364.95 5128.2 365.10 5130.7 365.17 5137.0 365.29\n", - " 5140.7 365.37 5145.8 365.47 5148.1 365.49 5150.7 365.53 5154.5 365.54\n", - " 5160.8 365.63 5163.3 365.68 5166.8 365.72 5170.8 365.79 5172.1 365.83\n", - " 5176.2 365.93 5180.8 366.07 5189.6 366.19 5190.9 366.19 5194.9 366.20\n", - " 5198.4 366.19 5200.9 366.20 5204.2 366.24 5207.2 366.33 5210.9 366.50\n", - " 5216.0 366.71 5218.3 366.79 5220.9 366.84 5223.0 366.83 5224.7 366.84\n", - " 5231.0 366.80 5232.3 366.80 5233.5 366.81 5241.0 367.06 5242.3 367.10\n", - " 5251.0 367.43 5259.8 367.71 5260.4 367.72 5261.0 367.72 5268.6 367.69\n", - " 5269.8 367.68 5271.1 367.67 5277.4 367.81 5281.1 368.05 5288.5 368.62\n", - " 5291.1 368.84 5293.2 368.99 5294.9 369.13 5301.2 369.68 5303.7 369.94\n", - " 5311.2 370.53 5312.5 370.58 5321.2 370.40 5330.0 370.73 5330.6 370.75\n", - " 5331.3 370.78 5338.8 370.91 5341.3 370.93 5344.6 370.93 5347.6 371.10\n", - " 5351.3 371.44 5354.0 371.72 5356.4 371.90 5361.3 372.29 5363.4 372.46\n", - " 5365.1 372.68 5371.4 373.40 5373.9 373.67 5377.4 374.03 5381.4 374.30\n", - " 5382.7 374.37 5386.8 374.69 5391.4 375.14 5391.7 375.17 5400.2 376.03\n", - " 5401.5 376.11 5409.0 376.54 5411.5 376.64 5414.8 376.80 5417.8 377.04\n", - " 5421.5 377.26 5426.6 377.50 5431.5 377.75 5435.3 377.88 5438.3 378.04\n", - " 5441.6 378.27 5442.9 378.37 5444.1 378.45 5451.6 378.97 5452.9 379.02\n", - " 5461.6 379.17 5471.0 379.47 5479.2 380.05 5480.4 380.12 5485.1 380.37\n", - " 5488.0 380.55 5489.7 380.64 5491.7 380.72 5496.8 380.94 5501.7 381.18\n", - " 5503.8 381.29 5505.5 381.40 5511.8 381.61 5514.3 381.63 5517.8 381.64\n", - " 5521.8 381.68 5523.1 381.71 5530.7 381.88\n", - "#Mann=3,-1,0\n", - " 4538.2 0.07 0 4996.8 0.07 0 5003.2 0.07 0\n", - "Bank Sta=4996.8,5003.2\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1,516,405,405,405\n", - "BEGIN DESCRIPTION:\n", - "Stream0512 1.0\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 807324.99 369375.03 807175.00 369775.00\n", - " 806824.99 370175.02\n", - "#Sta/Elev=275\n", - " 4573.3 404.53 4574.0 404.50 4579.4 403.89 4581.1 403.75 4583.8 403.37\n", - " 4588.1 402.73 4590.7 402.30 4595.2 401.56 4602.0 400.11 4602.2 400.06\n", - " 4602.6 399.98 4607.5 398.79 4609.3 398.41 4613.3 397.70 4616.3 397.13\n", - " 4617.7 396.88 4621.4 396.42 4623.4 396.17 4628.0 395.50 4630.5 395.15\n", - " 4635.9 394.18 4637.5 393.90 4643.4 392.95 4644.6 392.78 4648.5 392.29\n", - " 4651.6 391.89 4658.5 391.14 4658.7 391.11 4659.0 391.08 4663.9 390.60\n", - " 4665.7 390.44 4669.0 390.11 4672.8 389.65 4677.8 389.15 4679.8 388.97\n", - " 4686.9 388.37 4689.5 388.19 4693.9 387.74 4694.7 387.67 4696.6 387.45\n", - " 4701.0 386.99 4703.6 386.78 4708.0 386.39 4710.1 386.21 4715.1 385.79\n", - " 4715.4 385.74 4722.1 384.88 4726.2 384.34 4729.2 383.98 4734.3 383.49\n", - " 4736.2 383.29 4737.4 383.19 4743.3 382.66 4748.7 382.25 4750.4 382.11\n", - " 4753.1 381.84 4756.2 381.51 4757.4 381.40 4761.4 381.10 4764.5 380.83\n", - " 4766.5 380.67 4771.5 380.07 4771.6 380.06 4771.9 380.03 4778.6 379.38\n", - " 4782.6 379.16 4785.6 379.02 4790.7 378.57 4792.7 378.36 4797.3 377.70\n", - " 4799.7 377.40 4802.4 376.92 4806.8 376.01 4809.5 375.34 4812.6 374.52\n", - " 4813.8 374.24 4820.9 372.49 4827.7 370.73 4827.9 370.67 4828.3 370.59\n", - " 4835.0 369.19 4839.0 368.62 4842.0 368.19 4847.1 367.67 4849.1 367.50\n", - " 4853.7 367.04 4856.2 366.91 4858.8 366.72 4863.2 366.60 4865.9 366.49\n", - " 4870.3 366.31 4872.9 366.22 4877.3 366.09 4884.1 366.22 4884.4 366.23\n", - " 4884.5 366.23 4884.7 366.23 4891.4 366.37 4895.4 366.19 4898.5 366.02\n", - " 4899.8 365.92 4903.5 365.61 4905.5 365.42 4910.1 365.10 4912.6 365.02\n", - " 4915.2 364.97 4919.6 364.88 4920.4 364.86 4925.5 364.64 4926.7 364.62\n", - " 4929.3 364.59 4933.7 364.52 4940.6 364.48 4940.8 364.48 4941.1 364.48\n", - " 4946.0 364.43 4947.8 364.38 4951.8 364.21 4954.9 364.12 4960.0 363.99\n", - " 4961.4 363.97 4961.9 363.95 4963.1 363.94 4969.0 363.79 4971.7 363.73\n", - " 4976.1 363.66 4978.8 363.60 4983.1 363.49 4987.0 363.37 4990.2 363.29\n", - " 4992.2 363.24 4996.7 361.14 4997.2 360.64 5002.9 360.64 5003.4 361.14\n", - " 5005.2 362.65 5009.0 361.89 5011.6 361.36 5014.0 361.14 5019.0 361.55\n", - " 5022.7 362.05 5025.7 362.54 5029.0 362.89 5031.5 363.01 5035.0 362.93\n", - " 5039.0 362.66 5039.7 362.65 5040.3 362.68 5044.4 362.73 5049.1 363.15\n", - " 5057.8 363.43 5059.1 363.45 5066.6 363.45 5067.8 363.50 5069.1 363.54\n", - " 5072.5 363.73 5075.4 363.79 5079.2 363.92 5081.8 364.00 5084.2 364.03\n", - " 5089.2 364.10 5092.9 364.12 5095.9 364.08 5101.7 364.19 5109.2 364.29\n", - " 5109.9 364.29 5110.5 364.29 5119.2 364.27 5128.0 364.22 5128.6 364.22\n", - " 5129.3 364.23 5136.8 364.29 5138.0 364.28 5139.3 364.31 5145.6 364.29\n", - " 5147.4 364.26 5149.4 364.23 5152.0 364.18 5154.4 364.13 5156.7 364.04\n", - " 5159.4 364.01 5163.1 364.02 5166.1 364.02 5169.4 364.02 5171.9 364.02\n", - " 5175.4 364.04 5179.5 364.09 5180.1 364.09 5180.7 364.08 5184.8 364.11\n", - " 5189.5 364.17 5194.2 364.14 5198.2 364.14 5199.5 364.15 5203.5 364.26\n", - " 5209.5 364.40 5212.9 364.44 5215.8 364.45 5219.6 364.45 5224.6 364.50\n", - " 5226.9 364.50 5233.3 364.64 5239.6 364.66 5242.1 364.65 5249.6 364.61\n", - " 5250.9 364.59 5259.6 364.69 5268.4 365.28 5269.7 365.33 5277.2 365.64\n", - " 5279.7 365.69 5286.0 365.78 5289.8 365.83 5294.8 366.04 5299.8 366.17\n", - " 5303.5 366.32 5306.5 366.47 5309.8 366.55 5312.3 366.65 5319.8 367.15\n", - " 5321.1 367.24 5329.8 367.80 5338.6 368.04 5339.2 368.04 5339.9 368.06\n", - " 5343.9 368.12 5347.4 368.34 5349.9 368.43 5356.2 368.72 5360.0 369.07\n", - " 5365.0 369.36 5367.3 369.45 5370.0 369.51 5373.7 369.71 5376.7 369.80\n", - " 5380.0 370.00 5382.5 370.20 5390.1 370.91 5390.7 370.94 5391.3 371.00\n", - " 5400.1 371.76 5408.8 372.27 5410.1 372.35 5414.1 372.59 5417.6 372.73\n", - " 5420.1 372.85 5426.4 373.30 5435.2 374.01 5440.2 374.27 5443.9 374.57\n", - " 5446.9 374.86 5450.2 375.16 5451.6 375.26 5452.7 375.39 5460.3 376.15\n", - " 5460.9 376.19 5461.5 376.22 5470.2 376.90 5479.1 377.54 5480.3 377.56\n", - " 5484.3 377.56 5487.8 377.70 5490.3 377.84 5496.6 378.07 5500.4 378.24\n", - " 5505.4 378.50 5510.4 378.85 5514.1 379.03 5517.1 379.11 5520.4 379.19\n", - " 5522.9 379.16 5530.5 379.16 5531.1 379.18 5531.7 379.18 5532.0 379.18\n", - "#Mann=3,-1,0\n", - " 4573.3 0.07 0 4996.7 0.07 0 5003.4 0.07 0\n", - "Bank Sta=4996.7,5003.4\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1,111,0,0,0\n", - "BEGIN DESCRIPTION:\n", - "Stream0511 0.5\n", - "END DESCRIPTION:\n", - "XS GIS Cut Line=3\n", - " 807148.05 369310.04 806728.26 369906.93\n", - " 806800.42 370208.69\n", - "#Sta/Elev=322\n", - " 4369.8 391.25 4374.7 390.62 4377.4 390.29 4378.6 390.13 4382.6 389.58\n", - " 4385.5 389.14 4387.4 388.81 4390.1 388.35 4393.6 387.77 4401.5 386.59\n", - " 4401.9 386.55 4409.7 385.54 4411.1 385.36 4413.0 385.06 4417.8 384.34\n", - " 4424.5 383.41 4425.9 383.23 4430.0 382.68 4433.9 382.19 4439.5 381.71\n", - " 4442.0 381.49 4447.5 381.07 4450.1 380.80 4453.7 380.31 4458.2 379.64\n", - " 4458.5 379.57 4458.9 379.49 4466.2 378.32 4470.4 377.74 4472.7 377.45\n", - " 4474.3 377.31 4481.9 376.70 4482.4 376.66 4483.5 376.57 4490.5 376.04\n", - " 4491.7 375.95 4493.4 375.80 4496.4 375.54 4498.5 375.33 4504.9 374.69\n", - " 4505.9 374.60 4506.6 374.54 4510.7 374.22 4514.7 373.93 4516.3 373.81\n", - " 4522.7 373.48 4527.8 373.11 4529.6 372.97 4530.8 372.86 4534.3 372.40\n", - " 4538.9 371.93 4539.3 371.88 4547.0 371.07 4550.8 370.78 4553.3 370.57\n", - " 4555.0 370.51 4562.3 370.07 4563.1 370.01 4565.1 369.81 4571.2 369.17\n", - " 4572.2 369.08 4573.7 368.96 4577.0 368.81 4579.3 368.64 4585.2 368.25\n", - " 4586.5 368.15 4587.3 368.05 4592.4 367.64 4595.4 367.41 4596.7 367.34\n", - " 4603.5 367.06 4605.4 367.02 4608.2 367.02 4611.6 367.05 4614.9 367.05\n", - " 4619.6 367.16 4629.1 367.17 4631.1 367.16 4633.9 367.11 4635.8 367.10\n", - " 4642.6 366.98 4643.9 366.96 4651.9 366.95 4654.1 366.93 4660.0 366.91\n", - " 4665.6 366.67 4668.1 366.48 4671.8 366.26 4676.2 366.17 4676.5 366.15\n", - " 4677.1 366.14 4684.2 365.97 4686.0 365.90 4688.5 365.85 4690.7 365.76\n", - " 4692.3 365.65 4695.5 365.47 4700.0 365.18 4700.4 365.17 4701.2 365.16\n", - " 4708.5 365.12 4711.5 365.08 4716.5 364.93 4723.0 364.55 4723.9 364.49\n", - " 4724.6 364.44 4728.7 364.17 4732.7 364.01 4734.5 364.01 4740.7 364.11\n", - " 4745.9 364.12 4748.8 364.08 4752.4 364.04 4756.9 364.12 4757.4 364.12\n", - " 4765.0 364.14 4766.6 364.17 4768.9 364.12 4773.0 364.08 4780.4 364.06\n", - " 4780.8 364.06 4781.1 364.06 4785.5 363.99 4789.2 363.92 4790.3 363.90\n", - " 4791.9 363.89 4797.3 363.88 4803.3 363.84 4804.5 363.82 4805.3 363.80\n", - " 4810.0 363.72 4813.4 363.66 4814.0 363.63 4814.8 363.65 4821.5 363.68\n", - " 4823.5 363.68 4826.3 363.73 4828.2 363.74 4829.6 363.74 4837.3 363.89\n", - " 4837.6 363.89 4837.8 363.90 4842.4 363.99 4845.7 364.05 4847.2 364.07\n", - " 4849.3 364.09 4851.9 364.10 4853.8 364.12 4860.7 364.07 4861.4 364.06\n", - " 4861.8 364.07 4864.5 363.98 4869.9 363.77 4872.2 363.65 4878.0 363.32\n", - " 4883.7 363.29 4885.1 363.27 4886.1 363.26 4891.7 363.32 4894.1 363.35\n", - " 4895.2 363.35 4902.2 363.29 4904.1 363.18 4906.7 363.09 4908.8 363.02\n", - " 4910.3 362.97 4918.1 362.76 4918.3 362.75 4923.0 362.75 4926.4 362.69\n", - " 4929.6 362.68 4932.5 362.69 4934.5 362.69 4941.1 362.66 4942.0 362.67\n", - " 4946.7 362.55 4950.7 362.41 4952.6 362.37 4958.7 362.24 4960.9 362.17\n", - " 4964.1 362.00 4965.7 361.95 4966.8 361.86 4970.4 361.53 4974.9 361.23\n", - " 4975.5 361.25 4983.0 361.56 4987.0 361.92 4989.4 362.00 4991.0 362.12\n", - " 4996.7 361.23 4997.2 360.73 5002.9 360.73 5003.4 361.23 5003.6 362.55\n", - " 5007.2 362.60 5008.3 362.50 5010.0 362.40 5015.3 362.03 5021.5 361.64\n", - " 5023.3 361.56 5027.3 361.49 5031.4 361.68 5032.9 361.72 5036.8 361.77\n", - " 5039.5 361.85 5041.5 361.91 5044.4 362.02 5046.3 362.05 5047.5 362.10\n", - " 5051.0 362.36 5055.6 362.80 5055.9 362.82 5063.7 363.40 5067.4 363.49\n", - " 5071.8 363.57 5078.9 363.67 5079.8 363.69 5082.2 363.74 5087.9 363.88\n", - " 5090.3 363.88 5096.0 363.88 5099.5 363.93 5101.5 364.04 5103.3 364.12\n", - " 5107.0 364.41 5110.1 364.55 5115.2 364.81 5116.9 364.92 5123.4 365.14\n", - " 5123.7 365.14 5124.2 365.15 5134.4 365.11 5137.3 365.13 5139.9 365.22\n", - " 5144.1 365.33 5150.7 365.65 5150.8 365.66 5150.9 365.66 5157.7 365.92\n", - " 5161.8 366.14 5164.5 366.15 5167.3 366.30 5171.2 366.51 5172.7 366.54\n", - " 5178.0 366.80 5178.2 366.81 5179.1 366.83 5184.8 366.98 5189.2 367.06\n", - " 5191.6 367.15 5195.2 367.28 5196.0 367.28 5198.4 367.38 5200.2 367.51\n", - " 5205.2 367.98 5205.6 368.01 5207.5 368.16 5212.0 368.47 5216.6 368.55\n", - " 5218.8 368.65 5222.3 368.82 5225.6 368.95 5231.3 369.29 5232.4 369.36\n", - " 5233.0 369.42 5235.8 369.70 5238.5 369.97 5244.0 370.48 5245.9 370.62\n", - " 5249.5 370.77 5252.7 370.91 5254.9 370.97 5259.5 371.11 5260.4 371.14\n", - " 5264.3 371.31 5265.9 371.37 5266.3 371.40 5271.4 371.79 5273.1 371.92\n", - " 5276.9 372.15 5279.9 372.42 5282.3 372.69 5286.7 373.19 5292.6 373.60\n", - " 5293.5 373.66 5298.8 374.12 5300.3 374.33 5302.6 374.74 5307.0 375.42\n", - " 5313.8 376.50 5315.2 376.66 5320.6 377.11 5321.1 377.15 5327.4 377.59\n", - " 5329.4 377.80 5334.2 378.20 5337.1 378.52 5341.0 378.97 5347.3 379.36\n", - " 5347.8 379.42 5349.4 379.46 5354.6 379.50 5359.1 379.68 5361.4 379.73\n", - " 5365.1 379.87 5368.2 379.97 5374.0 380.34 5374.9 380.38 5377.8 380.52\n", - " 5381.0 380.66 5381.7 380.70 5386.5 380.85 5388.5 380.96 5391.9 381.21\n", - " 5395.3 381.41 5397.4 381.56 5402.1 381.97 5406.2 382.16 5408.4 382.22\n", - " 5408.9 382.22 5409.8 382.22\n", - "#Mann=3,-1,0\n", - " 4369.8 0.07 0 4996.7 0.07 0 5003.4 0.07 0\n", - "Bank Sta=4996.7,5003.4\n", - "Exp/Cntr=0.3,0.1\n", - "\n", - "Chan Stop Cuts=-1\"]]\n", - "station_elevation_points: [[\"[(4499.1, 241.3), (4502.6, 241.08), (4508.1, 240.72), (4525.5, 239.49), (4538.6, 238.45), (4541.0, 238.23), (4556.0, 236.88), (4574.5, 235.11), (4604.0, 232.37), (4610.5, 231.73), (4646.5, 228.57), (4648.8, 228.37), (4652.0, 228.13), (4669.4, 226.75), (4682.5, 225.74), (4689.9, 225.13), (4699.9, 224.39), (4718.4, 223.19), (4747.9, 221.12), (4754.4, 220.79), (4772.2, 220.03), (4790.4, 218.68), (4795.9, 218.36), (4826.4, 218.5), (4843.8, 220.2), (4854.4, 222.24), (4862.3, 223.75), (4874.9, 226.81), (4891.8, 230.2), (4895.9, 230.7), (4898.3, 230.99), (4917.8, 231.53), (4934.3, 232.1), (4939.7, 231.87), (4970.2, 228.16), (4987.7, 225.95), (4998.0, 218.36), (4998.2, 217.86), (5001.8, 217.86), (5002.1, 218.36), (5010.7, 223.52), (5024.3, 222.97), (5041.7, 222.97), (5056.7, 223.02), (5057.3, 223.02), (5061.7, 223.03), (5188.4, 223.05), (5198.0, 223.04), (5245.5, 223.01), (5255.4, 223.01), (5304.2, 222.95), (5321.4, 223.06), (5336.4, 223.19), (5362.9, 223.22), (5387.5, 223.49), (5420.5, 223.97), (5453.5, 225.03), (5463.2, 225.49), (5480.3, 226.15), (5494.5, 226.65), (5519.6, 227.58), (5539.0, 230.01), (5552.6, 231.77), (5568.8, 234.4), (5578.4, 236.1), (5585.6, 237.38), (5597.7, 238.56), (5611.1, 239.63), (5618.6, 240.34), (5645.5, 241.06), (5651.6, 241.22), (5684.7, 241.94), (5715.1, 242.4), (5716.8, 242.42), (5717.7, 242.44), (5721.0, 242.51), (5750.7, 243.15), (5759.0, 243.35), (5773.8, 243.81), (5783.7, 244.06), (5816.7, 244.98), (5832.5, 245.51), (5849.7, 246.1), (5871.9, 247.04), (5882.8, 247.48), (5906.9, 248.45), (5915.8, 248.77), (5917.0, 248.82)]\",\"[(4596.9, 238.12), (4607.4, 237.3), (4614.9, 236.74), (4665.4, 232.76), (4694.4, 230.63), (4718.0, 228.96), (4723.4, 228.56), (4743.8, 226.84), (4752.4, 226.14), (4769.6, 224.66), (4781.4, 223.73), (4810.4, 221.66), (4829.7, 220.47), (4846.9, 219.48), (4868.4, 218.38), (4872.7, 218.13), (4897.4, 216.99), (4898.5, 216.96), (4926.4, 216.05), (4955.4, 215.76), (4984.4, 215.76), (4998.0, 215.76), (4998.2, 215.26), (5001.8, 215.26), (5002.1, 215.76), (5013.8, 216.26), (5023.9, 216.41), (5038.6, 216.88), (5048.4, 217.27), (5075.8, 218.08), (5080.1, 218.22), (5083.0, 218.36), (5097.4, 219.05), (5117.6, 219.94), (5127.7, 220.6), (5142.4, 221.45), (5152.2, 222.35), (5163.1, 223.12), (5168.7, 223.66), (5179.6, 224.7), (5183.9, 224.98), (5186.8, 225.21), (5201.2, 226.23), (5221.4, 227.59), (5231.5, 228.34), (5256.0, 229.8), (5283.3, 231.49), (5287.7, 231.68), (5290.5, 231.86), (5308.4, 232.7), (5325.1, 233.87), (5329.2, 234.17), (5335.2, 234.67), (5339.5, 234.93), (5359.7, 236.16), (5360.6, 236.21)]\",\"[(4433.8, 246.73), (4461.2, 244.11), (4463.3, 243.94), (4482.6, 241.76), (4501.9, 240.4), (4502.9, 240.29), (4504.0, 240.22), (4523.3, 237.89), (4542.6, 236.45), (4544.7, 236.25), (4583.3, 232.69), (4585.4, 232.51), (4624.0, 229.18), (4625.0, 229.09), (4626.1, 229.0), (4664.7, 225.22), (4665.7, 225.11), (4666.8, 225.02), (4686.1, 223.11), (4705.4, 221.69), (4707.5, 221.53), (4746.1, 219.33), (4748.2, 219.25), (4767.5, 218.53), (4786.8, 217.91), (4788.8, 217.86), (4827.5, 216.55), (4829.5, 216.5), (4848.9, 216.01), (4868.2, 215.56), (4869.2, 215.53), (4870.3, 215.52), (4909.9, 214.99), (4911.0, 214.98), (4930.3, 214.96), (4951.7, 214.68), (4971.0, 214.53), (4992.4, 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'5002.4']\",\"['4997.5', '5002.6']\",...,\"['4997.2', '5002.8']\",\"['4997.1', '5002.9']\",\"['4996.8', '5003.2']\",\"['4996.7', '5003.4']\",\"['4996.7', '5003.4']\"]]\n", - "number_of_station_elevation_points: [[88,56,82,104,138,...,373,411,273,275,322]]\n", - "number_of_coords: [[3,3,3,3,3,...,3,3,3,3,3]]\n", - "flows: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "profile_names: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "geometry: [[01020000000300000051D82178BFE13F418B8DCDCF0A03464163A5A3B9F3DF3F41DEC07FA06C0346414848E85781DD3F4106877FA6BA044641,010200000003000000C908AB77A6E13F41BDB3B473D4024641B8223D432DE03F4131A40DC78C0246419B4CE3F923DF3F41A38D4E5506034641,010200000003000000A823A14D64E13F419F50E5944B014641191211EC80DF3F41C88DAED5DD01464114E39692E7DE3F416B4D989918034641,0102000000030000006EA7910A92E13F4189118FF1830046418D5FD38E2CDE3F413F15D84DB401464176F943F0B6DE3F417D1C7BAA12034641,0102000000030000004E06BF6BFEDF3F41317CDB199FFF454130D740445EDD3F417D14E8C8B401464182D23FB655DE3F415FD376D106034641,...,0102000000030000008D015B39241640415605BE8E8FA545412A6FC13093154041382078E3D2A5454101C89951721440410860DD5B27A74541,0102000000030000004D624513F3154041612340CD1CA545412160B21A1F1540415C18F81C36A5454141FDFDF46B134041A1E1B01B00A64541,010200000003000000BE699A542A154041447683BA9EA44541C8839BE35C144041A72757B106A54541FC6B7F4371134041845B4FF780A54541,01020000000300000067F8B4257F1440416EB87174A7A34541374AC1FF06144041A088659957A44541C15F15BA2D1340418FF5200DF0A44541,010200000003000000A02869ED301440418B117EF772A34541C802227B1E1340416A4E959962A4454150193BD01D134041BF158676FDA44541]]\n", - "huc: [[\"01020002\",\"01020002\",\"01020002\",\"01020002\",\"01020002\",...,\"01020002\",\"01020002\",\"01020002\",\"01020002\",\"01020002\"]]\n", - "model_id: [[\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",...,\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\"]]\n", - "...\n" - ] - } - ], + "outputs": [], "source": [ - "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "catalog_name = \"xs\"\n", + "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema [xs]\n", + "catalog_name = \"fema_xs\"\n", "namespace = \"mip\"\n", "tablename = \"huc_01020002\"\n", "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", @@ -1273,20 +192,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "aec525f7-6753-4ca5-ba5e-916c8c6659ec", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "List of tables under mip within xs:\n", - "\n", - "[('mip', 'huc_01020002'), ('mip', 'huc_02060006'), ('mip', 'huc_03170004'), ('mip', 'huc_04030101'), ('mip', 'huc_05020006'), ('mip', 'huc_06040002'), ('mip', 'huc_07130010'), ('mip', 'huc_08060202'), ('mip', 'huc_09020301'), ('mip', 'huc_10120203'), ('mip', 'huc_11060002'), ('mip', 'huc_12030107'), ('mip', 'huc_13020102'), ('mip', 'huc_14050005'), ('mip', 'huc_15040007'), ('mip', 'huc_16020203'), ('mip', 'huc_17060305'), ('mip', 'huc_18070204')]\n" - ] - } - ], + "outputs": [], "source": [ "# All tables created within the namespace of interest.\n", "print(f\"List of tables under {namespace} within {catalog_name}:\\n\\n{catalog.list_tables(namespace)}\")" @@ -1302,62 +211,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "28153d1a-46de-43f0-962a-5b613bdbaa73", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyarrow.Table\n", - "hf_source: large_string\n", - "owp_ahg_c: double\n", - "owp_ahg_f: double\n", - "owp_ahg_a: double\n", - "owp_ahg_b: double\n", - "owp_ahg_k: double\n", - "owp_ahg_m: double\n", - "owp_dingman_r: double\n", - "LENGTHKM: double\n", - "slope: double\n", - "owp_bf_channel_area: double\n", - "owp_inchan_channel_area: double\n", - "owp_bf_channel_perimeter: double\n", - "owp_inchan_channel_perimeter: double\n", - "owp_bf_channel_volume: double\n", - "owp_inchan_channel_volume: double\n", - "owp_bf_channel_bed_area: double\n", - "owp_inchan_channel_bed_area: double\n", - "owp_y_bf: double\n", - "owp_y_inchan: double\n", - "owp_tw_bf: double\n", - "owp_tw_inchan: double\n", - "owp_roughness_bathy: double\n", - "owp_roughness_bathy_lower_bound: double\n", - "owp_roughness_bathy_upper_bound: double\n", - "hf_id: int64\n", - "owp_roughness_no_bathy: double\n", - "hand_source: large_string\n", - "vpuid: int64\n", - "----\n", - "hf_source: [[\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",...,\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\"]]\n", - "owp_ahg_c: [[0.24283623695373535,0.24679405987262726,0.24796637892723083,0.24558784067630768,0.2416917383670807,...,0.24669642746448517,0.2410922795534134,0.21650205552577972,0.23524419963359833,0.2453560084104538]]\n", - "owp_ahg_f: [[0.44655272364616394,0.45140621066093445,0.45361408591270447,0.4597383141517639,0.46237292885780334,...,0.46750980615615845,0.46937182545661926,0.45957159996032715,0.4573363959789276,0.46542882919311523]]\n", - "owp_ahg_a: [[16.292556762695312,16.71356201171875,15.591700553894043,17.455158233642578,18.136899948120117,...,14.39522933959961,15.831462860107422,17.09531593322754,15.73424243927002,15.698975563049316]]\n", - "owp_ahg_b: [[0.1877126842737198,0.18782523274421692,0.19405822455883026,0.1825941652059555,0.17806944251060486,...,0.22100567817687988,0.20211540162563324,0.1899542361497879,0.19244758784770966,0.20660847425460815]]\n", - "owp_ahg_k: [[0.2711319327354431,0.26460614800453186,0.28298744559288025,0.2645978033542633,0.25294309854507446,...,0.30960214138031006,0.3056097626686096,0.3103371262550354,0.3096386194229126,0.2898109257221222]]\n", - "owp_ahg_m: [[0.4176878035068512,0.40634584426879883,0.3849862217903137,0.4011600911617279,0.39922311902046204,...,0.31428271532058716,0.34859946370124817,0.38616055250167847,0.37841135263442993,0.31649747490882874]]\n", - "owp_dingman_r: [[163.10582763253834,121.4828569903629,109.42969777415078,131.98548478571865,144.5459281918684,...,87.87201916851028,120.53569949076702,196.1542010519919,183.4062312745436,144.50062488154873]]\n", - "LENGTHKM: [[1.1828318619868186,3.453164835389148,3.7720762279021254,2.444968856687704,2.735238005445334,...,11.041326186301118,2.2220571382098906,2.3020617663270158,7.381776970395373,10.90124605539771]]\n", - "slope: [[0.00489451,0.00399073,0.00354641,0.00291035,0.00222913,...,0.00064764,0.00001,0.00286086,0.0031118,0.00064093]]\n", - "..." - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Filter Iceberg table based on threshold set for a given feature\n", "namespace = \"bathymetry_ml_auxiliary\"\n", @@ -1369,62 +226,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "6320114d-8115-4e1f-b8fd-3c80aeaa07de", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyarrow.Table\n", - "hf_source: large_string\n", - "owp_ahg_c: double\n", - "owp_ahg_f: double\n", - "owp_ahg_a: double\n", - "owp_ahg_b: double\n", - "owp_ahg_k: double\n", - "owp_ahg_m: double\n", - "owp_dingman_r: double\n", - "LENGTHKM: double\n", - "slope: double\n", - "owp_bf_channel_area: double\n", - "owp_inchan_channel_area: double\n", - "owp_bf_channel_perimeter: double\n", - "owp_inchan_channel_perimeter: double\n", - "owp_bf_channel_volume: double\n", - "owp_inchan_channel_volume: double\n", - "owp_bf_channel_bed_area: double\n", - "owp_inchan_channel_bed_area: double\n", - "owp_y_bf: double\n", - "owp_y_inchan: double\n", - "owp_tw_bf: double\n", - "owp_tw_inchan: double\n", - "owp_roughness_bathy: double\n", - "owp_roughness_bathy_lower_bound: double\n", - "owp_roughness_bathy_upper_bound: double\n", - "hf_id: int64\n", - "owp_roughness_no_bathy: double\n", - "hand_source: large_string\n", - "vpuid: int64\n", - "----\n", - "hf_source: [[\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",...,\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\"]]\n", - "owp_ahg_c: [[0.2783956527709961,0.26645627617836,0.2778419256210327,0.2656676173210144,0.2778984308242798,...,0.26968732476234436,0.262911856174469,0.2565535306930542,0.27186259627342224,0.2509637176990509]]\n", - "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.4091697037220001,0.4002934396266937,0.3982747495174408,0.41463810205459595,0.4620860517024994]]\n", - "owp_ahg_a: [[15.474262237548828,15.589872360229492,15.27043628692627,15.695556640625,15.373209953308105,...,13.57089614868164,15.591622352600098,17.110021591186523,15.198208808898926,16.834657669067383]]\n", - "owp_ahg_b: [[0.21297027170658112,0.20660685002803802,0.22359369695186615,0.21279571950435638,0.21274369955062866,...,0.22416476905345917,0.20449289679527283,0.20677334070205688,0.2223099172115326,0.20692850649356842]]\n", - "owp_ahg_k: [[0.2632668912410736,0.2649047374725342,0.2646264433860779,0.26899534463882446,0.26056915521621704,...,0.29988086223602295,0.27692529559135437,0.2727428376674652,0.28107815980911255,0.2823072373867035]]\n", - "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.3715367913246155,0.4299010634422302,0.4299301207065582,0.40392443537712097,0.3803613483905792]]\n", - "owp_dingman_r: [[34.94359157777524,78.50221647993335,23.816824026591785,71.37071616896621,68.16839964475069,...,66.4125603175732,47.97490105010014,49.70555612325059,44.49719316488429,221.7325794960283]]\n", - "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,1.3556982962034625,1.7761013055059551,3.1182578724544983,5.662275879263775,0.760075523461262]]\n", - "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.0154941,0.02633596,0.01911396,0.04333816,0.00001]]\n", - "..." - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "namespace = \"bathymetry_ml_auxiliary\"\n", "tablename = \"vpuid=01\"\n", @@ -1437,30 +242,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "3acdfde0-d34d-442e-87eb-724dbcef5d1b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyarrow.Table\n", - "owp_ahg_f: double\n", - "owp_ahg_m: double\n", - "LENGTHKM: double\n", - "slope: double\n", - "----\n", - "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.4067888855934143,0.3982747495174408,0.4000049829483032,0.41463810205459595,0.4620860517024994]]\n", - "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.3738446533679962,0.4299301207065582,0.4191203713417053,0.40392443537712097,0.3803613483905792]]\n", - "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,2.891987367172746,3.1182578724544983,3.6554421303734443,5.662275879263775,0.760075523461262]]\n", - "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.02001729,0.01911396,0.02483032,0.04333816,0.00001]]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "namespace = \"bathymetry_ml_auxiliary\"\n", "tablename = \"vpuid=01\"\n", @@ -1483,58 +268,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "5117aae1-40fb-4392-b424-b488245ace52", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyarrow.Table\n", - "river: string\n", - "reach: string\n", - "river_reach: string\n", - "river_station: double\n", - "river_reach_rs: string\n", - "thalweg: double\n", - "xs_max_elevation: double\n", - "left_reach_length: double\n", - "right_reach_length: double\n", - "channel_reach_length: double\n", - "ras_data: string\n", - "station_elevation_points: string\n", - "bank_stations: string\n", - "number_of_station_elevation_points: int64\n", - "number_of_coords: int64\n", - "flows: string\n", - "profile_names: string\n", - "geometry: binary\n", - "huc: string\n", - "model_id: string\n", - "us_reach_id: string\n", - "us_network_to_id: string\n", - "ds_reach_id: string\n", - "ds_network_to_id: string\n", - "__index_level_0__: int64\n", - "----\n", - "river: [[\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",...,\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\"]]\n", - "reach: [[\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",...,\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\"]]\n", - "river_reach: [[\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",...,\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\"]]\n", - "river_station: [[7300,6833,6346,5913,5505,...,1842,1400,970,516,111]]\n", - "river_reach_rs: [[\"Stream0528 Reach-1 7300.0\",\"Stream0528 Reach-1 6833.0\",\"Stream0528 Reach-1 6346.0\",\"Stream0528 Reach-1 5913.0\",\"Stream0528 Reach-1 5505.0\",...,\"Stream0512 Reach-1 1842.0\",\"Stream0512 Reach-1 1400.0\",\"Stream0512 Reach-1 970.0\",\"Stream0512 Reach-1 516.0\",\"Stream0512 Reach-1 111.0\"]]\n", - "thalweg: [[217.86,215.26,213.94,213.33,213.34,...,368.01,365.61,364.04,360.64,360.73]]\n", - "xs_max_elevation: [[248.82,238.12,246.73,241.95,242.87,...,404.79,398.04,385.23,404.53,391.25]]\n", - "left_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", - "right_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", - "channel_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", - "..." - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Load a XS parquet file into a DataFrame\n", "parquet_file_path = f\"{os.getcwd()}//xs_data/mip_01020002/huc_01020002.parquet\"\n", @@ -1544,62 +281,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "b365d647-bbfa-45a9-8673-52c564cf0ea7", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyarrow.Table\n", - "hf_source: string\n", - "owp_ahg_c: double\n", - "owp_ahg_f: double\n", - "owp_ahg_a: double\n", - "owp_ahg_b: double\n", - "owp_ahg_k: double\n", - "owp_ahg_m: double\n", - "owp_dingman_r: double\n", - "LENGTHKM: double\n", - "slope: double\n", - "owp_bf_channel_area: double\n", - "owp_inchan_channel_area: double\n", - "owp_bf_channel_perimeter: double\n", - "owp_inchan_channel_perimeter: double\n", - "owp_bf_channel_volume: double\n", - "owp_inchan_channel_volume: double\n", - "owp_bf_channel_bed_area: double\n", - "owp_inchan_channel_bed_area: double\n", - "owp_y_bf: double\n", - "owp_y_inchan: double\n", - "owp_tw_bf: double\n", - "owp_tw_inchan: double\n", - "owp_roughness_bathy: double\n", - "owp_roughness_bathy_lower_bound: double\n", - "owp_roughness_bathy_upper_bound: double\n", - "hf_id: int64\n", - "owp_roughness_no_bathy: double\n", - "hand_source: string\n", - "vpuid: dictionary\n", - "----\n", - "hf_source: [[\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",...,\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\",\"reference_fabric\"]]\n", - "owp_ahg_c: [[0.2783956527709961,0.26645627617836,0.2778419256210327,0.2656676173210144,0.2778984308242798,...,0.24669642746448517,0.2410922795534134,0.21650205552577972,0.23524419963359833,0.2453560084104538]]\n", - "owp_ahg_f: [[0.403207927942276,0.4721261262893677,0.3986799418926239,0.4429498612880707,0.4048362374305725,...,0.46750980615615845,0.46937182545661926,0.45957159996032715,0.4573363959789276,0.46542882919311523]]\n", - "owp_ahg_a: [[15.474262237548828,15.589872360229492,15.27043628692627,15.695556640625,15.373209953308105,...,14.39522933959961,15.831462860107422,17.09531593322754,15.73424243927002,15.698975563049316]]\n", - "owp_ahg_b: [[0.21297027170658112,0.20660685002803802,0.22359369695186615,0.21279571950435638,0.21274369955062866,...,0.22100567817687988,0.20211540162563324,0.1899542361497879,0.19244758784770966,0.20660847425460815]]\n", - "owp_ahg_k: [[0.2632668912410736,0.2649047374725342,0.2646264433860779,0.26899534463882446,0.26056915521621704,...,0.30960214138031006,0.3056097626686096,0.3103371262550354,0.3096386194229126,0.2898109257221222]]\n", - "owp_ahg_m: [[0.4113325774669647,0.36840832233428955,0.40038490295410156,0.3914453685283661,0.4115554094314575,...,0.31428271532058716,0.34859946370124817,0.38616055250167847,0.37841135263442993,0.31649747490882874]]\n", - "owp_dingman_r: [[34.94359157777524,78.50221647993335,23.816824026591785,71.37071616896621,68.16839964475069,...,87.87201916851028,120.53569949076702,196.1542010519919,183.4062312745436,144.50062488154873]]\n", - "LENGTHKM: [[2.967968432144912,1.359883622971128,2.0899864298839312,0.5329819884305866,1.276767180876021,...,11.041326186301118,2.2220571382098906,2.3020617663270158,7.381776970395373,10.90124605539771]]\n", - "slope: [[0.01388814,0.00057352,0.01573711,0.00500938,0.02304347,...,0.00064764,0.00001,0.00286086,0.0031118,0.00064093]]\n", - "..." - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Load a m1_auxiliary parquet file into a DataFrame\n", "parquet_file_path = f\"{os.getcwd()}/data/bathymetry/ml_auxiliary_data/vpuid=01/ml_auxiliary_data.parquet\"\n", @@ -1632,9 +317,9 @@ ], "metadata": { "kernelspec": { - "display_name": "iceberg", + "display_name": ".venv", "language": "python", - "name": "iceberg" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1646,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.2" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index 709ad31..116b8eb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,9 +23,7 @@ maintainers = [ dependencies = [] [project.optional-dependencies] -docs = [ - "mkdocs-material", -] +docs = ["mkdocs-material"] [tool.uv.sources] icefabric_api = { workspace = true } @@ -33,11 +31,7 @@ icefabric_manage = { workspace = true } icefabric_tools = { workspace = true } [tool.uv.workspace] -members = [ - "src/icefabric_api", - "src/icefabric_manage", - "src/icefabric_tools", -] +members = ["src/icefabric_api", "src/icefabric_manage", "src/icefabric_tools"] [tool.uv] dev-dependencies = [ @@ -121,11 +115,11 @@ convention = "numpy" [tool.mypy] python_version = "3.11" -warn_return_any = true +warn_return_any = false +disallow_any_unimported = false warn_unused_configs = true strict_optional = true ignore_missing_imports = true -disallow_any_unimported = true check_untyped_defs = true disallow_untyped_defs = true no_implicit_optional = true diff --git a/src/icefabric_manage/builds/__init__.py b/src/icefabric_manage/builds/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py b/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py new file mode 100644 index 0000000..4f3b2ea --- /dev/null +++ b/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py @@ -0,0 +1,40 @@ +from pathlib import Path + +from dotenv import load_dotenv +from pyiceberg.exceptions import NamespaceAlreadyExistsError + +from icefabric_manage import IcebergTable + +load_dotenv() + +if __name__ == "__main__": + warehouse_path = Path("/tmp/warehouse") + warehouse_path.mkdir(exist_ok=True) + catalog_name = "pyiceberg_catalog" + catalog_settings = { + "type": "sql", + "uri": f"sqlite:///{warehouse_path}/{catalog_name}.db", + "warehouse": f"file://{warehouse_path}", + } + + iceberg_table = IcebergTable() + + try: + # Establish new Iceberg catalog + iceberg_table.establish_catalog( + catalog_name=catalog_name, + namespace="bathymetry_ml_auxiliary", + catalog_settings=catalog_settings, + ) + + # Generate tables w/in the new Iceberg catalog for all parquets detected in s3 + iceberg_table.create_table_for_all_s3parquets( + app_name="bathymetry_ml_auxiliary", bucket_name="ngwpc-bathymetry" + ) + + except NamespaceAlreadyExistsError as e: + raise FileExistsError( + f"Iceberg table and namespace already exist. Remove catalog from {warehouse_path} to continue" + ) from e + + print(f"Build successful. Files written into metadata store @ {warehouse_path}") diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index b3cec73..f358619 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -24,7 +24,7 @@ dependencies = [ "s3fs==2025.5.0", "pyarrow==20.0.0", "pyiceberg[sql-sqlite]==0.9.0", - "python-dotenv==1.1.0" + "python-dotenv==1.1.0", ] [build-system] diff --git a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py index 91a4bdc..61f1855 100644 --- a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py +++ b/src/icefabric_manage/src/icefabric_manage/iceberg_table.py @@ -1,7 +1,5 @@ import os -import re -import boto3 import pyarrow as pa import pyarrow.parquet as pq import s3fs @@ -27,18 +25,15 @@ class IcebergTable: """ - def __init__(self): + def __init__(self) -> None: # Generate folder for iceberg catalog if not os.path.exists(f"{os.getcwd()}/iceberg_catalog"): os.makedirs(f"{os.getcwd()}/iceberg_catalog") - # Set location of where the iceberg catalog will reside - os.environ["PYICEBERG_HOME"] = os.getcwd() - # Initialize namespace to be set for Iceberg catalog self.namespace = "" - def read_data_dirs(self, data_dir): + def read_data_dirs(self, data_dir: str) -> list: """ Extract the list of parquet directories. @@ -52,14 +47,14 @@ def read_data_dirs(self, data_dir): """ parquet_list = [] - for folder, subfolders, files in os.walk(data_dir): + for folder, _subfolders, files in os.walk(data_dir): if folder != data_dir: for file in files: parquet_list.append(f"{folder}/{file}") return parquet_list - def read_data(self, parquet_file_path): + def read_data(self, parquet_file_path: str) -> pa.Table: """ Load a single parquet as a Pyarrow table. @@ -74,21 +69,43 @@ def read_data(self, parquet_file_path): return data - def establish_catalog(self, catalog_name, namespace): + def establish_catalog( + self, catalog_name: str, namespace: str, catalog_settings: dict[str, str] | None = None + ) -> None: """ Creates a new Iceberg catalog. + Defaults to saving in ./iceberg_catalog/{catalog_name}_catalog.db if no uri + specified in catalog_settings + Specify 'uri' and 'warehouse' to select location for catalog and files + Args: catalog_name (str): Name of the catalog to be created. Default: 'dev' for development catalog namespace (str): Name of namespace. + catalog_settings (str): Optional catalog settings accepted by pyiceberg.load_catalog() Return: None """ + # Check if catalog settings exist, if not initialize a URI and warehouse to default location + if not catalog_settings or not isinstance(catalog_settings, dict): + catalog_settings = {} + catalog_settings["uri"] = ( + f"sqlite:///iceberg_catalog/{catalog_name}_catalog.db" + if "uri" not in catalog_settings.keys() + else catalog_settings["uri"] + ) + catalog_settings["warehouse"] = ( + "file://iceberg_catalog" + if "warehouse" not in catalog_settings.keys() + else catalog_settings["warehouse"] + ) + # Establish a new Iceberg catalog & its configuration self.catalog = load_catalog( - name=catalog_name, **{"uri": f"sqlite:///iceberg_catalog/{catalog_name}_catalog.db"} + name=catalog_name, + **catalog_settings, ) # Establish namespace to be create w/in catalog @@ -98,7 +115,7 @@ def establish_catalog(self, catalog_name, namespace): return - def convert_pyarrow_to_iceberg_schema(self, arrow_schema): + def convert_pyarrow_to_iceberg_schema(self, arrow_schema: Schema) -> Schema: """ Translate a given Pyarrow schema into a schema acceptable by Iceberg. @@ -106,7 +123,7 @@ def convert_pyarrow_to_iceberg_schema(self, arrow_schema): arrow_schema (object): Pyarrow schema read from the loaded parquet of interest. - Return (object): Iceberg schema + Return (Iceberge.Schema): Iceberg schema """ fields = [] @@ -145,7 +162,7 @@ def convert_pyarrow_to_iceberg_schema(self, arrow_schema): return schema - def create_table_for_parquet(self, iceberg_tablename, data_table, schema): + def create_table_for_parquet(self, iceberg_tablename: str, data_table: pa.Table, schema: Schema) -> None: """ Convert parquet Pyarrow table to iceberg table & allocate Iceberg catalog under the ./iceberg_catalog directory. @@ -164,9 +181,7 @@ def create_table_for_parquet(self, iceberg_tablename, data_table, schema): """ # Create an Iceberg table iceberg_table = self.catalog.create_table( - identifier=f"{self.namespace}.{iceberg_tablename}", - schema=schema, - location=f"{os.environ['PYICEBERG_HOME']}/iceberg_catalog", + identifier=f"{self.namespace}.{iceberg_tablename}", schema=schema ) # Updates the Iceberg table with data of interest. @@ -174,7 +189,7 @@ def create_table_for_parquet(self, iceberg_tablename, data_table, schema): return - def create_table_for_all_parquets(self, parquet_files, app_name="mip-xs"): + def create_table_for_all_parquets(self, parquet_files: list[str], app_name: str = "mip-xs") -> None: """ Convert parquets to Iceberg tables - each w/ their inherited schema. @@ -190,7 +205,7 @@ def create_table_for_all_parquets(self, parquet_files, app_name="mip-xs"): 'bathymetry_ml_auxiliary' S3 buckets differ. """ - for idx, parquet_file in enumerate(parquet_files): + for _idx, parquet_file in enumerate(parquet_files): if app_name == "mip_xs": iceberg_tablename = f"{os.path.split(os.path.split(parquet_file)[1])[1].split('.')[0]}" @@ -203,48 +218,51 @@ def create_table_for_all_parquets(self, parquet_files, app_name="mip-xs"): self.create_table_for_parquet(iceberg_tablename, data_table, schema) return - def create_table_for_all_s3parquets(self, app_name, bucket_name, profile_name="default"): + def create_table_for_all_s3parquets(self, app_name: str, bucket_name: str) -> None: """ Convert parquets from S3 to Iceberg tables - each w/ their inherited schema. - Args: - bucket_name (list): S3 bucket name. - - app_name (str): Application to create Iceberg tables. - Options: 'mip_xs' & 'bathymetry_ml_auxiliary' + Parameters + ---------- + app_name : str + Application to create Iceberg tables. + Options: 'mip_xs', 'ble_xs' & 'bathymetry_ml_auxiliary' + bucket_name : str + S3 bucket name. - namespace (str): Namespace for which the Iceberg table will reside within - the Iceberg catalog. - - profile_name (str: Profile name declared in the AWS configuration file. - Default: 'default' - - Return: None + Returns + ------- + None """ - # Instantiate bucket of interest. - session = boto3.Session(profile_name=profile_name) - s3 = session.resource("s3") - bucket_ob = s3.Bucket(bucket_name) - pyarrow_table = {} - for s3obj in bucket_ob.objects.all(): - # For sourcing the preprocessed XS parquets from S3 - if app_name == "mip_xs" and re.match(r"^xs_data/.*\.parquet$", s3obj.key): - iceberg_tablename = f"{os.path.split(os.path.split(s3obj.key)[1])[1].split('.')[0]}" - pyarrow_table[iceberg_tablename] = pq.read_table( - f"s3://{bucket_name}/{s3obj.key}", filesystem=s3fs.S3FileSystem() - ) - - # For sourcing the bathymetry_ml_auxiliary parquets from S3 - elif app_name == "bathymetry_ml_auxiliary": - iceberg_tablename = f"{os.path.split(os.path.split(s3obj.key)[0])[1]}" - pyarrow_table[iceberg_tablename] = pq.read_table( - f"s3://{bucket_name}/{s3obj.key}", filesystem=s3fs.S3FileSystem() - ) + fs = s3fs.S3FileSystem( + key=os.environ["AWS_ACCESS_KEY_ID"], + secret=os.environ["AWS_SECRET_ACCESS_KEY"], + token=os.environ["AWS_SESSION_TOKEN"], + ) + glob_patterns = { + "mip_xs": f"{bucket_name}/full_mip_xs_data/**/*.parquet", + "ble_xs": f"{bucket_name}/full_ble_xs_data/**/*.parquet", + "bathymetry_ml_auxiliary": f"{bucket_name}/ml_auxiliary_data/**/*.parquet", + } + if app_name not in glob_patterns: + raise KeyError(f"App {app_name} not supported. Please add your app to the glob_patterns") + + # Table Name Factory + parquet_files = fs.glob(glob_patterns[app_name]) + pyarrow_tables = {} + for file_path in parquet_files: + if app_name in {"mip_xs", "ble_xs"}: + # Extracts the HUC as the table name + table_name = file_path.split("/")[-1].removesuffix(".parquet") + elif app_name in {"bathymetry_ml_auxiliary"}: + # Extract vpuid from directory structure + table_name = file_path.split("/")[-2] + else: + raise KeyError(f"App {app_name} not supported. Please add your app the table name factory") + s3_uri = f"s3://{file_path}" + pyarrow_tables[table_name] = pq.read_table(s3_uri, filesystem=fs) - # Xforming each unique parquet to an iceberg table - for key, data_table in pyarrow_table.items(): - data_pyarrow_schema = data_table.schema - schema = self.convert_pyarrow_to_iceberg_schema(data_pyarrow_schema) - self.create_table_for_parquet(key, data_table, schema) - return + for table_name, data_table in pyarrow_tables.items(): + schema = self.convert_pyarrow_to_iceberg_schema(data_table.schema) + self.create_table_for_parquet(table_name, data_table, schema) diff --git a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py index d1c7b00..be1a4ed 100644 --- a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py +++ b/src/icefabric_manage/src/icefabric_manage/map_fim_data.py @@ -15,44 +15,44 @@ class MapData: """ Maps FIM MIP & BLE XS datasets to relevant IDs & categorize by HUC. - At this time, ensure FIM datasets are saved to local disk. + At this time, ensure FIM datasets are saved to local disk. """ - def __init__(self, data_dir, subfolder_key_prefix): + def __init__(self, data_dir: str, subfolder_key_prefix: str) -> None: self.data_dir = data_dir self.subfolder_key_prefix = subfolder_key_prefix # Parent directory of the FIM files. # Note: All the jsons & geopackages are relevant # to map the files to IDs. - self.fim_data_dirs = [] + self.fim_data_dirs: list[str] = [] # List of directories associated with each file type of # the FIM data sample (e.g. geopackage of a given model @ HUC#, json, # source_models.gpkg, ripple.gpkg) - self.model_gpkg_dirs = [] - self.src_models_gpkg_dirs = [] - self.rip_gpkg_dirs = [] - self.gpkg_dirs = [] - self.json_dirs = [] - self.xs_df_list = [] + self.model_gpkg_dirs: list[str] = [] + self.src_models_gpkg_dirs: list[str] = [] + self.rip_gpkg_dirs: list[str] = [] + self.gpkg_dirs: list[str] = [] + self.json_dirs: list[str] = [] + self.xs_df_list: list[geopandas.GeoDataFrame] = [] # Variables to be used later - self.model_gpkg_tablenames = [] - self.src_models_gpkg_tablenames = [] - self.rip_gpkg_tablenames = [] - self.gpkg_tablenames = [] - self.json_tablenames = [] - - self.id2json = collections.defaultdict(dict) - self.model_id2gpkg = collections.defaultdict(dict) - self.us_ref_dict = collections.defaultdict(dict) - self.ds_ref_dict = collections.defaultdict(dict) - self.rip_huc2gpkg = collections.defaultdict(dict) - self.groupbyriver_dict = collections.defaultdict(dict) - self.crs_dict = collections.defaultdict(dict) - self.consolidated_id2xs = geopandas.GeoDataFrame() + self.model_gpkg_tablenames: list[str] = [] + self.src_models_gpkg_tablenames: list[str] = [] + self.rip_gpkg_tablenames: list[str] = [] + self.gpkg_tablenames: list[str] = [] + self.json_tablenames: list[str] = [] + + self.id2json: dict = collections.defaultdict(dict) + self.model_id2gpkg: dict = collections.defaultdict(dict) + self.us_ref_dict: dict = collections.defaultdict(dict) + self.ds_ref_dict: dict = collections.defaultdict(dict) + self.rip_huc2gpkg: dict = collections.defaultdict(dict) + self.groupbyriver_dict: dict = collections.defaultdict(dict) + self.crs_dict: dict = collections.defaultdict(dict) + self.consolidated_id2xs: geopandas.GeoDataFrame = geopandas.GeoDataFrame() self.read_data_dirs() self.cat_data_dirs(self.subfolder_key_prefix) @@ -84,9 +84,10 @@ def __init__(self, data_dir, subfolder_key_prefix): self.consolidate_id2xs_dfs() # Save HEC RAS models' cross-sections consolidated by HUC as geoparquets & geopackages + # TODO: does this need to be called with a `xs_data_type` ? self.save_xsbyhuc_data() - def read_data_dirs(self): + def read_data_dirs(self) -> None: """ Extract the list of FIM data sample's directories. @@ -97,15 +98,14 @@ def read_data_dirs(self): the FIM data sample. """ - fim_data_dirs = [] - for folder, subfolders, files in os.walk(self.data_dir): + for folder, _subfolders, files in os.walk(self.data_dir): if folder != self.data_dir: for file in files: self.fim_data_dirs.append(f"{folder}/{file}") return - def cat_data_dirs(self, subfolder_key_prefix): + def cat_data_dirs(self, subfolder_key_prefix: str) -> None: """ Categorize FIM data sample files. @@ -124,7 +124,7 @@ def cat_data_dirs(self, subfolder_key_prefix): if re.search("ripple.gpkg", x): self.rip_gpkg_dirs.append(x) t = re.search(f"/{subfolder_key_prefix}(.*)", x) - rip_gpkg_tblname = t.group() + rip_gpkg_tblname = t.group() # type: ignore[union-attr] self.rip_gpkg_tablenames.append(rip_gpkg_tblname.lstrip("/").replace("/", "_")) # Covers all HEC-RAS models gpkg featuring XS per HUC (contains model_id) @@ -135,33 +135,33 @@ def cat_data_dirs(self, subfolder_key_prefix): ): self.model_gpkg_dirs.append(x) t = re.search(f"/{subfolder_key_prefix}(.*)", x) - model_gpkg_tblname = t.group() + model_gpkg_tblname = t.group() # type: ignore[union-attr] self.model_gpkg_tablenames.append(model_gpkg_tblname.lstrip("/").replace("/", "_")) # Covers all HEC-RAS models gpkg featuring 1D model flowlines per HUC (contains model_id & their HEC-RAS 1D model flowlines) elif x.endswith("source_models.gpkg"): self.src_models_gpkg_dirs.append(x) t = re.search(f"/{subfolder_key_prefix}(.*)", x) - src_models_gpkg_tblname = t.group() + src_models_gpkg_tblname = t.group() # type: ignore[union-attr] self.src_models_gpkg_tablenames.append(src_models_gpkg_tblname.lstrip("/").replace("/", "_")) # Covers all HEC-RAS models + Ripple gpkg per HUC if x.endswith(".gpkg"): self.gpkg_dirs.append(x) t = re.search(f"/{subfolder_key_prefix}(.*)", x) - gpkg_tblname = t.group() + gpkg_tblname = t.group() # type: ignore[union-attr] self.gpkg_tablenames.append(gpkg_tblname.lstrip("/").replace("/", "_")) # Covers each HEC-RAS models' result of conflating its model w/ the NWM network elif x.endswith(".json"): self.json_dirs.append(x) t = re.search(f"/{subfolder_key_prefix}(.*)", x) - json_tblname = t.group() + json_tblname = t.group() # type: ignore[union-attr] self.json_tablenames.append(json_tblname.lstrip("/").replace("/", "_")) return - def drop_nested_keys(self, map_dict, keys_to_drop): + def drop_nested_keys(self, map_dict: dict, keys_to_drop: dict) -> dict | list: """ Drop keys irrelevant for linking each XS to IDs @@ -184,7 +184,7 @@ def drop_nested_keys(self, map_dict, keys_to_drop): else: return map_dict - def map_model2huc(self): + def map_model2huc(self) -> None: """ Map each conflation json file to their corresponding model ID & HUC #. @@ -209,7 +209,7 @@ def map_model2huc(self): return - def filter_model2huc_map(self, keys_to_drop): + def filter_model2huc_map(self, keys_to_drop: dict) -> None: """ Extract only relevant keys from model2huc map for linking each XS to a feature ID. @@ -225,7 +225,7 @@ def filter_model2huc_map(self, keys_to_drop): return - def map_modelhuc_xs2ids(self): + def map_modelhuc_xs2ids(self) -> None: """ Parse JSONs & map model_id & HUC # to xs to reach ID & "network_to_id" @@ -294,7 +294,7 @@ def map_modelhuc_xs2ids(self): return - def map_model2huc_gpkg(self): + def map_model2huc_gpkg(self) -> None: """ Map model ID & HUC # to each HEC-RAS model's geopackage. @@ -335,7 +335,7 @@ def map_model2huc_gpkg(self): return - def map_huc2ripple_gpkg(self): + def map_huc2ripple_gpkg(self) -> None: """ Map HUC # to ripple geopackage (features HEC RAS 1D model flowlines). @@ -395,7 +395,7 @@ def map_huc2ripple_gpkg(self): pass return - def map_model_xs2ids(self): + def map_model_xs2ids(self) -> None: """ Map each cross-section instance featured in HEC-RAS model's cross-section layer to their corresponding IDs. @@ -464,7 +464,7 @@ def map_model_xs2ids(self): return - def save_xs_data(self): + def save_xs_data(self) -> None: """ Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage @@ -476,7 +476,7 @@ def save_xs_data(self): Note: These saved parquet files will preserve each river @ HUC's inherited CRS. """ - for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items(): + for (model_id, huc_num), _model_gpkg_dict in self.model_id2gpkg.items(): # Generate data folder per HUC if not os.path.exists(f"{os.getcwd()}/xs_data/huc_{huc_num}"): os.makedirs(f"{os.getcwd()}/xs_data/huc_{huc_num}") @@ -500,7 +500,7 @@ def save_xs_data(self): return - def save_crs_map(self): + def save_crs_map(self) -> None: """ Consolidate HEC-RAS models cross-sections based on HUC & river & save to storage @@ -513,7 +513,7 @@ def save_crs_map(self): analyze & reference. """ - for (model_id, huc_num), model_gpkg_dict in self.model_id2gpkg.items(): + for (model_id, huc_num), _model_gpkg_dict in self.model_id2gpkg.items(): # Generate data folder per HUC if not os.path.exists(f"{os.getcwd()}/xs_data/crs_map"): os.makedirs(f"{os.getcwd()}/xs_data/crs_map") @@ -524,9 +524,11 @@ def save_crs_map(self): filterbyriver = grouped_xslayers.get_group(river_name) self.crs_dict[(huc_num, model_id)].update( { - river_name: f"""ESPG: { - str(filterbyriver.crs.to_epsg()) if filterbyriver.crs.to_epsg() else None - }, {filterbyriver.crs.name},""" + river_name: ( + f"""ESPG: { + str(filterbyriver.crs.to_epsg()) if filterbyriver.crs.to_epsg() else None + }, {filterbyriver.crs.name},""" + ) } ) @@ -536,7 +538,7 @@ def save_crs_map(self): return - def consolidate_id2xs_dfs(self): + def consolidate_id2xs_dfs(self) -> None: """ Consolidate HEC-RAS models cross-sections featuring their corresponding IDs. @@ -576,7 +578,7 @@ def consolidate_id2xs_dfs(self): return - def save_xsbyhuc_data(self, xs_data_type): + def save_xsbyhuc_data(self, xs_data_type: str) -> None: """ Consolidate HEC-RAS models cross-sections based on HUC & save to storage From b61262b5b7eeefa61b41c6a5c1f7565beb56dba7 Mon Sep 17 00:00:00 2001 From: Brock Hinkson Date: Tue, 3 Jun 2025 17:56:06 -0500 Subject: [PATCH 51/97] NGWPC-6384 - Create Icechunk VC Example Notebook --- .../icechunk_version_control_examples.ipynb | 493 ++++++++++++++++++ .../icechunk/icechunk_s3_module.py | 62 ++- 2 files changed, 549 insertions(+), 6 deletions(-) create mode 100644 examples/icechunk_version_control_examples.ipynb diff --git a/examples/icechunk_version_control_examples.ipynb b/examples/icechunk_version_control_examples.ipynb new file mode 100644 index 0000000..b5023ae --- /dev/null +++ b/examples/icechunk_version_control_examples.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "25341568-feb1-46b7-acaf-d6b76f066e57", + "metadata": {}, + "source": [ + "## Icechunk Version Control and Branching\n", + "Showcase for adding new data over time to an icechunk store, \"time traveling\", and making new branches" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f0fe850-0e5e-40e5-93b1-1c9f468a2d28", + "metadata": {}, + "outputs": [], + "source": [ + "import boto3\n", + "import os\n", + "import warnings\n", + "import xarray as xr\n", + "from dotenv import load_dotenv\n", + "\n", + "import icefabric_tools.icechunk.arch_weather_file_utils as awf_utils\n", + "from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations, S3Path\n", + "\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "# Before running this cell, make sure you created a .env file in this directory with your AWS credentials in it\n", + "# NOTE - if you authenticate with AWS SSO, leave it commented out\n", + "load_dotenv()\n", + "\n", + "# Create icechunk repo at s3://hydrofabric-data/ic_testing/snodas_yearly_append_test\n", + "# NOTE - make sure this S3 directory doesn't exist prior to running this cell\n", + "new_repo_s3_path = S3Path(bucket=\"hydrofabric-data\", prefix=\"ic_testing/snodas_yearly_append_test\")\n", + "new_repo = IcechunkS3Repo(location=new_repo_s3_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "61ff84b0-9dba-494e-b9c4-187923f504e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", + "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Message:\tRepository initialized\n", + "\n" + ] + } + ], + "source": [ + "# Print repo ancestry\n", + "new_repo.print_history()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ef762477-5c6b-4876-b5a2-a708b9f3d02f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zz_ssmv11034tS__T0001TTNATS2009*.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [00:11<00:00, 2.34s/files]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset is uploaded. Commit: 0B2CMBJJWH52P5Q4HSJ0\n" + ] + } + ], + "source": [ + "# Collect first five SNODAS netcdf files from 2009 and combine/virtualize them together into a single dataset\n", + "snodas_09_vds = awf_utils.virtualize_and_concat_archival_files_on_time(\n", + " location=NGWPCLocations.SNODAS_REF.path,\n", + " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", + " file_type=awf_utils.FileType.NETCDF,\n", + " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2009*.nc\",\n", + " loadable_vars=['crs'],\n", + " testing_file_quantity=5\n", + ")\n", + "\n", + "# Add 09 data to SNODAS repo with a new snapshot\n", + "new_repo.write_dataset(ds=snodas_09_vds, virtualized=True, commit=\"First commit! 09 data added.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b3b6e582-ba59-44c2-9753-74afa7c48fd0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Snapshot ID:\t0B2CMBJJWH52P5Q4HSJ0\n", + "Timestamp:\t2025-06-03 22:52:27.746862+00:00\n", + "Message:\tFirst commit! 09 data added.\n", + "\n", + "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", + "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Message:\tRepository initialized\n", + "\n" + ] + } + ], + "source": [ + "# Now that we have a new snapshot, reprint the repo ancestry\n", + "new_repo.print_history()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "41d0467d-84da-45f2-94e2-f911e88bbf68", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 671MB\n", + "Dimensions: (time: 5, lat: 4096, lon: 8192)\n", + "Coordinates:\n", + " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", + " * time (time) datetime64[ns] 40B 2009-12-09 2009-12-10 ... 2009-12-13\n", + " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", + "Data variables:\n", + " Band1 (time, lat, lon) float32 671MB dask.array\n", + " crs (time) object 40B dask.array\n", + "Attributes:\n", + " Conventions: CF-1.5\n", + " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", + " history: Tue Feb 04 18:40:28 2025: GDAL CreateCopy( /data/unmasked/2...\n" + ] + } + ], + "source": [ + "# Print the data now contained within the SNODAS repo\n", + "snodas_data = new_repo.retrieve_dataset()\n", + "print(snodas_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bbefb68a-b747-449e-b7da-4c61c6f4a2e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zz_ssmv11034tS__T0001TTNATS2010*.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [00:11<00:00, 2.35s/files]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset has been appended on the time dimension. Commit: 8DCP1J64FV48Z13XWK6G\n" + ] + } + ], + "source": [ + "# Much like the 09 SNODAS files were collected, do the same for 2010\n", + "snodas_10_vds = awf_utils.virtualize_and_concat_archival_files_on_time(\n", + " location=NGWPCLocations.SNODAS_REF.path,\n", + " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", + " file_type=awf_utils.FileType.NETCDF,\n", + " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2010*.nc\",\n", + " loadable_vars=['crs'],\n", + " testing_file_quantity=5\n", + ")\n", + "\n", + "# Append 2010 data to SNODAS repo with a new snapshot\n", + "new_repo.append_virt_data_to_store(vds=snodas_10_vds, append_dim=\"time\", commit=\"Appended new data from the year 2010\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c5cfc9ee-0357-404f-8535-c414409ac58e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Snapshot ID:\t8DCP1J64FV48Z13XWK6G\n", + "Timestamp:\t2025-06-03 22:52:40.716732+00:00\n", + "Message:\tAppended new data from the year 2010\n", + "\n", + "Snapshot ID:\t0B2CMBJJWH52P5Q4HSJ0\n", + "Timestamp:\t2025-06-03 22:52:27.746862+00:00\n", + "Message:\tFirst commit! 09 data added.\n", + "\n", + "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", + "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Message:\tRepository initialized\n", + "\n" + ] + } + ], + "source": [ + "# Now that we have another new snapshot with 2010 data, reprint the repo ancestry\n", + "new_repo.print_history()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f9a3d55d-7d51-4317-9dc8-cf41316d3fea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 1GB\n", + "Dimensions: (lat: 4096, lon: 8192, time: 10)\n", + "Coordinates:\n", + " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", + " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", + " * time (time) datetime64[ns] 80B 2009-12-09 2009-12-10 ... 2010-01-05\n", + "Data variables:\n", + " crs (time) object 80B dask.array\n", + " Band1 (time, lat, lon) float32 1GB dask.array\n", + "Attributes:\n", + " Conventions: CF-1.5\n", + " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", + " history: Tue Feb 04 19:54:02 2025: GDAL CreateCopy( /data/unmasked/2...\n" + ] + } + ], + "source": [ + "# Print the new repo collection with both 2009 and 2010 data\n", + "snodas_data = new_repo.retrieve_dataset()\n", + "print(snodas_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e8c16d28-1933-48fd-8cfc-31dd18d8ed7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 671MB\n", + "Dimensions: (time: 5, lat: 4096, lon: 8192)\n", + "Coordinates:\n", + " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", + " * time (time) datetime64[ns] 40B 2009-12-09 2009-12-10 ... 2009-12-13\n", + " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", + "Data variables:\n", + " Band1 (time, lat, lon) float32 671MB dask.array\n", + " crs (time) object 40B dask.array\n", + "Attributes:\n", + " Conventions: CF-1.5\n", + " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", + " history: Tue Feb 04 18:40:28 2025: GDAL CreateCopy( /data/unmasked/2...\n" + ] + } + ], + "source": [ + "# Retrieve and print the data from the previous snapshot, before 2010 data was added\n", + "prev_snap_snodas_data = new_repo.retrieve_prev_snapshot()\n", + "print(prev_snap_snodas_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3d940692-0407-4c46-8df2-5a0036f4420b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zz_ssmv11034tS__T0001TTNATS2011*.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [00:11<00:00, 2.36s/files]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset has been appended on the time dimension. Commit: ZESC6X2MJVJSYDASQY6G\n" + ] + } + ], + "source": [ + "# Make a new feature branch based on \"main\" to add 2011 data\n", + "new_repo.create_new_branch(name=\"2011_feature\")\n", + "\n", + "# Much like the 09 SNODAS files were collected, do the same for 2010\n", + "snodas_11_vds = awf_utils.virtualize_and_concat_archival_files_on_time(\n", + " location=NGWPCLocations.SNODAS_REF.path,\n", + " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", + " file_type=awf_utils.FileType.NETCDF,\n", + " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2011*.nc\",\n", + " loadable_vars=['crs'],\n", + " testing_file_quantity=5\n", + ")\n", + "\n", + "# Append 2011 data to SNODAS repo's new branch with a new snapshot\n", + "new_repo.append_virt_data_to_store(vds=snodas_11_vds, append_dim=\"time\", commit=\"Appended new data from the year 2011\", branch=\"2011_feature\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8386ab8e-02ef-4d87-8b55-91a26bb76675", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NEW BRANCH =====================================\n", + "Snapshot ID:\tZESC6X2MJVJSYDASQY6G\n", + "Timestamp:\t2025-06-03 22:52:53.279869+00:00\n", + "Message:\tAppended new data from the year 2011\n", + "\n", + "Snapshot ID:\t8DCP1J64FV48Z13XWK6G\n", + "Timestamp:\t2025-06-03 22:52:40.716732+00:00\n", + "Message:\tAppended new data from the year 2010\n", + "\n", + "Snapshot ID:\t0B2CMBJJWH52P5Q4HSJ0\n", + "Timestamp:\t2025-06-03 22:52:27.746862+00:00\n", + "Message:\tFirst commit! 09 data added.\n", + "\n", + "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", + "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Message:\tRepository initialized\n", + "\n", + "MAIN BRANCH ====================================\n", + "Snapshot ID:\t8DCP1J64FV48Z13XWK6G\n", + "Timestamp:\t2025-06-03 22:52:40.716732+00:00\n", + "Message:\tAppended new data from the year 2010\n", + "\n", + "Snapshot ID:\t0B2CMBJJWH52P5Q4HSJ0\n", + "Timestamp:\t2025-06-03 22:52:27.746862+00:00\n", + "Message:\tFirst commit! 09 data added.\n", + "\n", + "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", + "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Message:\tRepository initialized\n", + "\n" + ] + } + ], + "source": [ + "# Now that we have a new branch with new 2011 data, print the history of both branches\n", + "print(\"NEW BRANCH =====================================\")\n", + "new_repo.print_history(branch=\"2011_feature\")\n", + "print(\"MAIN BRANCH ====================================\")\n", + "new_repo.print_history(branch=\"main\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "43814441-7461-4d93-99f0-a839b9324d99", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NEW BRANCH ========================================================\n", + " Size: 2GB\n", + "Dimensions: (time: 15, lat: 4096, lon: 8192)\n", + "Coordinates:\n", + " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", + " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", + " * time (time) datetime64[ns] 120B 2009-12-09 2009-12-10 ... 2011-01-05\n", + "Data variables:\n", + " Band1 (time, lat, lon) float32 2GB dask.array\n", + " crs (time) object 120B dask.array\n", + "Attributes:\n", + " Conventions: CF-1.5\n", + " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", + " history: Tue Feb 04 19:00:12 2025: GDAL CreateCopy( /data/unmasked/2...\n", + "===================================================================\n", + "MAIN BRANCH =======================================================\n", + " Size: 1GB\n", + "Dimensions: (lat: 4096, lon: 8192, time: 10)\n", + "Coordinates:\n", + " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", + " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", + " * time (time) datetime64[ns] 80B 2009-12-09 2009-12-10 ... 2010-01-05\n", + "Data variables:\n", + " crs (time) object 80B dask.array\n", + " Band1 (time, lat, lon) float32 1GB dask.array\n", + "Attributes:\n", + " Conventions: CF-1.5\n", + " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", + " history: Tue Feb 04 19:54:02 2025: GDAL CreateCopy( /data/unmasked/2...\n", + "===================================================================\n" + ] + } + ], + "source": [ + "# Print both branch's datasets - notice the new one has 2011 data\n", + "snodas_data_feat_branch = new_repo.retrieve_dataset(branch=\"2011_feature\")\n", + "print(\"NEW BRANCH ========================================================\")\n", + "print(snodas_data_feat_branch)\n", + "print(\"===================================================================\")\n", + "print(\"MAIN BRANCH =======================================================\")\n", + "print(snodas_data)\n", + "print(\"===================================================================\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a69a8c8e-0003-4738-964e-7bd136412264", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Icechunk repo @ s3://hydrofabric-data/ic_testing/snodas_yearly_append_test in its entirety was successfully deleted.\n" + ] + } + ], + "source": [ + "# Cleanup - delete the test repo entirely\n", + "new_repo.delete_repo(quiet=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py index b294750..3babe5f 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py @@ -182,7 +182,7 @@ def gen_virtual_chunk_containers( ] return v_chunks - def create_session(self, read_only: bool | None = True, branch: str | None = "main") -> ic.Session: + def create_session(self, read_only: bool | None = True, snap_id: str | None = None, branch: str | None = "main") -> ic.Session: """ Open a session under the repo defined by an instance of IcechunkS3Repo @@ -190,6 +190,9 @@ def create_session(self, read_only: bool | None = True, branch: str | None = "ma ---------- read_only : bool | None, optional Denotes if the session will be read-only or writable. By default True + snap_id: str | None, optional + The Snapshot ID of a specific commit to base the session on. Leave out if you want the + latest. By default None branch : str | None, optional Icechunk repo branch to be opened. By default "main" @@ -200,26 +203,34 @@ def create_session(self, read_only: bool | None = True, branch: str | None = "ma can be configured. """ if read_only: - return self.repo.readonly_session(branch) + if snap_id: + return self.repo.readonly_session(snapshot_id=snap_id) + else: + return self.repo.readonly_session(branch) return self.repo.writable_session(branch) - def retrieve_dataset(self, read_only: bool | None = True, branch: str | None = "main") -> xr.Dataset: + def retrieve_dataset(self, branch: str | None = "main", snap_id: str | None = None) -> xr.Dataset: """ Returns the repo's store contents as an Xarray dataset Parameters ---------- - read_only : bool | None, optional - Denotes if the session will be read-only or writable, by default True + branch : str | None, optional Icechunk repo branch to be opened, by default "main" + snap_id : str | None, optional + The Snapshot ID of a specific commit you want to retrieve. Leave out if you want the + latest. By default None. Returns ------- xr.Dataset Xarray dataset representation of the Icechunk store """ - session = self.create_session(read_only, branch) + if snap_id: + session = self.create_session(read_only=True, branch=branch, snap_id=snap_id) + else: + session = self.create_session(read_only=True, branch=branch) ds = xr.open_zarr(session.store, consolidated=False, chunks={}) # geotiff rasters saved in zarr need to be convereted to spatial-aware xarray with rioxarray @@ -228,6 +239,22 @@ def retrieve_dataset(self, read_only: bool | None = True, branch: str | None = " return ds + def retrieve_rollback_to_snapshot(self, snap_id: str, branch: str | None = "main") -> xr.Dataset: + """Retrieves the repo data a specific snapshot ID""" + return self.retrieve_dataset(branch=branch, snap_id=snap_id) + + def retrieve_rollback_n_snapshots(self, n: int, branch: str | None = "main") -> xr.Dataset: + """Retrieves the repo data from snapshot(s) ago""" + try: + snap_id = list(self.repo.ancestry(branch=branch))[n].id + except IndexError: + print(f"Rolled back too far! Branch ({branch}) has fewer previous commits than was specified") + return self.retrieve_rollback_to_snapshot(snap_id, branch=branch) + + def retrieve_prev_snapshot(self, branch: str | None = "main") -> xr.Dataset: + """Retrieves the repo data one snapshot ago""" + return self.retrieve_rollback_n_snapshots(n=1, branch=branch) + def write_dataset( self, ds: xr.Dataset, commit: str, virtualized: bool | None = False, branch: str | None = "main" ): @@ -282,6 +309,29 @@ def append_virt_data_to_store( snapshot = session.commit(commit) print(f"Dataset has been appended on the {append_dim} dimension. Commit: {snapshot}") + def create_new_branch_from_snapshot(self, name: str, snap_id: str): + """Create a new branch that is based on a specific snapshot ID""" + self.repo.create_branch(name, snapshot_id=snap_id) + + def create_new_branch(self, name: str, origin: str | None = "main"): + """Create a new branch that is based on the most recent snapshot on a given branch""" + branch_latest_snap_id = self.repo.lookup_branch(origin) + self.create_new_branch_from_snapshot(name, snap_id=branch_latest_snap_id) + + def print_history(self, branch: str | None = "main"): + """ + Prints a nicely-formatted summary of the history of the icechunk repo branch. + + Parameters + ---------- + branch : str | None, optional + The branch whose history will be printed. By default "main" + """ + for ancestor in self.repo.ancestry(branch=branch): + print(f"Snapshot ID:\t{ancestor.id}") + print(f"Timestamp:\t{ancestor.written_at}") + print(f"Message:\t{ancestor.message}\n") + def retrieve_and_convert_to_tif( self, dest: str | Path, From d0afb92105e6aafeabfb437e005d0c5883bb7baa Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Tue, 10 Jun 2025 11:03:40 -0400 Subject: [PATCH 52/97] NGWPC 6318 + NGWPC 6510: Iceberg Terraform + S3 --- .gitignore | 16 + .pyiceberg.yaml | 5 - docker/Dockerfile.api | 4 - src/icefabric_api/app/conftest.py | 12 +- src/icefabric_api/app/main.py | 8 +- .../app/routers/hydrofabric/router.py | 8 +- .../routers/streamflow_observations/router.py | 10 +- src/icefabric_manage/builds/README.md | 47 + src/icefabric_manage/builds/build_fema_ble.py | 48 + src/icefabric_manage/builds/build_fema_mip.py | 48 + .../builds/build_hydrofabric.py | 39 +- .../build_iceberg_bathymetry_ml_auxiliary.py | 80 +- .../build_usgs_streamflow_observations.py | 36 +- src/icefabric_manage/pyproject.toml | 4 +- .../src/icefabric_manage/_api.py | 7 +- src/icefabric_manage/terraform/README.md | 137 + .../environments/test/.terraform.lock.hcl | 24 + .../terraform/environments/test/backend.tf | 9 + .../terraform/environments/test/main.tf | 28 + .../environments/test/terraform.tfvars | 8 + .../terraform/environments/test/variables.tf | 33 + .../terraform/iceberg_demo.py | 58 + .../terraform/modules/iceberg/main.tf | 142 + .../terraform/modules/iceberg/variables.tf | 33 + src/icefabric_tools/pyproject.toml | 6 +- src/icefabric_tools/tests/conftest.py | 29 +- uv.lock | 2803 ++++++++--------- 27 files changed, 2154 insertions(+), 1528 deletions(-) delete mode 100644 .pyiceberg.yaml create mode 100644 src/icefabric_manage/builds/README.md create mode 100644 src/icefabric_manage/builds/build_fema_ble.py create mode 100644 src/icefabric_manage/builds/build_fema_mip.py create mode 100644 src/icefabric_manage/terraform/README.md create mode 100644 src/icefabric_manage/terraform/environments/test/.terraform.lock.hcl create mode 100644 src/icefabric_manage/terraform/environments/test/backend.tf create mode 100644 src/icefabric_manage/terraform/environments/test/main.tf create mode 100644 src/icefabric_manage/terraform/environments/test/terraform.tfvars create mode 100644 src/icefabric_manage/terraform/environments/test/variables.tf create mode 100644 src/icefabric_manage/terraform/iceberg_demo.py create mode 100644 src/icefabric_manage/terraform/modules/iceberg/main.tf create mode 100644 src/icefabric_manage/terraform/modules/iceberg/variables.tf diff --git a/.gitignore b/.gitignore index aff49d6..45cdff4 100644 --- a/.gitignore +++ b/.gitignore @@ -186,3 +186,19 @@ data/ *.tiff *.tif examples/iceberg_catalog/metadata/ + +# Local Terraform state +*.tfstate +*.tfstate.backup + +# Crash log files +crash.log + +# CLI configuration +.terraform/ + +# Pyiceberg config file +.pyiceberg.yaml + +# Terraform plan output (optional unless you want to inspect later) +*.tfplan diff --git a/.pyiceberg.yaml b/.pyiceberg.yaml deleted file mode 100644 index 0cef7d0..0000000 --- a/.pyiceberg.yaml +++ /dev/null @@ -1,5 +0,0 @@ -catalog: - hydrofabric: - type: sql - uri: sqlite:///../..//warehouse/icefabric_catalog.db - warehouse: file:///../..//warehouse diff --git a/docker/Dockerfile.api b/docker/Dockerfile.api index 31c449f..cd44310 100644 --- a/docker/Dockerfile.api +++ b/docker/Dockerfile.api @@ -33,10 +33,6 @@ ENV PATH="/app/.venv/bin:$PATH" RUN uv sync RUN uv pip install -e src/icefabric_api -WORKDIR /app/src/icefabric_manage/scripts -RUN python build_hydrofabric.py -RUN python build_usgs_streamflow_observations.py - # Run the API WORKDIR /app/src/icefabric_api CMD ["python", "-m", "app.main"] diff --git a/src/icefabric_api/app/conftest.py b/src/icefabric_api/app/conftest.py index 0dc546f..40ba5d7 100644 --- a/src/icefabric_api/app/conftest.py +++ b/src/icefabric_api/app/conftest.py @@ -1,7 +1,13 @@ -import pytest -from fastapi.testclient import TestClient +import os +from pathlib import Path -from app.main import app +os.environ["PYICEBERG_HOME"] = str(Path(__file__).parents[3]) +print(f"PYICEBERG_HOME set to: {os.environ['PYICEBERG_HOME']}") + +import pytest # noqa: E402 +from fastapi.testclient import TestClient # noqa: E402 + +from app.main import app # noqa: E402 @pytest.fixture(scope="session") diff --git a/src/icefabric_api/app/main.py b/src/icefabric_api/app/main.py index 9a579ee..ec9180c 100644 --- a/src/icefabric_api/app/main.py +++ b/src/icefabric_api/app/main.py @@ -1,10 +1,12 @@ +import os +from pathlib import Path + import uvicorn from fastapi import FastAPI, status from pydantic import BaseModel from app.routers.hydrofabric.router import api_router as hydrofabric_api_router from app.routers.streamflow_observations.router import api_router as streamflow_api_router -from app.routers.module_params.router import api_router as module_params_router app = FastAPI( title="Icefabric API", @@ -24,7 +26,7 @@ class HealthCheck(BaseModel): # Include routers app.include_router(hydrofabric_api_router, prefix="/v1") app.include_router(streamflow_api_router, prefix="/v1") -app.include_router(module_params_router, prefix="/v1") +# app.include_router(module_params_router, prefix="/v1") @app.head( @@ -41,4 +43,6 @@ def get_health() -> HealthCheck: if __name__ == "__main__": + os.environ["PYICEBERG_HOME"] = str(Path(__file__).parents[3]) + print(os.environ["PYICEBERG_HOME"]) uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True, log_level="info") diff --git a/src/icefabric_api/app/routers/hydrofabric/router.py b/src/icefabric_api/app/routers/hydrofabric/router.py index ac864e5..e284ce9 100644 --- a/src/icefabric_api/app/routers/hydrofabric/router.py +++ b/src/icefabric_api/app/routers/hydrofabric/router.py @@ -11,12 +11,6 @@ api_router = APIRouter(prefix="/hydrofabric") -catalog_settings: dict = { - "type": "sql", - "uri": "sqlite:////tmp/warehouse/pyiceberg_catalog.db", - "warehouse": "file:///tmp/warehouse", -} - @api_router.get("/{identifier}/gpkg") async def get_hydrofabric_subset_gpkg( @@ -34,7 +28,7 @@ async def get_hydrofabric_subset_gpkg( from a given identifier and returns all related geospatial layers as a downloadable geopackage file. """ - catalog = load_catalog("hydrofabric", **catalog_settings) + catalog = load_catalog("glue") unique_id = str(uuid.uuid4())[:8] temp_dir = pathlib.Path(tempfile.gettempdir()) tmp_path = temp_dir / f"hydrofabric_subset_{identifier}_{unique_id}.gpkg" diff --git a/src/icefabric_api/app/routers/streamflow_observations/router.py b/src/icefabric_api/app/routers/streamflow_observations/router.py index e2c3055..fc0ee79 100644 --- a/src/icefabric_api/app/routers/streamflow_observations/router.py +++ b/src/icefabric_api/app/routers/streamflow_observations/router.py @@ -8,12 +8,6 @@ api_router = APIRouter(prefix="/streamflow_observations") -catalog_settings: dict = { - "type": "sql", - "uri": "sqlite:////tmp/warehouse/pyiceberg_catalog.db", - "warehouse": "file:///tmp/warehouse", -} - # TODO add other gauges used by NWM class DataSource(str, Enum): @@ -25,7 +19,7 @@ class DataSource(str, Enum): # Configuration for each data source DATA_SOURCE_CONFIG = { DataSource.USGS: { - "namespace": "observations", + "namespace": "streamflow_observations", "table": "usgs_hourly", "time_column": "time", "units": "cms", @@ -37,7 +31,7 @@ class DataSource(str, Enum): def get_catalog_and_table(data_source: DataSource): """Get catalog and table for a given data source""" config = DATA_SOURCE_CONFIG[data_source] - catalog = load_catalog(config["namespace"], **catalog_settings) + catalog = load_catalog("glue") table = catalog.load_table(f"{config['namespace']}.{config['table']}") return catalog, table, config diff --git a/src/icefabric_manage/builds/README.md b/src/icefabric_manage/builds/README.md new file mode 100644 index 0000000..d5c555d --- /dev/null +++ b/src/icefabric_manage/builds/README.md @@ -0,0 +1,47 @@ +This folder is set up to build remote iceberg catalogs to the S3 endpoints. The following template can be used to easily create a build script: + +The following variables must always be set when building + +```python +import argparse + +from dotenv import load_dotenv +from pyiceberg.catalog import load_catalog +from pyiceberg.exceptions import NamespaceAlreadyExistsError + +from icefabric_manage import build + +load_dotenv() + +def build_table(file_dir: str): + """Builds the hydrofabric namespace and tables + + Parameters + ---------- + file_dir : str + The directory to hydrofabric parquet files + """ + catalog = load_catalog("glue") # Using an AWS Glue Endpoint + namespace = "" + . + . + . + build( + catalog=catalog, + parquet_file=f"{file_dir}/{layer}.parquet", + namespace=namespace, + table_name=layer, + location="s3://fim-services-data-test/icefabric_metadata/" + ) + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="A script to build a pyiceberg catalog in the S3 endpoint" + ) + + parser.add_argument("--files", help="The local file dir where the files are located") + + args = parser.parse_args() + build_table(file_dir=args.files) + +``` diff --git a/src/icefabric_manage/builds/build_fema_ble.py b/src/icefabric_manage/builds/build_fema_ble.py new file mode 100644 index 0000000..8ad21d2 --- /dev/null +++ b/src/icefabric_manage/builds/build_fema_ble.py @@ -0,0 +1,48 @@ +import argparse +from pathlib import Path + +from dotenv import load_dotenv +from pyiceberg.catalog import load_catalog +from pyiceberg.exceptions import NamespaceAlreadyExistsError + +from icefabric_manage import build + +load_dotenv() + + +def build_table(file_dir: str): + """Builds the hydrofabric namespace and tables + + Parameters + ---------- + file_dir : str + The directory to hydrofabric parquet files + """ + catalog = load_catalog("glue") # Using an AWS Glue Endpoint + namespace = "ble_xs" + try: + catalog.create_namespace(namespace) + except NamespaceAlreadyExistsError: + print(f"Namespace {namespace} already exists") + for folder in Path(file_dir).glob("ble_*"): + huc_number = folder.name.split("_", 1)[1] + print(f"building HUC XS: {huc_number}") + build( + catalog=catalog, + parquet_file=f"{file_dir}/ble_{huc_number}/huc_{huc_number}.parquet", + namespace=namespace, + table_name=huc_number, + location="s3://fim-services-data-test/icefabric_metadata/", + ) + print(f"Build successful. Files written into metadata store @ {catalog.name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="A script to build a pyiceberg catalog in the S3 endpoint for the FEMA-BLE data" + ) + + parser.add_argument("--files", help="The local file dir where the files are located") + + args = parser.parse_args() + build_table(file_dir=args.files) diff --git a/src/icefabric_manage/builds/build_fema_mip.py b/src/icefabric_manage/builds/build_fema_mip.py new file mode 100644 index 0000000..cae4e2b --- /dev/null +++ b/src/icefabric_manage/builds/build_fema_mip.py @@ -0,0 +1,48 @@ +import argparse +from pathlib import Path + +from dotenv import load_dotenv +from pyiceberg.catalog import load_catalog +from pyiceberg.exceptions import NamespaceAlreadyExistsError + +from icefabric_manage import build + +load_dotenv() + + +def build_table(file_dir: str): + """Builds the hydrofabric namespace and tables + + Parameters + ---------- + file_dir : str + The directory to hydrofabric parquet files + """ + catalog = load_catalog("glue") # Using an AWS Glue Endpoint + namespace = "mip_xs" + try: + catalog.create_namespace(namespace) + except NamespaceAlreadyExistsError: + print(f"Namespace {namespace} already exists") + for folder in Path(file_dir).glob("mip_*"): + huc_number = folder.name.split("_", 1)[1] + print(f"building HUC XS: {huc_number}") + build( + catalog=catalog, + parquet_file=f"{file_dir}/mip_{huc_number}/huc_{huc_number}.parquet", + namespace=namespace, + table_name=huc_number, + location="s3://fim-services-data-test/icefabric_metadata/", + ) + print(f"Build successful. Files written into metadata store @ {catalog.name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="A script to build a pyiceberg catalog in the S3 endpoint for the FEMA-BLE data" + ) + + parser.add_argument("--files", help="The local file dir where the files are located") + + args = parser.parse_args() + build_table(file_dir=args.files) diff --git a/src/icefabric_manage/builds/build_hydrofabric.py b/src/icefabric_manage/builds/build_hydrofabric.py index e438391..c4d444a 100644 --- a/src/icefabric_manage/builds/build_hydrofabric.py +++ b/src/icefabric_manage/builds/build_hydrofabric.py @@ -1,23 +1,28 @@ -from pathlib import Path +import argparse from dotenv import load_dotenv from pyiceberg.catalog import load_catalog +from pyiceberg.exceptions import NamespaceAlreadyExistsError from icefabric_manage import build load_dotenv() -if __name__ == "__main__": - warehouse_path = Path("/tmp/warehouse") - warehouse_path.mkdir(exist_ok=True) - catalog_settings = { - "type": "sql", - "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", - "warehouse": f"file://{warehouse_path}", - } +def build_table(file_dir: str): + """Builds the hydrofabric namespace and tables + + Parameters + ---------- + file_dir : str + The directory to hydrofabric parquet files + """ + catalog = load_catalog("glue") # Using an AWS Glue Endpoint namespace = "hydrofabric" - catalog = load_catalog(namespace, **catalog_settings) + try: + catalog.create_namespace(namespace) + except NamespaceAlreadyExistsError: + print(f"Namespace {namespace} already exists") layers = [ "divide-attributes", "divides", @@ -34,8 +39,18 @@ print(f"building layer: {layer}") build( catalog=catalog, - parquet_file=f"s3://hydrofabric-data/icefabric/hydrofabric/{layer}.parquet", + parquet_file=f"{file_dir}/{layer}.parquet", namespace=namespace, table_name=layer, + location="s3://fim-services-data-test/icefabric_metadata/", ) - print(f"Build successful. Files written into metadata store @ {warehouse_path}") + print(f"Build successful. Files written into metadata store @ {catalog.name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="A script to build a pyiceberg catalog in the S3 endpoint") + + parser.add_argument("--files", help="The local file dir where the files are located") + + args = parser.parse_args() + build_table(file_dir=args.files) diff --git a/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py b/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py index 4f3b2ea..f7f2655 100644 --- a/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py +++ b/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py @@ -1,40 +1,68 @@ +import argparse from pathlib import Path from dotenv import load_dotenv +from pyiceberg.catalog import load_catalog from pyiceberg.exceptions import NamespaceAlreadyExistsError -from icefabric_manage import IcebergTable +from icefabric_manage import build load_dotenv() -if __name__ == "__main__": - warehouse_path = Path("/tmp/warehouse") - warehouse_path.mkdir(exist_ok=True) - catalog_name = "pyiceberg_catalog" - catalog_settings = { - "type": "sql", - "uri": f"sqlite:///{warehouse_path}/{catalog_name}.db", - "warehouse": f"file://{warehouse_path}", - } - - iceberg_table = IcebergTable() +def build_table(file_dir: Path): + catalog = load_catalog("glue") # Using an AWS Glue Endpoint + namespace = "bathymetry_ml_auxiliary" try: - # Establish new Iceberg catalog - iceberg_table.establish_catalog( - catalog_name=catalog_name, - namespace="bathymetry_ml_auxiliary", - catalog_settings=catalog_settings, - ) + catalog.create_namespace(namespace) + except NamespaceAlreadyExistsError: + print(f"Namespace {namespace} already exists") + + layers = [ + "vpuid=01", + "vpuid=02", + "vpuid=03N", + "vpuid=03S", + "vpuid=03W", + "vpuid=04", + "vpuid=05", + "vpuid=06", + "vpuid=07", + "vpuid=08", + "vpuid=09", + "vpuid=10L", + "vpuid=10U", + "vpuid=11", + "vpuid=12", + "vpuid=13", + "vpuid=14", + "vpuid=15", + "vpuid=16", + "vpuid=17", + ] - # Generate tables w/in the new Iceberg catalog for all parquets detected in s3 - iceberg_table.create_table_for_all_s3parquets( - app_name="bathymetry_ml_auxiliary", bucket_name="ngwpc-bathymetry" + for layer in layers: + print(f"building layer: {layer}") + # The following warning is expected: + # Iceberg does not have a dictionary type. will be inferred as int32 on read. + # Arrow will make columns with a single, non-unique, value into a dictionary for ease of writing/loading + # Thus, when writing to pyiceberg it needs to remove that. + build( + catalog=catalog, + parquet_file=f"{file_dir}/{layer}/ml_auxiliary_data.parquet", + namespace=namespace, + table_name=layer, + location="s3://fim-services-data-test/icefabric_metadata/", ) + print(f"Build successful. Files written into metadata store @ {catalog.name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="A script to build a pyiceberg catalog for bathymetric data in the S3 endpoint" + ) - except NamespaceAlreadyExistsError as e: - raise FileExistsError( - f"Iceberg table and namespace already exist. Remove catalog from {warehouse_path} to continue" - ) from e + parser.add_argument("--files", help="The local file dir where the files are located") - print(f"Build successful. Files written into metadata store @ {warehouse_path}") + args = parser.parse_args() + build_table(file_dir=args.files) diff --git a/src/icefabric_manage/builds/build_usgs_streamflow_observations.py b/src/icefabric_manage/builds/build_usgs_streamflow_observations.py index f112418..0b594ff 100644 --- a/src/icefabric_manage/builds/build_usgs_streamflow_observations.py +++ b/src/icefabric_manage/builds/build_usgs_streamflow_observations.py @@ -1,27 +1,35 @@ -from pathlib import Path +import argparse from dotenv import load_dotenv from pyiceberg.catalog import load_catalog +from pyiceberg.exceptions import NamespaceAlreadyExistsError from icefabric_manage import build load_dotenv() -if __name__ == "__main__": - warehouse_path = Path("/tmp/warehouse") - warehouse_path.mkdir(exist_ok=True) - catalog_settings = { - "type": "sql", - "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", - "warehouse": f"file://{warehouse_path}", - } - - namespace = "observations" - catalog = load_catalog(namespace, **catalog_settings) + +def build_table(file_dir): + catalog = load_catalog("glue") # Using an AWS Glue Endpoint + namespace = "streamflow_observations" + try: + catalog.create_namespace(namespace) + except NamespaceAlreadyExistsError: + print(f"Namespace {namespace} already exists") build( catalog=catalog, - parquet_file="s3://hydrofabric-data/icefabric/observations/usgs_hourly.parquet", + parquet_file=f"{file_dir}/usgs_hourly.parquet", namespace=namespace, table_name="usgs_hourly", + location="s3://fim-services-data-test/icefabric_metadata/", ) - print(f"Build successful. Files written into metadata store @ {warehouse_path}") + print(f"Build successful. Files written into metadata store @ {catalog.name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="A script to build a pyiceberg catalog in the S3 endpoint") + + parser.add_argument("--files", help="The local file dir where the files are located") + + args = parser.parse_args() + build_table(file_dir=args.files) diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index f358619..aac45b4 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -21,9 +21,9 @@ dependencies = [ "numpy==2.2.6", "pandas==2.2.3", "geopandas==1.0.1", - "s3fs==2025.5.0", + "s3fs==2025.5.1", "pyarrow==20.0.0", - "pyiceberg[sql-sqlite]==0.9.0", + "pyiceberg[s3fs,glue]==0.9.1", "python-dotenv==1.1.0", ] diff --git a/src/icefabric_manage/src/icefabric_manage/_api.py b/src/icefabric_manage/src/icefabric_manage/_api.py index 7d61784..b626c19 100644 --- a/src/icefabric_manage/src/icefabric_manage/_api.py +++ b/src/icefabric_manage/src/icefabric_manage/_api.py @@ -4,7 +4,7 @@ from pyiceberg.catalog import Catalog -def build(catalog: Catalog, parquet_file: str, namespace: str, table_name: str) -> None: +def build(catalog: Catalog, parquet_file: str, namespace: str, table_name: str, location: str) -> None: """Builds the hydrofabric catalog based on the .pyiceberg.yaml config and defined parquet files. Parameters @@ -14,10 +14,6 @@ def build(catalog: Catalog, parquet_file: str, namespace: str, table_name: str) file_dir : Path The path to the parquet files to add into the iceberg catalog """ - if not any(ns == (namespace,) for ns in catalog.list_namespaces()): - catalog.create_namespace(namespace) - print(f"Created {namespace} namespace") - if catalog.table_exists(f"{namespace}.{table_name}"): print(f"Table {table_name} already exists. Skipping build") else: @@ -25,5 +21,6 @@ def build(catalog: Catalog, parquet_file: str, namespace: str, table_name: str) iceberg_table = catalog.create_table( f"{namespace}.{table_name}", schema=arrow_table.schema, + location=location, ) iceberg_table.append(arrow_table) diff --git a/src/icefabric_manage/terraform/README.md b/src/icefabric_manage/terraform/README.md new file mode 100644 index 0000000..13940e0 --- /dev/null +++ b/src/icefabric_manage/terraform/README.md @@ -0,0 +1,137 @@ +# AWS S3 Tables with Apache Iceberg - Terraform Implementation + +This directory contains PoC Terraform IaC for deploying Apache Iceberg tables using the AWS S3 Tables service with AWS Glue catalog integration. It also contains a basic demo / test python script used to verify things. + +## Architecture Overview + +The infrastructure creates: +- **AWS S3 Tables bucket** - Managed storage for Iceberg table data +- **S3 Tables namespace and table** - Logical organization for tables +- **AWS Glue Catalog database** - Metadata storage for table schemas +- **Lake Formation permissions** - Access control and governance +- **IAM policies** - Secure access between services + +## Prerequisites + +### AWS Requirements +- AWS CLI configured with appropriate credentials. (Older versions may not support AWS S3 Tables) +- Terraform >= 1.0 +- AWS Account with permissions (basically Admin due to IAM requirements) for: + - S3 Tables + - AWS Glue + - Lake Formation + - IAM + +### ⚠️ Critical: Enable S3 Table Buckets Integration + +**This step must be completed before running Terraform**, otherwise the deployment will fail. + +1. Navigate to the [S3 Table Buckets Console](https://console.aws.amazon.com/s3tables/home) in your target region +2. Locate the section titled **"Integration with AWS analytics services"** +3. Click the **"Enable integration"** button +4. Confirm that the integration status shows **"Enabled"** for your deployment region + +This integration allows services like Athena, Glue, Redshift, and EMR to interact with S3 Table Buckets. Without this step, your Iceberg tables won't be accessible through these analytics services. + +> **Note**: This is a one-time setup per AWS region. Once enabled, all future S3 Table Buckets in that region will have access to AWS analytics services integration. + +### Python Requirements +- Python 3.8+ +- pyiceberg python module w/deps +- boto3 (for AWS SDK) + +## Quick Start + +### 1. High Level Deploy Infrastructure + +Create a `terraform.tfvars` file replacing the values below as appropriate for your environment or deploy: + +```hcl +env = "dev" +application = "myapp" +team = "NGWPC" +region = "us-east-1" +identity_center_role_arn = "arn:aws:iam::123456789012:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_DataRole" + +# Optional: Specify Lake Formation admins +lakeformation_admin_arns = [ + "arn:aws:iam::123456789012:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_DataRole", + "arn:aws:iam::123456789012:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_Admin" +] +``` + +Deploy the infrastructure: + +```bash +terraform init +terraform plan +terraform apply +``` + +### 3. Set Environment Variables + +After deployment, set these environment variables for the Python demo: + +```bash +# From Terraform outputs +export ICEBERG_WAREHOUSE_PATH=$(terraform output -raw s3tables_table_warehouse_location) +export AWS_DEFAULT_REGION="us-east-1" +``` + +### 4. Install Python Dependencies in your preferred active virtual environment + +```bash +pip install pyiceberg boto3 mypy_boto3_glue pyarrow +``` + +### 5. Run the Demo + +```bash +python iceberg_demo.py +``` + +## Terraform Configuration + +### Variables + +| Variable | Description | Type | Default | Required | +|----------|-------------|------|---------|----------| +| `region` | AWS region | string | `us-east-1` | No | +| `env` | Environment name (test/oe/other) | string | - | Yes | +| `application` | Application name | string | - | Yes | +| `team` | Team name (for future tagging if supported) | string | - | Yes | +| `identity_center_role_arn` | IAM role ARN for accessing resources | string | - | Yes | +| `lakeformation_admin_arns` | Lake Formation administrator ARNs | list(string) | `[]` | No | + +### Outputs + +| Output | Description | +|--------|-------------| +| `s3tables_bucket_arn` | ARN of the S3 Tables bucket | +| `s3tables_table_warehouse_location` | Warehouse location for Iceberg tables (devs need this!!!) | +| `glue_database_name` | Name of the Glue catalog database | +| `lakeformation_admins` | List of Lake Formation administrators | + +## Python Integration + +### Basic Usage + +The provided `iceberg_demo.py` demonstrates: +- Connecting to AWS Glue catalog +- Creating/loading Iceberg tables +- Very Basic schema definition + +### Configuration + +The Python script uses these environment variables: +- `ICEBERG_WAREHOUSE_PATH` - S3 Tables warehouse location +- `AWS_REGION` - AWS region for services +- `AWS_DEFAULT_REGION` - Default AWS region + +## Permissions and Security + +### Lake Formation Integration + +The infrastructure automatically configures basic Lake Formation settings. This can get very granular in the future. +- Database-level permissions for the specified Identity Center role (SoftwareEngineersFull) +- Table-level permissions are supported, but have not been tested diff --git a/src/icefabric_manage/terraform/environments/test/.terraform.lock.hcl b/src/icefabric_manage/terraform/environments/test/.terraform.lock.hcl new file mode 100644 index 0000000..abf9ba6 --- /dev/null +++ b/src/icefabric_manage/terraform/environments/test/.terraform.lock.hcl @@ -0,0 +1,24 @@ +# This file is maintained automatically by "terraform init". +# Manual edits may be lost in future updates. + +provider "registry.terraform.io/hashicorp/aws" { + version = "5.99.1" + hashes = [ + "h1:967WCGUW/vgrjUMBvC+HCie1DVgOXHwUkhm2ng3twJw=", + "zh:00b0a61c6d295300f0aa7a79a7d40e9f836164f1fff816d38324c148cd846887", + "zh:1ee9d5ccb67378704642db62113ac6c0d56d69408a9c1afb9a8e14b095fc0733", + "zh:2035977ed418dcb18290785c1eeb79b7133b39f718c470346e043ac48887ffc7", + "zh:67e3ca1bf7061900f81cf958d5c771a2fd6048c2b185bec7b27978349b173a90", + "zh:87fadbe5de7347ede72ad879ff8d8d9334103cd9aa4a321bb086bfac91654944", + "zh:901d170c457c2bff244a2282d9de595bdb3ebecc33a2034c5ce8aafbcff66db9", + "zh:92c07d6cf530679565b87934f9f98604652d787968cce6a3d24c148479b7e34b", + "zh:9b12af85486a96aedd8d7984b0ff811a4b42e3d88dad1a3fb4c0b580d04fa425", + "zh:a7d4803b4c5ff17f029f8b270c91480442ece27cec7922c38548bcfea2ac2d26", + "zh:afda848da7993a07d29018ec25ab6feda652e01d4b22721da570ce4fcc005292", + "zh:baaf16c98b81bad070e0908f057a97108ecd6e8c9f754d7a79b18df4c8453279", + "zh:c3dd496c5014427599d6b6b1c14c7ebb09a15df78918ae0be935e7bfa83b894c", + "zh:e2b84c1d40b3f2c4b1d74bf170b9e932983b61bac0e6dab2e36f5057ddcc997f", + "zh:e49c92cb29c53b4573ed4d9c946486e6bcfc1b63f1aee0c79cc7626f3d9add03", + "zh:efae8e339c4b13f546e0f96c42eb95bf8347de22e941594849b12688574bf380", + ] +} diff --git a/src/icefabric_manage/terraform/environments/test/backend.tf b/src/icefabric_manage/terraform/environments/test/backend.tf new file mode 100644 index 0000000..75551d5 --- /dev/null +++ b/src/icefabric_manage/terraform/environments/test/backend.tf @@ -0,0 +1,9 @@ +terraform { + backend "s3" { + bucket = "ngwpc-infra-test" + key = "terraform/icefabric/test/edfs/terraform.tfstate" + region = "us-east-1" + encrypt = true # Encrypt the state file + #dynamodb_table = "dynamodb-lock-table" # Optional / FUTURE for state locking + } +} diff --git a/src/icefabric_manage/terraform/environments/test/main.tf b/src/icefabric_manage/terraform/environments/test/main.tf new file mode 100644 index 0000000..55aa3bd --- /dev/null +++ b/src/icefabric_manage/terraform/environments/test/main.tf @@ -0,0 +1,28 @@ +provider "aws" { + region = var.region +} + +module "iceberg" { + source = "../../modules/iceberg" # Path to the iceberg module + env = var.env + team = var.team + application = var.application + identity_center_role_arn = var.identity_center_role_arn + lakeformation_admin_arns = var.lakeformation_admin_arns +} + +output "s3tables_bucket_arn" { + value = module.iceberg.s3tables_bucket_arn +} + +output "s3tables_table_warehouse_location" { + value = module.iceberg.s3tables_table_warehouse_location +} + +output "glue_database_name" { + value = module.iceberg.glue_database_name +} + +output "lakeformation_admins" { + value = module.iceberg.lakeformation_admins +} diff --git a/src/icefabric_manage/terraform/environments/test/terraform.tfvars b/src/icefabric_manage/terraform/environments/test/terraform.tfvars new file mode 100644 index 0000000..518b338 --- /dev/null +++ b/src/icefabric_manage/terraform/environments/test/terraform.tfvars @@ -0,0 +1,8 @@ +# Test Deploy +env = "test" +application = "icefabric" +region = "us-east-1" +team = "EDFS" +identity_center_role_arn = "arn:aws:iam::591210920133:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_SoftwareEngineersFull_529cc40c0355e893" +lakeformation_admin_arns = ["arn:aws:iam::591210920133:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_SoftwareEngineersFull_529cc40c0355e893", + "arn:aws:iam::591210920133:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_AdminLogin_fe24f9db156f282d"] diff --git a/src/icefabric_manage/terraform/environments/test/variables.tf b/src/icefabric_manage/terraform/environments/test/variables.tf new file mode 100644 index 0000000..ad7adc0 --- /dev/null +++ b/src/icefabric_manage/terraform/environments/test/variables.tf @@ -0,0 +1,33 @@ +# variables.tf + +variable "region" { + description = "AWS region" + type = string + default = "us-east-1" +} + +variable "env" { + description = "Environment name" + type = string +} + +variable "application" { + description = "Application name" + type = string +} + +variable "team" { + description = "Team name" + type = string +} + +variable "identity_center_role_arn" { + description = "ARN of the Identity Center role that will access the resources" + type = string +} + +variable "lakeformation_admin_arns" { + description = "List of ARNs to set as Lake Formation administrators" + type = list(string) + default = [] # Will be populated with current account or specific users/roles +} \ No newline at end of file diff --git a/src/icefabric_manage/terraform/iceberg_demo.py b/src/icefabric_manage/terraform/iceberg_demo.py new file mode 100644 index 0000000..d80186b --- /dev/null +++ b/src/icefabric_manage/terraform/iceberg_demo.py @@ -0,0 +1,58 @@ +import os + +from pyiceberg.catalog import load_catalog +from pyiceberg.schema import Schema +from pyiceberg.types import LongType, NestedField, StringType + +# Define warehouse path as a variable at the top (replace with your actual path or load dynamically) +WAREHOUSE_PATH = os.getenv("ICEBERG_WAREHOUSE_PATH") +TABLE_SUBPATH = "tables/icefabric" # Please avoid restricted directories + +# Debug: Print AWS region and Warehouse environment variables +print(f"AWS_DEFAULT_REGION from env: {os.getenv('AWS_DEFAULT_REGION')}") +print(f"Using warehouse path: {WAREHOUSE_PATH}") + +# Configure the catalog to use AWS Glue +config = { + "type": "glue", + "s3.endpoint": "s3.us-east-1.amazonaws.com", + "warehouse": f"{WAREHOUSE_PATH}", + "region": "us-east-1", + "glue_region": "us-east-1", +} +print(f"Catalog configuration: {config}") + +try: + catalog = load_catalog("glue", **config) + print("Catalog loaded successfully") +except Exception as e: + print(f"Error loading catalog: {e}") + raise + +# Define a schema for the Iceberg table (used if creating the table) +schema = Schema( + NestedField(field_id=1, name="id", field_type=LongType(), is_optional=False), + NestedField(field_id=2, name="name", field_type=StringType(), is_optional=True), +) + +# Load or create a table in the Glue catalog, pointing to S3 for storage +try: + table = catalog.load_table(("icefabric_db", "icefabric")) + print(f"Table loaded: {str(table)}") +except Exception as e: + print(f"Table not found, creating it: {e}") + try: + table = catalog.create_table( + identifier=("icefabric_db", "icefabric"), schema=schema, location=f"{TABLE_SUBPATH}" + ) + print(f"Table created: {str(table)}") + except Exception as create_error: + print(f"Error creating table: {create_error}") + raise + +# Example: List tables in the catalog to verify +try: + tables = catalog.list_tables("icefabric_db") + print(f"Tables in namespace: {tables}") +except Exception as list_error: + print(f"Error listing tables: {list_error}") diff --git a/src/icefabric_manage/terraform/modules/iceberg/main.tf b/src/icefabric_manage/terraform/modules/iceberg/main.tf new file mode 100644 index 0000000..c99b6d8 --- /dev/null +++ b/src/icefabric_manage/terraform/modules/iceberg/main.tf @@ -0,0 +1,142 @@ +provider "aws" { + region = var.region +} + +data "aws_caller_identity" "current" {} + +resource "aws_s3tables_table_bucket" "icefabric" { + name = "${var.env}-${var.application}" +} + +resource "aws_s3tables_namespace" "icefabric" { + namespace = var.application + table_bucket_arn = aws_s3tables_table_bucket.icefabric.arn +} + +resource "aws_s3tables_table" "icefabric" { + name = var.application + namespace = aws_s3tables_namespace.icefabric.namespace + table_bucket_arn = aws_s3tables_table_bucket.icefabric.arn + format = "ICEBERG" +} + +data "aws_iam_policy_document" "icefabric_bucket_policy_document" { + statement { + sid = "AllowGlueAccess" + effect = "Allow" + principals { + type = "Service" + identifiers = ["glue.amazonaws.com"] + } + actions = [ + "s3tables:*" + ] + resources = [ + "${aws_s3tables_table_bucket.icefabric.arn}/*", + aws_s3tables_table_bucket.icefabric.arn + ] + } + statement { + sid = "AllowAccountAccess" + effect = "Allow" + principals { + type = "AWS" + identifiers = ["arn:aws:iam::${data.aws_caller_identity.current.account_id}:root"] + } + actions = [ + "s3tables:*" + ] + resources = [ + "${aws_s3tables_table_bucket.icefabric.arn}/*", + aws_s3tables_table_bucket.icefabric.arn + ] + } +} + +resource "aws_s3tables_table_bucket_policy" "icefabric_policy" { + resource_policy = data.aws_iam_policy_document.icefabric_bucket_policy_document.json + table_bucket_arn = aws_s3tables_table_bucket.icefabric.arn +} + +# AWS Glue Catalog Database for Iceberg Tables Metadata +resource "aws_glue_catalog_database" "icefabric_db" { + name = "icefabric_db" + description = "Glue database for Iceberg tables in ${var.application} namespace" +} + +# Grant Lake Formation permissions for the database +resource "aws_lakeformation_permissions" "database_permissions" { + principal = var.identity_center_role_arn + permissions = [ + "CREATE_TABLE", + "DESCRIBE", + "ALTER" + ] + permissions_with_grant_option = [ + "CREATE_TABLE", + "DESCRIBE", + "ALTER" + ] + database { + name = aws_glue_catalog_database.icefabric_db.name + } +} + +# EDFS currently plans to manage Tables outside of Terraform, but one can grant permissions +# for table(s) (if it exists or after creation) in terraform +#resource "aws_lakeformation_permissions" "table_permissions" { +# principal = var.identity_center_role_arn +# permissions = [ +# "SELECT", +# "INSERT", +# "DELETE", +# "ALTER", +# "DESCRIBE" +# ] +# permissions_with_grant_option = [ +# "SELECT", +# "INSERT", +# "DELETE", +# "ALTER", +# "DESCRIBE" +# ] +# table { +# database_name = aws_glue_catalog_database.icefabric_db.name +# name = "icefabric" +# } +#} + +# Set Lake Formation Data Lake Settings (initialize Lake Formation) +resource "aws_lakeformation_data_lake_settings" "main" { + # Define Lake Formation administrators (This shows up as a popup when you enter the console if we don't set it via IaC.) + admins = length(var.lakeformation_admin_arns) > 0 ? var.lakeformation_admin_arns : [ + "arn:aws:iam::${data.aws_caller_identity.current.account_id}:root" + ] + + # Optional: Allow external data filtering (for cross-account access) + allow_external_data_filtering = false + + # Optional: Allow full table external data access + allow_full_table_external_data_access = false + + # Trusted resource owners (for cross-account scenarios) + trusted_resource_owners = [data.aws_caller_identity.current.account_id] +} + + +# Outputs +output "s3tables_bucket_arn" { + value = aws_s3tables_table_bucket.icefabric.arn +} + +output "s3tables_table_warehouse_location" { + value = aws_s3tables_table.icefabric.warehouse_location +} + +output "glue_database_name" { + value = aws_glue_catalog_database.icefabric_db.name +} + +output "lakeformation_admins" { + value = aws_lakeformation_data_lake_settings.main.admins +} diff --git a/src/icefabric_manage/terraform/modules/iceberg/variables.tf b/src/icefabric_manage/terraform/modules/iceberg/variables.tf new file mode 100644 index 0000000..ad7adc0 --- /dev/null +++ b/src/icefabric_manage/terraform/modules/iceberg/variables.tf @@ -0,0 +1,33 @@ +# variables.tf + +variable "region" { + description = "AWS region" + type = string + default = "us-east-1" +} + +variable "env" { + description = "Environment name" + type = string +} + +variable "application" { + description = "Application name" + type = string +} + +variable "team" { + description = "Team name" + type = string +} + +variable "identity_center_role_arn" { + description = "ARN of the Identity Center role that will access the resources" + type = string +} + +variable "lakeformation_admin_arns" { + description = "List of ARNs to set as Lake Formation administrators" + type = list(string) + default = [] # Will be populated with current account or specific users/roles +} \ No newline at end of file diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index 9c8a277..7e1489e 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -22,9 +22,9 @@ dependencies = [ "pandas==2.2.3", "geopandas==1.0.1", "matplotlib==3.10.3", - "s3fs==2025.5.0", + "s3fs==2025.5.1", "pyarrow==20.0.0", - "pyiceberg[sql-sqlite]==0.9.0", + "pyiceberg[s3fs,glue]==0.9.1", 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Please make sure your AWS credentials are accurate" + # ) # type: ignore @pytest.fixture(params=sample_gauges) diff --git a/uv.lock b/uv.lock index aca5b2d..5d1335c 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 2 requires-python = ">=3.11, <3.13" resolution-markers = [ "python_full_version >= '3.12'", @@ -17,9 +18,9 @@ members = [ name = "affine" version = "2.4.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132 } +sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" } wheels = [ - { url = 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Jupyter Notebook showing how to query RAS XS from the EDFS Apache iceberg tables --- examples/ras_xs/querying.ipynb | 744 +++++++++++++++++++++++++++++++++ 1 file changed, 744 insertions(+) create mode 100644 examples/ras_xs/querying.ipynb diff --git a/examples/ras_xs/querying.ipynb b/examples/ras_xs/querying.ipynb new file mode 100644 index 0000000..0e7bf1b --- /dev/null +++ b/examples/ras_xs/querying.ipynb @@ -0,0 +1,744 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Querying RAS XS through PyIceberg\n", + "\n", + "The following notebook is to walk you through the process of querying a RAS XS through PyIceberg. \n", + "\n", + "For the warehouse path, please put the path to your S3 tables URI\n", + "\n", + "Requires `.env` containing `test` account credentials AND default region: `AWS_DEFAULT_REGION=\"us-east-1\"`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import os\n", + "\n", + "from dotenv import load_dotenv\n", + "from pyiceberg.catalog import load_catalog\n", + "\n", + "from icefabric_tools import to_geopandas\n", + "\n", + "load_dotenv()\n", + "\n", + "os.environ[\"WAREHOUSE_PATH\"] = \"INSERT PATH TO URI\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Catalog\n", + "catalog_settings = {\n", + " \"type\": \"glue\",\n", + " \"s3.endpoint\": \"s3.us-east-1.amazonaws.com\",\n", + " \"warehouse\": os.environ[\"WAREHOUSE_PATH\"],\n", + " \"glue_region\": \"us-east-1\",\n", + "}\n", + "catalog = load_catalog(\"glue\", **catalog_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('mip_xs', '02030102'),\n", + " ('mip_xs', '02030103'),\n", + " ('mip_xs', '02030104'),\n", + " ('mip_xs', '02030105'),\n", + " ('mip_xs', '02040101'),\n", + " ('mip_xs', '02040102'),\n", + " ('mip_xs', '02040103'),\n", + " ('mip_xs', '02040104'),\n", + " ('mip_xs', '02040105'),\n", + " ('mip_xs', '02040106')]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catalog.list_tables(\"mip_xs\")[40:50]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `catalog.load_table()` we can directly call the XS data. Each is stored based on HUC8" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    2Middle CreekReach 1Middle Creek ,Reach 115972.0Middle Creek Reach 1 15972.0856.647865.25141.8214.1191.2Type RM Length L Ch R = 1 ,15972 ,141.8,191....[(0.0, 865.25), (0.757, 865.23), (4.075, 865.0...['177.08', '192.51']10472113.0\\n1371.0\\n1920.0\\n1109.0\\n877.0\\n611.00.2% Chance\\n1% Chance\\n1+% Chance\\n2% Chance\\...LINESTRING (1699527.966 2176278.803, 1699473.4...02040106MiddleCreekAENoneNoneNoneNone89160
    3Middle CreekReach 1Middle Creek ,Reach 115781.0Middle Creek Reach 1 15781.0853.81864.813128.0205.3183.5Type RM Length L Ch R = 1 ,15781 ,128,183.5,...[(0.0, 864.22), (19.555, 864.768), (22.379, 86...['249.38', '264.58']12962113.0\\n1371.0\\n1920.0\\n1109.0\\n877.0\\n611.00.2% Chance\\n1% Chance\\n1+% Chance\\n2% Chance\\...LINESTRING (1699583.967 2176242.774, 1699513.8...02040106MiddleCreekAENoneNoneNoneNone89161
    4Middle CreekReach 1Middle Creek ,Reach 115598.0Middle Creek Reach 1 15598.0851.72861.82215.395.0209.6Type RM Length L Ch R = 1 ,15598 ,215.3,209....[(0.0, 861.81), (0.4, 861.82), (10.0, 861.02),...['298.1', '328.6']35422113.0\\n1371.0\\n1920.0\\n1109.0\\n877.0\\n611.00.2% Chance\\n1% Chance\\n1+% Chance\\n2% Chance\\...LINESTRING (1699625.84 2176240.53, 1699373.415...02040106MiddleCreekAENoneNoneNoneNone89162
    \n", + "
    " + ], + "text/plain": [ + " river reach ... ds_network_to_id __index_level_0__\n", + "0 Middle Creek Reach 1 ... None 89158\n", + "1 Middle Creek Reach 1 ... None 89159\n", + "2 Middle Creek Reach 1 ... None 89160\n", + "3 Middle Creek Reach 1 ... None 89161\n", + "4 Middle Creek Reach 1 ... None 89162\n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Reading MIP XS\n", + "namespace = \"mip_xs\"\n", + "huc_number = \"02040106\"\n", + "df = catalog.load_table(f\"{namespace}.{huc_number}\").scan().to_pandas()\n", + "gdf = to_geopandas(df)\n", + "gdf.head()\n", + "# gdf.explore()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To query individual river reaches, we can use the scan feature to query based on the table schema" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catalog.load_table(f\"{namespace}.{huc_number}\").schema" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's query by the river name" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id__index_level_0__
    4916Lehigh RiverMainLehigh River ,Main970.0563Lehigh River Main 970.0563161.24315.72205.44257.81237.58Type RM Length L Ch R = 1 ,970.0563,205.44,237...[(0.0, 212.27), (10.0, 212.07), (13.99, 212.11...['1691.53', '1986.6']396446235.0\\n56895.0\\n64915.0\\n73200.0\\n88600.0\\n9...10-yr\\n25-yr\\n50-yr\\n100-yr\\n100-yr+\\n500-yrb'\\x01\\x02\\x00\\x00\\x00\\x04\\x00\\x00\\x00\\xb7^\\xf...02040106LehighRiverFEMA_SupNoneNoneNoneNone99205
    4917Lehigh RiverMainLehigh River ,Main732.4796Lehigh River Main 732.4796160.27307.0103.30103.30103.30Type RM Length L Ch R = 1 ,732.4796,103.3,103....[(0.0, 211.27), (3.99, 210.98), (9.98, 210.82)...['1649.13', '1981.56']400446235.0\\n56895.0\\n64915.0\\n73200.0\\n88600.0\\n9...10-yr\\n25-yr\\n50-yr\\n100-yr\\n100-yr+\\n500-yrb\"\\x01\\x02\\x00\\x00\\x00\\x04\\x00\\x00\\x000\\xdc\\xe...02040106LehighRiverFEMA_SupNoneNoneNoneNone99206
    4918Lehigh RiverMainLehigh River ,Main640.0000Lehigh River Main 640.0160.25211.874.2074.2074.20Type RM Length L Ch R = 1 ,640 ,74.2,74.2,...[(0.0, 209.86), (7.7, 209.81), (19.2, 209.34),...['1628.6', '2020.7']440946235.0\\n56895.0\\n64915.0\\n73200.0\\n88600.0\\n9...10-yr\\n25-yr\\n50-yr\\n100-yr\\n100-yr+\\n500-yrb'\\x01\\x02\\x00\\x00\\x00\\t\\x00\\x00\\x00\\xba!\\x0b\\...02040106LehighRiverFEMA_SupNoneNoneNoneNone99207
    4919Lehigh RiverMainLehigh River ,Main573.0077Lehigh River Main 573.0077160.76296.63201.11302.23224.98Type RM Length L Ch R = 1 ,573.0077,201.11,224...[(0.0, 210.06), (9.99, 209.84), (15.98, 209.82...['1657.49', '2078.85']380746235.0\\n56895.0\\n64915.0\\n73200.0\\n88600.0\\n9...10-yr\\n25-yr\\n50-yr\\n100-yr\\n100-yr+\\n500-yrb'\\x01\\x02\\x00\\x00\\x00\\x07\\x00\\x00\\x00*e)r\\xa7...02040106LehighRiverFEMA_SupNoneNoneNoneNone99208
    4920Lehigh RiverMainLehigh River ,Main348.0278Lehigh River Main 348.0278152.95283.66140.55152.84348.03Type RM Length L Ch R = 1 ,348.0278,140.55,348...[(0.0, 208.28), (9.99, 207.72), (37.95, 206.87...['1743.75', '2358.16']400846235.0\\n56895.0\\n64915.0\\n73200.0\\n88600.0\\n9...10-yr\\n25-yr\\n50-yr\\n100-yr\\n100-yr+\\n500-yrb'\\x01\\x02\\x00\\x00\\x00\\x08\\x00\\x00\\x00\\xb0\\x8f...02040106LehighRiverFEMA_SupNoneNone4188251258846199209
    \n", + "
    " + ], + "text/plain": [ + " river reach ... ds_network_to_id __index_level_0__\n", + "4916 Lehigh River Main ... None 99205\n", + "4917 Lehigh River Main ... None 99206\n", + "4918 Lehigh River Main ... None 99207\n", + "4919 Lehigh River Main ... None 99208\n", + "4920 Lehigh River Main ... 2588461 99209\n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pyiceberg.expressions import EqualTo\n", + "\n", + "df = (\n", + " catalog.load_table(f\"{namespace}.{huc_number}\")\n", + " .scan(row_filter=EqualTo(\"river\", \"Lehigh River\"))\n", + " .to_pandas()\n", + ")\n", + "display(df.tail())\n", + "# to_geopandas(df).explore()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's query by an individual river station ID" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id__index_level_0__
    0Lehigh RiverMainLehigh River ,Main573.0077Lehigh River Main 573.0077160.76296.63201.11302.23224.98Type RM Length L Ch R = 1 ,573.0077,201.11,224...[(0.0, 210.06), (9.99, 209.84), (15.98, 209.82...['1657.49', '2078.85']380746235.0\\n56895.0\\n64915.0\\n73200.0\\n88600.0\\n9...10-yr\\n25-yr\\n50-yr\\n100-yr\\n100-yr+\\n500-yrb'\\x01\\x02\\x00\\x00\\x00\\x07\\x00\\x00\\x00*e)r\\xa7...02040106LehighRiverFEMA_SupNoneNoneNoneNone99208
    \n", + "
    " + ], + "text/plain": [ + " river reach ... ds_network_to_id __index_level_0__\n", + "0 Lehigh River Main ... None 99208\n", + "\n", + "[1 rows x 25 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pyiceberg.expressions import EqualTo\n", + "\n", + "df = (\n", + " catalog.load_table(f\"{namespace}.{huc_number}\")\n", + " .scan(row_filter=EqualTo(\"river_station\", 573.0077))\n", + " .to_pandas()\n", + ")\n", + "display(df.head())\n", + "# to_geopandas(df).explore()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To extend this notebook to other HUCs, just change the HUC number, then change the XS reference" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 7876cb49f4c7a236920128026bdfd34d0828c6c9 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Tue, 10 Jun 2025 12:13:09 -0400 Subject: [PATCH 54/97] Docs + API bug cleanup (#29) --- README.md | 3 + conftest.py | 124 ++++++++++ docs/index.md | 36 ++- docs/user_guide/icefabric_api.md | 14 -- docs/user_guide/icefabric_manage.md | 226 +----------------- docs/user_guide/icefabric_tools.md | 7 +- docs/user_guide/terraform.md | 140 +++++++++++ examples/Demo_Iceberg.ipynb | 2 +- mkdocs.yml | 1 + pyproject.toml | 15 +- .../src => scripts}/get_module_parameters.py | 9 +- src/icefabric_api/app/conftest.py | 29 --- src/icefabric_api/app/main.py | 3 +- .../app/routers/hydrofabric/conftest.py | 32 --- .../app/routers/hydrofabric/test_router.py | 34 --- .../app/routers/module_params/router.py | 18 +- .../streamflow_observations/conftest.py | 18 -- src/icefabric_api/data/gauge_06710385.gpkg | Bin 319488 -> 0 bytes src/icefabric_api/pyproject.toml | 8 +- src/icefabric_api/src/__init__.py | 0 src/icefabric_api/src/cfe_params.csv | 26 -- src/icefabric_api/src/gauge_06710385.gpkg | Bin 319488 -> 0 bytes src/icefabric_api/src/hf_attributes.py | 96 -------- .../src/icefabric_api/__init__.py | 6 + .../src/icefabric_api/create_ipes.py | 217 +++++++++++++++++ src/icefabric_api/src/requirements.txt | 2 - src/icefabric_api/src/run_modules.py | 127 ---------- ...ta_from_20211231_1400_to_20220101_1400.csv | 26 -- ...rom_20211231_1400_to_20220101_1400.parquet | Bin 3415 -> 0 bytes .../tests/test_hydrofabric_router.py | 11 + src/icefabric_api/{app => tests}/test_main.py | 0 .../test_streamflow_observations_router.py} | 0 src/icefabric_manage/builds/README.md | 15 +- src/icefabric_manage/builds/build_fema_ble.py | 2 +- src/icefabric_manage/builds/build_fema_mip.py | 2 +- .../build_iceberg_bathymetry_ml_auxiliary.py | 7 + .../build_usgs_streamflow_observations.py | 7 + src/icefabric_manage/pyproject.toml | 2 +- .../src/icefabric_manage/_api.py | 23 +- src/icefabric_tools/pyproject.toml | 2 +- .../src/icefabric_tools/__init__.py | 9 +- src/icefabric_tools/tests/conftest.py | 55 ----- src/icefabric_tools/tests/test_subset.py | 2 +- tests/README.md | 7 + uv.lock | 12 +- 45 files changed, 654 insertions(+), 721 deletions(-) create mode 100644 conftest.py create mode 100644 docs/user_guide/terraform.md rename {src/icefabric_api/src => scripts}/get_module_parameters.py (73%) delete mode 100644 src/icefabric_api/app/conftest.py delete mode 100644 src/icefabric_api/app/routers/hydrofabric/conftest.py delete mode 100644 src/icefabric_api/app/routers/hydrofabric/test_router.py delete mode 100644 src/icefabric_api/app/routers/streamflow_observations/conftest.py delete mode 100644 src/icefabric_api/data/gauge_06710385.gpkg delete mode 100644 src/icefabric_api/src/__init__.py delete mode 100644 src/icefabric_api/src/cfe_params.csv delete mode 100644 src/icefabric_api/src/gauge_06710385.gpkg delete mode 100644 src/icefabric_api/src/hf_attributes.py create mode 100644 src/icefabric_api/src/icefabric_api/__init__.py create mode 100644 src/icefabric_api/src/icefabric_api/create_ipes.py delete mode 100644 src/icefabric_api/src/requirements.txt delete mode 100644 src/icefabric_api/src/run_modules.py delete mode 100644 src/icefabric_api/tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv delete mode 100644 src/icefabric_api/tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.parquet create mode 100644 src/icefabric_api/tests/test_hydrofabric_router.py rename src/icefabric_api/{app => tests}/test_main.py (100%) rename src/icefabric_api/{app/routers/streamflow_observations/test_router.py => tests/test_streamflow_observations_router.py} (100%) delete mode 100644 src/icefabric_tools/tests/conftest.py create mode 100644 tests/README.md diff --git a/README.md b/README.md index 776b525..3e3111f 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,9 @@ An [Apache Iceberg](https://py.iceberg.apache.org/) implementation of the Hydrofabric to disseminate continental hydrologic data +> [!NOTE] +> To run any of the functions in this repo your AWS test account credentials need to be in your `.env` file and your `.pyiceberg.yaml` settings need to up to date + ### Getting Started This repo is managed through [UV](https://docs.astral.sh/uv/getting-started/installation/) and can be installed through: ```sh diff --git a/conftest.py b/conftest.py new file mode 100644 index 0000000..279747d --- /dev/null +++ b/conftest.py @@ -0,0 +1,124 @@ +import os +import sys +from pathlib import Path + +# Hard coding a reference to the icefabric_api/ folder to test the API +project_root = Path(__file__).parents[1] +sys.path.insert(0, str(project_root / "src" / "icefabric_api")) + +os.environ["AWS_DEFAULT_REGION"] = "us-east-1" +os.environ["PYICEBERG_HOME"] = str(Path(__file__).parents[1]) + +import pandas as pd # noqa: E402 +import pytest # noqa: E402 +from app.main import app # noqa: E402 +from fastapi.testclient import TestClient # noqa: E402 +from pyiceberg.catalog import Catalog, load_catalog # noqa: E402 + +sample_gauges = [ + "01010000", + "02450825", + "03173000", + "04100500", + "05473450", + "06823500", + "07060710", + "08070000", + "09253000", + "10316500", + "11456000", + "12411000", + "13337000", + "14020000", +] + +sample_hf_uri = [ + "gages-01010000", + "gages-02450825", + "gages-03173000", + "gages-04100500", + "gages-05473450", + "gages-06823500", + "gages-07060710", + "gages-08070000", + "gages-09253000", + "gages-10316500", + "gages-11456000", + "gages-12411000", + "gages-13337000", + "gages-14020000", +] + + +@pytest.fixture(params=sample_gauges) +def gauge_ids(request) -> str: + """Returns individual gauge identifiers for parameterized testing""" + return request.param + + +@pytest.fixture(params=sample_hf_uri) +def gauge_hf_uri(request) -> str: + """Returns individual gauge identifiers for parameterized testing""" + return request.param + + +@pytest.fixture +def testing_dir() -> Path: + """Returns the testing data dir""" + return Path(__file__).parent / "tests/data/" + + +@pytest.fixture(scope="session") +def client(): + """Create a test client for the FastAPI app.""" + return TestClient(app) + + +@pytest.fixture +def local_usgs_streamflow_csv(): + """Returns a locally downloaded CSV file from a specific gauge and time""" + file_path = ( + Path(__file__).parent / "tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv" + ) + return pd.read_csv(file_path) + + +@pytest.fixture +def local_usgs_streamflow_parquet(): + """Returns a locally downloaded CSV file from a specific gauge and time""" + file_path = ( + Path(__file__).parent / "tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.parquet" + ) + return pd.read_parquet(file_path) + + +@pytest.fixture +def hydrofabric_catalog() -> Catalog: + """Returns an iceberg catalog object for the hydrofabric""" + return load_catalog("glue") + + +def pytest_addoption(parser): + """Adds the --run-slow tag""" + parser.addoption( + "--run-slow", + action="store_true", + default=False, + help="Run slow tests", + ) + + +def pytest_collection_modifyitems(config, items): + """Allows pytest to read the --run-slow tag""" + if not config.getoption("--run-slow"): + skipper = pytest.mark.skip(reason="Only run when --run-slow is given") + for item in items: + if "slow" in item.keywords: + item.add_marker(skipper) + + +def pytest_configure(config): + """Configure pytest markers.""" + config.addinivalue_line("markers", "performance: marks tests as performance tests") + config.addinivalue_line("markers", "integration: marks tests as integration tests") + config.addinivalue_line("markers", "unit: marks tests as unit tests") diff --git a/docs/index.md b/docs/index.md index d400578..6892352 100644 --- a/docs/index.md +++ b/docs/index.md @@ -3,4 +3,38 @@ !!! warning "In Progress" These docs are a work in progress and will continously be updated -This is the local build of the docs +# Icefabric + +An [Apache Iceberg](https://py.iceberg.apache.org/)/[Icechunk](https://icechunk.io/en/latest/) implementation of the Hydrofabric to disseminate continental hydrologic data + +!!! note + To run any of the functions in this repo your AWS test account credentials + `AWS_DEFAULT_REGION="us-east-1"` need to be in your `.env` file and your `.pyiceberg.yaml` settings need to up to date + +### Getting Started +This repo is managed through [UV](https://docs.astral.sh/uv/getting-started/installation/) and can be installed through: +```sh +uv venv +source .venv/bin/activate +uv sync +``` + +### Running the API locally +To run the API locally, ensure your `.env` file in your project root has the right credentials, then run +```sh +uv pip install -e src/icefabric_api +cd src/icefabric_api +python -m app.main +``` +This should spin up the API services + +### Building the API through Docker +To run the API locally with Docker, ensure your `.env` file in your project root has the right credentials, then run +```sh +docker compose -f docker/compose.yaml build --no-cache +docker compose -f docker/compose.yaml up +``` +This should spin up the API services + + +### Development +To ensure that icefabric follows the specified structure, be sure to install the local dev dependencies and run `pre-commit install` diff --git a/docs/user_guide/icefabric_api.md b/docs/user_guide/icefabric_api.md index ca26d09..df2d212 100644 --- a/docs/user_guide/icefabric_api.md +++ b/docs/user_guide/icefabric_api.md @@ -121,20 +121,6 @@ else: | > 50,000 records | Parquet | Better compression, faster processing | | > 200,000 records | Parquet + date filters | Reduced data transfer | -## Configuration - -### Catalog Settings - -The default catalog is pointing to a local instance. To build this catalog, one must use the build scripts in `icefabric_manage` - -```python -catalog_settings = { - "type": "sql", - "uri": "sqlite:////tmp/warehouse/pyiceberg_catalog.db", - "warehouse": "file:///tmp/warehouse", -} -``` - ## Development ### Running the API diff --git a/docs/user_guide/icefabric_manage.md b/docs/user_guide/icefabric_manage.md index 2199631..20eaac2 100644 --- a/docs/user_guide/icefabric_manage.md +++ b/docs/user_guide/icefabric_manage.md @@ -6,41 +6,23 @@ IceFabric Manage is a Python package that simplifies building Apache Iceberg cat ## Architecture -The system consists of two main components: +The system consists of two main components from within the `icefabric/src/icefabric_manage/` structure: -1. **Core API** (`src/icefabric_manage/_api.py`) - Contains the table building logic -2. **Build Scripts** (`builds/`) - Orchestrates the catalog creation process +1. **Source Code** (`src/icefabric_manage/`) - Contains the table building logic and objects +2. **Build Scripts** (`builds/`) - Orchestrates the catalog creation process from the S3 Tables ## How It Works !!! info "Build Process Overview" - The system converts individual Parquet files (local or S3) into queryable Iceberg tables through a simple file-by-file conversion process. + The system converts local Parquet file into queryable Iceberg tables through a simple file-by-file conversion process. ### The Build Process -1. **Create Catalog** - Sets up a SQLite-backed Apache Iceberg catalog in `/tmp/warehouse` -2. **Create Namespace** - Creates the specified namespace if it doesn't exist -3. **Create Tables** - For each specified Parquet file, creates a corresponding Iceberg table -4. **Populate Data** - Appends the Parquet data to the newly created Iceberg tables +1. **Create Namespace** - Creates the specified namespace if it doesn't exist +2. **Create Tables** - For each specified Parquet file, creates a corresponding Iceberg table +3. **Populate Data** - Appends the Parquet data to the newly created Iceberg tables -### File Structure - -!!! note "Project Structure" - The warehouse is now stored in `/tmp/warehouse` and supports both S3 and local Parquet files. - -```text -project/ -├── src/icefabric_manage/ -│ └── _api.py # Core table building functions -├── build_hydrofabric.py # Main build script -└── data/ - ├── warehouse/ # Iceberg catalog storage (auto-created) - │ └── pyiceberg_catalog.db - └── hydrofabric/ # Source Parquet files - ├── divides.parquet - ├── nexus.parquet - └── ... -``` +See `builds/README.md` for a template on how to create your own build script ## Usage @@ -53,6 +35,8 @@ project/ ### Running the Build Scripts +Build scripts have to be only run once as the files will live/persist in S3. + To build the hydrofabric dataset, run from the `scripts/` directory: ```bash python build_hydrofabric.py @@ -60,7 +44,7 @@ python build_hydrofabric.py To build USGS streamflow observations: ```bash -python build_usgs_streamflow_observations.py +python builds/build_hydrofabric.py ``` !!! success "Expected Output" @@ -71,191 +55,3 @@ python build_usgs_streamflow_observations.py ... Build successful ``` - -### What Happens During Build - -1. **Warehouse Setup** - Creates `/tmp/warehouse/` directory if it doesn't exist -2. **Catalog Configuration** - Sets up SQLite catalog with file-based storage -3. **Namespace Creation** - Creates the specified namespace if it doesn't exist -4. **Table Creation** - For each specified Parquet file: - - Reads the Parquet file (from S3 or local path) - - Creates an Iceberg table with matching schema - - Appends all data from the Parquet file to the Iceberg table - - Skips files if tables already exist - -## API Reference - -### `build(catalog, parquet_file, namespace, table_name)` - -!!! function "Main Build Function" - Creates an Iceberg table from a single Parquet file (local or S3). - -**Parameters:** - -| Parameter | Type | Description | -|-----------|------|-------------| -| `catalog` | `Catalog` | PyIceberg catalog instance | -| `parquet_file` | `str` | Path to Parquet file (local path or S3 URL) | -| `namespace` | `str` | Iceberg namespace to create the table in | -| `table_name` | `str` | Name for the new table | - -**Behavior:** -- Creates namespace if it doesn't exist -- Reads Parquet file schema using PyArrow -- Creates Iceberg table with matching schema -- Appends all data from the Parquet file -- Skips table creation if table already exists - -**Example Usage:** -```python -from pyiceberg.catalog import load_catalog -from icefabric_manage import build - -catalog = load_catalog("hydrofabric", **catalog_settings) -build( - catalog=catalog, - parquet_file="s3://hydrofabric-data/icefabric/hydrofabric/divides.parquet", - namespace="hydrofabric", - table_name="divides" -) -``` - -## Build Scripts - -### Hydrofabric Build Script - -The `build_hydrofabric.py` script processes multiple hydrofabric layers: - -```python -layers = [ - "divide-attributes", - "divides", - "flowpath-attributes-ml", - "flowpath-attributes", - "flowpaths", - "hydrolocations", - "lakes", - "network", - "nexus", - "pois" -] -``` - -Each layer is read from `s3://hydrofabric-data/icefabric/hydrofabric/{layer}.parquet` and created as a table in the `hydrofabric` namespace. - -### USGS Streamflow Observations Build Script - -The `build_usgs_streamflow_observations.py` script processes USGS hourly streamflow data: -- Source: `s3://hydrofabric-data/icefabric/streamflow_observations/usgs_hourly.parquet` -- Namespace: `observations` -- Table: `usgs_hourly` - -## Configuration - -### Catalog Settings - -Both build scripts use these default settings: - -```python -warehouse_path = Path("/tmp/warehouse") -catalog_settings = { - "type": "sql", # SQLite-backed catalog - "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", # Database location - "warehouse": f"file://{warehouse_path}", # Data storage location -} -``` - -### Data Sources - -!!! note "Supported File Locations" - - **S3 URLs**: `s3://bucket-name/path/to/file.parquet` - - **Local paths**: `/path/to/local/file.parquet` - - **Relative paths**: `./data/file.parquet` - -!!! danger "Common Issues" - - **S3 Access** - Ensure AWS credentials are properly configured - - **Nanosecond timestamps** - Convert to microseconds before building - - **Invalid schemas** - Verify Parquet files can be read with `pyarrow.parquet.read_table()` - -## Customization - -### Adding New Datasets - -Create a new build script following this pattern: - -```python -from pathlib import Path -from dotenv import load_dotenv -from pyiceberg.catalog import load_catalog -from icefabric_manage import build - -load_dotenv() - -if __name__ == "__main__": - warehouse_path = Path("/tmp/warehouse") - warehouse_path.mkdir(exist_ok=True) - catalog_settings = { - "type": "sql", - "uri": f"sqlite:///{warehouse_path}/pyiceberg_catalog.db", - "warehouse": f"file://{warehouse_path}", - } - - namespace = "your_namespace" - catalog = load_catalog(namespace, **catalog_settings) - - build( - catalog=catalog, - parquet_file="path/to/your/file.parquet", - namespace=namespace, - table_name="your_table_name", - ) - print("Build successful") -``` - -### Changing Warehouse Location - -Modify the `warehouse_path` in your build script: - -```python -warehouse_path = Path("/your/custom/warehouse/path") -``` - -### Alternative Catalog Backend - -Replace SQLite with other supported backends: - -```python -catalog_settings = { - "type": "sql", - "uri": "postgresql://user:pass@host:port/db", - "warehouse": "s3://your-bucket/warehouse/", -} -``` - -## Best Practices - -!!! tip "Recommendations" - 1. **Table Naming** - Use descriptive table names that reflect the data content - 2. **Data Preparation** - Ensure timestamps are properly formatted before building - -In most functions, it's predefined to the user's `/tmp` dir since it's the same per each user's machine - -## Customization - -The following customizations can be done to update where/what is being added to the PyIceberg table - -### Changing Data Directory - -Modify the `data_dir` path in `build_hydrofabric.py`: - -```python -data_dir = Path("/path/to/your/parquet/files") -``` - -### Different Namespace - -Change the namespace name: - -```python -namespace = "your_namespace" -``` diff --git a/docs/user_guide/icefabric_tools.md b/docs/user_guide/icefabric_tools.md index a6c9a2e..48946b2 100644 --- a/docs/user_guide/icefabric_tools.md +++ b/docs/user_guide/icefabric_tools.md @@ -22,12 +22,7 @@ from pyiceberg.catalog import load_catalog from icefabric_tools import subset, IdType # Load the catalog using default settings -catalog_settings: dict = { - "type": "sql", - "uri": "sqlite:////tmp/warehouse/pyiceberg_catalog.db", - "warehouse": "file:///tmp/warehouse", -} -catalog = load_catalog("hydrofabric", **catalog_settings) +catalog = load_catalog("glue") # Basic subset using a hydrofabric ID result = subset( diff --git a/docs/user_guide/terraform.md b/docs/user_guide/terraform.md new file mode 100644 index 0000000..43c052a --- /dev/null +++ b/docs/user_guide/terraform.md @@ -0,0 +1,140 @@ +!!! note + These docs are taken from `src/icefabric_manage/terraform/README.md` + +# AWS S3 Tables with Apache Iceberg - Terraform Implementation + +This directory contains PoC Terraform IaC for deploying Apache Iceberg tables using the AWS S3 Tables service with AWS Glue catalog integration. It also contains a basic demo / test python script used to verify things. + +## Architecture Overview + +The infrastructure creates: +- **AWS S3 Tables bucket** - Managed storage for Iceberg table data +- **S3 Tables namespace and table** - Logical organization for tables +- **AWS Glue Catalog database** - Metadata storage for table schemas +- **Lake Formation permissions** - Access control and governance +- **IAM policies** - Secure access between services + +## Prerequisites + +### AWS Requirements +- AWS CLI configured with appropriate credentials. (Older versions may not support AWS S3 Tables) +- Terraform >= 1.0 +- AWS Account with permissions (basically Admin due to IAM requirements) for: + - S3 Tables + - AWS Glue + - Lake Formation + - IAM + +### ⚠️ Critical: Enable S3 Table Buckets Integration + +**This step must be completed before running Terraform**, otherwise the deployment will fail. + +1. Navigate to the [S3 Table Buckets Console](https://console.aws.amazon.com/s3tables/home) in your target region +2. Locate the section titled **"Integration with AWS analytics services"** +3. Click the **"Enable integration"** button +4. Confirm that the integration status shows **"Enabled"** for your deployment region + +This integration allows services like Athena, Glue, Redshift, and EMR to interact with S3 Table Buckets. Without this step, your Iceberg tables won't be accessible through these analytics services. + +> **Note**: This is a one-time setup per AWS region. Once enabled, all future S3 Table Buckets in that region will have access to AWS analytics services integration. + +### Python Requirements +- Python 3.8+ +- pyiceberg python module w/deps +- boto3 (for AWS SDK) + +## Quick Start + +### 1. High Level Deploy Infrastructure + +Create a `terraform.tfvars` file replacing the values below as appropriate for your environment or deploy: + +```hcl +env = "dev" +application = "myapp" +team = "NGWPC" +region = "us-east-1" +identity_center_role_arn = "arn:aws:iam::123456789012:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_DataRole" + +# Optional: Specify Lake Formation admins +lakeformation_admin_arns = [ + "arn:aws:iam::123456789012:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_DataRole", + "arn:aws:iam::123456789012:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_Admin" +] +``` + +Deploy the infrastructure: + +```bash +terraform init +terraform plan +terraform apply +``` + +### 3. Set Environment Variables + +After deployment, set these environment variables for the Python demo: + +```bash +# From Terraform outputs +export ICEBERG_WAREHOUSE_PATH=$(terraform output -raw s3tables_table_warehouse_location) +export AWS_DEFAULT_REGION="us-east-1" +``` + +### 4. Install Python Dependencies in your preferred active virtual environment + +```bash +pip install pyiceberg boto3 mypy_boto3_glue pyarrow +``` + +### 5. Run the Demo + +```bash +python iceberg_demo.py +``` + +## Terraform Configuration + +### Variables + +| Variable | Description | Type | Default | Required | +|----------|-------------|------|---------|----------| +| `region` | AWS region | string | `us-east-1` | No | +| `env` | Environment name (test/oe/other) | string | - | Yes | +| `application` | Application name | string | - | Yes | +| `team` | Team name (for future tagging if supported) | string | - | Yes | +| `identity_center_role_arn` | IAM role ARN for accessing resources | string | - | Yes | +| `lakeformation_admin_arns` | Lake Formation administrator ARNs | list(string) | `[]` | No | + +### Outputs + +| Output | Description | +|--------|-------------| +| `s3tables_bucket_arn` | ARN of the S3 Tables bucket | +| `s3tables_table_warehouse_location` | Warehouse location for Iceberg tables (devs need this!!!) | +| `glue_database_name` | Name of the Glue catalog database | +| `lakeformation_admins` | List of Lake Formation administrators | + +## Python Integration + +### Basic Usage + +The provided `iceberg_demo.py` demonstrates: +- Connecting to AWS Glue catalog +- Creating/loading Iceberg tables +- Very Basic schema definition + +### Configuration + +The Python script uses these environment variables: +- `ICEBERG_WAREHOUSE_PATH` - S3 Tables warehouse location +- `AWS_REGION` - AWS region for services +- `AWS_DEFAULT_REGION` - Default AWS region + +## Permissions and Security + +### Lake Formation Integration + +The infrastructure automatically configures basic Lake Formation settings. This can get very granular in the future. +- Database-level permissions for the specified Identity Center role (SoftwareEngineersFull) +- Table-level permissions are supported, but have not been tested diff --git a/examples/Demo_Iceberg.ipynb b/examples/Demo_Iceberg.ipynb index e9242cb..d4665d1 100644 --- a/examples/Demo_Iceberg.ipynb +++ b/examples/Demo_Iceberg.ipynb @@ -331,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/mkdocs.yml b/mkdocs.yml index 8f176e3..16eb786 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -53,3 +53,4 @@ nav: - Icefabric Manage: user_guide/icefabric_manage.md - Icefabric Tools: user_guide/icefabric_tools.md - Icefabric API: user_guide/icefabric_api.md + - Terraform S3 Glue Endpoint: user_guide/terraform.md diff --git a/pyproject.toml b/pyproject.toml index 116b8eb..be270a9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "icefabric" -version = "2025.5" +version = "2025.6.0" description = "An Apache Iceberg implementation of the Hydrofabric" readme = "README.md" requires-python = ">=3.11,<3.13" @@ -125,3 +125,16 @@ disallow_untyped_defs = true no_implicit_optional = true show_error_codes = true warn_unused_ignores = true + +[tool.pytest.ini_options] +rootdir = "." # Ensures pytest always treats project root as the root +pythonpath = [ + "tests", + "src/icefabric_api", + "src/icefabric_tools" +] +testpaths = [ + "src/icefabric_api/tests", + "src/icefabric_tools/tests", + "tests" +] diff --git a/src/icefabric_api/src/get_module_parameters.py b/scripts/get_module_parameters.py similarity index 73% rename from src/icefabric_api/src/get_module_parameters.py rename to scripts/get_module_parameters.py index f9adb26..d36e1c4 100644 --- a/src/icefabric_api/src/get_module_parameters.py +++ b/scripts/get_module_parameters.py @@ -1,8 +1,10 @@ +"""A sample script to generate CFE IPEs""" + import geopandas as gpd import pandas as pd -param_file = "cfe_params.csv" -gpkg_file = "gauge_06710385.gpkg" +param_file = "../src/icefabric_api/data/cfe_params.csv" +gpkg_file = "../src/icefabric_tools/test/data/gages-08070000.gpkg" divides = gpd.read_file(gpkg_file, layer="divides") @@ -16,13 +18,12 @@ cfg_file = f"{divide}_bmi_cfg_cfe.txt" f = open(cfg_file, "x") - for index, row in param_values.iterrows(): + for _, row in param_values.iterrows(): key = row["name"] value = row["default_value"] f.write(f"{key}={value}\n") f.close() - params_calibratable = module_params.loc[module_params["calibratable"] == "TRUE"] params_calibratable.to_json("out.json", orient="split") diff --git a/src/icefabric_api/app/conftest.py b/src/icefabric_api/app/conftest.py deleted file mode 100644 index 40ba5d7..0000000 --- a/src/icefabric_api/app/conftest.py +++ /dev/null @@ -1,29 +0,0 @@ -import os -from pathlib import Path - -os.environ["PYICEBERG_HOME"] = str(Path(__file__).parents[3]) -print(f"PYICEBERG_HOME set to: {os.environ['PYICEBERG_HOME']}") - -import pytest # noqa: E402 -from fastapi.testclient import TestClient # noqa: E402 - -from app.main import app # noqa: E402 - - -@pytest.fixture(scope="session") -def client(): - """Create a test client for the FastAPI app.""" - return TestClient(app) - - -@pytest.fixture(scope="function") -def fresh_client(): - """Create a fresh test client for each test function.""" - return TestClient(app) - - -def pytest_configure(config): - """Configure pytest markers.""" - config.addinivalue_line("markers", "performance: marks tests as performance tests") - config.addinivalue_line("markers", "integration: marks tests as integration tests") - config.addinivalue_line("markers", "unit: marks tests as unit tests") diff --git a/src/icefabric_api/app/main.py b/src/icefabric_api/app/main.py index ec9180c..31833ba 100644 --- a/src/icefabric_api/app/main.py +++ b/src/icefabric_api/app/main.py @@ -6,6 +6,7 @@ from pydantic import BaseModel from app.routers.hydrofabric.router import api_router as hydrofabric_api_router +from app.routers.module_params.router import api_router as module_params_router from app.routers.streamflow_observations.router import api_router as streamflow_api_router app = FastAPI( @@ -26,7 +27,7 @@ class HealthCheck(BaseModel): # Include routers app.include_router(hydrofabric_api_router, prefix="/v1") app.include_router(streamflow_api_router, prefix="/v1") -# app.include_router(module_params_router, prefix="/v1") +app.include_router(module_params_router, prefix="/v1") @app.head( diff --git a/src/icefabric_api/app/routers/hydrofabric/conftest.py b/src/icefabric_api/app/routers/hydrofabric/conftest.py deleted file mode 100644 index 19850f9..0000000 --- a/src/icefabric_api/app/routers/hydrofabric/conftest.py +++ /dev/null @@ -1,32 +0,0 @@ -from pathlib import Path - -import pytest - -sample_gauges = [ - "01010000", - "02450825", - "03173000", - "04100500", - "05473450", - "06823500", - "07060710", - "08070000", - "09253000", - "10316500", - "11456000", - "12411000", - "13337000", - "14020000", -] - - -@pytest.fixture(params=sample_gauges) -def gauge_hf_uri(request) -> str: - """Returns individual gauge identifiers for parameterized testing""" - return request.param - - -@pytest.fixture -def testing_dir() -> Path: - """Returns the testing data dir""" - return Path(__file__).parents[4] / "icefabric_tools/tests/data/" diff --git a/src/icefabric_api/app/routers/hydrofabric/test_router.py b/src/icefabric_api/app/routers/hydrofabric/test_router.py deleted file mode 100644 index 697b645..0000000 --- a/src/icefabric_api/app/routers/hydrofabric/test_router.py +++ /dev/null @@ -1,34 +0,0 @@ -from pathlib import Path - -import geopandas as gpd -import pytest - - -@pytest.mark.integration -def test_subset_hl_uri(client, gauge_hf_uri: str, testing_dir: Path, tmp_path: Path): - """Test: GET /streamflow_observations/usgs/csv""" - response = client.get( - f"/v1/hydrofabric/{gauge_hf_uri}/gpkg", - ) - - assert response.status_code == 200, "Incorrect response" - - if response.status_code == 200: - temp_gpkg = tmp_path / "structure_test.gpkg" - temp_gpkg.write_bytes(response.content) - - output_gdf = gpd.read_file(temp_gpkg, layer="network") - correct_gdf = gpd.read_file(testing_dir / f"gages-{gauge_hf_uri}.gpkg", layer="network") - assert output_gdf.equals(correct_gdf) - - output_gdf = gpd.read_file(temp_gpkg, layer="divides") - correct_gdf = gpd.read_file(testing_dir / f"gages-{gauge_hf_uri}.gpkg", layer="divides") - assert output_gdf.equals(correct_gdf) - - output_gdf = gpd.read_file(temp_gpkg, layer="flowpaths") - correct_gdf = gpd.read_file(testing_dir / f"gages-{gauge_hf_uri}.gpkg", layer="flowpaths") - assert output_gdf.equals(correct_gdf) - - output_gdf = gpd.read_file(temp_gpkg, layer="nexus") - correct_gdf = gpd.read_file(testing_dir / f"gages-{gauge_hf_uri}.gpkg", layer="nexus") - assert output_gdf.equals(correct_gdf) diff --git a/src/icefabric_api/app/routers/module_params/router.py b/src/icefabric_api/app/routers/module_params/router.py index bb28cbc..7e9b8c6 100644 --- a/src/icefabric_api/app/routers/module_params/router.py +++ b/src/icefabric_api/app/routers/module_params/router.py @@ -1,13 +1,11 @@ import json -import re import os +import re -from fastapi import APIRouter, HTTPException, Path, Query -from fastapi.responses import Response +from fastapi import APIRouter, HTTPException from pydantic import BaseModel -from src.hf_attributes import get_hydrofabric_attributes -from src.run_modules import module_ipe +from icefabric_api.create_ipes import get_hydrofabric_attributes, module_ipe api_router = APIRouter(prefix="/modules") ROOT_DIR = os.path.abspath(os.curdir) @@ -22,14 +20,12 @@ class Parameters(BaseModel): @api_router.post("/parameters") -async def get_ipes( - query: Parameters -): - if query.version != '2.1' and query.version != '2.2': +async def get_ipes(query: Parameters): + if query.version != "2.1" and query.version != "2.2": raise HTTPException(status_code=422, detail="Icefabric version must be 2.2 or 2.1") - elif query.version == '2.1' and query.domain != 'CONUS': + elif query.version == "2.1" and query.domain != "CONUS": raise HTTPException(status_code=422, detail="oCONUS domains not availiable in Icefabric version 2.1") - + gpkg_file = f"{ROOT_DIR}/data/gauge_{query.gage_id}.gpkg" if not os.path.exists(gpkg_file): raise HTTPException(status_code=422, detail="Gage ID/geopackage not found") diff --git a/src/icefabric_api/app/routers/streamflow_observations/conftest.py b/src/icefabric_api/app/routers/streamflow_observations/conftest.py deleted file mode 100644 index ea8b020..0000000 --- a/src/icefabric_api/app/routers/streamflow_observations/conftest.py +++ /dev/null @@ -1,18 +0,0 @@ -from pathlib import Path - -import pandas as pd -import pytest - - -@pytest.fixture -def local_usgs_streamflow_csv(): - """Returns a locally downloaded CSV file from a specific gauge and time""" - return pd.read_csv(Path.cwd() / "tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv") - - -@pytest.fixture -def local_usgs_streamflow_parquet(): - """Returns a locally downloaded CSV file from a specific gauge and time""" - return pd.read_parquet( - Path.cwd() / "tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.parquet" - ) diff --git a/src/icefabric_api/data/gauge_06710385.gpkg b/src/icefabric_api/data/gauge_06710385.gpkg deleted file mode 100644 index 8f8a14a4968a83ac42f8b1fd7beb05637f218975..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 319488 zcmeF)2RxO3|3C0!@0}6JN#Q7aD+wi(m64*7aqN{tAtf12Ekqg;4WvY&WE@EoDO8$T zN<(RC{$D2@QNG{b_x|4Z|Gw|v|9^Qr>Uf@Ooa=gD@6Wt0IL@;6kD!u$Lc@bSBFNe# zVG;`q$%ITMkw}y9&ldlfUoQBDoe8|MkVs0`7`CvVX));i!QW0Ism_G z4BZW9;6EM4pk7`T{|U?CKOtcO{Ljv3M-pTc*emFVZ-_4<0z?Fe2oMnypG-R|k`9O{W=Xlhw&nLqj5IWYZu|YBlfe#T#lGn)*7r+FFKMx|)WDI@;PQrp`{5b`DlfbKT?_ry#E) 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z##n5UD^Xhh^he#)&rrtI^1H$V6X-I;JFsHI<$-RztmB+Z79480q$ItzSQKpPEGw*8 zJ&KNA24e5U4E*q{@2J}b}Se6AeKXgvva(v;~ z7Q%P&d9m&C&nL7+gC1Uc>)2T*(&8jygN1mQj9n>84L3Sj@9^L-!;=oyI|ARQGH8&{ zoV6`Z;Z=`fgHBVw*R0Q3|DyJOmZK-liZbC+E3aQ@`Z(6bq`PDE_$tgQvKPl!fa`g` zQwDZ%vF&O-5&uJ7tUOdvmusS>krs%0QVS8p*A?6F1W!q~PL%<}2I9@Mp^C^OaG`A> zb@09$q@!q@?ml!!+zZsp{!nFW*Ep0pZ&UluZ!96xSGmM+wxe6GOYsuIpcKL)*yV{- zW)Pe8#`oEbT(%*reYt0S%CPovmaINYkdHKveOkZl8)d?!HVtprc)-r5Cf#kba)DyJ z1W6Hs>*bPCg2c8z0vVi^!d@AGg{aAx7TJ3vTTdeDixk-7d%mRi-Q(( zjyBQtokms}p=3HD4GuMO))>kuT7zaM)kVg4H$n~fayDc54p8Kh&jwlN0N5%{IHp zC?UkxHM>&9>j@ok*SV~_wz)-aO4k;hyXA>yflIcdwC_#}v(vso16O}`_mq_tX?BL& zqarfR#E1KaU0zrO+1kb%He$n8rHhTvki!jUD%e|Fa&nJ)mmFuVBB{?w(gH<{|JDz? zM451@4=3.11, <3.13" @@ -9,3 +9,7 @@ dependencies = [ "pydantic==2.11.5", "pydantic-settings==2.9.1", ] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/src/icefabric_api/src/__init__.py b/src/icefabric_api/src/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/icefabric_api/src/cfe_params.csv b/src/icefabric_api/src/cfe_params.csv deleted file mode 100644 index 894fa0a..0000000 --- a/src/icefabric_api/src/cfe_params.csv +++ /dev/null @@ -1,26 +0,0 @@ -name,description,units,data_type,calibratable,source_file,min,max,default_value,divide_attr_name -soil_params.b,beta exponent on Clapp-Hornberger (1978) soil water relations,NULL,double,TRUE,attr,2,15,4.05,mode.bexp_soil_layers_stag=1 -soil_params.satdk,saturated hydraulic conductivity,m/s,double,TRUE,attr,0.000000195,0.00141,0.00000338,geom_mean.dksat_soil_layers_stag=1 -soil_params.satpsi,saturated capillary head,m,double,TRUE,attr,0.036,0.955,0.355,geom_mean.psisat_soil_layers_stag=1 -soil_params.slop,this factor (0-1) modifies the gradient of the hydraulic head at the soil bottom. 0=no-flow.,m/m,double,TRUE,attr,0.0000598,1,0.05,mean.slope_1km -soil_params.smcmax,saturated soil moisture content (Maximum soil moisture content),m/m,double,TRUE,attr,0.16,0.58,0.439,mean.smcmax_soil_layers_stag=1 -soil_params.wltsmc,wilting point soil moisture content (< soil_params.smcmax),m/m,double,TRUE,attr,0.05,0.3,per soil type,mean.smcwlt_soil_layers_stag=1 -soil_params.expon,"optional; defaults to 1, This parameter defines the soil reservoirs to be linear, Use linear reservoirs",NULL,double,FALSE,const,NULL,NULL,1, -soil_params.expon_secondary,"optional; defaults to 1, This parameter defines the soil reservoirs to be linear, Use linear reservoirs ",NULL,double,FALSE,const,NULL,NULL,1, -max_gw_storage,maximum storage in the conceptual reservoir,m,double,TRUE,attr,0.01,0.25,0.05,mean.Zmax -Cgw,the primary outlet coefficient,m/hr,double,TRUE,attr,0.0000018,0.0018,0.000018,mean.Coeff -expon,exponent parameter for nonlinear ground water reservoir (1.0 for linear reservoir),NULL,double,TRUE,attr,1,8,3,mode.Expon -gw_storage,initial condition for groundwater reservoir - it is the ground water as a decimal fraction of the maximum groundwater storage (max_gw_storage) for the initial timestep,m/m,double,FALSE,const,NULL,NULL,0.05, -alpha_fc,alpha at fc for clapp hornberger (field capacity),NULL,double,FALSE,const,NULL,NULL,0.33, -soil_storage,initial condition for soil reservoir - it is the water in the soil as a decimal fraction of maximum soil water storage (smcmax x depth) for the initial timestep. Default = 0.5,m/m,double,FALSE,const,NULL,NULL,0.5, -K_nash,Nash Config param for lateral subsurface runoff (Nash discharge to storage ratio),1/m,double,TRUE,const,0,1,0.003, -K_lf,Nash Config param - primary reservoir,NULL,double,TRUE,const,0,1,0.01, -nash_storage,Nash Config param - secondary reservoir ,NULL,double,FALSE,const,NULL,NULL,"0.0,0.0", -giuh_ordinates,Giuh (geomorphological instantaneous unit hydrograph) ordinates in dt time steps,NULL,double,FALSE,const,NULL,NULL,"0.55, 0.25, 0.2", -a_Xinanjiang_inflection_point_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,TRUE,CFE-X_params,-0.5,0.5,-0.2,AXAJ -b_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,TRUE,CFE-X_params,0.01,10,0.66,BXAJ -x_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,TRUE,CFE-X_params,0.01,10,0.02,XXAJ -urban_decimal_fraction,when surface_water_partitioning_scheme=Xinanjiang,NULL,double ,FALSE,const,0,1,0.01, -refkdt,Reference Soil Infiltration Parameter (used in runoff formulation),NULL,double,TRUE,attr,0.1,4,1,mean.refkdt -soil_params.depth,soil depth,m,double,FALSE,const,NULL,NULL,2, - diff --git a/src/icefabric_api/src/gauge_06710385.gpkg b/src/icefabric_api/src/gauge_06710385.gpkg deleted file mode 100644 index 8f8a14a4968a83ac42f8b1fd7beb05637f218975..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 319488 zcmeF)2RxO3|3C0!@0}6JN#Q7aD+wi(m64*7aqN{tAtf12Ekqg;4WvY&WE@EoDO8$T zN<(RC{$D2@QNG{b_x|4Z|Gw|v|9^Qr>Uf@Ooa=gD@6Wt0IL@;6kD!u$Lc@bSBFNe# zVG;`q$%ITMkw}y9&ldlfUoQBDoe8|MkVs0`7`CvVX));i!QW0Ism_G z4BZW9;6EM4pk7`T{|U?CKOtcO{Ljv3M-pTc*emFVZ-_4<0z?Fe2oMnypG-R|k`9O{W=Xlhw&nLqj5IWYZu|YBlfe#T#lGn)*7r+FFKMx|)WDI@;PQrp`{5b`DlfbKT?_ry#E) zZ>XuIC668czC=e$&ycZ%ahB#&oviF^-Q@ev-W{Rv_i{I16zLJPYkjGl44yHEd zPUa47@6SJBb^Wi20%AR5)3>OqU}5AppTt3e0czc7SxWtdC*BN8 zKZNQ{_N9jUhI@qh`FoKa9UN`#Y|SH?%Lcv~ypmUFXt=k3hzIWVhEsj0;nWZ>Dw!5d 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zyvG+r<8H9Ue7wWuf(>#n%CTmmj2g*sJc#RlHO`_UXsIgCP7sWwJkd^+lk#jDMe$Yc z*}*IH3xxc5h~p*z9R!{v9!OINrE#l)MJSXZ!U7&iKOjrPUo>(Bl5hpvOGjbA?Ib-- zI?EKXC9mX-^rz^W`amf4G&nsrkNv@@$4BDn<4a%wlE-YY!;fd%1$G+81APtSPfqvz z!zFBq*2jx`CL;HegfkiK+J`X+Bm??h0fm}I3c8@!C4^ddLINK8;vxAHvcWQ+PvYzu x{v>KJOrs|$#$DxVry7#JTc}odlV1YvZj*N#?caF<{%Y<2p|), namespace=namespace, - table_name=layer, + table_name=, location="s3://fim-services-data-test/icefabric_metadata/" ) diff --git a/src/icefabric_manage/builds/build_fema_ble.py b/src/icefabric_manage/builds/build_fema_ble.py index 8ad21d2..d22ddb7 100644 --- a/src/icefabric_manage/builds/build_fema_ble.py +++ b/src/icefabric_manage/builds/build_fema_ble.py @@ -11,7 +11,7 @@ def build_table(file_dir: str): - """Builds the hydrofabric namespace and tables + """Builds the FEMA BLE XS tables Parameters ---------- diff --git a/src/icefabric_manage/builds/build_fema_mip.py b/src/icefabric_manage/builds/build_fema_mip.py index cae4e2b..ee21ccb 100644 --- a/src/icefabric_manage/builds/build_fema_mip.py +++ b/src/icefabric_manage/builds/build_fema_mip.py @@ -11,7 +11,7 @@ def build_table(file_dir: str): - """Builds the hydrofabric namespace and tables + """Builds the FEMA MIP XS tables Parameters ---------- diff --git a/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py b/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py index f7f2655..453c3a6 100644 --- a/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py +++ b/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py @@ -11,6 +11,13 @@ def build_table(file_dir: Path): + """Builds the bathymetric channel data for the Hydrofabric + + Parameters + ---------- + file_dir : str + The directory to hydrofabric parquet files + """ catalog = load_catalog("glue") # Using an AWS Glue Endpoint namespace = "bathymetry_ml_auxiliary" try: diff --git a/src/icefabric_manage/builds/build_usgs_streamflow_observations.py b/src/icefabric_manage/builds/build_usgs_streamflow_observations.py index 0b594ff..7d7661a 100644 --- a/src/icefabric_manage/builds/build_usgs_streamflow_observations.py +++ b/src/icefabric_manage/builds/build_usgs_streamflow_observations.py @@ -10,6 +10,13 @@ def build_table(file_dir): + """Builds the streamflow observation namespace and tables + + Parameters + ---------- + file_dir : str + The directory to hydrofabric parquet files + """ catalog = load_catalog("glue") # Using an AWS Glue Endpoint namespace = "streamflow_observations" try: diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index aac45b4..693f536 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "icefabric_manage" -version = "2025.5" +version = "2025.6.0" description = "All functionality for creating, updating, and deleting information from the icefabric catalog" authors = [ { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, diff --git a/src/icefabric_manage/src/icefabric_manage/_api.py b/src/icefabric_manage/src/icefabric_manage/_api.py index b626c19..70eae83 100644 --- a/src/icefabric_manage/src/icefabric_manage/_api.py +++ b/src/icefabric_manage/src/icefabric_manage/_api.py @@ -7,12 +7,27 @@ def build(catalog: Catalog, parquet_file: str, namespace: str, table_name: str, location: str) -> None: """Builds the hydrofabric catalog based on the .pyiceberg.yaml config and defined parquet files. + Creates a new Iceberg table from a parquet file if the table doesn't already exist. + If the table exists, the function will skip the build process and print a message. + Parameters ---------- - catalog: Catalog - The Apache Iceberg Catalog - file_dir : Path - The path to the parquet files to add into the iceberg catalog + catalog : Catalog + The Apache Iceberg Catalog instance used to manage tables + parquet_file : str + Path to the parquet file to be loaded into the Iceberg table + namespace : str + The namespace (database/schema) where the table will be created + table_name : str + The name of the table to be created in the catalog + location : str + The storage location where the Iceberg table data will be stored + + Notes + ----- + - The function will automatically infer the schema from the parquet file + - If the table already exists, no action is taken and a message is printed + - The parquet data is appended to the newly created Iceberg table """ if catalog.table_exists(f"{namespace}.{table_name}"): print(f"Table {table_name} already exists. Skipping build") diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index 7e1489e..723b688 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "icefabric_tools" -version = "2025.5" +version = "2025.6.0" description = "All functionality for creating, updating, and deleting information from the icefabric catalog" authors = [ { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, diff --git a/src/icefabric_tools/src/icefabric_tools/__init__.py b/src/icefabric_tools/src/icefabric_tools/__init__.py index 68c7c48..8a7da3d 100644 --- a/src/icefabric_tools/src/icefabric_tools/__init__.py +++ b/src/icefabric_tools/src/icefabric_tools/__init__.py @@ -2,4 +2,11 @@ from icefabric_tools.iceberg.rnr import get_rnr_segment from icefabric_tools.iceberg.utils import find_origin, table_to_geopandas, to_geopandas -__all__ = ["IdType", "subset", "get_rnr_segment", "find_origin", "to_geopandas", "table_to_geopandas"] +__all__ = [ + "IdType", + "subset", + "get_rnr_segment", + "find_origin", + "to_geopandas", + "table_to_geopandas", +] diff --git a/src/icefabric_tools/tests/conftest.py b/src/icefabric_tools/tests/conftest.py deleted file mode 100644 index 32059d1..0000000 --- a/src/icefabric_tools/tests/conftest.py +++ /dev/null @@ -1,55 +0,0 @@ -import os -from pathlib import Path - -os.environ["PYICEBERG_HOME"] = str(Path(__file__).parents[3]) -print(f"PYICEBERG_HOME set to: {os.environ['PYICEBERG_HOME']}") - -import pytest # noqa: E402 -from pyiceberg.catalog import Catalog, load_catalog # noqa: E402 - -sample_gauges = [ - "gages-01010000", - "gages-02450825", - "gages-03173000", - "gages-04100500", - "gages-05473450", - "gages-06823500", - "gages-07060710", - "gages-08070000", - "gages-09253000", - "gages-10316500", - "gages-11456000", - "gages-12411000", - "gages-13337000", - "gages-14020000", -] - - -@pytest.fixture -def hydrofabric_catalog() -> Catalog: - """Returns an iceberg catalog object for the hydrofabric""" - # try: - return load_catalog("glue") - # except Exception as e: - # raise type(e)( - # f"Cannot find warehouse @ {warehouse_path}. Please make sure your AWS credentials are accurate" - # ) # type: ignore - - -@pytest.fixture(params=sample_gauges) -def gauge_hf_uri(request) -> str: - """Returns individual gauge identifiers for parameterized testing""" - return request.param - - -@pytest.fixture -def testing_dir() -> Path: - """Returns the testing data dir""" - return Path(__file__).parent / "data/" - - -def pytest_configure(config): - """Configure pytest markers.""" - config.addinivalue_line("markers", "performance: marks tests as performance tests") - config.addinivalue_line("markers", "integration: marks tests as integration tests") - config.addinivalue_line("markers", "unit: marks tests as unit tests") diff --git a/src/icefabric_tools/tests/test_subset.py b/src/icefabric_tools/tests/test_subset.py index dc76c47..57e059d 100644 --- a/src/icefabric_tools/tests/test_subset.py +++ b/src/icefabric_tools/tests/test_subset.py @@ -7,7 +7,7 @@ from icefabric_tools import IdType, subset -@pytest.mark.integration +@pytest.mark.slow def test_subset_hl_uri(hydrofabric_catalog: Catalog, gauge_hf_uri: str, testing_dir: Path, tmp_path: Path): """Tests all subset gauges in the sample data using hl_uri""" subset( diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 0000000..4184d08 --- /dev/null +++ b/tests/README.md @@ -0,0 +1,7 @@ +# Pytests + +This folder is for all testing data so the global confest can pick it up. This allows all tests in the namespace packages to share the same scope without having to reference one another in tests + +To run tests, run `pytest -s` from project root. + +To run the subsetter tests, run `pytest --run-slow` as these tests take some time. Otherwise, they will be skipped diff --git a/uv.lock b/uv.lock index 5d1335c..f879d18 100644 --- a/uv.lock +++ b/uv.lock @@ -1015,7 +1015,7 @@ wheels = [ [[package]] name = "icefabric" -version = "2025.5" +version = "2025.6.0" source = { virtual = "." } [package.optional-dependencies] @@ -1048,7 +1048,7 @@ dev = [ { name = "black", specifier = "==25.1.0" }, { name = "boto3" }, { name = "examples" }, - { name = "icefabric-api", virtual = "src/icefabric_api" }, + { name = "icefabric-api", editable = "src/icefabric_api" }, { name = "icefabric-manage", editable = "src/icefabric_manage" }, { name = "icefabric-tools", editable = "src/icefabric_tools" }, { name = "ipykernel", specifier = ">=6.29.5" }, @@ -1061,8 +1061,8 @@ dev = [ [[package]] name = "icefabric-api" -version = "2025.5.0" -source = { virtual = "src/icefabric_api" } +version = "2025.6.0" +source = { editable = "src/icefabric_api" } dependencies = [ { name = "fastapi", extra = ["standard"] }, { name = "pydantic" }, @@ -1078,7 +1078,7 @@ requires-dist = [ [[package]] name = "icefabric-manage" -version = "2025.5" +version = "2025.6.0" source = { editable = "src/icefabric_manage" } dependencies = [ { name = "geopandas" }, @@ -1103,7 +1103,7 @@ requires-dist = [ [[package]] name = "icefabric-tools" -version = "2025.5" +version = "2025.6.0" source = { editable = "src/icefabric_tools" } dependencies = [ { name = "dask" }, From b6ba4e9872b5339f45139119d4366103be535eef Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 12 Jun 2025 13:30:56 -0400 Subject: [PATCH 55/97] Feature add TIFF conversion to xr.Dataset --- .../src/icefabric_tools/icechunk/arch_weather_file_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py b/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py index dadf8e5..0e337bf 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py @@ -17,7 +17,6 @@ from icefabric_tools.icechunk import S3Path - class FileType(Enum): """ Archival weather file types @@ -55,6 +54,7 @@ def load_tiff_file(fp: str) -> xr.Dataset | xr.DataArray | list[xr.Dataset]: if os.path.exists(fp) is False: raise FileNotFoundError(f"Cannot find: {fp}") ds = rxr.open_rasterio(fp) + ds = ds.to_dataset(name="elevation") return ds From 3ba4fcd2781b556b8e3a2b6bd0c7e011c42d7d3c Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 12 Jun 2025 13:34:58 -0400 Subject: [PATCH 56/97] Fixed arg doest exist for retrieve_dataset() TypeError: IcechunkS3Repo.retrieve_dataset() got an unexpected keyword argument 'read_only' --- .../src/icefabric_tools/icechunk/icechunk_s3_module.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py index 3babe5f..0f6987e 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py @@ -378,7 +378,7 @@ def retrieve_and_convert_to_tif( If an xarray dataset does not have a "band" attribute in coordinates, the file is not deemed a raster and will raise error. """ - ds = self.retrieve_dataset(read_only=True, branch=branch) + ds = self.retrieve_dataset(branch=branch) if "band" not in ds.coords.dims: raise AttributeError("Dataset needs a 'band' coordinate to export geotiff") From 4f1cd3440c6a1687980f84696f2ac97433ee02ce Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 12 Jun 2025 13:46:09 -0400 Subject: [PATCH 57/97] Demo tif to xr conversion verification --- .../Demo_Verification_TIF_to_xrDataset.ipynb | 640 ++++++++++++++++++ 1 file changed, 640 insertions(+) create mode 100644 examples/Demo_Verification_TIF_to_xrDataset.ipynb diff --git a/examples/Demo_Verification_TIF_to_xrDataset.ipynb b/examples/Demo_Verification_TIF_to_xrDataset.ipynb new file mode 100644 index 0000000..9119813 --- /dev/null +++ b/examples/Demo_Verification_TIF_to_xrDataset.ipynb @@ -0,0 +1,640 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4ccf1cb2-05ff-4a86-ad4f-87a209349c86", + "metadata": {}, + "source": [ + "# Demo: Verification of TIF to xr.Dataset Conversion\n", + "\n", + "_Note:_\n", + "\n", + "1) To verify TIFF conversion to xr.dataset, drop this notebook into \"/src/icefabric_tools/src/icefabric_tools/icechunk\"\n", + "\n", + "2) Just for the purpose of using this notebook, one can replace \"from icefabric_tools.icechunk import S3Path\" to \"from \n", + "icechunk_s3_module import S3Path\" from within the \"/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py\"\n", + "\n", + "3) One can now verify that TIFFs can be converted to xr.Dataset as seen as below.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3eb9f5ee-29a3-44bd-8cc0-47ad146705df", + "metadata": {}, + "source": [ + "# Import Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a51dff72-3f4a-4015-9d2d-fb7590709a94", + "metadata": {}, + "outputs": [], + "source": [ + "from arch_weather_file_utils import *\n", + "from icechunk_s3_module import IcechunkS3Repo, NGWPCLocations" + ] + }, + { + "cell_type": "markdown", + "id": "02e4a87f-7384-475e-a938-e64e69f54754", + "metadata": {}, + "source": [ + "# Retrieves a TIFF from S3 & Saves to Local" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e54f73ed-a735-4ad7-9470-47c511ff4c40", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved dataset to ./hawaii_topo_30.tif\n" + ] + } + ], + "source": [ + "repo = IcechunkS3Repo(location=NGWPCLocations.TOPO_HA_30M_IC.path)\n", + "ds = repo.retrieve_dataset()\n", + "\n", + "var_name = \"__xarray_dataarray_variable__\"\n", + "dest = \"./hawaii_topo_30.tif\"\n", + "repo.retrieve_and_convert_to_tif(dest=dest, var_name=var_name)" + ] + }, + { + "cell_type": "markdown", + "id": "73fa9bed-1dc6-4fe5-b447-8e62c3fafcc5", + "metadata": {}, + "source": [ + "### Code Findings:\n", + "\n", + "- An error will occur under because \"read_only=True\" is not an argument in icechunk_s3_module.py\"s 'retrieve_dataset()' method. Refer to of L381. Putting in a PR for this change as a result." + ] + }, + { + "cell_type": "markdown", + "id": "018b944c-3adc-4454-88b7-55b149187607", + "metadata": {}, + "source": [ + "# After Code Modification, the following code is able to convert TIFF to xr.Dataset\n", + "\n", + "- This conversion effectively allows _xarray_data_variable_ to be converted to `elevation`. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "826c5ffa-99cc-49db-b153-c6dc0466e871", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    elevation    (band, y, x) float32 1GB ...
    " + ], + "text/plain": [ + " Size: 1GB\n", + "Dimensions: (band: 1, x: 22226, y: 14819)\n", + "Coordinates:\n", + " * band (band) int64 8B 1\n", + " * x (x) float64 178kB -160.5 -160.5 -160.5 ... -154.5 -154.5 -154.5\n", + " * y (y) float64 119kB 22.5 22.5 22.5 22.5 ... 18.5 18.5 18.5 18.5\n", + " spatial_ref int64 8B 0\n", + "Data variables:\n", + " elevation (band, y, x) float32 1GB ..." + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dest = \"./hawaii_topo_30.tif\"\n", + "ds = load_tiff_file(fp = dest)\n", + "ds" + ] + }, + { + "attachments": { + "098a3552-002d-4ea7-9b15-bfc1264a18c8.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "fc36554a-1ac0-4260-9553-72dd07795f93", + "metadata": {}, + "source": [ + "# Prior to Code Modification, the following was being generated for TIFF\n", + "\n", + "![image.png](attachment:098a3552-002d-4ea7-9b15-bfc1264a18c8.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23b5e6ec-1887-4595-88a8-ffa5916968d1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "“icefabric_uv”", + "language": "python", + "name": "icefabric_uv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 2df53ccf586c5024f9a304005681dd4c8c535f73 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 12 Jun 2025 15:18:14 -0400 Subject: [PATCH 58/97] Adjst made to accom name attribute in arg --- .../icechunk/arch_weather_file_utils.py | 10 +++++++--- .../src/icefabric_tools/icechunk/icechunk_testing.py | 4 ++-- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py b/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py index 0e337bf..d2639ae 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py @@ -15,7 +15,8 @@ from tqdm import tqdm from virtualizarr import open_virtual_dataset -from icefabric_tools.icechunk import S3Path +# from icefabric_tools.icechunk import S3Path +from icechunk_s3_module import S3Path class FileType(Enum): """ @@ -29,7 +30,7 @@ class FileType(Enum): NETCDF = ".nc" -def load_tiff_file(fp: str) -> xr.Dataset | xr.DataArray | list[xr.Dataset]: +def load_tiff_file(fp: str, attr_name: str) -> xr.Dataset | xr.DataArray | list[xr.Dataset]: """ Loads a GEOTIFF @@ -41,6 +42,9 @@ def load_tiff_file(fp: str) -> xr.Dataset | xr.DataArray | list[xr.Dataset]: ---------- fp : str File path to the TIFF that will be returned as a dataset. + attr_name : str + Name of the attribute of interest. Ex: "elevation". + Note: Not all rasters will be elevation in future. Returns ------- @@ -54,7 +58,7 @@ def load_tiff_file(fp: str) -> xr.Dataset | xr.DataArray | list[xr.Dataset]: if os.path.exists(fp) is False: raise FileNotFoundError(f"Cannot find: {fp}") ds = rxr.open_rasterio(fp) - ds = ds.to_dataset(name="elevation") + ds = ds.to_dataset(name=attr_name) return ds diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py index 285daa0..a2ff7af 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py @@ -118,7 +118,7 @@ def land_cover_virtualize_push_test( nlcd_repo.delete_repo(quiet=True) -def topo_push_test(tiff_fp: str, new_ic_repo: S3Path, clean_up: bool | None = False): +def topo_push_test(tiff_fp: str, attr_name: str, new_ic_repo: S3Path, clean_up: bool | None = False): """ Push a topobathy GEOTIFF @@ -131,7 +131,7 @@ def topo_push_test(tiff_fp: str, new_ic_repo: S3Path, clean_up: bool | None = Fa NOTE: Take care to not overwrite an existing IC repo """ topo_repo = IcechunkS3Repo(location=new_ic_repo) - topo_ds = awf_utils.load_tiff_file(tiff_fp) + topo_ds = awf_utils.load_tiff_file(tiff_fp, attr_name) topo_repo.write_dataset(ds=topo_ds, commit="first commit") print(topo_repo.retrieve_dataset()) if clean_up: From 85dc1225ee3e4df8af2de9a54ec2db0867ec3dbb Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Thu, 12 Jun 2025 15:18:47 -0400 Subject: [PATCH 59/97] Modified import modules --- .../Demo_Verification_TIF_to_xrDataset.ipynb | 39 ++++++++----------- 1 file changed, 17 insertions(+), 22 deletions(-) diff --git a/examples/Demo_Verification_TIF_to_xrDataset.ipynb b/examples/Demo_Verification_TIF_to_xrDataset.ipynb index 9119813..fbd1742 100644 --- a/examples/Demo_Verification_TIF_to_xrDataset.ipynb +++ b/examples/Demo_Verification_TIF_to_xrDataset.ipynb @@ -11,7 +11,7 @@ "\n", "1) To verify TIFF conversion to xr.dataset, drop this notebook into \"/src/icefabric_tools/src/icefabric_tools/icechunk\"\n", "\n", - "2) Just for the purpose of using this notebook, one can replace \"from icefabric_tools.icechunk import S3Path\" to \"from \n", + "2) Just for the purpose of using this notebook, one could replace \"from icefabric_tools.icechunk import S3Path\" to \"from \n", "icechunk_s3_module import S3Path\" from within the \"/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py\"\n", "\n", "3) One can now verify that TIFFs can be converted to xr.Dataset as seen as below.\n" @@ -32,8 +32,10 @@ "metadata": {}, "outputs": [], "source": [ - "from arch_weather_file_utils import *\n", - "from icechunk_s3_module import IcechunkS3Repo, NGWPCLocations" + "from dotenv import load_dotenv\n", + "from icefabric_tools.icechunk.arch_weather_file_utils import load_tiff_file\n", + "from icefabric_tools.icechunk.icechunk_s3_module import IcechunkS3Repo, NGWPCLocations\n", + "load_dotenv()" ] }, { @@ -63,18 +65,9 @@ "ds = repo.retrieve_dataset()\n", "\n", "var_name = \"__xarray_dataarray_variable__\"\n", - "dest = \"./hawaii_topo_30.tif\"\n", - "repo.retrieve_and_convert_to_tif(dest=dest, var_name=var_name)" - ] - }, - { - "cell_type": "markdown", - "id": "73fa9bed-1dc6-4fe5-b447-8e62c3fafcc5", - "metadata": {}, - "source": [ - "### Code Findings:\n", - "\n", - "- An error will occur under because \"read_only=True\" is not an argument in icechunk_s3_module.py\"s 'retrieve_dataset()' method. Refer to of L381. Putting in a PR for this change as a result." + "fp = \"./hawaii_topo_30.tif\"\n", + "repo.retrieve_and_convert_to_tif(dest=fp,\n", + " var_name=var_name)" ] }, { @@ -547,9 +540,9 @@ " * y (y) float64 119kB 22.5 22.5 22.5 22.5 ... 18.5 18.5 18.5 18.5\n", " spatial_ref int64 8B 0\n", "Data variables:\n", - " elevation (band, y, x) float32 1GB ...
    2. " ], "text/plain": [ " Size: 1GB\n", @@ -587,8 +580,10 @@ } ], "source": [ - "dest = \"./hawaii_topo_30.tif\"\n", - "ds = load_tiff_file(fp = dest)\n", + "fp = \"./hawaii_topo_30.tif\"\n", + "attr_name = \"elevation\"\n", + "ds = load_tiff_file(fp,\n", + " attr_name)\n", "ds" ] }, From d01e24b407e9c33ed7eef6f54aa5492871871194 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Mon, 16 Jun 2025 11:44:48 -0400 Subject: [PATCH 60/97] Add env setup requirement --- src/icefabric_tools/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/icefabric_tools/README.md b/src/icefabric_tools/README.md index c522fc6..7ee2c9f 100644 --- a/src/icefabric_tools/README.md +++ b/src/icefabric_tools/README.md @@ -1,3 +1,6 @@ # Icefabric tools A collection of compute functions/services build on top of existing Iceberg data + +Prior to exectuing the "src/icefabric_tools", one must establish their environment via +"uv pip install -e" against the .toml file residing within "https://github.com/NGWPC/icefabric/blob/main/pyproject.toml." From c63565b4bc51ff4ff8481faa8f7f4fbb83a993f5 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Mon, 16 Jun 2025 11:46:33 -0400 Subject: [PATCH 61/97] Update import of icechunk module --- .../src/icefabric_tools/icechunk/arch_weather_file_utils.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py b/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py index d2639ae..e3128c5 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py @@ -7,16 +7,13 @@ import os from enum import Enum - import fsspec import pandas as pd import rioxarray as rxr import xarray as xr from tqdm import tqdm from virtualizarr import open_virtual_dataset - -# from icefabric_tools.icechunk import S3Path -from icechunk_s3_module import S3Path +from icefabric_tools.icechunk import S3Path class FileType(Enum): """ From cb01040ad8a628fc5741ba1df0ced3f1a4498a61 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Mon, 16 Jun 2025 12:12:13 -0400 Subject: [PATCH 62/97] Migrates icechunks of TIFFs from local to cloud. Script for which will covers those TIFFs declared in icechunk_s3_module.py's NGWPCLocations() for migration to S3 as icechunks. --- .../icechunk/icechunk_s3_migration.py | 58 +++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_migration.py diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_migration.py b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_migration.py new file mode 100644 index 0000000..bd08485 --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_migration.py @@ -0,0 +1,58 @@ +import os +from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations, S3Path +from icefabric_tools.icechunk.icechunk_testing import * + +def migrate_all_icechunks(attr_name): + ''' + Converts & migrates TIFFs from local to cloud. + + Args: + attr_name (str): Attribute name of interest for a given TIFF. + + Return: None + + ''' + # Extract list of TIFFs from local + local_list = [] + for root, _, files in os.walk("./"): + for file in files: + if file.endswith(f".tiff"): + local_list.append(file.lower()) + + # Maps the S3 paths to TIFFs + topo_fn2s3_map = {} + for fn in local_list: + if 'hawaii_10m' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_HA_10M_IC.path] = 'tbdem_hawaii_10m.tiff' + elif 'hawaii_30m' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_HA_30M_IC.path] = 'tbdem_hawaii_30m.tiff' + elif 'conus_atlantic_gulf_30m' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_CONUS_ATL_GULF_30M_IC.path] = 'tbdem_conus_atlantic_gulf_30m.tiff' + elif 'conus_pacific_30m' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_CONUS_PAC_30M_IC.path] = 'tbdem_conus_pacific_30m.tiff' + elif 'pr_usvi_30m' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_PR_USVI_30M_IC.path] = 'tbdem_pr_usvi_30m.tiff' + elif 'pr_usvi_10m' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_PR_USVI_10M_IC.path] = 'tbdem_pr_usvi_10m.tiff' + elif 'alaska_10m' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_AK_10M_IC.path] = 'tbdem_alaska_10m.tiff' + elif 'alaska_30m' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_AK_30M_IC.path] = 'tbdem_alaska_30m.tiff' + elif 'great_lakes_30m' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_GREAT_LAKES_30M_IC.path] = 'tbdem_great_lakes_30m.tiff' + elif 'albemarle_sound_nos_ncei' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_ALBEMARLE_SOUND_IC.path] = 'Albemarle_Sound_NOS_NCEI.tiff' + elif 'chesapeake_bay_nos_ncei' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_CHESAPEAKE_BAY_IC.path] = 'Chesapeake_Bay_NOS_NCEI.tiff' + elif 'mobile_bay_nos_ncei' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_MOBILE_BAY_IC.path] = 'Mobile_Bay_NOS_NCEI.tiff' + elif 'tangier_sound_nos_ncei-002' in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_TANGIER_SOUND_IC.path] = 'Tangier_Sound_NOS_NCEI-002.tiff' + + # Migration of all captured TIFFs to cloud. + for s3_path, fn in topo_fn2s3_map.items(): + if fn: + topo_push_test(tiff_fp=f"./{fn}", + attr_name=attr_name, + new_ic_repo=s3_path) + return \ No newline at end of file From 10ee82a632009c7a347ea9372d81874742c89273 Mon Sep 17 00:00:00 2001 From: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> Date: Tue, 17 Jun 2025 11:49:59 -0400 Subject: [PATCH 63/97] NGWPC-6502: Move the hydrofabric-ui codes into the icefabric repo --- .pre-commit-config.yaml | 2 +- docker/notebook/docker-compose.yml | 19 + examples/geopackage_viewer/geopackage.ipynb | 855 +++++ examples/geopackage_viewer/requirements.txt | 9 + examples/{ => iceberg}/Demo_Iceberg.ipynb | 0 examples/iceberg/pyiceberg_timetravel.ipynb | 376 +++ examples/{ => iceberg}/quick_start.ipynb | 6 +- examples/icechunk/README.icechunk.md | 19 + .../icechunk/icechunk_data_ret_and_plot.ipynb | 301 ++ .../{ => icechunk}/icechunk_to_tiff.ipynb | 82 +- .../icechunk_version_control_examples.ipynb | 131 +- pyproject.toml | 16 +- src/icefabric_manage/pyproject.toml | 2 +- src/icefabric_tools/pyproject.toml | 5 + .../src/icefabric_tools/geo/__init__.py | 0 .../src/icefabric_tools/geo/geo_utils.py | 91 + .../icechunk/icechunk_s3_module.py | 5 +- tests/integration/test_topobathy_icechunk.py | 48 + uv.lock | 2948 +++++++++-------- 19 files changed, 3442 insertions(+), 1473 deletions(-) create mode 100644 docker/notebook/docker-compose.yml create mode 100644 examples/geopackage_viewer/geopackage.ipynb create mode 100644 examples/geopackage_viewer/requirements.txt rename examples/{ => iceberg}/Demo_Iceberg.ipynb (100%) create mode 100644 examples/iceberg/pyiceberg_timetravel.ipynb rename examples/{ => iceberg}/quick_start.ipynb (99%) create mode 100644 examples/icechunk/README.icechunk.md create mode 100644 examples/icechunk/icechunk_data_ret_and_plot.ipynb rename examples/{ => icechunk}/icechunk_to_tiff.ipynb (70%) rename examples/{ => icechunk}/icechunk_version_control_examples.ipynb (84%) create mode 100644 src/icefabric_tools/src/icefabric_tools/geo/__init__.py create mode 100644 src/icefabric_tools/src/icefabric_tools/geo/geo_utils.py create mode 100644 tests/integration/test_topobathy_icechunk.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 88b32b5..a258935 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,4 +22,4 @@ repos: - repo: https://github.com/astral-sh/uv-pre-commit rev: 0.5.7 hooks: - - id: uv-lock \ No newline at end of file + - id: uv-lock diff --git a/docker/notebook/docker-compose.yml b/docker/notebook/docker-compose.yml new file mode 100644 index 0000000..f09cefc --- /dev/null +++ b/docker/notebook/docker-compose.yml @@ -0,0 +1,19 @@ +# NOTE: Unsure the purpose of this compose at this time - personal directories should be removed +version: "3.8" + +services: + jupyterlab: + image: jupyter/base-notebook:latest + ports: + - "8888:8888" + volumes: + - ~/efs/jupyterlab-compose:/home/jovyan/work + environment: + JUPYTER_ENABLE_LAB: "yes" + NB_UID: "1000" + NB_GID : "100" + AWS_ACCESS_KEY_ID: "${AWS_ACCESS_KEY_ID}" + AWS_SECRET_ACCESS_KEY: "${AWS_SECRET_ACCESS_KEY}" + AWS_SESSION_TOKEN: "${AWS_SESSION_TOKEN}" + command: > + bash -c "pip install boto3 && chown -R 1000:100 /home/jovyan/work && start-notebook.sh --NotebookApp.token=''" diff --git a/examples/geopackage_viewer/geopackage.ipynb b/examples/geopackage_viewer/geopackage.ipynb new file mode 100644 index 0000000..1355d34 --- /dev/null +++ b/examples/geopackage_viewer/geopackage.ipynb @@ -0,0 +1,855 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5c66ee09-04ee-47c3-bc36-8f620a207626", + "metadata": {}, + "source": [ + "# Hydrofabric Geopackage Viewer" + ] + }, + { + "cell_type": "markdown", + "id": "df0435b9-0fd0-4681-9c10-4ac2faaf83aa", + "metadata": {}, + "source": [ + "Read geopackage with geopandas. Get list of layers, loop and read all layers and store in a dictionary.\n", + "\n", + "\n", + "Requires:\n", + "- To be run on machine with access to https://hydroapi.oe.nextgenwaterprediction.com/\n", + "- `.env` with AWS credentials" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "784799a5-df23-4971-ad1b-ea4573610279", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import ipywidgets as widgets\n", + "import pandas as pd\n", + "from dotenv import load_dotenv\n", + "from ipywidgets import interact\n", + "\n", + "from icefabric_tools.geo.geo_utils import create_time_series_widget, get_geopackage_uri, get_observational_uri" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "515b8c7d-c575-42f3-a9b3-414659e619f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# before running this cell, make sure you created a .env file in this directory with your AWS credentials in it\n", + "load_dotenv()" + ] + }, + { + "cell_type": "markdown", + "id": "3278d5ca-d9c5-4950-8731-77b9dd5d3367", + "metadata": {}, + "source": [ + "## Getting streamflow observations for different gages\n" + ] + }, + { + "cell_type": "markdown", + "id": "ac3f8c61-8218-4d6b-bba7-285053166a7e", + "metadata": {}, + "source": [ + "Step 1) getting the data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5124f78a-2f24-4e9c-ba73-93d246bfc2e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " dateTime q_cms\n", + "0 1990-10-01 05:00:00 19.623575\n", + "1 1990-10-01 06:00:00 20.416447\n", + "2 1990-10-01 07:00:00 21.237635\n", + "3 1990-10-01 08:00:00 21.860605\n", + "4 1990-10-01 09:00:00 22.285358\n", + "... ... ...\n", + "297303 2024-08-30 20:00:00 13.280601\n", + "297304 2024-08-30 21:00:00 13.280601\n", + "297305 2024-08-30 22:00:00 13.280601\n", + "297306 2024-08-30 23:00:00 13.280601\n", + "297307 2024-08-31 00:00:00 13.280601\n", + "\n", + "[297308 rows x 2 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "streamflow_obs_uri = get_observational_uri(gage_id=\"01013500\")\n", + "streamflow_obs_df = pd.read_csv(streamflow_obs_uri)\n", + "streamflow_obs_df" + ] + }, + { + "cell_type": "markdown", + "id": "3ef286cb-c57b-471c-b235-0c7becd81bb4", + "metadata": {}, + "source": [ + "Step 2) streamflow visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a53b0ff1-be18-4baf-af51-f331888974da", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "02a4caac921e415092fb25289f697d6b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(SelectionSlider(description='Start:', layout=Layout(width='95%'), options=('1990-10-01 0…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "streamflow_obs_df[\"dateTime\"] = pd.to_datetime(streamflow_obs_df[\"dateTime\"])\n", + "\n", + "# Create sliders\n", + "start_slider = widgets.SelectionSlider(\n", + " options=streamflow_obs_df[\"dateTime\"].dt.strftime(\"%Y-%m-%d %H:%M:%S\").tolist(),\n", + " description=\"Start:\",\n", + " layout=widgets.Layout(width=\"95%\"),\n", + ")\n", + "\n", + "end_slider = widgets.SelectionSlider(\n", + " options=streamflow_obs_df[\"dateTime\"].dt.strftime(\"%Y-%m-%d %H:%M:%S\").tolist(),\n", + " description=\"End:\",\n", + " layout=widgets.Layout(width=\"95%\"),\n", + ")\n", + "\n", + "# Scatter Plot of observations\n", + "create_time_series_widget(streamflow_obs_df, start_slider, end_slider, point_size=5)" + ] + }, + { + "cell_type": "markdown", + "id": "721e55a1-798e-4122-b69f-d178381086ec", + "metadata": {}, + "source": [ + "## Geopackage Summary" + ] + }, + { + "cell_type": "markdown", + "id": "3adcb9a2-4623-4117-99cf-ad29501bea42", + "metadata": {}, + "source": [ + "Print list of layers, number of catchments, and list of hydrolocations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "434b7c40-4a11-4db2-b073-8794848833d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'s3://ngwpc-hydrofabric-oe/2.1/CONUS/06710385/GEOPACKAGE/USGS/2024_Dec_16_20_29_12/gauge_06710385.gpkg'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geopackage_uri" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7fb96ccb-b383-42eb-b8bd-b2355c1e20cc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Layers:\n", + "flowpaths, divides, nexus, lakes, pois, hydrolocations, flowpath-attributes, flowpath-attributes-ml, network, divide-attributes\n", + "Number of catchments:\n", + "26\n", + "Hydrolocations:\n", + "huc12-101900020801, huc12-101900020802, gages-06710385\n" + ] + } + ], + "source": [ + "geopackage_uri = get_geopackage_uri(gage_id=\"06710385\", version=2.2)\n", + "\n", + "layers = gpd.list_layers(geopackage_uri).name.tolist()\n", + "\n", + "layer_df = {}\n", + "\n", + "for layer in layers:\n", + " df = gpd.read_file(geopackage_uri, layer=layer)\n", + " layer_df[layer] = df\n", + "\n", + "layers_str = \", \".join(layers)\n", + "print(\"Layers:\")\n", + "print(layers_str)\n", + "row, col = layer_df[\"divides\"].shape\n", + "print(\"Number of catchments:\")\n", + "print(row)\n", + "print(\"Hydrolocations:\")\n", + "hl = layer_df[\"hydrolocations\"].hl_uri.tolist()\n", + "hl_str = \", \".join(hl)\n", + "print(hl_str)" + ] + }, + { + "cell_type": "markdown", + "id": "77cd7974-cda9-4931-b80c-8d5dd31da250", + "metadata": {}, + "source": [ + "## Map of divides, nexuses, and flowpaths" + ] + }, + { + "cell_type": "markdown", + "id": "b3689b76-8a61-48b8-b827-1b17eb3e3caf", + "metadata": {}, + "source": [ + "Plot divides, nexuses, and flowpaths on a map." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "798ddd22", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    Make this Notebook Trusted to load map: File -> Trust Notebook
    " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "divides = layer_df[\"divides\"].to_crs(epsg=3857)\n", + "\n", + "flowpaths = layer_df[\"flowpaths\"].to_crs(epsg=3857)\n", + "nexus = layer_df[\"nexus\"].to_crs(epsg=3857)\n", + "\n", + "div_ex = divides.explore()\n", + "fl_ex = flowpaths.explore(m=div_ex, color=\"yellow\")\n", + "nexus.explore(m=fl_ex, color=\"red\")" + ] + }, + { + "cell_type": "markdown", + "id": "460fc320-ffd7-4509-a252-d3eedb25b292", + "metadata": {}, + "source": [ + "## View Layers" + ] + }, + { + "cell_type": "markdown", + "id": "c4e1f9d4-ab23-4606-86dd-69432e84a523", + "metadata": {}, + "source": [ + "Select layer and print table" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "858e0398-81ee-4d57-8889-11002174c5ea", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "70048138c0e24c9b99123493f978d25a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(Dropdown(description='layer_name', options=('flowpaths', 'divides', 'nexus', 'lakes', 'p…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def show_layer(layer_name):\n", + " display(layer_df[layer_name])\n", + "\n", + "\n", + "interact(show_layer, layer_name=layers)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccc9a36b-0c8e-456e-8b5b-97215626a630", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "464f2243-2d93-44a6-8101-5b7b6812495b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/geopackage_viewer/requirements.txt b/examples/geopackage_viewer/requirements.txt new file mode 100644 index 0000000..bfa17c6 --- /dev/null +++ b/examples/geopackage_viewer/requirements.txt @@ -0,0 +1,9 @@ +folium==0.19.5 +geodatasets==2024.8.0 +geopandas==1.0.1 +httpx==0.28.1 +ipywidgets==8.1.6 +matplotlib==3.10.3 +pandas==2.2.3 +python-dotenv==1.1.0 +mapclassify==2.9.0 diff --git a/examples/Demo_Iceberg.ipynb b/examples/iceberg/Demo_Iceberg.ipynb similarity index 100% rename from examples/Demo_Iceberg.ipynb rename to examples/iceberg/Demo_Iceberg.ipynb diff --git a/examples/iceberg/pyiceberg_timetravel.ipynb b/examples/iceberg/pyiceberg_timetravel.ipynb new file mode 100644 index 0000000..eb06391 --- /dev/null +++ b/examples/iceberg/pyiceberg_timetravel.ipynb @@ -0,0 +1,376 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c015f447", + "metadata": {}, + "source": [ + "# Demo: Timetravel in with iceberg tables\n", + "\n", + "Create a demo catalog, make changes, and see the changes with \"snapshot\" history.\n", + "\n", + "Requires:\n", + "- pyiceberg[sql-sqlite] installed\n", + "- `.env` with your AWS credentials" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9f07637f", + "metadata": {}, + "outputs": [], + "source": [ + "import shlex\n", + "import subprocess\n", + "from pathlib import Path\n", + "\n", + "import pyarrow.parquet as pq\n", + "from dotenv import load_dotenv\n", + "from pyiceberg.catalog import load_catalog" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "db86d0ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# before running this cell, make sure you created a .env file in this directory with your _current_ AWS credentials in it\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3f901988", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "download: s3://ngwpc-hydrofabric/hydrofabric_parquet/2.2/CONUS/divides.parquet to data/parquet/divides.parquet\n" + ] + }, + { + "data": { + "text/plain": [ + "CompletedProcess(args=['aws', 's3', 'cp', 's3://ngwpc-hydrofabric/hydrofabric_parquet/2.2/CONUS/divides.parquet', 'data/parquet'], returncode=0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Retrieve sample parquet from s3\n", + "parquet_path = Path(\"./data/parquet\")\n", + "parquet_path.mkdir(parents=True, exist_ok=True)\n", + "\n", + "subprocess.run(\n", + " shlex.split(\n", + " f\"aws s3 cp s3://ngwpc-hydrofabric/hydrofabric_parquet/2.2/CONUS/divides.parquet {parquet_path}\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f41913d7-1cc8-42d0-8cea-053b4c23d6bb", + "metadata": {}, + "source": [ + "Read divides parquet file" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6651c9e7-c38d-46d1-b6a4-491baf12cc90", + "metadata": {}, + "outputs": [], + "source": [ + "df = pq.read_table(\"data/parquet/divides.parquet\")" + ] + }, + { + "cell_type": "markdown", + "id": "0e4d5aaf-d51c-486c-86ac-32eeb2b7e74f", + "metadata": {}, + "source": [ + "Create data catalog stored in \"warehouse\" directory." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "96974839-0308-4256-bb3e-0cddfea9d037", + "metadata": {}, + "outputs": [], + "source": [ + "warehouse_path = Path(\"./warehouse\")\n", + "warehouse_path.mkdir(exist_ok=True)\n", + "catalog = load_catalog(\n", + " \"default\",\n", + " **{\n", + " \"type\": \"sql\",\n", + " \"uri\": f\"sqlite:///{warehouse_path}/pyiceberg_catalog.db\",\n", + " \"warehouse\": f\"file://{warehouse_path}\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2fe13a4d-0c18-4f2b-953e-a9c658b7225a", + "metadata": {}, + "source": [ + "Create Iceberg table for divides" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "efed8866-134c-40f9-969b-1e4c20b5f11b", + "metadata": {}, + "outputs": [], + "source": [ + "catalog.create_namespace(\"default\")\n", + "table = catalog.create_table(\n", + " \"default.divides\",\n", + " schema=df.schema,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f29ed9b6-ba8d-4a54-bbc5-eec900f1cec6", + "metadata": {}, + "source": [ + "Add divides data to Iceberg table and print the number of rows. There should be 831777 divides for CONUS." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0a9d4717-9055-4ac7-819c-aca6c2435c95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "831777" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table.append(df)\n", + "len(table.scan().to_arrow())" + ] + }, + { + "cell_type": "markdown", + "id": "a1c2b449-667a-4bcd-9c94-d0e604c33f8c", + "metadata": {}, + "source": [ + "A snapshot was created for the initial append. Store this snapshot id for later use." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "78ee6462-5db1-4a40-89b8-3837e63711cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Snapshot ID: 6392225911450853109; Summary: operation=Operation.APPEND\n" + ] + } + ], + "source": [ + "for snapshot in table.snapshots():\n", + " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")\n", + "snapshot_id = table.metadata.snapshots[0].snapshot_id" + ] + }, + { + "cell_type": "markdown", + "id": "8f245529-a3c9-4a99-bcb3-fe1078779498", + "metadata": {}, + "source": [ + "Add a new column for flowpath length in m. Overwrite original table." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "975224d2-deb0-420f-be80-6b5cac7bbe60", + "metadata": {}, + "outputs": [], + "source": [ + "import pyarrow.compute as pc\n", + "\n", + "df = df.append_column(\"lengthm\", pc.multiply(df[\"lengthkm\"], 1000))\n", + "with table.update_schema() as update_schema:\n", + " update_schema.union_by_name(df.schema)\n", + "table.overwrite(df)" + ] + }, + { + "cell_type": "markdown", + "id": "b3ff7a72-70f6-44e8-a6da-d1c9766c6782", + "metadata": {}, + "source": [ + "There should be a new \"lengthm\" column." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d826f290-79bf-4bab-a508-cd73bb3aadb6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table.schema" + ] + }, + { + "cell_type": "markdown", + "id": "668d3f57-e9ef-47e7-a268-05699debcf8f", + "metadata": {}, + "source": [ + "There should now be three snapshots. The original, a delete, and an append with the new column." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6b3a70f2-2271-4bcd-b5fb-83683f36705c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Snapshot ID: 6392225911450853109; Summary: operation=Operation.APPEND\n", + "Snapshot ID: 3770483438741118773; Summary: operation=Operation.DELETE\n", + "Snapshot ID: 6082380623201209864; Summary: operation=Operation.APPEND\n" + ] + } + ], + "source": [ + "for snapshot in table.snapshots():\n", + " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3685eddd-31bb-4c07-a656-6cfb5e97881a", + "metadata": {}, + "source": [ + "You can use the scan function and the first snapshot ID (this variable was saved earlier) to look at the table before the\n", + "new column was added. This table doesn't have lengthm." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2f47f9e3-7e6b-468b-a978-2c433e662c12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pyarrow.Table\n", + "divide_id: large_string\n", + "toid: large_string\n", + "type: large_string\n", + "ds_id: double\n", + "areasqkm: double\n", + "vpuid: large_string\n", + "id: large_string\n", + "lengthkm: double\n", + "tot_drainage_areasqkm: double\n", + "has_flowline: bool\n", + "geometry: large_binary\n" + ] + } + ], + "source": [ + "# scan = table.scan(row_filter=\"divide_id\" == \"cat-276\", selected_fields=('divide_id', 'lengthm')).to_arrow()\n", + "# print(scan)\n", + "print(table.scan(snapshot_id=snapshot_id).to_arrow().to_string())\n", + "# table.scan(snapshot_id=snapshot_id)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/quick_start.ipynb b/examples/iceberg/quick_start.ipynb similarity index 99% rename from examples/quick_start.ipynb rename to examples/iceberg/quick_start.ipynb index 6e18088..24680e4 100644 --- a/examples/quick_start.ipynb +++ b/examples/iceberg/quick_start.ipynb @@ -14,7 +14,6 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "from pathlib import Path\n", "\n", "import geopandas as gpd\n", @@ -58,7 +57,7 @@ "warehouse_path.mkdir(exist_ok=True)\n", "\n", "catalog_settings = {\n", - " 'type': 'sql',\n", + " \"type\": \"sql\",\n", " \"uri\": f\"sqlite:///{warehouse_path}/pyiceberg_catalog.db\",\n", " \"warehouse\": f\"file://{warehouse_path}\",\n", "}\n", @@ -830,7 +829,7 @@ "from icefabric_tools.rnr import get_rnr_segment\n", "\n", "catalog_settings = {\n", - " 'type': 'sql',\n", + " \"type\": \"sql\",\n", " \"uri\": \"sqlite:///../data/warehouse/pyiceberg_catalog.db\",\n", " \"warehouse\": \"file://../data/warehouse\",\n", "}\n", @@ -1036,7 +1035,6 @@ } ], "source": [ - "import geopandas as gpd\n", "gdf = gpd.read_file(\"../data/test_ds_rnr.gpkg\", layer=\"network\")\n", "gdf.head()" ] diff --git a/examples/icechunk/README.icechunk.md b/examples/icechunk/README.icechunk.md new file mode 100644 index 0000000..322a9c7 --- /dev/null +++ b/examples/icechunk/README.icechunk.md @@ -0,0 +1,19 @@ +# Icechunk Data Viewer +Interface to load, create and view S3 Icechunk-versioned NGWPC datasets + +## Install Icechunk Data Viewer: +1. Clone repo: `git clone ` +2. CD to repo dir: `cd /icechunk_data_viewer` +3. Create virtual environment: `python3.11 -m venv .venv` +4. Start virtual environment: `source .venv/bin/activate` +5. Load dependedencies: `pip install -r requirements.txt` +6. Prepare AWS credentials. Either: + - [Set up IAM Identity Center and run `aws sso login` prior to notebook launch](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-sso.html#sso-configure-profile-token-auto-sso) + - [Copy/paste credentials into `~/.aws/credentials`](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html#cli-configure-files-format) +7. Start Jupyter notebook: `jupyter notebook --no-browser` +8. Using your browser, navigate to the notebook server instance (i.e. `http://127.0.0.1:8888/tree?token=XXX`). Your specific URL is listed in the terminal output. +9. Open `icechunk_data_ret_and_plot.jpynb` in the GUI. +10. Prior to execution, depending on if you've configured AWS credentials with SSO or copy/pasted them: + - SSO: Comment out line #11 in block 1 (`os.environ['AWS_PROFILE'] = "XXX_SoftwareEngineersFull"`) + - Copy/paste: Alter line #11 to overwrite the placeholder string with your profile name found in `~/.aws/credentials` +11. Run the notebook diff --git a/examples/icechunk/icechunk_data_ret_and_plot.ipynb b/examples/icechunk/icechunk_data_ret_and_plot.ipynb new file mode 100644 index 0000000..f1986b1 --- /dev/null +++ b/examples/icechunk/icechunk_data_ret_and_plot.ipynb @@ -0,0 +1,301 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d2960e7e-b0ce-4fc7-bc91-509ff38180db", + "metadata": {}, + "source": [ + "## Retrieving Icechunk Data\n", + "Intantiate and retrieve the data from the NLCD Land Cover Icechunk repo. Print and plot the data.\n", + "\n", + "Requires:\n", + "- `.env` with your AWS credentials" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "92608737-174f-42fd-ab07-b599edd9d1de", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from dotenv import load_dotenv" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "28c1bab2-5106-4ea5-94fd-502de7859a4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# before running this cell, make sure you created a .env file in this directory with your AWS credentials in it\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2b3b1ced-6760-4ef5-8cdc-4bde82a287fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 874GB\n", + "Dimensions: (year: 39, Y: 105000, X: 160000, Y1: 52500, X1: 80000, Y2: 26250,\n", + " X2: 40000, Y3: 13125, X3: 20000, Y4: 6563, X4: 10000, Y5: 3282,\n", + " X5: 5000, Y6: 1641, X6: 2500)\n", + "Coordinates:\n", + " * year (year) int32 156B 1985 1986 1987 1988 1989 ... 2020 2021 2022 2023\n", + "Dimensions without coordinates: Y, X, Y1, X1, Y2, X2, Y3, X3, Y4, X4, Y5, X5,\n", + " Y6, X6\n", + "Data variables:\n", + " 0 (year, Y, X) uint8 655GB dask.array\n", + " 1 (year, Y1, X1) uint8 164GB dask.array\n", + " 2 (year, Y2, X2) uint8 41GB dask.array\n", + " 3 (year, Y3, X3) uint8 10GB dask.array\n", + " 4 (year, Y4, X4) uint8 3GB dask.array\n", + " 5 (year, Y5, X5) uint8 640MB dask.array\n", + " 6 (year, Y6, X6) uint8 160MB dask.array\n", + "Attributes: (12/24)\n", + " multiscales: [{'datasets': [{'path': '0'}, {'path': '1'}, {'...\n", + " KeyDirectoryVersion: 1\n", + " KeyRevision: 1\n", + " KeyRevisionMinor: 0\n", + " GTModelTypeGeoKey: 1\n", + " GTRasterTypeGeoKey: 1\n", + " ... ...\n", + " ProjNatOriginLongGeoKey: -96.0\n", + " ProjNatOriginLatGeoKey: 23.0\n", + " ProjFalseEastingGeoKey: 0.0\n", + " ProjFalseNorthingGeoKey: 0.0\n", + " ModelPixelScale: [30.0, 30.0, 0.0]\n", + " ModelTiepoint: [0.0, 0.0, 0.0, -2415585.0, 3314805.0, 0.0]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", + " super().__init__(**codec_config)\n", + "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", + " super().__init__(**codec_config)\n", + "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", + " super().__init__(**codec_config)\n", + "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", + " super().__init__(**codec_config)\n", + "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", + " super().__init__(**codec_config)\n", + "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", + " super().__init__(**codec_config)\n", + "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", + " super().__init__(**codec_config)\n" + ] + } + ], + "source": [ + "from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations\n", + "\n", + "# Retrive data and print\n", + "land_cover_repo = IcechunkS3Repo(location=NGWPCLocations.NLCD_IC.path)\n", + "lc_data = land_cover_repo.retrieve_dataset()\n", + "print(lc_data)" + ] + }, + { + "cell_type": "markdown", + "id": "e4c698c4-49cd-445c-ac4a-2929fa01185f", + "metadata": {}, + "source": [ + "### Plot 2023 data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9d70e76d-82aa-4724-93fb-9e53b979363c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 16MB\n", + "dask.array\n", + "Coordinates:\n", + " year int32 4B 2023\n", + "Dimensions without coordinates: Y5, X5\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Print data for year 2023\n", + "print(lc_data[\"5\"].sel(year=2023))\n", + "\n", + "# Set up plot for 2023 land cover\n", + "lc_data[\"5\"].sel(year=2023).plot(x=\"X5\", y=\"Y5\")\n", + "\n", + "# Invert the y-axis to show the CONUS region correctly\n", + "plt.gca().invert_yaxis()\n", + "\n", + "# Add labels and show the plot\n", + "plt.xlabel(\"LON\")\n", + "plt.ylabel(\"LAT\")\n", + "plt.title(\"CONUS Land Cover\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "982fa54a-cd71-43d0-af65-a60184956bba", + "metadata": {}, + "source": [ + "## Topobathy\n", + "Intantiate and retrieve the data from one Topobathy Icechunk repo. Print and plot the data.\n", + "\n", + "### Retrieve Data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "617115d8-a7d0-486e-aab9-2daedad9074e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 681MB\n", + "Dimensions: (band: 1, y: 6304, x: 13504)\n", + "Coordinates:\n", + " * y (y) float64 50kB 19.0 19.0 ... 17.25 17.25\n", + " * x (x) float64 108kB -68.0 -68.0 ... -64.25\n", + " spatial_ref int64 8B 0\n", + " * band (band) int64 8B 1\n", + "Data variables:\n", + " __xarray_dataarray_variable__ (band, y, x) float64 681MB dask.array\n" + ] + } + ], + "source": [ + "# Retrive data and print\n", + "topo_repo = IcechunkS3Repo(location=NGWPCLocations.TOPO_PR_USVI_30M_IC.path)\n", + "topo_data = topo_repo.retrieve_dataset()\n", + "print(topo_data)" + ] + }, + { + "cell_type": "markdown", + "id": "9ac68b4d-0601-43b5-8cc2-ce328305e4fe", + "metadata": {}, + "source": [ + "### Plot Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5a687c9-57ea-4fa9-ae0b-d88595fc03b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 681MB\n", + "dask.array\n", + "Coordinates:\n", + " * y (y) float64 50kB 19.0 19.0 19.0 19.0 ... 17.25 17.25 17.25\n", + " * x (x) float64 108kB -68.0 -68.0 -68.0 ... -64.25 -64.25 -64.25\n", + " spatial_ref int64 8B 0\n", + " * band (band) int64 8B 1\n", + "Attributes: (12/13)\n", + " DataType: Generic\n", + " AREA_OR_POINT: Area\n", + " RepresentationType: ATHEMATIC\n", + " STATISTICS_COUNT: 64069221.0\n", + " STATISTICS_COVARIANCES: 2043434.806372322\n", + " STATISTICS_MAXIMUM: 1328.0447998047\n", + " ... ...\n", + " STATISTICS_MEDIAN: -1039.828272\n", + " STATISTICS_MINIMUM: -6616.7924804688\n", + " STATISTICS_SKIPFACTORX: 1\n", + " STATISTICS_SKIPFACTORY: 1\n", + " STATISTICS_STDDEV: 1429.4876027347\n", + " units: metre\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Print Dataarray\n", + "print(topo_data[\"elevation\"])\n", + "\n", + "# Set up plot\n", + "topo_data[\"elevation\"].plot(x=\"x\", y=\"y\")\n", + "\n", + "# Add labels and show the plot\n", + "plt.xlabel(\"LON\")\n", + "plt.ylabel(\"LAT\")\n", + "plt.title(\"Puerto Rico & USVI 30M Topobathy\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/icechunk_to_tiff.ipynb b/examples/icechunk/icechunk_to_tiff.ipynb similarity index 70% rename from examples/icechunk_to_tiff.ipynb rename to examples/icechunk/icechunk_to_tiff.ipynb index 1e07c9e..03bb4e2 100644 --- a/examples/icechunk_to_tiff.ipynb +++ b/examples/icechunk/icechunk_to_tiff.ipynb @@ -7,19 +7,35 @@ "source": [ "# Demo: Export icechunk raster data to geotiff\n", "\n", - "Raster topobathymetry data made into icechunk stores is not inherently visibile with traditional GIS methods. Use this method to retrieve a dataset and export to geotiff. " + "Raster topobathymetry data made into icechunk stores is not inherently visibile with traditional GIS methods. Use this method to retrieve a dataset and export to geotiff. \n", + "\n", + "Requires:\n", + "- `.env` with AWS credentials" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "7cba65ef", "metadata": {}, "outputs": [], "source": [ + "from dotenv import load_dotenv\n", + "\n", "from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "d479759f", + "metadata": {}, + "outputs": [], + "source": [ + "# before running this cell, make sure you created a .env file in this directory with your AWS credentials in it\n", + "load_dotenv()" + ] + }, { "cell_type": "markdown", "id": "bcebeab5", @@ -408,16 +424,16 @@ " fill: currentColor;\n", "}\n", "
    <xarray.Dataset> Size: 3GB\n",
-       "Dimensions:                        (band: 1, y: 14819, x: 22226)\n",
+       "Dimensions:      (band: 1, y: 14819, x: 22226)\n",
        "Coordinates:\n",
-       "    spatial_ref                    int64 8B 0\n",
-       "  * band                           (band) int64 8B 1\n",
-       "  * x                              (x) float64 178kB -160.5 -160.5 ... -154.5\n",
-       "  * y                              (y) float64 119kB 22.5 22.5 ... 18.5 18.5\n",
+       "  * band         (band) int64 8B 1\n",
+       "    spatial_ref  int64 8B 0\n",
+       "  * y            (y) float64 119kB 22.5 22.5 22.5 22.5 ... 18.5 18.5 18.5 18.5\n",
+       "  * x            (x) float64 178kB -160.5 -160.5 -160.5 ... -154.5 -154.5 -154.5\n",
        "Data variables:\n",
-       "    __xarray_dataarray_variable__  (band, y, x) float64 3GB dask.array<chunksize=(1, 464, 1390), meta=np.ndarray>
    " + " dtype='float64', name='y', length=14819))
  2. x
    PandasIndex
    PandasIndex(Index([-160.5005550000003, -160.5002850000003, -160.5000150000003,\n",
+       "       -160.4997450000003, -160.4994750000003, -160.4992050000003,\n",
+       "       -160.4989350000003, -160.4986650000003, -160.4983950000003,\n",
+       "       -160.4981250000003,\n",
+       "       ...\n",
+       "       -154.5022350000003, -154.5019650000003, -154.5016950000003,\n",
+       "       -154.5014250000003, -154.5011550000003, -154.5008850000003,\n",
+       "       -154.5006150000003, -154.5003450000003, -154.5000750000003,\n",
+       "       -154.4998050000003],\n",
+       "      dtype='float64', name='x', length=22226))
    4. " ], "text/plain": [ " Size: 3GB\n", - "Dimensions: (band: 1, y: 14819, x: 22226)\n", + "Dimensions: (band: 1, y: 14819, x: 22226)\n", "Coordinates:\n", - " spatial_ref int64 8B 0\n", - " * band (band) int64 8B 1\n", - " * x (x) float64 178kB -160.5 -160.5 ... -154.5\n", - " * y (y) float64 119kB 22.5 22.5 ... 18.5 18.5\n", + " * band (band) int64 8B 1\n", + " spatial_ref int64 8B 0\n", + " * y (y) float64 119kB 22.5 22.5 22.5 22.5 ... 18.5 18.5 18.5 18.5\n", + " * x (x) float64 178kB -160.5 -160.5 -160.5 ... -154.5 -154.5 -154.5\n", "Data variables:\n", - " __xarray_dataarray_variable__ (band, y, x) float64 3GB dask.array" + " elevation (band, y, x) float64 3GB dask.array" ] }, "execution_count": 2, @@ -614,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "9e2935a8", "metadata": {}, "outputs": [ @@ -622,12 +638,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Saved dataset to ./hawaii_topo_30.tiff\n" + "Saved dataset to ./hawaii_topo_30.tif\n" ] } ], "source": [ - "var_name = \"__xarray_dataarray_variable__\"\n", + "var_name = \"elevation\"\n", "dest = \"./hawaii_topo_30.tif\"\n", "repo.retrieve_and_convert_to_tif(dest=dest, var_name=var_name)" ] @@ -642,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "5493c79b", "metadata": {}, "outputs": [ @@ -650,12 +666,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Saved clipped window to ./hawaii_clipped_topo_30.tiff\n" + "Saved clipped window to ./hawaii_clipped_topo_30.tif\n" ] } ], "source": [ - "var_name = \"__xarray_dataarray_variable__\"\n", + "var_name = \"elevation\"\n", "dest = \"./hawaii_clipped_topo_30.tif\"\n", "minx, miny, maxx, maxy = -159, 21, -158, 22\n", "repo.retrieve_and_convert_to_tif(dest=dest, var_name=var_name, minx=minx, miny=miny, maxx=maxx, maxy=maxy)" diff --git a/examples/icechunk_version_control_examples.ipynb b/examples/icechunk/icechunk_version_control_examples.ipynb similarity index 84% rename from examples/icechunk_version_control_examples.ipynb rename to examples/icechunk/icechunk_version_control_examples.ipynb index b5023ae..d096bd0 100644 --- a/examples/icechunk_version_control_examples.ipynb +++ b/examples/icechunk/icechunk_version_control_examples.ipynb @@ -11,22 +11,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "4f0fe850-0e5e-40e5-93b1-1c9f468a2d28", "metadata": {}, "outputs": [], "source": [ - "import boto3\n", - "import os\n", "import warnings\n", - "import xarray as xr\n", + "\n", "from dotenv import load_dotenv\n", "\n", "import icefabric_tools.icechunk.arch_weather_file_utils as awf_utils\n", "from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations, S3Path\n", "\n", - "\n", - "warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings(\"ignore\")\n", "\n", "# Before running this cell, make sure you created a .env file in this directory with your AWS credentials in it\n", "# NOTE - if you authenticate with AWS SSO, leave it commented out\n", @@ -48,8 +45,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", - "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", + "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", "Message:\tRepository initialized\n", "\n" ] @@ -77,14 +74,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [00:11<00:00, 2.34s/files]\u001b[0m\n" + "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [01:27<00:00, 17.52s/files]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Dataset is uploaded. Commit: 0B2CMBJJWH52P5Q4HSJ0\n" + "Dataset is uploaded. Commit: 9CNDKXE65PVGCHK4CHBG\n" ] } ], @@ -95,8 +92,8 @@ " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", " file_type=awf_utils.FileType.NETCDF,\n", " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2009*.nc\",\n", - " loadable_vars=['crs'],\n", - " testing_file_quantity=5\n", + " loadable_vars=[\"crs\"],\n", + " testing_file_quantity=5,\n", ")\n", "\n", "# Add 09 data to SNODAS repo with a new snapshot\n", @@ -113,12 +110,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Snapshot ID:\t0B2CMBJJWH52P5Q4HSJ0\n", - "Timestamp:\t2025-06-03 22:52:27.746862+00:00\n", + "Snapshot ID:\t9CNDKXE65PVGCHK4CHBG\n", + "Timestamp:\t2025-06-17 00:01:17.067141+00:00\n", "Message:\tFirst commit! 09 data added.\n", "\n", - "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", - "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", + "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", "Message:\tRepository initialized\n", "\n" ] @@ -140,11 +137,11 @@ "output_type": "stream", "text": [ " Size: 671MB\n", - "Dimensions: (time: 5, lat: 4096, lon: 8192)\n", + "Dimensions: (lon: 8192, time: 5, lat: 4096)\n", "Coordinates:\n", " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", - " * time (time) datetime64[ns] 40B 2009-12-09 2009-12-10 ... 2009-12-13\n", " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", + " * time (time) datetime64[ns] 40B 2009-12-09 2009-12-10 ... 2009-12-13\n", "Data variables:\n", " Band1 (time, lat, lon) float32 671MB dask.array\n", " crs (time) object 40B dask.array\n", @@ -178,14 +175,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [00:11<00:00, 2.35s/files]\u001b[0m\n" + "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [01:27<00:00, 17.54s/files]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Dataset has been appended on the time dimension. Commit: 8DCP1J64FV48Z13XWK6G\n" + "Dataset has been appended on the time dimension. Commit: 6FMVKF4CS7N5RW6PC86G\n" ] } ], @@ -196,12 +193,14 @@ " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", " file_type=awf_utils.FileType.NETCDF,\n", " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2010*.nc\",\n", - " loadable_vars=['crs'],\n", - " testing_file_quantity=5\n", + " loadable_vars=[\"crs\"],\n", + " testing_file_quantity=5,\n", ")\n", "\n", "# Append 2010 data to SNODAS repo with a new snapshot\n", - "new_repo.append_virt_data_to_store(vds=snodas_10_vds, append_dim=\"time\", commit=\"Appended new data from the year 2010\")" + "new_repo.append_virt_data_to_store(\n", + " vds=snodas_10_vds, append_dim=\"time\", commit=\"Appended new data from the year 2010\"\n", + ")" ] }, { @@ -214,16 +213,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Snapshot ID:\t8DCP1J64FV48Z13XWK6G\n", - "Timestamp:\t2025-06-03 22:52:40.716732+00:00\n", + "Snapshot ID:\t6FMVKF4CS7N5RW6PC86G\n", + "Timestamp:\t2025-06-17 00:03:27.965202+00:00\n", "Message:\tAppended new data from the year 2010\n", "\n", - "Snapshot ID:\t0B2CMBJJWH52P5Q4HSJ0\n", - "Timestamp:\t2025-06-03 22:52:27.746862+00:00\n", + "Snapshot ID:\t9CNDKXE65PVGCHK4CHBG\n", + "Timestamp:\t2025-06-17 00:01:17.067141+00:00\n", "Message:\tFirst commit! 09 data added.\n", "\n", - "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", - "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", + "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", "Message:\tRepository initialized\n", "\n" ] @@ -245,14 +244,14 @@ "output_type": "stream", "text": [ " Size: 1GB\n", - "Dimensions: (lat: 4096, lon: 8192, time: 10)\n", + "Dimensions: (time: 10, lat: 4096, lon: 8192)\n", "Coordinates:\n", - " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", " * time (time) datetime64[ns] 80B 2009-12-09 2009-12-10 ... 2010-01-05\n", + " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", "Data variables:\n", - " crs (time) object 80B dask.array\n", " Band1 (time, lat, lon) float32 1GB dask.array\n", + " crs (time) object 80B dask.array\n", "Attributes:\n", " Conventions: CF-1.5\n", " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", @@ -280,8 +279,8 @@ "Dimensions: (time: 5, lat: 4096, lon: 8192)\n", "Coordinates:\n", " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", - " * time (time) datetime64[ns] 40B 2009-12-09 2009-12-10 ... 2009-12-13\n", " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", + " * time (time) datetime64[ns] 40B 2009-12-09 2009-12-10 ... 2009-12-13\n", "Data variables:\n", " Band1 (time, lat, lon) float32 671MB dask.array\n", " crs (time) object 40B dask.array\n", @@ -315,14 +314,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [00:11<00:00, 2.36s/files]\u001b[0m\n" + "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [01:28<00:00, 17.79s/files]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Dataset has been appended on the time dimension. Commit: ZESC6X2MJVJSYDASQY6G\n" + "Dataset has been appended on the time dimension. Commit: T30V4R9GM9TCKHFFBQFG\n" ] } ], @@ -336,12 +335,14 @@ " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", " file_type=awf_utils.FileType.NETCDF,\n", " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2011*.nc\",\n", - " loadable_vars=['crs'],\n", - " testing_file_quantity=5\n", + " loadable_vars=[\"crs\"],\n", + " testing_file_quantity=5,\n", ")\n", "\n", "# Append 2011 data to SNODAS repo's new branch with a new snapshot\n", - "new_repo.append_virt_data_to_store(vds=snodas_11_vds, append_dim=\"time\", commit=\"Appended new data from the year 2011\", branch=\"2011_feature\")" + "new_repo.append_virt_data_to_store(\n", + " vds=snodas_11_vds, append_dim=\"time\", commit=\"Appended new data from the year 2011\", branch=\"2011_feature\"\n", + ")" ] }, { @@ -355,33 +356,33 @@ "output_type": "stream", "text": [ "NEW BRANCH =====================================\n", - "Snapshot ID:\tZESC6X2MJVJSYDASQY6G\n", - "Timestamp:\t2025-06-03 22:52:53.279869+00:00\n", + "Snapshot ID:\tT30V4R9GM9TCKHFFBQFG\n", + "Timestamp:\t2025-06-17 00:05:10.559344+00:00\n", "Message:\tAppended new data from the year 2011\n", "\n", - "Snapshot ID:\t8DCP1J64FV48Z13XWK6G\n", - "Timestamp:\t2025-06-03 22:52:40.716732+00:00\n", + "Snapshot ID:\t6FMVKF4CS7N5RW6PC86G\n", + "Timestamp:\t2025-06-17 00:03:27.965202+00:00\n", "Message:\tAppended new data from the year 2010\n", "\n", - "Snapshot ID:\t0B2CMBJJWH52P5Q4HSJ0\n", - "Timestamp:\t2025-06-03 22:52:27.746862+00:00\n", + "Snapshot ID:\t9CNDKXE65PVGCHK4CHBG\n", + "Timestamp:\t2025-06-17 00:01:17.067141+00:00\n", "Message:\tFirst commit! 09 data added.\n", "\n", - "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", - "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", + "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", "Message:\tRepository initialized\n", "\n", "MAIN BRANCH ====================================\n", - "Snapshot ID:\t8DCP1J64FV48Z13XWK6G\n", - "Timestamp:\t2025-06-03 22:52:40.716732+00:00\n", + "Snapshot ID:\t6FMVKF4CS7N5RW6PC86G\n", + "Timestamp:\t2025-06-17 00:03:27.965202+00:00\n", "Message:\tAppended new data from the year 2010\n", "\n", - "Snapshot ID:\t0B2CMBJJWH52P5Q4HSJ0\n", - "Timestamp:\t2025-06-03 22:52:27.746862+00:00\n", + "Snapshot ID:\t9CNDKXE65PVGCHK4CHBG\n", + "Timestamp:\t2025-06-17 00:01:17.067141+00:00\n", "Message:\tFirst commit! 09 data added.\n", "\n", - "Snapshot ID:\tBANR3MYSD920Z3WTB500\n", - "Timestamp:\t2025-06-03 22:52:13.292198+00:00\n", + "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", + "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", "Message:\tRepository initialized\n", "\n" ] @@ -407,14 +408,14 @@ "text": [ "NEW BRANCH ========================================================\n", " Size: 2GB\n", - "Dimensions: (time: 15, lat: 4096, lon: 8192)\n", + "Dimensions: (time: 15, lon: 8192, lat: 4096)\n", "Coordinates:\n", - " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", - " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", " * time (time) datetime64[ns] 120B 2009-12-09 2009-12-10 ... 2011-01-05\n", + " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", + " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", "Data variables:\n", - " Band1 (time, lat, lon) float32 2GB dask.array\n", " crs (time) object 120B dask.array\n", + " Band1 (time, lat, lon) float32 2GB dask.array\n", "Attributes:\n", " Conventions: CF-1.5\n", " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", @@ -422,14 +423,14 @@ "===================================================================\n", "MAIN BRANCH =======================================================\n", " Size: 1GB\n", - "Dimensions: (lat: 4096, lon: 8192, time: 10)\n", + "Dimensions: (time: 10, lat: 4096, lon: 8192)\n", "Coordinates:\n", - " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", " * time (time) datetime64[ns] 80B 2009-12-09 2009-12-10 ... 2010-01-05\n", + " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", "Data variables:\n", - " crs (time) object 80B dask.array\n", " Band1 (time, lat, lon) float32 1GB dask.array\n", + " crs (time) object 80B dask.array\n", "Attributes:\n", " Conventions: CF-1.5\n", " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", @@ -467,11 +468,19 @@ "# Cleanup - delete the test repo entirely\n", "new_repo.delete_repo(quiet=True)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da2aa6f1", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -485,7 +494,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0rc1" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index be270a9..8f524f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,7 +58,7 @@ target-version = ['py311'] [tool.ruff] line-length = 110 -exclude = [".csv", "LICENSE"] +exclude = [".csv", "LICENSE", ".tf", ".tfvars"] lint.select = [ "F", # Errors detected by Pyflakes "E", # Error detected by Pycodestyle @@ -127,14 +127,6 @@ show_error_codes = true warn_unused_ignores = true [tool.pytest.ini_options] -rootdir = "." # Ensures pytest always treats project root as the root -pythonpath = [ - "tests", - "src/icefabric_api", - "src/icefabric_tools" -] -testpaths = [ - "src/icefabric_api/tests", - "src/icefabric_tools/tests", - "tests" -] +rootdir = "." # Ensures pytest always treats project root as the root +pythonpath = ["tests", "src/icefabric_api", "src/icefabric_tools"] +testpaths = ["src/icefabric_api/tests", "src/icefabric_tools/tests", "tests"] diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml index 693f536..ba9ddec 100644 --- a/src/icefabric_manage/pyproject.toml +++ b/src/icefabric_manage/pyproject.toml @@ -23,7 +23,7 @@ dependencies = [ "geopandas==1.0.1", "s3fs==2025.5.1", "pyarrow==20.0.0", - "pyiceberg[s3fs,glue]==0.9.1", + "pyiceberg[s3fs,glue,sql-sqlite]==0.9.1", "python-dotenv==1.1.0", ] diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index 723b688..9aa31f0 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -40,6 +40,11 @@ dependencies = [ "dask==2025.5.1", "netCDF4==1.7.2", "python-dotenv==1.1.0", + "folium==0.19.5", + "geodatasets==2024.8.0", + "httpx==0.28.1", + "ipywidgets==8.1.6", + "mapclassify==2.9.0", ] [build-system] diff --git a/src/icefabric_tools/src/icefabric_tools/geo/__init__.py b/src/icefabric_tools/src/icefabric_tools/geo/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/icefabric_tools/src/icefabric_tools/geo/geo_utils.py b/src/icefabric_tools/src/icefabric_tools/geo/geo_utils.py new file mode 100644 index 0000000..b64719b --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/geo/geo_utils.py @@ -0,0 +1,91 @@ +import httpx +import ipywidgets as widgets +import matplotlib.pyplot as plt +import pandas as pd + + +def get_observational_uri( + gage_id: str, source: str = "USGS", domain: str = "CONUS", version: str = "2.1", timeout=None +) -> str: + """ + Fetches the observational data URI from the NextGen Water Prediction API. + + Args: + gage_id (str): The gage identifier. + source (str): Data source, e.g., "USGS". + domain (str): Domain name, e.g., "CONUS". + version (str): API version, e.g., "2.2". + + Returns + ------- + str: The URI of the observational dataset. + """ + base_url = f"https://hydroapi.oe.nextgenwaterprediction.com/hydrofabric/{version}/observational" + params = {"gage_id": gage_id, "source": source, "domain": domain} + + response = httpx.get(base_url, params=params, timeout=timeout) + response.raise_for_status() # Raise an error if request failed + data = response.json() + + return data["uri"] + + +def get_geopackage_uri( + gage_id: str, source: str = "USGS", domain: str = "CONUS", version: str = "2.2", timeout=None +) -> str: + """ + Fetches the GeoPackage URI for a given gage ID from the NextGen Water Prediction API. + + Returns + ------- + str: The URI pointing to the GeoPackage in S3. + """ + import httpx + + base_url = "https://hydroapi.oe.nextgenwaterprediction.com/hydrofabric/geopackages" + params = {"gage_id": gage_id, "source": source, "domain": domain, "version": version} + + response = httpx.get(base_url, params=params, timeout=timeout) + response.raise_for_status() + data = response.json() + + return data["uri"] + + +def create_time_series_widget( + df: pd.DataFrame, + start_slider: widgets.SelectionSlider, + end_slider: widgets.SelectionSlider, + point_size: float = 30, +): + """ + Creates an interactive time series plot using matplotlib and ipywidgets. + + Parameters + ---------- + df (pd.DataFrame): DataFrame with 'DateTime' and 'q_cms' columns + start_slider (widgets.SelectionSlider): Widget for selecting start time + end_slider (widgets.SelectionSlider): Widget for selecting end time + """ + + @widgets.interact(start=start_slider, end=end_slider) + def plot_flow(start, end): + start_dt = pd.to_datetime(start) + end_dt = pd.to_datetime(end) + + if start_dt >= end_dt: + print("⚠️ Start must be before End.") + return + + filtered = df[(df["dateTime"] >= start_dt) & (df["dateTime"] <= end_dt)] + + fig, ax = plt.subplots(figsize=(10, 4)) + ax.scatter(filtered["dateTime"], filtered["q_cms"], s=point_size, label="Flow rate (cms)", alpha=0.7) + ax.set_xlabel("Date Time") + ax.set_ylabel("Discharge (cms)") + ax.set_title("Streamflow Time Series (Scatter Plot)") + ax.grid(True) + ax.legend() + plt.xticks(rotation=45) + plt.tight_layout() + plt.show() diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py index 0f6987e..a1fecae 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py @@ -182,7 +182,9 @@ def gen_virtual_chunk_containers( ] return v_chunks - def create_session(self, read_only: bool | None = True, snap_id: str | None = None, branch: str | None = "main") -> ic.Session: + def create_session( + self, read_only: bool | None = True, snap_id: str | None = None, branch: str | None = "main" + ) -> ic.Session: """ Open a session under the repo defined by an instance of IcechunkS3Repo @@ -215,7 +217,6 @@ def retrieve_dataset(self, branch: str | None = "main", snap_id: str | None = No Parameters ---------- - branch : str | None, optional Icechunk repo branch to be opened, by default "main" snap_id : str | None, optional diff --git a/tests/integration/test_topobathy_icechunk.py b/tests/integration/test_topobathy_icechunk.py new file mode 100644 index 0000000..97fc2f9 --- /dev/null +++ b/tests/integration/test_topobathy_icechunk.py @@ -0,0 +1,48 @@ +import os +from pathlib import Path + +import numpy as np +import pytest +import rasterio +from dotenv import load_dotenv + +from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations + +load_dotenv() + +ic_rasters = [pytest.param(f, id=f) for f in NGWPCLocations._member_names_ if "TOPO" in f] + + +@pytest.mark.parametrize("ic_raster", ic_rasters) +@pytest.mark.slow +def test_topobathy(ic_raster: str) -> None: + """This test is SLOW. It will temporarily download all topobathy layers, up to 9 GB individually. + To run, call `pytest tests --run-slow` + Corrupted rasters will load correctly in xarray but incorrectly in rasterio and cannot be exported. + This test checks that when exported, a dataset has values that are non-no data. + """ + data_dir = Path(__file__).parents[0] / "data" + os.makedirs(data_dir, exist_ok=True) + + temp_path = data_dir / "temp_raster.tif" + + try: + # export icechunk zarr to geotiff raster + repo = IcechunkS3Repo(location=NGWPCLocations[ic_raster].path) + ds = repo.retrieve_dataset() + raster = ds.elevation + raster.rio.to_raster(temp_path, tiled=True, compress="LZW", bigtiff="YES") + + # open raster version + with rasterio.open(temp_path, "r") as f: + profile = f.profile + ras = f.read(1) + + # assert all values are not nodata + total_nd = np.where(ras == profile["nodata"], 1, 0).sum() + assert total_nd != ras.size + assert ras.min() != ras.max() + + finally: + if 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"sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931", size = 9630 }, ] From 0381a790fb2b7af00c89a60d2cb49d2209bceef5 Mon Sep 17 00:00:00 2001 From: Daniel Cumpton Date: Tue, 17 Jun 2025 13:55:34 -0600 Subject: [PATCH 64/97] add file to convert all domains to parquet --- scripts/gpkg_to_parquet_all_domains.py | 28 ++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 scripts/gpkg_to_parquet_all_domains.py diff --git a/scripts/gpkg_to_parquet_all_domains.py b/scripts/gpkg_to_parquet_all_domains.py new file mode 100644 index 0000000..5a7e7d6 --- /dev/null +++ b/scripts/gpkg_to_parquet_all_domains.py @@ -0,0 +1,28 @@ +"""A simple script to convert the v2.2 hydrofabric to parquet""" + +import fiona +import geopandas as gpd + +#Location of Hydrofabric geopackage files organized by domain +input_file_root = '/home/daniel.cumpton/Hydrofabric/data/hydrofabric/v2.2/nextgen' + +#Location of data/domain directories for output Parquet files +output_file_root = '/home/daniel.cumpton/icefabric' + +input_file = {'CONUS':'CONUS/conus_nextgen.gpkg', 'GL':'CONUS/gl_nextgen_divide_attr.gpkg', + 'Alaska':'Alaska/ak_nextgen_workaround.gpkg', 'Hawaii':'Hawaii/hi_nextgen_workaround.gpkg', + 'Puerto_Rico':'Puerto_Rico/prvi_nextgen_workaround.gpkg'} + +domains = ['CONUS', 'GL', 'Alaska', 'Hawaii', 'Puerto_Rico'] + +for domain in domains: + + file = f"{input_file_root}/{input_file[domain]}" + available_layers = fiona.listlayers(file) + print(f"For {domain}, layers in GeoPackage: {available_layers}") + + for layer in available_layers: + gdf = gpd.read_file(file, layer=layer) + output_file = f"{output_file_root}/data/parquet/{domain}/{layer}.parquet" + gdf.to_parquet(output_file) + print(f"For {domain}, converted layer '{layer}' to {output_file}") From e0db0bbbf2cab80acbb9eabc16808806988035ec Mon Sep 17 00:00:00 2001 From: Daniel Cumpton Date: Thu, 26 Jun 2025 10:15:01 -0600 Subject: [PATCH 65/97] updates for csv parameters moving to iceberg tables --- src/icefabric_api/data/modules.csv | 2 +- src/icefabric_api/data/sac_sma_params.csv | 2 +- src/icefabric_api/data/ueb_params.csv | 10 ++-- .../src/icefabric_api/create_ipes.py | 42 ++++++++++++---- .../builds/build_parameters.py | 50 +++++++++++++++++++ 5 files changed, 90 insertions(+), 16 deletions(-) create mode 100644 src/icefabric_manage/builds/build_parameters.py diff --git a/src/icefabric_api/data/modules.csv b/src/icefabric_api/data/modules.csv index d291784..b12d663 100644 --- a/src/icefabric_api/data/modules.csv +++ b/src/icefabric_api/data/modules.csv @@ -1,7 +1,7 @@ module,file,outputs CFE-X,cfe_params.csv, Noah-OWP-Modular,noah_owp_modular_params.csv, -Snow17,snow17_params.csv, +Snow-17,snow17_params.csv, Sac-SMA,sac_sma_params.csv, TopModel,topmodel_params.csv, UEB,ueb_params.csv, diff --git a/src/icefabric_api/data/sac_sma_params.csv b/src/icefabric_api/data/sac_sma_params.csv index dc490b6..59ab937 100644 --- a/src/icefabric_api/data/sac_sma_params.csv +++ b/src/icefabric_api/data/sac_sma_params.csv @@ -1,4 +1,4 @@ -name,description,units,data_type,calibratable,source_file,min,max,default_value,divide_attr_name,source_file +name,description,units,data_type,calibratable,source,min,max,default_value,divide_attr_name,source_file hru_id,Identification string for each hrus,NULL,char,False,const,25,125,NA,divide_id, hru_area,Area of each HRU,NULL,double,False,attr,10,100,NA,areasqkm, uztwm,upper zone tension water maximum storage,mm,double,True,csv,25,125,75,,sac_sma_params diff --git a/src/icefabric_api/data/ueb_params.csv b/src/icefabric_api/data/ueb_params.csv index b51bddc..abe86fb 100644 --- a/src/icefabric_api/data/ueb_params.csv +++ b/src/icefabric_api/data/ueb_params.csv @@ -1,10 +1,10 @@ name,description,units,data_type,calibratable,source,min,max,default_value,divide_attr_name,source_file -USic,Energy content initial condition,kg m-3,double,False,const,,,,, -WSis,Snow water equivalent initial condition,m,double,False,const,,,,, -Tic,Snow surface dimensionless age initial condition,NULL,double,False,const,,,,, -WCic,Snow water equivalent dimensionless age initial condition,m,double,False,const,,,,, +USic,Energy content initial condition,kg m-3,double,False,const,,,0,, +WSis,Snow water equivalent initial condition,m,double,False,const,,,0,, +Tic,Snow surface dimensionless age initial condition,NULL,double,False,const,,,0,, +WCic,Snow water equivalent dimensionless age initial condition,m,double,False,const,,,0,, df,Drift factor multiplier,NULL,double,True,const,0.5,6,1,, -apr,Average atmospheric pressure,Pa,double,True,attr,30000,101325,,atm_pres, +apr,Average atmospheric pressure,Pa,double,True,csv,30000,101325,,atm_pres, Aep,Albedo extinction coefficient,m,double,False,const,NULL,NULL,0.1,, cc,Canopy coverage fraction,NULL,double,True,const,0,0.8,0.4,, hcan,Canopy height,m,integer,True,const,0,10,5,, diff --git a/src/icefabric_api/src/icefabric_api/create_ipes.py b/src/icefabric_api/src/icefabric_api/create_ipes.py index 15aa8dc..21e3596 100644 --- a/src/icefabric_api/src/icefabric_api/create_ipes.py +++ b/src/icefabric_api/src/icefabric_api/create_ipes.py @@ -4,11 +4,12 @@ import geopandas as gpd import pandas as pd from pyproj import Transformer +from pyiceberg.catalog import load_catalog +import pyiceberg.exceptions as ex logger = logging.getLogger(__name__) ROOT_DIR = os.path.abspath(os.curdir) - def module_ipe(module, attr, domain, version, cfg_write=None): # Create empty dataframe for csv parameters csv_params = pd.DataFrame() @@ -55,7 +56,9 @@ def module_ipe(module, attr, domain, version, cfg_write=None): attr_list = module_params["source"].to_list() # Create dictionary with divide IDs as the keys and parameter dictionary for the values. - all_cats = dict.fromkeys(divides, param_values) + all_cats = {} + for divide in divides: + all_cats[divide] = param_values.copy() # For attributes, get list of attribute names attr_list = module_params.loc[module_params["source"] == "attr"]["divide_attr_name"].to_list() @@ -70,10 +73,7 @@ def module_ipe(module, attr, domain, version, cfg_write=None): # Get CSV file parameters if "csv" in module_params["source"].values: - csv_file = module_params.loc[module_params["source"] == "csv"]["source_file"].to_list() - csv_file = csv_file[0] - csv_file = f"{csv_file}_{domain}_{version}.csv" - csv_params = pd.read_csv(f"{ROOT_DIR}/data/{csv_file}") + csv_params = divide_parameters(divides, module, domain) for divide in divides: attr_div = attr.loc[attr["divide_id"] == divide] @@ -83,7 +83,12 @@ def module_ipe(module, attr, domain, version, cfg_write=None): csv_params_div = csv_params.loc[csv_params["divide_id"] == divide] csv_params_div = csv_params_div.to_dict(orient="list") all_cats[divide].update(csv_params_div) - # print(all_cats[divide]) + + for key in all_cats[divide].keys(): + value = all_cats[divide][key] + if isinstance(value, list): + all_cats[divide][key] = value[0] + if cfg_write: write_cfg(all_cats) @@ -91,11 +96,10 @@ def module_ipe(module, attr, domain, version, cfg_write=None): module_json = write_json(all_cats[divides[1]], module_params, output_vars, module, s3_uri) return module_json - def write_cfg(params): divides = params.keys() for divide in divides: - cfg_file = f"{divide}_bmi_config_cfe.txt" + cfg_file = f"{ROOT_DIR}/{divide}_bmi_config_cfe.txt" f = open(cfg_file, "w") for key, value in params[divide].items(): @@ -215,3 +219,23 @@ def get_hydrofabric_attributes(gpkg_file, version, domain): divide_attr.loc[index, "centroid_x"] = wgs84_latlon[1] # longitude return divide_attr + +def divide_parameters(divides, module, domain): + module = module.lower() + domain = domain.lower() + namespace = "divide_parameters" + table_name = f"{namespace}.{module}_{domain}" + catalog = load_catalog("glue") + try: + table = catalog.load_table(table_name) + except ex.NoSuchTableError as e: + print(f"Table {table_name} does not exist") + return pd.DataFrame() + except Exception as e: + print(f"Iceberg table load exception: {e}") + return pd.DataFrame() + df = table.scan().to_pandas() + filtered = df[df['divide_id'].isin(divides)] + return filtered + + diff --git a/src/icefabric_manage/builds/build_parameters.py b/src/icefabric_manage/builds/build_parameters.py new file mode 100644 index 0000000..7064ebb --- /dev/null +++ b/src/icefabric_manage/builds/build_parameters.py @@ -0,0 +1,50 @@ +import argparse + +from dotenv import load_dotenv +from pyiceberg.catalog import load_catalog +from pyiceberg.exceptions import NamespaceAlreadyExistsError + +from icefabric_manage import build + +load_dotenv() + + +def build_table(file_dir): + """Builds the divide parameters namespace and tables + + Parameters + ---------- + file_dir : str + The directory to hydrofabric parquet files + """ + parquet_tables = {'CFE-X_params_Alaska_2.2.parquet':'CFE-X_Alaska', 'CFE-X_params_GL_2.2.parquet':'CFE-X_GL', + 'CFE-X_params_Puerto_Rico_2.2.parquet':'CFE-X_Puerto_Rico', 'snow17_params_2.2.parquet':'Snow-17_CONUS', + 'CFE-X_params_CONUS_2.2.parquet':'CFE-X_CONUS', 'CFE-X_params_Hawaii_2.2.parquet':'CFE-X_Hawaii', + 'sac_sma_params_2.2.parquet':'Sac-SMA_CONUS', 'ueb_params.parquet':'UEB_CONUS'} + + catalog = load_catalog("glue") # Using an AWS Glue Endpoint + namespace = "divide_parameters" + try: + catalog.create_namespace(namespace) + except NamespaceAlreadyExistsError: + print(f"Namespace {namespace} already exists") + + for file in parquet_tables.keys(): + + build( + catalog=catalog, + parquet_file=f"{file_dir}/{file}", + namespace=namespace, + table_name=parquet_tables[file], + location="s3://fim-services-data-test/icefabric_metadata/", + ) + print(f"Build successful. Files written into metadata store @ {catalog.name}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="A script to build a pyiceberg catalog in the S3 endpoint") + + parser.add_argument("--files", help="The local file dir where the files are located") + + args = parser.parse_args() + build_table(file_dir=args.files) From 64314a961b6c1b95de825415e9cb381ec7588931 Mon Sep 17 00:00:00 2001 From: Daniel Cumpton Date: Thu, 26 Jun 2025 12:19:19 -0600 Subject: [PATCH 66/97] change csv variable names to iceberg --- .../app/routers/module_params/router.py | 2 +- src/icefabric_api/data/cfe_params.csv | 6 ++--- src/icefabric_api/data/sac_sma_params.csv | 22 ++++++++-------- src/icefabric_api/data/snow17_params.csv | 26 +++++++++---------- src/icefabric_api/data/ueb_params.csv | 26 +++++++++---------- .../src/icefabric_api/create_ipes.py | 24 ++++++++++------- 6 files changed, 55 insertions(+), 51 deletions(-) diff --git a/src/icefabric_api/app/routers/module_params/router.py b/src/icefabric_api/app/routers/module_params/router.py index 7e9b8c6..1f3eb22 100644 --- a/src/icefabric_api/app/routers/module_params/router.py +++ b/src/icefabric_api/app/routers/module_params/router.py @@ -35,7 +35,7 @@ async def get_ipes(query: Parameters): all_modules = [] for mod in query.modules: - mod_json = module_ipe(mod, attr, query.domain, query.version) + mod_json = module_ipe(mod, attr, query.domain, query.version, cfg_write=True) all_modules.append(mod_json) final_json = json.dumps({"modules": all_modules}) diff --git a/src/icefabric_api/data/cfe_params.csv b/src/icefabric_api/data/cfe_params.csv index eea0fa2..b829027 100644 --- a/src/icefabric_api/data/cfe_params.csv +++ b/src/icefabric_api/data/cfe_params.csv @@ -17,9 +17,9 @@ K_nash,Nash Config param for lateral subsurface runoff (Nash discharge to storag K_lf,Nash Config param - primary reservoir,NULL,double,True,const,0,1,0.01,, nash_storage,Nash Config param - secondary reservoir ,NULL,double,False,const,NULL,NULL,"0.0,0.0",, giuh_ordinates,Giuh (geomorphological instantaneous unit hydrograph) ordinates in dt time steps,NULL,double,False,const,NULL,NULL,"0.55, 0.25, 0.2",, -a_Xinanjiang_inflection_point_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,True,csv,-0.5,0.5,-0.2,AXAJ,CFE-X_params -b_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,True,csv,0.01,10,0.66,BXAJ,CFE-X_params -x_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,True,csv,0.01,10,0.02,XXAJ,CFE-X_params +a_Xinanjiang_inflection_point_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,True,iceberg,-0.5,0.5,-0.2,AXAJ,CFE-X_params +b_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,True,iceberg,0.01,10,0.66,BXAJ,CFE-X_params +x_Xinanjiang_shape_parameter,when surface_water_partitioning_scheme=Xinanjiang ,NULL,double,True,iceberg,0.01,10,0.02,XXAJ,CFE-X_params urban_decimal_fraction,when surface_water_partitioning_scheme=Xinanjiang,NULL,double ,False,const,0,1,0.01,, refkdt,Reference Soil Infiltration Parameter (used in runoff formulation),NULL,double,True,attr,0.1,4,1,mean.refkdt, soil_params.depth,soil depth,m,double,False,const,NULL,NULL,2,, \ No newline at end of file diff --git a/src/icefabric_api/data/sac_sma_params.csv b/src/icefabric_api/data/sac_sma_params.csv index 59ab937..c8ce61e 100644 --- a/src/icefabric_api/data/sac_sma_params.csv +++ b/src/icefabric_api/data/sac_sma_params.csv @@ -1,19 +1,19 @@ name,description,units,data_type,calibratable,source,min,max,default_value,divide_attr_name,source_file hru_id,Identification string for each hrus,NULL,char,False,const,25,125,NA,divide_id, hru_area,Area of each HRU,NULL,double,False,attr,10,100,NA,areasqkm, -uztwm,upper zone tension water maximum storage,mm,double,True,csv,25,125,75,,sac_sma_params -uzfwm,Maximum upper zone free water,mm,double,True,csv,10,100,30,,sac_sma_params -lztwm,Maximum lower zone tension water,mm,double,True,csv,75,300,150,,sac_sma_params -lzfsm,"Maximum lower zone free water, secondary (aka supplemental)",mm,double,True,csv,15,300,150,,sac_sma_params -lzfpm,"Maximum lower zone free water, primary",mm,double,True,csv,40,600,300,,sac_sma_params +uztwm,upper zone tension water maximum storage,mm,double,True,iceberg,25,125,75,,sac_sma_params +uzfwm,Maximum upper zone free water,mm,double,True,iceberg,10,100,30,,sac_sma_params +lztwm,Maximum lower zone tension water,mm,double,True,iceberg,75,300,150,,sac_sma_params +lzfsm,"Maximum lower zone free water, secondary (aka supplemental)",mm,double,True,iceberg,15,300,150,,sac_sma_params +lzfpm,"Maximum lower zone free water, primary",mm,double,True,iceberg,40,600,300,,sac_sma_params adimp,Additional impervious area due to saturation,decimal percent,double,True,const,0,0.2,0,, -uzk,Upper zone recession coefficient,per day ,double,True,csv,0.2,0.5,0.3,,sac_sma_params -lzpk,"Lower zone recession coefficient, primary",decimal percent,double,True,csv,0.001,0.015,0.01,,sac_sma_params -lzsk,"Lower zone recession coefficient, secondary (aka supplemental)",decimal percent,double,True,csv,0.03,0.2,0.1,,sac_sma_params -zperc,Minimum percolation rate coefficient,NULL,double,True,csv,20,300,10,,sac_sma_params -rexp,Percolation equation exponent,NULL,double,True,csv,1.4,3.5,2,,sac_sma_params +uzk,Upper zone recession coefficient,per day ,double,True,iceberg,0.2,0.5,0.3,,sac_sma_params +lzpk,"Lower zone recession coefficient, primary",decimal percent,double,True,iceberg,0.001,0.015,0.01,,sac_sma_params +lzsk,"Lower zone recession coefficient, secondary (aka supplemental)",decimal percent,double,True,iceberg,0.03,0.2,0.1,,sac_sma_params +zperc,Minimum percolation rate coefficient,NULL,double,True,iceberg,20,300,10,,sac_sma_params +rexp,Percolation equation exponent,NULL,double,True,iceberg,1.4,3.5,2,,sac_sma_params pctim,impervious fraction of the watershed area ,decimal percent,double,True,const,0,0.05,0,, -pfree,fraction of water percolating from upper zone directly to lower zone free water storage. ,decimal percent,double,True,csv,0,0.5,0.1,,sac_sma_params +pfree,fraction of water percolating from upper zone directly to lower zone free water storage. ,decimal percent,double,True,iceberg,0,0.5,0.1,,sac_sma_params riva,Percent of the basin that is riparian area,decimal percent,double,True,const,0,0.2,0,, side,Portion of the baseflow which does not go to the stream,decimal percent,double,False,const,0,0.2,0,, rserv,Percent of lower zone free water not transferable to the lower zone tension water,decimal percent,double,False,const,0.2,0.4,0.3,, diff --git a/src/icefabric_api/data/snow17_params.csv b/src/icefabric_api/data/snow17_params.csv index dd07923..671cbe1 100644 --- a/src/icefabric_api/data/snow17_params.csv +++ b/src/icefabric_api/data/snow17_params.csv @@ -1,9 +1,9 @@ name,description,units,data_type,calibratable,source,min,max,default_value,divide_attr_name,source_file -mfmax, Maximum melt factor during non-rain periods – assumed to occur on June 21,mm/˚C/hr,double,True,csv,0.1,2.2,1,,sac_sma_params +mfmax, Maximum melt factor during non-rain periods – assumed to occur on June 21,mm/˚C/hr,double,True,iceberg,0.1,2.2,1,,sac_sma_params hru_area, needed for combination and routing conv, sq-km,double,False,attr,,,,areasqkm, latitude, centroid latitude of hru, decimal degrees,double,False,attr,,,,centroid_y, elev, mean elevation of hru, m,double,False,attr,,,,mean.elevation, -uadj, The average wind function during rain-on-snow periods,mm/mb/6 hr,double,True,csv,0.01,0.2,0.05,, +uadj, The average wind function during rain-on-snow periods,mm/mb/6 hr,double,True,iceberg,0.01,0.2,0.05,, si, The mean areal water equivalent above which there is always 100 percent areal snow cover,mm,double,True,const,0,10000,500,, adc1, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,const,0.05,0.05,0.05,, adc2, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,const,0.1,0.1,0.1,, @@ -13,15 +13,15 @@ adc5, Curve that defines the areal extent of the snow cover as a function of how adc6, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,const,0.5,0.5,0.5,, adc7, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,const,0.6,0.6,0.6,, adc8, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,const,0.7,0.7,0.7,, -mfmin, Minimum melt factor during non-rain periods – assumed to occur on December 21,mm/˚C/hr,double,True,csv,0.01,0.6,0.2,, +mfmin, Minimum melt factor during non-rain periods – assumed to occur on December 21,mm/˚C/hr,double,True,iceberg,0.01,0.6,0.2,, hru_id, local hru ids for multiple hrus,,string,False,const,,,,, -scf,The multiplying factor which adjusts precipitation that is determined to be in the form of snow,,double,True,csv,0.9,1.8,1.1,, -adc9, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,csv,0.8,0.8,0.8,, -adc10, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,csv,0.9,0.9,0.9,, -adc11, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,csv,1,1,1,, -nmf, Maximum negative melt factor,mm/˚C/hr,double,True,csv,0.01,0.3,0.15,, -tipm, Antecedent temperature index parameter,,double,True,csv,0,1,0.1,, -pxtemp, Antecedent temperature index parameter,˚C,double,True,csv,0.5,5,1,, -mbase, Base temperature for snowmelt computations during non-rain periods,˚C,double,False,csv,0,0,0,, -plwhc, maximum amount of liquid water as a fraction of the ice portion of the snow that can be held against gravity drainage, %,double,True,csv,0.01,0.3,0.03,, -daygm, Constant daily amount of melt which takes place at the snow-soil interface whenever there is a snow cover,mm/day,double,True,csv,0,0.5,0,, +scf,The multiplying factor which adjusts precipitation that is determined to be in the form of snow,,double,True,const,0.9,1.8,1.1,, +adc9, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,const,0.8,0.8,0.8,, +adc10, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,const,0.9,0.9,0.9,, +adc11, Curve that defines the areal extent of the snow cover as a function of how much of the original snow cover remains after significant bare ground shows up,NULL,double,False,const,1,1,1,, +nmf, Maximum negative melt factor,mm/˚C/hr,double,True,const,0.01,0.3,0.15,, +tipm, Antecedent temperature index parameter,,double,True,const,0,1,0.1,, +pxtemp, Antecedent temperature index parameter,˚C,double,True,const,0.5,5,1,, +mbase, Base temperature for snowmelt computations during non-rain periods,˚C,double,False,const,0,0,0,, +plwhc, maximum amount of liquid water as a fraction of the ice portion of the snow that can be held against gravity drainage, %,double,True,const,0.01,0.3,0.03,, +daygm, Constant daily amount of melt which takes place at the snow-soil interface whenever there is a snow cover,mm/day,double,True,const,0,0.5,0,, diff --git a/src/icefabric_api/data/ueb_params.csv b/src/icefabric_api/data/ueb_params.csv index abe86fb..05d0ab2 100644 --- a/src/icefabric_api/data/ueb_params.csv +++ b/src/icefabric_api/data/ueb_params.csv @@ -4,7 +4,7 @@ WSis,Snow water equivalent initial condition,m,double,False,const,,,0,, Tic,Snow surface dimensionless age initial condition,NULL,double,False,const,,,0,, WCic,Snow water equivalent dimensionless age initial condition,m,double,False,const,,,0,, df,Drift factor multiplier,NULL,double,True,const,0.5,6,1,, -apr,Average atmospheric pressure,Pa,double,True,csv,30000,101325,,atm_pres, +apr,Average atmospheric pressure,Pa,double,True,iceberg,30000,101325,,atm_pres, Aep,Albedo extinction coefficient,m,double,False,const,NULL,NULL,0.1,, cc,Canopy coverage fraction,NULL,double,True,const,0,0.8,0.4,, hcan,Canopy height,m,integer,True,const,0,10,5,, @@ -17,18 +17,18 @@ latitude,A 2-D grid that contains the latitude at each grid point,degrees,double subalb,The fraction of shortwave radiation (fraction 0-1) reflected by the substrate beneath the snow (ground or glacier),NULL,double,True,const,0.25,0.7,0.25,, subtype,Type of beneath snow substrate ,NULL,integer,False,const,0,3,0,, gsurf,The fraction of surface melt that runs off (e.g. from a glacier),NULL,double,False,const,,,0,, -b01,Monthly mean of daily temperature range for January used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b02,Monthly mean of daily temperature range for February used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b03,Monthly mean of daily temperature range for March used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b04,Monthly mean of daily temperature range for April used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b05,Monthly mean of daily temperature range for May used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b06,Monthly mean of daily temperature range for June used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b07,Monthly mean of daily temperature range for July used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b08,Monthly mean of daily temperature range for August used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b09,Monthly mean of daily temperature range for September used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b10,Monthly mean of daily temperature range for October used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b11,Monthly mean of daily temperature range for November used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat -b12,Monthly mean of daily temperature range for December used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,csv,,,,,ueb_deltat +b01,Monthly mean of daily temperature range for January used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b02,Monthly mean of daily temperature range for February used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b03,Monthly mean of daily temperature range for March used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b04,Monthly mean of daily temperature range for April used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b05,Monthly mean of daily temperature range for May used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b06,Monthly mean of daily temperature range for June used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b07,Monthly mean of daily temperature range for July used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b08,Monthly mean of daily temperature range for August used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b09,Monthly mean of daily temperature range for September used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b10,Monthly mean of daily temperature range for October used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b11,Monthly mean of daily temperature range for November used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat +b12,Monthly mean of daily temperature range for December used in Bristow Campbell formulas for atmospheric transmissivity,C,double,False,iceberg,,,,,ueb_deltat ts_last,Snow surface temperature one day prior to the model starting time,C,double,False,const,,,-9999,,ueb_deltat longitude,A 2-D grid that contains the longitude at each grid,degrees,double,False,attr,,,,centroid_x, ems,Emissivity of snow,NULL,double,True,const,0.98,0.99,0.99,, diff --git a/src/icefabric_api/src/icefabric_api/create_ipes.py b/src/icefabric_api/src/icefabric_api/create_ipes.py index 21e3596..a2866a5 100644 --- a/src/icefabric_api/src/icefabric_api/create_ipes.py +++ b/src/icefabric_api/src/icefabric_api/create_ipes.py @@ -41,7 +41,7 @@ def module_ipe(module, attr, domain, version, cfg_write=None): } # Get output vars out_datatypes = {"variable": "object", "description": "object"} - + module_params = pd.read_csv(f"{ROOT_DIR}/data/{param_file}", dtype=datatypes) output_vars = pd.DataFrame() if has_output_vars: @@ -70,20 +70,24 @@ def module_ipe(module, attr, domain, version, cfg_write=None): attr = attr[attr_list] # Rename attribute names to module names attr.rename(columns=attr_name_map, inplace=True) + + # Get iceberg table parameters + if "iceberg" in module_params["source"].values: + iceberg_params = divide_parameters(divides, module, domain) - # Get CSV file parameters - if "csv" in module_params["source"].values: - csv_params = divide_parameters(divides, module, domain) - + # Loop through divides and update default values with divide attributes from geopackage + # and iceberg tables for divide in divides: attr_div = attr.loc[attr["divide_id"] == divide] attr_div = attr_div.to_dict(orient="list") all_cats[divide].update(attr_div) - if not csv_params.empty: - csv_params_div = csv_params.loc[csv_params["divide_id"] == divide] - csv_params_div = csv_params_div.to_dict(orient="list") - all_cats[divide].update(csv_params_div) - + if not iceberg_params.empty: + iceberg_params_div = iceberg_params.loc[iceberg_params["divide_id"] == divide] + iceberg_params_div = iceberg_params_div.to_dict(orient="list") + all_cats[divide].update(iceberg_params_div) + + # When dataframe is converted to a dictionary, the values are contained in a list + # Remove the value from the list. for key in all_cats[divide].keys(): value = all_cats[divide][key] if isinstance(value, list): From ae437493f732f98d6fe96665c6e7893db5c0f8f0 Mon Sep 17 00:00:00 2001 From: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> Date: Fri, 27 Jun 2025 09:58:20 -0400 Subject: [PATCH 67/97] NGWPC-6591: Complete the topobathy viewer UI workflow --- .gitignore | 6 + docker/notebook/docker-compose.yml | 19 -- examples/icechunk_data_viewer/README.md | 70 +++++ .../martin/compose.martin.yaml | 10 + .../martin/tiles/.gitkeep | 0 .../martin/tiles/martin_config.yaml | 8 + .../icechunk_data_viewer/requirements.txt | 3 + examples/icechunk_data_viewer/viewer.ipynb | 155 +++++++++++ pyproject.toml | 2 +- src/icefabric_tools/pyproject.toml | 2 + .../icechunk/topobathy_ic_to_tif.py | 33 +++ uv.lock | 256 +++++++++++++++++- 12 files changed, 537 insertions(+), 27 deletions(-) delete mode 100644 docker/notebook/docker-compose.yml create mode 100644 examples/icechunk_data_viewer/README.md create mode 100644 examples/icechunk_data_viewer/martin/compose.martin.yaml create mode 100644 examples/icechunk_data_viewer/martin/tiles/.gitkeep create mode 100644 examples/icechunk_data_viewer/martin/tiles/martin_config.yaml create mode 100644 examples/icechunk_data_viewer/requirements.txt create mode 100644 examples/icechunk_data_viewer/viewer.ipynb create mode 100644 src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py diff --git a/.gitignore b/.gitignore index 45cdff4..e714cfe 100644 --- a/.gitignore +++ b/.gitignore @@ -202,3 +202,9 @@ crash.log # Terraform plan output (optional unless you want to inspect later) *.tfplan + +# tiles +*.pmtiles +examples/icechunk_data_viewer/martin/tiles/quantiles/* +examples/icechunk_data_viewer/martin/tiles/legends/* +examples/icechunk_data_viewer/martin/tiles/legend.png \ No newline at end of file diff --git a/docker/notebook/docker-compose.yml b/docker/notebook/docker-compose.yml deleted file mode 100644 index f09cefc..0000000 --- a/docker/notebook/docker-compose.yml +++ /dev/null @@ -1,19 +0,0 @@ -# NOTE: Unsure the purpose of this compose at this time - personal directories should be removed -version: "3.8" - -services: - jupyterlab: - image: jupyter/base-notebook:latest - ports: - - "8888:8888" - volumes: - - ~/efs/jupyterlab-compose:/home/jovyan/work - environment: - JUPYTER_ENABLE_LAB: "yes" - NB_UID: "1000" - NB_GID : "100" - AWS_ACCESS_KEY_ID: "${AWS_ACCESS_KEY_ID}" - AWS_SECRET_ACCESS_KEY: "${AWS_SECRET_ACCESS_KEY}" - AWS_SESSION_TOKEN: "${AWS_SESSION_TOKEN}" - command: > - bash -c "pip install boto3 && chown -R 1000:100 /home/jovyan/work && start-notebook.sh --NotebookApp.token=''" diff --git a/examples/icechunk_data_viewer/README.md b/examples/icechunk_data_viewer/README.md new file mode 100644 index 0000000..09380ab --- /dev/null +++ b/examples/icechunk_data_viewer/README.md @@ -0,0 +1,70 @@ +# Topobathy Data Viewer +The purpose of this data viewer is to visualize version-controlled tif files hosted by the NGWPC program. The data viewer can be run either on pre-processed tiles, or by creating said tiles on an end-to-end full workflow. Details on how to run each are below +## If tiles are pre-created and stored on s3: +1. Move to icechunk_data-viewer folder: + + `cd examples/icechunk_data_viewer` +2. Export AWS data account credentials to your environment and run in CLI: + + `aws s3 sync s3://hydrofabric-data/surface/nws-topobathy/tiles ./martin/tiles` + +3. Start martin tile server: + + `cd martin` + + `docker compose -f compose.martin.yaml up` + +4. Tile server is now running. Confirm by checking `localhost:3000/catalog`. You should see a list of tile sources. Debug logs should be populating in console. +5. Open a new terminal and move to `icechunk_data_viewer` root + + `cd examples/icechunk_data_viewer` +6. Start jupyter lab in activated icefabric virtual environment. This will start the jupyter server at `localhost:8888/lab` + + `jupyter lab` + +7. Open `viewer.ipynb` in Jupyter Lab +8. Execute cells in `viewer.ipynb`. The map should show the tiles served from Martin. + + +## Full Pipeline - from creating tiles to viewing +1. __In icefabric repo__: Export topobathy from icechunk to TIFF using `icefabric_tools/icechunk/topobathy_ic_to_tif.py`. These will be stored locally. + + __NOTE__: Some files may require more memory than average desktop. If 'killed', move to a cluster with more memory. + +2. Clone hydrofabric-ui-tools +3. __In hydrofabric-ui-tools repo__: Copy saved icechunk TIFs to `data` folder in `hydrofabric-ui-tools` +5. Create or modify `config` files to match TIF +6. Run `build_topobathy_tiles.py` using docker or local environment as described in `README.md`. Some regions may require more memory than average desktop. +7. Tiles will be uploaded to s3 if specified in config. +8. __Return to icefabric repo__ +9. Sync from s3 or paste `.pmtiles` files into + + `icefabric/examples/icechunk_data_viewer/martin/tiles` + +AWS option with data account credentials in env vars. + + cd examples/icechunk_data_viewer + aws s3 sync s3://hydrofabric-data/surface/nws-topobathy/tiles ./martin/tiles + +10. Open `martin_config.yaml` + + `icefabric/examples/icechunk_data_viewer/martin/martin_config.yaml` + +11. Match tile names in `tiles` folders to source name if not correct. Source name will be the URI for tile serving. + +12. Start martin tile server. This must be done in the `martin` working directory to copy the files correctly. + ``` + cd examples/icechunk_data/viewer/martin + docker compose -f compose.martin.yaml up + ``` + +13. Tile server is now running. Confirm by checking `localhost:3000/catalog`. You should see a list of tile sources. +14. Open a new terminal and move to `icechunk_data_viewer` root + + `cd examples/icechunk_data_viewer` +15. Start jupyter lab in activated icefabric virtual environment. This will start the jupyter server at `localhost:8888/lab` + + `jupyter lab` + +16. Open `viewer.ipynb` in Jupyter Lab +17. Execute cells in `viewer.ipynb`. The map should show the tiles served from Martin. diff --git a/examples/icechunk_data_viewer/martin/compose.martin.yaml b/examples/icechunk_data_viewer/martin/compose.martin.yaml new file mode 100644 index 0000000..1916d37 --- /dev/null +++ b/examples/icechunk_data_viewer/martin/compose.martin.yaml @@ -0,0 +1,10 @@ +services: + martin: + image: ghcr.io/maplibre/martin:v0.17.0 + ports: + - 3000:3000 + environment: + - RUST_LOG=debug + volumes: + - "${PWD}/tiles:/tiles" + command: -c /tiles/martin_config.yaml diff --git a/examples/icechunk_data_viewer/martin/tiles/.gitkeep b/examples/icechunk_data_viewer/martin/tiles/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/examples/icechunk_data_viewer/martin/tiles/martin_config.yaml b/examples/icechunk_data_viewer/martin/tiles/martin_config.yaml new file mode 100644 index 0000000..e2b11ee --- /dev/null +++ b/examples/icechunk_data_viewer/martin/tiles/martin_config.yaml @@ -0,0 +1,8 @@ +pmtiles: + sources: + alaska: /tiles/alaska.pmtiles + atlantic: /tiles/conus_atlantic_gulf.pmtiles + pacific: /tiles/conus_pacific.pmtiles + great_lakes: /tiles/great_lakes.pmtiles + hawaii: /tiles/hawaii.pmtiles + pr_usvi: /tiles/pr_usvi.pmtiles diff --git a/examples/icechunk_data_viewer/requirements.txt b/examples/icechunk_data_viewer/requirements.txt new file mode 100644 index 0000000..9115ce8 --- /dev/null +++ b/examples/icechunk_data_viewer/requirements.txt @@ -0,0 +1,3 @@ +jupyterlab==4.4.3 +jupytergis==0.5.0 +matplotlib==3.10.3 \ No newline at end of file diff --git a/examples/icechunk_data_viewer/viewer.ipynb b/examples/icechunk_data_viewer/viewer.ipynb new file mode 100644 index 0000000..02812c5 --- /dev/null +++ b/examples/icechunk_data_viewer/viewer.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f3c3eb3", + "metadata": {}, + "source": [ + "# Data Viewer: Topobathy\n", + "\n", + "## NOTE: This must viewed in Jupyter Lab\n", + "\n", + "A JupyterGIS project to interactively view topobathy and other spatial files. \n", + "\n", + "Maps are served as pmtiles to allow high performance loading and seamless transition. Maps are classified in quantiles - each color represents 10% of the dataset in its region. Legend can be viewed within notebook.\n", + "\n", + "The layer list can be viewed by clicking the bottom tab on left with \"globe\" symbol after layers are loaded.\n", + "\n", + "Opacity can be adjusted on the layers by clicking the right tab \"globe\" > layer properties > opacity > okay\n", + "\n", + "Requirements:\n", + "- Pre-computed map tiles\n", + "- Running local tile server \"Martin\" on port 3000" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a3120a17", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.image as mpimg\n", + "import matplotlib.pyplot as plt\n", + "from jupytergis import GISDocument" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7439900c", + "metadata": {}, + "outputs": [], + "source": [ + "# create the map doc\n", + "doc = GISDocument()\n", + "\n", + "# open the map\n", + "doc.sidecar()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a05cbcdc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'6e20a7fa-e8ce-40b9-92cb-f2e13f4ff566'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add Open Street Map basemap\n", + "doc.add_raster_layer(\"https://tile.openstreetmap.org/{z}/{x}/{y}.png\", name=\"Basemap\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "09b6ae09", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ae71a180-bc06-4582-91bb-c0265f59fbae'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add topobathy tile layers\n", + "doc.add_raster_layer(\"http://localhost:3000/pr_usvi/{z}/{x}/{y}\", name=\"Puerto Rico/VI\")\n", + "doc.add_raster_layer(\"http://localhost:3000/alaska/{z}/{x}/{y}\", name=\"Alaska\")\n", + "doc.add_raster_layer(\"http://localhost:3000/atlantic/{z}/{x}/{y}\", name=\"Atlantic/Gulf\")\n", + "doc.add_raster_layer(\"http://localhost:3000/hawaii/{z}/{x}/{y}\", name=\"Hawaii\")\n", + "doc.add_raster_layer(\"http://localhost:3000/pacific/{z}/{x}/{y}\", name=\"Pacific\")\n", + "doc.add_raster_layer(\"http://localhost:3000/great_lakes/{z}/{x}/{y}\", name=\"Great Lakes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dd6e4c55", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# show the legend\n", + "image = mpimg.imread(\"./martin/tiles/legend.png\")\n", + "fig, ax = plt.subplots(figsize=(25, 8))\n", + "ax.imshow(image)\n", + "ax.set_axis_off()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e67f5f73-6c5d-44ba-b8c9-6376307445f6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 8f524f3..844bda6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,7 +37,7 @@ members = ["src/icefabric_api", "src/icefabric_manage", "src/icefabric_tools"] dev-dependencies = [ "pre-commit>=3.8.0", "ipykernel>=6.29.5", - "jupyterlab==4.4.2", + "jupyterlab==4.4.3", "pytest>=8.3.2", "ruff==0.11.0", "black==25.1.0", diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml index 9aa31f0..24cbf59 100644 --- a/src/icefabric_tools/pyproject.toml +++ b/src/icefabric_tools/pyproject.toml @@ -45,6 +45,8 @@ dependencies = [ "httpx==0.28.1", "ipywidgets==8.1.6", "mapclassify==2.9.0", + "jupyterlab==4.4.3", + "jupytergis==0.5.0", ] [build-system] diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py b/src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py new file mode 100644 index 0000000..e799a23 --- /dev/null +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py @@ -0,0 +1,33 @@ +import os +from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations + +from dotenv import load_dotenv + +load_dotenv() + + +def convert_topobathy_to_tiff(output_dir: str, ic_rasters: list[NGWPCLocations]) -> None: + """Converts topobathy layers from icechunk to tiff for use in tiles + + Parameters + ---------- + output_dir : str + Directory to save outputs to + ic_rasters : list[NGWPCLocations] + list of NGWPCLocation raster paths. eg. [NGWPCLocations[TOPO_AK_30M_IC].path] + """ + for ic_raster in ic_rasters: + repo = IcechunkS3Repo(location=NGWPCLocations[ic_raster].path) + output = os.path.join(output_dir, f"{str.split(str(NGWPCLocations[ic_raster].path), '/')[-1]}.tif") + + repo.retrieve_and_convert_to_tif(dest=output, var_name='elevation') + + +if __name__ == '__main__': + output_dir = "./temp_tb_data" + os.makedirs(output_dir, exist_ok=True) + + # all 30 m topobathy layers + ic_rasters = [f for f in NGWPCLocations._member_names_ if "TOPO" and "30M" in f] + + convert_topobathy_to_tiff(output_dir, ic_rasters) diff --git a/uv.lock b/uv.lock index 62d28ce..9d1b77d 100644 --- a/uv.lock +++ b/uv.lock @@ -514,14 +514,14 @@ wheels = [ [[package]] name = "comm" -version = "0.2.2" +version = "0.1.4" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "traitlets" }, ] -sdist = { url = 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recommended to include uv.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. -#uv.lock +uv.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. @@ -207,4 +207,4 @@ crash.log *.pmtiles examples/icechunk_data_viewer/martin/tiles/quantiles/* examples/icechunk_data_viewer/martin/tiles/legends/* -examples/icechunk_data_viewer/martin/tiles/legend.png \ No newline at end of file +examples/icechunk_data_viewer/martin/tiles/legend.png diff --git a/examples/icechunk_data_viewer/README.md b/examples/icechunk_data_viewer/README.md index 09380ab..8f0618c 100644 --- a/examples/icechunk_data_viewer/README.md +++ b/examples/icechunk_data_viewer/README.md @@ -54,7 +54,7 @@ AWS option with data account credentials in env vars. 12. Start martin tile server. This must be done in the `martin` working directory to copy the files correctly. ``` - cd examples/icechunk_data/viewer/martin + cd examples/icechunk_data_viewer/martin docker compose -f compose.martin.yaml up ``` diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py b/src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py index e799a23..5d14e0f 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py +++ b/src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py @@ -1,7 +1,9 @@ import os -from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations from dotenv import load_dotenv +from tqdm import tqdm + +from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations load_dotenv() @@ -16,14 +18,14 @@ def convert_topobathy_to_tiff(output_dir: str, ic_rasters: list[NGWPCLocations]) ic_rasters : list[NGWPCLocations] list of NGWPCLocation raster paths. eg. 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From 4d86adf0d3a48a40cfb9f0d618f6667c8956a1b6 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Wed, 2 Jul 2025 12:10:52 -0400 Subject: [PATCH 70/97] initial commit: added jira story issue template and PR template (#42) --- .../ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE.md | 31 +++++++++++++++ .github/ISSUE_TEMPLATE/capability.md | 38 +++++++++++++++++++ 2 files changed, 69 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE.md create mode 100644 .github/ISSUE_TEMPLATE/capability.md diff --git a/.github/ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE.md b/.github/ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..d73c01d --- /dev/null +++ b/.github/ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,31 @@ +## Issue Addressed + + + +Fixes # (issue number) + +## Description + + + +## Type of Change + +- [ ] Bug fix (non-breaking change which fixes an issue) +- [ ] New feature (non-breaking change which adds functionality) +- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected) +- [ ] Code cleanup/refactor +- [ ] Documentation update + +Other (please specify): + +## Checklist + +- [ ] Branch is up to date with master +- [ ] Updated tests or added new tests +- [ ] Tests & pre-commit hooks pass +- [ ] Updated documentation (if applicable) +- [ ] Code follows established style and conventions diff --git a/.github/ISSUE_TEMPLATE/capability.md b/.github/ISSUE_TEMPLATE/capability.md new file mode 100644 index 0000000..41378f6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/capability.md @@ -0,0 +1,38 @@ +--- +name: JIRA Story/Capability +about: The structure for outlining work being done on a JIRA story +labels: JIRA Story +--- + +# Capability + + + +## Task + + + +## Plan/Outline + + + + +### TODOS + + +- [ ] + +### Additional components / Context + + From 54a733f1f79517b80b4243dc301b5bb9919ceb6d Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Wed, 2 Jul 2025 12:12:47 -0400 Subject: [PATCH 71/97] chore: update PULL_REQUEST_TEMPLATE.md path --- .github/{ISSUE_TEMPLATE => }/PULL_REQUEST_TEMPLATE.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .github/{ISSUE_TEMPLATE => }/PULL_REQUEST_TEMPLATE.md (100%) diff --git a/.github/ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md similarity index 100% rename from .github/ISSUE_TEMPLATE/PULL_REQUEST_TEMPLATE.md rename to .github/PULL_REQUEST_TEMPLATE.md From 9f39384aca58c8c07c2d4804d4068eb4a496ba78 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Mon, 7 Jul 2025 17:31:30 -0400 Subject: [PATCH 72/97] NGWPC 6903: initial commit: refactored the repo based on #41 (#43) * initial commit: refactored the repo based on #41 * patch: updated pyiceberg security settings * updated load_creds() to replace doad_dotenv() * docs: updating api/manage/tools docs in the mkdocs user guide * patch: updating martin compose to look in local dir rather than PWD * tests: added local tests for icechunk topobathy --- .gitignore | 4 +- LICENSE | 757 +- README.md | 13 +- {src/icefabric_api/app => app}/__init__.py | 0 {src/icefabric_api/app => app}/main.py | 13 +- .../app => app}/routers/__init__.py | 0 .../routers/hydrofabric/__init__.py | 0 .../app => app}/routers/hydrofabric/router.py | 3 +- .../routers/module_params/__init__.py | 0 .../routers/module_params/router.py | 2 +- .../streamflow_observations/__init__.py | 0 .../routers/streamflow_observations/router.py | 10 +- conftest.py | 124 - data/LICENSE | 540 ++ .../data => data/module_ipes}/cfe_params.csv | 0 .../data => data/module_ipes}/lasam_out.csv | 0 .../module_ipes}/lasam_params.csv | 0 .../data => data/module_ipes}/modules.csv | 0 .../module_ipes}/noah_owp_modular_params.csv | 0 .../module_ipes}/sac_sma_params.csv | 0 .../data => data/module_ipes}/sft_params.csv | 0 .../data => data/module_ipes}/smp_params.csv | 0 .../module_ipes}/snow17_params.csv | 0 .../module_ipes}/topmodel_params.csv | 0 .../data => data/module_ipes}/ueb_params.csv | 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.../test_streamflow_observations_router.py | 0 tests/conftest.py | 159 + tests/data/gages-06710385.gpkg | Bin 0 -> 319488 bytes ...ta_from_20211231_1400_to_20220101_1400.csv | 26 + ...rom_20211231_1400_to_20220101_1400.parquet | Bin 0 -> 3415 bytes tests/integration/test_topobathy_icechunk.py | 52 +- .../tests => tests}/test_subset.py | 3 +- .../tests => tests}/test_utils.py | 3 +- tools/hydrofabric/hfsubset.py | 32 + tools/icechunk/convert_to_tif.py | 18 + tools/icechunk/icechunk_s3_migration.py | 72 + .../icechunk/icechunk_testing.py | 42 +- tools/ipes/get_module_parameters.py | 52 + tools/pyiceberg/gpkg_to_parquet.py | 36 + .../pyiceberg/remote_builds}/README.md | 0 .../remote_builds}/build_fema_ble.py | 4 +- .../remote_builds}/build_fema_mip.py | 4 +- .../remote_builds}/build_hydrofabric.py | 4 +- .../build_iceberg_bathymetry_ml_auxiliary.py | 4 +- .../remote_builds}/build_parameters.py | 23 +- .../build_usgs_streamflow_observations.py | 4 +- 118 files changed, 2008 insertions(+), 10868 deletions(-) rename {src/icefabric_api/app => app}/__init__.py (100%) rename {src/icefabric_api/app => app}/main.py (79%) rename {src/icefabric_api/app => app}/routers/__init__.py (100%) rename {src/icefabric_api/app => app}/routers/hydrofabric/__init__.py (100%) rename {src/icefabric_api/app => app}/routers/hydrofabric/router.py (97%) rename {src/icefabric_api/app => app}/routers/module_params/__init__.py (100%) rename {src/icefabric_api/app => app}/routers/module_params/router.py (94%) rename {src/icefabric_api/app => app}/routers/streamflow_observations/__init__.py (100%) rename {src/icefabric_api/app => app}/routers/streamflow_observations/router.py (97%) delete mode 100644 conftest.py create mode 100644 data/LICENSE rename {src/icefabric_api/data => data/module_ipes}/cfe_params.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/lasam_out.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/lasam_params.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/modules.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/noah_owp_modular_params.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/sac_sma_params.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/sft_params.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/smp_params.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/snow17_params.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/topmodel_params.csv (100%) rename {src/icefabric_api/data => data/module_ipes}/ueb_params.csv (100%) delete mode 100644 docs/user_guide/icefabric_manage.md delete mode 100644 examples/Demo_Verification_TIF_to_xrDataset.ipynb delete mode 100644 examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb delete mode 100644 examples/geopackage_viewer/requirements.txt rename examples/geopackage_viewer/{geopackage.ipynb => viewer.ipynb} (98%) delete mode 100644 examples/iceberg/Demo_Iceberg.ipynb rename examples/iceberg/{pyiceberg_timetravel.ipynb => pyiceberg_time_travel.ipynb} (95%) rename examples/{ras_xs/querying.ipynb => iceberg/querying_ras_xs.ipynb} (86%) delete mode 100644 examples/iceberg/quick_start.ipynb delete mode 100644 examples/icechunk/README.icechunk.md delete mode 100644 examples/icechunk_data_viewer/requirements.txt create mode 100644 examples/quick_start.ipynb delete mode 100644 scripts/get_module_parameters.py delete mode 100644 scripts/gpkg_to_parquet.py delete mode 100644 scripts/gpkg_to_parquet_all_domains.py create mode 100644 src/icefabric/__init__.py create mode 100644 src/icefabric/builds/__init__.py rename src/{icefabric_manage/src/icefabric_manage/_api.py => icefabric/builds/build.py} (92%) rename src/{icefabric_manage/src/icefabric_manage => icefabric/builds}/iceberg_table.py (100%) rename src/{icefabric_tools/src/icefabric_tools/icechunk => icefabric/builds}/icechunk_s3_module.py (79%) rename src/{icefabric_manage/src/icefabric_manage => icefabric/builds}/map_fim_data.py (100%) create mode 100644 src/icefabric/helpers/__init__.py rename src/{icefabric_tools/src/icefabric_tools/icechunk => icefabric/helpers}/arch_weather_file_utils.py (93%) create mode 100644 src/icefabric/helpers/creds.py create mode 100644 src/icefabric/helpers/geopackage.py rename src/{icefabric_tools/src/icefabric_tools/icechunk => icefabric/helpers}/nc_conv_utils.py (97%) rename src/{icefabric_tools/src/icefabric_tools/icechunk => icefabric/helpers}/topobathy_ic_to_tif.py (57%) create mode 100644 src/icefabric/hydrofabric/__init__.py rename src/{icefabric_tools/src/icefabric_tools/iceberg/utils.py => icefabric/hydrofabric/origin.py} (51%) rename src/{icefabric_tools/src/icefabric_tools/iceberg/hydrofabric.py => icefabric/hydrofabric/subset.py} (94%) create mode 100644 src/icefabric/modules/__init__.py rename src/{icefabric_api/src/icefabric_api => icefabric/modules}/create_ipes.py (88%) rename src/{icefabric_tools/src/icefabric_tools/iceberg => icefabric/modules}/rnr.py (98%) create mode 100644 src/icefabric/schemas/__init__.py create mode 100644 src/icefabric/schemas/hydrofabric.py create mode 100644 src/icefabric/schemas/topobathy.py create mode 100644 src/icefabric/ui/__init__.py rename src/{icefabric_tools/src/icefabric_tools/geo => icefabric/ui}/geo_utils.py (60%) delete mode 100644 src/icefabric_api/README.md delete mode 100644 src/icefabric_api/pyproject.toml delete mode 100644 src/icefabric_api/src/icefabric_api/__init__.py delete mode 100644 src/icefabric_manage/README.md delete mode 100644 src/icefabric_manage/pyproject.toml delete mode 100644 src/icefabric_manage/src/icefabric_manage/__init__.py delete mode 100644 src/icefabric_tools/README.md delete mode 100644 src/icefabric_tools/pyproject.toml delete mode 100644 src/icefabric_tools/src/icefabric_tools/__init__.py delete mode 100644 src/icefabric_tools/src/icefabric_tools/geo/__init__.py delete mode 100644 src/icefabric_tools/src/icefabric_tools/iceberg/__init__.py delete mode 100644 src/icefabric_tools/src/icefabric_tools/icechunk/__init__.py delete mode 100644 src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_migration.py rename {src/icefabric_manage/terraform => terraform}/README.md (100%) rename {src/icefabric_manage/terraform => terraform}/environments/test/.terraform.lock.hcl (100%) rename {src/icefabric_manage/terraform => terraform}/environments/test/backend.tf (100%) rename {src/icefabric_manage/terraform => terraform}/environments/test/main.tf (100%) rename {src/icefabric_manage/terraform => terraform}/environments/test/terraform.tfvars (96%) rename {src/icefabric_manage/terraform => terraform}/environments/test/variables.tf (99%) rename {src/icefabric_manage/terraform => terraform}/iceberg_demo.py (100%) rename {src/icefabric_manage/terraform => terraform}/modules/iceberg/main.tf (99%) rename {src/icefabric_manage/terraform => terraform}/modules/iceberg/variables.tf (99%) delete mode 100644 tests/README.md rename {src/icefabric_manage/builds => tests}/__init__.py (100%) rename {src/icefabric_api/tests => tests/app}/test_hydrofabric_router.py (100%) rename {src/icefabric_api/tests => tests/app}/test_main.py (100%) rename {src/icefabric_api/tests => tests/app}/test_streamflow_observations_router.py (100%) create mode 100644 tests/conftest.py create mode 100644 tests/data/gages-06710385.gpkg create mode 100644 tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv create mode 100644 tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.parquet rename {src/icefabric_tools/tests => tests}/test_subset.py (94%) rename {src/icefabric_tools/tests => tests}/test_utils.py (89%) create mode 100644 tools/hydrofabric/hfsubset.py create mode 100644 tools/icechunk/convert_to_tif.py create mode 100644 tools/icechunk/icechunk_s3_migration.py rename {src/icefabric_tools/src/icefabric_tools => tools}/icechunk/icechunk_testing.py (85%) create mode 100644 tools/ipes/get_module_parameters.py create mode 100644 tools/pyiceberg/gpkg_to_parquet.py rename {src/icefabric_manage/builds => tools/pyiceberg/remote_builds}/README.md (100%) rename {src/icefabric_manage/builds => tools/pyiceberg/remote_builds}/build_fema_ble.py (94%) rename {src/icefabric_manage/builds => tools/pyiceberg/remote_builds}/build_fema_mip.py (94%) rename {src/icefabric_manage/builds => tools/pyiceberg/remote_builds}/build_hydrofabric.py (95%) rename {src/icefabric_manage/builds => tools/pyiceberg/remote_builds}/build_iceberg_bathymetry_ml_auxiliary.py (96%) rename {src/icefabric_manage/builds => tools/pyiceberg/remote_builds}/build_parameters.py (69%) rename {src/icefabric_manage/builds => tools/pyiceberg/remote_builds}/build_usgs_streamflow_observations.py (94%) diff --git a/.gitignore b/.gitignore index 54e6449..c126ca8 100644 --- a/.gitignore +++ b/.gitignore @@ -178,8 +178,7 @@ cython_debug/ Pipfile .qodo -_version.py -data/ +src/icefabric/_version.py *.db *.zarr .zarr @@ -208,3 +207,4 @@ crash.log examples/icechunk_data_viewer/martin/tiles/quantiles/* examples/icechunk_data_viewer/martin/tiles/legends/* examples/icechunk_data_viewer/martin/tiles/legend.png +tests/data/topo_tifs diff --git a/LICENSE b/LICENSE index b03120f..51ef095 100644 --- a/LICENSE +++ b/LICENSE @@ -1,540 +1,217 @@ -## ODC Open Database License (ODbL) - -### Preamble - -The Open Database License (ODbL) is a license agreement intended to -allow users to freely share, modify, and use this Database while -maintaining this same freedom for others. 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This allows all tests in the namespace packages to share the same scope without having to reference one another in tests + +To run tests, run `pytest -s` from project root. + +To run the subsetter tests, run `pytest --run-slow` as these tests take some time. Otherwise, they will be skipped diff --git a/src/icefabric_api/app/__init__.py b/app/__init__.py similarity index 100% rename from src/icefabric_api/app/__init__.py rename to app/__init__.py diff --git a/src/icefabric_api/app/main.py b/app/main.py similarity index 79% rename from src/icefabric_api/app/main.py rename to app/main.py index 771825a..270d4a7 100644 --- a/src/icefabric_api/app/main.py +++ b/app/main.py @@ -18,8 +18,15 @@ redoc_url="/redoc", ) -env_path = Path(__file__).parents[3] / ".env" -load_dotenv(dotenv_path=env_path) +# Setting .env/.pyiceberg creds based on project root +load_dotenv(dotenv_path=Path.cwd() / ".env") +pyiceberg_file = Path.cwd() / ".pyiceberg.yaml" +if pyiceberg_file.exists(): + os.environ["PYICEBERG_HOME"] = str(pyiceberg_file) +else: + raise FileNotFoundError( + "Cannot find .pyiceberg.yaml. Please download this from NGWPC confluence or create " + ) class HealthCheck(BaseModel): @@ -48,6 +55,4 @@ def get_health() -> HealthCheck: if __name__ == "__main__": - os.environ["PYICEBERG_HOME"] = str(Path(__file__).parents[3]) - print(os.environ["PYICEBERG_HOME"]) uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True, log_level="info") diff --git a/src/icefabric_api/app/routers/__init__.py b/app/routers/__init__.py similarity index 100% rename from src/icefabric_api/app/routers/__init__.py rename to app/routers/__init__.py diff --git a/src/icefabric_api/app/routers/hydrofabric/__init__.py b/app/routers/hydrofabric/__init__.py similarity index 100% rename from src/icefabric_api/app/routers/hydrofabric/__init__.py rename to app/routers/hydrofabric/__init__.py diff --git a/src/icefabric_api/app/routers/hydrofabric/router.py b/app/routers/hydrofabric/router.py similarity index 97% rename from src/icefabric_api/app/routers/hydrofabric/router.py rename to app/routers/hydrofabric/router.py index e284ce9..6c23345 100644 --- a/src/icefabric_api/app/routers/hydrofabric/router.py +++ b/app/routers/hydrofabric/router.py @@ -7,7 +7,8 @@ from pyiceberg.catalog import load_catalog from starlette.background import BackgroundTask -from icefabric_tools import IdType, subset +from icefabric.hydrofabric import subset +from icefabric.schemas import IdType api_router = APIRouter(prefix="/hydrofabric") diff --git a/src/icefabric_api/app/routers/module_params/__init__.py b/app/routers/module_params/__init__.py similarity index 100% rename from src/icefabric_api/app/routers/module_params/__init__.py rename to app/routers/module_params/__init__.py diff --git a/src/icefabric_api/app/routers/module_params/router.py b/app/routers/module_params/router.py similarity index 94% rename from src/icefabric_api/app/routers/module_params/router.py rename to app/routers/module_params/router.py index 1f3eb22..ff38f12 100644 --- a/src/icefabric_api/app/routers/module_params/router.py +++ b/app/routers/module_params/router.py @@ -5,7 +5,7 @@ from fastapi import APIRouter, HTTPException from pydantic import BaseModel -from icefabric_api.create_ipes import get_hydrofabric_attributes, module_ipe +from icefabric.modules import get_hydrofabric_attributes, module_ipe api_router = APIRouter(prefix="/modules") ROOT_DIR = os.path.abspath(os.curdir) diff --git a/src/icefabric_api/app/routers/streamflow_observations/__init__.py b/app/routers/streamflow_observations/__init__.py similarity index 100% rename from src/icefabric_api/app/routers/streamflow_observations/__init__.py rename to app/routers/streamflow_observations/__init__.py diff --git a/src/icefabric_api/app/routers/streamflow_observations/router.py b/app/routers/streamflow_observations/router.py similarity index 97% rename from src/icefabric_api/app/routers/streamflow_observations/router.py rename to app/routers/streamflow_observations/router.py index fc0ee79..f869482 100644 --- a/src/icefabric_api/app/routers/streamflow_observations/router.py +++ b/app/routers/streamflow_observations/router.py @@ -2,6 +2,7 @@ from datetime import datetime from enum import Enum +from botocore.exceptions import ClientError from fastapi import APIRouter, HTTPException, Path, Query from fastapi.responses import Response from pyiceberg.catalog import load_catalog @@ -31,8 +32,13 @@ class DataSource(str, Enum): def get_catalog_and_table(data_source: DataSource): """Get catalog and table for a given data source""" config = DATA_SOURCE_CONFIG[data_source] - catalog = load_catalog("glue") - table = catalog.load_table(f"{config['namespace']}.{config['table']}") + try: + catalog = load_catalog("glue") + table = catalog.load_table(f"{config['namespace']}.{config['table']}") + except ClientError as e: + msg = "AWS Test account credentials expired. Can't access remote S3 endpoint" + print(msg) + raise e return catalog, table, config diff --git a/conftest.py b/conftest.py deleted file mode 100644 index 279747d..0000000 --- a/conftest.py +++ /dev/null @@ -1,124 +0,0 @@ -import os -import sys -from pathlib import Path - -# Hard coding a reference to the icefabric_api/ folder to test the API -project_root = Path(__file__).parents[1] -sys.path.insert(0, str(project_root / "src" / "icefabric_api")) - -os.environ["AWS_DEFAULT_REGION"] = "us-east-1" -os.environ["PYICEBERG_HOME"] = str(Path(__file__).parents[1]) - -import pandas as pd # noqa: E402 -import pytest # noqa: E402 -from app.main import app # noqa: E402 -from fastapi.testclient import TestClient # noqa: E402 -from pyiceberg.catalog import Catalog, load_catalog # noqa: E402 - -sample_gauges = [ - "01010000", - "02450825", - "03173000", - "04100500", - "05473450", - "06823500", - "07060710", - "08070000", - "09253000", - "10316500", - "11456000", - "12411000", - "13337000", - "14020000", -] - -sample_hf_uri = [ - "gages-01010000", - "gages-02450825", - "gages-03173000", - "gages-04100500", - "gages-05473450", - "gages-06823500", - "gages-07060710", - "gages-08070000", - "gages-09253000", - "gages-10316500", - "gages-11456000", - "gages-12411000", - "gages-13337000", - "gages-14020000", -] - - -@pytest.fixture(params=sample_gauges) -def gauge_ids(request) -> str: - """Returns individual gauge identifiers for parameterized testing""" - return request.param - - -@pytest.fixture(params=sample_hf_uri) -def gauge_hf_uri(request) -> str: - """Returns individual gauge identifiers for parameterized testing""" - return request.param - - -@pytest.fixture -def testing_dir() -> Path: - """Returns the testing data dir""" - return Path(__file__).parent / "tests/data/" - - -@pytest.fixture(scope="session") -def client(): - """Create a test client for the FastAPI app.""" - return TestClient(app) - - -@pytest.fixture -def local_usgs_streamflow_csv(): - """Returns a locally downloaded CSV file from a specific gauge and time""" - file_path = ( - Path(__file__).parent / "tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv" - ) - return pd.read_csv(file_path) - - -@pytest.fixture -def local_usgs_streamflow_parquet(): - """Returns a locally downloaded CSV file from a specific gauge and time""" - file_path = ( - Path(__file__).parent / "tests/data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.parquet" - ) - return pd.read_parquet(file_path) - - -@pytest.fixture -def hydrofabric_catalog() -> Catalog: - """Returns an iceberg catalog object for the hydrofabric""" - return load_catalog("glue") - - -def pytest_addoption(parser): - """Adds the --run-slow tag""" - parser.addoption( - "--run-slow", - action="store_true", - default=False, - help="Run slow tests", - ) - - -def pytest_collection_modifyitems(config, items): - """Allows pytest to read the --run-slow tag""" - if not config.getoption("--run-slow"): - skipper = pytest.mark.skip(reason="Only run when --run-slow is given") - for item in items: - if "slow" in item.keywords: - item.add_marker(skipper) - - -def pytest_configure(config): - """Configure pytest markers.""" - config.addinivalue_line("markers", "performance: marks tests as performance tests") - config.addinivalue_line("markers", "integration: marks tests as integration tests") - config.addinivalue_line("markers", "unit: marks tests as unit tests") diff --git a/data/LICENSE b/data/LICENSE new file mode 100644 index 0000000..b03120f --- /dev/null +++ b/data/LICENSE @@ -0,0 +1,540 @@ +## ODC Open Database License (ODbL) + +### Preamble + +The Open Database License (ODbL) is a license agreement intended to +allow users to freely share, modify, and use this Database while +maintaining this same freedom for others. 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If the standard suite of rights granted under +applicable copyright law and Database Rights in the relevant +jurisdiction includes additional rights not granted under this License, +these additional rights are granted in this License in order to meet the +terms of this License. diff --git a/src/icefabric_api/data/cfe_params.csv b/data/module_ipes/cfe_params.csv similarity index 100% rename from src/icefabric_api/data/cfe_params.csv rename to data/module_ipes/cfe_params.csv diff --git a/src/icefabric_api/data/lasam_out.csv b/data/module_ipes/lasam_out.csv similarity index 100% rename from src/icefabric_api/data/lasam_out.csv rename to data/module_ipes/lasam_out.csv diff --git a/src/icefabric_api/data/lasam_params.csv b/data/module_ipes/lasam_params.csv similarity index 100% rename from src/icefabric_api/data/lasam_params.csv rename to data/module_ipes/lasam_params.csv diff --git a/src/icefabric_api/data/modules.csv b/data/module_ipes/modules.csv similarity index 100% rename from src/icefabric_api/data/modules.csv rename to data/module_ipes/modules.csv diff --git a/src/icefabric_api/data/noah_owp_modular_params.csv b/data/module_ipes/noah_owp_modular_params.csv similarity index 100% rename from src/icefabric_api/data/noah_owp_modular_params.csv rename to data/module_ipes/noah_owp_modular_params.csv diff --git a/src/icefabric_api/data/sac_sma_params.csv b/data/module_ipes/sac_sma_params.csv similarity index 100% rename from src/icefabric_api/data/sac_sma_params.csv rename to data/module_ipes/sac_sma_params.csv diff --git a/src/icefabric_api/data/sft_params.csv b/data/module_ipes/sft_params.csv similarity index 100% rename from src/icefabric_api/data/sft_params.csv rename to data/module_ipes/sft_params.csv diff --git a/src/icefabric_api/data/smp_params.csv b/data/module_ipes/smp_params.csv similarity index 100% rename from src/icefabric_api/data/smp_params.csv rename to data/module_ipes/smp_params.csv diff --git a/src/icefabric_api/data/snow17_params.csv b/data/module_ipes/snow17_params.csv similarity index 100% rename from src/icefabric_api/data/snow17_params.csv rename to data/module_ipes/snow17_params.csv diff --git a/src/icefabric_api/data/topmodel_params.csv b/data/module_ipes/topmodel_params.csv similarity index 100% rename from src/icefabric_api/data/topmodel_params.csv rename to data/module_ipes/topmodel_params.csv diff --git a/src/icefabric_api/data/ueb_params.csv b/data/module_ipes/ueb_params.csv similarity index 100% rename from src/icefabric_api/data/ueb_params.csv rename to data/module_ipes/ueb_params.csv diff --git a/docs/index.md b/docs/index.md index 6892352..ff196b6 100644 --- a/docs/index.md +++ b/docs/index.md @@ -13,19 +13,16 @@ An [Apache Iceberg](https://py.iceberg.apache.org/)/[Icechunk](https://icechunk. ### Getting Started This repo is managed through [UV](https://docs.astral.sh/uv/getting-started/installation/) and can be installed through: ```sh -uv venv -source .venv/bin/activate uv sync +source .venv/bin/activate ``` ### Running the API locally To run the API locally, ensure your `.env` file in your project root has the right credentials, then run ```sh -uv pip install -e src/icefabric_api -cd src/icefabric_api python -m app.main ``` -This should spin up the API services +This should spin up the API services. ### Building the API through Docker To run the API locally with Docker, ensure your `.env` file in your project root has the right credentials, then run @@ -38,3 +35,19 @@ This should spin up the API services ### Development To ensure that icefabric follows the specified structure, be sure to install the local dev dependencies and run `pre-commit install` + +### Documentation +To build the user guide documentation for Icefabric locally, run the following commands: +```sh +uv sync --extra docs +mkdocs serve -a localhost:8080 +``` +Docs will be spun up at localhost:8080/ + +### Pytests + +The `tests` folder is for all testing data so the global confest can pick it up. This allows all tests in the namespace packages to share the same scope without having to reference one another in tests + +To run tests, run `pytest -s` from project root. + +To run the subsetter tests, run `pytest --run-slow` as these tests take some time. Otherwise, they will be skipped diff --git a/docs/user_guide/icefabric_api.md b/docs/user_guide/icefabric_api.md index df2d212..86a8850 100644 --- a/docs/user_guide/icefabric_api.md +++ b/docs/user_guide/icefabric_api.md @@ -15,8 +15,8 @@ The API consists of several key components: ### Running the API locally To run the API locally, ensure your `.env` file in your project root has the right credentials, then run ```sh -uv pip install -e src/icefabric_api -cd src/icefabric_api +uv sync +source python -m app.main ``` This should spin up the API services @@ -130,7 +130,7 @@ else: uv sync # Start development server -python src/icefabric_api/app/main.py +python -m app.main ``` ### Adding New Data Observation Sources diff --git a/docs/user_guide/icefabric_manage.md b/docs/user_guide/icefabric_manage.md deleted file mode 100644 index 20eaac2..0000000 --- a/docs/user_guide/icefabric_manage.md +++ /dev/null @@ -1,57 +0,0 @@ -# Table Building Guide - -## Overview - -IceFabric Manage is a Python package that simplifies building Apache Iceberg catalogs from Parquet files stored on S3 or locally. - -## Architecture - -The system consists of two main components from within the `icefabric/src/icefabric_manage/` structure: - -1. **Source Code** (`src/icefabric_manage/`) - Contains the table building logic and objects -2. **Build Scripts** (`builds/`) - Orchestrates the catalog creation process from the S3 Tables - -## How It Works - -!!! info "Build Process Overview" - The system converts local Parquet file into queryable Iceberg tables through a simple file-by-file conversion process. - -### The Build Process - -1. **Create Namespace** - Creates the specified namespace if it doesn't exist -2. **Create Tables** - For each specified Parquet file, creates a corresponding Iceberg table -3. **Populate Data** - Appends the Parquet data to the newly created Iceberg tables - -See `builds/README.md` for a template on how to create your own build script - -## Usage - -### Prerequisites - -!!! warning "Important Requirements" - - The `icefabric_manage` package must be installed through `uv sync` - - AWS credentials configured for S3 access (if using S3 files) - - Environment variables loaded via `.env` file - -### Running the Build Scripts - -Build scripts have to be only run once as the files will live/persist in S3. - -To build the hydrofabric dataset, run from the `scripts/` directory: -```bash -python build_hydrofabric.py -``` - -To build USGS streamflow observations: -```bash -python builds/build_hydrofabric.py -``` - -!!! success "Expected Output" - ```text - building layer: divide-attributes - building layer: divides - building layer: flowpath-attributes-ml - ... - Build successful - ``` diff --git a/examples/Demo_Verification_TIF_to_xrDataset.ipynb b/examples/Demo_Verification_TIF_to_xrDataset.ipynb deleted file mode 100644 index fbd1742..0000000 --- a/examples/Demo_Verification_TIF_to_xrDataset.ipynb +++ /dev/null @@ -1,635 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4ccf1cb2-05ff-4a86-ad4f-87a209349c86", - "metadata": {}, - "source": [ - "# Demo: Verification of TIF to xr.Dataset Conversion\n", - "\n", - "_Note:_\n", - "\n", - "1) To verify TIFF conversion to xr.dataset, drop this notebook into \"/src/icefabric_tools/src/icefabric_tools/icechunk\"\n", - "\n", - "2) Just for the purpose of using this notebook, one could replace \"from icefabric_tools.icechunk import S3Path\" to \"from \n", - "icechunk_s3_module import S3Path\" from within the \"/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py\"\n", - "\n", - "3) One can now verify that TIFFs can be converted to xr.Dataset as seen as below.\n" - ] - }, - { - "cell_type": "markdown", - "id": "3eb9f5ee-29a3-44bd-8cc0-47ad146705df", - "metadata": {}, - "source": [ - "# Import Modules" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a51dff72-3f4a-4015-9d2d-fb7590709a94", - "metadata": {}, - "outputs": [], - "source": [ - "from dotenv import load_dotenv\n", - "from icefabric_tools.icechunk.arch_weather_file_utils import load_tiff_file\n", - "from icefabric_tools.icechunk.icechunk_s3_module import IcechunkS3Repo, NGWPCLocations\n", - "load_dotenv()" - ] - }, - { - "cell_type": "markdown", - "id": "02e4a87f-7384-475e-a938-e64e69f54754", - "metadata": {}, - "source": [ - "# Retrieves a TIFF from S3 & Saves to Local" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e54f73ed-a735-4ad7-9470-47c511ff4c40", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved dataset to ./hawaii_topo_30.tif\n" - ] - } - ], - "source": [ - "repo = IcechunkS3Repo(location=NGWPCLocations.TOPO_HA_30M_IC.path)\n", - "ds = repo.retrieve_dataset()\n", - "\n", - "var_name = \"__xarray_dataarray_variable__\"\n", - "fp = \"./hawaii_topo_30.tif\"\n", - "repo.retrieve_and_convert_to_tif(dest=fp,\n", - " var_name=var_name)" - ] - }, - { - "cell_type": "markdown", - "id": "018b944c-3adc-4454-88b7-55b149187607", - "metadata": {}, - "source": [ - "# After Code Modification, the following code is able to convert TIFF to xr.Dataset\n", - "\n", - "- This conversion effectively allows _xarray_data_variable_ to be converted to `elevation`. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "826c5ffa-99cc-49db-b153-c6dc0466e871", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    " - ], - "text/plain": [ - " Size: 1GB\n", - "Dimensions: (band: 1, x: 22226, y: 14819)\n", - "Coordinates:\n", - " * band (band) int64 8B 1\n", - " * x (x) float64 178kB -160.5 -160.5 -160.5 ... -154.5 -154.5 -154.5\n", - " * y (y) float64 119kB 22.5 22.5 22.5 22.5 ... 18.5 18.5 18.5 18.5\n", - " spatial_ref int64 8B 0\n", - "Data variables:\n", - " elevation (band, y, x) float32 1GB ..." - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fp = \"./hawaii_topo_30.tif\"\n", - "attr_name = \"elevation\"\n", - "ds = load_tiff_file(fp,\n", - " attr_name)\n", - "ds" - ] - }, - { - "attachments": { - "098a3552-002d-4ea7-9b15-bfc1264a18c8.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "fc36554a-1ac0-4260-9553-72dd07795f93", - "metadata": {}, - "source": [ - "# Prior to Code Modification, the following was being generated for TIFF\n", - "\n", - "![image.png](attachment:098a3552-002d-4ea7-9b15-bfc1264a18c8.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "23b5e6ec-1887-4595-88a8-ffa5916968d1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "“icefabric_uv”", - "language": "python", - "name": "icefabric_uv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb b/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb deleted file mode 100644 index 18efcf7..0000000 --- a/examples/XS_Mapping_With_IDs_&_Iceberg_Demo.ipynb +++ /dev/null @@ -1,7043 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a24f20d0-abb4-413a-a70b-0fb6d3ca1e22", - "metadata": {}, - "source": [ - "# Introduction:\n", - "#### - Mapping cross-sections with IDs\n", - "#### - Converting the proccessed cross-sections to parquets, geopackages, Iceberg tables by HUC \n", - "#### - Querying for cross-sections By IDs " - ] - }, - { - "cell_type": "markdown", - "id": "4318dbc0-f266-4078-a4e6-59898e1e2c43", - "metadata": {}, - "source": [ - "## Modules" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6788bf1c-12cf-4d48-a880-2e30762b0378", - "metadata": {}, - "outputs": [], - "source": [ - "import collections\n", - "import os\n", - "import pickle\n", - "import warnings\n", - "\n", - "import pandas as pd\n", - "import pyarrow.parquet as pq\n", - "\n", - "from icefabric_manage import MapData\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "id": "a4372bf1-9031-41c8-8567-5cfe3c56a0b1", - "metadata": {}, - "source": [ - "## Demo I:\n", - "### Mapping FIM-C Data to Model ID, HUC #, Reach ID, Network ID, etc" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3bd859e1-1b0a-45e9-a3aa-03aaca2cc5e2", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model_id @ HUC# (('SHR6_1', '09020301')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('SandHillRiver7', '09020301')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('SandHillRiver7_1', '09020301')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('SHR6', '09020301')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('Kelly_Creek_T1', '06040002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('Duck_River_Trib4', '06040002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('Bell_Buckle_CrT1', '06040002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('Trib_to_Holland', '06040002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('Stream0510', '01020002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('Martin_Stream', '01020002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('Stream0526', '01020002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('Stream0527', '01020002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT115_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT79_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT61_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT119_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT45_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT84_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT91_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT115_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT66_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT120_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT107_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT34_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT84_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT55_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT2_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT106_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT103_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT71_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT103_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT120_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT25_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT8_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT65_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT110_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT105_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT73_in_BluffCr_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT55_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT72_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT72_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT35_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT129_in_Edith_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT89_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT25_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT89_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT102_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT120_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT114_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT33_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT95_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT62_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT70_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT52_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT110_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT35_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT85_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT146_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT53_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT34_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT94_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT96_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT52_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT148_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT112_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT127_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT119_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT112_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT94_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT104_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT120_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT100_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT95_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT33_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT147_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT61_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT129_in_Edith_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT162_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT45_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT235_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT82_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT98_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT86_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT2_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT53_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT86_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT118_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT85_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT235_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT62_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT98_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT100_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT152_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT73_in_BluffCr_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT118_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT96_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT108_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT127_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT107_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT103_in_Liberal_Watershed_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT108_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT66_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT147_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT106_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT71_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT105_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT102_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT79_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT70_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT114_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT113_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT82_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT103_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT113_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT91_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT65_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT8_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT152_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT104_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT162_in_Liberal_Watershed', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT146_in_UPPER_SALT_FRK_HUC8', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UNT148_in_UPPER_SALT_FRK_HUC8_1', '11060002')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioTusasTrib2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaJarita', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadadeBorracho', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoMiranda', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeRancho', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoPunche', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoPuncheT4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadadelaOrill', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('PueblodeTaosT3b1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaSarcoT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ElRito', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('FernandodeTaos', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('AguajePetacaT1a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('PueblodeTaosT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioTusas_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoPuncheT3a1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioVallecitos', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ElRitoT4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioPuebloTrib1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoPino', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT11b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioOjoCalienteT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioVallecitosT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT12c', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyolaMina', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ElRitoT6', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('AguajedelaPetacaT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaAnchaNorth', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoRancho', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ElRitoT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoelRitoT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaPueblo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RedRiver', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT13', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT10f', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CabrestoCreek', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RiodeTruchas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('GrandeRanchoT1b4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT11c', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioOjoCaliente_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaCorral', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('PuebloArroyo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('FernandodeTaosT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT10e', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioTusasTrib3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadadeApodaca', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('AguajePetacaT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ElRitoT7', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadadelAgua', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoPuncheT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('EmbudoCreekTrib1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('LaMaderaT1a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT9', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioVallecitos_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('PiedraLumbre', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('GrandeRanchoT1_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('GrandeRanchoT1b2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioVallecitosT2b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoLagunitas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('AngelCanyon', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('LagunitasT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoelRito', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT9a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('GrandeRanchoT1a1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('LaMaderaT1b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioPueblo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT10d1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaLemitas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('GrandeRanchoT1d', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoPuncheT3b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioVallecitosT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ChamizalCreek', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadadeOso', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaEntranasT', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioVallecitosT4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioOjoCaliente', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaTioPula', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('PueblodeTaosT4a1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadadeOso_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaAlamos', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioHondo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioOjoCalienteT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RedRiverTrib2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaOjoSarco', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaAbeque', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioLucero', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('LatirCreekTrib2a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ElRitoT8a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RiodeTruchasT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaMontecito', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioTusas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT10c', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT12', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadadeOjito', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('AlmagreT3a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT12a', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('GrandeRanchoT1b1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrande', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT10b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('GrandeRanchoT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT12b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadadeLorenzo', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('PueblodeTaosT4b', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeRancho_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('AcequiadelJaral', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT9c2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('JarosoCreek', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaEntranas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ArroyoPuncheT6', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ElRito_1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('LagunitasT2', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RiodelasTrampas', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('PueblodeTaosT3', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('GrandeRanchoT1c', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioVallecitosT9', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaPinoReale', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioTusasTrib4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadadeJacinto', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT11c1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('LatirCreek', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('PueblodeTaosT4', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('ElRitoT5', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CanadaAncha', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('AnchaNorthT1', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('RioGrandeT10d', '13020102')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('KingsCreekTrib1B_4', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('KingsCreekTrib1B_1', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('KingsCreekTrib1B_3', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('KingsCreekTrib1B_2', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('KingsCreekTrib1B', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('KC1_KC1B_LOMR', '12030107')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('NP708', '05020006')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('NP720', '05020006')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('NP719', '05020006')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('NP180', '05020006')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CB18_CorrectedEffective', '02060006')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('CB18_Proposed', '02060006')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UT81Milw', '04030101')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The model_id @ HUC# (('UT5Milw', '04030101')) IS NOT featured in current model @ HUC's conflation json file.\n", - "The consolidated XS geopandas dataframes now has a standardized CRS of:\n", - "NAD83 / Conus Albers\n" - ] - } - ], - "source": [ - "# Instantiate class\n", - "obj = MapData(data_dir=f\"{os.getcwd()}/segment_data/\", subfolder_key_prefix=\"ble\")\n", - "\n", - "# Extract list of fim-c data sample's directories.\n", - "fim_data_dirs = obj.fim_data_dirs\n", - "\n", - "# Categorize FIM-C data sample files (e.g. geopackage of a given model @ HUC#, json, source_models.gpkg, ripple.gpkg)\n", - "model_gpkg_dirs, src_models_gpkg_dirs, rip_gpkg_dirs = (\n", - " obj.model_gpkg_dirs,\n", - " obj.src_models_gpkg_dirs,\n", - " obj.rip_gpkg_dirs,\n", - ")\n", - "gpkg_dirs, json_dirs = obj.gpkg_dirs, obj.json_dirs\n", - "\n", - "model_gpkg_tablenames, src_models_gpkg_tablenames, rip_gpkg_tablenames = (\n", - " obj.model_gpkg_tablenames,\n", - " obj.src_models_gpkg_tablenames,\n", - " obj.rip_gpkg_tablenames,\n", - ")\n", - "gpkg_tablenames, json_tablenames = obj.gpkg_tablenames, obj.json_tablenames\n", - "\n", - "# Generate a dictionary to map each conflation json files to their corresponding model_id & HUC #.\n", - "model2huc_map = obj.id2json\n", - "\n", - "# Generated maps of model_id & HUC # to xs (for both us & ds cross-section) to reach ID & \"network_to_id\"\n", - "us_xs2id_map = obj.us_ref_dict\n", - "ds_xs2id_map = obj.ds_ref_dict\n", - "\n", - "# Generated map of model_id & HUC # to each HEC-RAS model's geopackage.\n", - "model_id2gpkg_map = obj.model_id2gpkg\n", - "\n", - "# Generated map of HUC # to each ripple geopackage.\n", - "rip_huc2gpkg_map = obj.rip_huc2gpkg\n", - "\n", - "# Map IDs to each model's cross-section instance\n", - "id2xs_dfs = obj.xs_df_list\n", - "\n", - "# [OPTIONAL: If a user wants to extract individual river geopandas, parquet, &/or gpkg]\n", - "# Map of XS as geopandas per river per HUC w/ their inherited CRS by HUC\n", - "# xsbyriver_map = obj.groupbyriver_dict\n", - "\n", - "# [OPTIONAL: Map of inherited CRS to HUC, model_id, river name]\n", - "crs_map = obj.crs_dict\n", - "\n", - "# Conslidate all HEC-RAS models' cross-sections featuring their corresponding IDs.\n", - "consolidated_id2xs = obj.consolidated_id2xs" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "86bfa6f3-0c8d-4cdc-8f1d-2b4e73b4d00b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_length...number_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id
    0BapCrReach1BapCr ,Reach148077.80BapCr Reach1 48077.8743.50756.11207.08207.08207.08...3607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436369.214 1962609.838, 436369.691...07130010sa_11380162613801634NoneNone
    1BapCrReach1BapCr ,Reach147870.72BapCr Reach1 47870.72742.54755.74259.78259.78259.78...2607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436352.44 1962575.103, 436311.185 ...07130010sa_1NoneNoneNoneNone
    2BapCrReach1BapCr ,Reach147610.94BapCr Reach1 47610.94741.94755.17374.57374.57374.57...2607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436323.593 1962531.597, 436264.538...07130010sa_1NoneNoneNoneNone
    3BapCrReach1BapCr ,Reach147236.37BapCr Reach1 47236.37740.62754.18333.59333.59333.59...2607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436310.242 1962416.052, 436236.357...07130010sa_1NoneNoneNoneNone
    4BapCrReach1BapCr ,Reach146902.78BapCr Reach1 46902.78739.04757.19375.07375.07375.07...2607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (436292.364 1962322.688, 436219.117...07130010sa_1NoneNoneNoneNone
    \n", - "

    5 rows × 24 columns

    \n", - "
    " - ], - "text/plain": [ - " river reach river_reach river_station \\\n", - "0 BapCr Reach1 BapCr ,Reach1 48077.80 \n", - "1 BapCr Reach1 BapCr ,Reach1 47870.72 \n", - "2 BapCr Reach1 BapCr ,Reach1 47610.94 \n", - "3 BapCr Reach1 BapCr ,Reach1 47236.37 \n", - "4 BapCr Reach1 BapCr ,Reach1 46902.78 \n", - "\n", - " river_reach_rs thalweg xs_max_elevation left_reach_length \\\n", - "0 BapCr Reach1 48077.8 743.50 756.11 207.08 \n", - "1 BapCr Reach1 47870.72 742.54 755.74 259.78 \n", - "2 BapCr Reach1 47610.94 741.94 755.17 374.57 \n", - "3 BapCr Reach1 47236.37 740.62 754.18 333.59 \n", - "4 BapCr Reach1 46902.78 739.04 757.19 375.07 \n", - "\n", - " right_reach_length channel_reach_length ... number_of_coords \\\n", - "0 207.08 207.08 ... 3 \n", - "1 259.78 259.78 ... 2 \n", - "2 374.57 374.57 ... 2 \n", - "3 333.59 333.59 ... 2 \n", - "4 375.07 375.07 ... 2 \n", - "\n", - " flows profile_names \\\n", - "0 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", - "1 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", - "2 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", - "3 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", - "4 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", - "\n", - " geometry huc model_id \\\n", - "0 LINESTRING (436369.214 1962609.838, 436369.691... 07130010 sa_1 \n", - "1 LINESTRING (436352.44 1962575.103, 436311.185 ... 07130010 sa_1 \n", - "2 LINESTRING (436323.593 1962531.597, 436264.538... 07130010 sa_1 \n", - "3 LINESTRING (436310.242 1962416.052, 436236.357... 07130010 sa_1 \n", - "4 LINESTRING (436292.364 1962322.688, 436219.117... 07130010 sa_1 \n", - "\n", - " us_reach_id us_network_to_id ds_reach_id ds_network_to_id \n", - "0 13801626 13801634 None None \n", - "1 None None None None \n", - "2 None None None None \n", - "3 None None None None \n", - "4 None None None None \n", - "\n", - "[5 rows x 24 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "consolidated_id2xs.head(5)\n", - "# consolidated_id2xs.shape" - ] - }, - { - "attachments": { - "6667e33a-9f14-4aeb-9078-49fc7dd265d2.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "id": "00d552bf-d39e-40f0-ace5-54a09f1f35e8", - "metadata": {}, - "source": [ - "## Demo I Result:\n", - "![image.png](attachment:6667e33a-9f14-4aeb-9078-49fc7dd265d2.png)\n" - ] - }, - { - "cell_type": "markdown", - "id": "2959553a-f779-46ef-9dca-2f2a20eb6554", - "metadata": {}, - "source": [ - "## Demo II: \n", - "### Querying XS Instance by Network ID" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8d337a5c-0f7a-42c4-91cf-72e7f0da98cb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_length...number_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id
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    " - ], - "text/plain": [ - " river reach river_reach \\\n", - "28507 BattleCrCypress 1 BattleCrCypress ,1 \n", - "\n", - " river_station river_reach_rs thalweg xs_max_elevation \\\n", - "28507 13227.35 BattleCrCypress 1 13227.35 17.88 82.69 \n", - "\n", - " left_reach_length right_reach_length channel_reach_length ... \\\n", - "28507 464.51 459.84 462.47 ... \n", - "\n", - " number_of_coords flows profile_names \\\n", - "28507 2 1230.0\\n1330.0 1%\\n1% Ultimate \n", - "\n", - " geometry huc \\\n", - "28507 LINESTRING (1665550.543 1889977.407, 1664479.0... 02060006 \n", - "\n", - " model_id us_reach_id us_network_to_id ds_reach_id \\\n", - "28507 BattleCreekCypressSwamp 11908190 11908200 None \n", - "\n", - " ds_network_to_id \n", - "28507 None \n", - "\n", - "[1 rows x 24 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario I\n", - "us_network_to_id = \"11908200\"\n", - "queried_xsbyid = consolidated_id2xs[consolidated_id2xs[\"us_network_to_id\"] == us_network_to_id]\n", - "queried_xsbyid" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "0e5adb81-5ba0-41ad-a1f1-1196b822f1f7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    " - ], - "text/plain": [ - " river reach river_reach river_station \\\n", - "0 BapCr Reach1 BapCr ,Reach1 48077.8 \n", - "\n", - " river_reach_rs thalweg xs_max_elevation left_reach_length \\\n", - "0 BapCr Reach1 48077.8 743.5 756.11 207.08 \n", - "\n", - " right_reach_length channel_reach_length ... number_of_coords \\\n", - "0 207.08 207.08 ... 3 \n", - "\n", - " flows profile_names \\\n", - "0 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", - "\n", - " geometry huc model_id \\\n", - "0 LINESTRING (436369.214 1962609.838, 436369.691... 07130010 sa_1 \n", - "\n", - " us_reach_id us_network_to_id ds_reach_id ds_network_to_id \n", - "0 13801626 13801634 None None \n", - "\n", - "[1 rows x 24 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario II\n", - "us_network_to_id = \"13801634\"\n", - "queried_xsbyid = consolidated_id2xs[consolidated_id2xs[\"us_network_to_id\"] == us_network_to_id]\n", - "queried_xsbyid" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "38a9c0ba-388b-41a5-a282-2c245e42c12c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    3 rows × 24 columns

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    " - ], - "text/plain": [ - " river reach river_reach river_station \\\n", - "30796 1242 15 1242,15 789.6214 \n", - "30878 1195 109 1195 ,109 6071.4750 \n", - "30966 1236 61 1236,61 725.2979 \n", - "\n", - " river_reach_rs thalweg xs_max_elevation left_reach_length \\\n", - "30796 1242 15 789.6214 3520.558 3568.420 85.14416 \n", - "30878 1195 109 6071.475 3534.450 3616.780 206.69170 \n", - "30966 1236 61 725.2979 3533.829 3587.055 151.74160 \n", - "\n", - " right_reach_length channel_reach_length ... number_of_coords \\\n", - "30796 93.27657 93.45487 ... 10 \n", - "30878 147.98750 195.85400 ... 8 \n", - "30966 139.83360 148.79750 ... 7 \n", - "\n", - " flows \\\n", - "30796 193.0\\n359.0\\n522.0\\n723.0\\n919.0\\n569.0\\n1347.0 \n", - "30878 1746.0\\n3146.0\\n4514.0\\n6152.0\\n7819.0\\n4840.0... \n", - "30966 433.0\\n796.0\\n1152.0\\n1584.0\\n2014.0\\n1247.0\\n... \n", - "\n", - " profile_names \\\n", - "30796 10yr\\n25yr\\n50yr\\n100yr\\n100plus\\n100minus\\n5... \n", - "30878 10yr\\n25yr\\n50yr\\n100yr\\n100plus\\n100minus\\n500yr \n", - "30966 10yr\\n25yr\\n50yr\\n100yr\\n100plus\\n100minus\\n5... \n", - "\n", - " geometry huc model_id \\\n", - "30796 LINESTRING (-1355141.15 1253454.057, -1355086.... 15040007 1242_ras \n", - "30878 LINESTRING (-1355056.984 1253633.24, -1355173.... 15040007 1195_ras \n", - "30966 LINESTRING (-1355130.287 1253615.579, -1355168... 15040007 1236_ras \n", - "\n", - " us_reach_id us_network_to_id ds_reach_id ds_network_to_id \n", - "30796 21294309 21294311 None None \n", - "30878 21294309 21294311 21294243 21294309 \n", - "30966 21294309 21294311 21295551 21294309 \n", - "\n", - "[3 rows x 24 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario III\n", - "us_network_to_id = \"21294311\"\n", - "queried_xsbyid = consolidated_id2xs[consolidated_id2xs[\"us_network_to_id\"] == us_network_to_id]\n", - "queried_xsbyid" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "94b5df36-a194-402b-b795-e47487fef291", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    riverreachriver_reachriver_stationriver_reach_rsthalwegxs_max_elevationleft_reach_lengthright_reach_lengthchannel_reach_length...number_of_coordsflowsprofile_namesgeometryhucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_id
    19216cottonwood_creek45cottonwood_creek,451773.175cottonwood_creek 45 1773.1751342.2951450.915166.0375330.7853294.9549...147096.0\\n9743.0\\n4449.0100yr\\n100yrplus\\n100yrminusLINESTRING (-1536837.566 2728068.167, -1536849...17060305cottonwood_creek_1_ras23597195235971932359719723597195
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    1 rows × 24 columns

    \n", - "
    " - ], - "text/plain": [ - " river reach river_reach river_station \\\n", - "19216 cottonwood_creek 45 cottonwood_creek,45 1773.175 \n", - "\n", - " river_reach_rs thalweg xs_max_elevation \\\n", - "19216 cottonwood_creek 45 1773.175 1342.295 1450.915 \n", - "\n", - " left_reach_length right_reach_length channel_reach_length ... \\\n", - "19216 166.0375 330.7853 294.9549 ... \n", - "\n", - " number_of_coords flows profile_names \\\n", - "19216 14 7096.0\\n9743.0\\n4449.0 100yr\\n100yrplus\\n100yrminus \n", - "\n", - " geometry huc \\\n", - "19216 LINESTRING (-1536837.566 2728068.167, -1536849... 17060305 \n", - "\n", - " model_id us_reach_id us_network_to_id ds_reach_id \\\n", - "19216 cottonwood_creek_1_ras 23597195 23597193 23597197 \n", - "\n", - " ds_network_to_id \n", - "19216 23597195 \n", - "\n", - "[1 rows x 24 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario IV\n", - "us_network_to_id = \"23597193\"\n", - "queried_xsbyid = consolidated_id2xs[consolidated_id2xs[\"us_network_to_id\"] == us_network_to_id]\n", - "queried_xsbyid" - ] - }, - { - "cell_type": "markdown", - "id": "64d121ff-4622-4863-ab1e-16514c28581f", - "metadata": {}, - "source": [ - "## Demo III: \n", - "### Querying Each XS Layer by Model ID & HUC Number" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c8659230-af75-48ff-b988-d1b0dffc260b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    hucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_idriver_reach_rsthalwegxs_max_elevationriver_station...right_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometry
    009020301GF_EGF_FIS_Submittal120493982327066766NoneNoneRRN GFEGF 170.0774.37834.80170.0...700.0934.0Type RM Length L Ch R = 1 ,170 ,900,934,70...[(44883.4, 834.0), (47186.0, 832.44), (49148.8...['50124', '50484']238749000.0\\n71000.0\\n89000.0\\n139000.010-Year\\n50-Year\\n100-Year\\n500 YearLINESTRING (2818284.13 354233.597, 2818770.518...
    109020301GF_EGF_FIS_Submittal1204NoneNoneNoneNoneRRN GFEGF 169.0772.69842.37169.0...340.0344.0Type RM Length L Ch R = 1 ,169 ,450,344,34...[(45195.48, 833.55), (45220.07, 833.6), (45256...['50192', '50563']263849000.0\\n71000.0\\n89000.0\\n139000.010-Year\\n50-Year\\n100-Year\\n500 YearLINESTRING (2815855.489 354032.803, 2816473.50...
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    2 rows × 24 columns

    \n", - "
    " - ], - "text/plain": [ - " huc model_id us_reach_id us_network_to_id \\\n", - "0 09020301 GF_EGF_FIS_Submittal1204 9398232 7066766 \n", - "1 09020301 GF_EGF_FIS_Submittal1204 None None \n", - "\n", - " ds_reach_id ds_network_to_id river_reach_rs thalweg xs_max_elevation \\\n", - "0 None None RRN GFEGF 170.0 774.37 834.80 \n", - "1 None None RRN GFEGF 169.0 772.69 842.37 \n", - "\n", - " river_station ... right_reach_length channel_reach_length \\\n", - "0 170.0 ... 700.0 934.0 \n", - "1 169.0 ... 340.0 344.0 \n", - "\n", - " ras_data \\\n", - "0 Type RM Length L Ch R = 1 ,170 ,900,934,70... \n", - "1 Type RM Length L Ch R = 1 ,169 ,450,344,34... \n", - "\n", - " station_elevation_points bank_stations \\\n", - "0 [(44883.4, 834.0), (47186.0, 832.44), (49148.8... ['50124', '50484'] \n", - "1 [(45195.48, 833.55), (45220.07, 833.6), (45256... ['50192', '50563'] \n", - "\n", - " number_of_station_elevation_points number_of_coords \\\n", - "0 238 7 \n", - "1 263 8 \n", - "\n", - " flows profile_names \\\n", - "0 49000.0\\n71000.0\\n89000.0\\n139000.0 10-Year\\n50-Year\\n100-Year\\n500 Year \n", - "1 49000.0\\n71000.0\\n89000.0\\n139000.0 10-Year\\n50-Year\\n100-Year\\n500 Year \n", - "\n", - " geometry \n", - "0 LINESTRING (2818284.13 354233.597, 2818770.518... \n", - "1 LINESTRING (2815855.489 354032.803, 2816473.50... \n", - "\n", - "[2 rows x 24 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario I\n", - "model_id = \"GF_EGF_FIS_Submittal1204\"\n", - "huc_num = \"09020301\"\n", - "\n", - "tt = model_id2gpkg_map[(model_id, huc_num)][\"XS\"]\n", - "tt.insert(0, \"huc\", tt.pop(\"huc\"))\n", - "tt.insert(1, \"model_id\", tt.pop(\"model_id\"))\n", - "tt.insert(2, \"us_reach_id\", tt.pop(\"us_reach_id\"))\n", - "tt.insert(3, \"us_network_to_id\", tt.pop(\"us_network_to_id\"))\n", - "tt.insert(4, \"ds_reach_id\", tt.pop(\"ds_reach_id\"))\n", - "tt.insert(5, \"ds_network_to_id\", tt.pop(\"ds_network_to_id\"))\n", - "tt.insert(6, \"river_reach_rs\", tt.pop(\"river_reach_rs\"))\n", - "tt.insert(7, \"thalweg\", tt.pop(\"thalweg\"))\n", - "tt.insert(8, \"xs_max_elevation\", tt.pop(\"xs_max_elevation\"))\n", - "tt.insert(9, \"river_station\", tt.pop(\"river_station\"))\n", - "tt.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "eb406b80-c188-47ec-8025-2446f54e0575", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    hucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_idriver_reach_rsthalwegxs_max_elevationriver_station...right_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometry
    007130010sa_11380162613801634NoneNoneBapCr Reach1 48077.8743.50756.1148077.80...207.08207.08Type RM Length L Ch R = 1 ,48077.80,207.08,207...[(0.0, 754.17), (3.42, 753.96), (6.85, 753.88)...['99.27', '143.86']673607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (2120750.469 1421598.418, 2120758.2...
    107130010sa_1NoneNoneNoneNoneBapCr Reach1 47870.72742.54755.7447870.72...259.78259.78Type RM Length L Ch R = 1 ,47870.72,259.78,259...[(0.0, 755.74), (6.92, 755.26), (10.38, 755.11...['65.75', '107.27']582607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.010%\\n4%\\n2%\\n1%\\n0.2%\\n1%+LINESTRING (2120687.969 1421489.043, 2120560.3...
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    2 rows × 24 columns

    \n", - "
    " - ], - "text/plain": [ - " huc model_id us_reach_id us_network_to_id ds_reach_id \\\n", - "0 07130010 sa_1 13801626 13801634 None \n", - "1 07130010 sa_1 None None None \n", - "\n", - " ds_network_to_id river_reach_rs thalweg xs_max_elevation \\\n", - "0 None BapCr Reach1 48077.8 743.50 756.11 \n", - "1 None BapCr Reach1 47870.72 742.54 755.74 \n", - "\n", - " river_station ... right_reach_length channel_reach_length \\\n", - "0 48077.80 ... 207.08 207.08 \n", - "1 47870.72 ... 259.78 259.78 \n", - "\n", - " ras_data \\\n", - "0 Type RM Length L Ch R = 1 ,48077.80,207.08,207... \n", - "1 Type RM Length L Ch R = 1 ,47870.72,259.78,259... \n", - "\n", - " station_elevation_points bank_stations \\\n", - "0 [(0.0, 754.17), (3.42, 753.96), (6.85, 753.88)... ['99.27', '143.86'] \n", - "1 [(0.0, 755.74), (6.92, 755.26), (10.38, 755.11... ['65.75', '107.27'] \n", - "\n", - " number_of_station_elevation_points number_of_coords \\\n", - "0 67 3 \n", - "1 58 2 \n", - "\n", - " flows profile_names \\\n", - "0 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", - "1 607.0\\n798.0\\n943.0\\n1100.0\\n1460.0\\n1650.0 10%\\n4%\\n2%\\n1%\\n0.2%\\n1%+ \n", - "\n", - " geometry \n", - "0 LINESTRING (2120750.469 1421598.418, 2120758.2... \n", - "1 LINESTRING (2120687.969 1421489.043, 2120560.3... \n", - "\n", - "[2 rows x 24 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario II\n", - "model_id = \"sa_1\"\n", - "huc_num = \"07130010\"\n", - "\n", - "tt = model_id2gpkg_map[(model_id, huc_num)][\"XS\"]\n", - "tt.insert(0, \"huc\", tt.pop(\"huc\"))\n", - "tt.insert(1, \"model_id\", tt.pop(\"model_id\"))\n", - "tt.insert(2, \"us_reach_id\", tt.pop(\"us_reach_id\"))\n", - "tt.insert(3, \"us_network_to_id\", tt.pop(\"us_network_to_id\"))\n", - "tt.insert(4, \"ds_reach_id\", tt.pop(\"ds_reach_id\"))\n", - "tt.insert(5, \"ds_network_to_id\", tt.pop(\"ds_network_to_id\"))\n", - "tt.insert(6, \"river_reach_rs\", tt.pop(\"river_reach_rs\"))\n", - "tt.insert(7, \"thalweg\", tt.pop(\"thalweg\"))\n", - "tt.insert(8, \"xs_max_elevation\", tt.pop(\"xs_max_elevation\"))\n", - "tt.insert(9, \"river_station\", tt.pop(\"river_station\"))\n", - "tt.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f65f2e37-c7b3-4061-8990-b0f73a98e01e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    hucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_idriver_reach_rsthalwegxs_max_elevationriver_station...right_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometry
    018070204BorregoCyn_ExistingNoneNoneNoneNoneBorrego Cyn Reach 1 US 1345.913681.12697.361345.913...149.24148.80Type RM Length L Ch R = 1 ,1345.913,155.13,148...[(0.0, 692.38), (121.89, 691.88), (205.0, 692....['205', '255']2321126.0PF 1LINESTRING (6127868.943 2194456.933, 6127820.9...
    118070204BorregoCyn_ExistingNoneNoneNoneNoneBorrego Cyn Reach 1 US 1197.11680.18697.561197.110...200.51200.29Type RM Length L Ch R = 1 ,1197.11 ,203.67,200...[(0.0, 692.64), (14.99, 692.64), (16.4, 692.64...['164', '256']3721126.0PF 1LINESTRING (6127720.709 2194447.582, 6127672.7...
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    2 rows × 24 columns

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    " - ], - "text/plain": [ - " huc model_id us_reach_id us_network_to_id ds_reach_id \\\n", - "0 18070204 BorregoCyn_Existing None None None \n", - "1 18070204 BorregoCyn_Existing None None None \n", - "\n", - " ds_network_to_id river_reach_rs thalweg \\\n", - "0 None Borrego Cyn Reach 1 US 1345.913 681.12 \n", - "1 None Borrego Cyn Reach 1 US 1197.11 680.18 \n", - "\n", - " xs_max_elevation river_station ... right_reach_length \\\n", - "0 697.36 1345.913 ... 149.24 \n", - "1 697.56 1197.110 ... 200.51 \n", - "\n", - " channel_reach_length ras_data \\\n", - "0 148.80 Type RM Length L Ch R = 1 ,1345.913,155.13,148... \n", - "1 200.29 Type RM Length L Ch R = 1 ,1197.11 ,203.67,200... \n", - "\n", - " station_elevation_points bank_stations \\\n", - "0 [(0.0, 692.38), (121.89, 691.88), (205.0, 692.... ['205', '255'] \n", - "1 [(0.0, 692.64), (14.99, 692.64), (16.4, 692.64... ['164', '256'] \n", - "\n", - " number_of_station_elevation_points number_of_coords flows profile_names \\\n", - "0 23 2 1126.0 PF 1 \n", - "1 37 2 1126.0 PF 1 \n", - "\n", - " geometry \n", - "0 LINESTRING (6127868.943 2194456.933, 6127820.9... \n", - "1 LINESTRING (6127720.709 2194447.582, 6127672.7... \n", - "\n", - "[2 rows x 24 columns]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario III\n", - "model_id = \"BorregoCyn_Existing\"\n", - "huc_num = \"18070204\"\n", - "\n", - "tt = model_id2gpkg_map[(model_id, huc_num)][\"XS\"]\n", - "tt.insert(0, \"huc\", tt.pop(\"huc\"))\n", - "tt.insert(1, \"model_id\", tt.pop(\"model_id\"))\n", - "tt.insert(2, \"us_reach_id\", tt.pop(\"us_reach_id\"))\n", - "tt.insert(3, \"us_network_to_id\", tt.pop(\"us_network_to_id\"))\n", - "tt.insert(4, \"ds_reach_id\", tt.pop(\"ds_reach_id\"))\n", - "tt.insert(5, \"ds_network_to_id\", tt.pop(\"ds_network_to_id\"))\n", - "tt.insert(6, \"river_reach_rs\", tt.pop(\"river_reach_rs\"))\n", - "tt.insert(7, \"thalweg\", tt.pop(\"thalweg\"))\n", - "tt.insert(8, \"xs_max_elevation\", tt.pop(\"xs_max_elevation\"))\n", - "tt.insert(9, \"river_station\", tt.pop(\"river_station\"))\n", - "tt.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "28c8bfb6-9cf9-4215-9bcd-605dcd0797c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    hucmodel_idus_reach_idus_network_to_idds_reach_idds_network_to_idriver_reach_rsthalwegxs_max_elevationriver_station...right_reach_lengthchannel_reach_lengthras_datastation_elevation_pointsbank_stationsnumber_of_station_elevation_pointsnumber_of_coordsflowsprofile_namesgeometry
    017060305cottonwood_creek_ras2359728323597281NoneNonecottonwood_creek 874 89457.283764.9973862.72489457.28...99.942199.9421Type RM Length L Ch R = 1 ,89457.28,99.942100,...[(9649.015, 3806.544), (9649.124, 3806.528), (...['9649.015', '10350.99']86892.0PF 1LINESTRING (2462395.976 1602716.665, 2462389.5...
    117060305cottonwood_creek_rasNoneNoneNoneNonecottonwood_creek 874 89357.333762.6663867.24889357.33...99.942199.9421Type RM Length L Ch R = 1 ,89357.33,99.942100,...[(9649.021, 3808.923), (9652.402, 3808.42), (9...['9649.021', '10350.98']83992.0PF 1LINESTRING (2462481.133 1602698.905, 2462480.8...
    \n", - "

    2 rows × 24 columns

    \n", - "
    " - ], - "text/plain": [ - " huc model_id us_reach_id us_network_to_id ds_reach_id \\\n", - "0 17060305 cottonwood_creek_ras 23597283 23597281 None \n", - "1 17060305 cottonwood_creek_ras None None None \n", - "\n", - " ds_network_to_id river_reach_rs thalweg xs_max_elevation \\\n", - "0 None cottonwood_creek 874 89457.28 3764.997 3862.724 \n", - "1 None cottonwood_creek 874 89357.33 3762.666 3867.248 \n", - "\n", - " river_station ... right_reach_length channel_reach_length \\\n", - "0 89457.28 ... 99.9421 99.9421 \n", - "1 89357.33 ... 99.9421 99.9421 \n", - "\n", - " ras_data \\\n", - "0 Type RM Length L Ch R = 1 ,89457.28,99.942100,... \n", - "1 Type RM Length L Ch R = 1 ,89357.33,99.942100,... \n", - "\n", - " station_elevation_points \\\n", - "0 [(9649.015, 3806.544), (9649.124, 3806.528), (... \n", - "1 [(9649.021, 3808.923), (9652.402, 3808.42), (9... \n", - "\n", - " bank_stations number_of_station_elevation_points \\\n", - "0 ['9649.015', '10350.99'] 86 \n", - "1 ['9649.021', '10350.98'] 83 \n", - "\n", - " number_of_coords flows profile_names \\\n", - "0 8 92.0 PF 1 \n", - "1 9 92.0 PF 1 \n", - "\n", - " geometry \n", - "0 LINESTRING (2462395.976 1602716.665, 2462389.5... \n", - "1 LINESTRING (2462481.133 1602698.905, 2462480.8... \n", - "\n", - "[2 rows x 24 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario IV\n", - "model_id = \"cottonwood_creek_ras\"\n", - "huc_num = \"17060305\"\n", - "\n", - "tt = model_id2gpkg_map[(model_id, huc_num)][\"XS\"]\n", - "tt.insert(0, \"huc\", tt.pop(\"huc\"))\n", - "tt.insert(1, \"model_id\", tt.pop(\"model_id\"))\n", - "tt.insert(2, \"us_reach_id\", tt.pop(\"us_reach_id\"))\n", - "tt.insert(3, \"us_network_to_id\", tt.pop(\"us_network_to_id\"))\n", - "tt.insert(4, \"ds_reach_id\", tt.pop(\"ds_reach_id\"))\n", - "tt.insert(5, \"ds_network_to_id\", tt.pop(\"ds_network_to_id\"))\n", - "tt.insert(6, \"river_reach_rs\", tt.pop(\"river_reach_rs\"))\n", - "tt.insert(7, \"thalweg\", tt.pop(\"thalweg\"))\n", - "tt.insert(8, \"xs_max_elevation\", tt.pop(\"xs_max_elevation\"))\n", - "tt.insert(9, \"river_station\", tt.pop(\"river_station\"))\n", - "tt.head(2)\n", - "# tt.tail()" - ] - }, - { - "cell_type": "markdown", - "id": "dcbab67e-335d-4d5c-89b6-892228fd1e67", - "metadata": {}, - "source": [ - "## Demo IVa: \n", - "### Per HUC, save & verify each river's XS data as parquet & geopackage" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ccc7bb03-6669-4bca-8f2e-161f683c6b9a", - "metadata": {}, - "outputs": [], - "source": [ - "# # # Scenario I\n", - "# # Read Parquet file\n", - "# df1 = pd.read_parquet('/home/sylvia.chin/Desktop/iceberg/xs_data/huc_07130010/BapCr.parquet',\n", - "# engine='pyarrow')\n", - "\n", - "# # Display the first few rows\n", - "# print(df1.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c907f518-7d65-490a-a31b-395fc02eaa81", - "metadata": {}, - "outputs": [], - "source": [ - "# # # Scenario II\n", - "\n", - "# # Read Parquet file\n", - "# df2 = pd.read_parquet('/home/sylvia.chin/Desktop/iceberg/xs_data/huc_01020002/Martin Stream.parquet',\n", - "# engine='pyarrow')\n", - "\n", - "# # Display the first few rows\n", - "# print(df2.head())" - ] - }, - { - "cell_type": "markdown", - "id": "bbd6c174-53b6-4472-b5a2-1ed76e736600", - "metadata": {}, - "source": [ - "## Demo IVb: \n", - "### Per HUC, save & verify each HUC's XS data as parquet & geopackage " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8304726d-8564-4f4a-9729-f3fe887757d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    hucrivermodel_idus_reach_idds_reach_id
    704210120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
    704310120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
    704410120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
    704510120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
    704610120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSE_1NoneNone
    ..................
    722510120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
    722610120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
    722710120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
    722810120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
    722910120203Spearfish CreekSpearfish_Cr_LOMR_FEMA_RESPONSENoneNone
    \n", - "

    188 rows × 5 columns

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    " - ], - "text/plain": [ - " huc river model_id \\\n", - "7042 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", - "7043 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", - "7044 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", - "7045 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", - "7046 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE_1 \n", - "... ... ... ... \n", - "7225 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", - "7226 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", - "7227 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", - "7228 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", - "7229 10120203 Spearfish Creek Spearfish_Cr_LOMR_FEMA_RESPONSE \n", - "\n", - " us_reach_id ds_reach_id \n", - "7042 None None \n", - "7043 None None \n", - "7044 None None \n", - "7045 None None \n", - "7046 None None \n", - "... ... ... \n", - "7225 None None \n", - "7226 None None \n", - "7227 None None \n", - "7228 None None \n", - "7229 None None \n", - "\n", - "[188 rows x 5 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# # Scenario I\n", - "# Read Parquet file\n", - "df1 = pd.read_parquet(f\"/{os.getcwd()}/xs_data/mip_10120203/huc_10120203.parquet\", engine=\"pyarrow\")\n", - "\n", - "# set(df1['river'])\n", - "# set(df1['huc'])\n", - "df1[[\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\"]]" - ] - }, - { - "cell_type": "markdown", - "id": "32a5e23d-ba5e-45fc-ab95-a894d69f70a3", - "metadata": {}, - "source": [ - "#### Cross-checking iceberg table with parquet @ huc_10120203\n", - "\n", - "huc: [[\"10120203\",\"10120203\",\"10120203\",\"10120203\",\"10120203\",...,\"10120203\",\"10120203\",\"10120203\",\"10120203\",\"10120203\"]]\n", - "model_id: [[\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",...,\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\"]]\n", - "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "us_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "ds_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "__index_level_0__: [[7042,7043,7044,7045,7046,...,7225,7226,7227,7228,7229]]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a5806256-a6a2-4e47-b206-cd1c4d378ebb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    hucrivermodel_idus_reach_idds_reach_id
    1917817060305cottonwood_creekcottonwood_creek_1_ras23597201None
    1917917060305cottonwood_creekcottonwood_creek_1_rasNoneNone
    1918017060305cottonwood_creekcottonwood_creek_1_ras2359719923597201
    1918117060305cottonwood_creekcottonwood_creek_1_rasNoneNone
    1918217060305cottonwood_creekcottonwood_creek_1_rasNoneNone
    ..................
    2440317060305south_fork_clearsouth_fork_clearwater_river_1_rasNoneNone
    2440417060305south_fork_clearsouth_fork_clearwater_river_1_rasNoneNone
    2440517060305south_fork_clearsouth_fork_clearwater_river_1_rasNoneNone
    2440617060305south_fork_clearsouth_fork_clearwater_river_1_rasNoneNone
    2440717060305south_fork_clearsouth_fork_clearwater_river_1_rasNone23597347
    \n", - "

    5230 rows × 5 columns

    \n", - "
    " - ], - "text/plain": [ - " huc river model_id \\\n", - "19178 17060305 cottonwood_creek cottonwood_creek_1_ras \n", - "19179 17060305 cottonwood_creek cottonwood_creek_1_ras \n", - "19180 17060305 cottonwood_creek cottonwood_creek_1_ras \n", - "19181 17060305 cottonwood_creek cottonwood_creek_1_ras \n", - "19182 17060305 cottonwood_creek cottonwood_creek_1_ras \n", - "... ... ... ... \n", - "24403 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", - "24404 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", - "24405 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", - "24406 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", - "24407 17060305 south_fork_clear south_fork_clearwater_river_1_ras \n", - "\n", - " us_reach_id ds_reach_id \n", - "19178 23597201 None \n", - "19179 None None \n", - "19180 23597199 23597201 \n", - "19181 None None \n", - "19182 None None \n", - "... ... ... \n", - "24403 None None \n", - "24404 None None \n", - "24405 None None \n", - "24406 None None \n", - "24407 None 23597347 \n", - "\n", - "[5230 rows x 5 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario II\n", - "# Read Parquet file\n", - "df2 = pd.read_parquet(f\"/{os.getcwd()}/xs_data/mip_17060305/huc_17060305.parquet\", engine=\"pyarrow\")\n", - "\n", - "# set(df2['river'])\n", - "# set(df2['huc'])\n", - "df2[[\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\"]]" - ] - }, - { - "cell_type": "markdown", - "id": "8784d040-695c-421a-85bf-27f585b4186e", - "metadata": {}, - "source": [ - "#### Cross-checking iceberg table with parquet @ huc_17060305\n", - "\n", - "huc: [[\"17060305\",\"17060305\",\"17060305\",\"17060305\",\"17060305\",...,\"17060305\",\"17060305\",\"17060305\",\"17060305\",\"17060305\"]]\n", - "model_id: [[\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",...,\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\"]]\n", - "us_reach_id: [[\"23597201\",\"None\",\"23597199\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "ds_reach_id: [[\"None\",\"None\",\"23597201\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"23597347\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "abc4da53-c0d5-45a1-a0d6-0c4ee28df1b0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    hucrivermodel_idus_reach_idds_reach_id
    307871504000712421242_rasNoneNone
    307881504000712421242_rasNoneNone
    307891504000712421242_rasNoneNone
    307901504000712421242_rasNoneNone
    307911504000712421242_rasNoneNone
    ..................
    309661504000712361236_ras2129430921295551
    309671504000712361236_rasNoneNone
    309681504000712361236_rasNoneNone
    309691504000712361236_rasNoneNone
    309701504000712361236_rasNone21294309
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    184 rows × 5 columns

    \n", - "
    " - ], - "text/plain": [ - " huc river model_id us_reach_id ds_reach_id\n", - "30787 15040007 1242 1242_ras None None\n", - "30788 15040007 1242 1242_ras None None\n", - "30789 15040007 1242 1242_ras None None\n", - "30790 15040007 1242 1242_ras None None\n", - "30791 15040007 1242 1242_ras None None\n", - "... ... ... ... ... ...\n", - "30966 15040007 1236 1236_ras 21294309 21295551\n", - "30967 15040007 1236 1236_ras None None\n", - "30968 15040007 1236 1236_ras None None\n", - "30969 15040007 1236 1236_ras None None\n", - "30970 15040007 1236 1236_ras None 21294309\n", - "\n", - "[184 rows x 5 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Scenario III\n", - "# Read Parquet file\n", - "df3 = pd.read_parquet(f\"/{os.getcwd()}/xs_data/mip_15040007/huc_15040007.parquet\", engine=\"pyarrow\")\n", - "\n", - "# set(df2['river'])\n", - "# set(df2['huc'])\n", - "df3[[\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\"]]" - ] - }, - { - "cell_type": "markdown", - "id": "d576de82-bed6-4d89-953f-cde2eeb7ba81", - "metadata": {}, - "source": [ - "#### Cross-checking iceberg table with parquet @ huc_15040007\n", - "\n", - "huc: [[\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\",...,\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\"]]\n", - "model_id: [[\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",...,\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\"]]\n", - "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"None\"]]\n", - "us_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294311\",\"None\",\"None\",\"None\",\"None\"]]\n", - "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21295551\",\"None\",\"None\",\"None\",\"21294309\"]]\n", - "ds_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"21294311\"]]\n", - "__index_level_0__: [[30787,30788,30789,30790,30791,...,30966,30967,30968,30969,30970]]" - ] - }, - { - "cell_type": "markdown", - "id": "a4738ea4-8890-4f50-a372-a66eb5865830", - "metadata": { - "scrolled": true - }, - "source": [ - "## Demo Va: \n", - "### Transform parquet into Iceberg tables via calling iceberg_xs_table.py (scenario for sourcing parquet from local)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "a2bf23b0-3449-43e9-a461-934a1fabd0d7", - "metadata": {}, - "outputs": [], - "source": [ - "from iceberg_table import *\n", - "\n", - "# Instantiate iceberg class\n", - "iceberg_obj = IcebergTable()\n", - "\n", - "# Extract list of parquets\n", - "file_list = iceberg_obj.read_data_dirs(data_dir=f\"{os.getcwd()}/xs_data/\")\n", - "parquet_list = [file for file in file_list if file.endswith(\".parquet\")]\n", - "\n", - "# Establish new Iceberg catalog for XS data\n", - "iceberg_obj.establish_catalog(catalog_name=\"xs\", namespace=\"mip\")\n", - "\n", - "# Generate tables w/in the new Iceberg catalog for all parquets detected\n", - "iceberg_obj.create_table_for_all_parquets(parquet_list, app_name=\"mip_xs\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "39a187bf-cfba-4aaa-9c8f-69146b699307", - "metadata": {}, - "source": [ - "## Demo Vb: \n", - "### Establish A New Iceberg Catalog & Generate Tables For All Parquets in S3 (scenario for sourcing parquet from s3)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "5082de29-3548-48e6-a741-ddd6342d3341", - "metadata": {}, - "outputs": [], - "source": [ - "# # Instantiate class\n", - "# obj = IcebergTable()\n", - "\n", - "# # Establish new Iceberg catalog\n", - "# obj.establish_catalog(catalog_name='xs_dev2',\n", - "# namespace='mip')\n", - "\n", - "# # Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", - "# obj.create_table_for_all_s3parquets(app_name='mip_xs',\n", - "# bucket_name='ngwpc-xs',\n", - "# profile_name='default')" - ] - }, - { - "attachments": { - "ca580451-2f38-43d8-8a8e-7e8767aedc95.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "365d1563-c753-4b72-a202-04cf8c7a95d8", - "metadata": {}, - "source": [ - "## Demo V Result:\n", - "![image.png](attachment:ca580451-2f38-43d8-8a8e-7e8767aedc95.png)" - ] - }, - { - "cell_type": "markdown", - "id": "f20bbfc9-6806-434d-afbe-45fd932105a2", - "metadata": {}, - "source": [ - "### Query XS by HUC & additional attributes of interest @ a given HUC from Iceberg Tables" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7bf79944-1da0-4aa2-a6d3-c6f9a976e3ae", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pyarrow.Table\n", - "river: large_string\n", - "reach: large_string\n", - "river_reach: large_string\n", - "river_station: double\n", - "river_reach_rs: large_string\n", - "thalweg: double\n", - "xs_max_elevation: double\n", - "left_reach_length: double\n", - "right_reach_length: double\n", - "channel_reach_length: double\n", - "ras_data: large_string\n", - "station_elevation_points: large_string\n", - "bank_stations: large_string\n", - "number_of_station_elevation_points: int64\n", - "number_of_coords: int64\n", - "flows: large_string\n", - "profile_names: large_string\n", - "geometry: large_binary\n", - "huc: large_string\n", - "model_id: large_string\n", - "us_reach_id: large_string\n", - "us_network_to_id: large_string\n", - "ds_reach_id: large_string\n", - "ds_network_to_id: large_string\n", - "__index_level_0__: int64\n", - "----\n", - "river: [[\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",...,\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\",\"Spearfish Creek\"]]\n", - "reach: [[\"Breakout Channel\",\"Breakout Channel\",\"Breakout Channel\",\"Breakout Channel\",\"Breakout Channel\",...,\"Old US14 Breakou\",\"Old US14 Breakou\",\"Old US14 Breakou\",\"Old US14 Breakou\",\"Old US14 Breakou\"]]\n", - "river_reach: [[\"Spearfish Creek ,Breakout Channel\",\"Spearfish Creek ,Breakout Channel\",\"Spearfish Creek ,Breakout Channel\",\"Spearfish Creek ,Breakout Channel\",\"Spearfish Creek ,Breakout Channel\",...,\"Spearfish Creek ,Old US14 Breakou\",\"Spearfish Creek ,Old US14 Breakou\",\"Spearfish Creek ,Old US14 Breakou\",\"Spearfish Creek ,Old US14 Breakou\",\"Spearfish Creek ,Old US14 Breakou\"]]\n", - "river_station: [[733,665,553,356,254,...,1796,1502,1236,814,253]]\n", - "river_reach_rs: [[\"Spearfish Creek Breakout Channel 733.0\",\"Spearfish Creek Breakout Channel 665.0\",\"Spearfish Creek Breakout Channel 553.0\",\"Spearfish Creek Breakout Channel 356.0\",\"Spearfish Creek Breakout Channel 254.0\",...,\"Spearfish Creek Old US14 Breakou 1796.0\",\"Spearfish Creek Old US14 Breakou 1502.0\",\"Spearfish Creek Old US14 Breakou 1236.0\",\"Spearfish Creek Old US14 Breakou 814.0\",\"Spearfish Creek Old US14 Breakou 253.0\"]]\n", - "thalweg: [[3563.87,3562.9,3561.47,3558.17,3557.05,...,3488.63,3486.35,3484.18,3481.92,3477.64]]\n", - "xs_max_elevation: [[3574.46,3573.83,3570.78,3568.95,3566,...,3499.78,3496.02,3490.04,3488,3482]]\n", - "left_reach_length: [[73.3,113.86,195.76,101.29,62.19,...,289.59,293.56,321.77,551.53,0]]\n", - "right_reach_length: [[64.03,108.6,199.45,101.26,67.59,...,304.33,193.56,284.8,570.49,0]]\n", - "channel_reach_length: [[68.79,111.37,197.46,101.84,65.19,...,293.78,265.51,422.78,560.74,0]]\n", - "ras_data: [[\"Type RM Length L Ch R = 1 ,733 ,73.3,68.79,64.03\n", - "XS GIS Cut Line=6\n", - "960048.214101717268250.980046898960110.800918803268271.043983147\n", - " 960143.17913489 268329.91263181960142.516406551268354.240339056\n", - "960184.587844715268375.098565057960362.015967548 268336.91753906\n", - "Node Last Edited Time=Sep/04/2015 10:51:51\n", - "#Sta/Elev= 134 \n", - " 0 3574.41 2.99 3574.46 5.97 3574.38 8.96 3574.3 11.95 3574.26\n", - " 14.94 3574.29 17.92 3574.21 20.91 3574.12 23.9 3574.1 26.89 3574.15\n", - " 29.87 3574.2 32.86 3574.21 35.85 3574.1 38.84 3573.99 41.82 3574.02\n", - " 44.81 3573.91 47.8 3573.74 50.79 3573.66 53.77 3573.6 56.76 3573.39\n", - " 59.75 3573.24 62.74 3573.13 65.72 3572.96 68.65 3572.78 71.57 3572.64\n", - " 74.49 3572.58 77.41 3572.28 80.33 3572.15 83.25 3572.04 86.17 3571.89\n", - " 89.09 3571.76 92.01 3571.57 94.94 3571.33 97.86 3571.17 100.78 3571.08\n", - " 103.7 3570.9 106.62 3570.73 109.54 3570.64 112.46 3570.53 115.38 3570.39\n", - " 118.3 3570.28 121.23 3570.09 124.15 3569.84 127.07 3569.54 129.99 3569.35\n", - " 132.91 3569.06 135.61 3568.76 138.32 3568.55 141.02 3568.31 143.73 3568.06\n", - " 146.43 3567.8 149.13 3567.56 151.84 3567.36 154.54 3567.21 157.25 3567.02\n", - " 160.18 3566.79 163.12 3566.64 166.05 3566.52 166.06 3566.52 168.99 3566.11\n", - " 171.92 3565.19 174.86 3564.5 177.79 3564.13 180.73 3563.93 183.66 3563.87\n", - " 186.6 3563.95 189.53 3564.27 192.46 3564.58 193.82 3564.71 195.4 3564.86\n", - " 198.33 3565.06 201.27 3565.12 204.2 3565 207.18 3565.01 210.15 3565.06\n", - " 213.13 3565.15 216.11 3565.32 219.08 3565.42 222.06 3565.53 225.03 3565.65\n", - " 228.01 3565.71 230.98 3565.72 233.96 3565.74 236.93 3565.77 239.91 3565.88\n", - " 242.88 3566.02 245.86 3566.22 248.83 3566.48 251.81 3566.79 254.78 3567.09\n", - " 257.76 3567.32 260.73 3567.56 263.71 3567.78 266.68 3568.01 269.66 3568.22\n", - " 272.64 3568.35 275.61 3568.51 278.59 3568.61 281.56 3568.69 284.54 3568.74\n", - " 287.51 3568.86 290.49 3569.03 293.46 3569.18 296.44 3569.26 299.41 3569.36\n", - " 302.39 3569.47 305.36 3569.52 308.34 3569.63 311.31 3569.71 314.29 3569.84\n", - " 317.26 3569.98 320.24 3570.07 323.21 3570.1 326.19 3570.23 329.16 3570.3\n", - " 332.14 3570.47 335.12 3570.49 338.09 3570.6 341.07 3570.65 344.04 3570.73\n", - " 347.02 3570.84 349.99 3570.94 352.97 3571.02 355.94 3571 358.92 3570.94\n", - " 361.89 3570.85 364.87 3570.83 367.84 3571.01 370.82 3571.24 373.79 3571.35\n", - " 376.77 3571.52 379.74 3571.52 382.72 3571.53 385.69 3571.63\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .05 0 171.92 .035 0 201.27 .05 0\n", - "Bank Sta=171.92,201.27\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,665 ,113.86,111.37,108.6\n", - "XS GIS Cut Line=4\n", - "959963.651606798268352.968359977960118.028922722268425.752335146\n", - "960197.031061307268438.418320313960326.476996645268399.388870806\n", - "Node Last Edited Time=Sep/17/2015 21:01:33\n", - "#Sta/Elev= 133 \n", - " 0 3573.83 2.99 3573.73 5.99 3573.64 8.98 3573.59 11.98 3573.52\n", - " 14.97 3573.43 17.97 3573.35 20.96 3573.27 23.95 3573.19 26.95 3573.15\n", - " 29.94 3573.07 32.94 3573 35.93 3572.88 38.93 3572.75 41.92 3572.6\n", - " 44.91 3572.47 47.91 3572.33 50.9 3572.27 53.9 3572.11 56.89 3572.04\n", - " 59.89 3571.88 62.88 3571.78 65.87 3571.61 68.87 3571.5 71.86 3571.43\n", - " 74.86 3571.35 77.85 3571.19 80.85 3571.02 83.84 3570.89 86.83 3570.71\n", - " 89.83 3570.61 92.82 3570.53 95.82 3570.4 98.81 3570.36 101.81 3570.32\n", - " 104.8 3570.21 107.79 3570.06 110.79 3569.88 113.78 3569.74 116.78 3569.53\n", - " 119.77 3569.46 122.77 3569.35 125.76 3569.2 128.75 3569 131.75 3568.93\n", - " 134.74 3568.8 137.74 3568.58 140.73 3568.35 143.73 3568.31 146.72 3568.24\n", - " 149.71 3568.16 152.71 3568.02 155.7 3567.87 158.7 3567.75 161.69 3567.67\n", - " 164.69 3567.51 167.68 3567.23 170.67 3567.03 173.64 3566.75 176.6 3566.31\n", - " 179.56 3565.72 182.53 3565.25 184.9 3564.95 185.49 3564.87 188.46 3564.75\n", - " 191.42 3564.48 194.38 3564.03 197.35 3563.46 200.31 3563.18 203.27 3562.95\n", - " 206.24 3562.9 209.2 3562.95 212.16 3563.24 215.13 3563.64 218.09 3563.91\n", - " 221.05 3564.27 222.45 3564.39 224.02 3564.52 226.98 3564.69 229.94 3564.95\n", - " 232.91 3565.16 235.87 3565.3 238.83 3565.39 241.8 3565.53 244.76 3565.61\n", - " 247.72 3565.66 250.69 3565.9 253.62 3566.02 256.56 3566.12 259.5 3566.15\n", - " 262.44 3566.16 265.38 3566.21 268.32 3566.27 271.26 3566.39 274.2 3566.52\n", - " 277.14 3566.55 280.08 3566.59 283.02 3566.71 285.96 3566.87 288.9 3567.05\n", - " 291.83 3567.22 294.77 3567.39 297.71 3567.51 300.65 3567.66 303.59 3567.79\n", - " 306.53 3567.85 309.47 3567.92 312.41 3568.06 315.35 3568.16 318.29 3568.19\n", - " 321.23 3568.21 324.16 3568.2 327.1 3568.32 330.04 3568.45 332.98 3568.68\n", - " 335.92 3568.85 338.86 3569.06 341.8 3569.2 344.74 3569.26 347.68 3569.32\n", - " 350.62 3569.44 353.56 3569.58 356.5 3569.64 359.43 3569.78 362.37 3569.94\n", - " 365.31 3570.04 368.25 3569.95 371.19 3570.05 374.13 3570.09 377.07 3570.2\n", - " 380.01 3570.18 382.95 3570.13 385.89 3570.06\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .035 0 191.42 .016 0 222.45 .035 0\n", - "Bank Sta=191.42,222.45\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,553 ,195.76,197.46,199.45\n", - "XS GIS Cut Line=5\n", - " 959968.78676714268478.844748646960110.713976726 268541.34462364\n", - "960190.140983224268542.646786392960266.963992298268490.563557222\n", - "960335.974352971268474.938260391\n", - "Node Last Edited Time=Sep/17/2015 21:02:28\n", - "#Sta/Elev= 137 \n", - " 0 3570.7 2.98 3570.67 5.96 3570.54 8.95 3570.46 11.93 3570.37\n", - " 14.91 3570.31 17.89 3570.32 20.88 3570.41 23.86 3570.38 26.84 3570.33\n", - " 29.82 3570.23 32.81 3570.09 35.79 3570.01 38.77 3569.89 41.75 3569.84\n", - " 44.73 3569.74 47.72 3569.6 50.7 3569.57 53.68 3569.5 56.66 3569.43\n", - " 59.65 3569.35 62.63 3569.29 65.61 3569.25 68.59 3569.22 71.57 3569.13\n", - " 74.56 3569.04 77.54 3568.99 80.52 3568.82 83.5 3568.78 86.49 3568.64\n", - " 89.47 3568.44 92.45 3568.22 95.43 3568.09 98.42 3568.07 101.4 3567.9\n", - " 104.38 3567.66 107.36 3567.43 110.34 3567.29 113.33 3567.14 116.31 3567.01\n", - " 119.29 3566.81 122.27 3566.66 125.26 3566.47 128.24 3566.27 131.22 3566.08\n", - " 134.2 3565.8 137.19 3565.57 140.17 3565.64 143.15 3565.47 146.13 3565.26\n", - " 149.11 3565.11 152.1 3564.97 155.08 3564.75 158.02 3564.61 160.96 3564.31\n", - " 163.91 3563.79 166.85 3563.39 168.12 3563.29 169.79 3563.16 172.73 3562.91\n", - " 175.67 3562.73 178.62 3562.48 181.56 3562.03 184.5 3561.71 187.44 3561.54\n", - " 190.38 3561.47 193.33 3561.53 196.27 3561.93 199.21 3562.46 202.15 3562.81\n", - " 204.26 3562.99 205.1 3563.06 208.04 3563.26 210.98 3563.46 213.92 3563.65\n", - " 216.86 3563.83 219.81 3564.03 222.75 3564.18 225.69 3564.38 228.63 3564.53\n", - " 231.57 3564.53 234.52 3564.51 237.51 3564.54 240.5 3564.69 243.5 3564.75\n", - " 246.49 3564.81 249.49 3564.9 252.48 3565.03 255.47 3565.14 258.47 3565.19\n", - " 261.46 3565.27 264.46 3565.32 267.45 3565.38 270.44 3565.45 273.44 3565.56\n", - " 276.43 3565.58 279.43 3565.67 282.42 3565.78 285.41 3565.84 288.41 3565.78\n", - " 291.4 3565.67 294.4 3565.81 297.39 3566.03 300.38 3566.24 303.38 3566.34\n", - " 306.37 3566.5 309.37 3566.61 312.36 3566.65 315.35 3566.65 318.35 3566.71\n", - " 321.34 3566.81 324.34 3566.91 327.33 3566.81 330.28 3566.73 333.23 3566.61\n", - " 336.18 3566.45 339.12 3566.43 342.07 3566.56 345.02 3566.79 347.97 3567.08\n", - " 350.92 3567.41 353.86 3567.81 356.81 3568.22 359.76 3568.63 362.71 3569.06\n", - " 365.66 3569.42 368.61 3569.76 371.55 3570.05 374.5 3570.21 377.45 3570.34\n", - " 380.4 3570.39 383.35 3570.54 386.3 3570.67 389.24 3570.69 392.19 3570.75\n", - " 395.14 3570.75 398.09 3570.78\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .035 0 172.73 .016 0 204.26 .035 0\n", - "Bank Sta=172.73,204.26\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,356 ,101.29,101.84,101.26\n", - "Node Name=Q\n", - "XS GIS Cut Line=5\n", - "959963.578444228268601.240501225960097.693005383268735.355062395\n", - "960161.495043144268743.167710811960259.151343882268653.323730394\n", - "960343.787001386 268632.49043873\n", - "Node Last Edited Time=Sep/17/2015 21:02:30\n", - "#Sta/Elev= 164 \n", - " 0 3568.28 2.96 3568.25 5.93 3568.17 8.89 3568.1 11.85 3568.07\n", - " 14.82 3567.98 17.78 3567.89 20.75 3567.93 23.71 3567.88 26.67 3567.74\n", - " 29.64 3567.71 32.6 3567.67 35.56 3567.68 38.53 3567.64 41.49 3567.61\n", - " 44.45 3567.61 47.42 3567.61 50.38 3567.64 53.34 3567.67 56.31 3567.68\n", - " 59.27 3567.71 62.23 3567.7 65.2 3567.64 68.16 3567.58 71.12 3567.6\n", - " 74.09 3567.58 77.05 3567.51 80.02 3567.41 82.98 3567.34 85.94 3567.22\n", - " 88.91 3567.11 91.87 3567.06 94.83 3566.93 97.8 3566.8 100.76 3566.71\n", - " 103.72 3566.64 106.69 3566.52 109.65 3566.42 112.61 3566.36 115.58 3566.32\n", - " 118.54 3566.31 121.51 3566.28 124.47 3566.29 127.43 3566.23 130.4 3566.19\n", - " 133.36 3566.08 136.32 3565.96 139.29 3565.79 142.25 3565.65 145.21 3565.53\n", - " 148.18 3565.37 151.14 3565.17 154.1 3565 157.07 3564.95 160.03 3564.88\n", - " 162.99 3564.73 165.96 3564.68 168.92 3564.61 171.89 3564.38 174.85 3564.18\n", - " 177.81 3563.99 180.78 3563.8 183.74 3563.5 186.7 3563.23 189.67 3562.81\n", - " 192.59 3562.07 195.51 3561.34 198.43 3560.82 201.35 3560.51 201.51 3560.49\n", - " 204.28 3560.21 207.2 3560.04 210.12 3559.89 213.04 3559.3 215.96 3558.68\n", - " 218.88 3558.47 221.81 3558.17 224.73 3558.18 227.65 3558.32 230.57 3558.75\n", - " 233.49 3559.13 234.76 3559.3 236.41 3559.53 239.34 3559.91 242.26 3560.17\n", - " 245.18 3560.32 248.1 3560.49 251.02 3560.57 253.95 3560.62 256.89 3560.76\n", - " 259.84 3560.95 262.79 3561.15 265.74 3561.33 268.69 3561.52 271.64 3561.62\n", - " 274.59 3561.61 277.54 3561.62 280.48 3561.66 283.43 3561.74 286.38 3561.89\n", - " 289.33 3561.98 292.28 3562.06 295.23 3562.19 298.18 3562.2 301.13 3562.24\n", - " 304.08 3562.3 307.02 3562.34 309.97 3562.36 312.92 3562.42 315.87 3562.49\n", - " 318.82 3562.57 321.77 3562.71 324.72 3562.9 327.67 3563 330.62 3563.11\n", - " 333.56 3563.23 336.51 3563.44 339.46 3563.69 342.41 3563.88 345.36 3563.88\n", - " 348.31 3563.89 351.26 3563.94 354.21 3564.01 357.15 3564.13 360.1 3564.19\n", - " 363.05 3564.19 366 3564.22 368.95 3564.31 371.9 3564.44 374.85 3564.69\n", - " 377.8 3564.71 380.75 3564.65 383.69 3564.69 386.64 3564.81 389.55 3564.91\n", - " 392.45 3564.81 395.36 3564.75 398.26 3564.81 401.17 3564.45 404.08 3564.39\n", - " 406.98 3564.28 409.89 3564.27 412.79 3564.45 415.7 3564.69 418.6 3565.01\n", - " 421.51 3565.34 424.41 3565.59 427.32 3565.81 430.22 3566.07 433.13 3566.35\n", - " 436.03 3566.59 438.94 3566.74 441.85 3567.05 444.75 3567.19 447.66 3567.22\n", - " 450.56 3567.28 453.47 3567.38 456.37 3567.41 459.28 3567.47 462.18 3567.43\n", - " 465.09 3567.47 467.99 3568.3 470.9 3568.92 473.8 3568.95\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .035 0 213.04 .016 0 234.76 .035 0\n", - "Bank Sta=213.04,234.76\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,254 ,62.19,65.19,67.59\n", - "XS GIS Cut Line=7\n", - "960051.049069807268716.112318724960078.876770049 268794.23322548\n", - "960092.627726808268835.981501564960164.099368632268846.032334477\n", - "960274.776312634268825.198714733 960325.67743562268790.328950138\n", - "960429.483335241268797.924503769\n", - "Node Last Edited Time=Sep/17/2015 21:02:32\n", - "#Sta/Elev= 131 \n", - " 0 3565.98 2.96 3565.75 5.92 3565.56 8.89 3565.4 11.85 3565.21\n", - " 14.81 3564.97 17.77 3564.71 20.73 3564.5 23.69 3564.37 26.66 3564.25\n", - " 29.62 3564.14 32.58 3564.02 35.54 3563.97 38.5 3563.87 41.46 3563.61\n", - " 44.43 3563.51 47.39 3563.55 50.35 3563.54 53.31 3563.49 56.27 3563.46\n", - " 59.24 3563.37 62.2 3563.22 65.16 3563.06 68.12 3562.89 71.08 3562.76\n", - " 74.04 3562.66 77.01 3562.58 79.97 3562.48 82.93 3562.33 85.86 3562.2\n", - " 88.79 3562.09 91.72 3562.01 94.65 3561.9 97.58 3561.86 100.51 3561.84\n", - " 103.44 3561.75 106.37 3561.71 109.3 3561.72 112.23 3561.7 115.16 3561.63\n", - " 118.09 3561.51 121.02 3561.34 123.95 3561.15 126.88 3560.98 129.77 3560.58\n", - " 132.66 3560.17 135.54 3559.8 138.43 3559.51 140.72 3559.31 141.32 3559.26\n", - " 144.21 3559.02 147.09 3558.67 149.98 3558.03 152.87 3557.32 155.75 3557.05\n", - " 158.64 3557.08 161.53 3557.14 164.41 3557.6 167.3 3558.09 167.51 3558.11\n", - " 170.19 3558.41 173.08 3558.6 175.96 3558.67 178.85 3558.82 181.74 3559.05\n", - " 184.62 3559.19 187.51 3559.28 190.4 3559.47 193.28 3559.63 196.17 3559.64\n", - " 199.06 3559.65 202.02 3559.73 204.99 3559.83 207.95 3559.89 210.91 3559.93\n", - " 213.88 3560.01 216.84 3560.13 219.8 3560.25 222.77 3560.38 225.73 3560.49\n", - " 228.7 3560.52 231.66 3560.53 234.62 3560.6 237.59 3560.65 240.55 3560.72\n", - " 243.51 3560.82 246.48 3560.9 249.44 3560.98 252.41 3561.11 255.37 3561.21\n", - " 258.33 3561.38 261.3 3561.41 264.26 3561.41 267.22 3561.53 270.19 3561.6\n", - " 273.15 3561.56 276.11 3561.67 279.08 3561.89 282.04 3562.09 285.01 3562.13\n", - " 287.97 3562.01 290.93 3562.09 293.9 3562.25 296.86 3562.29 299.82 3562.34\n", - " 302.79 3562.5 305.75 3562.61 308.72 3562.64 311.68 3562.64 314.59 3562.57\n", - " 317.51 3562.52 320.42 3562.5 323.34 3562.44 326.25 3562.41 329.17 3562.41\n", - " 332.08 3562.49 335 3562.69 337.91 3563.01 340.83 3563.15 343.74 3563.31\n", - " 346.66 3563.41 349.57 3563.54 352.49 3563.65 355.4 3563.77 358.32 3563.95\n", - " 361.23 3564.03 364.15 3564.16 367.06 3564.27 369.98 3564.34 372.89 3564.34\n", - " 478 3566\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .035 0 149.98 .016 0 167.51 .035 0\n", - "Bank Sta=149.98,167.51\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\",...,\"Type RM Length L Ch R = 1 ,1796 ,289.59,293.78,304.33\n", - "Node Name=C\n", - "XS GIS Cut Line=5\n", - "957019.704410717274956.661189303957880.381495133274810.827819556\n", - "958159.027263299274750.931941971958409.027419478274678.015421137\n", - "959028.818830803274605.098572224\n", - "Node Last Edited Time=Sep/17/2015 21:28:25\n", - "#Sta/Elev= 230 \n", - " 0 3499.57 5 3499.78 11 3499.56 21 3499.33 24 3499.05\n", - " 35 3498.91 43 3498.68 48 3498.42 53 3498.67 55 3498.43\n", - " 57 3498.17 60 3498.44 71 3498.25 76 3498.04 81 3497.81\n", - " 89.99 3497.59 95.99 3497.37 102.99 3497.59 108.99 3497.35 117.99 3497.15\n", - " 123.99 3496.92 133.99 3496.78 138.99 3497.02 143.99 3496.79 146.99 3496.57\n", - " 150.99 3496.36 161.99 3496.22 172.99 3496.13 183.99 3496.17 191.99 3495.92\n", - " 202.99 3495.75 211.99 3495.54 222.99 3495.65 226.99 3495.35 237.99 3495.22\n", - " 250.98 3494.95 260.98 3494.73 272.98 3494.72 283.98 3494.61 289.98 3494.41\n", - " 300.98 3494.39 311.98 3494.44 318.98 3494.21 329.98 3494.14 340.98 3494.21\n", - " 351.98 3494.2 362.98 3493.97 368.98 3493.74 379.98 3493.73 390.98 3493.72\n", - " 401.98 3493.59 412.98 3493.43 423.97 3493.36 434.97 3493.18 445.97 3493.11\n", - " 456.97 3493.02 465.97 3493.25 471.97 3492.96 478.97 3492.75 489.97 3492.75\n", - " 497.97 3492.51 508.97 3492.45 519.97 3492.31 530.97 3492.28 536.97 3492.05\n", - " 547.97 3491.93 558.97 3491.98 569.97 3491.84 576.97 3491.57 587.97 3491.66\n", - " 598.96 3491.49 601.96 3491.28 612.96 3491.15 623.96 3491.08 634.96 3491.32\n", - " 642.96 3491.08 653.96 3490.92 664.96 3490.9 675.96 3490.91 686.96 3490.74\n", - " 692.96 3490.95 703.96 3491 714.96 3491.05 722.96 3490.81 733.96 3490.8\n", - " 744.96 3490.66 750.96 3490.35 755.96 3490.14 764.96 3489.85 769.96 3489.64\n", - " 774.95 3489.43 778.95 3489.21 783.95 3488.99 790.95 3488.68 801.95 3488.63\n", - " 812.95 3488.76 823.95 3488.68 826.95 3488.9 830.95 3489.13 836.95 3489.39\n", - " 838.95 3489.63 842.95 3489.88 845.95 3490.11 849.95 3490.34 855.95 3490.58\n", - " 861.95 3490.84 867.95 3490.63 872.95 3490.92 883.91 3490.94 894.87 3491.04\n", - " 905.84 3491.27 916.8 3491.07 924.77 3491.29 930.75 3491.5 941.71 3491.6\n", - " 946.69 3491.82 952.67 3492.04 954.67 3492.26 965.63 3492.1 973.6 3492.32\n", - " 982.57 3492.53 993.53 3492.72 1004.49 3492.67 1015.46 3492.72 1026.42 3492.75\n", - " 1030.4 3492.47 1036.38 3492.69 1047.34 3492.63 1058.31 3492.5 1069.27 3492.52\n", - " 1080.23 3492.41 1091.19 3492.46 1102.15 3492.57 1105.14 3492.31 1112.12 3492.59\n", - " 1123.08 3492.58 1134.04 3492.49 1145 3492.62 1150.98 3492.85 1157.96 3493.07\n", - " 1160.95 3493.32 1166.94 3492.87 1169.93 3492.61 1172.92 3492.31 1174.92 3492.01\n", - " 1176.92 3491.78 1187.89 3491.69 1197.87 3491.48 1208.84 3491.62 1216.83 3491.93\n", - " 1218.82 3492.25 1220.82 3492.6 1226.8 3492.32 1235.78 3492.53 1246.76 3492.5\n", - " 1257.73 3492.44 1268.71 3492.47 1279.68 3492.48 1290.66 3492.47 1301.64 3492.65\n", - " 1312.61 3492.69 1323.59 3492.6 1334.56 3492.65 1340.55 3492.85 1351.52 3492.84\n", - " 1362.5 3493.03 1373.47 3493.11 1384.45 3493.26 1396.42 3493.14 1407.4 3493.22\n", - " 1418.37 3493.1 1429.36 3493.25 1440.34 3493.18 1451.32 3492.99 1462.31 3493.04\n", - " 1473.29 3493.13 1484.27 3493.06 1495.26 3493.12 1506.24 3493.09 1515.23 3492.86\n", - " 1526.21 3492.94 1537.2 3492.8 1548.18 3492.59 1559.16 3492.8 1570.15 3493\n", - " 1581.13 3493.09 1592.11 3493.13 1603.1 3493.26 1614.08 3493.43 1625.06 3493.48\n", - " 1636.05 3493.65 1647.03 3493.51 1658.02 3493.57 1671.99 3493.43 1682.98 3493.33\n", - " 1693.96 3493.45 1704.95 3493.58 1715.93 3493.69 1725.91 3493.9 1736.9 3493.94\n", - " 1747.88 3494.03 1758.87 3493.94 1769.85 3493.96 1780.83 3494.15 1791.82 3494.06\n", - " 1801.8 3494.29 1812.79 3494.39 1823.77 3494.34 1834.75 3494.41 1845.74 3494.45\n", - " 1856.72 3494.51 1867.7 3494.31 1878.69 3494.33 1881.68 3494.12 1892.67 3494.23\n", - " 1903.65 3494.35 1914.63 3494.45 1925.62 3494.47 1936.6 3494.35 1947.58 3494.42\n", - " 1958.57 3494.4 1969.55 3494.4 1980.54 3494.52 1991.52 3494.74 1995.51 3495.01\n", - " 2006.5 3494.82 2017.48 3494.86 2028.46 3494.94 2035.45 3495.15 2042.44 3495.26\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .035 0 714.96 .035 0 861.95 .035 0\n", - "#XS Ineff= 3 ,-1 \n", - " 0 200 3499 200 677.95 3496.65 923.95 2050 3496.65\n", - "Permanent Ineff=\n", - " F F F\n", - "Bank Sta=714.96,861.95\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,1502 ,293.56,265.51,193.56\n", - "Node Name=B\n", - "XS GIS Cut 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445.99 3490.37 456.98 3490.37 467.98 3490.45 477.98 3490.21\n", - " 483.98 3489.96 490.98 3490.24 498.98 3490.02 509.98 3489.85 513.98 3489.64\n", - " 524.98 3489.49 535.98 3489.48 544.98 3489.27 555.98 3489.06 566.98 3488.94\n", - " 577.98 3488.84 583.98 3488.64 595.98 3488.51 606.98 3488.54 610.98 3488.78\n", - " 619.98 3488.55 630.98 3488.41 638.98 3488.62 644.98 3488.41 651.98 3488.2\n", - " 662.98 3487.95 665.98 3487.69 675.98 3487.48 682.98 3487.72 685.98 3487.98\n", - " 694.98 3487.75 697.98 3487.46 708.98 3487.27 719.98 3487.2 724.98 3487.45\n", - " 735.98 3487.53 741.98 3487.77 752.97 3487.58 755.97 3487.21 757.97 3486.87\n", - " 759.97 3486.58 765.97 3486.35 774.97 3486.59 776.97 3486.85 778.97 3487.1\n", - " 780.97 3487.41 783.97 3487.63 787.97 3487.85 790.97 3488.09 801.97 3488.06\n", - " 806.97 3487.82 812.97 3487.49 823.97 3487.63 831.97 3487.84 842.97 3487.83\n", - " 846.97 3488.05 857.97 3488.22 861.97 3488.01 864.97 3487.77 875.97 3487.67\n", - " 886.97 3487.56 897.97 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3493.32\n", - " 1426.8 3493.47 1437.79 3493.59 1448.78 3493.72 1459.77 3493.83 1470.76 3494.02\n", - " 1481.75 3494.07 1492.74 3494.23 1503.73 3494.3 1514.72 3494.34 1525.71 3494.3\n", - " 1536.7 3494.35 1547.69 3494.4 1558.68 3494.53 1569.67 3494.7 1580.66 3494.68\n", - " 1591.65 3494.73 1602.64 3494.77 1610.64 3494.98 1621.63 3494.91 1632.62 3494.88\n", - " 1642.61 3495.09 1653.6 3495.06 1664.59 3494.92 1669.58 3495.13 1680.57 3495.11\n", - " 1691.56 3495.17 1702.55 3495.11 1713.54 3495.1 1724.53 3495.21 1735.52 3495.32\n", - " 1746.51 3495.12 1751.51 3495.4 1762.5 3495.42 1773.49 3495.35 1784.48 3495.3\n", - " 1795.47 3495.27 1806.46 3495.3 1817.45 3495.42 1828.44 3495.5 1839.43 3495.72\n", - " 1850.42 3495.66 1861.41 3495.72 1872.4 3495.68 1883.39 3495.85 1894.38 3495.8\n", - " 1905.37 3495.99 1916.36 3495.9 1927.35 3496.02 1938.34 3495.9 1949.33 3495.87\n", - " 1960.32 3495.86 1971.31 3495.88 1982.3 3495.77 1993.29 3495.79 2004.28 3495.84\n", - " 2015.27 3495.88 2026.26 3495.85 2037.26 3495.95 2048.25 3495.84 2057.24 3495.9\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .035 0 638.98 .035 0 857.97 .035 0\n", - "#XS Ineff= 2 , 0 \n", - " 0 736.72 3488.65 789.59 0 3488.65\n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=638.98,857.97\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,1236 ,321.77,422.78,284.8\n", - "XS GIS Cut Line=5\n", - "957333.506604642275382.442489982957761.891902715275433.223556399\n", - "958113.454519808275266.556894973958215.016980723275237.911282972\n", - "958216.318815395275106.400719225\n", - "Node Last Edited Time=Sep/03/2015 14:13:08\n", - "#Sta/Elev= 122 \n", - " 0 3490.01 10.98 3489.9 21.97 3489.72 27.96 3489.5 31.95 3489.73\n", - " 39.94 3489.49 52.92 3489.24 63.91 3489.27 74.89 3489.2 85.88 3489\n", - " 96.86 3489.03 104.85 3488.78 115.83 3488.73 126.82 3488.79 137.8 3488.66\n", - " 146.79 3488.41 157.77 3488.28 168.76 3488.2 179.74 3488.18 190.73 3488.21\n", - " 201.71 3488.23 212.7 3488.21 223.68 3488.08 234.67 3488.09 245.65 3488.05\n", - " 256.63 3488.19 267.62 3488.01 278.6 3487.87 289.59 3487.97 298.57 3487.75\n", - " 309.56 3487.68 320.54 3487.66 331.53 3487.64 342.51 3487.46 353.5 3487.48\n", - " 359.49 3487.26 370.47 3487.42 377.46 3487.19 388.45 3487.21 399.43 3487.23\n", - " 406.42 3487.02 408.42 3486.81 413.41 3486.58 424.4 3486.56 435.38 3486.58\n", - " 441.36 3486.79 452.34 3486.69 460.32 3486.48 471.29 3486.54 482.26 3486.51\n", - " 492.24 3486.27 500.22 3486.06 506.21 3485.84 517.18 3485.64 521.17 3485.4\n", - " 532.14 3485.33 537.13 3485.13 548.11 3485.01 553.09 3484.78 564.07 3484.69\n", - " 575.04 3484.58 579.03 3484.82 584.02 3484.6 589.01 3484.39 592 3484.18\n", - " 602.98 3484.3 613.95 3484.3 624.92 3484.29 635.9 3484.34 646.87 3484.38\n", - " 657.84 3484.42 668.82 3484.61 675.8 3484.88 682.78 3485.1 690.76 3484.9\n", - " 701.74 3484.94 704.73 3485.23 715.71 3485.37 726.68 3485.53 737.65 3485.44\n", - " 748.63 3485.57 759.6 3485.53 765.59 3485.76 776.56 3485.9 787.53 3486.08\n", - " 792.52 3486.34 797.51 3486.56 806.49 3486.77 810.48 3486.99 813.47 3487.25\n", - " 824.44 3487.19 828.42 3487.42 835.39 3487.91 848.33 3488.17 859.28 3488.06\n", - " 863.26 3487.68 864.26 3487.43 875.21 3487.57 886.16 3487.5 898.1 3487.38\n", - " 905.07 3487.07 909.06 3486.84 913.04 3486.59 917.02 3486.81 920.01 3487.06\n", - " 922.99 3487.3 933.95 3487.44 946.9 3487.57 957.86 3487.75 965.83 3487.97\n", - " 976.79 3487.9 986.76 3488.11 997.72 3488.22 1008.68 3488.35 1019.64 3488.59\n", - " 1030.6 3488.75 1035.58 3488.98 1041.56 3489.19 1046.54 3489.41 1051.52 3489.65\n", - " 1056.5 3489.95 1057.5 3490.04\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .035 0 548.11 .035 0 682.78 .035 0\n", - "#XS Ineff= 2 , 0 \n", - " 0 480.89 3486.65 684.5 0 3486.65\n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=548.11,682.78\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,814 ,551.53,560.74,570.49\n", - "Node Name=A\n", - "XS 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473.68 3483.06 484.67 3482.93 495.66 3482.88 506.65 3482.69 517.63 3482.72\n", - " 528.62 3482.78 539.61 3482.84 550.59 3482.84 561.58 3482.88 572.57 3482.88\n", - " 583.56 3482.78 594.54 3482.87 605.53 3482.8 616.52 3482.75 627.5 3482.73\n", - " 638.49 3482.74 649.48 3482.56 660.46 3482.57 671.45 3482.64 682.44 3482.56\n", - " 693.43 3482.59 704.41 3482.53 715.4 3482.67 718.4 3482.88 722.39 3482.67\n", - " 725.39 3482.46 729.38 3482.21 739.37 3482.57 741.37 3482.9 743.37 3483.18\n", - " 750.36 3483.43 761.34 3483.23 764.34 3482.95 767.34 3482.71 771.33 3482.45\n", - " 774.33 3482.17 778.32 3481.92 784.32 3482.16 786.32 3482.42 788.31 3482.63\n", - " 793.31 3482.85 796.3 3483.06 807.29 3483.16 818.29 3483.13 829.28 3483.12\n", - " 840.28 3483.27 851.27 3483.13 855.27 3483.36 866.27 3483.3 877.26 3483.45\n", - " 883.26 3483.67 891.26 3483.87 897.26 3484.09 908.25 3484.03 919.25 3483.96\n", - " 930.25 3484.06 941.24 3484.19 952.24 3484.34 963.23 3484.44 974.23 3484.59\n", - " 985.23 3484.57 996.22 3484.71 1007.22 3484.55 1015.22 3484.77 1021.21 3485\n", - " 1032.21 3485.16 1044.2 3485.15 1052.2 3485.37 1250 3488\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .035 0 718.4 .035 0 796.3 .035 0\n", - "#XS Ineff= 2 , 0 \n", - " 0 796 0 3484\n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=718.4,796.3\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,253 ,0,0,0\n", - "XS GIS Cut Line=5\n", - "957737.846675217276018.727306649957894.096690804276104.143802479\n", - "958321.874378562276067.338429898958435.763258383 275768.72715047\n", - "958471.391777778275675.310850499\n", - "Node Last Edited Time=Sep/23/2015 12:07:50\n", - "#Sta/Elev= 101 \n", - " 0 3481.6 10.94 3481.46 21.89 3481.25 32.83 3481.37 37.8 3481.16\n", - " 48.75 3480.89 59.69 3480.79 70.63 3480.69 79.59 3480.48 91.52 3480.47\n", - " 102.47 3480.28 113.41 3480.17 124.35 3479.99 132.31 3479.78 143.25 3479.55\n", - " 154.2 3479.48 165.14 3479.32 176.08 3479.25 187.06 3479.25 191.05 3479.03\n", - " 202.04 3478.85 213.02 3478.79 218.01 3478.49 229 3478.38 239.98 3478.2\n", - " 250.96 3478.1 261.95 3478.06 272.93 3478.19 283.91 3478.24 294.9 3478.22\n", - " 305.88 3478.25 316.86 3478.35 327.85 3478.44 338.83 3478.6 349.82 3478.62\n", - " 360.8 3478.62 371.78 3478.83 382.77 3478.95 389.76 3478.68 400.74 3478.59\n", - " 411.72 3478.43 422.71 3478.28 433.69 3478.28 444.67 3478.27 455.66 3478.21\n", - " 466.64 3478.31 477.62 3478.36 488.61 3478.56 499.59 3478.69 508.58 3478.94\n", - " 514.57 3479.22 520.56 3478.95 523.55 3478.67 525.55 3478.34 527.55 3478.1\n", - " 529.54 3477.87 534.54 3478.26 537.53 3478.51 541.53 3478.74 552.51 3478.91\n", - " 557.5 3478.69 561.5 3478.37 563.49 3478.11 568.49 3477.76 579.47 3477.64\n", - " 582.47 3477.89 593.45 3477.89 597.44 3478.14 608.43 3478.18 619.41 3478.41\n", - " 630.4 3478.34 641.38 3478.62 652.37 3478.75 657.36 3478.54 669.35 3478.42\n", - " 676.34 3478.63 687.33 3478.68 698.31 3478.51 709.3 3478.63 720.28 3478.8\n", - " 731.27 3478.98 742.26 3479.09 753.24 3479.04 764.23 3479.2 774.21 3479.42\n", - " 785.2 3479.55 796.19 3479.6 804.18 3479.82 815.16 3479.92 819.16 3480.17\n", - " 830.14 3480.29 841.13 3480.31 852.11 3480.38 863.1 3480.55 875.08 3480.54\n", - " 886.07 3480.61 897.06 3480.61 908.04 3480.71 919.03 3480.84 927.02 3480.9\n", - " 1027 3482\n", - "#Mann= 3 , 0 , 0 \n", - " 0 .035 0 514.57 .035 0 652.37 .035 0\n", - "Bank Sta=514.57,652.37\n", - "XS Rating Curve= 0 ,0\n", - "Exp/Cntr=0.3,0.1\n", - "\n", - "Stream Node=CityDrain ,CityDrain , 0 , 0 ,Upstream end \n", - "\n", - "Stream Node=CityDrain ,CityDrain , 0 , 1 ,Downstream end \n", - "\n", - "Stream Node=Evans-Tonn ,US , 0 , 0 ,Upstream end \n", - "\n", - "Stream Node=Evans-Tonn ,US , 0 , 1 ,Downstream end \n", - "\n", - "Stream Node=Spearfish_Cr ,Old_Channel , 0 , 0 ,Upstream end \n", - "\n", - "Stream Node=Spearfish_Cr ,Old_Channel , 0 , 1 ,Downstream end \n", - "\n", - "Stream Node=Spearfish_cr1 ,Old_us14 , 0 , 0 ,Upstream end \n", - "\n", - "Stream Node=Spearfish_cr1 ,Old_us14 , 0 , 1 ,Downstream end \n", - "\n", - "Stream Node=Spearfish_Creek ,Spearfish_Creek , 0 , 0 ,Upstream end \n", - "\n", - "Stream Node=Spearfish_Creek ,Spearfish_Creek , 0 , 1 ,Downstream end \n", - "\n", - "Stream Node=Spearfish_Creek ,SpearfishDS , 0 , 0 ,Upstream end \n", - "\n", - "Stream Node=Spearfish_Creek ,SpearfishDS , 0 , 1 ,Downstream end \n", - "\n", - "Stream Node=Spearfish_Creek ,Spearfish_Cr_DS , 0 , 0 ,Upstream end \n", - "\n", - "Stream Node=Spearfish_Creek ,Spearfish_Cr_DS , 0 , 1 ,Downstream end \n", - "\n", - "Chan Stop Cuts=-1 \n", - "\n", - "\n", - "\n", - "Geom Raster=..\\3748.jpg,True,image,, 0 \n", - "Geom Raster=..\\FW US.jpg,True,image,, 0 \n", - "Geom Raster=..\\FW MID.jpg,True,image,, 0 \n", - "Geom Raster=..\\FW DS.jpg,True,image,, 0 \n", - "Use User Specified Reach Order=0\n", - "GIS Units=\n", - "GIS DTM Type=TIN\n", - "GIS DTM=\\\n", - "GIS Stream Layer=\\CityDrainageChannel\n", - "GIS Cross Section Layer=\\XSCutLines_citydrainage\n", - "GIS Map Projection=\n", - "GIS Projection Zone=\n", - "GIS Datum=\n", - "GIS Vertical Datum=\n", - "GIS Data Extents=960025.2397382,268286.1364726,960233.5437836,269067.0043335\n", - "\n", - "GIS Ratio Cuts To Invert=-1\n", - "GIS Limit At Bridges=0\n", - "Composite Channel Slope=5\"]]\n", - "station_elevation_points: [[\"[(0.0, 3574.41), (2.99, 3574.46), (5.97, 3574.38), (8.96, 3574.3), (11.95, 3574.26), (14.94, 3574.29), (17.92, 3574.21), (20.91, 3574.12), (23.9, 3574.1), (26.89, 3574.15), (29.87, 3574.2), (32.86, 3574.21), (35.85, 3574.1), (38.84, 3573.99), (41.82, 3574.02), (44.81, 3573.91), (47.8, 3573.74), (50.79, 3573.66), (53.77, 3573.6), (56.76, 3573.39), (59.75, 3573.24), (62.74, 3573.13), (65.72, 3572.96), (68.65, 3572.78), (71.57, 3572.64), (74.49, 3572.58), (77.41, 3572.28), (80.33, 3572.15), (83.25, 3572.04), (86.17, 3571.89), (89.09, 3571.76), (92.01, 3571.57), (94.94, 3571.33), (97.86, 3571.17), (100.78, 3571.08), (103.7, 3570.9), 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(1927.35, 3496.02), (1938.34, 3495.9), (1949.33, 3495.87), (1960.32, 3495.86), (1971.31, 3495.88), (1982.3, 3495.77), (1993.29, 3495.79), (2004.28, 3495.84), (2015.27, 3495.88), (2026.26, 3495.85), (2037.26, 3495.95), (2048.25, 3495.84), (2057.24, 3495.9)]\",\"[(0.0, 3490.01), (10.98, 3489.9), (21.97, 3489.72), (27.96, 3489.5), (31.95, 3489.73), (39.94, 3489.49), (52.92, 3489.24), (63.91, 3489.27), (74.89, 3489.2), (85.88, 3489.0), (96.86, 3489.03), (104.85, 3488.78), (115.83, 3488.73), (126.82, 3488.79), (137.8, 3488.66), (146.79, 3488.41), (157.77, 3488.28), (168.76, 3488.2), (179.74, 3488.18), (190.73, 3488.21), (201.71, 3488.23), (212.7, 3488.21), (223.68, 3488.08), (234.67, 3488.09), (245.65, 3488.05), (256.63, 3488.19), (267.62, 3488.01), (278.6, 3487.87), (289.59, 3487.97), (298.57, 3487.75), (309.56, 3487.68), (320.54, 3487.66), (331.53, 3487.64), (342.51, 3487.46), (353.5, 3487.48), (359.49, 3487.26), (370.47, 3487.42), (377.46, 3487.19), (388.45, 3487.21), (399.43, 3487.23), (406.42, 3487.02), (408.42, 3486.81), (413.41, 3486.58), (424.4, 3486.56), (435.38, 3486.58), (441.36, 3486.79), (452.34, 3486.69), (460.32, 3486.48), (471.29, 3486.54), (482.26, 3486.51), (492.24, 3486.27), (500.22, 3486.06), (506.21, 3485.84), (517.18, 3485.64), (521.17, 3485.4), (532.14, 3485.33), (537.13, 3485.13), (548.11, 3485.01), (553.09, 3484.78), (564.07, 3484.69), (575.04, 3484.58), (579.03, 3484.82), (584.02, 3484.6), (589.01, 3484.39), (592.0, 3484.18), (602.98, 3484.3), (613.95, 3484.3), (624.92, 3484.29), (635.9, 3484.34), (646.87, 3484.38), (657.84, 3484.42), (668.82, 3484.61), (675.8, 3484.88), (682.78, 3485.1), (690.76, 3484.9), (701.74, 3484.94), (704.73, 3485.23), (715.71, 3485.37), (726.68, 3485.53), (737.65, 3485.44), (748.63, 3485.57), (759.6, 3485.53), (765.59, 3485.76), (776.56, 3485.9), (787.53, 3486.08), (792.52, 3486.34), (797.51, 3486.56), (806.49, 3486.77), (810.48, 3486.99), (813.47, 3487.25), (824.44, 3487.19), (828.42, 3487.42), (835.39, 3487.91), (848.33, 3488.17), (859.28, 3488.06), (863.26, 3487.68), (864.26, 3487.43), (875.21, 3487.57), (886.16, 3487.5), (898.1, 3487.38), (905.07, 3487.07), (909.06, 3486.84), (913.04, 3486.59), (917.02, 3486.81), (920.01, 3487.06), (922.99, 3487.3), (933.95, 3487.44), (946.9, 3487.57), (957.86, 3487.75), (965.83, 3487.97), (976.79, 3487.9), (986.76, 3488.11), (997.72, 3488.22), (1008.68, 3488.35), (1019.64, 3488.59), (1030.6, 3488.75), (1035.58, 3488.98), (1041.56, 3489.19), (1046.54, 3489.41), (1051.52, 3489.65), (1056.5, 3489.95), (1057.5, 3490.04)]\",\"[(0.0, 3486.61), (7.0, 3486.41), (17.99, 3486.27), (24.98, 3486.06), (35.98, 3485.99), (42.97, 3485.78), (53.97, 3485.65), (63.96, 3485.43), (74.95, 3485.53), (85.95, 3485.38), (96.94, 3485.42), (107.93, 3485.34), (118.93, 3485.33), (129.92, 3485.25), (140.91, 3485.21), (151.91, 3485.3), (162.9, 3485.26), (173.89, 3485.41), (184.89, 3485.27), (195.88, 3485.32), (206.87, 3485.45), (217.87, 3485.48), (228.86, 3485.39), (239.85, 3485.48), (249.85, 3485.23), (260.84, 3485.34), (271.83, 3485.17), (277.83, 3484.96), (288.82, 3484.96), (298.82, 3484.65), (309.81, 3484.52), (319.8, 3484.29), (330.8, 3484.06), (342.79, 3483.95), (353.78, 3483.99), (363.78, 3483.75), (374.77, 3483.77), (385.76, 3483.79), (396.76, 3483.84), (407.75, 3483.82), (418.74, 3483.69), (429.74, 3483.78), (440.72, 3483.51), (451.71, 3483.3), (462.7, 3483.2), (473.68, 3483.06), (484.67, 3482.93), (495.66, 3482.88), (506.65, 3482.69), (517.63, 3482.72), (528.62, 3482.78), (539.61, 3482.84), (550.59, 3482.84), (561.58, 3482.88), (572.57, 3482.88), (583.56, 3482.78), (594.54, 3482.87), (605.53, 3482.8), (616.52, 3482.75), (627.5, 3482.73), (638.49, 3482.74), (649.48, 3482.56), (660.46, 3482.57), (671.45, 3482.64), (682.44, 3482.56), (693.43, 3482.59), (704.41, 3482.53), (715.4, 3482.67), (718.4, 3482.88), (722.39, 3482.67), (725.39, 3482.46), (729.38, 3482.21), (739.37, 3482.57), (741.37, 3482.9), (743.37, 3483.18), (750.36, 3483.43), (761.34, 3483.23), (764.34, 3482.95), (767.34, 3482.71), (771.33, 3482.45), (774.33, 3482.17), (778.32, 3481.92), (784.32, 3482.16), (786.32, 3482.42), (788.31, 3482.63), (793.31, 3482.85), (796.3, 3483.06), (807.29, 3483.16), (818.29, 3483.13), (829.28, 3483.12), (840.28, 3483.27), (851.27, 3483.13), (855.27, 3483.36), (866.27, 3483.3), (877.26, 3483.45), (883.26, 3483.67), (891.26, 3483.87), (897.26, 3484.09), (908.25, 3484.03), (919.25, 3483.96), (930.25, 3484.06), (941.24, 3484.19), (952.24, 3484.34), (963.23, 3484.44), (974.23, 3484.59), (985.23, 3484.57), (996.22, 3484.71), (1007.22, 3484.55), (1015.22, 3484.77), (1021.21, 3485.0), (1032.21, 3485.16), (1044.2, 3485.15), (1052.2, 3485.37), (1250.0, 3488.0)]\",\"[(0.0, 3481.6), (10.94, 3481.46), (21.89, 3481.25), (32.83, 3481.37), (37.8, 3481.16), (48.75, 3480.89), (59.69, 3480.79), (70.63, 3480.69), (79.59, 3480.48), (91.52, 3480.47), (102.47, 3480.28), (113.41, 3480.17), (124.35, 3479.99), (132.31, 3479.78), (143.25, 3479.55), (154.2, 3479.48), (165.14, 3479.32), (176.08, 3479.25), (187.06, 3479.25), (191.05, 3479.03), (202.04, 3478.85), (213.02, 3478.79), (218.01, 3478.49), (229.0, 3478.38), (239.98, 3478.2), (250.96, 3478.1), (261.95, 3478.06), (272.93, 3478.19), (283.91, 3478.24), (294.9, 3478.22), (305.88, 3478.25), (316.86, 3478.35), (327.85, 3478.44), (338.83, 3478.6), (349.82, 3478.62), (360.8, 3478.62), (371.78, 3478.83), (382.77, 3478.95), (389.76, 3478.68), (400.74, 3478.59), (411.72, 3478.43), (422.71, 3478.28), (433.69, 3478.28), (444.67, 3478.27), (455.66, 3478.21), (466.64, 3478.31), (477.62, 3478.36), (488.61, 3478.56), (499.59, 3478.69), (508.58, 3478.94), (514.57, 3479.22), (520.56, 3478.95), (523.55, 3478.67), (525.55, 3478.34), (527.55, 3478.1), (529.54, 3477.87), (534.54, 3478.26), (537.53, 3478.51), (541.53, 3478.74), (552.51, 3478.91), (557.5, 3478.69), (561.5, 3478.37), (563.49, 3478.11), (568.49, 3477.76), (579.47, 3477.64), (582.47, 3477.89), (593.45, 3477.89), (597.44, 3478.14), (608.43, 3478.18), (619.41, 3478.41), (630.4, 3478.34), (641.38, 3478.62), (652.37, 3478.75), (657.36, 3478.54), (669.35, 3478.42), (676.34, 3478.63), (687.33, 3478.68), (698.31, 3478.51), (709.3, 3478.63), (720.28, 3478.8), (731.27, 3478.98), (742.26, 3479.09), (753.24, 3479.04), (764.23, 3479.2), (774.21, 3479.42), (785.2, 3479.55), (796.19, 3479.6), (804.18, 3479.82), (815.16, 3479.92), (819.16, 3480.17), (830.14, 3480.29), (841.13, 3480.31), (852.11, 3480.38), (863.1, 3480.55), (875.08, 3480.54), (886.07, 3480.61), (897.06, 3480.61), (908.04, 3480.71), (919.03, 3480.84), (927.02, 3480.9), (1027.0, 3482.0)]\"]]\n", - "bank_stations: [[\"['171.92', '201.27']\",\"['191.42', '222.45']\",\"['172.73', '204.26']\",\"['213.04', '234.76']\",\"['149.98', '167.51']\",...,\"['714.96', '861.95']\",\"['638.98', '857.97']\",\"['548.11', '682.78']\",\"['718.4', '796.3']\",\"['514.57', '652.37']\"]]\n", - "number_of_station_elevation_points: [[134,133,137,164,131,...,230,230,122,114,101]]\n", - "number_of_coords: [[6,4,5,5,7,...,5,4,5,5,5]]\n", - "flows: [[\"1.0\n", - "255.0\n", - "1540.0\n", - "9405.0\",\"1.0\n", - "255.0\n", - "1540.0\n", - "9405.0\",\"1.0\n", - "255.0\n", - "1540.0\n", - "9405.0\",\"1.0\n", - "255.0\n", - "1540.0\n", - "9405.0\",\"1.0\n", - "255.0\n", - "1540.0\n", - "9405.0\",...,\"1.0\n", - "1.0\n", - "1.0\n", - "1.0\",\"1.0\n", - "1.0\n", - "1.0\n", - "1.0\",\"1.0\n", - "1.0\n", - "1.0\n", - "1.0\",\"1.0\n", - "1.0\n", - "1.0\n", - "1.0\",\"1.0\n", - "1.0\n", - "1.0\n", - "1.0\"]]\n", - "profile_names: [[\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\",\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\",\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\",\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\",\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\",...,\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\",\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\",\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\",\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\",\"10yr FIS\n", - "50yr FIS\n", - "100yr FIS\n", - "500yr FIS\"]]\n", - "geometry: [[010200000006000000BE4C8CE1A50523C1206B1025266F42419AC8EC697F0523C132F6A6E0286F42419A0006786A0523C17F92F0B1316F4241F0D4F7586A0523C13C43C669356F42418A5AA654500523C159EC2A60386F424133B5B753E50423C10A0FA39B316F4241,01020000000400000016D0FC12D70523C15084122A366F4241E79442B0770523C186A13776406F4241DAB89867470523C1B04E6AFA416F4241157F3194F90423C15BF0A5553B6F4241,0102000000050000008AF5EF3DD10523C185E9765B496F4241D1DFD6A4790523C13D297626526F42417349BC58490523C19953E3ED516F42417EDA28C81B0523C104EC1292496F4241417CFD2DF20423C155BDEBD1466F4241,010200000005000000FC7247C5D10523C1218ACB125C6F42418538B3607D0523C1CFECADD76F6F424144418C6F560523C199DFBBB2706F4241E59308061D0523C14394B776626F4241481EA709EA0423C1D703D9D55E6F4241,0102000000070000003B4C7E239A0523C170FC9E266D6F42412A7B5E8B870523C1148A9AEE786F4241F267D3497E0523C155BEE03B7F6F4241B1E51DA3520523C1C7AE0A64806F424177B850D30F0523C1E0B9EC9F7C6F4241B9769DA4F10423C1B8CB0908776F42412D6C0265B20423C1D74578A4776F4241,...,0102000000050000008A09B101460C23C17CAB3305367342412DA30C133E0A23C1BEF7F0341B734241DAC1C704960923C1AB66DF9610734241C71810A7FF0823C13D30972204734241622B728C880723C177CF00BAF5724241,010200000004000000FB552DF0210C23C12829C54D607342410FF4975B790923C182EB27853E734241EAB104AF3C0923C1176B4AC641734241AD573D4A570723C158C2304029734241,010200000005000000EEF043217E0B23C1830A685F7573424133FBEFAF780A23C14C14E6DC7A73424187CC4B9DA60923C1A9B2D98E5F73424117D0CA81690923C1C554CBA55A7342416F09108C6B0923C175CBA88F46734241,010200000005000000C1089CB40B0B23C1159AB7CEA673424106CF01A81A0A23C15C7FFC8A8F7342416180C58E370923C1992DFC3882734241B17690D0DB0823C1194EEDF56473424124F1636A910823C1CD36043B4D734241,010200000005000000A0EC58B27A0A23C1B0660763D473424126DCAAE5190A23C1327A5B9AE07342411DFA3EB5160923C1F1541AB9D87342417C1FCCEBD70823C1B0E03987AA734241330E7747C40823C17EE9A9139C734241]]\n", - "huc: [[\"10120203\",\"10120203\",\"10120203\",\"10120203\",\"10120203\",...,\"10120203\",\"10120203\",\"10120203\",\"10120203\",\"10120203\"]]\n", - "model_id: [[\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE_1\",...,\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\",\"Spearfish_Cr_LOMR_FEMA_RESPONSE\"]]\n", - "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "us_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "ds_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "__index_level_0__: [[7042,7043,7044,7045,7046,...,7225,7226,7227,7228,7229]]\n" - ] - } - ], - "source": [ - "# Scenario I\n", - "# User Args\n", - "catalog_name = \"xs\"\n", - "huc_num = 10120203\n", - "\n", - "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "tablename = f\"huc_{huc_num}\"\n", - "namespace = \"mip\"\n", - "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", - "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", - "print(iceberg_table.to_string(preview_cols=30))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "8b8254a1-dce8-42f0-8e38-87b0d1192760", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pyarrow.Table\n", - "river: large_string\n", - "reach: large_string\n", - "river_reach: large_string\n", - "river_station: double\n", - "river_reach_rs: large_string\n", - "thalweg: double\n", - "xs_max_elevation: double\n", - "left_reach_length: double\n", - "right_reach_length: double\n", - "channel_reach_length: double\n", - "ras_data: large_string\n", - "station_elevation_points: large_string\n", - "bank_stations: large_string\n", - "number_of_station_elevation_points: int64\n", - "number_of_coords: int64\n", - "flows: large_string\n", - "profile_names: large_string\n", - "geometry: large_binary\n", - "huc: large_string\n", - "model_id: large_string\n", - "us_reach_id: large_string\n", - "us_network_to_id: large_string\n", - "ds_reach_id: large_string\n", - "ds_network_to_id: large_string\n", - "__index_level_0__: int64\n", - "----\n", - "river: [[\"cottonwood_creek\",\"cottonwood_creek\",\"cottonwood_creek\",\"cottonwood_creek\",\"cottonwood_creek\",...,\"south_fork_clear\",\"south_fork_clear\",\"south_fork_clear\",\"south_fork_clear\",\"south_fork_clear\"]]\n", - "reach: [[\"45\",\"45\",\"45\",\"45\",\"45\",...,\"173\",\"173\",\"173\",\"173\",\"173\"]]\n", - "river_reach: [[\"cottonwood_creek,45\",\"cottonwood_creek,45\",\"cottonwood_creek,45\",\"cottonwood_creek,45\",\"cottonwood_creek,45\",...,\"south_fork_clear,173\",\"south_fork_clear,173\",\"south_fork_clear,173\",\"south_fork_clear,173\",\"south_fork_clear,173\"]]\n", - "river_station: [[12970.38,12672.1,12299.53,12153.37,11649.77,...,1595.506,1196.63,797.7532,398.8766,0]]\n", - "river_reach_rs: [[\"cottonwood_creek 45 12970.38\",\"cottonwood_creek 45 12672.1\",\"cottonwood_creek 45 12299.53\",\"cottonwood_creek 45 12153.37\",\"cottonwood_creek 45 11649.77\",...,\"south_fork_clear 173 1595.506\",\"south_fork_clear 173 1196.63\",\"south_fork_clear 173 797.7532\",\"south_fork_clear 173 398.8766\",\"south_fork_clear 173 0.0\"]]\n", - "thalweg: [[1441.132,1440.028,1435.111,1432.966,1430.307,...,1573.805,1570.469,1570.177,1569.928,1568.771]]\n", - "xs_max_elevation: [[1588.847,1669.48,1654.026,1558.699,1527.284,...,1786.711,1767.658,1763.571,1818.624,1873.877]]\n", - "left_reach_length: [[284.287,323.3077,227.909,360.3609,210.6718,...,401.7363,330.3242,391.7664,429.1667,0]]\n", - "right_reach_length: [[303.1325,369.12,136.7996,342.1801,217.7682,...,396.9099,438.9131,407.5078,364.8997,0]]\n", - "channel_reach_length: [[298.2864,372.5664,146.1611,503.6007,210.0612,...,398.8766,398.8766,398.8766,398.8766,0]]\n", - "ras_data: [[\"Type RM Length L Ch R = 1 ,12970.38,284.287,298.2864,303.1325\n", - "XS GIS Cut Line= 10\n", - " 2557013.62452871613905.405064322557170.01356711 1613144.049169\n", - "2557179.463565481613034.761952912557187.667479591612919.42676206\n", - "2557187.802633431612917.431333932557190.014987551612880.65587562\n", - "2557193.545626411612777.654764992557194.857639491612645.33335081\n", - "2557193.914288551612519.076795222557191.264243681612417.61287668\n", - "#Sta/Elev=194\n", - "8996.4271486.1168999.6651484.945 9009.751481.6349012.3051480.7349019.8341478.072\n", - "9029.9191474.7179040.0031471.7099050.0881468.0159057.7621465.1049060.1721464.198\n", - "9070.2571460.2889080.3411456.826 9088.941455.1989090.4251454.917 9100.511452.727\n", - "9103.2191452.4039110.5941451.4969120.6791450.4729130.763 1450.229140.8481449.938\n", - "9148.6771449.7829150.9321449.7419161.0171449.4439171.1011449.1259181.1861449.709\n", - "9187.5131449.774 9191.271449.8129194.1341449.8399201.3551449.9599211.4391449.922\n", - "9221.524 1450.199231.6081450.4949239.5921450.4839241.6931450.4759251.7771450.553\n", - "9261.8621451.0499271.946 1451.569282.0311451.5039285.0491451.3289286.0871451.265\n", - "9292.115 1450.9 9302.21450.8589312.2841450.7749322.3691450.3739330.5061450.322\n", - "9332.453 1450.349342.5381450.4939352.6221450.5529362.7071449.8589372.7911449.142\n", - "9375.9641448.9989382.8761448.817 9384.661448.841 9392.961448.9519403.0451449.256\n", - "9413.1291449.2399421.4211449.0479423.2141449.0229433.2981448.6999443.383 1449.3\n", - "9453.4671449.9689463.5521450.3829466.8781450.4069473.6361450.4379483.7211450.084\n", - "9493.8051450.313 9503.891450.3519512.3361450.3999513.9741450.4169524.0591450.226\n", - "9534.1431450.2029544.227 1450.399554.3121450.3999557.7931450.4069564.3961450.395\n", - "9574.4811450.2219584.5651450.199 9594.651450.1179603.2511449.8449604.7341449.797\n", - "9614.8191449.5739624.9031449.2749634.9881448.5759645.0721447.8959648.7081447.717\n", - "9655.1571447.4279665.241 1447.569675.3261447.676 9685.411447.5339694.165 1447.33\n", - "9695.4951447.2969705.5791446.9549715.6641446.6819725.7481446.8869735.8331446.944\n", - "9739.6231446.9839745.917 1447.039756.002 1447.039766.0861446.9499773.6781446.636\n", - "9776.1241446.4949786.0211446.6349795.9171446.7399797.9361446.6559805.8141446.378\n", - " 9815.711445.9549825.6071445.8099835.5041445.575 9845.41445.4169855.2971446.119\n", - "9865.1931446.894 9875.091446.7479883.3731445.9719884.9841445.8099894.866 1445.09\n", - "9896.6261445.0669904.7481444.9529914.6311446.6029924.5131445.3079934.3951443.526\n", - "9944.2781443.559 9954.161443.4459964.042 1443.569973.9241443.2379977.7911443.054\n", - " 99781443.0449983.807 1442.779993.6891442.084 99991441.571 100011441.378\n", - "10003.571441.13210010.011444.45910011.711445.33710013.441446.24710023.321454.194\n", - "10033.19 1456.4710037.841456.86310043.051457.32510052.911458.83210062.771460.466\n", - "10072.631462.36510077.581463.48410082.481464.59110092.341466.139 10102.21468.718\n", - "10112.061472.43610121.911476.67310131.771482.372 10140.91486.71610141.631487.069\n", - "10151.481492.71410161.321499.17210171.171505.26910177.83 1509.1310181.02 1510.98\n", - "10190.871517.24110200.721524.20110210.571529.88110220.411535.70610230.261541.737\n", - "10240.111547.268 10246.91550.70910249.961552.26810259.811557.01510269.661561.212\n", - "10273.231562.24710278.381563.776 10279.51564.10910289.351565.99310299.191567.332\n", - "10309.041567.87610318.881567.67110328.731565.63110338.58 1563.9210348.42 1563.27\n", - "10358.271563.71210368.111563.92410377.961564.78610379.291564.924 10387.81565.808\n", - "10397.651568.82410399.491569.15310407.491570.67410417.341573.67110427.191576.583\n", - "10437.031579.69510446.881580.83610456.721582.29110466.571583.43810476.41 1583.68\n", - "10480.531584.50110486.261585.64310496.11 1587.6910500.991588.847\n", - "#Mann= 13 , -1 , 0 \n", - "8996.427 0.035 0 9088.94 0.05 09187.513 0.035 0\n", - "9286.087 0.06 0 9384.66 0.035 09914.631 0.03 0\n", - "9977.791 0.05 010013.44 0.12 010077.58 0.035 0\n", - "10177.83 0.12 010278.38 0.035 010379.29 0.12 0\n", - "10480.53 0.035 0\n", - "Bank Sta=9914.631,10013.44\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,12672.1,323.3077,372.5664,369.12\n", - "XS GIS Cut Line= 10\n", - " 2557287.06202871613874.155064322557404.471304131613263.03441516\n", - "2557476.989646171612950.247379152557481.560806741612927.83306148\n", - "2557481.932942281612925.867987662557501.930132851612812.78317349\n", - "2557513.530362521612724.937654172557521.67234582 1612630.8707731\n", - "2557525.149238181612532.339724532557523.51763574 1612428.8570291\n", - "#Sta/Elev=188\n", - "9014.974 1564.519020.8471563.3889030.9041560.9069040.4921556.855 9040.96 1556.66\n", - "9051.0171551.9619061.0741547.3229071.1311539.7779081.1881531.6769088.7321523.734\n", - "9091.2441521.5199101.3011514.7229111.3581505.0389121.4151493.8269131.4721471.925\n", - "9136.973 1467.449141.5281463.8429151.5851457.4919161.6421454.4489171.6991453.282\n", - "9181.756 1454.889185.2141454.8839191.812 1454.59201.8691453.0079211.926 1450.38\n", - "9221.9831449.5499232.0391447.4079233.4551447.3179242.0961446.7999252.1531446.579\n", - " 9262.211446.3059272.2671446.5669281.695 1446.529282.3231446.515 9292.381446.515\n", - "9302.437 1446.049312.4941445.5149316.2161445.1149322.5511444.4339329.9361444.398\n", - "9332.6071444.3779342.6641444.3649352.7211445.8049362.7781446.8449372.8351446.533\n", - "9378.1771446.3459382.8911446.1839392.9481446.0189403.005 1445.919413.062 1445.87\n", - "9423.1191446.0769426.4181446.1479433.1751446.2729443.2321446.2069453.2891446.235\n", - "9463.3461446.4039473.4021446.4629474.6581446.4399483.4591446.2819493.5161446.047\n", - "9503.5731445.9719513.3921445.975 9513.631445.9759522.899 1445.759523.6861445.732\n", - "9533.7431445.612 9543.81445.6859553.8571445.7919563.9141445.998 9571.141446.223\n", - " 9573.971446.3149584.0271446.6699594.0841446.9349604.1411446.9949614.1981446.909\n", - "9619.3811446.9729624.2541447.0119634.3111447.0529637.2711447.026 9644.431446.996\n", - "9654.5741446.6029662.9571446.3439664.7191446.2769674.8631446.1459685.0081447.103\n", - "9695.1521447.1989703.7851447.5629705.2971447.6149715.4411447.7489725.586 1447.41\n", - " 9735.731446.3459744.6131445.4529745.8751445.3459756.0191445.2779766.1641444.929\n", - "9776.3081444.593 9785.441444.4119786.453 1444.399796.5971444.2119806.7421443.952\n", - "9816.8861443.5529826.2681443.4459827.0311443.4359837.1751443.045 9847.321442.712\n", - "9857.4641442.1039867.0961441.6469867.6091441.6179877.753 1441.889887.8981441.987\n", - "9898.042 1442.079907.923 1442.279908.1871442.2739918.3311441.6829928.4751441.859\n", - " 9938.621442.0559948.7511441.7269948.7641441.7259958.3551441.0869958.9051441.047\n", - "9968.9871440.2549977.0581440.0739979.0681440.028 9981.23 1440.15 9983.231440.266\n", - "9989.1121440.6149994.8531440.7569999.1391440.87910006.351440.98910009.171441.032\n", - "10019.191441.44810025.311441.24910029.221441.12210039.251445.66610046.781447.947\n", - "10049.271448.687 10059.3 1448.3610069.331449.83310079.35 1452.7610089.381454.956\n", - "10098.071456.006 10098.91456.123 10099.41456.18810109.35 1455.56 10119.31455.611\n", - "10129.261457.42610139.211458.30610149.161458.99110159.111460.44210166.111461.487\n", - "10169.071461.93610179.02 1464.6710186.681468.52210188.96 1469.6810198.851475.282\n", - "10208.751481.60210218.651488.73710228.541496.13310238.441504.14110248.34 1509.76\n", - "10253.741514.08710258.231517.68210268.131524.92710278.031534.054 10281.11536.538\n", - " 10287.91542.13310297.761549.95110307.611557.99410317.471565.18510327.331572.197\n", - "10337.191579.24310347.051585.504 10356.91592.38310366.76 1600.4110376.621606.408\n", - "10379.691608.89210386.471614.32510396.311620.80610406.161626.685 104161633.488\n", - "10425.851639.77410435.691645.09910445.541650.30610455.38 1655.710465.231660.954\n", - "10475.071665.69310476.051666.15110483.18 1669.48\n", - "#Mann= 7 , -1 , 0 \n", - "9014.974 0.035 09316.216 0.06 09513.392 0.035 0\n", - " 9938.62 0.03 010006.35 0.05 010039.25 0.12 0\n", - "10476.05 0.07 0\n", - "#XS Ineff= 2 , 0 \n", - " 9716.005 0 \n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=9938.62,10039.25\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,12299.53,227.909,146.1611,136.7996\n", - "XS GIS Cut Line= 16\n", - " 2557535.24779261613918.048293492557639.174358991613468.19124465\n", - "2557659.220609351613370.060636922557683.373493861613274.19429828\n", - "2557727.511369281613120.553925662557735.40572159 1613081.3753766\n", - "2557738.736435691613051.186009912557738.897791651613049.19252947\n", - " 2557741.02184811613026.416694342557745.293586961613000.61611636\n", - "2557778.035496571612884.934559932557790.467898631612830.30736832\n", - "2557800.564067161612756.439908522557803.672983651612693.70734845\n", - "2557802.275557361612619.59424265 2557797.69423381612556.62741067\n", - "#Sta/Elev=189\n", - "8796.0531654.0268800.4951652.6918810.6381649.6818812.1561649.1938820.7811646.419\n", - "8830.9231642.9178841.0661639.5998851.2081636.5618853.1121635.8898861.3511633.015\n", - "8871.4931629.6028881.6361626.4658891.7781623.2968894.0691622.2378901.9211618.516\n", - "8912.0631614.1918922.2061610.5138932.3481607.5588935.0261606.7538942.4911604.455\n", - "8952.6331601.1418962.7761598.2488972.9181595.7138975.9821594.9968983.0611593.262\n", - "8993.2031590.5799003.3461587.4269013.4881584.3589016.9391583.4739023.6311581.758\n", - "9033.7731578.2889043.9161574.4189054.0581570.2279057.895 1568.329064.2011565.235\n", - "9074.3441557.8179084.4861547.4599094.6291541.6019098.8521538.8679104.7711534.571\n", - "9114.9141523.8349125.0561509.7549135.1991499.9929139.8081497.6449145.3411495.136\n", - "9155.4841489.6629165.6261483.5869175.7691468.5059180.7651460.6429185.9111451.301\n", - "9196.0541447.7489206.1961446.4869216.339 1445.199221.7221445.2089226.4811445.296\n", - "9236.6241445.0989246.766 1444.659256.9091444.1459257.7591444.1289263.2461443.982\n", - "9266.9961443.8699277.0781443.659 9287.161443.5369297.2421443.4129307.3241443.314\n", - "9308.9261443.3229317.4061443.3619327.4881443.239 9337.571443.2219347.6531443.148\n", - "9354.6061443.1439357.7351443.1239357.9161443.1149367.9271442.5269378.1211442.655\n", - "9384.7691442.9459388.316 1443.19393.103 1443.11 9398.511443.0999408.705 1442.97\n", - "9418.8991441.5229429.0931442.3039431.0391442.2129439.2881441.7999449.4821441.956\n", - "9456.7781442.0529459.7041442.1149467.6481442.0119469.995 1441.989480.2861441.749\n", - "9483.4631441.6589490.5771441.4549500.8691441.3319501.459 1441.33 9511.161441.267\n", - "9521.4511441.2999531.7421441.3399535.2691441.3859542.0331441.4529552.3241441.639\n", - "9562.6151441.908 9569.081441.6699572.9061441.5239583.1971441.4259593.4881441.445\n", - "9602.8911441.5569603.7791441.565 9614.071441.6159616.6331441.614 9624.21441.634\n", - "9634.2761441.7629644.0791441.6259644.352 1441.629654.4291441.2639656.5991441.175\n", - "9664.3851440.8359674.3081439.6399684.2311435.1119686.971 1435.339688.9711435.504\n", - "9694.138 14369704.0411437.0639711.8461437.7179713.9671437.9049715.7621437.808\n", - "9723.973 1437.27 9733.981437.7339737.9981437.9129744.1491438.4189754.4281439.416\n", - "9758.4161439.4939764.7071439.6549774.9861440.1179785.265 1440.639792.6681440.317\n", - "9795.5431440.2389805.8221440.4119816.1011440.118 9826.381440.0649826.9211440.055\n", - "9836.6591439.9159846.938 1438.859857.2171437.4459858.2241437.6289861.798 1438.32\n", - "9867.3651439.546 9877.51440.6839887.6341439.7489897.7681440.3159903.3021440.228\n", - "9907.9031440.1889914.2481439.9349917.972 1439.799927.9311439.625 9937.891439.473\n", - " 9947.851439.0819957.8091438.766 9962.261438.3159967.7681437.7159977.6761437.002\n", - "9977.7271436.9989987.6871437.0159988.8021436.8449997.5631435.48710007.431435.938\n", - "10010.53 1437.81 10017.31441.89810027.161442.61810037.031443.75310046.891449.367\n", - "10051.611453.19610056.751457.40710066.591462.82310076.441463.889 10077.51464.003\n", - "10086.281464.94910096.13 1470.4410105.971486.16410115.821489.21510125.671489.507\n", - "10125.74 1489.5410135.531493.99110145.391508.98410155.251515.44810165.111520.333\n", - "10174.971526.87810182.271531.31310184.831532.86810188.871535.676\n", - "#Mann= 7 , -1 , 0 \n", - "8796.053 0.035 09384.769 0.06 09483.463 0.035 0\n", - "9977.676 0.03 010010.53 0.035 0 10077.5 0.12 0\n", - "10182.27 0.035 0\n", - "#XS Ineff= 2 , 0 \n", - " 9634.593 0 \n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=9977.676,10010.53\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,12153.37,360.3609,503.6007,342.1801\n", - "XS GIS Cut Line= 13\n", - "2558005.837845531613629.286573512558001.071123921613447.86874134\n", - "2558001.936595571613301.260953872558004.91408231 1613240.9687236\n", - "2558009.284893191613190.459930822558017.914136131613130.27051659\n", - "2558018.205822091613128.291901082558024.661629811613084.20905222\n", - "2558025.23134723 1613041.9582129 2558021.85871811613013.41343009\n", - "2558017.051115461612989.615294132557969.286263831612813.16956088\n", - "2557878.207862041612525.19673098\n", - "#Sta/Elev=147\n", - "9100.3271558.6999108.8191555.0969118.6651550.465 9128.511546.3879138.3561541.929\n", - "9148.2021537.2249158.0471533.0689167.8931529.0679177.7391525.6539187.584 1521.84\n", - " 9197.431517.8619207.2761513.0119217.1211508.6059226.9671504.5189236.8131500.944\n", - "9246.6581497.8529256.5041494.312 9266.351490.7979276.1951488.1899281.8071487.128\n", - "9286.0421486.3219295.8891485.0549305.7371483.2319315.5841481.1819325.4321478.986\n", - "9335.2791476.8729345.1261474.9639354.9741473.4839364.8211471.5219374.669 1469.3\n", - "9384.516 1467.29394.3641465.2289404.2111461.5549414.0581461.3349415.4771461.294\n", - "9423.9061461.0559428.4181459.7329430.1261459.2339433.7631458.1669443.6291452.886\n", - "9453.4951448.3489463.361 1444.939473.2271442.3299483.0931440.8549488.7831440.392\n", - "9492.9721440.0589502.8691439.6669512.7661439.4339515.2661439.3899522.6621439.258\n", - "9532.5591439.1019539.481 1439.199542.4771439.2239544.9891439.2519552.4471439.325\n", - "9562.4161439.0729572.3861438.4499582.3551433.7069592.325 1433.469600.2861433.061\n", - "9602.2861432.9679602.2951432.9669607.3681433.183 9612.271433.3989622.2441434.224\n", - "9632.2181435.6179642.1931436.8689646.8391437.095 9652.11437.339 9661.951437.471\n", - "9671.7991437.2269681.6481436.1739689.0931436.5159691.5091436.6369701.4061436.855\n", - "9711.3021436.9879717.8371436.6829721.2381436.5319731.2521436.5399741.2651436.546\n", - "9742.1161436.4639742.9681436.381 9751.411435.6539761.5681436.6819771.7251436.609\n", - "9781.8831436.7369782.9731436.8359792.0411437.5879802.1981437.9569812.3561438.299\n", - "9822.5131438.3259822.9781438.3239832.671 1438.239842.8281436.3659852.9861434.769\n", - "9862.9831437.1929863.1431437.2299873.3011437.3029883.4581437.3989893.6161436.822\n", - "9902.9881436.9459903.7731436.9519913.9311436.9969924.0881436.5099924.9121436.475\n", - "9934.3551436.1239940.4421436.2269944.6311436.2669954.9071436.6889965.1831436.605\n", - "9974.8021436.3349975.4591436.3149976.0031436.2789985.7351435.6419996.0111433.617\n", - "10006.291435.39510009.161435.43910016.561435.50710022.421437.23410026.841438.537\n", - "10037.111439.09510043.521439.13310047.391439.14410048.87 1439.1510057.671439.183\n", - "10067.941439.00710077.88 1440.1810078.22 1440.2310088.491441.97310098.771446.052\n", - "10109.05 1452.0410112.241454.09410119.321458.404 10129.61463.25510139.871467.458\n", - " 10146.61470.83910150.151472.85810160.431479.171 10170.71481.27910180.961486.683\n", - "10180.981486.69310191.251493.79610201.531499.66810211.811506.57610215.321508.435\n", - "10222.081511.70510226.941514.078\n", - "#Mann= 6 , -1 , 0 \n", - "9100.327 0.035 09415.477 0.06 09515.266 0.035 0\n", - "9976.003 0.03 010022.42 0.035 010048.87 0.12 0\n", - "Bank Sta=9976.003,10022.42\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,11649.77,210.6718,210.0612,217.7682\n", - "XS GIS Cut Line= 4\n", - "2558491.236043721613362.263242862558300.723085611612976.14609283\n", - "2558025.777306481612611.134230982557961.150826171612534.09331362\n", - "#Sta/Elev=149\n", - "9506.1761527.2849511.8241526.4519519.6191525.4329522.7191524.9299533.6141522.478\n", - "9542.604 1519.76 9544.511519.1989555.4051516.5989565.5891514.284 9566.31514.153\n", - "9577.1951512.603 9588.091510.3939588.5741510.2689598.9851507.4379609.8811504.865\n", - "9611.5591504.5889620.7761502.9729631.6711501.0859634.544 1500.579642.5661499.227\n", - "9653.4611496.5849657.5291495.3749664.3561493.2879675.2511487.8169680.5141485.129\n", - "9686.1471482.2459688.2431482.1819697.0421481.9119703.499 1481.489707.9371481.173\n", - "9718.8321475.7579726.4841474.0159729.7271473.2599740.6221471.8329749.4691470.431\n", - "9751.5181470.1229756.352 1469.49762.4131468.4949772.4541467.0679773.3081466.946\n", - "9784.2031465.4789795.0981463.7769795.4391463.7289805.993 1462.339813.6381461.388\n", - "9816.8891460.9889818.4241460.8049827.7841459.5869838.6791458.0019841.4091457.637\n", - "9849.5741456.5549860.4691455.2049864.3941454.7289871.3641453.779 9882.261452.831\n", - "9887.3791451.6869893.1551450.355 9904.051447.1079910.3641445.0749914.9451443.675\n", - "9915.6111443.463 9925.84 1440.219933.3491438.1149936.7351436.9349939.4831435.857\n", - "9948.918 1432.169950.9871431.6329961.1011431.1569967.7011431.3299973.2841431.357\n", - " 99781431.2659984.4151431.1419985.4671431.089 9997.651430.30710001.13 1430.33\n", - "10009.831430.32510017.841430.342 100221430.66110022.021430.663 10034.21437.179\n", - "10034.561437.23310046.381438.82810051.271437.39210058.561435.54910067.991435.717\n", - "10070.751435.67810082.931435.326 10084.71435.32410089.451435.39610095.111435.482\n", - "10101.411435.556 10107.31435.51610118.131436.35410119.481436.48110131.661435.154\n", - "10134.841435.18510143.841435.78710151.561436.26410156.031436.36410168.21 1436.64\n", - "10168.271436.64710180.391437.47410184.981437.57810192.581438.155 10201.71437.726\n", - "10204.761437.59310216.941437.49610218.411437.47610229.131437.20510235.131437.226\n", - "10241.311437.25810251.841437.05510253.491437.01910265.671437.22210268.551437.341\n", - "10277.861437.79910285.27 1438.1110290.041438.19610301.981438.37210302.221438.376\n", - "10314.411438.283 10318.71438.16510326.591438.04710335.411438.07410338.771438.116\n", - "10350.951438.09510352.12 1438.110363.141438.05110368.841438.00410375.321438.049\n", - "10385.551438.645 10387.51438.82110389.371438.99410393.711439.39510399.931440.086\n", - " 10401.71440.36610412.621442.30410417.321444.291 10425.31448.52410432.931453.957\n", - "10437.991457.69110448.541466.08910450.671467.66510463.361472.58810464.161472.801\n", - "10476.041475.49610479.771476.37510488.721480.09310494.271483.038\n", - "#Mann= 9 , -1 , 0 \n", - "9506.176 0.035 09688.243 0.06 09756.352 0.035 0\n", - "9813.638 0.05 09915.611 0.035 09939.483 0.05 0\n", - " 10034.2 0.12 010089.45 0.035 010389.37 0.12 0\n", - "#XS Ineff= 2 , 0 \n", - " 0 10198.34 \n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=9939.483,10034.2\n", - "Exp/Cntr=0.3,0.1\n", - "\",...,\"Type RM Length L Ch R = 1 ,1595.506,401.7363,398.8766,396.9099\n", - "XS GIS Cut Line= 11\n", - "2569866.855332031569170.360780212569930.926198271569166.69678087\n", - " 2570006.2559641 1569159.9022742570068.28737536 1569152.1884285\n", - "2570141.366381391569140.409234492570195.624875831569129.64314001\n", - "2570257.327370751569115.202519972570358.472903061569087.20764276\n", - "2570360.393819351569086.650793942570461.87762149 1569059.3065258\n", - "2570477.554173541569056.01259492\n", - "#Sta/Elev=87\n", - "9499.0231786.7119505.066 1783.769514.9191782.2499524.7731780.4139534.6261776.614\n", - " 9544.481773.2269554.3331770.7029563.1991768.4059564.1891768.1419574.0611765.398\n", - "9583.9331762.6119591.8091760.1999593.8061759.5879603.6781756.031 9613.551752.038\n", - "9623.4221746.9359633.2951742.4739638.8341738.601 9643.181735.6029653.0831729.608\n", - "9662.986 1727.939672.8891726.5149682.7911719.4619692.6941713.8439697.7821711.044\n", - "9701.3431709.0559702.6031708.3459712.5511702.0669722.4991696.6599732.4471691.508\n", - "9742.3951686.5139752.3431681.2889762.2921676.3699767.2171673.832 9772.241671.195\n", - "9775.3661669.6549782.2281666.2839792.2351661.6089802.2421656.835 9812.251652.599\n", - "9822.2571648.6719823.768 1648.129830.6821645.5589832.2741644.955 9842.351641.233\n", - "9852.4251637.243 9862.5 1632.499871.1661628.6879872.5761628.0669882.6511623.418\n", - "9892.7261617.2279894.0521616.5089902.8861611.9049913.0581608.0639913.8451607.623\n", - " 9923.231602.3369933.4031593.6629943.5751584.7739952.9751580.5029953.7471580.155\n", - "9963.9191576.4719969.9981574.8789974.0921573.8059984.2641573.8129992.1051573.817\n", - "9994.4361573.819 99991573.824 100011573.82610004.611573.82910014.761573.838\n", - "10024.921573.84510030.011573.84910031.961573.85110035.071573.85410045.231573.862\n", - "10055.381573.86910065.541573.87810072.141574.22610075.691574.48410077.981575.238\n", - "10085.841577.827 100961581.007 10106.11591.97110106.151592.02710114.121592.944\n", - "10116.181593.10110122.121593.023\n", - "#Mann= 3 , -1 , 0 \n", - "9499.023 0.12 09953.747 0.05 010085.84 0.06 0\n", - "Bank Sta=9953.747,10085.84\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,1196.63,330.3242,398.8766,438.9131\n", - "XS GIS Cut Line= 12\n", - " 2569973.83429441569523.920553592570074.614466151569526.71344698\n", - "2570143.887681351569526.113518212570201.084971381569523.59966273\n", - "2570268.613317321569517.59688189 2570319.03802861569510.39962544\n", - "2570376.176272521569498.677135652570421.021740971569486.32491607\n", - "2570467.553377281569470.860472212570469.443049681569470.20538578\n", - "2570537.419084781569448.859015632570577.076295591569439.29613939\n", - "#Sta/Elev=87\n", - "9499.0441767.6589502.116 1767.12 9511.971765.3949521.8241764.0059531.6791762.473\n", - "9541.5331762.2149551.3871760.1399561.2411757.3799571.0961755.655 9580.951754.537\n", - "9590.8041753.1369597.9551752.0899599.8631751.7989600.6581751.6779610.5031748.984\n", - "9620.3491745.9329630.1941742.6639640.039 1738.999649.8841735.936 9659.731732.624\n", - "9669.1391729.1069669.5751728.9419679.4241725.5879689.2731721.8239689.3141721.807\n", - "9699.1221717.8429708.9711714.446 9718.821708.5269726.3921707.9369728.674 1707.76\n", - "9738.5451704.9649748.4171700.3479758.2881696.4319768.1591692.5399778.0311690.202\n", - "9787.9021687.1019794.186 1685.569797.7921684.6599807.7161682.208 9817.641679.506\n", - "9824.8451677.4339827.5641676.6749837.4881673.7199845.1221670.5129847.4331669.371\n", - "9857.4521664.1929867.4711655.9919877.4911645.0969884.4311642.052 9887.511640.739\n", - "9897.5291635.133 9903.451632.0249907.6091629.9819917.7781624.2799927.9471622.071\n", - "9928.6721620.5019938.1161598.7729948.2851584.6919949.9661582.642 9955.92 1575.44\n", - "9958.5821572.7479964.9871571.4319968.904 1570.649969.9971570.6229979.2261570.469\n", - "9989.548 1570.479997.754 1570.47 9999 1570.479999.873 1570.47 100011570.471\n", - "10010.151570.47110020.421570.47310030.011570.47210030.691570.47210032.081570.472\n", - "10040.961570.64610051.231575.051 10061.51581.59910066.671585.63410071.771589.385\n", - "10072.251589.46410081.871590.81110091.961591.04210102.05 1591.1410109.93 1597.35\n", - "10112.141599.32610113.04 1600.57\n", - "#Mann= 3 , -1 , 0 \n", - "9499.044 0.12 0 9955.92 0.05 010051.23 0.06 0\n", - "Bank Sta=9955.92,10051.23\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,797.7532,391.7664,398.8766,407.5078\n", - "XS GIS Cut Line= 12\n", - "2569950.142438951569685.925139022570048.453772321569708.27646943\n", - "2570115.47023832 1569725.82628252570193.948911681569749.71266599\n", - " 2570251.99762231569770.426299572570343.346052731569809.42124964\n", - "2570421.221551271569847.912041382570423.004423261569848.81833464\n", - " 2570514.30180711569894.483971712570612.319257981569939.66964625\n", - "2570723.795974861569986.118438442570882.17984215 1570045.6387451\n", - "#Sta/Elev=150\n", - "9499.0451760.1459499.8811759.9029508.9351757.2739519.0681754.2149529.2021751.713\n", - "9539.3361748.6779541.4281748.1389549.4691746.1769559.6031744.0319569.7371741.689\n", - " 9579.871739.9229582.9741739.3549590.0041738.1119599.8651734.969 9600.141734.879\n", - " 9610.361730.5419620.5811730.0769621.6271729.9839630.8011728.9819641.0211726.543\n", - "9651.2411724.7539658.2971723.8569661.4621723.4459669.1411722.1059671.7121721.657\n", - 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"10037.771570.17710047.731570.17710050.04 1570.4110058.821571.29610059.11 1571.52\n", - "10063.54 1575.7510069.921581.70210080.451588.52110081.011588.79110092.111589.495\n", - "10101.791589.72110103.08 1589.7510103.211589.75310114.131591.30610124.451598.483\n", - "10125.051598.86810135.971605.57910146.891610.947 10147.21611.09110157.811616.286\n", - "10168.731621.33310169.941621.89110179.651626.65910190.57 1631.5910192.691632.637\n", - " 10201.51637.02410211.011641.64110212.391642.32710215.811643.96410223.131647.531\n", - "10233.871652.65710240.481656.144 10244.61658.30910251.821661.89310255.34 1663.64\n", - "10265.151668.30110266.081668.74410276.811673.79910287.551678.82310289.821679.919\n", - "10298.291683.95610309.031689.08610314.491691.50410319.76 1693.93 10330.51699.035\n", - "10331.781699.60910339.821703.274 10341.11703.85910351.681708.23710362.261712.604\n", - "10366.691714.48210372.841717.09110383.421721.60110393.561725.405 103941725.564\n", - "10404.581729.47410415.171733.58110420.431735.63510425.751737.71110436.33 1741.85\n", - "10446.911746.07410447.291746.21310451.211747.56210457.491749.72410468.071753.488\n", - "10474.161755.50210478.651756.95110489.231760.31310499.811763.26910500.981763.571\n", - "#Mann= 5 , -1 , 0 \n", - "9499.045 0.12 09952.002 0.05 010063.54 0.06 0\n", - "10251.82 0.12 010451.21 0.035 0\n", - "Bank Sta=9952.002,10063.54\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,398.8766,429.1667,398.8766,364.8997\n", - "XS GIS Cut Line= 12\n", - "2569757.246055221569959.51847817 2569814.56280451569980.11342874\n", - "2569885.318804391570008.405213072569967.552871981570045.55613195\n", - "2570028.083227011570076.09690963 2570137.49744321570138.27234724\n", - "2570203.802289011570181.144349242570205.433244961570182.30192545\n", - "2570271.536427261570225.492995912570395.693569811570299.81847222\n", - "2570510.975880571570362.558521342570641.118864671570426.59477915\n", - "#Sta/Elev=155\n", - 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1607.349881.9751604.9199893.402 15989896.3921596.172\n", - "9904.8291591.4869915.7861585.8149916.2561585.5199920.0421582.4469927.9631575.781\n", - "9933.8611572.3789939.8081570.3929951.5661569.9399951.6531569.9399963.4991569.938\n", - " 9969.271569.9379969.9731569.9379975.3441569.9369986.9741569.9359987.1891569.935\n", - " 99991569.9339999.0361569.933 100011569.93310004.531569.93310010.971569.932\n", - "10022.111569.93110022.861569.93110030.02 1569.9310034.741569.92910039.681569.929\n", - "10046.621569.92810057.261570.30410058.511570.38710070.391570.57710074.84 1570.22\n", - "10079.961570.04210082.211570.123 10093.21573.386 10093.81573.53310101.141574.419\n", - "10105.381574.93110111.881578.69710116.971581.53210128.551588.59410130.561589.137\n", - "10140.141591.61510149.241592.07410151.721592.13210163.311592.19810167.921592.684\n", - "10174.891594.41510186.481606.578 10186.61606.66610198.061612.17810205.281613.909\n", - "10209.641614.92410221.231618.63710223.961619.68410224.671619.96410232.621623.076\n", - "10243.921627.03910243.931627.04110255.231631.18910263.951634.44410266.541635.437\n", - "10277.841639.73410283.97 1641.8810289.151643.63410300.461646.78610303.991647.778\n", - "10308.021648.80110311.761649.75110323.071652.70610324.011653.03710334.381656.457\n", - "10344.041659.59110345.681660.14810355.921663.39510356.971663.728 10364.71666.051\n", - "10368.031667.05410379.081669.78810386.321671.32510390.141672.087 10401.21673.664\n", - "10407.93 1674.3910412.261674.82310423.321675.67310429.541675.88210434.381675.954\n", - "10445.441676.05610451.161676.401 10456.51676.65710467.561676.32710472.771676.053\n", - "10478.62 1675.7210489.671675.48310494.381675.31510500.731675.10910500.96 1675.11\n", - "#Mann= 5 , -1 , 0 \n", - "9499.054 0.12 09927.963 0.05 0 10093.8 0.12 0\n", - "10101.14 0.06 010308.02 0.12 0\n", - "Bank Sta=9927.963,10093.8\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,0,0,0,0\n", - "XS GIS Cut Line= 10\n", - "2569556.392519861570394.849359422569690.133119311570435.64742042\n", - "2569933.359502841570513.352483592569991.295723561570529.88925296\n", - "2570034.571152841570540.58333442 2570036.52014131570541.03215952\n", - "2570079.734807231570551.962645572570137.595155541570569.73952895\n", - "2570271.679746921570618.656330962570506.19388341 1570711.2792319\n", - "#Sta/Elev=144\n", - " 9499.011873.8779499.997 1873.11 9510.341865.4919519.8111859.1689520.6841858.597\n", - "9531.0281851.5389541.3721844.9459551.7151838.6519551.9061838.5379562.0591832.394\n", - "9572.4031825.4559582.7461818.2339584.0011817.144 9593.091809.0379603.4341802.518\n", - "9613.7781798.3199616.0961796.5729624.121 1790.939634.4651782.8959638.8351779.932\n", - "9644.8351775.9999647.8251774.0819655.2231769.0939665.6121761.888 96761753.752\n", - "9678.6651751.7229686.389 1745.899696.7781737.5479707.1661729.8059709.5051728.147\n", - 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"10099.341579.42910104.831581.29110106.111581.70410109.751582.846 10120.31585.176\n", - "10130.841588.90610132.551589.29910141.381590.23110151.921590.69810160.091590.545\n", - "10162.46 1590.49 101731590.28310183.541591.69710187.341595.19910187.641595.475\n", - "10194.091601.85810204.631608.45410215.171612.18810215.181612.19310225.711617.106\n", - "10236.251620.31210242.721621.06410246.791621.32110248.831621.53910257.421622.471\n", - "10268.081623.748 10268.91623.86210278.731625.02710289.381625.88610294.681626.305\n", - "10300.031626.64810310.69 1627.510320.46 1628.8210321.341628.89910331.991628.687\n", - "10342.641628.81810346.241629.31910353.291630.36310363.951632.14610372.021632.802\n", - " 10374.61633.03610385.251633.84110386.531633.911 10395.91634.421 10397.81634.615\n", - "10406.561635.68510417.211636.91210423.581637.87410427.861638.61410438.511640.054\n", - "10449.171641.34110449.361641.37410459.821643.45610470.471644.98410475.131645.411\n", - "10481.121645.97310491.781646.60310500.911646.89710500.981646.905\n", - "#Mann= 5 , -1 , 0 \n", - " 9499.01 0.12 09904.446 0.05 010088.99 0.12 0\n", - "10187.34 0.06 010386.53 0.12 0\n", - "Bank Sta=9904.446,10088.99\n", - "Exp/Cntr=0.3,0.1\n", - "\n", - "LCMann Time=Dec/30/1899 00:00:00\n", - "LCMann Region Time=Dec/30/1899 00:00:00\n", - "LCMann Table=0\n", - "Chan Stop Cuts=-1\n", - "Use User Specified Reach Order=0\n", - "GIS Ratio Cuts To Invert=-1\n", - "GIS Limit At Bridges=0\n", - "Composite Channel Slope=5\"]]\n", - "station_elevation_points: [[\"[(8996.427, 1486.116), (8999.665, 1484.945), (9009.75, 1481.634), (9012.305, 1480.734), (9019.834, 1478.072), (9029.919, 1474.717), (9040.003, 1471.709), (9050.088, 1468.015), (9057.762, 1465.104), (9060.172, 1464.198), (9070.257, 1460.288), (9080.341, 1456.826), (9088.94, 1455.198), (9090.425, 1454.917), (9100.51, 1452.727), (9103.219, 1452.403), (9110.594, 1451.496), (9120.679, 1450.472), (9130.763, 1450.22), (9140.848, 1449.938), 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1643.456), (10470.47, 1644.984), (10475.13, 1645.411), (10481.12, 1645.973), (10491.78, 1646.603), (10500.91, 1646.897), (10500.98, 1646.905)]\"]]\n", - "bank_stations: [[\"['9914.631', '10013.44']\",\"['9938.62', '10039.25']\",\"['9977.676', '10010.53']\",\"['9976.003', '10022.42']\",\"['9939.483', '10034.2']\",...,\"['9953.747', '10085.84']\",\"['9955.92', '10051.23']\",\"['9952.002', '10063.54']\",\"['9927.963', '10093.8']\",\"['9904.446', '10088.99']\"]]\n", - "number_of_station_elevation_points: [[194,188,189,147,149,...,87,87,150,155,144]]\n", - "number_of_coords: [[10,10,16,13,4,...,11,12,12,12,10]]\n", - "flows: [[\"7012.0\n", - "9628.0\n", - "4397.0\",\"7012.0\n", - "9628.0\n", - "4397.0\",\"7023.0\n", - "9643.0\n", - "4404.0\",\"7023.0\n", - "9643.0\n", - "4404.0\",\"7023.0\n", - "9643.0\n", - "4404.0\",...,\"12672.0\n", - "14408.0\n", - "11146.0\",\"12676.0\n", - "14413.0\n", - "11149.0\",\"12676.0\n", - "14413.0\n", - "11149.0\",\"12676.0\n", - "14413.0\n", - "11149.0\",\"12676.0\n", - "14413.0\n", - "11149.0\"]]\n", - "profile_names: [[\" 100yr\n", - "100yrplus\n", - "100yrminus\",\" 100yr\n", - "100yrplus\n", - "100yrminus\",\" 100yr\n", - "100yrplus\n", - "100yrminus\",\" 100yr\n", - "100yrplus\n", - "100yrminus\",\" 100yr\n", - "100yrplus\n", - "100yrminus\",...,\" 100yr\n", - "100yrplus\n", - "100yrminus\",\" 100yr\n", - "100yrplus\n", - "100yrminus\",\" 100yr\n", - "100yrplus\n", - "100yrminus\",\" 100yr\n", - "100yrplus\n", - "100yrminus\",\" 100yr\n", - "100yrplus\n", - "100yrminus\"]]\n", - "geometry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n", - "huc: [[\"17060305\",\"17060305\",\"17060305\",\"17060305\",\"17060305\",...,\"17060305\",\"17060305\",\"17060305\",\"17060305\",\"17060305\"]]\n", - "model_id: [[\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",\"cottonwood_creek_1_ras\",...,\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\",\"south_fork_clearwater_river_1_ras\"]]\n", - "us_reach_id: [[\"23597201\",\"None\",\"23597199\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "us_network_to_id: [[\"23597199\",\"None\",\"23597197\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"None\"]]\n", - "ds_reach_id: [[\"None\",\"None\",\"23597201\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"23597347\"]]\n", - "ds_network_to_id: [[\"None\",\"None\",\"23597199\",\"None\",\"None\",...,\"None\",\"None\",\"None\",\"None\",\"23597345\"]]\n", - "__index_level_0__: [[19178,19179,19180,19181,19182,...,24403,24404,24405,24406,24407]]\n" - ] - } - ], - "source": [ - "# Scenario II\n", - "# User Args\n", - "catalog_name = \"xs\"\n", - "huc_num = 17060305\n", - "\n", - "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "tablename = f\"huc_{huc_num}\"\n", - "namespace = \"mip\"\n", - "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", - "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", - "print(iceberg_table.to_string(preview_cols=30))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "e10ffe0a-3811-4a48-9524-d55e3c26807a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pyarrow.Table\n", - "river: large_string\n", - "reach: large_string\n", - "river_reach: large_string\n", - "river_station: double\n", - "river_reach_rs: large_string\n", - "thalweg: double\n", - "xs_max_elevation: double\n", - "left_reach_length: double\n", - "right_reach_length: double\n", - "channel_reach_length: double\n", - "ras_data: large_string\n", - "station_elevation_points: large_string\n", - "bank_stations: large_string\n", - "number_of_station_elevation_points: int64\n", - "number_of_coords: int64\n", - "flows: large_string\n", - "profile_names: large_string\n", - "geometry: large_binary\n", - "huc: large_string\n", - "model_id: large_string\n", - "us_reach_id: large_string\n", - "us_network_to_id: large_string\n", - "ds_reach_id: large_string\n", - "ds_network_to_id: large_string\n", - "__index_level_0__: int64\n", - "----\n", - "river: [[\"1242\",\"1242\",\"1242\",\"1242\",\"1242\",...,\"1236\",\"1236\",\"1236\",\"1236\",\"1236\"]]\n", - "reach: [[\"15\",\"15\",\"15\",\"15\",\"15\",...,\"61\",\"61\",\"61\",\"61\",\"61\"]]\n", - "river_reach: [[\"1242,15\",\"1242,15\",\"1242,15\",\"1242,15\",\"1242,15\",...,\"1236,61\",\"1236,61\",\"1236,61\",\"1236,61\",\"1236,61\"]]\n", - "river_station: [[1645.9,1552.446,1458.841,1365.072,1271.617,...,725.2979,576.5005,427.703,231.8489,35.9949]]\n", - "river_reach_rs: [[\"1242 15 1645.9\",\"1242 15 1552.446\",\"1242 15 1458.841\",\"1242 15 1365.072\",\"1242 15 1271.617\",...,\"1236 61 725.2979\",\"1236 61 576.5005\",\"1236 61 427.703\",\"1236 61 231.8489\",\"1236 61 35.9949\"]]\n", - "thalweg: [[3550.146,3547.128,3543.629,3540.886,3537.79,...,3533.829,3531.006,3528.091,3525.665,3521.963]]\n", - "xs_max_elevation: [[3577.866,3562.288,3580.739,3569.751,3569.955,...,3587.055,3592.946,3565.975,3588.964,3583.562]]\n", - "left_reach_length: [[96.61742,96.73559,95.70554,94.16392,92.93386,...,151.7416,150.1448,198.9483,156.1958,0]]\n", - "right_reach_length: [[89.80285,90.02276,91.58722,92.05128,94.53484,...,139.8336,130.2688,186.9168,195.4554,0]]\n", - "channel_reach_length: [[93.45487,93.60503,93.76862,93.45487,93.45487,...,148.7975,148.7975,195.854,195.854,0]]\n", - "ras_data: [[\"Type RM Length L Ch R = 1 ,1645.9,96.61742,93.45487,89.80285\n", - "XS GIS Cut Line= 8\n", - "528894.279452703873971.621611987528907.966560143873946.488329754\n", - " 528925.01586739 873921.27277097528926.208395569 873919.66719427\n", - " 528948.49023709873885.841779061528972.766208581873836.497337306\n", - "528991.974043957873781.439473717529008.465616347 873712.3025376\n", - "#Sta/Elev=85\n", - "9939.9433565.3739944.636 3565.289946.6773565.0169950.3293564.4819956.0233562.989\n", - "9957.1313562.6999961.716 3561.11 9967.413558.7359967.5863558.6679968.5613558.316\n", - "9973.3763556.6879976.6553555.5799979.4123554.5829985.4473552.5949985.5823552.554\n", - "9989.947 3551.419991.4833551.0079994.5083550.4899997.5193550.146 99993550.156\n", - " 100013550.17710003.353550.234 10003.6 3550.2510009.583550.66310012.443550.833\n", - "10012.953550.88810015.573551.17110021.533551.94810021.563551.95110027.543551.831\n", - "10030.623551.68510033.533551.50210039.523551.507 10039.73551.502 10041.53551.458\n", - "10045.233551.39110050.593551.513 10050.83551.52310056.373551.82710061.913551.735\n", - "10061.953551.73510067.52 3551.8910073.093551.94810073.243551.94810078.663551.913\n", - "10084.243551.87410084.573551.87810089.813551.78510095.38 3551.3310095.893551.318\n", - " 10096.53551.32810100.73 3551.4210106.03 3551.510110.87 3551.6110111.323551.618\n", - "10116.623551.61310121.913551.37610126.053551.38510127.213551.406 10132.53551.669\n", - " 10137.8 3551.8810141.233553.033 10143.13553.61110148.393555.77510153.693558.897\n", - "10154.813559.54510157.083560.85410158.863561.722 101643562.68510169.143564.403\n", - "10174.283566.15410178.633567.54110179.423567.77110184.563569.051 10189.73570.504\n", - "10194.843571.85610199.983572.85910200.183572.89210205.123573.82610210.263574.735\n", - " 10215.43576.35310220.543577.12910221.723577.28210225.683577.80710225.893577.866\n", - "#Mann= 3 , 0 , 0 \n", - "9939.943 0.065 09989.947 0.04 010012.95 0.065 0\n", - "#XS Ineff= 2 , 0 \n", - " 0 10026.37 \n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=9989.947,10012.95\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,1552.446,96.73559,93.60503,90.02276\n", - "XS GIS Cut Line= 8\n", - "528995.492298483874005.011416777529006.956371507873965.884739773\n", - "529007.476454129873963.953544789529024.844213126873907.219206723\n", - "529036.479831966873862.946674337529049.075311191873800.032525587\n", - " 529056.98887746873744.890768309529062.422368085873707.030186743\n", - "#Sta/Elev=83\n", - "9958.2283555.8889960.8453556.1439965.3693554.7539966.0563554.6179971.2663553.203\n", - "9976.4763551.1859981.6863549.0219983.1513548.7059986.8963548.0059987.5453547.907\n", - "9992.1073547.2179997.3173547.168 99993547.157 10001 3547.1410001.083547.139\n", - "10002.523547.12810007.753547.873 10010 3548.3610012.983549.00410018.163549.255\n", - "10018.213549.25710023.443549.08610028.673548.62510033.893548.38710035.243548.332\n", - "10039.123548.19310044.353548.14810049.58 3548.3510052.323548.42210054.813548.473\n", - "10060.043548.82310060.333548.81410065.213548.64610070.383548.63410070.783548.623\n", - "10075.553548.52710080.723548.32710085.893548.34410090.453548.35510091.063548.349\n", - "10096.233548.271 10101.43548.32710106.113548.43710106.563548.44610111.313548.733\n", - "10111.663548.75610116.763549.14710121.863549.31510126.96 3549.7410132.063549.646\n", - "10136.783549.66610137.163549.66410142.263550.17410147.363550.21810152.463550.027\n", - "10157.56 3550.110162.25 3550.0210162.663550.01710167.763549.85510170.273549.796\n", - "10172.833549.73210177.883549.56410182.93 3549.8110187.983549.65110193.043549.445\n", - "10194.383549.39710198.093549.23210203.143549.61410208.19 3550.1610213.243550.864\n", - "10218.293551.86610223.343552.58110225.983553.16410228.39 3553.6810229.583553.997\n", - "10233.443555.068 10238.53556.43410243.553557.723 10248.63559.01210253.653560.127\n", - " 10258.73561.51510263.753562.18910264.233562.288\n", - "#Mann= 3 , 0 , 0 \n", - "9958.228 0.065 09987.545 0.04 0 10010 0.065 0\n", - "#XS Ineff= 2 , 0 \n", - " 0 10020.61 \n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=9987.545,10010\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,1458.841,95.70554,93.76862,91.58722\n", - "XS GIS Cut Line= 7\n", - "528939.960137047874200.795523874529100.822893173874025.728232764\n", - "529099.580756519873976.581178352529099.535324253873974.581694439\n", - " 529096.29245545 873887.78082598529098.715297006873749.681707554\n", - "529099.584481761873700.139198773\n", - "#Sta/Elev=143\n", - "9714.2233580.7399717.9773579.8719718.6993579.6989725.3673577.5819725.4893577.538\n", - 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"529575.760648519874884.549223147529481.701193254874788.105902128\n", - "529340.709909569874655.622377299529308.019624803874622.156504385\n", - "529306.846605174874620.536620133529286.260644916874599.150820695\n", - "528993.921709903874402.011202961\n", - "#Sta/Elev=222\n", - "9624.0323592.1739626.8943592.3779628.7023592.6439633.8783592.8039635.8633592.946\n", - "9640.8623592.7149643.0243592.6389647.8473591.8669650.1853591.6179654.8313591.228\n", - "9657.3473591.2529661.8153591.1429664.5083591.1719668.7993591.1029671.6693591.313\n", - "9675.7843591.689 9678.833591.8159682.7683592.2289685.9913592.5369689.7523592.653\n", - "9693.1533592.4059696.7363591.6319700.3143590.741 9703.723590.0389707.4753589.124\n", - "9710.7053588.4529714.6363587.9589717.689 3587.539721.7983587.2279724.6733587.044\n", - "9728.9593586.8449731.6573586.754 9736.123586.4459738.642 3586.359743.2813585.914\n", - "9745.6263585.5999750.4433584.926 9752.613584.5989757.6043583.6059758.7483583.361\n", - 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3532.2810086.733532.20410092.223531.80410097.563531.795\n", - "10097.713531.79810103.213531.877 10108.73532.02810109.643532.16610114.193533.046\n", - "10119.683534.49810121.723534.55710125.183534.28210130.673534.254 10133.83534.279\n", - "10136.163534.25610141.663534.01710145.883533.53210147.153533.35310152.643532.693\n", - "10157.963532.71610158.133532.72210163.633533.26310169.123534.58510170.043534.653\n", - "10174.613535.112 10180.13535.31110182.123535.352 10185.63535.39310191.093535.432\n", - " 10194.23535.49810196.583535.56310202.073536.58110206.28 3537.7410207.573538.073\n", - "10213.063539.63310218.363541.64110218.553541.70810224.043543.75310229.543546.488\n", - "10230.443546.78110235.03 3547.5310240.523546.51610242.523545.88210243.283545.636\n", - " 10246.13544.77510251.75 3543.7510253.313543.655 10257.43543.47410263.063543.396\n", - "10264.03 3543.3810268.71 3543.2910274.363543.23510274.743543.22710280.023543.119\n", - 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"10649.283560.18410653.673560.78510658.583561.589 10659.6 3561.7810665.533562.864\n", - "10667.873563.25710671.463563.83310677.173564.56910677.393564.57710683.323564.727\n", - "10686.473565.41910689.253566.23510694.71 3567.97\n", - "#Mann= 3 , 0 , 0 \n", - "9662.966 0.065 09985.772 0.04 010032.64 0.065 0\n", - "#XS Ineff= 2 , 0 \n", - " 9917.352 10107.52 \n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=9985.772,10032.64\n", - "Exp/Cntr=0.3,0.1\n", - "\",\"Type RM Length L Ch R = 1 ,35.9949,0,0,0\n", - "XS GIS Cut Line= 11\n", - "529817.641340002874447.270478971529780.274598278874420.271368474\n", - " 529701.30426788874357.422959477529648.732257009874309.460134774\n", - "529615.700806044874273.585569665529592.399235025874241.299609289\n", - "529577.611305378874213.201117232 529565.60607497 874181.94687143\n", - "529564.967540465874180.051541992529554.304202974874151.542926691\n", - "528985.086335212873718.068140216\n", - "#Sta/Elev=325\n", - 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3540.59819.7813539.3849825.8383533.6559826.5493533.068\n", - "9833.2563528.5069833.3173528.4749840.0853524.8219840.6753524.6869845.1853524.158\n", - "9847.0053524.012 9847.853524.0639854.3863524.3519854.6463524.3539861.4433524.837\n", - "9861.7683524.873 9868.24 3525.279869.1493525.3339875.0363525.8889876.5313526.042\n", - "9881.8333526.2869883.9133526.283 9888.633526.0729891.2943525.8659893.9513525.697\n", - "9895.2893525.616 9899.423525.5429901.4563525.5459907.6223525.3479907.9643525.344\n", - "9913.7883525.4929916.5073525.5699919.9543525.6619925.0513525.916 9926.123525.958\n", - "9932.2873526.3369933.5953526.3699933.7673526.3699938.0613526.3629943.711 3525.72\n", - "9944.2873525.6599949.3613525.1289955.0113524.1619955.0223524.1599960.6613523.747\n", - "9965.5193523.685 9965.833523.678 9966.273523.6639971.6273523.4569974.1713523.264\n", - "9976.9833523.0529979.774 3522.899982.3393522.7279987.6953522.5459993.0513522.205\n", - "9993.718 3522.189998.4073521.963 99993521.982 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3532.48\n", - "10197.113532.52510203.173532.72110203.39 3532.7310209.683532.83210211.423532.907\n", - "10215.963533.11610219.673533.05510222.253533.13110227.933533.56210228.533533.589\n", - "10234.823534.98110236.183535.447 10241.13537.11910244.433538.56210247.393539.332\n", - "10252.68 3539.9510253.673539.96310259.963539.60910260.943539.43810266.243538.371\n", - "10269.193537.83410272.533537.01710277.44 3536.710278.813536.642 10285.13536.353\n", - " 10285.73536.34810291.383536.49810293.953536.52510297.663536.683 10302.23536.663\n", - "10303.953536.60410310.233536.64310310.453536.64310316.523536.55710318.713536.531\n", - " 10322.83536.51310326.963536.52110329.093536.52510335.213536.91910335.373536.925\n", - "10341.663538.12310343.473538.39610347.943538.70310351.723538.74910354.233538.728\n", - "10359.973538.54410360.513538.593 10366.83538.82110368.223538.65910373.083538.616\n", - "10376.483538.39610379.373538.25910384.733538.11210385.653538.12410391.943538.324\n", - "10392.98 3538.3510398.223538.47710401.243538.55310404.513538.69710409.493539.102\n", - "10410.793539.11910417.083539.09210417.743539.11810423.36 3539.4910425.993539.643\n", - "10429.643539.80410434.253539.84910435.933539.85610442.213539.753 10442.53539.777\n", - " 10448.5 3540.3810450.753540.47210454.783540.644 104593541.04810461.07 3541.19\n", - "10467.263541.23810467.353541.23710473.643541.43410475.513541.51710479.92 3541.77\n", - "10483.763542.06510486.213542.24310492.023542.44910492.493542.45910498.783542.505\n", - "10500.273542.51410505.063542.71510508.523543.17210511.353543.46910516.773543.545\n", - "10517.633543.54310523.923543.55710525.033543.617 10530.23543.58810533.28 3543.56\n", - "10536.493543.70610541.53 3543.910542.77 3543.9710549.053544.24910549.793544.289\n", - "10555.343544.75910558.043545.10910561.623545.73810566.293546.71410567.913546.934\n", - "10574.193547.26910574.543547.24410580.483547.297 10582.83547.31210586.763547.195\n", - "10591.053547.35410593.05 3547.4 10599.33547.42310599.333547.42210605.623547.539\n", - "10607.563547.482 10611.93547.39210615.813547.41610618.193547.48210624.063547.955\n", - "10624.473547.98410630.763548.48910632.313548.55210637.043548.70810640.573548.788\n", - "10643.333548.90610648.823548.92910649.613548.931 10655.93548.88110657.07 3548.93\n", - "10662.183549.55110665.333550.10410668.473550.78410673.58 3552.3310674.753552.793\n", - "10681.033555.24310681.833555.57110687.323558.07810690.083559.295 10693.63560.674\n", - "10698.343562.85810699.893563.52910706.173566.25510706.593566.47410712.463569.467\n", - "10714.843570.49810718.74 3572.24 10723.13573.74110725.033574.39410731.313576.874\n", - "10731.353576.887 10737.63579.189 10739.63579.84410743.883581.37310746.923582.122\n", - "#Mann= 3 , 0 , 0 \n", - "9626.995 0.065 09974.171 0.04 010010.04 0.065 0\n", - "#XS Ineff= 2 , 0 \n", - " 9893.349 10140.11 \n", - "Permanent Ineff=\n", - " F F\n", - "Bank Sta=9974.171,10010.04\n", - "Exp/Cntr=0.3,0.1\n", - "\"]]\n", - 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"633.0\n", - "805.0\n", - "498.0\n", - "1185.0\",\"168.0\n", - "313.0\n", - "457.0\n", - "634.0\n", - "805.0\n", - "499.0\n", - "1185.0\",\"169.0\n", - "316.0\n", - "460.0\n", - "638.0\n", - "811.0\n", - "502.0\n", - "1194.0\",...,\"433.0\n", - "796.0\n", - "1152.0\n", - "1584.0\n", - "2014.0\n", - "1247.0\n", - "2926.0\",\"433.0\n", - "796.0\n", - "1152.0\n", - "1585.0\n", - "2014.0\n", - "1247.0\n", - "2927.0\",\"437.0\n", - "803.0\n", - "1162.0\n", - "1599.0\n", - "2032.0\n", - "1258.0\n", - "2952.0\",\"440.0\n", - "808.0\n", - "1169.0\n", - "1608.0\n", - "2043.0\n", - "1265.0\n", - "2967.0\",\"440.0\n", - "808.0\n", - "1169.0\n", - "1608.0\n", - "2043.0\n", - "1265.0\n", - "2967.0\"]]\n", - "profile_names: [[\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\",\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\",\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\",\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\",\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\",...,\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\",\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\",\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\",\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\",\" 10yr\n", - "25yr\n", - "50yr\n", - "100yr\n", - "100plus\n", - "100minus\n", - "500yr\"]]\n", - "geometry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n", - "huc: [[\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\",...,\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\"]]\n", - "model_id: [[\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",...,\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\"]]\n", - "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"None\"]]\n", - "us_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294311\",\"None\",\"None\",\"None\",\"None\"]]\n", - "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21295551\",\"None\",\"None\",\"None\",\"21294309\"]]\n", - "ds_network_to_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"21294311\"]]\n", - "__index_level_0__: [[30787,30788,30789,30790,30791,...,30966,30967,30968,30969,30970]]\n" - ] - } - ], - "source": [ - "# Scenario III\n", - "# User Args\n", - "catalog_name = \"xs\"\n", - "huc_num = 15040007\n", - "\n", - "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema\n", - "tablename = f\"huc_{huc_num}\"\n", - "namespace = \"mip\"\n", - "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", - "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", - "print(iceberg_table.to_string(preview_cols=30))" - ] - }, - { - "cell_type": "markdown", - "id": "42d97ebb-f690-4dce-80fd-a01e61f9e6ec", - "metadata": {}, - "source": [ - "#### Demo:\n", - "#### Load Iceberg Catalog & Table of Interest\n", - "#### Cross-Check & Verify Parquet Data Has Been Written To Its Associated Iceberg Table Sucessfully" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "64bfeb93-15f2-4c20-affa-af8b215172de", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyarrow.Table\n", - "river: large_string\n", - "huc: large_string\n", - "model_id: large_string\n", - "us_reach_id: large_string\n", - "ds_reach_id: large_string\n", - "----\n", - "river: [[\"1242\",\"1242\",\"1242\",\"1242\",\"1242\",...,\"1236\",\"1236\",\"1236\",\"1236\",\"1236\"]]\n", - "huc: [[\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\",...,\"15040007\",\"15040007\",\"15040007\",\"15040007\",\"15040007\"]]\n", - "model_id: [[\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",\"1242_ras\",...,\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\",\"1236_ras\"]]\n", - "us_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21294309\",\"None\",\"None\",\"None\",\"None\"]]\n", - "ds_reach_id: [[\"None\",\"None\",\"None\",\"None\",\"None\",...,\"21295551\",\"None\",\"None\",\"None\",\"21294309\"]]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Query iceberg table for specific features of interest\n", - "catalog.load_table(f\"{namespace}.{tablename}\").scan(\n", - " selected_fields=(\"huc\", \"river\", \"model_id\", \"us_reach_id\", \"ds_reach_id\")\n", - ").to_arrow()" - ] - }, - { - "cell_type": "markdown", - "id": "42103887-3d43-421a-819d-8689940267d5", - "metadata": {}, - "source": [ - "### Demo VI:\n", - "### Load CRS Mapping" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "2c3de676-42ec-4e15-8e04-5469afcbae10", - "metadata": {}, - "outputs": [], - "source": [ - "with open(f\"{os.getcwd()}/xs_data/crs_map/crs_mapping.pickle\", \"rb\") as handle:\n", - " crs_map = pickle.load(handle)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "c36c0954-2150-4441-a546-3b455ab871cc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(dict,\n", - " {('07130010',\n", - " 'sa_1'): {'LitCr': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaMoRv_us': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'SBLaMoRv': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaHaCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'BapCr': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'RocCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", - " ('07130010',\n", - " 'Killjordan'): {'Killjordan Cr': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", - " ('07130010',\n", - " 'sa_6'): {'CedCkTrbA': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaMoRv': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'CedCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaMoRvTrbA': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'LaMoRvTrbB': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", - " ('07130010',\n", - " 'sa_7'): {'TowFk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'DroFk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'DroFkTrbAa': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'KepCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'NFEFLaMoRv': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'FarFk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'EFLaMoRv': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'SprCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'ShoFk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", - " ('07130010',\n", - " 'sa_5'): {'TroCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'KilCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", - " ('07130010',\n", - " 'sa_4'): {'CamCr': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'CarCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'GriCk': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'CamCrTrbA': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", - " ('07130010',\n", - " 'LaMoine'): {'LaMoine River': 'ESPG: 3436, NAD83 / Illinois West (ftUS),', 'Spring Creek': 'ESPG: 3436, NAD83 / Illinois West (ftUS),'},\n", - " ('09020301',\n", - " 'SHRWDS'): {'SHRWDS': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SHR5_1'): {'SHR5': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SHRUS_1'): {'SHRUS': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'GF_EGF_FIS_Submittal1204'): {'RRN': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,'},\n", - " ('09020301',\n", - " 'SHR6_1'): {'SHR6': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SHRup'): {'SHRup': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'UnnamedTrib1_1'): {'UnnamedTrib1': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SHR2'): {'SHR2': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'Maple_Creek_1'): {'Maple Creek': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SHR2_1'): {'SHR2': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'RedRiverTA'): {'RedRiverTA': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'Maple_Creek'): {'Maple Creek': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SandHill29_1'): {'SandHill29': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SHRUS'): {'SHRUS': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SHRWDS_1'): {'SHRWDS': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'UnnamedTrib1'): {'UnnamedTrib1': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SandHill29'): {'SandHill29': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'Ditch16So_1'): {'Ditch16So': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'Kittleson'): {'Kittleson': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SandHillRiver7'): {'SandHillRiver7': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SHR5'): {'SHR5': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SandHillRiver7_1'): {'SandHillRiver7': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'Ditch16So'): {'Ditch16So': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'GFEGFFIS0807'): {'RRN': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,', 'Heartsville': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,', 'Red Lake River': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,', 'English Coulee': 'ESPG: None, NAD_1983_StatePlane_North_Dakota_North_FIPS_3301_Feet,'},\n", - " ('09020301',\n", - " 'SHR6'): {'SHR6': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'Kittleson_1'): {'Kittleson': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'SHRup_1'): {'SHRup': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('09020301',\n", - " 'RedRiverTA_1'): {'RedRiverTA': 'ESPG: 6503, NAD83(2011) / Minnesota North (ftUS),'},\n", - " ('06040002',\n", - " 'Bell_Buckle_Cr'): {'Bell Buckle Cr': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Kelly_Creek_T1'): {'Kelly Creek T1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Bomar_Creek'): {'Bomar Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Trib_to_Bomar_Cr'): {'Trib to Bomar Cr': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Rabbit_Branch'): {'Rabbit Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Thompson_Branch'): {'Thompson Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Sinking_Creek'): {'Sinking Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Duck_River_DS'): {'Duck River DS': 'ESPG: 2915, NAD83(HARN) / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'North_Fork_Creek'): {'North Fork Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Butler_Creek'): {'Butler Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Noah_Fork_Creek'): {'Noah Fork Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Duck_River_Trib4'): {'Duck River Trib4': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Wartrace_Creek'): {'Wartrace Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Dry_Branch'): {'Dry Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Doddy_Branch'): {'Doddy Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Stokes_Branch'): {'Stokes Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'GarrisonForkCrT1'): {'GarrisonForkCrT1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Little_Flat_Cr'): {'Little Flat Cr': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Sugar_Creek'): {'Sugar Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Bell_Buckle_CrT2'): {'Bell Buckle CrT2': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Fall_Creek'): {'Fall Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Muse_Branch'): {'Muse Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Fay_Branch'): {'Fay Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Crouch_Branch'): {'Crouch Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Hurricane_Branch'): {'Hurricane Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'GarrisonForkCr'): {'GarrisonForkCr': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Flat_Creek'): {'Flat Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Powell_Creek'): {'Powell Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Dryland_Creek'): {'Dryland Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Bell_Buckle_CrT1'): {'Bell Buckle CrT1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Norman_Creek_T1'): {'Norman Creek T1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Anthony_Branch'): {'Anthony Branch': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Trib_to_Holland'): {'Trib to Holland': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Cannon_Creek'): {'Cannon Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Little_Hurricane'): {'Little Hurricane': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Norman_Creek_T2'): {'Norman Creek T2': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Kelly_Creek'): {'Kelly Creek': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('06040002',\n", - " 'Wartrace_CreekT1'): {'Wartrace CreekT1': 'ESPG: 2274, NAD83 / Tennessee (ftUS),'},\n", - " ('03170004',\n", - " 'MixonLOMR-Existing3_17_17'): {'Mixons Creek Tri': 'ESPG: 2254, NAD83 / Mississippi East (ftUS),'},\n", - " ('01020002',\n", - " 'Stream0510'): {'Stream0510': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", - " ('01020002',\n", - " 'Stream0528'): {'Stream0528': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", - " ('01020002',\n", - " 'Martin_Stream'): {'Martin Stream': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", - " ('01020002',\n", - " 'Stream0526'): {'Stream0526': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", - " ('01020002',\n", - " 'Stream0527'): {'Stream0527': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", - " ('01020002',\n", - " 'Stream0512'): {'Stream0512': 'ESPG: 2852, NAD83(HARN) / Vermont,'},\n", - " ('10120203',\n", - " 'Spearfish_Cr_LOMR_FEMA_RESPONSE_1'): {'Spearfish Creek': 'ESPG: 3458, NAD83(HARN) / South Dakota North (ftUS),'},\n", - " ('10120203',\n", - " 'Spearfish_Cr_LOMR_FEMA_RESPONSE'): {'Spearfish Creek': 'ESPG: 3458, NAD83(HARN) / South Dakota North (ftUS),'},\n", - " ('11060002',\n", - " 'YELLOWSTONE_CREEK'): {'YELLOWSTONE CREE': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT64_in_UPPER_SALT_FRK_HUC8_1'): {'UNT64 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT115_in_UPPER_SALT_FRK_HUC8_1'): {'UNT115 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT64_in_UPPER_SALT_FRK_HUC8'): {'UNT64 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'JUG_MOTTE_CREEK_1'): {'JUG MOTTE CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT80_in_UPPER_SALT_FRK_HUC8'): {'UNT80 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'MUSTANG_CREEK_1'): {'MUSTANG CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT79_in_UPPER_SALT_FRK_HUC8'): {'UNT79 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT23_in_UPPER_SALT_FRK_HUC8'): {'UNT23 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT61_in_UPPER_SALT_FRK_HUC8'): {'UNT61 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT83_in_UPPER_SALT_FRK_HUC8_1'): {'UNT83 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT119_in_UPPER_SALT_FRK_HUC8_1'): {'UNT119 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT14_in_UPPER_SALT_FRK_HUC8_1'): {'UNT14 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT45_in_UPPER_SALT_FRK_HUC8'): {'UNT45 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT101_in_UPPER_SALT_FRK_HUC8_1'): {'UNT101 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT84_in_UPPER_SALT_FRK_HUC8_1'): {'UNT84 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'COTTONWOOD_CREEK_1'): {'COTTONWOOD CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT91_in_UPPER_SALT_FRK_HUC8_1'): {'UNT91 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT115_in_UPPER_SALT_FRK_HUC8'): {'UNT115 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT66_in_UPPER_SALT_FRK_HUC8'): {'UNT66 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT75_in_BluffCr_Watershed'): {'UNT75 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", - " ('11060002',\n", - " 'UNT111_in_UPPER_SALT_FRK_HUC8_1'): {'UNT111 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT92_in_UPPER_SALT_FRK_HUC8_1'): {'UNT92 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT75_in_UPPER_SALT_FRK_HUC8_1'): {'UNT75 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT120_in_Liberal_Watershed_1'): {'UNT120 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT9_in_UPPER_SALT_FRK_HUC8_1'): {'UNT9 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT92_in_UPPER_SALT_FRK_HUC8'): {'UNT92 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT107_in_UPPER_SALT_FRK_HUC8'): {'UNT107 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT34_in_UPPER_SALT_FRK_HUC8'): {'UNT34 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT84_in_UPPER_SALT_FRK_HUC8'): {'UNT84 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT27_in_UPPER_SALT_FRK_HUC8'): {'UNT27 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT93_in_UPPER_SALT_FRK_HUC8_1'): {'UNT93 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT55_in_UPPER_SALT_FRK_HUC8'): {'UNT55 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT2_in_UPPER_SALT_FRK_HUC8_1'): {'UNT2 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT106_in_UPPER_SALT_FRK_HUC8_1'): {'UNT106 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT9_in_UPPER_SALT_FRK_HUC8'): {'UNT9 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'MUSTANG_CREEK'): {'MUSTANG CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT14_in_UPPER_SALT_FRK_HUC8'): {'UNT14 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT54_in_UPPER_SALT_FRK_HUC8'): {'UNT54 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT133_in_Liberal_Watershed_1'): {'UNT133 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT21_in_UPPER_SALT_FRK_HUC8_1'): {'UNT21 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT18_in_UPPER_SALT_FRK_HUC8_1'): {'UNT18 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT103_in_UPPER_SALT_FRK_HUC8'): {'UNT103 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT11_in_UPPER_SALT_FRK_HUC8_1'): {'UNT11 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT71_in_UPPER_SALT_FRK_HUC8'): {'UNT71 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT139_in_UPPER_SALT_FRK_HUC8_1'): {'UNT139 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT139_in_UPPER_SALT_FRK_HUC8'): {'UNT139 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT68_in_UPPER_SALT_FRK_HUC8_1'): {'UNT68 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT111_in_UPPER_SALT_FRK_HUC8'): {'UNT111 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT60_in_UPPER_SALT_FRK_HUC8_1'): {'UNT60 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT103_in_Liberal_Watershed'): {'UNT103 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT31_in_UPPER_SALT_FRK_HUC8_1'): {'UNT31 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT19_in_UPPER_SALT_FRK_HUC8_1'): {'UNT19 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT120_in_UPPER_SALT_FRK_HUC8'): {'UNT120 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT25_in_UPPER_SALT_FRK_HUC8_1'): {'UNT25 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT67_in_UPPER_SALT_FRK_HUC8_1'): {'UNT67 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT8_in_UPPER_SALT_FRK_HUC8_1'): {'UNT8 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT29_in_UPPER_SALT_FRK_HUC8'): {'UNT29 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT65_in_UPPER_SALT_FRK_HUC8'): {'UNT65 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT110_in_Liberal_Watershed'): {'UNT110 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT19_in_UPPER_SALT_FRK_HUC8'): {'UNT19 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT27_in_UPPER_SALT_FRK_HUC8_1'): {'UNT27 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT105_in_UPPER_SALT_FRK_HUC8_1'): {'UNT105 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'GREENLEAF_CREEK'): {'GREENLEAF CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT32_in_UPPER_SALT_FRK_HUC8'): {'UNT32 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT73_in_UPPER_SALT_FRK_HUC8_1'): {'UNT73 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT73_in_BluffCr_Watershed'): {'UNT73 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", - " ('11060002',\n", - " 'UNT55_in_UPPER_SALT_FRK_HUC8_1'): {'UNT55 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT87_in_UPPER_SALT_FRK_HUC8_1'): {'UNT87 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT72_in_UPPER_SALT_FRK_HUC8_1'): {'UNT72 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'HACKBERRY_CREEK'): {'HACKBERRY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT72_in_UPPER_SALT_FRK_HUC8'): {'UNT72 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT144_in_UPPER_SALT_FRK_HUC8_1'): {'UNT144 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT35_in_UPPER_SALT_FRK_HUC8'): {'UNT35 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT129_in_Edith_Watershed_1'): {'UNT129 in Edith': 'ESPG: 2763, NAD83(HARN) / Arizona West,'},\n", - " ('11060002',\n", - " 'UNT144_in_UPPER_SALT_FRK_HUC8'): {'UNT144 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT89_in_UPPER_SALT_FRK_HUC8_1'): {'UNT89 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT25_in_UPPER_SALT_FRK_HUC8'): {'UNT25 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT89_in_UPPER_SALT_FRK_HUC8'): {'UNT89 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT102_in_UPPER_SALT_FRK_HUC8'): {'UNT102 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT94_in_Liberal_Watershed'): {'UNT94 in Liberal': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'TURKEY_CREEK'): {'TURKEY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT120_in_UPPER_SALT_FRK_HUC8_1'): {'UNT120 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT12_in_UPPER_SALT_FRK_HUC8_1'): {'UNT12 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT36_in_UPPER_SALT_FRK_HUC8'): {'UNT36 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT26_in_UPPER_SALT_FRK_HUC8'): {'UNT26 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT143_in_UPPER_SALT_FRK_HUC8'): {'UNT143 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT36_in_UPPER_SALT_FRK_HUC8_1'): {'UNT36 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT114_in_UPPER_SALT_FRK_HUC8'): {'UNT114 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT24_in_UPPER_SALT_FRK_HUC8_1'): {'UNT24 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT116_in_UPPER_SALT_FRK_HUC8'): {'UNT116 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT33_in_UPPER_SALT_FRK_HUC8_1'): {'UNT33 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT97_in_UPPER_SALT_FRK_HUC8'): {'UNT97 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT76_in_UPPER_SALT_FRK_HUC8'): {'UNT76 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT32_in_UPPER_SALT_FRK_HUC8_1'): {'UNT32 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'SALT_FORK_ARKANSAS_RIVER'): {'SALT FORK ARKANS': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT95_in_UPPER_SALT_FRK_HUC8'): {'UNT95 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT24_in_UPPER_SALT_FRK_HUC8'): {'UNT24 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT74_in_UPPER_SALT_FRK_HUC8'): {'UNT74 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT20_in_UPPER_SALT_FRK_HUC8'): {'UNT20 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT62_in_UPPER_SALT_FRK_HUC8'): {'UNT62 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT70_in_UPPER_SALT_FRK_HUC8'): {'UNT70 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT140_in_UPPER_SALT_FRK_HUC8'): {'UNT140 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT44_in_UPPER_SALT_FRK_HUC8'): {'UNT44 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT52_in_UPPER_SALT_FRK_HUC8_1'): {'UNT52 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT29_in_UPPER_SALT_FRK_HUC8_1'): {'UNT29 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT331_in_Edith_Watershed_1'): {'UNT331 in Edith': 'ESPG: 2763, NAD83(HARN) / Arizona West,'},\n", - " ('11060002',\n", - " 'UNT110_in_Liberal_Watershed_1'): {'UNT110 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT133_in_Liberal_Watershed'): {'UNT133 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT35_in_UPPER_SALT_FRK_HUC8_1'): {'UNT35 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT85_in_UPPER_SALT_FRK_HUC8_1'): {'UNT85 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT146_in_UPPER_SALT_FRK_HUC8_1'): {'UNT146 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT57_in_UPPER_SALT_FRK_HUC8'): {'UNT57 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT13_in_UPPER_SALT_FRK_HUC8_1'): {'UNT13 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT53_in_UPPER_SALT_FRK_HUC8'): {'UNT53 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT34_in_UPPER_SALT_FRK_HUC8_1'): {'UNT34 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT30_in_UPPER_SALT_FRK_HUC8'): {'UNT30 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT11_in_UPPER_SALT_FRK_HUC8'): {'UNT11 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT94_in_UPPER_SALT_FRK_HUC8_1'): {'UNT94 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT81_in_UPPER_SALT_FRK_HUC8'): {'UNT81 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT90_in_UPPER_SALT_FRK_HUC8_1'): {'UNT90 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT20_in_UPPER_SALT_FRK_HUC8_1'): {'UNT20 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT96_in_UPPER_SALT_FRK_HUC8'): {'UNT96 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT99_in_UPPER_SALT_FRK_HUC8'): {'UNT99 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT52_in_UPPER_SALT_FRK_HUC8'): {'UNT52 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT97_in_UPPER_SALT_FRK_HUC8_1'): {'UNT97 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT99_in_UPPER_SALT_FRK_HUC8_1'): {'UNT99 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT140_in_UPPER_SALT_FRK_HUC8_1'): {'UNT140 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT143_in_UPPER_SALT_FRK_HUC8_1'): {'UNT143 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT74_in_UPPER_SALT_FRK_HUC8_1'): {'UNT74 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT59_in_UPPER_SALT_FRK_HUC8_1'): {'UNT59 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT148_in_UPPER_SALT_FRK_HUC8'): {'UNT148 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT112_in_UPPER_SALT_FRK_HUC8'): {'UNT112 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT127_in_Liberal_Watershed_1'): {'UNT127 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT18_in_UPPER_SALT_FRK_HUC8'): {'UNT18 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT119_in_UPPER_SALT_FRK_HUC8'): {'UNT119 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT112_in_UPPER_SALT_FRK_HUC8_1'): {'UNT112 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'GREENLEAF_CREEK_1'): {'GREENLEAF CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT94_in_UPPER_SALT_FRK_HUC8'): {'UNT94 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT48_in_UPPER_SALT_FRK_HUC8'): {'UNT48 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT3_in_UPPER_SALT_FRK_HUC8_1'): {'UNT3 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT81_in_UPPER_SALT_FRK_HUC8_1'): {'UNT81 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT104_in_UPPER_SALT_FRK_HUC8_1'): {'UNT104 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT117_in_UPPER_SALT_FRK_HUC8'): {'UNT117 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT120_in_Liberal_Watershed'): {'UNT120 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'BOGGY_CREEK'): {'BOGGY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT100_in_UPPER_SALT_FRK_HUC8_1'): {'UNT100 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT21_in_UPPER_SALT_FRK_HUC8'): {'UNT21 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT22_in_UPPER_SALT_FRK_HUC8_1'): {'UNT22 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT95_in_UPPER_SALT_FRK_HUC8_1'): {'UNT95 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT33_in_UPPER_SALT_FRK_HUC8'): {'UNT33 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT147_in_UPPER_SALT_FRK_HUC8'): {'UNT147 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT234_in_Liberal_Watershed_1'): {'UNT234 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT61_in_UPPER_SALT_FRK_HUC8_1'): {'UNT61 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT145_in_UPPER_SALT_FRK_HUC8_1'): {'UNT145 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT87_in_UPPER_SALT_FRK_HUC8'): {'UNT87 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT47_in_UPPER_SALT_FRK_HUC8_1'): {'UNT47 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT129_in_Edith_Watershed'): {'UNT129 in Edith': 'ESPG: 2763, NAD83(HARN) / Arizona West,'},\n", - " ('11060002',\n", - " 'UNT162_in_Liberal_Watershed_1'): {'UNT162 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT57_in_UPPER_SALT_FRK_HUC8_1'): {'UNT57 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'HACKBERRY_CREEK_1'): {'HACKBERRY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT90_in_UPPER_SALT_FRK_HUC8'): {'UNT90 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT28_in_UPPER_SALT_FRK_HUC8_1'): {'UNT28 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT63_in_UPPER_SALT_FRK_HUC8_1'): {'UNT63 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT16_in_UPPER_SALT_FRK_HUC8'): {'UNT16 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT45_in_UPPER_SALT_FRK_HUC8_1'): {'UNT45 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT235_in_Liberal_Watershed'): {'UNT235 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT82_in_UPPER_SALT_FRK_HUC8'): {'UNT82 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT98_in_UPPER_SALT_FRK_HUC8'): {'UNT98 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT59_in_UPPER_SALT_FRK_HUC8'): {'UNT59 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT48_in_UPPER_SALT_FRK_HUC8_1'): {'UNT48 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT73_in_UPPER_SALT_FRK_HUC8'): {'UNT73 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT86_in_UPPER_SALT_FRK_HUC8'): {'UNT86 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT2_in_UPPER_SALT_FRK_HUC8'): {'UNT2 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT69_in_UPPER_SALT_FRK_HUC8_1'): {'UNT69 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT53_in_UPPER_SALT_FRK_HUC8_1'): {'UNT53 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT101_in_UPPER_SALT_FRK_HUC8'): {'UNT101 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT13_in_UPPER_SALT_FRK_HUC8'): {'UNT13 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT145_in_UPPER_SALT_FRK_HUC8'): {'UNT145 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT94_in_Liberal_Watershed_1'): {'UNT94 in Liberal': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'YELLOWSTONE_CREEK_1'): {'YELLOWSTONE CREE': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT86_in_UPPER_SALT_FRK_HUC8_1'): {'UNT86 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'SALT_FORK_ARKANSAS_RIVER_1'): {'SALT FORK ARKANS': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT118_in_UPPER_SALT_FRK_HUC8'): {'UNT118 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT51_in_UPPER_SALT_FRK_HUC8_1'): {'UNT51 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT85_in_UPPER_SALT_FRK_HUC8'): {'UNT85 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT235_in_Liberal_Watershed_1'): {'UNT235 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT16_in_UPPER_SALT_FRK_HUC8_1'): {'UNT16 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT62_in_UPPER_SALT_FRK_HUC8_1'): {'UNT62 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT98_in_UPPER_SALT_FRK_HUC8_1'): {'UNT98 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'BOGGY_CREEK_1'): {'BOGGY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT100_in_UPPER_SALT_FRK_HUC8'): {'UNT100 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT12_in_UPPER_SALT_FRK_HUC8'): {'UNT12 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'RED_FORK_CREEK_1'): {'RED FORK CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT54_in_UPPER_SALT_FRK_HUC8_1'): {'UNT54 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT152_in_Liberal_Watershed_1'): {'UNT152 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT117_in_UPPER_SALT_FRK_HUC8_1'): {'UNT117 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT73_in_BluffCr_Watershed_1'): {'UNT73 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", - " ('11060002',\n", - " 'UNT77_in_UPPER_SALT_FRK_HUC8_1'): {'UNT77 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT118_in_UPPER_SALT_FRK_HUC8_1'): {'UNT118 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT93_in_UPPER_SALT_FRK_HUC8'): {'UNT93 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT75_in_UPPER_SALT_FRK_HUC8'): {'UNT75 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT15_in_UPPER_SALT_FRK_HUC8'): {'UNT15 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT96_in_UPPER_SALT_FRK_HUC8_1'): {'UNT96 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT3_in_UPPER_SALT_FRK_HUC8'): {'UNT3 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT108_in_UPPER_SALT_FRK_HUC8_1'): {'UNT108 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'RED_FORK_CREEK'): {'RED FORK CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT127_in_Liberal_Watershed'): {'UNT127 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT107_in_UPPER_SALT_FRK_HUC8_1'): {'UNT107 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT103_in_Liberal_Watershed_1'): {'UNT103 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT108_in_UPPER_SALT_FRK_HUC8'): {'UNT108 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT66_in_UPPER_SALT_FRK_HUC8_1'): {'UNT66 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT56_in_UPPER_SALT_FRK_HUC8_1'): {'UNT56 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT56_in_UPPER_SALT_FRK_HUC8'): {'UNT56 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT147_in_UPPER_SALT_FRK_HUC8_1'): {'UNT147 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT80_in_UPPER_SALT_FRK_HUC8_1'): {'UNT80 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT106_in_UPPER_SALT_FRK_HUC8'): {'UNT106 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT22_in_UPPER_SALT_FRK_HUC8'): {'UNT22 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT71_in_UPPER_SALT_FRK_HUC8_1'): {'UNT71 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT105_in_UPPER_SALT_FRK_HUC8'): {'UNT105 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT331_in_Edith_Watershed'): {'UNT331 in Edith': 'ESPG: 2763, NAD83(HARN) / Arizona West,'},\n", - " ('11060002',\n", - " 'UNT102_in_UPPER_SALT_FRK_HUC8_1'): {'UNT102 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT47_in_UPPER_SALT_FRK_HUC8'): {'UNT47 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT79_in_UPPER_SALT_FRK_HUC8_1'): {'UNT79 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT70_in_UPPER_SALT_FRK_HUC8_1'): {'UNT70 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT114_in_UPPER_SALT_FRK_HUC8_1'): {'UNT114 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT113_in_UPPER_SALT_FRK_HUC8_1'): {'UNT113 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT82_in_UPPER_SALT_FRK_HUC8_1'): {'UNT82 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT68_in_UPPER_SALT_FRK_HUC8'): {'UNT68 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT26_in_UPPER_SALT_FRK_HUC8_1'): {'UNT26 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT60_in_UPPER_SALT_FRK_HUC8'): {'UNT60 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT103_in_UPPER_SALT_FRK_HUC8_1'): {'UNT103 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT67_in_UPPER_SALT_FRK_HUC8'): {'UNT67 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'JUG_MOTTE_CREEK'): {'JUG MOTTE CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT44_in_UPPER_SALT_FRK_HUC8_1'): {'UNT44 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT113_in_UPPER_SALT_FRK_HUC8'): {'UNT113 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT76_in_UPPER_SALT_FRK_HUC8_1'): {'UNT76 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT91_in_UPPER_SALT_FRK_HUC8'): {'UNT91 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT30_in_UPPER_SALT_FRK_HUC8_1'): {'UNT30 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT51_in_UPPER_SALT_FRK_HUC8'): {'UNT51 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT23_in_UPPER_SALT_FRK_HUC8_1'): {'UNT23 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT15_in_UPPER_SALT_FRK_HUC8_1'): {'UNT15 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT77_in_UPPER_SALT_FRK_HUC8'): {'UNT77 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT65_in_UPPER_SALT_FRK_HUC8_1'): {'UNT65 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT8_in_UPPER_SALT_FRK_HUC8'): {'UNT8 in UPPER SA': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT152_in_Liberal_Watershed'): {'UNT152 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT83_in_UPPER_SALT_FRK_HUC8'): {'UNT83 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT31_in_UPPER_SALT_FRK_HUC8'): {'UNT31 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT63_in_UPPER_SALT_FRK_HUC8'): {'UNT63 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'TURKEY_CREEK_1'): {'TURKEY CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT69_in_UPPER_SALT_FRK_HUC8'): {'UNT69 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT104_in_UPPER_SALT_FRK_HUC8'): {'UNT104 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT162_in_Liberal_Watershed'): {'UNT162 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'COTTONWOOD_CREEK'): {'COTTONWOOD CREEK': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT234_in_Liberal_Watershed'): {'UNT234 in Libera': 'ESPG: 2761, NAD83(HARN) / Arizona East,'},\n", - " ('11060002',\n", - " 'UNT6_in_BluffCr_Watershed'): {'UNT6 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", - " ('11060002',\n", - " 'UNT6_in_BluffCr_Watershed_1'): {'UNT6 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", - " ('11060002',\n", - " 'UNT75_in_BluffCr_Watershed_1'): {'UNT75 in BluffCr': 'ESPG: 2762, NAD83(HARN) / Arizona Central,'},\n", - " ('11060002',\n", - " 'UNT28_in_UPPER_SALT_FRK_HUC8'): {'UNT28 in UPPER S': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT146_in_UPPER_SALT_FRK_HUC8'): {'UNT146 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('11060002',\n", - " 'UNT148_in_UPPER_SALT_FRK_HUC8_1'): {'UNT148 in UPPER': 'ESPG: 2267, NAD83 / Oklahoma North (ftUS),'},\n", - " ('13020102',\n", - " 'RioGrandeT8'): {'RioGrandeT8': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaAnchaT1'): {'CanadaAnchaT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioTusasTrib2'): {'RioTusasT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaJarita'): {'CanadaJarita': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadeBorracho'): {'CanadadeBorracho': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaos_1'): {'PueblodeTaos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LatirCreekTrib1'): {'LatirCreekTrib1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoMiranda'): {'ArroyoMiranda': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1a_1'): {'GrandeRanchoT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPuncheT1a'): {'ArroyoPuncheT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeRancho'): {'RioGrandeRancho': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPunche'): {'Arroyo Punche': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPuncheT4'): {'ArroyoPuncheT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'SaltDraw'): {'SaltDraw': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'CanadadelaOrill'): {'CanadadelaOrill': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaosT3b1'): {'PueblodeTaosT3b1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaSarcoT1'): {'CanadaSarcoT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ElRito'): {'ElRito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'FernandodeTaos'): {'FernandodeTaos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AguajePetacaT1a'): {'AguajePetacaT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaosT2'): {'PueblodeTaosT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioTusas_1'): {'RioTusas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPuncheT3a1'): {'ArroyoPuncheT3a1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioVallecitos'): {'RioVallecitos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaosT2a'): {'PueblodeTaosT2a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ElRitoT4'): {'ElRitoT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LatirCreekTrib2'): {'LatirCreekTrib2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioPuebloTrib1'): {'RioPuebloTrib1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPino'): {'ArroyoPino': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT11b'): {'RioGrandeT11b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaSarcoT2'): {'CanadaSarcoT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioOjoCalienteT1'): {'RioOjoCalienteT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioVallecitosT3'): {'RioVallecitosT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT12c'): {'RioGrandeT12c': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'BobcatCreek'): {'BobcatCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaJarosita'): {'CanadaJarosita': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AguajedelaPetaca_1'): {'AguajedelaPetaca': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoOcole'): {'ArroyoOcole': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyolaMina'): {'ArroyolaMina': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ElRitoT6'): {'ElRitoT6': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AguajedelaPetacaT2'): {'AguajePetacaT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaAnchaNorth'): {'CanadaAnchaNorth': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1b3'): {'GrandeRanchoT1b3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoRancho'): {'ArroyoRancho': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ElRitoT3'): {'ElRitoT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoelRitoT1'): {'ArroyoelRitoT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaPueblo'): {'CanadaPueblo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaosT4a'): {'PueblodeTaosT4a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RedRiver'): {'RedRiver': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT13'): {'RioGrandeT13': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT10f'): {'RioGrandeT10f': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ElRitoT8'): {'ElRitoT8': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CabrestoCreek'): {'CabrestoCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT10a'): {'RioGrandeT10a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT11a'): {'RioGrandeT11a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RiodeTruchas'): {'RiodeTruchas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'MadredelPrado'): {'MadredelPrado': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadePiedra'): {'CanadadePiedra': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AlmagreT3'): {'AlmagreT3': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1b4'): {'GrandeRanchoT1b4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT11c'): {'RioGrandeT11c': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioOjoCaliente_1'): {'RioOjoCaliente': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RedRiverTrib1'): {'RedRiverTrib1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaCorral'): {'CanadaCorral': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PuebloArroyo'): {'PuebloArroyo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'FernandodeTaosT1'): {'FernandodeTaosT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'BitterCreek'): {'BitterCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT11'): {'RioGrandeT11': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT10e'): {'RioGrandeT10e': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1b'): {'GrandeRanchoT1b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioTusasTrib3'): {'RioTusasT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadeApodaca'): {'CanadadeApodaca': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AguajePetacaT3'): {'AguajePetacaT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ElRitoT7'): {'ElRitoT7': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadelAgua'): {'CanadadelAgua': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPuncheT2'): {'ArroyoPuncheT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AguajePetacaT1'): {'AguajePetacaT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AlmagreT2b1'): {'AlmagreT2b1': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'EmbudoCreekTrib1'): {'EmbudoCreekTrib1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LaMaderaT1a'): {'LaMaderaT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT9'): {'RioGrandeT9': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioVallecitos_1'): {'RioVallecitos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PiedraLumbre'): {'PiedraLumbre': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1_1'): {'GrandeRanchoT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LatirCreekTrib3a'): {'LatirCreekTrib3a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaos'): {'PueblodeTaos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1b2'): {'GrandeRanchoT1b2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioVallecitosT2b'): {'RioVallecitosT2b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoLagunitas'): {'ArroyoLagunitas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1e'): {'GrandeRanchoT1e': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AngelCanyon'): {'AngelCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanoncitoMadera'): {'CanoncitoMadera': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AlmagreT4'): {'AlmagreT4': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'RioVallecitosT7'): {'RioVallecitosT7': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1a'): {'GrandeRanchoT1a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LagunitasT3'): {'LagunitasT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoelRito'): {'ArroyoelRito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT9a'): {'RioGrandeT9a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoCerroNegro'): {'ArroyoCerroNegro': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1a1'): {'GrandeRanchoT1a1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LaMaderaT1b'): {'LaMaderaT1b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPuncheT1'): {'ArroyoPuncheT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioPueblo'): {'RioPueblo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1f'): {'GrandeRanchoT1f': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'DesagueAguita'): {'DesagueAguita': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT10d1'): {'RioGrandeT10d1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaBuenaVista'): {'CanadaBuenaVista': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaLemitas'): {'CanadaLemitas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1d'): {'GrandeRanchoT1d': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RitoCieneguilla'): {'RitoCieneguilla': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPuncheT3b'): {'ArroyoPuncheT3b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioVallecitosT1'): {'RioVallecitosT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ChamizalCreek'): {'ChamizalCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadeOso'): {'CanadadeOso': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaEntranasT'): {'CanadaEntranasT': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioVallecitosT4'): {'RioVallecitosT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT9b'): {'RioGrandeT9b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioOjoCaliente'): {'RioOjoCaliente': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaTioPula'): {'CanadaTioPula': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaosT4a1'): {'PueblodeTaosT4a1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioChiquito1'): {'RioChiquito1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadelaCruz'): {'CanadadelaCruz': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioVallecitosT6'): {'RioVallecitosT6': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LamyCanyon'): {'LamyCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadeOso_1'): {'CanadadeOso': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaAlamos'): {'CanadaAlamos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioHondo'): {'RioHondo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioVallecitosT2a'): {'RioVallecitosT2a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioOjoCalienteT2'): {'RioOjoCalienteT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'EmbudoCreek'): {'EmbudoCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'SandlinArroyo'): {'SandlinArroyo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LatirCreekTrib3'): {'LatirCreekTrib3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioVallecitosT8'): {'RioVallecitosT8': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RedRiverTrib2'): {'RedRiverTrib2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoGavilan'): {'ArroyoGavilan': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT9a2'): {'RioGrandeT9a2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'SpringCreekSouth'): {'SpringCreekSouth': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AlmagreT2b'): {'AlmagreT2b': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'RioGrandeT7'): {'RioGrandeT7': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaOjoSarco'): {'CanadaOjoSarco': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaAbeque'): {'CanadaAbeque': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT10'): {'RioGrandeT10': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LittleTusasCreek'): {'LittleTusasCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioTusasTrib1'): {'RioTusasT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaosT3A'): {'PueblodeTaosT3a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioLucero'): {'RioLucero': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LatirCreekTrib2a'): {'LatirCreekTrib2a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ElRitoT8a'): {'ElRitoT8a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RiodeTruchasT1'): {'RiodeTruchasT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaMontecito'): {'CanadaMontecito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioTusas'): {'RioTusas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT10c'): {'RioGrandeT10c': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT12'): {'RioGrandeT12': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'MartinezCanyon'): {'MartinezCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadeOjito'): {'CanadadeOjito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AlmagreT2'): {'AlmagreT2': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'URGUnnamedStream'): {'URGUnnamedStream': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AlmagreT3a'): {'AlmagreT3a': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'RioGrandeT12a'): {'RioGrandeT12a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1b1'): {'GrandeRanchoT1b1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoSecoNorth'): {'ArroyoSecoNorth': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrande'): {'RioGrande': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT10b'): {'RioGrandeT10b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1'): {'GrandeRanchoT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT12b'): {'RioGrandeT12b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadeLorenzo'): {'CanadadeLorenzo': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanondelaMadera'): {'CanondelaMadera': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'BarelaCanyon'): {'BarelaCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaosT4b'): {'PueblodeTaosT4b': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT7a'): {'RioGrandeT7a': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeRancho_1'): {'RioGrandeRancho': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadelOso'): {'CanadadelOso': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AcequiadelJaral'): {'AcequiadelJaral': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT9c2'): {'RioGrandeT9c2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPuncheT3b1'): {'ArroyoPuncheT3b1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AguajedelaPetaca'): {'AguajedelaPetaca': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoAlmagre'): {'ArroyoAlmagre': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'RioVallecitosT5'): {'RioVallecitosT5': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT9a1'): {'RioGrandeT9a1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'JarosoCreek'): {'JarosoCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LaMaderaT1'): {'LaMaderaT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaEntranas'): {'CanadaEntranas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PicurisCanyon'): {'PicurisCanyon': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ArroyoPuncheT6'): {'ArroyoPuncheT6': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'ElRito_1'): {'ElRito': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'LagunitasT2'): {'LagunitasT2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT9a11'): {'RioGrandeT9a11': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RiodelasTrampas'): {'RiodelasTrampas': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaApaches'): {'CanadaApaches': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaosT3'): {'PueblodeTaosT3': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'GrandeRanchoT1c'): {'GrandeRanchoT1c': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'BlancoSouthT5'): {'BlancoSouthT5': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'RioVallecitosT9'): {'RioVallecitosT9': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaPinoReale'): {'CanadaPinoReale': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioTusasTrib4'): {'RioTusasT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadadeJacinto'): {'CanadadeJacinto': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT11c1'): {'RioGrandeT11c1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AlmagreT2a'): {'AlmagreT2a': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'LatirCreek'): {'LatirCreek': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'PueblodeTaosT4'): {'PueblodeTaosT4': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioChiquito2'): {'RioChiquito2': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'BlancoSouthT4'): {'BlancoSouthT4': 'ESPG: 2258, NAD83 / New Mexico Central (ftUS),'},\n", - " ('13020102',\n", - " 'ElRitoT5'): {'ElRitoT5': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanadaAncha'): {'CanadaAncha': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'CanondelosAlamos'): {'CanondelosAlamos': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'AnchaNorthT1'): {'AnchaNorthT1': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('13020102',\n", - " 'RioGrandeT10d'): {'RioGrandeT10d': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('17060305',\n", - " 'cottonwood_creek_1_ras'): {'cottonwood_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'whitman_creek_ras'): {'whitman_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'shebang_creek_tributary1_ras'): {'shebang_creek_tr': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'shebang_creek_tributary2_ras'): {'shebang_creek_tr': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'meadow_creek_1_ras'): {'meadow_creek_1': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'orchard_creek_ras'): {'orchard_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'main_three_mile_ck_trib1_ras'): {'main_three_mile_': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'red_river_2_ras'): {'red_river_2': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'cottonwood_creek_tributary_ras'): {'cottonwood_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'threemile_creek_ras_1'): {'threemile_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'stockney_creek_tributary1_ras'): {'stockney_creek_t': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'stockney_creek_ras'): {'stockney_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'threemile_creek_trib1a_ras'): {'threemile_creek_': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'threemile_creek_ras'): {'threemile_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'long_haul_creek_ras'): {'long_haul_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'south_fork_clearwater_river_3_ras'): {'south_fork_clear': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'cole_creek_ras'): {'cole_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'south_fork_clearwater_river_2_ras'): {'south_fork_clear': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'threemile_creek_trib1b_ras'): {'threemile_creek_': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'elk_creek_ras'): {'elk_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'main_three_mile_ck_trib2_ras'): {'main_three_mile_': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'rabbit_creek_ras'): {'rabbit_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'clearwater_river1_tributary3_ras'): {'clearwater_river': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'south_fork_cottonwood_creek_ras'): {'south_fork_cotto': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'sally_ann_creek_ras'): {'sally_ann_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'shebang_creek_ras'): {'shebang_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'cottonwood_creek_ras'): {'cottonwood_creek': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('17060305',\n", - " 'south_fork_clearwater_river_1_ras'): {'south_fork_clear': 'ESPG: 2243, NAD83 / Idaho West (ftUS),'},\n", - " ('12030107',\n", - " 'KingsCreekTrib1B_4'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'ELD004_1'): {'Bachelor Creek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'ELD004'): {'Bachelor Creek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'KingsCreekTrib1B_1'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " '010-07-BrushyCreek'): {'Brushy': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'BrushyCr_1'): {'BrushyCreek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),', 'Trib': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'KingsCreekTrib1B_3'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'KingsCreekTrib1B_2'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'KingsCreekTrib1B'): {'Kng Crk -1B': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'ELD004_2'): {'Bachelor Creek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'KC1_KC1B_LOMR'): {'KC-1': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " 'BrushyCr'): {'BrushyCreek': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),', 'Trib': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('12030107',\n", - " '010-07-BrushyCreek_1'): {'Brushy': 'ESPG: 2276, NAD83 / Texas North Central (ftUS),'},\n", - " ('08060202',\n", - " 'Comm_Creek_Trib_1'): {'Commissioners Cr': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Big_Sand_Creek_Trib_1'): {'Big Sand Creek T': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Comm_Creek_Trib_1-1'): {'Commissioners Cr': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Porter_Creek_Trib_3'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Jones_Creek'): {'Jones Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Maxie_Creek'): {'Maxie Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Bogue_Falia_Creek_Trib_2-1'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Fivemile_Creek'): {'Fivemile Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Porter_Creek_Trib_1-2'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Bogue_Falia_Creek_Trib_3'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Harts_Creek'): {'Harts Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Porter_Creek_Trib_1'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Potter_Creek'): {'Potter Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Porter_Creek_Trib_1-3'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Bogue_Falia_Creek'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Bogue_Falia_Creek_Trib_2'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Porter_Creek_Trib_1-1'): {'Porter Creek Tri': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Bogue_Falia_Creek_Trib_5'): {'Bogue Falia Cree': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Big_Black_River_Trib_1'): {'Big Black River': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Murphy_Creek'): {'Murphy Creek': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('08060202',\n", - " 'Fivemile_Creek_Trib_3'): {'Fivemile Creek T': 'ESPG: 2255, NAD83 / Mississippi West (ftUS),'},\n", - " ('16020203',\n", - " 'S_EastWeberFOA'): {'Nobletts Creek': 'ESPG: 2259, NAD83 / New Mexico West (ftUS),', 'Weber River': 'ESPG: 2259, NAD83 / New Mexico West (ftUS),', 'SF Weber River': 'ESPG: 2259, NAD83 / New Mexico West (ftUS),'},\n", - " ('16020203',\n", - " 'Center_Creek'): {'Center Creek': 'ESPG: 2281, NAD83 / Utah Central (ft),'},\n", - " ('18070204',\n", - " 'BorregoCyn_Existing'): {'Borrego Cyn': 'ESPG: 2230, NAD83 / California zone 6 (ftUS),'},\n", - " ('05020006',\n", - " 'PIKERUN1'): {'PIKERUN1': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP771'): {'NP771': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'INDIANCR'): {'INDIANCR': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP708'): {'NP708': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'WILSONRUN'): {'WILSONRUN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP499'): {'NP499': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'T9YOUGH'): {'T9YOUGH': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'CEDARCR'): {'CEDARCR': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP535'): {'NP535': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'TWNSHPRN'): {'TWNSHPRN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP720'): {'NP720': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'BuffRnT1'): {'BuffRnT1': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'JcbsCrkT3'): {'JcbsCrkT3': 'ESPG: 3365, NAD83(HARN) / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP709'): {'NP709': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'JACOBCR1'): {'JACOBCR1': 'ESPG: 3365, NAD83(HARN) / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'JcbsCrkT2'): {'JcbsCrkT2': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'WEITYRUN'): {'WEITYRUN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'STAUFERN'): {'STAUFERN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'JcbsCrkT4'): {'JcbsCrkT4': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP500'): {'NP500': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP707'): {'NP707': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'BARRENRUN'): {'BARRENRUN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'LICKRUN'): {'LICKRUN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'YoughyRT1'): {'YoughyRT1': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP719'): {'NP719': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP180'): {'NP180': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'T8YOUGHI'): {'T8YOUGHI': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP537'): {'NP537': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'ANDREWS2'): {'ANDREWS2': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'NP620'): {'NP620': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('05020006',\n", - " 'LITTLERN'): {'LITTLERN': 'ESPG: 2272, NAD83 / Pennsylvania South (ftUS),'},\n", - " ('02060006',\n", - " 'streamcb4'): {'Stream CB-4': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'HallCreek'): {'Hall Creek': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'mosleybranch'): {'Mosley Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'CoxtownCreek'): {'Cocktowncr': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'CB_CorrectedEffective'): {'Clydes Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'NortheastBranchWB'): {'Northeast Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'clydesbranch'): {'Clydes Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'streamcb16_1'): {'Stream CB-16': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'streamcb1_1'): {'Stream CB-1': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'CB16_Proposed'): {'Stream CB-16': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'ChewCreek'): {'Chew Creek': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'PatuxentRiver_1'): {'Patuxent River': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'streamcb1'): {'Stream CB-1': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'MPR_Proposed'): {'MiddlePatuxent': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'StLeonardCreek'): {'St_Leonard_CL': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'MillCreek'): {'Mill_Cr_CL': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'streamcb18_1'): {'Stream CB-18': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'LaurelRaceTrack'): {'Patuxent River': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'HuntingCreek'): {'HuntingCr': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'CB16_CorrectedEffective'): {'Stream CB-16': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'middlepatuxent'): {'MiddlePatuxent': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'CB18_CorrectedEffective'): {'Stream CB-18': 'ESPG: 2248, NAD83 / Maryland (ftUS),', 'CB-18 Split Flow': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'JohnsCreek'): {'JohnMosleyCr': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'PatuxentRiver'): {'Patuxent River': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'SewellCreek'): {'Sewell_Branch_CL': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'streamcb16'): {'Stream CB-16': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'clydesbranch_1'): {'Clydes Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'BattleCreekTrib'): {'Battle Creek': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'LittleLyonsCreek'): {'LittleLyonsCL': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'StreamCB4Prop'): {'Stream CB-4': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'CB18_Proposed'): {'Stream CB-18': 'ESPG: 2248, NAD83 / Maryland (ftUS),', 'CB-18 Split Flow': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'CB_Proposed'): {'Clydes Branch': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'streamcb18'): {'Stream CB-18': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('02060006',\n", - " 'BattleCreekCypressSwamp'): {'BattleCrCypress': 'ESPG: 2248, NAD83 / Maryland (ftUS),'},\n", - " ('14050005',\n", - " 'MarvineCreek'): {'MarvineCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'StrawberryTrib'): {'Strawberry Trib': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'DryCreek'): {'Dry_Creek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'NorthForkWhiteRiver'): {'NorthFork_WR': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'CaveGulch'): {'Cave Gulch': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'MillerCreek'): {'Miller_Creek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'StrawberryCreek'): {'StrawberryCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'UteCreek'): {'UteCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'WhiteRiver_Seg3'): {'WhiteRiv_Seg3': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'BigBeaverCreek'): {'BigBeaverCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'FawnCreek'): {'FawnCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'NorthElkCreek'): {'NorthElkCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'SheepCreek'): {'SheepCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'LittleBeaverCreek'): {'LBC': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'FourmileGulch'): {'Fourmile Gulch': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'WhiteMeeker'): {'White_R_Meeker': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'UpperCurtisCreek'): {'UpperCurtisCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'WhiteRiver_Seg4'): {'WhiteRiv_Seg4': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'CoalCreek'): {'CoalCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'CurtisCreek_Meeker'): {'Curtis Creek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'SulphurCreek'): {'SulphurCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'EastMillerCreek'): {'EastMillerCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'DevilsHole'): {'Devils Hole': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'EFlagCreek'): {'EFlagCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'SouthForkWhiteRiver'): {'thForkWhiteRiver': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'OylerGulch'): {'Oyler Gulch': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('14050005',\n", - " 'FlagCreek'): {'FlagCreek': 'ESPG: 2231, NAD83 / Colorado North (ftUS),'},\n", - " ('15040007',\n", - " '1242_ras'): {'1242': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('15040007',\n", - " '1195_ras'): {'1195': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('15040007',\n", - " '1236_ras'): {'1236': 'ESPG: 2222, NAD83 / Arizona East (ft),'},\n", - " ('04030101',\n", - " 'UT15Milw'): {'UT15Milw': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", - " ('04030101',\n", - " 'EastTwin_trib'): {'East Twin': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'},\n", - " ('04030101',\n", - " 'UT13_Milw'): {'UT13Milw': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", - " ('04030101',\n", - " 'Jambo'): {'Jambo': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'},\n", - " ('04030101',\n", - " 'UT81Milw'): {'UT81Milw': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", - " ('04030101',\n", - " 'UT5Milw'): {'UT5MIlw': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", - " ('04030101',\n", - " 'EastTwin'): {'East Twin': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'},\n", - " ('04030101',\n", - " 'UT2RiveredgeCrk'): {'UT2RiveredgeCrk': 'ESPG: 8313, NAD83(2011) / Oregon Canyon City-Burns zone (m),'},\n", - " ('04030101',\n", - " 'Krok'): {'Krok': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'},\n", - " ('04030101',\n", - " 'Buck'): {'Buck': 'ESPG: 2288, NAD83 / Wisconsin Central (ftUS),'}})" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "crs_map" - ] - }, - { - "cell_type": "markdown", - "id": "dd7a0bfe-921f-4b44-a7f5-887b1139d669", - "metadata": {}, - "source": [ - "__Findings:__\n", - "\n", - "To map each cross-section of a model @ HUC#, there has to be a shared attribute between a model @ HUC#'s cross section w/in its XS layer & the details provided within a model @ HUC#'s conflation json file.\n", - " \n", - "- Each conflation json file reveals ...\n", - " - Per reach, there is a set of cross-sections.\n", - " - Within each model's cross-section (XS) layers, there are a set of cross-section instances - each instance featuring a unique \"thalweg\" (aka \"min_elevation\"), \"xs_max_elevation\" (aka \"max_elevation\"), \"reach_id\" (\"reaches\"), & \"river_station\" (aka \"xs_id\")\n", - "\n", - "- Each model @ HUC#'s XS layer contains a collection of cross-section instancees. Thus, each unique cross-section w/in a given model @ HUC#'s XS layer will need to be mapped in such a way to allow each cross-section to be associated with a feature ID (aka \"reach_id\" and/or \"network_to_id\")\n", - "\n", - "- \"river_reach_rs\" is formatted differently across models' XS layers, however multplie keys referenced in the conflation jsons can be referenced to obtain the \"river_reach_rs\" from the jsons - as a result, the mapping of IDs to each model's cross-section instance will be based on the info extracted from a model @ HUC#'s conflation.json\n" - ] - }, - { - "cell_type": "markdown", - "id": "56e8ca07-a893-423a-8b35-865c9cf6c5bf", - "metadata": {}, - "source": [ - "##### Next Step: \n", - "##### Per HUC, consolidate each XS by River name (aka \"model\") & save as a geopandas dataframe (ensure CRS is consistent for each geopandas dataframe; check with QGIS)\n", - "\n", - "1) Consolidate XS by river. For each river, save their consolidate XS rows as a geopandas dataframe per river. Ensure their CRS are same.\n", - "Keep their unique inherited CRS. Track HUC\n", - "\n", - "2) Consolidate each \"river's\" geopandas dataframe by HUC & save them as a geoparquet, store in S3\n", - "\n", - "3) Each geoparquets can then be stored as Apache iceberg tables\n", - "\n", - "4) Store Apache iceberg tables in S3\n", - "\n", - "\n", - "Collection of geoparquets representing each river at a given HUC\n", - "\n", - "model_id2gpkg_map[" - ] - }, - { - "cell_type": "markdown", - "id": "2677869f-a354-4bc3-858a-b14da790e2b8", - "metadata": {}, - "source": [ - "## Demo VII: \n", - "### Querying Ripple Layers by HUC Number" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "06748e4a-1a94-492c-9d07-4f71cd956f33", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['processing', 'reaches', 'rating_curves', 'network', 'models', 'metadata', 'rating_curves_no_map'])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rip_huc2gpkg_map[\"07130010\"].keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "791efbaa-31f8-42fe-850f-67774d56a7c7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" 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" - } - }, - "cell_type": "markdown", - "id": "b3747f8a-8b23-4bdd-b7a7-4a9add4df61c", - "metadata": {}, - "source": [ - "# Write local files to S3 (for dev purposes only)\n", - "![image.png](attachment:cb93a3d1-2477-4ec1-bddd-0da089352aa6.png)\n", - "![image.png](attachment:2255f9dc-6db9-4a19-b160-559958de2563.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "2e54d9d3-b04e-434b-9576-2a861e4c9938", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(None,\n", - " {'xs_data/mip_07130010/huc_07130010.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_07130010/huc_07130010.gpkg',\n", - " 'xs_data/mip_07130010/huc_07130010.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_07130010/huc_07130010.parquet',\n", - " 'xs_data/mip_09020301/huc_09020301.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_09020301/huc_09020301.parquet',\n", - " 'xs_data/mip_09020301/huc_09020301.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_09020301/huc_09020301.gpkg',\n", - " 'xs_data/mip_06040002/huc_06040002.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_06040002/huc_06040002.parquet',\n", - " 'xs_data/mip_06040002/huc_06040002.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_06040002/huc_06040002.gpkg',\n", - " 'xs_data/mip_03170004/huc_03170004.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_03170004/huc_03170004.gpkg',\n", - " 'xs_data/mip_03170004/huc_03170004.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_03170004/huc_03170004.parquet',\n", - " 'xs_data/mip_01020002/huc_01020002.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_01020002/huc_01020002.gpkg',\n", - " 'xs_data/mip_01020002/huc_01020002.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_01020002/huc_01020002.parquet',\n", - " 'xs_data/mip_10120203/huc_10120203.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_10120203/huc_10120203.parquet',\n", - " 'xs_data/mip_10120203/huc_10120203.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_10120203/huc_10120203.gpkg',\n", - " 'xs_data/mip_11060002/huc_11060002.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_11060002/huc_11060002.gpkg',\n", - " 'xs_data/mip_11060002/huc_11060002.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_11060002/huc_11060002.parquet',\n", - " 'xs_data/mip_13020102/huc_13020102.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_13020102/huc_13020102.gpkg',\n", - " 'xs_data/mip_13020102/huc_13020102.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_13020102/huc_13020102.parquet',\n", - " 'xs_data/mip_17060305/huc_17060305.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_17060305/huc_17060305.gpkg',\n", - " 'xs_data/mip_17060305/huc_17060305.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_17060305/huc_17060305.parquet',\n", - " 'xs_data/mip_12030107/huc_12030107.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_12030107/huc_12030107.gpkg',\n", - " 'xs_data/mip_12030107/huc_12030107.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_12030107/huc_12030107.parquet',\n", - " 'xs_data/crs_map/crs_mapping.pickle': '/home/sylvia.chin/Desktop/iceberg/xs_data/crs_map/crs_mapping.pickle',\n", - " 'xs_data/mip_08060202/huc_08060202.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_08060202/huc_08060202.gpkg',\n", - " 'xs_data/mip_08060202/huc_08060202.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_08060202/huc_08060202.parquet',\n", - " 'xs_data/mip_16020203/huc_16020203.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_16020203/huc_16020203.gpkg',\n", - " 'xs_data/mip_16020203/huc_16020203.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_16020203/huc_16020203.parquet',\n", - " 'xs_data/mip_18070204/huc_18070204.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_18070204/huc_18070204.gpkg',\n", - " 'xs_data/mip_18070204/huc_18070204.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_18070204/huc_18070204.parquet',\n", - " 'xs_data/mip_05020006/huc_05020006.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_05020006/huc_05020006.parquet',\n", - " 'xs_data/mip_05020006/huc_05020006.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_05020006/huc_05020006.gpkg',\n", - " 'xs_data/mip_02060006/huc_02060006.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_02060006/huc_02060006.gpkg',\n", - " 'xs_data/mip_02060006/huc_02060006.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_02060006/huc_02060006.parquet',\n", - " 'xs_data/mip_14050005/huc_14050005.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_14050005/huc_14050005.gpkg',\n", - " 'xs_data/mip_14050005/huc_14050005.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_14050005/huc_14050005.parquet',\n", - " 'xs_data/mip_15040007/huc_15040007.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_15040007/huc_15040007.parquet',\n", - " 'xs_data/mip_15040007/huc_15040007.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_15040007/huc_15040007.gpkg',\n", - " 'xs_data/mip_04030101/huc_04030101.gpkg': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_04030101/huc_04030101.gpkg',\n", - " 'xs_data/mip_04030101/huc_04030101.parquet': '/home/sylvia.chin/Desktop/iceberg/xs_data/mip_04030101/huc_04030101.parquet'})" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s3_xs_keys2objs = collections.defaultdict()\n", - "for folder, subfolders, files in os.walk(f\"{os.getcwd()}/xs_data/\"):\n", - " for file in files:\n", - " if folder != f\"{os.getcwd()}/xs_data/\":\n", - " s3_xs_keys2objs[f\"{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}/{file}\"] = (\n", - " f\"{folder}/{file}\"\n", - " )\n", - "\n", - "s3_xs_keys2objs" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "0ef03ac5-4475-4a3c-8fbb-88ec50d832c4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(None,\n", - " {'iceberg_catalog/xs_dev2_catalog.db': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/xs_dev2_catalog.db',\n", - " 'iceberg_catalog/xs_catalog.db': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/xs_catalog.db',\n", - " 'iceberg_catalog/metadata/snap-1219395432228756396-0-e0c82612-49a1-4bec-9ffb-0363b2808c90.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-1219395432228756396-0-e0c82612-49a1-4bec-9ffb-0363b2808c90.avro',\n", - " 'iceberg_catalog/metadata/79d941be-d64b-420d-8258-ab9a19f60a3c-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/79d941be-d64b-420d-8258-ab9a19f60a3c-m0.avro',\n", - " 'iceberg_catalog/metadata/56302b1b-0145-4261-a75f-9fc822513965-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/56302b1b-0145-4261-a75f-9fc822513965-m0.avro',\n", - " 'iceberg_catalog/metadata/2b35b175-c393-402f-aefe-7ab21a7f86e3-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/2b35b175-c393-402f-aefe-7ab21a7f86e3-m0.avro',\n", - " 'iceberg_catalog/metadata/00000-d5bfb311-f654-4caa-a8bd-0988d5ea3f1c.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-d5bfb311-f654-4caa-a8bd-0988d5ea3f1c.metadata.json',\n", - " 'iceberg_catalog/metadata/00000-e005d193-1dbe-48f8-9c71-5d6acb118047.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-e005d193-1dbe-48f8-9c71-5d6acb118047.metadata.json',\n", - " 'iceberg_catalog/metadata/00000-a51a3d3f-c9d8-4491-ba4a-61b45450b482.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-a51a3d3f-c9d8-4491-ba4a-61b45450b482.metadata.json',\n", - " 'iceberg_catalog/metadata/snap-7751284475758926448-0-83a8e328-45aa-41c3-b7ba-aada589c1100.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-7751284475758926448-0-83a8e328-45aa-41c3-b7ba-aada589c1100.avro',\n", - " 'iceberg_catalog/metadata/7e31eefd-b1be-4270-8c56-55c3d7cee3ec-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/7e31eefd-b1be-4270-8c56-55c3d7cee3ec-m0.avro',\n", - " 'iceberg_catalog/metadata/00001-ff6bb0fd-545d-4c90-8687-d59e57f795f7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-ff6bb0fd-545d-4c90-8687-d59e57f795f7.metadata.json',\n", - " 'iceberg_catalog/metadata/eb72845f-a72d-4ec7-b515-3fa60bcb1a0e-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/eb72845f-a72d-4ec7-b515-3fa60bcb1a0e-m0.avro',\n", - " 'iceberg_catalog/metadata/00000-efb96525-596e-4f96-a5a4-6e930d9fa14d.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-efb96525-596e-4f96-a5a4-6e930d9fa14d.metadata.json',\n", - " 'iceberg_catalog/metadata/00000-e971734f-4fd6-4f57-8313-6578b345dd9f.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-e971734f-4fd6-4f57-8313-6578b345dd9f.metadata.json',\n", - " 'iceberg_catalog/metadata/00001-37052db6-879f-4d5d-a7eb-bf238795f174.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-37052db6-879f-4d5d-a7eb-bf238795f174.metadata.json',\n", - " 'iceberg_catalog/metadata/00000-566a22c6-9329-4cf1-b863-35034787428a.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-566a22c6-9329-4cf1-b863-35034787428a.metadata.json',\n", - " 'iceberg_catalog/metadata/0932a09d-7c95-4369-91ff-86187c292488-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/0932a09d-7c95-4369-91ff-86187c292488-m0.avro',\n", - " 'iceberg_catalog/metadata/f491d047-a07d-4dd1-9880-fb7cb524b729-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/f491d047-a07d-4dd1-9880-fb7cb524b729-m0.avro',\n", - " 'iceberg_catalog/metadata/94010da4-988a-4690-9744-544b69c4043d-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/94010da4-988a-4690-9744-544b69c4043d-m0.avro',\n", - " 'iceberg_catalog/metadata/snap-1423242798306539847-0-f1963f4e-533c-4349-90ce-fe651c3bd03f.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-1423242798306539847-0-f1963f4e-533c-4349-90ce-fe651c3bd03f.avro',\n", - " 'iceberg_catalog/metadata/74d5f5a8-00b1-4270-86e4-920f534462e2-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/74d5f5a8-00b1-4270-86e4-920f534462e2-m0.avro',\n", - " 'iceberg_catalog/metadata/00000-7bbe577e-8ca9-4a90-afe8-115ae5ad1e66.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-7bbe577e-8ca9-4a90-afe8-115ae5ad1e66.metadata.json',\n", - " 'iceberg_catalog/metadata/00000-0f5ff66b-9f53-4695-8fc7-cb317cab3ff7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-0f5ff66b-9f53-4695-8fc7-cb317cab3ff7.metadata.json',\n", - " 'iceberg_catalog/metadata/00001-5b0d7e69-1e56-4f5a-8f0e-b5c5f8c9e402.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-5b0d7e69-1e56-4f5a-8f0e-b5c5f8c9e402.metadata.json',\n", - " 'iceberg_catalog/metadata/snap-2864916062256581278-0-56302b1b-0145-4261-a75f-9fc822513965.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-2864916062256581278-0-56302b1b-0145-4261-a75f-9fc822513965.avro',\n", - " 'iceberg_catalog/metadata/c02f999d-b401-4394-8550-0524812a59fe-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/c02f999d-b401-4394-8550-0524812a59fe-m0.avro',\n", - " 'iceberg_catalog/metadata/2a5f2f6a-d7ed-424e-84ef-eaebb0fb2428-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/2a5f2f6a-d7ed-424e-84ef-eaebb0fb2428-m0.avro',\n", - " 'iceberg_catalog/metadata/00000-37fbbe54-240a-4d27-9c80-8af688986ec7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-37fbbe54-240a-4d27-9c80-8af688986ec7.metadata.json',\n", - " 'iceberg_catalog/metadata/snap-1547694616865413560-0-eb72845f-a72d-4ec7-b515-3fa60bcb1a0e.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-1547694616865413560-0-eb72845f-a72d-4ec7-b515-3fa60bcb1a0e.avro',\n", - " 'iceberg_catalog/metadata/4b224a68-251a-448b-a79a-e7a2ae6848c7-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/4b224a68-251a-448b-a79a-e7a2ae6848c7-m0.avro',\n", - " 'iceberg_catalog/metadata/83a8e328-45aa-41c3-b7ba-aada589c1100-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/83a8e328-45aa-41c3-b7ba-aada589c1100-m0.avro',\n", - " 'iceberg_catalog/metadata/e1c70f45-ac00-4f36-9f05-b402a92b17bb-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/e1c70f45-ac00-4f36-9f05-b402a92b17bb-m0.avro',\n", - " 'iceberg_catalog/metadata/snap-6643227147233749836-0-dd9f8708-2f34-4066-8420-63fd375262c4.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-6643227147233749836-0-dd9f8708-2f34-4066-8420-63fd375262c4.avro',\n", - " 'iceberg_catalog/metadata/88b277f5-8e5f-45cf-b3a7-3f645ccf3610-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/88b277f5-8e5f-45cf-b3a7-3f645ccf3610-m0.avro',\n", - " 'iceberg_catalog/metadata/00001-d3787857-4eac-4cfa-86a5-6b220bb197d3.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-d3787857-4eac-4cfa-86a5-6b220bb197d3.metadata.json',\n", - " 'iceberg_catalog/metadata/snap-6148936714446219898-0-928c6a28-c248-4fbc-bd5c-684b1b38d2d9.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-6148936714446219898-0-928c6a28-c248-4fbc-bd5c-684b1b38d2d9.avro',\n", - " 'iceberg_catalog/metadata/00001-e7962f83-a076-4a75-985e-d22f5c50d02e.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-e7962f83-a076-4a75-985e-d22f5c50d02e.metadata.json',\n", - " 'iceberg_catalog/metadata/00000-a6048e13-d05c-4e89-8da9-6781f37ad1f2.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-a6048e13-d05c-4e89-8da9-6781f37ad1f2.metadata.json',\n", - " 'iceberg_catalog/metadata/snap-5963977556171816944-0-31bea5d0-7d98-40f6-800b-8c651aea6750.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-5963977556171816944-0-31bea5d0-7d98-40f6-800b-8c651aea6750.avro',\n", - " 'iceberg_catalog/metadata/snap-612035466803620355-0-7e31eefd-b1be-4270-8c56-55c3d7cee3ec.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-612035466803620355-0-7e31eefd-b1be-4270-8c56-55c3d7cee3ec.avro',\n", - " 'iceberg_catalog/metadata/00000-4e2fd912-9308-4048-a8d1-252cf9122840.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-4e2fd912-9308-4048-a8d1-252cf9122840.metadata.json',\n", - " 'iceberg_catalog/metadata/00001-79175265-6e89-4e2a-9157-5db43dafa9d7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-79175265-6e89-4e2a-9157-5db43dafa9d7.metadata.json',\n", - " 'iceberg_catalog/metadata/snap-62392862022840883-0-79d941be-d64b-420d-8258-ab9a19f60a3c.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-62392862022840883-0-79d941be-d64b-420d-8258-ab9a19f60a3c.avro',\n", - " 'iceberg_catalog/metadata/snap-515960161385423435-0-994a22b9-0dae-40c2-b2bb-23f9962c61c5.avro': 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'iceberg_catalog/metadata/00000-9a694d60-0d5c-41b4-9b92-a81b1e4dabf4.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-9a694d60-0d5c-41b4-9b92-a81b1e4dabf4.metadata.json',\n", - " 'iceberg_catalog/metadata/994a22b9-0dae-40c2-b2bb-23f9962c61c5-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/994a22b9-0dae-40c2-b2bb-23f9962c61c5-m0.avro',\n", - " 'iceberg_catalog/metadata/snap-4622875834949791680-0-88b277f5-8e5f-45cf-b3a7-3f645ccf3610.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-4622875834949791680-0-88b277f5-8e5f-45cf-b3a7-3f645ccf3610.avro',\n", - " 'iceberg_catalog/metadata/00001-6fac6d1a-30ce-4822-89e5-f4d412208b5f.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00001-6fac6d1a-30ce-4822-89e5-f4d412208b5f.metadata.json',\n", - " 'iceberg_catalog/metadata/e0c82612-49a1-4bec-9ffb-0363b2808c90-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/e0c82612-49a1-4bec-9ffb-0363b2808c90-m0.avro',\n", - " 'iceberg_catalog/metadata/00000-0ecd14a5-3da4-47f0-8bd8-83c95e680afc.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-0ecd14a5-3da4-47f0-8bd8-83c95e680afc.metadata.json',\n", - " 'iceberg_catalog/metadata/snap-4996700114448263860-0-29a6c52d-eefc-475d-927b-8e74a04cb96c.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-4996700114448263860-0-29a6c52d-eefc-475d-927b-8e74a04cb96c.avro',\n", - " 'iceberg_catalog/metadata/00000-728df095-7195-4409-aae6-ed71991c6ef6.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-728df095-7195-4409-aae6-ed71991c6ef6.metadata.json',\n", - " 'iceberg_catalog/metadata/snap-6495503154204324342-0-0932a09d-7c95-4369-91ff-86187c292488.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/snap-6495503154204324342-0-0932a09d-7c95-4369-91ff-86187c292488.avro',\n", - " 'iceberg_catalog/metadata/8dea00e3-705a-4e9b-80ab-16d2728732f9-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/8dea00e3-705a-4e9b-80ab-16d2728732f9-m0.avro',\n", - " 'iceberg_catalog/metadata/1d64c282-3463-4db0-9914-78fef1158e5e-m0.avro': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/1d64c282-3463-4db0-9914-78fef1158e5e-m0.avro',\n", - " 'iceberg_catalog/metadata/00000-1c1fb8d7-0c4c-4b0a-9ae3-db09afa718c7.metadata.json': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/metadata/00000-1c1fb8d7-0c4c-4b0a-9ae3-db09afa718c7.metadata.json',\n", - " 'iceberg_catalog/data/00000-0-3a1ebbe7-11ac-4920-b628-91d42f2a9658.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-3a1ebbe7-11ac-4920-b628-91d42f2a9658.parquet',\n", - " 'iceberg_catalog/data/00000-0-1d64c282-3463-4db0-9914-78fef1158e5e.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-1d64c282-3463-4db0-9914-78fef1158e5e.parquet',\n", - " 'iceberg_catalog/data/00000-0-f491d047-a07d-4dd1-9880-fb7cb524b729.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-f491d047-a07d-4dd1-9880-fb7cb524b729.parquet',\n", - " 'iceberg_catalog/data/00000-0-a5fa37d5-ad11-4816-8bc1-907b2f384012.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-a5fa37d5-ad11-4816-8bc1-907b2f384012.parquet',\n", - " 'iceberg_catalog/data/00000-0-31bea5d0-7d98-40f6-800b-8c651aea6750.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-31bea5d0-7d98-40f6-800b-8c651aea6750.parquet',\n", - " 'iceberg_catalog/data/00000-0-25ee9bd7-8d6f-4ae3-93ae-0a7c74d1be51.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-25ee9bd7-8d6f-4ae3-93ae-0a7c74d1be51.parquet',\n", - " 'iceberg_catalog/data/00000-0-e1c70f45-ac00-4f36-9f05-b402a92b17bb.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-e1c70f45-ac00-4f36-9f05-b402a92b17bb.parquet',\n", - " 'iceberg_catalog/data/00000-0-94010da4-988a-4690-9744-544b69c4043d.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-94010da4-988a-4690-9744-544b69c4043d.parquet',\n", - " 'iceberg_catalog/data/00000-0-994a22b9-0dae-40c2-b2bb-23f9962c61c5.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-994a22b9-0dae-40c2-b2bb-23f9962c61c5.parquet',\n", - " 'iceberg_catalog/data/00000-0-2ec024d8-d33a-474b-a66d-578b741bb07b.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-2ec024d8-d33a-474b-a66d-578b741bb07b.parquet',\n", - " 'iceberg_catalog/data/00000-0-8dea00e3-705a-4e9b-80ab-16d2728732f9.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-8dea00e3-705a-4e9b-80ab-16d2728732f9.parquet',\n", - " 'iceberg_catalog/data/00000-0-4b224a68-251a-448b-a79a-e7a2ae6848c7.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-4b224a68-251a-448b-a79a-e7a2ae6848c7.parquet',\n", - " 'iceberg_catalog/data/00000-0-7e31eefd-b1be-4270-8c56-55c3d7cee3ec.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-7e31eefd-b1be-4270-8c56-55c3d7cee3ec.parquet',\n", - " 'iceberg_catalog/data/00000-0-29a6c52d-eefc-475d-927b-8e74a04cb96c.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-29a6c52d-eefc-475d-927b-8e74a04cb96c.parquet',\n", - " 'iceberg_catalog/data/00000-0-2b35b175-c393-402f-aefe-7ab21a7f86e3.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-2b35b175-c393-402f-aefe-7ab21a7f86e3.parquet',\n", - " 'iceberg_catalog/data/00000-0-0932a09d-7c95-4369-91ff-86187c292488.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-0932a09d-7c95-4369-91ff-86187c292488.parquet',\n", - " 'iceberg_catalog/data/00000-0-2a5f2f6a-d7ed-424e-84ef-eaebb0fb2428.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-2a5f2f6a-d7ed-424e-84ef-eaebb0fb2428.parquet',\n", - " 'iceberg_catalog/data/00000-0-f1963f4e-533c-4349-90ce-fe651c3bd03f.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-f1963f4e-533c-4349-90ce-fe651c3bd03f.parquet',\n", - " 'iceberg_catalog/data/00000-0-79d941be-d64b-420d-8258-ab9a19f60a3c.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-79d941be-d64b-420d-8258-ab9a19f60a3c.parquet',\n", - " 'iceberg_catalog/data/00000-0-dd9f8708-2f34-4066-8420-63fd375262c4.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-dd9f8708-2f34-4066-8420-63fd375262c4.parquet',\n", - " 'iceberg_catalog/data/00000-0-6092672e-c75b-4426-af48-40346808bdf7.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-6092672e-c75b-4426-af48-40346808bdf7.parquet',\n", - " 'iceberg_catalog/data/00000-0-fbaf2a80-3953-4b77-9f09-6398d6db52ea.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-fbaf2a80-3953-4b77-9f09-6398d6db52ea.parquet',\n", - " 'iceberg_catalog/data/00000-0-2dd9f725-e066-4163-a76c-934c075c6b69.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-2dd9f725-e066-4163-a76c-934c075c6b69.parquet',\n", - " 'iceberg_catalog/data/00000-0-928c6a28-c248-4fbc-bd5c-684b1b38d2d9.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-928c6a28-c248-4fbc-bd5c-684b1b38d2d9.parquet',\n", - " 'iceberg_catalog/data/00000-0-e0c82612-49a1-4bec-9ffb-0363b2808c90.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-e0c82612-49a1-4bec-9ffb-0363b2808c90.parquet',\n", - " 'iceberg_catalog/data/00000-0-88b277f5-8e5f-45cf-b3a7-3f645ccf3610.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-88b277f5-8e5f-45cf-b3a7-3f645ccf3610.parquet',\n", - " 'iceberg_catalog/data/00000-0-56302b1b-0145-4261-a75f-9fc822513965.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-56302b1b-0145-4261-a75f-9fc822513965.parquet',\n", - " 'iceberg_catalog/data/00000-0-eb72845f-a72d-4ec7-b515-3fa60bcb1a0e.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-eb72845f-a72d-4ec7-b515-3fa60bcb1a0e.parquet',\n", - " 'iceberg_catalog/data/00000-0-6ed7cdd8-37e9-4f05-af66-0ad179cad29e.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-6ed7cdd8-37e9-4f05-af66-0ad179cad29e.parquet',\n", - " 'iceberg_catalog/data/00000-0-ef901ac2-0781-4afb-94ca-b733dd048a70.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-ef901ac2-0781-4afb-94ca-b733dd048a70.parquet',\n", - " 'iceberg_catalog/data/00000-0-c02f999d-b401-4394-8550-0524812a59fe.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-c02f999d-b401-4394-8550-0524812a59fe.parquet',\n", - " 'iceberg_catalog/data/00000-0-74d5f5a8-00b1-4270-86e4-920f534462e2.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-74d5f5a8-00b1-4270-86e4-920f534462e2.parquet',\n", - " 'iceberg_catalog/data/00000-0-33337276-e026-455f-8cfd-7b05284abc36.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-33337276-e026-455f-8cfd-7b05284abc36.parquet',\n", - " 'iceberg_catalog/data/00000-0-b6e49d71-9d65-445d-8721-8549a8bfff63.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-b6e49d71-9d65-445d-8721-8549a8bfff63.parquet',\n", - " 'iceberg_catalog/data/00000-0-ff565f97-f743-4f7c-abd6-986fadcdd5ff.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-ff565f97-f743-4f7c-abd6-986fadcdd5ff.parquet',\n", - " 'iceberg_catalog/data/00000-0-83a8e328-45aa-41c3-b7ba-aada589c1100.parquet': '/home/sylvia.chin/Desktop/iceberg/iceberg_catalog/data/00000-0-83a8e328-45aa-41c3-b7ba-aada589c1100.parquet'})" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s3_xs_iceberg_keys2objs = collections.defaultdict()\n", - "for folder, subfolders, files in os.walk(f\"{os.getcwd()}/iceberg_catalog/\"):\n", - " for file in files:\n", - " if folder != f\"{os.getcwd()}/iceberg_catalog/\":\n", - " # For all files not the database\n", - " s3_xs_iceberg_keys2objs[f\"{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}/{file}\"] = (\n", - " f\"{folder}/{file}\"\n", - " )\n", - " else:\n", - " # For the database.\n", - " s3_xs_iceberg_keys2objs[f\"{folder.split('/')[-2:][0]}/{folder.split('/')[-2:][1]}{file}\"] = (\n", - " f\"{folder}{file}\"\n", - " )\n", - "\n", - "\n", - "s3_xs_iceberg_keys2objs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ddc9f129-b35b-453f-bad6-92350e053fb6", - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate bucket of interest.\n", - "profile_name = \"default\"\n", - "session = boto3.Session(profile_name=profile_name)\n", - "s3 = session.resource(\"s3\")\n", - "bucket_name = \"ngwpc-xs\"\n", - "\n", - "# # Uploading the xs by HUC parquets\n", - "# for key, file_dir in s3_xs_keys2objs.items():\n", - "# s3.Bucket(bucket_name).upload_file(file_dir, key)\n", - "\n", - "# Uploading the xs by HUC parquets\n", - "# for key, file_dir in s3_xs_iceberg_keys2objs.items():\n", - "# s3.Bucket(bucket_name).upload_file(file_dir, key)" - ] - }, - { - "cell_type": "markdown", - "id": "01dc1ff0-bfd5-4b7a-82d0-1d60e237e2cd", - "metadata": {}, - "source": [ - "# Cross-Check & Verify:\n", - "## Parquet Data Has Been Written To Its Associated Iceberg Table Sucessfully" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "b365d647-bbfa-45a9-8673-52c564cf0ea7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pyarrow.Table\n", - "river: string\n", - "reach: string\n", - "river_reach: string\n", - "river_station: double\n", - "river_reach_rs: string\n", - "thalweg: double\n", - "xs_max_elevation: double\n", - "left_reach_length: double\n", - "right_reach_length: double\n", - "channel_reach_length: double\n", - "ras_data: string\n", - "station_elevation_points: string\n", - "bank_stations: string\n", - "number_of_station_elevation_points: int64\n", - "number_of_coords: int64\n", - "flows: string\n", - "profile_names: string\n", - "geometry: binary\n", - "huc: string\n", - "model_id: string\n", - "us_reach_id: string\n", - "us_network_to_id: string\n", - "ds_reach_id: string\n", - "ds_network_to_id: string\n", - "__index_level_0__: int64\n", - "----\n", - "river: [[\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",\"Stream0528\",...,\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\",\"Stream0512\"]]\n", - "reach: [[\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",...,\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\",\"Reach-1\"]]\n", - "river_reach: [[\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",\"Stream0528,Reach-1\",...,\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\",\"Stream0512,Reach-1\"]]\n", - "river_station: [[7300,6833,6346,5913,5505,...,1842,1400,970,516,111]]\n", - "river_reach_rs: [[\"Stream0528 Reach-1 7300.0\",\"Stream0528 Reach-1 6833.0\",\"Stream0528 Reach-1 6346.0\",\"Stream0528 Reach-1 5913.0\",\"Stream0528 Reach-1 5505.0\",...,\"Stream0512 Reach-1 1842.0\",\"Stream0512 Reach-1 1400.0\",\"Stream0512 Reach-1 970.0\",\"Stream0512 Reach-1 516.0\",\"Stream0512 Reach-1 111.0\"]]\n", - "thalweg: [[217.86,215.26,213.94,213.33,213.34,...,368.01,365.61,364.04,360.64,360.73]]\n", - "xs_max_elevation: [[248.82,238.12,246.73,241.95,242.87,...,404.79,398.04,385.23,404.53,391.25]]\n", - "left_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", - "right_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", - "channel_reach_length: [[467,487,433,408,419,...,442,430,454,405,0]]\n", - "..." - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Load the Parquet file into a DataFrame\n", - "parquet_file_path = f\"{os.getcwd()}/xs_data/mip_01020002/huc_01020002.parquet\"\n", - "parquet_content = pq.read_table(parquet_file_path)\n", - "parquet_content" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86ec5e38-ab4c-4185-bc0f-3306e670f968", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "iceberg", - "language": "python", - "name": "iceberg" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/geopackage_viewer/requirements.txt b/examples/geopackage_viewer/requirements.txt deleted file mode 100644 index bfa17c6..0000000 --- a/examples/geopackage_viewer/requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -folium==0.19.5 -geodatasets==2024.8.0 -geopandas==1.0.1 -httpx==0.28.1 -ipywidgets==8.1.6 -matplotlib==3.10.3 -pandas==2.2.3 -python-dotenv==1.1.0 -mapclassify==2.9.0 diff --git a/examples/geopackage_viewer/geopackage.ipynb b/examples/geopackage_viewer/viewer.ipynb similarity index 98% rename from examples/geopackage_viewer/geopackage.ipynb rename to examples/geopackage_viewer/viewer.ipynb index 1355d34..a264560 100644 --- a/examples/geopackage_viewer/geopackage.ipynb +++ b/examples/geopackage_viewer/viewer.ipynb @@ -23,40 +23,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "784799a5-df23-4971-ad1b-ea4573610279", "metadata": {}, "outputs": [], "source": [ + "from pathlib import Path\n", + "\n", "import geopandas as gpd\n", "import ipywidgets as widgets\n", "import pandas as pd\n", - "from dotenv import load_dotenv\n", "from ipywidgets import interact\n", "\n", - "from icefabric_tools.geo.geo_utils import create_time_series_widget, get_geopackage_uri, get_observational_uri" + "from icefabric.ui import create_time_series_widget, get_geopackage_uri, get_observational_uri" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "515b8c7d-c575-42f3-a9b3-414659e619f5", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# before running this cell, make sure you created a .env file in this directory with your AWS credentials in it\n", - "load_dotenv()" + "from icefabric.helpers import load_creds\n", + "\n", + "# dir is where the .env file is located\n", + "load_creds(dir=Path.cwd().parents[1])" ] }, { @@ -77,118 +69,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "5124f78a-2f24-4e9c-ba73-93d246bfc2e4", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    dateTimeq_cms
    01990-10-01 05:00:0019.623575
    11990-10-01 06:00:0020.416447
    21990-10-01 07:00:0021.237635
    31990-10-01 08:00:0021.860605
    41990-10-01 09:00:0022.285358
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    297308 rows × 2 columns

    \n", - "
    " - ], - "text/plain": [ - " dateTime q_cms\n", - "0 1990-10-01 05:00:00 19.623575\n", - "1 1990-10-01 06:00:00 20.416447\n", - "2 1990-10-01 07:00:00 21.237635\n", - "3 1990-10-01 08:00:00 21.860605\n", - "4 1990-10-01 09:00:00 22.285358\n", - "... ... ...\n", - "297303 2024-08-30 20:00:00 13.280601\n", - "297304 2024-08-30 21:00:00 13.280601\n", - "297305 2024-08-30 22:00:00 13.280601\n", - "297306 2024-08-30 23:00:00 13.280601\n", - "297307 2024-08-31 00:00:00 13.280601\n", - "\n", - "[297308 rows x 2 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "streamflow_obs_uri = get_observational_uri(gage_id=\"01013500\")\n", "streamflow_obs_df = pd.read_csv(streamflow_obs_uri)\n", @@ -262,28 +146,7 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "434b7c40-4a11-4db2-b073-8794848833d7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'s3://ngwpc-hydrofabric-oe/2.1/CONUS/06710385/GEOPACKAGE/USGS/2024_Dec_16_20_29_12/gauge_06710385.gpkg'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geopackage_uri" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "7fb96ccb-b383-42eb-b8bd-b2355c1e20cc", "metadata": {}, "outputs": [ @@ -302,6 +165,7 @@ ], "source": [ "geopackage_uri = get_geopackage_uri(gage_id=\"06710385\", version=2.2)\n", + "print(geopackage_uri)\n", "\n", "layers = gpd.list_layers(geopackage_uri).name.tolist()\n", "\n", @@ -777,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "858e0398-81ee-4d57-8889-11002174c5ea", "metadata": {}, "outputs": [ @@ -807,33 +671,13 @@ } ], "source": [ - "def show_layer(layer_name):\n", - " display(layer_df[layer_name])\n", - "\n", - "\n", - "interact(show_layer, layer_name=layers)" + "interact(lambda layer_name: display(layer_df[layer_name]), layer_name=layers)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ccc9a36b-0c8e-456e-8b5b-97215626a630", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "464f2243-2d93-44a6-8101-5b7b6812495b", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "icefabric", "language": "python", "name": "python3" }, @@ -847,7 +691,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/examples/iceberg/Demo_Iceberg.ipynb b/examples/iceberg/Demo_Iceberg.ipynb deleted file mode 100644 index d4665d1..0000000 --- a/examples/iceberg/Demo_Iceberg.ipynb +++ /dev/null @@ -1,339 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a24f20d0-abb4-413a-a70b-0fb6d3ca1e22", - "metadata": {}, - "source": [ - "# Introduction:\n", - "## Generation of Iceberg Catalog for Parquet files\n", - "\n", - "Note:\n", - "\n", - "For building from S3, requires `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` in your environmental variables. The recommended method to set is to store in this directory's `.env` file.\n", - "\n", - "For building locally, store `ml_auxiliary_data.parquet` files in `./data/bathymetry/ml_axuiliary_data/vpuid==XX/ml_auxiliary_data.parquet`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e3f71a8-a823-4df9-90df-8d6e62969faa", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "\n", - "import pyarrow.parquet as pq\n", - "from dotenv import load_dotenv\n", - "from pyiceberg.catalog import load_catalog\n", - "from pyiceberg.exceptions import NamespaceAlreadyExistsError\n", - "from pyiceberg.expressions import And, GreaterThan, GreaterThanOrEqual, LessThan\n", - "\n", - "from icefabric_manage.iceberg_table import IcebergTable\n", - "\n", - "load_dotenv()\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e6e15027-cc49-4508-b434-fc4362ffb44e", - "metadata": {}, - "source": [ - "#### Demo:\n", - "#### Establish A New Iceberg Catalog & Generate Tables For All Parquets in Local" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c74f8432-f07f-430d-830f-d0910a699ea8", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Scenario I\n", - "# Instantiate class\n", - "obj = IcebergTable()\n", - "\n", - "# Extract list of parquets\n", - "parquet_list = obj.read_data_dirs(data_dir=f\"{os.getcwd()}/data/bathymetry/ml_auxiliary_data/\")\n", - "\n", - "try:\n", - " obj.establish_catalog(catalog_name=\"bathymetry_ml_auxiliary_dev\", namespace=\"bathymetry_ml_auxiliary\")\n", - "\n", - " # Generate tables w/in the new Iceberg catalog for all parquets detected\n", - " obj.create_table_for_all_parquets(parquet_list, app_name=\"bathymetry_ml_auxiliary\")\n", - "except NamespaceAlreadyExistsError:\n", - " print(\"Table already exists! Exiting\")" - ] - }, - { - "cell_type": "markdown", - "id": "b7f5dba5-6892-45a2-81b8-1a80a3ad9f49", - "metadata": {}, - "source": [ - "#### Demo\n", - "#### Establish A New Iceberg Catalog & Generate Tables For All Parquets in S3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad60621b-fc16-44c7-85b9-8a66a2b1835b", - "metadata": {}, - "outputs": [], - "source": [ - "# Scenario II\n", - "# Instantiate class\n", - "obj = IcebergTable()\n", - "\n", - "try:\n", - " # Establish new Iceberg catalog\n", - " obj.establish_catalog(catalog_name=\"bathymetry_ml_auxiliary_dev2\", namespace=\"bathymetry_ml_auxiliary\")\n", - "\n", - " # Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", - " obj.create_table_for_all_s3parquets(app_name=\"bathymetry_ml_auxiliary\", bucket_name=\"ngwpc-bathymetry\")\n", - "except NamespaceAlreadyExistsError:\n", - " print(\"Table already exists! Exiting\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1c58d4f-59e5-41d8-be31-536f3f790216", - "metadata": {}, - "outputs": [], - "source": [ - "# Scenario III\n", - "# Instantiate class\n", - "obj = IcebergTable()\n", - "\n", - "# Establish new Iceberg catalog\n", - "try:\n", - " obj.establish_catalog(catalog_name=\"fema_xs\", namespace=\"mip\")\n", - " obj.establish_catalog(catalog_name=\"fema_xs\", namespace=\"ble\")\n", - " # Generate tables w/in the new Iceberg catalog for all parquets detected in s3\n", - " obj.create_table_for_all_s3parquets(app_name=\"mip_xs\", bucket_name=\"ngwpc-xs\")\n", - " obj.create_table_for_all_s3parquets(app_name=\"ble_xs\", bucket_name=\"ngwpc-xs\")\n", - "except NamespaceAlreadyExistsError:\n", - " print(\"Table already exists! Exiting\")" - ] - }, - { - "cell_type": "markdown", - "id": "71949e8b-36f2-4e74-afb2-741a884d95dd", - "metadata": {}, - "source": [ - "#### Demo:\n", - "#### Load Iceberg Catalog & Table of Interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f449ec30-027e-45c8-91f2-9ad5402b6d17", - "metadata": {}, - "outputs": [], - "source": [ - "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema [local]\n", - "catalog_name = \"bathymetry_ml_auxiliary_dev\"\n", - "namespace = \"bathymetry_ml_auxiliary\"\n", - "tablename = \"vpuid=01\"\n", - "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", - "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", - "print(iceberg_table.to_string(preview_cols=20))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c69cdf0-90ae-4e32-8100-5aecc746edc9", - "metadata": {}, - "outputs": [], - "source": [ - "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema [s3]\n", - "catalog_name = \"bathymetry_ml_auxiliary_dev2\"\n", - "namespace = \"bathymetry_ml_auxiliary\"\n", - "tablename = \"vpuid=01\"\n", - "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", - "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", - "# print(iceberg_table.to_string(preview_cols=20))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57d3a536-70dc-4b1d-a162-73e376bca0dc", - "metadata": {}, - "outputs": [], - "source": [ - "# Load-in table from catalog of interest & observe the first N (e.g. 20) feature columns + schema [xs]\n", - "catalog_name = \"fema_xs\"\n", - "namespace = \"mip\"\n", - "tablename = \"huc_01020002\"\n", - "catalog = load_catalog(name=catalog_name, **{\"uri\": f\"sqlite:///iceberg_catalog/{catalog_name}_catalog.db\"})\n", - "iceberg_table = catalog.load_table(f\"{namespace}.{tablename}\").scan().to_arrow()\n", - "print(iceberg_table.to_string(preview_cols=20))" - ] - }, - { - "cell_type": "markdown", - "id": "d69402cf-d224-45e8-9680-74b28205ea44", - "metadata": {}, - "source": [ - "#### Observe List of Tables under Catalog of Interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aec525f7-6753-4ca5-ba5e-916c8c6659ec", - "metadata": {}, - "outputs": [], - "source": [ - "# All tables created within the namespace of interest.\n", - "print(f\"List of tables under {namespace} within {catalog_name}:\\n\\n{catalog.list_tables(namespace)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "c5b0cacc-8f92-49db-b080-5c1ecb72f57b", - "metadata": {}, - "source": [ - "#### Query Iceberg Catalog's Table by Conditions (Note: Against the catalog_name = 'bathymetry_ml_auxiliary_dev')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28153d1a-46de-43f0-962a-5b613bdbaa73", - "metadata": {}, - "outputs": [], - "source": [ - "# Filter Iceberg table based on threshold set for a given feature\n", - "namespace = \"bathymetry_ml_auxiliary\"\n", - "tablename = \"vpuid=01\"\n", - "feature_name = \"owp_ahg_c\"\n", - "thresh_val = 0.25\n", - "catalog.load_table(f\"{namespace}.{tablename}\").scan(row_filter=LessThan(feature_name, thresh_val)).to_arrow()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6320114d-8115-4e1f-b8fd-3c80aeaa07de", - "metadata": {}, - "outputs": [], - "source": [ - "namespace = \"bathymetry_ml_auxiliary\"\n", - "tablename = \"vpuid=01\"\n", - "feature_name = \"owp_ahg_c\"\n", - "thresh_lower, thresh_upper = 0.25, 0.28\n", - "catalog.load_table(f\"{namespace}.{tablename}\").scan(\n", - " row_filter=And(GreaterThan(feature_name, thresh_lower), LessThan(feature_name, thresh_upper))\n", - ").to_arrow()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3acdfde0-d34d-442e-87eb-724dbcef5d1b", - "metadata": {}, - "outputs": [], - "source": [ - "namespace = \"bathymetry_ml_auxiliary\"\n", - "tablename = \"vpuid=01\"\n", - "feature_name = \"owp_ahg_c\"\n", - "thresh = 0.25\n", - "catalog.load_table(f\"{namespace}.{tablename}\").scan(\n", - " row_filter=GreaterThanOrEqual(feature_name, thresh),\n", - " selected_fields=(\"owp_ahg_m\", \"slope\", \"LENGTHKM\", \"owp_ahg_f\"),\n", - ").to_arrow()" - ] - }, - { - "cell_type": "markdown", - "id": "01dc1ff0-bfd5-4b7a-82d0-1d60e237e2cd", - "metadata": {}, - "source": [ - "# Cross-Check & Verify:\n", - "## Parquet Data Has Been Written To Its Associated Iceberg Table Sucessfully" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5117aae1-40fb-4392-b424-b488245ace52", - "metadata": {}, - "outputs": [], - "source": [ - "# Load a XS parquet file into a DataFrame\n", - "parquet_file_path = f\"{os.getcwd()}//xs_data/mip_01020002/huc_01020002.parquet\"\n", - "df = pq.read_table(parquet_file_path)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b365d647-bbfa-45a9-8673-52c564cf0ea7", - "metadata": {}, - "outputs": [], - "source": [ - "# Load a m1_auxiliary parquet file into a DataFrame\n", - "parquet_file_path = f\"{os.getcwd()}/data/bathymetry/ml_auxiliary_data/vpuid=01/ml_auxiliary_data.parquet\"\n", - "df = pq.read_table(parquet_file_path)\n", - "df" - ] - }, - { - "attachments": { - "224c4550-7d87-455e-8193-0364425d439b.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "655127c1-3bc5-4f7e-b224-fb9655102134", - "metadata": {}, - "source": [ - "# Result:\n", - "![image.png](attachment:224c4550-7d87-455e-8193-0364425d439b.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0020ac6-1f0b-4344-b83f-33f9d73a390c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/iceberg/pyiceberg_timetravel.ipynb b/examples/iceberg/pyiceberg_time_travel.ipynb similarity index 95% rename from examples/iceberg/pyiceberg_timetravel.ipynb rename to examples/iceberg/pyiceberg_time_travel.ipynb index eb06391..a001312 100644 --- a/examples/iceberg/pyiceberg_timetravel.ipynb +++ b/examples/iceberg/pyiceberg_time_travel.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "9f07637f", "metadata": {}, "outputs": [], @@ -26,30 +26,20 @@ "from pathlib import Path\n", "\n", "import pyarrow.parquet as pq\n", - "from dotenv import load_dotenv\n", "from pyiceberg.catalog import load_catalog" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "db86d0ba", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# before running this cell, make sure you created a .env file in this directory with your _current_ AWS credentials in it\n", - "load_dotenv()" + "from icefabric.helpers import load_creds\n", + "\n", + "# dir is where the .env file is located\n", + "load_creds(dir=Path.cwd().parents[1])" ] }, { @@ -354,7 +344,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "icefabric", "language": "python", "name": "python3" }, @@ -368,7 +358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/examples/ras_xs/querying.ipynb b/examples/iceberg/querying_ras_xs.ipynb similarity index 86% rename from examples/ras_xs/querying.ipynb rename to examples/iceberg/querying_ras_xs.ipynb index 0e7bf1b..1f4aabb 100644 --- a/examples/ras_xs/querying.ipynb +++ b/examples/iceberg/querying_ras_xs.ipynb @@ -15,65 +15,72 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# imports\n", - "import os\n", + "from pathlib import Path\n", "\n", - "from dotenv import load_dotenv\n", "from pyiceberg.catalog import load_catalog\n", "\n", - "from icefabric_tools import to_geopandas\n", - "\n", - "load_dotenv()\n", - "\n", - "os.environ[\"WAREHOUSE_PATH\"] = \"INSERT PATH TO URI\"" + "from icefabric.helpers import to_geopandas" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# Load Catalog\n", - "catalog_settings = {\n", - " \"type\": \"glue\",\n", - " \"s3.endpoint\": \"s3.us-east-1.amazonaws.com\",\n", - " \"warehouse\": os.environ[\"WAREHOUSE_PATH\"],\n", - " \"glue_region\": \"us-east-1\",\n", - "}\n", - "catalog = load_catalog(\"glue\", **catalog_settings)" + "from icefabric.helpers import load_creds\n", + "\n", + "# dir is where the .env file is located\n", + "load_creds(dir=Path.cwd().parents[1])" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('mip_xs', '02030102'),\n", - " ('mip_xs', '02030103'),\n", - " ('mip_xs', '02030104'),\n", - " ('mip_xs', '02030105'),\n", - " ('mip_xs', '02040101'),\n", - " ('mip_xs', '02040102'),\n", - " ('mip_xs', '02040103'),\n", - " ('mip_xs', '02040104'),\n", - " ('mip_xs', '02040105'),\n", - " ('mip_xs', '02040106')]" + "'/Users/taddbindas/projects/NGWPC/icefabric/.pyiceberg.yaml'" ] }, - "execution_count": 21, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import os\n", + "\n", + "os.environ[\"PYICEBERG_HOME\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "URI missing, please provide using --uri, the config or environment variable PYICEBERG_CATALOG__GLUE__URI", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m catalog = \u001b[43mload_catalog\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mglue\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m catalog.list_tables(\u001b[33m\"\u001b[39m\u001b[33mmip_xs\u001b[39m\u001b[33m\"\u001b[39m)[\u001b[32m40\u001b[39m:\u001b[32m50\u001b[39m]\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.12/site-packages/pyiceberg/catalog/__init__.py:257\u001b[39m, in \u001b[36mload_catalog\u001b[39m\u001b[34m(name, **properties)\u001b[39m\n\u001b[32m 255\u001b[39m catalog_type = CatalogType(provided_catalog_type.lower())\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m provided_catalog_type:\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m catalog_type = \u001b[43minfer_catalog_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m catalog_type:\n\u001b[32m 260\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m AVAILABLE_CATALOGS[catalog_type](name, cast(Dict[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m], conf))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.12/site-packages/pyiceberg/catalog/__init__.py:210\u001b[39m, in \u001b[36minfer_catalog_type\u001b[39m\u001b[34m(name, catalog_properties)\u001b[39m\n\u001b[32m 208\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 209\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExpects the URI to be a string, got: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(uri)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m210\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 211\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mURI missing, please provide using --uri, the config or environment variable PYICEBERG_CATALOG__\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname.upper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m__URI\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 212\u001b[39m )\n", + "\u001b[31mValueError\u001b[39m: URI missing, please provide using --uri, the config or environment variable PYICEBERG_CATALOG__GLUE__URI" + ] + } + ], + "source": [ + "catalog = load_catalog(\"glue\")\n", "catalog.list_tables(\"mip_xs\")[40:50]" ] }, @@ -722,7 +729,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "icefabric", "language": "python", "name": "python3" }, diff --git a/examples/iceberg/quick_start.ipynb b/examples/iceberg/quick_start.ipynb deleted file mode 100644 index 24680e4..0000000 --- a/examples/iceberg/quick_start.ipynb +++ /dev/null @@ -1,1071 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Icefabric QuickStart\n", - "Author: Tadd Bindas" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "import geopandas as gpd\n", - "from pyiceberg.catalog import load_catalog\n", - "\n", - "from icefabric_manage import build\n", - "from icefabric_tools import find_origin, table_to_geopandas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, you will need to define where your local data warehouse will live. This file will be located on disk and requires you to already have some geoparquet files to load" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Table network already exists. Skipping build\n", - "Table nexus already exists. Skipping build\n", - "Table flowpath-attributes already exists. Skipping build\n", - "Table divides already exists. Skipping build\n", - "Table pois already exists. Skipping build\n", - "Table flowpath-attributes-ml already exists. Skipping build\n", - "Table divide-attributes already exists. Skipping build\n", - "Table flowpaths already exists. Skipping build\n", - "Table hydrolocations already exists. Skipping build\n", - "Table lakes already exists. Skipping build\n" - ] - } - ], - "source": [ - "warehouse_path = Path(\"../data/warehouse\")\n", - "warehouse_path.mkdir(exist_ok=True)\n", - "\n", - "catalog_settings = {\n", - " \"type\": \"sql\",\n", - " \"uri\": f\"sqlite:///{warehouse_path}/pyiceberg_catalog.db\",\n", - " \"warehouse\": f\"file://{warehouse_path}\",\n", - "}\n", - "\n", - "catalog = load_catalog(\"hydrofabric\", **catalog_settings)\n", - "build(catalog, Path(\"/Users/taddbindas/projects/NGWPC/icefabric/data/parquet\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "hydrofabric ()" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "catalog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once the catalog is created, you can load the table into memory, scan the file based on a query, then return the object via pandas dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    " - ], - "text/plain": [ - " id toid mainstem order hydroseq lengthkm \\\n", - "0 wb-20466 nex-20467 2613576.0 1.0 20292 2.443793 \n", - "1 wb-20474 nex-20475 2613597.0 1.0 20291 3.982518 \n", - "2 wb-20475 nex-20468 2613597.0 1.0 20290 1.006645 \n", - "3 wb-20473 nex-20467 2613587.0 1.0 20289 1.892781 \n", - "4 wb-20467 nex-20468 2613576.0 2.0 20288 1.616463 \n", - "... ... ... ... ... ... ... \n", - "828283 wb-3054945 nex-3054946 2523667.0 2.0 236 3.515287 \n", - "828284 wb-3054946 nex-3054947 2523667.0 2.0 235 4.365587 \n", - "828285 wb-3054961 nex-3054958 2523700.0 1.0 234 2.032194 \n", - "828286 wb-3054958 nex-3054959 2523693.0 1.0 233 6.466212 \n", - "828287 wb-3054960 nex-3054959 2523698.0 1.0 232 2.242424 \n", - "\n", - " areasqkm tot_drainage_areasqkm has_divide divide_id poi_id \\\n", - "0 4.304250 4.304250 True cat-20466 19534 \n", - "1 4.829400 4.829400 True cat-20474 None \n", - "2 6.713101 11.542501 True cat-20475 None \n", - "3 4.486050 4.486050 True cat-20473 None \n", - "4 2.583900 22.916701 True cat-20467 1193 \n", - "... ... ... ... ... ... \n", - "828283 9.335699 84.047850 True cat-3054945 None \n", - "828284 13.306500 97.354350 True cat-3054946 None \n", - "828285 5.235750 5.235750 True cat-3054961 None \n", - "828286 10.670850 15.906600 True cat-3054958 None \n", - "828287 3.640500 3.640500 True cat-3054960 None \n", - "\n", - " vpuid geometry \n", - "0 01 MULTILINESTRING ((1873700.565 2684810.523, 187... \n", - "1 01 MULTILINESTRING ((1875378.459 2678583.977, 187... \n", - "2 01 MULTILINESTRING ((1875781.183 2682056.744, 187... \n", - "3 01 MULTILINESTRING ((1875252.558 2684695.906, 187... \n", - "4 01 MULTILINESTRING ((1875278.618 2683667.5, 18754... \n", - "... ... ... \n", - "828283 17 MULTILINESTRING ((-2219263.794 2463211.731, -2... \n", - "828284 17 MULTILINESTRING ((-2222330.448 2464615.711, -2... \n", - "828285 17 MULTILINESTRING ((-2218810.457 2467656.224, -2... \n", - "828286 17 MULTILINESTRING ((-2217798.39 2468518.649, -22... \n", - "828287 17 MULTILINESTRING ((-2220749.538 2467137.344, -2... \n", - "\n", - "[828288 rows x 13 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "flowpaths = catalog.load_table(\"hydrofabric.flowpaths\")\n", - "flowpaths.scan().to_pandas()\n", - "table_to_geopandas(flowpaths)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Getting a Replace and Route Segment as a Geopackage" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from icefabric_tools import load_hydrofabric\n", - "from icefabric_tools.rnr import get_rnr_segment\n", - "\n", - "catalog_settings = {\n", - " \"type\": \"sql\",\n", - " \"uri\": \"sqlite:///../data/warehouse/pyiceberg_catalog.db\",\n", - " \"warehouse\": \"file://../data/warehouse\",\n", - "}\n", - "catalog = load_hydrofabric(catalog_settings)\n", - "get_rnr_segment(catalog, 18471000, \"../data/test_rnr.gpkg\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    idtoiddivide_idds_idmainstemhydroseqhf_sourcehf_idlengthkmareasqkmtot_drainage_areasqkmtypevpuidhf_hydroseqhf_lengthkmhf_mainstemtopopoi_idhl_uri
    0wb-1321815tnx-1000005117cat-1321815NaN473130.09408.0NOAA Reference Fabric22812067.07.5734567.18740129645.996881terminal08473132.03.072944473130.0fl-nexNaNNone
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    3wb-1321754nex-1321755cat-1321754NaN473130.09562.0NOAA Reference Fabric19269180.05.9468029.06615127052.965484nexus08473933.00.149239473130.0fl-nexNaNNone
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    \n", - "
    " - ], - "text/plain": [ - " id toid divide_id ds_id mainstem hydroseq \\\n", - "0 wb-1321815 tnx-1000005117 cat-1321815 NaN 473130.0 9408.0 \n", - "1 wb-1321815 tnx-1000005117 cat-1321815 NaN 473130.0 9408.0 \n", - "2 wb-1321815 tnx-1000005117 cat-1321815 NaN 473130.0 9408.0 \n", - "3 wb-1321754 nex-1321755 cat-1321754 NaN 473130.0 9562.0 \n", - "4 wb-1321754 nex-1321755 cat-1321754 NaN 473130.0 9562.0 \n", - "\n", - " hf_source hf_id lengthkm areasqkm \\\n", - "0 NOAA Reference Fabric 22812067.0 7.573456 7.18740 \n", - "1 NOAA Reference Fabric 22811613.0 7.573456 7.18740 \n", - "2 NOAA Reference Fabric 22811611.0 7.573456 7.18740 \n", - "3 NOAA Reference Fabric 19269180.0 5.946802 9.06615 \n", - "4 NOAA Reference Fabric 19269466.0 5.946802 9.06615 \n", - "\n", - " tot_drainage_areasqkm type vpuid hf_hydroseq hf_lengthkm \\\n", - "0 129645.996881 terminal 08 473132.0 3.072944 \n", - "1 129645.996881 terminal 08 473131.0 2.692083 \n", - "2 129645.996881 terminal 08 473130.0 1.808428 \n", - "3 127052.965484 nexus 08 473933.0 0.149239 \n", - "4 127052.965484 nexus 08 473932.0 2.638347 \n", - "\n", - " hf_mainstem topo poi_id hl_uri \n", - "0 473130.0 fl-nex NaN None \n", - "1 473130.0 fl-nex NaN None \n", - "2 473130.0 fl-nex NaN None \n", - "3 473130.0 fl-nex NaN None \n", - "4 473130.0 fl-nex NaN None " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf = gpd.read_file(\"../data/test_ds_rnr.gpkg\", layer=\"network\")\n", - "gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/icechunk/README.icechunk.md b/examples/icechunk/README.icechunk.md deleted file mode 100644 index 322a9c7..0000000 --- a/examples/icechunk/README.icechunk.md +++ /dev/null @@ -1,19 +0,0 @@ -# Icechunk Data Viewer -Interface to load, create and view S3 Icechunk-versioned NGWPC datasets - -## Install Icechunk Data Viewer: -1. Clone repo: `git clone ` -2. CD to repo dir: `cd /icechunk_data_viewer` -3. Create virtual environment: `python3.11 -m venv .venv` -4. Start virtual environment: `source .venv/bin/activate` -5. Load dependedencies: `pip install -r requirements.txt` -6. Prepare AWS credentials. Either: - - [Set up IAM Identity Center and run `aws sso login` prior to notebook launch](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-sso.html#sso-configure-profile-token-auto-sso) - - [Copy/paste credentials into `~/.aws/credentials`](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html#cli-configure-files-format) -7. Start Jupyter notebook: `jupyter notebook --no-browser` -8. Using your browser, navigate to the notebook server instance (i.e. `http://127.0.0.1:8888/tree?token=XXX`). Your specific URL is listed in the terminal output. -9. Open `icechunk_data_ret_and_plot.jpynb` in the GUI. -10. Prior to execution, depending on if you've configured AWS credentials with SSO or copy/pasted them: - - SSO: Comment out line #11 in block 1 (`os.environ['AWS_PROFILE'] = "XXX_SoftwareEngineersFull"`) - - Copy/paste: Alter line #11 to overwrite the placeholder string with your profile name found in `~/.aws/credentials` -11. Run the notebook diff --git a/examples/icechunk/icechunk_data_ret_and_plot.ipynb b/examples/icechunk/icechunk_data_ret_and_plot.ipynb index f1986b1..550989b 100644 --- a/examples/icechunk/icechunk_data_ret_and_plot.ipynb +++ b/examples/icechunk/icechunk_data_ret_and_plot.ipynb @@ -14,40 +14,32 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "92608737-174f-42fd-ab07-b599edd9d1de", "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "from dotenv import load_dotenv" + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "28c1bab2-5106-4ea5-94fd-502de7859a4d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# before running this cell, make sure you created a .env file in this directory with your AWS credentials in it\n", - "load_dotenv()" + "from icefabric.helpers import load_creds\n", + "\n", + "# dir is where the .env file is located\n", + "load_creds(dir=Path.cwd().parents[1])" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "2b3b1ced-6760-4ef5-8cdc-4bde82a287fe", "metadata": {}, "outputs": [ @@ -109,10 +101,11 @@ } ], "source": [ - "from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations\n", + "from icefabric.builds import IcechunkRepo\n", + "from icefabric.schemas import NGWPCLocations\n", "\n", "# Retrive data and print\n", - "land_cover_repo = IcechunkS3Repo(location=NGWPCLocations.NLCD_IC.path)\n", + "land_cover_repo = IcechunkRepo(location=NGWPCLocations.NLCD_IC.path)\n", "lc_data = land_cover_repo.retrieve_dataset()\n", "print(lc_data)" ] @@ -183,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "617115d8-a7d0-486e-aab9-2daedad9074e", "metadata": {}, "outputs": [ @@ -205,7 +198,7 @@ ], "source": [ "# Retrive data and print\n", - "topo_repo = IcechunkS3Repo(location=NGWPCLocations.TOPO_PR_USVI_30M_IC.path)\n", + "topo_repo = IcechunkRepo(location=NGWPCLocations.TOPO_PR_USVI_30M_IC.path)\n", "topo_data = topo_repo.retrieve_dataset()\n", "print(topo_data)" ] @@ -279,7 +272,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "icefabric", "language": "python", "name": "python3" }, @@ -293,7 +286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/examples/icechunk/icechunk_to_tiff.ipynb b/examples/icechunk/icechunk_to_tiff.ipynb index 03bb4e2..e752390 100644 --- a/examples/icechunk/icechunk_to_tiff.ipynb +++ b/examples/icechunk/icechunk_to_tiff.ipynb @@ -15,25 +15,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "7cba65ef", "metadata": {}, "outputs": [], "source": [ - "from dotenv import load_dotenv\n", + "from pathlib import Path\n", "\n", - "from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations" + "from icefabric.builds import IcechunkRepo\n", + "from icefabric.schemas import NGWPCLocations" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "d479759f", "metadata": {}, "outputs": [], "source": [ - "# before running this cell, make sure you created a .env file in this directory with your AWS credentials in it\n", - "load_dotenv()" + "from icefabric.helpers import load_creds\n", + "\n", + "# dir is where the .env file is located\n", + "load_creds(dir=Path.cwd().parents[1])" ] }, { @@ -46,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "7df8b014", "metadata": {}, "outputs": [ @@ -615,7 +618,7 @@ } ], "source": [ - "repo = IcechunkS3Repo(location=NGWPCLocations.TOPO_HA_30M_IC.path)\n", + "repo = IcechunkRepo(location=NGWPCLocations.TOPO_HA_30M_IC.path)\n", "ds = repo.retrieve_dataset()\n", "ds" ] @@ -680,7 +683,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "icefabric", "language": "python", "name": "python3" }, @@ -694,7 +697,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/examples/icechunk/icechunk_version_control_examples.ipynb b/examples/icechunk/icechunk_version_control_examples.ipynb index d096bd0..5429e4a 100644 --- a/examples/icechunk/icechunk_version_control_examples.ipynb +++ b/examples/icechunk/icechunk_version_control_examples.ipynb @@ -11,28 +11,36 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "id": "1a2ed407", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "from icefabric.helpers import load_creds\n", + "\n", + "# dir is where the .env file is located\n", + "load_creds(dir=Path.cwd().parents[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "4f0fe850-0e5e-40e5-93b1-1c9f468a2d28", "metadata": {}, "outputs": [], "source": [ "import warnings\n", "\n", - "from dotenv import load_dotenv\n", - "\n", - "import icefabric_tools.icechunk.arch_weather_file_utils as awf_utils\n", - "from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations, S3Path\n", + "from icefabric.builds import IcechunkRepo, S3Path\n", + "from icefabric.helpers import virtualize_and_concat_archival_files_on_time\n", + "from icefabric.schemas import FileType, NGWPCLocations\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", - "# Before running this cell, make sure you created a .env file in this directory with your AWS credentials in it\n", - "# NOTE - if you authenticate with AWS SSO, leave it commented out\n", - "load_dotenv()\n", - "\n", - "# Create icechunk repo at s3://hydrofabric-data/ic_testing/snodas_yearly_append_test\n", - "# NOTE - make sure this S3 directory doesn't exist prior to running this cell\n", "new_repo_s3_path = S3Path(bucket=\"hydrofabric-data\", prefix=\"ic_testing/snodas_yearly_append_test\")\n", - "new_repo = IcechunkS3Repo(location=new_repo_s3_path)" + "new_repo = IcechunkRepo(location=new_repo_s3_path)" ] }, { @@ -59,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "ef762477-5c6b-4876-b5a2-a708b9f3d02f", "metadata": {}, "outputs": [ @@ -87,10 +95,10 @@ ], "source": [ "# Collect first five SNODAS netcdf files from 2009 and combine/virtualize them together into a single dataset\n", - "snodas_09_vds = awf_utils.virtualize_and_concat_archival_files_on_time(\n", + "snodas_09_vds = virtualize_and_concat_archival_files_on_time(\n", " location=NGWPCLocations.SNODAS_REF.path,\n", " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", - " file_type=awf_utils.FileType.NETCDF,\n", + " file_type=FileType.NETCDF,\n", " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2009*.nc\",\n", " loadable_vars=[\"crs\"],\n", " testing_file_quantity=5,\n", @@ -160,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "bbefb68a-b747-449e-b7da-4c61c6f4a2e0", "metadata": {}, "outputs": [ @@ -188,10 +196,10 @@ ], "source": [ "# Much like the 09 SNODAS files were collected, do the same for 2010\n", - "snodas_10_vds = awf_utils.virtualize_and_concat_archival_files_on_time(\n", + "snodas_10_vds = virtualize_and_concat_archival_files_on_time(\n", " location=NGWPCLocations.SNODAS_REF.path,\n", " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", - " file_type=awf_utils.FileType.NETCDF,\n", + " file_type=FileType.NETCDF,\n", " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2010*.nc\",\n", " loadable_vars=[\"crs\"],\n", " testing_file_quantity=5,\n", @@ -299,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "3d940692-0407-4c46-8df2-5a0036f4420b", "metadata": {}, "outputs": [ @@ -330,10 +338,10 @@ "new_repo.create_new_branch(name=\"2011_feature\")\n", "\n", "# Much like the 09 SNODAS files were collected, do the same for 2010\n", - "snodas_11_vds = awf_utils.virtualize_and_concat_archival_files_on_time(\n", + "snodas_11_vds = virtualize_and_concat_archival_files_on_time(\n", " location=NGWPCLocations.SNODAS_REF.path,\n", " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", - " file_type=awf_utils.FileType.NETCDF,\n", + " file_type=FileType.NETCDF,\n", " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2011*.nc\",\n", " loadable_vars=[\"crs\"],\n", " testing_file_quantity=5,\n", @@ -480,7 +488,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "icefabric", "language": "python", "name": "python3" }, @@ -494,7 +502,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/examples/icechunk_data_viewer/martin/compose.martin.yaml b/examples/icechunk_data_viewer/martin/compose.martin.yaml index 1916d37..c4788a9 100644 --- a/examples/icechunk_data_viewer/martin/compose.martin.yaml +++ b/examples/icechunk_data_viewer/martin/compose.martin.yaml @@ -6,5 +6,5 @@ services: environment: - RUST_LOG=debug volumes: - - "${PWD}/tiles:/tiles" + - "./tiles:/tiles" command: -c /tiles/martin_config.yaml diff --git a/examples/icechunk_data_viewer/requirements.txt b/examples/icechunk_data_viewer/requirements.txt deleted file mode 100644 index 9115ce8..0000000 --- a/examples/icechunk_data_viewer/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -jupyterlab==4.4.3 -jupytergis==0.5.0 -matplotlib==3.10.3 \ No newline at end of file diff --git a/examples/quick_start.ipynb b/examples/quick_start.ipynb new file mode 100644 index 0000000..6ffd065 --- /dev/null +++ b/examples/quick_start.ipynb @@ -0,0 +1,44 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Icefabric QuickStart\n", + "\n", + "This notebook is meant to showcase the icefabric repo services, versioning, tools, and capabilities as an enterprise data services product from NGWPC\n", + "\n", + "The notebook is broken up into the following sections:\n", + "\n", + "#### TODO Finish Quickstart\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/mkdocs.yml b/mkdocs.yml index 16eb786..3f721b1 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -50,7 +50,6 @@ nav: - index.md - User Guide: - user_guide/index.md - - Icefabric Manage: user_guide/icefabric_manage.md - - Icefabric Tools: user_guide/icefabric_tools.md - - Icefabric API: user_guide/icefabric_api.md + - API: user_guide/icefabric_api.md + - Tools: user_guide/icefabric_tools.md - Terraform S3 Glue Endpoint: user_guide/terraform.md diff --git a/pyproject.toml b/pyproject.toml index 844bda6..fa6c4b9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,9 +1,20 @@ +[build-system] +build-backend = "hatchling.build" +requires = ["hatchling", "hatch-vcs"] + +[tool.hatch] +version.source = "vcs" +build.hooks.vcs.version-file = "src/icefabric/_version.py" + +[tool.hatch.build.targets.wheel] +packages = ["src/icefabric", "app", "tools"] + [project] name = "icefabric" -version = "2025.6.0" -description = "An Apache Iceberg implementation of the Hydrofabric" +version = "2025.7.0" +description = "An Apache Iceberg + Icechunk implementation of Hydrofabric data services" readme = "README.md" -requires-python = ">=3.11,<3.13" +requires-python = ">=3.11" license = { file = "LICENSE" } authors = [ { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, @@ -11,6 +22,7 @@ authors = [ { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, + { name = "Farshid Rahmani", email = "Farshid.Rahmani@rtx.com" }, ] maintainers = [ { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, @@ -18,37 +30,74 @@ maintainers = [ { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, + { name = "Farshid Rahmani", email = "Farshid.Rahmani@rtx.com" }, ] -dependencies = [] +dependencies = [ + "dask==2025.5.1", + "fiona==1.10.1", + "fsspec==2025.5.1", + "geodatasets==2024.8.0", + "geopandas==1.0.1", + "h5netcdf==1.6.1", + "h5py==3.13.0", + "icechunk==0.2.18", + "ipywidgets==8.1.6", + "jupytergis==0.5.0", + "jupyterlab==4.4.3", + "kerchunk==0.2.8", + "mapclassify==2.9.0", + "matplotlib==3.10.3", + "netCDF4==1.7.2", + "numpy==2.2.6", + "pandas==2.2.3", + "pyarrow==20.0.0", + "pyiceberg[s3fs,glue,sql-sqlite]==0.9.1", + "python-dotenv==1.1.0", + "rioxarray==0.19.0", + "s3fs==2025.5.1", + "scipy==1.15.3", + "tifffile==2025.5.21", + "tqdm==4.67.1", + "virtualizarr==1.3.2", + "xarray==2025.4.0", + "zarr==3.0.9", +] [project.optional-dependencies] -docs = ["mkdocs-material"] - -[tool.uv.sources] -icefabric_api = { workspace = true } -icefabric_manage = { workspace = true } -icefabric_tools = { workspace = true } - -[tool.uv.workspace] -members = ["src/icefabric_api", "src/icefabric_manage", "src/icefabric_tools"] +docs = [ + "mkdocs-material==9.6.14", + "sympy==1.14.0" +] -[tool.uv] -dev-dependencies = [ - "pre-commit>=3.8.0", - "ipykernel>=6.29.5", - "jupyterlab==4.4.3", - "pytest>=8.3.2", - "ruff==0.11.0", +[dependency-groups] +app = [ + "fastapi[standard]>=0.115.12", + "pydantic==2.11.5", + "pydantic-settings==2.9.1", +] +dev = [ + "pre-commit==3.8.0", + "ruff==0.11.13", "black==25.1.0", "mypy==1.15.0", - "examples", - "boto3", - "icefabric_api", - "icefabric_manage", - "icefabric_tools", + "nbstripout==0.8.1", +] +examples = [ + "folium==0.19.5", + "httpx==0.28.1", + "ipykernel==6.29.5", + "jupyterlab==4.4.3", + "jupytergis==0.5.0", + "matplotlib==3.10.3", +] +tests = [ + "pytest==8.4.1", + "pytest-cov==6.1.1", ] -package = false + +[tool.uv] +default-groups = ["app", "dev", "examples", "tests"] [tool.black] line-length = 110 @@ -103,7 +152,6 @@ lint.ignore = [ "E722", ] - [tool.ruff.lint.pydocstyle] convention = "numpy" @@ -112,7 +160,6 @@ convention = "numpy" "tests/*" = ["D"] "*/__init__.py" = ["F401"] - [tool.mypy] python_version = "3.11" warn_return_any = false diff --git a/scripts/get_module_parameters.py b/scripts/get_module_parameters.py deleted file mode 100644 index d36e1c4..0000000 --- a/scripts/get_module_parameters.py +++ /dev/null @@ -1,29 +0,0 @@ -"""A sample script to generate CFE IPEs""" - -import geopandas as gpd -import pandas as pd - -param_file = "../src/icefabric_api/data/cfe_params.csv" -gpkg_file = "../src/icefabric_tools/test/data/gages-08070000.gpkg" - - -divides = gpd.read_file(gpkg_file, layer="divides") -divides = divides["divide_id"].to_list() - - -module_params = pd.read_csv(param_file) -param_values = module_params[["name", "default_value"]] - -for divide in divides: - cfg_file = f"{divide}_bmi_cfg_cfe.txt" - f = open(cfg_file, "x") - - for _, row in param_values.iterrows(): - key = row["name"] - value = row["default_value"] - f.write(f"{key}={value}\n") - - f.close() - -params_calibratable = module_params.loc[module_params["calibratable"] == "TRUE"] -params_calibratable.to_json("out.json", orient="split") diff --git a/scripts/gpkg_to_parquet.py b/scripts/gpkg_to_parquet.py deleted file mode 100644 index 1f06b13..0000000 --- a/scripts/gpkg_to_parquet.py +++ /dev/null @@ -1,14 +0,0 @@ -"""A simple script to convert the v2.2 hydrofabric to parquet""" - -import fiona -import geopandas as gpd - -input_file = "data/conus_nextgen.gpkg" -available_layers = fiona.listlayers(input_file) -print(f"Layers in GeoPackage: {available_layers}") - -for layer in available_layers: - gdf = gpd.read_file(input_file, layer=layer) - output_file = f"data/parquet/{layer}.parquet" - gdf.to_parquet(output_file) - print(f"Converted layer '{layer}' to {output_file}") diff --git a/scripts/gpkg_to_parquet_all_domains.py b/scripts/gpkg_to_parquet_all_domains.py deleted file mode 100644 index 5a7e7d6..0000000 --- a/scripts/gpkg_to_parquet_all_domains.py +++ /dev/null @@ -1,28 +0,0 @@ -"""A simple script to convert the v2.2 hydrofabric to parquet""" - -import fiona -import geopandas as gpd - -#Location of Hydrofabric geopackage files organized by domain -input_file_root = '/home/daniel.cumpton/Hydrofabric/data/hydrofabric/v2.2/nextgen' - -#Location of data/domain directories for output Parquet files -output_file_root = '/home/daniel.cumpton/icefabric' - -input_file = {'CONUS':'CONUS/conus_nextgen.gpkg', 'GL':'CONUS/gl_nextgen_divide_attr.gpkg', - 'Alaska':'Alaska/ak_nextgen_workaround.gpkg', 'Hawaii':'Hawaii/hi_nextgen_workaround.gpkg', - 'Puerto_Rico':'Puerto_Rico/prvi_nextgen_workaround.gpkg'} - -domains = ['CONUS', 'GL', 'Alaska', 'Hawaii', 'Puerto_Rico'] - -for domain in domains: - - file = f"{input_file_root}/{input_file[domain]}" - available_layers = fiona.listlayers(file) - print(f"For {domain}, layers in GeoPackage: {available_layers}") - - for layer in available_layers: - gdf = gpd.read_file(file, layer=layer) - output_file = f"{output_file_root}/data/parquet/{domain}/{layer}.parquet" - gdf.to_parquet(output_file) - print(f"For {domain}, converted layer '{layer}' to {output_file}") diff --git a/src/icefabric/__init__.py b/src/icefabric/__init__.py new file mode 100644 index 0000000..e701dc1 --- /dev/null +++ b/src/icefabric/__init__.py @@ -0,0 +1,6 @@ +"""An Apache Iceberg + Icechunk implementation of Hydrofabric data services""" + +from . import builds, helpers, hydrofabric, modules, schemas, ui +from ._version import __version__ + +__all__ = ["__version__", "builds", "hydrofabric", "helpers", "modules", "schemas", "ui"] diff --git a/src/icefabric/builds/__init__.py b/src/icefabric/builds/__init__.py new file mode 100644 index 0000000..3030723 --- /dev/null +++ b/src/icefabric/builds/__init__.py @@ -0,0 +1,10 @@ +"""Functions / objects to be used for building tables/objects""" + +from .build import build_iceberg_table +from .icechunk_s3_module import IcechunkRepo, S3Path + +__all__ = [ + "build_iceberg_table", + "IcechunkRepo", + "S3Path", +] diff --git a/src/icefabric_manage/src/icefabric_manage/_api.py b/src/icefabric/builds/build.py similarity index 92% rename from src/icefabric_manage/src/icefabric_manage/_api.py rename to src/icefabric/builds/build.py index 70eae83..eeee091 100644 --- a/src/icefabric_manage/src/icefabric_manage/_api.py +++ b/src/icefabric/builds/build.py @@ -4,7 +4,9 @@ from pyiceberg.catalog import Catalog -def build(catalog: Catalog, parquet_file: str, namespace: str, table_name: str, location: str) -> None: +def build_iceberg_table( + catalog: Catalog, parquet_file: str, namespace: str, table_name: str, location: str +) -> None: """Builds the hydrofabric catalog based on the .pyiceberg.yaml config and defined parquet files. Creates a new Iceberg table from a parquet file if the table doesn't already exist. diff --git a/src/icefabric_manage/src/icefabric_manage/iceberg_table.py b/src/icefabric/builds/iceberg_table.py similarity index 100% rename from src/icefabric_manage/src/icefabric_manage/iceberg_table.py rename to src/icefabric/builds/iceberg_table.py diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py b/src/icefabric/builds/icechunk_s3_module.py similarity index 79% rename from src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py rename to src/icefabric/builds/icechunk_s3_module.py index a1fecae..e276e06 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_module.py +++ b/src/icefabric/builds/icechunk_s3_module.py @@ -7,7 +7,6 @@ import subprocess import warnings -from enum import Enum from pathlib import Path from typing import Any @@ -15,9 +14,6 @@ import xarray as xr from icechunk.xarray import to_icechunk -TOPO_BP = "surface/nws-topobathy" -TOPO_NOS = f"{TOPO_BP}/nws-nos-surveys" - class S3Path: """ @@ -42,7 +38,7 @@ class S3Path: def __init__(self, bucket: str, prefix: str, region: str | None = "us-east-1"): self.bucket = bucket self.prefix = prefix - self.region = region + self.region = region # type: ignore def __str__(self): """Returns the full S3 path""" @@ -53,47 +49,14 @@ def partial_path(self): return f"{self.bucket}/{self.prefix}" -class NGWPCLocations(Enum): - """ - Important NGWPC S3 locations - - Enum class for instantiating S3Paths corresponding to the - icechunk stores, as well as the reference locations for virtualized - stores. - """ - - SNODAS_REF = ("ngwpc-forcing", "snodas_nc_v4") - SNODAS_V3 = ("ngwpc-forcing", "snodas_nc") - SNODAS_IC = ("hydrofabric-data", "forcing/snodas") - NLCD_REF = ("ngwpc-hydrofabric", "NLCD_Land_Cover_CONUS") - NLCD_IC = ("hydrofabric-data", "land-cover/NLCD-Land-Cover") - USGS_IC = ("ngwpc-hydrofabric", "usgs_observations") - TOPO_AK_10M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_alaska_10m") - TOPO_AK_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_alaska_30m") - TOPO_CONUS_ATL_GULF_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_conus_atlantic_gulf_30m") - TOPO_CONUS_PAC_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_conus_pacific_30m") - TOPO_GREAT_LAKES_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_great_lakes_30m") - TOPO_HA_10M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_hawaii_10m") - TOPO_HA_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_hawaii_30m") - TOPO_PR_USVI_10M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_pr_usvi_10m") - TOPO_PR_USVI_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_pr_usvi_30m") - TOPO_ALBEMARLE_SOUND_IC = ("hydrofabric-data", f"{TOPO_NOS}/Albemarle_Sound_NOS_NCEI") - TOPO_CHESAPEAKE_BAY_IC = ("hydrofabric-data", f"{TOPO_NOS}/Chesapeake_Bay_NOS_NCEI") - TOPO_MOBILE_BAY_IC = ("hydrofabric-data", f"{TOPO_NOS}/Mobile_Bay_NOS_NCEI") - TOPO_TANGIER_SOUND_IC = ("hydrofabric-data", f"{TOPO_NOS}/Tangier_Sound_NOS_NCEI") - - def __init__(self, bucket, prefix): - self.path = S3Path(bucket, prefix) - - -class IcechunkS3Repo: +class IcechunkRepo: """ - Class representing an S3 bucket icechunk store + Class representing an S3 bucket or local icechunk store Parameters ---------- - location: S3Path - The S3Path of the repo. + location: S3Path | Path + The S3Path or local path of the repo. repo: ic.Repository The icechunk repo, derived from the bucket, prefix, and region. S3 credentials are provided from the environment. @@ -103,11 +66,11 @@ class IcechunkS3Repo: in virtualized datasets. """ - location: S3Path + location: S3Path | Path repo: ic.Repository virtual_chunks: list[ic.VirtualChunkContainer] | None - def __init__(self, location: S3Path, virtual_chunk_mapping: list[dict[str, str]] | None = None): + def __init__(self, location: S3Path | Path, virtual_chunk_mapping: list[dict[str, str]] | None = None): self.location = location self.virtual_chunks = self.gen_virtual_chunk_containers(virtual_chunk_mapping) self.repo = self.open_repo() @@ -124,18 +87,25 @@ def open_repo(self) -> ic.Repository: ic.Repository Icechunk repo corresponding to the S3 bucket path defined in the instance """ - storage = ic.s3_storage( - bucket=self.location.bucket, - prefix=self.location.prefix, - region=self.location.region, - from_env=True, - ) - config = ic.RepositoryConfig.default() - if self.virtual_chunks: - for vcc in self.virtual_chunks: - config.set_virtual_chunk_container(vcc) - credentials = ic.containers_credentials({self.location.bucket: ic.s3_credentials(from_env=True)}) - repo = ic.Repository.open_or_create(storage, config, credentials) + if isinstance(self.location, S3Path): + storage_config = ic.s3_storage( + bucket=self.location.bucket, + prefix=self.location.prefix, + region=self.location.region, + from_env=True, + ) + credentials = ic.containers_credentials({self.location.bucket: ic.s3_credentials(from_env=True)}) + config = ic.RepositoryConfig.default() + if self.virtual_chunks: + for vcc in self.virtual_chunks: + config.set_virtual_chunk_container(vcc) + else: + # self.location is a Path + storage_config = ic.local_filesystem_storage(str(self.location)) + credentials = None + config = None + + repo = ic.Repository.open_or_create(storage_config, config, credentials) return repo def delete_repo(self, quiet: bool | None = False): @@ -431,33 +401,32 @@ def _infer_var_name_for_geotiff(self, variable_list: list) -> str: ) return var_name + @staticmethod + def set_up_virtual_chunk_container(bucket: str, region: str) -> ic.VirtualChunkContainer: + """ + Create a virtual chunk container from a mapping -@staticmethod -def set_up_virtual_chunk_container(bucket: str, region: str) -> ic.VirtualChunkContainer: - """ - Create a virtual chunk container from a mapping + Given an S3 bucket/region, generate and return a VirtualChunkContainer + so Icechunk can point to virtualized data inside S3 buckets. - Given an S3 bucket/region, generate and return a VirtualChunkContainer - so Icechunk can point to virtualized data inside S3 buckets. + Parameters + ---------- + bucket : str + The S3 bucket the virtual chunk points to. + region : str + The region of the S3 bucket. - Parameters - ---------- - bucket : str - The S3 bucket the virtual chunk points to. - region : str - The region of the S3 bucket. - - Returns - ------- - ic.VirtualChunkContainer - A definition of a virtual chunk that the icechunk repo - uses to define access to virtualized data. - """ - return ic.VirtualChunkContainer( - name=bucket, url_prefix=f"s3://{bucket}/", store=ic.s3_store(region=region) - ) + Returns + ------- + ic.VirtualChunkContainer + A definition of a virtual chunk that the icechunk repo + uses to define access to virtualized data. + """ + return ic.VirtualChunkContainer( + name=bucket, url_prefix=f"s3://{bucket}/", store=ic.s3_store(region=region) + ) -def get_icechunk_data(repo: NGWPCLocations) -> xr.Dataset: - """Convenience wrapper to return data from a designated NGWPCLocations Icechunk repo""" - return IcechunkS3Repo(location=repo.path).retrieve_dataset() +# def get_icechunk_data(repo: NGWPCLocations) -> xr.Dataset: +# """Convenience wrapper to return data from a designated NGWPCLocations Icechunk repo""" +# return IcechunkRepo(location=repo.path).retrieve_dataset() diff --git a/src/icefabric_manage/src/icefabric_manage/map_fim_data.py b/src/icefabric/builds/map_fim_data.py similarity index 100% rename from src/icefabric_manage/src/icefabric_manage/map_fim_data.py rename to src/icefabric/builds/map_fim_data.py diff --git a/src/icefabric/helpers/__init__.py b/src/icefabric/helpers/__init__.py new file mode 100644 index 0000000..5bcf70b --- /dev/null +++ b/src/icefabric/helpers/__init__.py @@ -0,0 +1,24 @@ +"""Helper functions designed to assist with managing data. Similar to util functions""" + +from .arch_weather_file_utils import ( + get_archival_weather_files, + load_tiff_file, + virtualize_and_concat_archival_files_on_time, +) +from .creds import load_creds +from .geopackage import table_to_geopandas, to_geopandas +from .nc_conv_utils import conv_nc, convert_files_to_netcdf4, convert_nc_files_from_s3 +from .topobathy_ic_to_tif import convert_topobathy_to_tiff + +__all__ = [ + "get_archival_weather_files", + "load_tiff_file", + "virtualize_and_concat_archival_files_on_time", + "load_creds", + "table_to_geopandas", + "to_geopandas", + "convert_files_to_netcdf4", + "convert_nc_files_from_s3", + "conv_nc", + "convert_topobathy_to_tiff", +] diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py b/src/icefabric/helpers/arch_weather_file_utils.py similarity index 93% rename from src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py rename to src/icefabric/helpers/arch_weather_file_utils.py index e3128c5..dfd300b 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/arch_weather_file_utils.py +++ b/src/icefabric/helpers/arch_weather_file_utils.py @@ -6,25 +6,16 @@ """ import os -from enum import Enum + import fsspec import pandas as pd import rioxarray as rxr import xarray as xr from tqdm import tqdm from virtualizarr import open_virtual_dataset -from icefabric_tools.icechunk import S3Path - -class FileType(Enum): - """ - Archival weather file types - - Enum class for instantiating different archival weather file - formats. Used when virtualizing and collecting files. - """ - GEOTIFF = ".tif" - NETCDF = ".nc" +from icefabric.builds.icechunk_s3_module import S3Path +from icefabric.schemas.topobathy import FileType def load_tiff_file(fp: str, attr_name: str) -> xr.Dataset | xr.DataArray | list[xr.Dataset]: @@ -41,7 +32,7 @@ def load_tiff_file(fp: str, attr_name: str) -> xr.Dataset | xr.DataArray | list[ File path to the TIFF that will be returned as a dataset. attr_name : str Name of the attribute of interest. Ex: "elevation". - Note: Not all rasters will be elevation in future. + Note: Not all rasters will be elevation in future. Returns ------- @@ -109,7 +100,7 @@ def get_archival_weather_files( return files -def extract_dates_from_archival_files( +def _extract_dates_from_archival_files( file_paths: list[str], file_pattern: str, just_year: bool | None = False ) -> list[pd.DatetimeIndex] | list[int]: """ @@ -146,7 +137,7 @@ def extract_dates_from_archival_files( return date_dims -def virtualize_datasets( +def _virtualize_datasets( file_list: list[str], loadable_vars: list[str] | None = None, testing_file_quantity: int | None = None ) -> list[xr.Dataset]: """ @@ -187,7 +178,7 @@ def virtualize_datasets( return v_datasets -def add_time_dim_to_datasets( +def _add_time_dim_to_datasets( timeless_datasets: list[xr.Dataset], datetimes: list[pd.DatetimeIndex], just_year: bool | None = False, @@ -278,9 +269,9 @@ def virtualize_and_concat_archival_files_on_time( arch_files = get_archival_weather_files( loc=location, file_type=file_type, manual_file_pattern=manual_file_pattern ) - datetimes = extract_dates_from_archival_files(arch_files, file_date_pattern, just_year=just_year) - timeless_datasets = virtualize_datasets(arch_files, loadable_vars, testing_file_quantity) - time_added_datasets = add_time_dim_to_datasets( + datetimes = _extract_dates_from_archival_files(arch_files, file_date_pattern, just_year=just_year) + timeless_datasets = _virtualize_datasets(arch_files, loadable_vars, testing_file_quantity) + time_added_datasets = _add_time_dim_to_datasets( timeless_datasets, datetimes, just_year, testing_file_quantity ) concat_dim = "year" if just_year else "time" diff --git a/src/icefabric/helpers/creds.py b/src/icefabric/helpers/creds.py new file mode 100644 index 0000000..b8b10b2 --- /dev/null +++ b/src/icefabric/helpers/creds.py @@ -0,0 +1,29 @@ +"""A file for cred helpers""" + +import os +from pathlib import Path + +from dotenv import load_dotenv + + +def load_creds(dir: Path): + """Loads the .env and .pyiceberg.yaml files from the project root + + Parameters + ---------- + dir : Path + The directory where the creds exist + + Raises + ------ + FileNotFoundError + The .pyiceberg.yaml file does not exist + """ + load_dotenv(dotenv_path=dir / ".env") + pyiceberg_file = dir / ".pyiceberg.yaml" + if pyiceberg_file.exists(): + os.environ["PYICEBERG_HOME"] = str(pyiceberg_file) + else: + raise FileNotFoundError( + "Cannot find .pyiceberg.yaml. Please download this from NGWPC confluence or create " + ) diff --git a/src/icefabric/helpers/geopackage.py b/src/icefabric/helpers/geopackage.py new file mode 100644 index 0000000..1335da0 --- /dev/null +++ b/src/icefabric/helpers/geopackage.py @@ -0,0 +1,72 @@ +"""Contains all api functions that can be called outside of the icefabric_tools package""" + +import geopandas as gpd +import pandas as pd +from pyiceberg.expressions import BooleanExpression +from pyiceberg.table import ALWAYS_TRUE, Table +from shapely import wkb + + +def table_to_geopandas( + table: Table, + row_filter: str | BooleanExpression = ALWAYS_TRUE, + case_sensitive: bool | None = True, + snapshot_id: int | None = None, + limit: int | None = None, +) -> gpd.GeoDataFrame: + """Converts a table to a geopandas dataframe + + Parameters + ---------- + table : Table + The iceberg table you are trying to read from + row_filter : str | None, optional + A string or BooleanExpression that describes the desired rows, by default "" + case_sensitive : bool | None, optional + If True column matching is case sensitive, by default True + snapshot_id : int | None, optional + Optional Snapshot ID to time travel to. + If None, scans the table as of the current snapshot ID, by default None + limit : int | None, optional + An integer representing the number of rows to return in the scan result. + If None, fetches all matching rows., by default None + + Returns + ------- + gpd.DataFrame + The resulting queried row, but in a geodataframe + """ + df = table.scan( + row_filter=row_filter, + case_sensitive=case_sensitive, + snapshot_id=snapshot_id, + limit=limit, + ).to_pandas() + return to_geopandas(df) + + +def to_geopandas(df: pd.DataFrame, crs: str = "EPSG:5070") -> gpd.GeoDataFrame: + """Converts the geometries in a pandas df to a geopandas dataframe + + Parameters + ---------- + df: pd.DataFrame + The iceberg table you are trying to read from + crs: str, optional + A string representing the CRS to set in the gdf, by default "EPSG:5070" + + Returns + ------- + gpd.DataFrame + The resulting queried row, but in a geodataframe + + Raises + ------ + ValueError + Raised if the table does not have a geometry column + """ + if "geometry" not in df.columns: + raise ValueError("The provided table does not have a geometry column.") + + geometry = df["geometry"].apply(lambda x: wkb.loads(x) if x is not None else None) + return gpd.GeoDataFrame(df, geometry=geometry, crs=crs) diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/nc_conv_utils.py b/src/icefabric/helpers/nc_conv_utils.py similarity index 97% rename from src/icefabric_tools/src/icefabric_tools/icechunk/nc_conv_utils.py rename to src/icefabric/helpers/nc_conv_utils.py index 71476cb..185f500 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/nc_conv_utils.py +++ b/src/icefabric/helpers/nc_conv_utils.py @@ -6,8 +6,8 @@ from netCDF4 import Dataset as NCDataset from tqdm import tqdm -import icefabric_tools.icechunk.arch_weather_file_utils as awf_utils -from icefabric_tools.icechunk import S3Path +import icefabric.helpers.arch_weather_file_utils as awf_utils +from icefabric.builds.icechunk_s3_module import S3Path def convert_files_to_netcdf4( diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py b/src/icefabric/helpers/topobathy_ic_to_tif.py similarity index 57% rename from src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py rename to src/icefabric/helpers/topobathy_ic_to_tif.py index 5d14e0f..4ee6057 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/topobathy_ic_to_tif.py +++ b/src/icefabric/helpers/topobathy_ic_to_tif.py @@ -1,14 +1,12 @@ import os -from dotenv import load_dotenv from tqdm import tqdm -from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations +from icefabric.builds.icechunk_s3_module import IcechunkRepo +from icefabric.schemas.topobathy import NGWPCLocations -load_dotenv() - -def convert_topobathy_to_tiff(output_dir: str, ic_rasters: list[NGWPCLocations]) -> None: +def convert_topobathy_to_tiff(output_dir: str, ic_rasters: list[str]) -> None: """Converts topobathy layers from icechunk to tiff for use in tiles Parameters @@ -19,17 +17,7 @@ def convert_topobathy_to_tiff(output_dir: str, ic_rasters: list[NGWPCLocations]) list of NGWPCLocation raster paths. eg. [NGWPCLocations[TOPO_AK_30M_IC].path] """ for ic_raster in tqdm(ic_rasters, desc="Downloading IC Rasters to .tif"): - repo = IcechunkS3Repo(location=NGWPCLocations[ic_raster].path) + repo = IcechunkRepo(location=NGWPCLocations[ic_raster].path) output = os.path.join(output_dir, f"{str.split(str(NGWPCLocations[ic_raster].path), '/')[-1]}.tif") repo.retrieve_and_convert_to_tif(dest=output, var_name="elevation") - - -if __name__ == "__main__": - output_dir = "./temp_tb_data" - os.makedirs(output_dir, exist_ok=True) - - # all 30 m topobathy layers - ic_rasters = [f for f in NGWPCLocations._member_names_ if "TOPO" and "30M" in f] - - convert_topobathy_to_tiff(output_dir, ic_rasters) diff --git a/src/icefabric/hydrofabric/__init__.py b/src/icefabric/hydrofabric/__init__.py new file mode 100644 index 0000000..52eae63 --- /dev/null +++ b/src/icefabric/hydrofabric/__init__.py @@ -0,0 +1,9 @@ +"""Helper functions designed to assist with managing data. Similar to util functions""" + +from .origin import find_origin +from .subset import subset + +__all__ = [ + "find_origin", + "subset", +] diff --git a/src/icefabric_tools/src/icefabric_tools/iceberg/utils.py b/src/icefabric/hydrofabric/origin.py similarity index 51% rename from src/icefabric_tools/src/icefabric_tools/iceberg/utils.py rename to src/icefabric/hydrofabric/origin.py index 82dc46a..1678d21 100644 --- a/src/icefabric_tools/src/icefabric_tools/iceberg/utils.py +++ b/src/icefabric/hydrofabric/origin.py @@ -1,75 +1,7 @@ -"""Contains all api functions that can be called outside of the icefabric_tools package""" +"""Finds the origin of the Hydrofabric id""" -import geopandas as gpd import pandas as pd -from pyiceberg.expressions import BooleanExpression -from pyiceberg.table import ALWAYS_TRUE, Table -from shapely import wkb - - -def table_to_geopandas( - table: Table, - row_filter: str | BooleanExpression = ALWAYS_TRUE, - case_sensitive: bool | None = True, - snapshot_id: int | None = None, - limit: int | None = None, -) -> gpd.GeoDataFrame: - """Converts a table to a geopandas dataframe - - Parameters - ---------- - table : Table - The iceberg table you are trying to read from - row_filter : str | None, optional - A string or BooleanExpression that describes the desired rows, by default "" - case_sensitive : bool | None, optional - If True column matching is case sensitive, by default True - snapshot_id : int | None, optional - Optional Snapshot ID to time travel to. - If None, scans the table as of the current snapshot ID, by default None - limit : int | None, optional - An integer representing the number of rows to return in the scan result. - If None, fetches all matching rows., by default None - - Returns - ------- - gpd.DataFrame - The resulting queried row, but in a geodataframe - """ - df = table.scan( - row_filter=row_filter, - case_sensitive=case_sensitive, - snapshot_id=snapshot_id, - limit=limit, - ).to_pandas() - return to_geopandas(df) - - -def to_geopandas(df: pd.DataFrame, crs: str = "EPSG:5070") -> gpd.GeoDataFrame: - """Converts the geometries in a pandas df to a geopandas dataframe - - Parameters - ---------- - df: pd.DataFrame - The iceberg table you are trying to read from - crs: str, optional - A string representing the CRS to set in the gdf, by default "EPSG:5070" - - Returns - ------- - gpd.DataFrame - The resulting queried row, but in a geodataframe - - Raises - ------ - ValueError - Raised if the table does not have a geometry column - """ - if "geometry" not in df.columns: - raise ValueError("The provided table does not have a geometry column.") - - geometry = df["geometry"].apply(lambda x: wkb.loads(x) if x is not None else None) - return gpd.GeoDataFrame(df, geometry=geometry, crs=crs) +from pyiceberg.table import Table def find_origin(network_table: Table, identifier: str, id_type: str = "hl_uri") -> pd.DataFrame: diff --git a/src/icefabric_tools/src/icefabric_tools/iceberg/hydrofabric.py b/src/icefabric/hydrofabric/subset.py similarity index 94% rename from src/icefabric_tools/src/icefabric_tools/iceberg/hydrofabric.py rename to src/icefabric/hydrofabric/subset.py index 99db880..25a1158 100644 --- a/src/icefabric_tools/src/icefabric_tools/iceberg/hydrofabric.py +++ b/src/icefabric/hydrofabric/subset.py @@ -1,40 +1,17 @@ """A file to hold all hydrofabric geospatial tools""" -from enum import Enum from pathlib import Path import geopandas as gpd import pandas as pd +from botocore.exceptions import ClientError from pyiceberg.catalog import Catalog from pyiceberg.expressions import And, EqualTo, In from pyiceberg.table import Table -from icefabric_tools.iceberg.utils import find_origin, table_to_geopandas, to_geopandas - - -class IdType(str, Enum): - """All queriable HF fields. - - Attributes - ---------- - HL_URI : str - Hydrolocation URI identifier - HF_ID : str - Hydrofabric ID identifier - ID : str - Generic ID identifier - POI_ID : str - Point of Interest ID identifier - """ - - HL_URI = "hl_uri" - HF_ID = "hf_id" - ID = "id" - POI_ID = "poi_id" - - -# For catchments that may extend in many VPUs -UPSTREAM_VPUS: dict[str, list[str]] = {"08": ["11", "10U", "10L", "08", "07", "05"]} +from icefabric.helpers.geopackage import table_to_geopandas, to_geopandas +from icefabric.hydrofabric.origin import find_origin +from icefabric.schemas.hydrofabric import UPSTREAM_VPUS, IdType def get_sorted_network(network_df: pd.DataFrame, outlets: list[str] | str) -> pd.DataFrame: @@ -298,12 +275,15 @@ def subset( layers = [] layers.extend(["divides", "flowpaths", "network", "nexus"]) layers = list(set(layers)) - - network = catalog.load_table("hydrofabric.network") - divides = catalog.load_table("hydrofabric.divides") - flowpaths = catalog.load_table("hydrofabric.flowpaths") - nexus = catalog.load_table("hydrofabric.nexus") - + try: + network = catalog.load_table("hydrofabric.network") + divides = catalog.load_table("hydrofabric.divides") + flowpaths = catalog.load_table("hydrofabric.flowpaths") + nexus = catalog.load_table("hydrofabric.nexus") + except ClientError as e: + msg = "AWS Test account credentials expired. Can't access remote S3 endpoint" + print(msg) + raise e # Find the origin point origin_row = find_origin(network_table=network, identifier=identifier, id_type=id_type.value) origin_filter = EqualTo("id", origin_row["id"].iloc[0]) diff --git a/src/icefabric/modules/__init__.py b/src/icefabric/modules/__init__.py new file mode 100644 index 0000000..5b8f303 --- /dev/null +++ b/src/icefabric/modules/__init__.py @@ -0,0 +1,11 @@ +"""Contains helper functions to support NWM modules""" + +from .create_ipes import divide_parameters, get_hydrofabric_attributes, module_ipe +from .rnr import get_rnr_segment + +__all__ = [ + "divide_parameters", + "get_hydrofabric_attributes", + "module_ipe", + "get_rnr_segment", +] diff --git a/src/icefabric_api/src/icefabric_api/create_ipes.py b/src/icefabric/modules/create_ipes.py similarity index 88% rename from src/icefabric_api/src/icefabric_api/create_ipes.py rename to src/icefabric/modules/create_ipes.py index a2866a5..de0113b 100644 --- a/src/icefabric_api/src/icefabric_api/create_ipes.py +++ b/src/icefabric/modules/create_ipes.py @@ -1,16 +1,36 @@ -import logging import os import geopandas as gpd import pandas as pd -from pyproj import Transformer -from pyiceberg.catalog import load_catalog import pyiceberg.exceptions as ex +from botocore.exceptions import ClientError +from pyiceberg.catalog import load_catalog +from pyproj import Transformer -logger = logging.getLogger(__name__) ROOT_DIR = os.path.abspath(os.curdir) -def module_ipe(module, attr, domain, version, cfg_write=None): + +def module_ipe(module: str, attr: str, domain: str, version: str, cfg_write=None): + """Creates initial parameter estimates for a module + + Parameters + ---------- + module : str + _description_ + attr : str + _description_ + domain : str + _description_ + version : str + _description_ + cfg_write : str, optional + _description_, by default None + + Returns + ------- + _type_ + _description_ + """ # Create empty dataframe for csv parameters csv_params = pd.DataFrame() @@ -41,7 +61,7 @@ def module_ipe(module, attr, domain, version, cfg_write=None): } # Get output vars out_datatypes = {"variable": "object", "description": "object"} - + module_params = pd.read_csv(f"{ROOT_DIR}/data/{param_file}", dtype=datatypes) output_vars = pd.DataFrame() if has_output_vars: @@ -70,7 +90,7 @@ def module_ipe(module, attr, domain, version, cfg_write=None): attr = attr[attr_list] # Rename attribute names to module names attr.rename(columns=attr_name_map, inplace=True) - + # Get iceberg table parameters if "iceberg" in module_params["source"].values: iceberg_params = divide_parameters(divides, module, domain) @@ -85,22 +105,23 @@ def module_ipe(module, attr, domain, version, cfg_write=None): iceberg_params_div = iceberg_params.loc[iceberg_params["divide_id"] == divide] iceberg_params_div = iceberg_params_div.to_dict(orient="list") all_cats[divide].update(iceberg_params_div) - + # When dataframe is converted to a dictionary, the values are contained in a list - # Remove the value from the list. + # Remove the value from the list. for key in all_cats[divide].keys(): value = all_cats[divide][key] if isinstance(value, list): all_cats[divide][key] = value[0] - if cfg_write: - write_cfg(all_cats) + _write_cfg(all_cats) s3_uri = "s3://" - module_json = write_json(all_cats[divides[1]], module_params, output_vars, module, s3_uri) + module_json = _write_json(all_cats[divides[1]], module_params, output_vars, module, s3_uri) return module_json -def write_cfg(params): + +def _write_cfg(params: dict): + """Writes a config file from a dictionary of parameters""" divides = params.keys() for divide in divides: cfg_file = f"{ROOT_DIR}/{divide}_bmi_config_cfe.txt" @@ -112,7 +133,8 @@ def write_cfg(params): f.close() -def write_json(params, all_params, output_vars, module, s3_uri): +def _write_json(params, all_params, output_vars, module, s3_uri): + """Writes a JSON file from a dictionary of parameters""" json_columns = ["name", "description", "default_value", "min", "max", "data_type", "units"] all_params = all_params.loc[all_params["calibratable"] == True, json_columns] all_params = all_params.to_dict(orient="records") @@ -135,6 +157,7 @@ def write_json(params, all_params, output_vars, module, s3_uri): def get_hydrofabric_attributes(gpkg_file, version, domain): + """Gets attributes from the hydrofabric geopackage""" attr_layer = "divide-attributes" if version == "2.1": attr_layer = "model-attributes" @@ -178,7 +201,7 @@ def get_hydrofabric_attributes(gpkg_file, version, domain): except: # TODO: Replace 'except' with proper catch error_str = "Error opening " + gpkg_file error = dict(error=error_str) - logger.error(error_str) + print(error_str) return error # Get catchement area from divides layer and append to attributes data frame @@ -224,7 +247,9 @@ def get_hydrofabric_attributes(gpkg_file, version, domain): return divide_attr + def divide_parameters(divides, module, domain): + """Returns iceberg divide parameters""" module = module.lower() domain = domain.lower() namespace = "divide_parameters" @@ -232,14 +257,13 @@ def divide_parameters(divides, module, domain): catalog = load_catalog("glue") try: table = catalog.load_table(table_name) - except ex.NoSuchTableError as e: + except ex.NoSuchTableError: print(f"Table {table_name} does not exist") return pd.DataFrame() - except Exception as e: - print(f"Iceberg table load exception: {e}") - return pd.DataFrame() + except ClientError as e: + msg = "AWS Test account credentials expired. Can't access remote S3 endpoint" + print(msg) + raise e df = table.scan().to_pandas() - filtered = df[df['divide_id'].isin(divides)] + filtered = df[df["divide_id"].isin(divides)] return filtered - - diff --git a/src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py b/src/icefabric/modules/rnr.py similarity index 98% rename from src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py rename to src/icefabric/modules/rnr.py index 07598b6..6906a1b 100644 --- a/src/icefabric_tools/src/icefabric_tools/iceberg/rnr.py +++ b/src/icefabric/modules/rnr.py @@ -6,7 +6,7 @@ from pyiceberg.catalog import Catalog from pyiceberg.expressions import And, EqualTo, In, LessThanOrEqual -from icefabric_tools.iceberg.utils import table_to_geopandas, to_geopandas +from icefabric.helpers.geopackage import table_to_geopandas, to_geopandas def get_rnr_segment(catalog: Catalog, reach_id: str, output_file: str) -> gpd.GeoDataFrame | None: diff --git a/src/icefabric/schemas/__init__.py b/src/icefabric/schemas/__init__.py new file mode 100644 index 0000000..86d1c95 --- /dev/null +++ b/src/icefabric/schemas/__init__.py @@ -0,0 +1,6 @@ +"""Contains helper functions to support NWM modules""" + +from .hydrofabric import UPSTREAM_VPUS, IdType +from .topobathy import FileType, NGWPCLocations, NGWPCTestLocations + +__all__ = ["UPSTREAM_VPUS", "IdType", "FileType", "NGWPCLocations", "NGWPCTestLocations"] diff --git a/src/icefabric/schemas/hydrofabric.py b/src/icefabric/schemas/hydrofabric.py new file mode 100644 index 0000000..dbb6d8c --- /dev/null +++ b/src/icefabric/schemas/hydrofabric.py @@ -0,0 +1,28 @@ +"""Contains all schemas and enums for the NGWPC Enterprise Hydrofabric""" + +from enum import Enum + + +class IdType(str, Enum): + """All queriable HF fields. + + Attributes + ---------- + HL_URI : str + Hydrolocation URI identifier + HF_ID : str + Hydrofabric ID identifier + ID : str + Generic ID identifier + POI_ID : str + Point of Interest ID identifier + """ + + HL_URI = "hl_uri" + HF_ID = "hf_id" + ID = "id" + POI_ID = "poi_id" + + +# For catchments that may extend in many VPUs +UPSTREAM_VPUS: dict[str, list[str]] = {"08": ["11", "10U", "10L", "08", "07", "05"]} diff --git a/src/icefabric/schemas/topobathy.py b/src/icefabric/schemas/topobathy.py new file mode 100644 index 0000000..25ed9b6 --- /dev/null +++ b/src/icefabric/schemas/topobathy.py @@ -0,0 +1,81 @@ +"""Contains all schemas and enums for topobathy definitions in the NGWPC S3""" + +from enum import Enum + +from icefabric.builds.icechunk_s3_module import S3Path + +TOPO_BP = "surface/nws-topobathy" +TOPO_NOS = f"{TOPO_BP}/nws-nos-surveys" + + +class FileType(Enum): + """ + Archival weather file types + + Enum class for instantiating different archival weather file + formats. Used when virtualizing and collecting files. + """ + + GEOTIFF = ".tif" + NETCDF = ".nc" + + +class NGWPCLocations(Enum): + """ + Important NGWPC S3 locations + + Enum class for instantiating S3Paths corresponding to the + icechunk stores, as well as the reference locations for virtualized + stores. + """ + + SNODAS_REF = ("ngwpc-forcing", "snodas_nc_v4") + SNODAS_V3 = ("ngwpc-forcing", "snodas_nc") + SNODAS_IC = ("hydrofabric-data", "forcing/snodas") + NLCD_REF = ("ngwpc-hydrofabric", "NLCD_Land_Cover_CONUS") + NLCD_IC = ("hydrofabric-data", "land-cover/NLCD-Land-Cover") + TOPO_AK_10M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_alaska_10m") + TOPO_AK_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_alaska_30m") + TOPO_CONUS_ATL_GULF_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_conus_atlantic_gulf_30m") + TOPO_CONUS_PAC_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_conus_pacific_30m") + TOPO_GREAT_LAKES_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_great_lakes_30m") + TOPO_HA_10M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_hawaii_10m") + TOPO_HA_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_hawaii_30m") + TOPO_PR_USVI_10M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_pr_usvi_10m") + TOPO_PR_USVI_30M_IC = ("hydrofabric-data", f"{TOPO_BP}/tbdem_pr_usvi_30m") + TOPO_ALBEMARLE_SOUND_IC = ("hydrofabric-data", f"{TOPO_NOS}/Albemarle_Sound_NOS_NCEI") + TOPO_CHESAPEAKE_BAY_IC = ("hydrofabric-data", f"{TOPO_NOS}/Chesapeake_Bay_NOS_NCEI") + TOPO_MOBILE_BAY_IC = ("hydrofabric-data", f"{TOPO_NOS}/Mobile_Bay_NOS_NCEI") + TOPO_TANGIER_SOUND_IC = ("hydrofabric-data", f"{TOPO_NOS}/Tangier_Sound_NOS_NCEI") + + def __init__(self, bucket, prefix): + self.path = S3Path(bucket, prefix) + + +class NGWPCTestLocations(Enum): + """ + Important NGWPC S3 locations + + Enum class for instantiating S3Paths corresponding to the + icechunk stores, as well as the reference locations for virtualized + stores. + """ + + SNODAS_IC = ("edfs-data", "forcing/snodas") + NLCD_IC = ("edfs-data", "land-cover/NLCD-Land-Cover") + TOPO_AK_10M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_alaska_10m") + TOPO_AK_30M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_alaska_30m") + TOPO_CONUS_ATL_GULF_30M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_conus_atlantic_gulf_30m") + TOPO_CONUS_PAC_30M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_conus_pacific_30m") + TOPO_GREAT_LAKES_30M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_great_lakes_30m") + TOPO_HA_10M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_hawaii_10m") + TOPO_HA_30M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_hawaii_30m") + TOPO_PR_USVI_10M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_pr_usvi_10m") + TOPO_PR_USVI_30M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_pr_usvi_30m") + TOPO_ALBEMARLE_SOUND_IC = ("edfs-data", f"{TOPO_NOS}/Albemarle_Sound_NOS_NCEI") + TOPO_CHESAPEAKE_BAY_IC = ("edfs-data", f"{TOPO_NOS}/Chesapeake_Bay_NOS_NCEI") + TOPO_MOBILE_BAY_IC = ("edfs-data", f"{TOPO_NOS}/Mobile_Bay_NOS_NCEI") + TOPO_TANGIER_SOUND_IC = ("edfs-data", f"{TOPO_NOS}/Tangier_Sound_NOS_NCEI") + + def __init__(self, bucket, prefix): + self.path = S3Path(bucket, prefix) diff --git a/src/icefabric/ui/__init__.py b/src/icefabric/ui/__init__.py new file mode 100644 index 0000000..75174ee --- /dev/null +++ b/src/icefabric/ui/__init__.py @@ -0,0 +1,9 @@ +"""Contains helper functions to support NWM modules""" + +from .geo_utils import create_time_series_widget, get_geopackage_uri, get_observational_uri + +__all__ = [ + "create_time_series_widget", + "get_geopackage_uri", + "get_observational_uri", +] diff --git a/src/icefabric_tools/src/icefabric_tools/geo/geo_utils.py b/src/icefabric/ui/geo_utils.py similarity index 60% rename from src/icefabric_tools/src/icefabric_tools/geo/geo_utils.py rename to src/icefabric/ui/geo_utils.py index b64719b..3ef7846 100644 --- a/src/icefabric_tools/src/icefabric_tools/geo/geo_utils.py +++ b/src/icefabric/ui/geo_utils.py @@ -7,18 +7,28 @@ def get_observational_uri( gage_id: str, source: str = "USGS", domain: str = "CONUS", version: str = "2.1", timeout=None ) -> str: - """ - Fetches the observational data URI from the NextGen Water Prediction API. + """Fetch observational data URI from the NextGen Water Prediction API. - Args: - gage_id (str): The gage identifier. - source (str): Data source, e.g., "USGS". - domain (str): Domain name, e.g., "CONUS". - version (str): API version, e.g., "2.2". + Retrieves the URI for observational streamflow data for a specific gage + from the NextGen Water Prediction hydrofabric API. + + Parameters + ---------- + gage_id : str + The gage identifier (e.g., USGS station ID). + source : str, default "USGS" + Data source provider for the observational data. + domain : str, default "CONUS" + Geographic domain name for the data request. + version : str, default "2.1" + API version to use for the request. + timeout : float or None, optional + Request timeout in seconds. If None, uses httpx default. Returns ------- - str: The URI of the observational dataset. + str + The URI pointing to the observational dataset. """ base_url = f"https://hydroapi.oe.nextgenwaterprediction.com/hydrofabric/{version}/observational" params = {"gage_id": gage_id, "source": source, "domain": domain} @@ -33,15 +43,29 @@ def get_observational_uri( def get_geopackage_uri( gage_id: str, source: str = "USGS", domain: str = "CONUS", version: str = "2.2", timeout=None ) -> str: - """ - Fetches the GeoPackage URI for a given gage ID from the NextGen Water Prediction API. + """Fetch GeoPackage URI for a gage from the NextGen Water Prediction API. + + Retrieves the URI for a hydrofabric GeoPackage containing network topology + and catchment boundaries for a specific gage from the NextGen API. + + Parameters + ---------- + gage_id : str + The gage identifier for which to retrieve the hydrofabric GeoPackage. + source : str, default "USGS" + Data source provider for the gage data. + domain : str, default "CONUS" + Geographic domain name for the hydrofabric request. + version : str, default "2.2" + Hydrofabric version to retrieve. + timeout : float or None, optional + Request timeout in seconds. If None, uses httpx default. Returns ------- - str: The URI pointing to the GeoPackage in S3. + str + The URI pointing to the GeoPackage file in S3 storage. """ - import httpx - base_url = "https://hydroapi.oe.nextgenwaterprediction.com/hydrofabric/geopackages" params = {"gage_id": gage_id, "source": source, "domain": domain, "version": version} @@ -74,7 +98,7 @@ def plot_flow(start, end): end_dt = pd.to_datetime(end) if start_dt >= end_dt: - print("⚠️ Start must be before End.") + print("Warning: Start must be before End.") return filtered = df[(df["dateTime"] >= start_dt) & (df["dateTime"] <= end_dt)] diff --git a/src/icefabric_api/README.md b/src/icefabric_api/README.md deleted file mode 100644 index 5a8601a..0000000 --- a/src/icefabric_api/README.md +++ /dev/null @@ -1,102 +0,0 @@ -# Icefabric API - -FastAPI service providing access to hydrology and water resources data stored in Apache Iceberg format. - -## Features - -- **Streamflow Observations** - USGS hourly streamflow data in CSV/Parquet formats -- **Hydrofabric Subsets** - Upstream watershed data as downloadable geopackages -- **Interactive Documentation** - Built-in Swagger UI and ReDoc - -## Quick Start - -### Prerequisites - -- [uv](https://docs.astral.sh/uv/) package manager - -### Installation - -```bash -# Clone repository -git clone -cd icefabric - -# Install package in development mode -uv pip install src/icefabric_api -e . -``` - -### Running the API - -```bash -# Start development server -cd src/icefabric_api -python -m app.main -``` - -The API will be available at `http://localhost:8000` - -### Docker Alternative - -```bash -# Build and run with Docker -docker compose -f docker/compose.yaml build --no-cache -docker compose -f docker/compose.yaml up -``` - -## API Endpoints - -### Streamflow Observations - -- `GET /v1/streamflow_observations/sources` - List available data sources -- `GET /v1/streamflow_observations/usgs/available` - Get available station IDs -- `GET /v1/streamflow_observations/usgs/{identifier}/info` - Station metadata -- `GET /v1/streamflow_observations/usgs/csv` - Download CSV data -- `GET /v1/streamflow_observations/usgs/parquet` - Download Parquet data - -### Hydrofabric - -- `GET /v1/hydrofabric/{identifier}/gpkg` - Download watershed subset as geopackage - -### Health Check - -- `HEAD /health` - API health status - -## Usage Examples - -### Download Streamflow Data - -```python -import requests -import pandas as pd - -# Get station information -response = requests.get( - "http://localhost:8000/v1/streamflow_observations/usgs/01031500/info" -) -station_info = response.json() - -# Download CSV data -csv_response = requests.get( - "http://localhost:8000/v1/streamflow_observations/usgs/csv", - params={ - "identifier": "01031500", - "start_date": "2023-01-01T00:00:00", - "end_date": "2023-01-31T23:59:59" - } -) -df = pd.read_csv(csv_response.text) -``` - -## Documentation - -- **Interactive API Docs**: http://localhost:8000/docs -- **Alternative Docs**: http://localhost:8000/redoc -- **OpenAPI Schema**: http://localhost:8000/openapi.json - -## Initial Parameters - -CSV files for catchment specific values are too big to be checked in. They are stored here: s3://ngwpc-dev/DanielCumpton/divide_csv.tgz -Download and untar in the icefabric/src/icefabric_api/data directory. -For now, you can run modules by listing them in the module list defined on line 108 in run_modules.py. Text files showing all parameters as key/value -pairs will be written to a files per catchment. A JSON is output to the terminal. SFT, SMP, and LASAM csv files still need work to define the parameters properly. -See icefabric/src/icefabric_api/data/modules.csv for module name strings. diff --git a/src/icefabric_api/pyproject.toml b/src/icefabric_api/pyproject.toml deleted file mode 100644 index 3d529d8..0000000 --- a/src/icefabric_api/pyproject.toml +++ /dev/null @@ -1,15 +0,0 @@ -[project] -name = "icefabric_api" -version = "2025.6.0" -description = "A FastAPI API for querying apache iceberg tables" -readme = "README.md" -requires-python = ">=3.11, <3.13" -dependencies = [ - "fastapi[standard]>=0.115.12", - "pydantic==2.11.5", - "pydantic-settings==2.9.1", -] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" diff --git a/src/icefabric_api/src/icefabric_api/__init__.py b/src/icefabric_api/src/icefabric_api/__init__.py deleted file mode 100644 index becebe6..0000000 --- a/src/icefabric_api/src/icefabric_api/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from icefabric_api.create_ipes import get_hydrofabric_attributes, module_ipe - -__all__ = [ - "get_hydrofabric_attributes", - "module_ipe", -] diff --git a/src/icefabric_manage/README.md b/src/icefabric_manage/README.md deleted file mode 100644 index dbb02f0..0000000 --- a/src/icefabric_manage/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# Icefabric Manage - -A repo which contains scripts for building the icefabric from scratch - -### How to build locally -- The `icefabric_manage` package must be installed through `uv sync` -- There must be parquet files locally to build from. These files are to be downloaded from the NGWPC S3 bucket @ `s3://hydrofabric-data/icefabric/` and placed in `src/icefabric_manage/data/` - -Once you have parquet files locally in the `data/` dir, you can convert them to an icefabric warehouse through a build script in `scripts/` or make your own! diff --git a/src/icefabric_manage/pyproject.toml b/src/icefabric_manage/pyproject.toml deleted file mode 100644 index ba9ddec..0000000 --- a/src/icefabric_manage/pyproject.toml +++ /dev/null @@ -1,32 +0,0 @@ -[project] -name = "icefabric_manage" -version = "2025.6.0" -description = "All functionality for creating, updating, and deleting information from the icefabric catalog" -authors = [ - { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, - { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, - { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, - { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, - { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, -] -maintainers = [ - { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, - { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, - { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, - { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, - { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, -] -readme = "README.md" -dependencies = [ - "numpy==2.2.6", - "pandas==2.2.3", - "geopandas==1.0.1", - "s3fs==2025.5.1", - "pyarrow==20.0.0", - "pyiceberg[s3fs,glue,sql-sqlite]==0.9.1", - "python-dotenv==1.1.0", -] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" diff --git a/src/icefabric_manage/src/icefabric_manage/__init__.py b/src/icefabric_manage/src/icefabric_manage/__init__.py deleted file mode 100644 index 49b9a3e..0000000 --- a/src/icefabric_manage/src/icefabric_manage/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from icefabric_manage._api import build -from icefabric_manage.iceberg_table import IcebergTable -from icefabric_manage.map_fim_data import MapData - -__all__ = ["build", "IcebergTable", "MapData"] diff --git a/src/icefabric_tools/README.md b/src/icefabric_tools/README.md deleted file mode 100644 index 7ee2c9f..0000000 --- a/src/icefabric_tools/README.md +++ /dev/null @@ -1,6 +0,0 @@ -# Icefabric tools - -A collection of compute functions/services build on top of existing Iceberg data - -Prior to exectuing the "src/icefabric_tools", one must establish their environment via -"uv pip install -e" against the .toml file residing within "https://github.com/NGWPC/icefabric/blob/main/pyproject.toml." diff --git a/src/icefabric_tools/pyproject.toml b/src/icefabric_tools/pyproject.toml deleted file mode 100644 index 24cbf59..0000000 --- a/src/icefabric_tools/pyproject.toml +++ /dev/null @@ -1,54 +0,0 @@ -[project] -name = "icefabric_tools" -version = "2025.6.0" -description = "All functionality for creating, updating, and deleting information from the icefabric catalog" -authors = [ - { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, - { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, - { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, - { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, - { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, -] -maintainers = [ - { name = "Tadd Bindas", email = "tadd.bindas@ertcorp.com" }, - { name = "Sylvia Chin", email = "sylvia.c.chin@rtx.com" }, - { name = "Daniel Cumpton", email = "dcumpton@rtx.com" }, - { name = "Quercus Hamlin", email = "qhamlin@asrcfederal.com" }, - { name = "Brock Hinkson", email = "brock.w.hinkson@rtx.com" }, -] -readme = "README.md" -dependencies = [ - "numpy==2.2.6", - "pandas==2.2.3", - "geopandas==1.0.1", - "matplotlib==3.10.3", - "s3fs==2025.5.1", - "pyarrow==20.0.0", - "pyiceberg[s3fs,glue]==0.9.1", - "xarray==2025.4.0", - "rioxarray==0.19.0", - "icechunk==0.2.14", - "zarr==3.0.8", - "virtualizarr==1.3.2", - "tqdm==4.67.1", - "tifffile==2025.5.21", - "scipy==1.15.3", - "kerchunk==0.2.8", - "h5py==3.13.0", - "h5netcdf==1.6.1", - "fsspec==2025.5.1", - "dask==2025.5.1", - "netCDF4==1.7.2", - "python-dotenv==1.1.0", - "folium==0.19.5", - "geodatasets==2024.8.0", - "httpx==0.28.1", - "ipywidgets==8.1.6", - "mapclassify==2.9.0", - "jupyterlab==4.4.3", - "jupytergis==0.5.0", -] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" diff --git a/src/icefabric_tools/src/icefabric_tools/__init__.py b/src/icefabric_tools/src/icefabric_tools/__init__.py deleted file mode 100644 index 8a7da3d..0000000 --- a/src/icefabric_tools/src/icefabric_tools/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from icefabric_tools.iceberg.hydrofabric import IdType, subset -from icefabric_tools.iceberg.rnr import get_rnr_segment -from icefabric_tools.iceberg.utils import find_origin, table_to_geopandas, to_geopandas - -__all__ = [ - "IdType", - "subset", - "get_rnr_segment", - "find_origin", - "to_geopandas", - "table_to_geopandas", -] diff --git a/src/icefabric_tools/src/icefabric_tools/geo/__init__.py b/src/icefabric_tools/src/icefabric_tools/geo/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/icefabric_tools/src/icefabric_tools/iceberg/__init__.py b/src/icefabric_tools/src/icefabric_tools/iceberg/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/__init__.py b/src/icefabric_tools/src/icefabric_tools/icechunk/__init__.py deleted file mode 100644 index c0ef9ae..0000000 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from icefabric_tools.icechunk.icechunk_s3_module import ( - IcechunkS3Repo, - NGWPCLocations, - S3Path, - get_icechunk_data, -) diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_migration.py b/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_migration.py deleted file mode 100644 index bd08485..0000000 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_s3_migration.py +++ /dev/null @@ -1,58 +0,0 @@ -import os -from icefabric_tools.icechunk import IcechunkS3Repo, NGWPCLocations, S3Path -from icefabric_tools.icechunk.icechunk_testing import * - -def migrate_all_icechunks(attr_name): - ''' - Converts & migrates TIFFs from local to cloud. - - Args: - attr_name (str): Attribute name of interest for a given TIFF. - - Return: None - - ''' - # Extract list of TIFFs from local - local_list = [] - for root, _, files in os.walk("./"): - for file in files: - if file.endswith(f".tiff"): - local_list.append(file.lower()) - - # Maps the S3 paths to TIFFs - topo_fn2s3_map = {} - for fn in local_list: - if 'hawaii_10m' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_HA_10M_IC.path] = 'tbdem_hawaii_10m.tiff' - elif 'hawaii_30m' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_HA_30M_IC.path] = 'tbdem_hawaii_30m.tiff' - elif 'conus_atlantic_gulf_30m' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_CONUS_ATL_GULF_30M_IC.path] = 'tbdem_conus_atlantic_gulf_30m.tiff' - elif 'conus_pacific_30m' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_CONUS_PAC_30M_IC.path] = 'tbdem_conus_pacific_30m.tiff' - elif 'pr_usvi_30m' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_PR_USVI_30M_IC.path] = 'tbdem_pr_usvi_30m.tiff' - elif 'pr_usvi_10m' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_PR_USVI_10M_IC.path] = 'tbdem_pr_usvi_10m.tiff' - elif 'alaska_10m' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_AK_10M_IC.path] = 'tbdem_alaska_10m.tiff' - elif 'alaska_30m' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_AK_30M_IC.path] = 'tbdem_alaska_30m.tiff' - elif 'great_lakes_30m' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_GREAT_LAKES_30M_IC.path] = 'tbdem_great_lakes_30m.tiff' - elif 'albemarle_sound_nos_ncei' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_ALBEMARLE_SOUND_IC.path] = 'Albemarle_Sound_NOS_NCEI.tiff' - elif 'chesapeake_bay_nos_ncei' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_CHESAPEAKE_BAY_IC.path] = 'Chesapeake_Bay_NOS_NCEI.tiff' - elif 'mobile_bay_nos_ncei' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_MOBILE_BAY_IC.path] = 'Mobile_Bay_NOS_NCEI.tiff' - elif 'tangier_sound_nos_ncei-002' in fn: - topo_fn2s3_map[NGWPCLocations.TOPO_TANGIER_SOUND_IC.path] = 'Tangier_Sound_NOS_NCEI-002.tiff' - - # Migration of all captured TIFFs to cloud. - for s3_path, fn in topo_fn2s3_map.items(): - if fn: - topo_push_test(tiff_fp=f"./{fn}", - attr_name=attr_name, - new_ic_repo=s3_path) - return \ No newline at end of file diff --git a/src/icefabric_manage/terraform/README.md b/terraform/README.md similarity index 100% rename from src/icefabric_manage/terraform/README.md rename to terraform/README.md diff --git a/src/icefabric_manage/terraform/environments/test/.terraform.lock.hcl b/terraform/environments/test/.terraform.lock.hcl similarity index 100% rename from src/icefabric_manage/terraform/environments/test/.terraform.lock.hcl rename to terraform/environments/test/.terraform.lock.hcl diff --git a/src/icefabric_manage/terraform/environments/test/backend.tf b/terraform/environments/test/backend.tf similarity index 100% rename from src/icefabric_manage/terraform/environments/test/backend.tf rename to terraform/environments/test/backend.tf diff --git a/src/icefabric_manage/terraform/environments/test/main.tf b/terraform/environments/test/main.tf similarity index 100% rename from src/icefabric_manage/terraform/environments/test/main.tf rename to terraform/environments/test/main.tf diff --git a/src/icefabric_manage/terraform/environments/test/terraform.tfvars b/terraform/environments/test/terraform.tfvars similarity index 96% rename from src/icefabric_manage/terraform/environments/test/terraform.tfvars rename to terraform/environments/test/terraform.tfvars index 518b338..71c8ea7 100644 --- a/src/icefabric_manage/terraform/environments/test/terraform.tfvars +++ b/terraform/environments/test/terraform.tfvars @@ -4,5 +4,5 @@ application = "icefabric" region = "us-east-1" team = "EDFS" identity_center_role_arn = "arn:aws:iam::591210920133:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_SoftwareEngineersFull_529cc40c0355e893" -lakeformation_admin_arns = ["arn:aws:iam::591210920133:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_SoftwareEngineersFull_529cc40c0355e893", +lakeformation_admin_arns = ["arn:aws:iam::591210920133:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_SoftwareEngineersFull_529cc40c0355e893", "arn:aws:iam::591210920133:role/aws-reserved/sso.amazonaws.com/AWSReservedSSO_AdminLogin_fe24f9db156f282d"] diff --git a/src/icefabric_manage/terraform/environments/test/variables.tf b/terraform/environments/test/variables.tf similarity index 99% rename from src/icefabric_manage/terraform/environments/test/variables.tf rename to terraform/environments/test/variables.tf index ad7adc0..dc5c863 100644 --- a/src/icefabric_manage/terraform/environments/test/variables.tf +++ b/terraform/environments/test/variables.tf @@ -30,4 +30,4 @@ variable "lakeformation_admin_arns" { description = "List of ARNs to set as Lake Formation administrators" type = list(string) default = [] # Will be populated with current account or specific users/roles -} \ No newline at end of file +} diff --git a/src/icefabric_manage/terraform/iceberg_demo.py b/terraform/iceberg_demo.py similarity index 100% rename from src/icefabric_manage/terraform/iceberg_demo.py rename to terraform/iceberg_demo.py diff --git a/src/icefabric_manage/terraform/modules/iceberg/main.tf b/terraform/modules/iceberg/main.tf similarity index 99% rename from src/icefabric_manage/terraform/modules/iceberg/main.tf rename to terraform/modules/iceberg/main.tf index c99b6d8..229e27a 100644 --- a/src/icefabric_manage/terraform/modules/iceberg/main.tf +++ b/terraform/modules/iceberg/main.tf @@ -82,7 +82,7 @@ resource "aws_lakeformation_permissions" "database_permissions" { } } -# EDFS currently plans to manage Tables outside of Terraform, but one can grant permissions +# EDFS currently plans to manage Tables outside of Terraform, but one can grant permissions # for table(s) (if it exists or after creation) in terraform #resource "aws_lakeformation_permissions" "table_permissions" { # principal = var.identity_center_role_arn diff --git a/src/icefabric_manage/terraform/modules/iceberg/variables.tf b/terraform/modules/iceberg/variables.tf similarity index 99% rename from src/icefabric_manage/terraform/modules/iceberg/variables.tf rename to terraform/modules/iceberg/variables.tf index ad7adc0..dc5c863 100644 --- a/src/icefabric_manage/terraform/modules/iceberg/variables.tf +++ b/terraform/modules/iceberg/variables.tf @@ -30,4 +30,4 @@ variable "lakeformation_admin_arns" { description = "List of ARNs to set as Lake Formation administrators" type = list(string) default = [] # Will be populated with current account or specific users/roles -} \ No newline at end of file +} diff --git a/tests/README.md b/tests/README.md deleted file mode 100644 index 4184d08..0000000 --- a/tests/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Pytests - -This folder is for all testing data so the global confest can pick it up. This allows all tests in the namespace packages to share the same scope without having to reference one another in tests - -To run tests, run `pytest -s` from project root. - -To run the subsetter tests, run `pytest --run-slow` as these tests take some time. Otherwise, they will be skipped diff --git a/src/icefabric_manage/builds/__init__.py b/tests/__init__.py similarity index 100% rename from src/icefabric_manage/builds/__init__.py rename to tests/__init__.py diff --git a/src/icefabric_api/tests/test_hydrofabric_router.py b/tests/app/test_hydrofabric_router.py similarity index 100% rename from src/icefabric_api/tests/test_hydrofabric_router.py rename to tests/app/test_hydrofabric_router.py diff --git a/src/icefabric_api/tests/test_main.py b/tests/app/test_main.py similarity index 100% rename from src/icefabric_api/tests/test_main.py rename to tests/app/test_main.py diff --git a/src/icefabric_api/tests/test_streamflow_observations_router.py b/tests/app/test_streamflow_observations_router.py similarity index 100% rename from src/icefabric_api/tests/test_streamflow_observations_router.py rename to tests/app/test_streamflow_observations_router.py diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..a1bbdee --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,159 @@ +import os +from pathlib import Path + +import pandas as pd +import pytest +from dotenv import load_dotenv +from fastapi.testclient import TestClient +from pyiceberg.catalog import Catalog, load_catalog + +from app.main import app +from icefabric.schemas import NGWPCTestLocations + +# Setting .env/.pyiceberg creds based on project root +env_path = Path.cwd() / ".env" +load_dotenv(dotenv_path=env_path) +pyiceberg_file = Path.cwd() / ".pyiceberg.yaml" +if pyiceberg_file.exists(): + os.environ["PYICEBERG_HOME"] = str(Path(__file__).parents[1]) +else: + raise FileNotFoundError( + "Cannot find .pyiceberg.yaml. Please download this from NGWPC confluence or create " + ) + + +sample_gauges = [ + # "01010000", + # "02450825", + # "03173000", + # "04100500", + # "05473450", + # "06823500", + # "07060710", + # "08070000", + # "09253000", + # "10316500", + # "11456000", + # "12411000", + # "13337000", + # "14020000", + "06710385", +] + +sample_hf_uri = [ + # "gages-01010000", + # "gages-02450825", + # "gages-03173000", + # "gages-04100500", + # "gages-05473450", + # "gages-06823500", + # "gages-07060710", + # "gages-08070000", + # "gages-09253000", + # "gages-10316500", + # "gages-11456000", + # "gages-12411000", + # "gages-13337000", + # "gages-14020000", + "gages-06710385" +] + +test_ic_rasters = [f for f in NGWPCTestLocations._member_names_ if "TOPO" in f] +local_ic_rasters = [ + Path(__file__).parent / "data/topo_tifs/nws-nos-surveys/Albemarle_Sound_NOS_NCEI", + Path(__file__).parent / "data/topo_tifs/nws-nos-surveys/Chesapeake_Bay_NOS_NCEI", + Path(__file__).parent / "data/topo_tifs/nws-nos-surveys/Mobile_Bay_NOS_NCEI", + Path(__file__).parent / "data/topo_tifs/nws-nos-surveys/Tangier_Sound_NOS_NCEI", + Path(__file__).parent / "data/topo_tifs/tbdem_alaska_10m", + Path(__file__).parent / "data/topo_tifs/tbdem_alaska_30m", + Path(__file__).parent / "data/topo_tifs/tbdem_conus_atlantic_gulf_30m", + Path(__file__).parent / "data/topo_tifs/tbdem_conus_pacific_30m", + Path(__file__).parent / "data/topo_tifs/tbdem_great_lakes_30m", + Path(__file__).parent / "data/topo_tifs/tbdem_hawaii_10m", + Path(__file__).parent / "data/topo_tifs/tbdem_hawaii_30m", + Path(__file__).parent / "data/topo_tifs/tbdem_pr_usvi_10m", + Path(__file__).parent / "data/topo_tifs/tbdem_pr_usvi_30m", +] + + +@pytest.fixture(params=test_ic_rasters) +def ic_raster(request) -> str: + """Returns AWS S3 icechunk stores/rasters for checking correctness""" + return request.param + + +@pytest.fixture(params=local_ic_rasters) +def local_ic_raster(request) -> Path: + """Returns local icechunk stores/rasters for checking correctness""" + return request.param + + +@pytest.fixture(params=sample_gauges) +def gauge_ids(request) -> str: + """Returns individual gauge identifiers for parameterized testing""" + return request.param + + +@pytest.fixture(params=sample_hf_uri) +def gauge_hf_uri(request) -> str: + """Returns individual gauge identifiers for parameterized testing""" + return request.param + + +@pytest.fixture +def testing_dir() -> Path: + """Returns the testing data dir""" + return Path(__file__).parent / "data/" + + +@pytest.fixture(scope="session") +def client(): + """Create a test client for the FastAPI app.""" + return TestClient(app) + + +@pytest.fixture +def local_usgs_streamflow_csv(): + """Returns a locally downloaded CSV file from a specific gauge and time""" + file_path = Path(__file__).parent / "data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.csv" + return pd.read_csv(file_path) + + +@pytest.fixture +def local_usgs_streamflow_parquet(): + """Returns a locally downloaded CSV file from a specific gauge and time""" + file_path = Path(__file__).parent / "data/usgs_01010000_data_from_20211231_1400_to_20220101_1400.parquet" + return pd.read_parquet(file_path) + + +@pytest.fixture +def hydrofabric_catalog() -> Catalog: + """Returns an iceberg catalog object for the hydrofabric""" + return load_catalog("glue") + + +def pytest_addoption(parser): + """Adds the --run-slow tag""" + parser.addoption( + "--run-slow", + action="store_true", + default=False, + help="Run slow tests", + ) + + +def pytest_collection_modifyitems(config, items): + """Allows pytest to read the --run-slow tag""" + if not config.getoption("--run-slow"): + skipper = pytest.mark.skip(reason="Only run when --run-slow is given") + for item in items: + if "slow" in item.keywords: + item.add_marker(skipper) + + +def pytest_configure(config): + """Configure pytest markers.""" + config.addinivalue_line("markers", "slow: marks tests as slow tests") + 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zyvG+r<8H9Ue7wWuf(>#n%CTmmj2g*sJc#RlHO`_UXsIgCP7sWwJkd^+lk#jDMe$Yc z*}*IH3xxc5h~p*z9R!{v9!OINrE#l)MJSXZ!U7&iKOjrPUo>(Bl5hpvOGjbA?Ib-- zI?EKXC9mX-^rz^W`amf4G&nsrkNv@@$4BDn<4a%wlE-YY!;fd%1$G+81APtSPfqvz z!zFBq*2jx`CL;HegfkiK+J`X+Bm??h0fm}I3c8@!C4^ddLINK8;vxAHvcWQ+PvYzu x{v>KJOrs|$#$DxVry7#JTc}odlV1YvZj*N#?caF<{%Y<2p|) None: """This test is SLOW. It will temporarily download all topobathy layers, up to 9 GB individually. @@ -21,14 +19,56 @@ def test_topobathy(ic_raster: str) -> None: Corrupted rasters will load correctly in xarray but incorrectly in rasterio and cannot be exported. This test checks that when exported, a dataset has values that are non-no data. """ - data_dir = Path(__file__).parents[0] / "data" + data_dir = Path(__file__).parent / "data" + os.makedirs(data_dir, exist_ok=True) + + temp_path = data_dir / "temp_raster.tif" + + local_creds_file = Path(__file__).parents[2] / ".env" + if local_creds_file.exists is False: + pytest.skip("Skipping as AWS creds are not available") + + try: + # export icechunk zarr to geotiff raster + repo = IcechunkRepo(location=NGWPCTestLocations[ic_raster].path) + ds = repo.retrieve_dataset() + raster = ds.elevation + raster.rio.to_raster(temp_path, tiled=True, compress="LZW", bigtiff="YES") + + # open raster version + with rasterio.open(temp_path, "r") as f: + profile = f.profile + ras = f.read(1) + + # assert all values are not nodata + total_nd = np.where(ras == profile["nodata"], 1, 0).sum() + assert total_nd != ras.size + assert ras.min() != ras.max() + + finally: + if os.path.exists(temp_path): + os.remove(temp_path) + + +def test_local_topobathy(local_ic_raster: Path) -> None: + """Tests local topobathy against local icechunk stores + + Parameters + ---------- + local_ic_raster : str + _description_ + """ + data_dir = Path(__file__).parent / "data" os.makedirs(data_dir, exist_ok=True) temp_path = data_dir / "temp_raster.tif" + if local_ic_raster.exists() is False: + pytest.skip("Local file for topobathy missing. Skipping test") + try: # export icechunk zarr to geotiff raster - repo = IcechunkS3Repo(location=NGWPCLocations[ic_raster].path) + repo = IcechunkRepo(local_ic_raster) ds = repo.retrieve_dataset() raster = ds.elevation raster.rio.to_raster(temp_path, tiled=True, compress="LZW", bigtiff="YES") diff --git a/src/icefabric_tools/tests/test_subset.py b/tests/test_subset.py similarity index 94% rename from src/icefabric_tools/tests/test_subset.py rename to tests/test_subset.py index 57e059d..9e6bf0e 100644 --- a/src/icefabric_tools/tests/test_subset.py +++ b/tests/test_subset.py @@ -4,7 +4,8 @@ import pytest from pyiceberg.catalog import Catalog -from icefabric_tools import IdType, subset +from icefabric.hydrofabric import subset +from icefabric.schemas import IdType @pytest.mark.slow diff --git a/src/icefabric_tools/tests/test_utils.py b/tests/test_utils.py similarity index 89% rename from src/icefabric_tools/tests/test_utils.py rename to tests/test_utils.py index 60f5398..c4cfac2 100644 --- a/src/icefabric_tools/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,7 +1,8 @@ import pytest from pyiceberg.catalog import Catalog -from icefabric_tools import IdType, find_origin +from icefabric.hydrofabric import find_origin +from icefabric.schemas import IdType @pytest.mark.unit diff --git a/tools/hydrofabric/hfsubset.py b/tools/hydrofabric/hfsubset.py new file mode 100644 index 0000000..ab0767a --- /dev/null +++ b/tools/hydrofabric/hfsubset.py @@ -0,0 +1,32 @@ +"""A script to subset the hydrofabric""" + +import argparse +from pathlib import Path + +from pyiceberg.catalog import load_catalog + +from icefabric.helpers import load_creds +from icefabric.hydrofabric import subset +from icefabric.schemas import IdType + +load_creds(dir=Path.cwd()) + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="A script to build a pyiceberg catalog in the S3 endpoint") + + parser.add_argument("--hf-uri", help="the gauge # to subset from") + parser.add_argument("--output-file", help="Where the subset should be output [as a geopackage]") + + args = parser.parse_args() + + if args.output_file is None: + output_file = Path("subset.gpkg") + else: + output_file = Path(args.output_file) + subset( + catalog=load_catalog("glue"), + identifier=f"gages-{args.hf_uri}", + id_type=IdType.HL_URI, + output_file=output_file, + ) + print(f"Subset file written to: {output_file}") diff --git a/tools/icechunk/convert_to_tif.py b/tools/icechunk/convert_to_tif.py new file mode 100644 index 0000000..3375125 --- /dev/null +++ b/tools/icechunk/convert_to_tif.py @@ -0,0 +1,18 @@ +"""A function to convert the topobathy data into a tif file""" + +import os +from pathlib import Path + +from icefabric.helpers import convert_topobathy_to_tiff, load_creds +from icefabric.schemas import NGWPCLocations + +load_creds(dir=Path.cwd()) + +if __name__ == "__main__": + output_dir = "./temp_tb_data" + os.makedirs(output_dir, exist_ok=True) + + # all 30 m topobathy layers + ic_rasters = [f for f in NGWPCLocations._member_names_ if "TOPO" and "30M" in f] + + convert_topobathy_to_tiff(output_dir, ic_rasters) diff --git a/tools/icechunk/icechunk_s3_migration.py b/tools/icechunk/icechunk_s3_migration.py new file mode 100644 index 0000000..456231a --- /dev/null +++ b/tools/icechunk/icechunk_s3_migration.py @@ -0,0 +1,72 @@ +import argparse +import os + +from dotenv import load_dotenv + +from icefabric.schemas import NGWPCLocations + +from .icechunk_testing import topo_push_test + +load_dotenv() + + +def migrate_all_icechunks(attr_name: str) -> None: + """Converts & migrates TIFFs from local to cloud. + + Parameters + ---------- + attr_name: str + Attribute name of interest for a given TIFF. + """ + # Extract list of TIFFs from local + local_list = [] + for _, _, files in os.walk("./"): + for file in files: + if file.endswith(".tiff"): + local_list.append(file.lower()) + + # Maps the S3 paths to TIFFs + topo_fn2s3_map = {} + for fn in local_list: + if "hawaii_10m" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_HA_10M_IC.path] = "tbdem_hawaii_10m.tiff" + elif "hawaii_30m" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_HA_30M_IC.path] = "tbdem_hawaii_30m.tiff" + elif "conus_atlantic_gulf_30m" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_CONUS_ATL_GULF_30M_IC.path] = ( + "tbdem_conus_atlantic_gulf_30m.tiff" + ) + elif "conus_pacific_30m" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_CONUS_PAC_30M_IC.path] = "tbdem_conus_pacific_30m.tiff" + elif "pr_usvi_30m" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_PR_USVI_30M_IC.path] = "tbdem_pr_usvi_30m.tiff" + elif "pr_usvi_10m" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_PR_USVI_10M_IC.path] = "tbdem_pr_usvi_10m.tiff" + elif "alaska_10m" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_AK_10M_IC.path] = "tbdem_alaska_10m.tiff" + elif "alaska_30m" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_AK_30M_IC.path] = "tbdem_alaska_30m.tiff" + elif "great_lakes_30m" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_GREAT_LAKES_30M_IC.path] = "tbdem_great_lakes_30m.tiff" + elif "albemarle_sound_nos_ncei" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_ALBEMARLE_SOUND_IC.path] = "Albemarle_Sound_NOS_NCEI.tiff" + elif "chesapeake_bay_nos_ncei" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_CHESAPEAKE_BAY_IC.path] = "Chesapeake_Bay_NOS_NCEI.tiff" + elif "mobile_bay_nos_ncei" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_MOBILE_BAY_IC.path] = "Mobile_Bay_NOS_NCEI.tiff" + elif "tangier_sound_nos_ncei-002" in fn: + topo_fn2s3_map[NGWPCLocations.TOPO_TANGIER_SOUND_IC.path] = "Tangier_Sound_NOS_NCEI-002.tiff" + + # Migration of all captured TIFFs to cloud. + for s3_path, fn in topo_fn2s3_map.items(): + if fn: + topo_push_test(tiff_fp=f"./{fn}", attr_name=attr_name, new_ic_repo=s3_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="A script to write an icechunk store") + + parser.add_argument("--attr", help="The attribute that is to be built to icechunk") + + args = parser.parse_args() + migrate_all_icechunks(attr_name=args.attr) diff --git a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py b/tools/icechunk/icechunk_testing.py similarity index 85% rename from src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py rename to tools/icechunk/icechunk_testing.py index a2ff7af..690a526 100644 --- a/src/icefabric_tools/src/icefabric_tools/icechunk/icechunk_testing.py +++ b/tools/icechunk/icechunk_testing.py @@ -5,15 +5,19 @@ file virtualization/concatenation & uploading/appending/retrieving data to/from repos. """ -import icefabric_tools.icechunk.arch_weather_file_utils as awf_utils -import icefabric_tools.icechunk.nc_conv_utils as ncc_utils -from icefabric_tools.icechunk.icechunk_s3_module import ( - IcechunkS3Repo, - NGWPCLocations, - S3Path, +from dotenv import load_dotenv + +import icefabric.helpers.nc_conv_utils as ncc_utils +from icefabric.builds import IcechunkRepo, S3Path +from icefabric.helpers import ( + get_archival_weather_files, + load_tiff_file, + virtualize_and_concat_archival_files_on_time, ) +from icefabric.schemas import FileType, NGWPCLocations ICECHUNK_STORES = [loc for loc in NGWPCLocations if "_IC" in loc.name] +load_dotenv() def output_icechunk_stores(): @@ -35,13 +39,13 @@ def snodas_virtualize_push_test( NOTE: Take care to not overwrite an existing IC repo """ - snodas_repo = IcechunkS3Repo( + snodas_repo = IcechunkRepo( location=new_ic_repo, virtual_chunk_mapping=[{"bucket": "ngwpc-forcing", "region": "us-east-1"}] ) - nc_virt_ds = awf_utils.virtualize_and_concat_archival_files_on_time( + nc_virt_ds = virtualize_and_concat_archival_files_on_time( location=NGWPCLocations.SNODAS_REF.path, file_date_pattern="zz_ssmv11034tS__T0001TTNATS*05HP001.nc", - file_type=awf_utils.FileType.NETCDF, + file_type=FileType.NETCDF, loadable_vars=["crs"], testing_file_quantity=test_quant, ) @@ -65,15 +69,15 @@ def snodas_yearly_virt_append_test( NOTE: Take care to not overwrite an existing IC repo """ - snodas_repo = IcechunkS3Repo( + snodas_repo = IcechunkRepo( location=new_ic_repo, virtual_chunk_mapping=[{"bucket": "ngwpc-forcing", "region": "us-east-1"}] ) for y in years: - nc_virt_ds = awf_utils.virtualize_and_concat_archival_files_on_time( + nc_virt_ds = virtualize_and_concat_archival_files_on_time( location=NGWPCLocations.SNODAS_REF.path, file_date_pattern="zz_ssmv11034tS__T0001TTNATS*05HP001.nc", manual_file_pattern=f"zz_ssmv11034tS__T0001TTNATS{y}*.nc", - file_type=awf_utils.FileType.NETCDF, + file_type=FileType.NETCDF, loadable_vars=["crs"], testing_file_quantity=test_quant, ) @@ -102,13 +106,13 @@ def land_cover_virtualize_push_test( NOTE: Take care to not overwrite an existing IC repo """ - nlcd_repo = IcechunkS3Repo( + nlcd_repo = IcechunkRepo( location=new_ic_repo, virtual_chunk_mapping=[{"bucket": "ngwpc-hydrofabric", "region": "us-east-1"}] ) - nlcd_vrt_ds = awf_utils.virtualize_and_concat_archival_files_on_time( + nlcd_vrt_ds = virtualize_and_concat_archival_files_on_time( location=NGWPCLocations.NLCD_REF.path, file_date_pattern="Annual_NLCD_LndCov_*_CU_C1V0.tif", - file_type=awf_utils.FileType.GEOTIFF, + file_type=FileType.GEOTIFF, just_year=True, testing_file_quantity=test_quant, ) @@ -130,8 +134,8 @@ def topo_push_test(tiff_fp: str, attr_name: str, new_ic_repo: S3Path, clean_up: NOTE: Take care to not overwrite an existing IC repo """ - topo_repo = IcechunkS3Repo(location=new_ic_repo) - topo_ds = awf_utils.load_tiff_file(tiff_fp, attr_name) + topo_repo = IcechunkRepo(location=new_ic_repo) + topo_ds = load_tiff_file(tiff_fp, attr_name) topo_repo.write_dataset(ds=topo_ds, commit="first commit") print(topo_repo.retrieve_dataset()) if clean_up: @@ -143,9 +147,9 @@ def get_nc_by_year(years: range): files = [] for y in years: print(y) - files += awf_utils.get_archival_weather_files( + files += get_archival_weather_files( loc=NGWPCLocations.SNODAS_REF.path, - file_type=awf_utils.FileType.NETCDF, + file_type=FileType.NETCDF, manual_file_pattern=f"zz_ssmv11034tS__T0001TTNATS{y}*.nc", ) return files diff --git a/tools/ipes/get_module_parameters.py b/tools/ipes/get_module_parameters.py new file mode 100644 index 0000000..05ac679 --- /dev/null +++ b/tools/ipes/get_module_parameters.py @@ -0,0 +1,52 @@ +"""A sample script to generate CFE IPEs""" + +import argparse + +import geopandas as gpd +import pandas as pd +from dotenv import load_dotenv + +# param_file = "../src/icefabric_api/data/cfe_params.csv" +# gpkg_file = "../src/icefabric_tools/test/data/gages-08070000.gpkg" +load_dotenv() + + +def create_module_params(param_file: str, gpkg_file: str) -> None: + """Creates module initial parameter estimates + + Parameters + ---------- + param_file : str + the initial parameters file + gpkg_file : str + the hydrofabric gpkg file + """ + divides = gpd.read_file(gpkg_file, layer="divides") + divides = divides["divide_id"].to_list() + + module_params = pd.read_csv(param_file) + param_values = module_params[["name", "default_value"]] + + for divide in divides: + cfg_file = f"{divide}_bmi_cfg_cfe.txt" + f = open(cfg_file, "x") + + for _, row in param_values.iterrows(): + key = row["name"] + value = row["default_value"] + f.write(f"{key}={value}\n") + + f.close() + + params_calibratable = module_params.loc[module_params["calibratable"] == "TRUE"] + params_calibratable.to_json("out.json", orient="split") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="A script to make initial parameter estimates") + + parser.add_argument("--params", help="The initial parameters file") + parser.add_argument("--gpkg", help="The hydrofabric gpkg file") + + args = parser.parse_args() + create_module_params(param_file=args.params, gpkg_file=args.gpkg) diff --git a/tools/pyiceberg/gpkg_to_parquet.py b/tools/pyiceberg/gpkg_to_parquet.py new file mode 100644 index 0000000..05e04f0 --- /dev/null +++ b/tools/pyiceberg/gpkg_to_parquet.py @@ -0,0 +1,36 @@ +"""A simple script to convert the v2.2 hydrofabric to parquet""" + +import argparse + +import fiona +import geopandas as gpd +from dotenv import load_dotenv + +load_dotenv() + + +def gpkg_to_parquet(input_file: str) -> None: + """A function to convert geopackages to parquet files + + Parameters + ---------- + input_file : str + the gpkg to convert + """ + available_layers = fiona.listlayers(input_file) + print(f"Layers in GeoPackage: {available_layers}") + + for layer in available_layers: + gdf = gpd.read_file(input_file, layer=layer) + output_file = f"../../data/{layer}.parquet" + gdf.to_parquet(output_file) + print(f"Converted layer '{layer}' to {output_file}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="A script to build a pyiceberg catalog in the S3 endpoint") + + parser.add_argument("--gpkg", help="The local gpkg to convert into a parquet file") + + args = parser.parse_args() + gpkg_to_parquet(input_file=args.gpkg) diff --git a/src/icefabric_manage/builds/README.md b/tools/pyiceberg/remote_builds/README.md similarity index 100% rename from src/icefabric_manage/builds/README.md rename to tools/pyiceberg/remote_builds/README.md diff --git a/src/icefabric_manage/builds/build_fema_ble.py b/tools/pyiceberg/remote_builds/build_fema_ble.py similarity index 94% rename from src/icefabric_manage/builds/build_fema_ble.py rename to tools/pyiceberg/remote_builds/build_fema_ble.py index d22ddb7..553e308 100644 --- a/src/icefabric_manage/builds/build_fema_ble.py +++ b/tools/pyiceberg/remote_builds/build_fema_ble.py @@ -5,7 +5,7 @@ from pyiceberg.catalog import load_catalog from pyiceberg.exceptions import NamespaceAlreadyExistsError -from icefabric_manage import build +from icefabric.builds import build_iceberg_table load_dotenv() @@ -27,7 +27,7 @@ def build_table(file_dir: str): for folder in Path(file_dir).glob("ble_*"): huc_number = folder.name.split("_", 1)[1] print(f"building HUC XS: {huc_number}") - build( + build_iceberg_table( catalog=catalog, parquet_file=f"{file_dir}/ble_{huc_number}/huc_{huc_number}.parquet", namespace=namespace, diff --git a/src/icefabric_manage/builds/build_fema_mip.py b/tools/pyiceberg/remote_builds/build_fema_mip.py similarity index 94% rename from src/icefabric_manage/builds/build_fema_mip.py rename to tools/pyiceberg/remote_builds/build_fema_mip.py index ee21ccb..c6e16d5 100644 --- a/src/icefabric_manage/builds/build_fema_mip.py +++ b/tools/pyiceberg/remote_builds/build_fema_mip.py @@ -5,7 +5,7 @@ from pyiceberg.catalog import load_catalog from pyiceberg.exceptions import NamespaceAlreadyExistsError -from icefabric_manage import build +from icefabric.builds import build_iceberg_table load_dotenv() @@ -27,7 +27,7 @@ def build_table(file_dir: str): for folder in Path(file_dir).glob("mip_*"): huc_number = folder.name.split("_", 1)[1] print(f"building HUC XS: {huc_number}") - build( + build_iceberg_table( catalog=catalog, parquet_file=f"{file_dir}/mip_{huc_number}/huc_{huc_number}.parquet", namespace=namespace, diff --git a/src/icefabric_manage/builds/build_hydrofabric.py b/tools/pyiceberg/remote_builds/build_hydrofabric.py similarity index 95% rename from src/icefabric_manage/builds/build_hydrofabric.py rename to tools/pyiceberg/remote_builds/build_hydrofabric.py index c4d444a..81755d3 100644 --- a/src/icefabric_manage/builds/build_hydrofabric.py +++ b/tools/pyiceberg/remote_builds/build_hydrofabric.py @@ -4,7 +4,7 @@ from pyiceberg.catalog import load_catalog from pyiceberg.exceptions import NamespaceAlreadyExistsError -from icefabric_manage import build +from icefabric.builds import build_iceberg_table load_dotenv() @@ -37,7 +37,7 @@ def build_table(file_dir: str): ] for layer in layers: print(f"building layer: {layer}") - build( + build_iceberg_table( catalog=catalog, parquet_file=f"{file_dir}/{layer}.parquet", namespace=namespace, diff --git a/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py b/tools/pyiceberg/remote_builds/build_iceberg_bathymetry_ml_auxiliary.py similarity index 96% rename from src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py rename to tools/pyiceberg/remote_builds/build_iceberg_bathymetry_ml_auxiliary.py index 453c3a6..fa1033c 100644 --- a/src/icefabric_manage/builds/build_iceberg_bathymetry_ml_auxiliary.py +++ b/tools/pyiceberg/remote_builds/build_iceberg_bathymetry_ml_auxiliary.py @@ -5,7 +5,7 @@ from pyiceberg.catalog import load_catalog from pyiceberg.exceptions import NamespaceAlreadyExistsError -from icefabric_manage import build +from icefabric.builds import build_iceberg_table load_dotenv() @@ -54,7 +54,7 @@ def build_table(file_dir: Path): # Iceberg does not have a dictionary type. will be inferred as int32 on read. # Arrow will make columns with a single, non-unique, value into a dictionary for ease of writing/loading # Thus, when writing to pyiceberg it needs to remove that. - build( + build_iceberg_table( catalog=catalog, parquet_file=f"{file_dir}/{layer}/ml_auxiliary_data.parquet", namespace=namespace, diff --git a/src/icefabric_manage/builds/build_parameters.py b/tools/pyiceberg/remote_builds/build_parameters.py similarity index 69% rename from src/icefabric_manage/builds/build_parameters.py rename to tools/pyiceberg/remote_builds/build_parameters.py index 7064ebb..becc453 100644 --- a/src/icefabric_manage/builds/build_parameters.py +++ b/tools/pyiceberg/remote_builds/build_parameters.py @@ -4,7 +4,7 @@ from pyiceberg.catalog import load_catalog from pyiceberg.exceptions import NamespaceAlreadyExistsError -from icefabric_manage import build +from icefabric.builds import build_iceberg_table load_dotenv() @@ -17,21 +17,26 @@ def build_table(file_dir): file_dir : str The directory to hydrofabric parquet files """ - parquet_tables = {'CFE-X_params_Alaska_2.2.parquet':'CFE-X_Alaska', 'CFE-X_params_GL_2.2.parquet':'CFE-X_GL', - 'CFE-X_params_Puerto_Rico_2.2.parquet':'CFE-X_Puerto_Rico', 'snow17_params_2.2.parquet':'Snow-17_CONUS', - 'CFE-X_params_CONUS_2.2.parquet':'CFE-X_CONUS', 'CFE-X_params_Hawaii_2.2.parquet':'CFE-X_Hawaii', - 'sac_sma_params_2.2.parquet':'Sac-SMA_CONUS', 'ueb_params.parquet':'UEB_CONUS'} - + parquet_tables = { + "CFE-X_params_Alaska_2.2.parquet": "CFE-X_Alaska", + "CFE-X_params_GL_2.2.parquet": "CFE-X_GL", + "CFE-X_params_Puerto_Rico_2.2.parquet": "CFE-X_Puerto_Rico", + "snow17_params_2.2.parquet": "Snow-17_CONUS", + "CFE-X_params_CONUS_2.2.parquet": "CFE-X_CONUS", + "CFE-X_params_Hawaii_2.2.parquet": "CFE-X_Hawaii", + "sac_sma_params_2.2.parquet": "Sac-SMA_CONUS", + "ueb_params.parquet": "UEB_CONUS", + } + catalog = load_catalog("glue") # Using an AWS Glue Endpoint namespace = "divide_parameters" try: catalog.create_namespace(namespace) except NamespaceAlreadyExistsError: print(f"Namespace {namespace} already exists") - - for file in parquet_tables.keys(): - build( + for file in parquet_tables.keys(): + build_iceberg_table( catalog=catalog, parquet_file=f"{file_dir}/{file}", namespace=namespace, diff --git a/src/icefabric_manage/builds/build_usgs_streamflow_observations.py b/tools/pyiceberg/remote_builds/build_usgs_streamflow_observations.py similarity index 94% rename from src/icefabric_manage/builds/build_usgs_streamflow_observations.py rename to tools/pyiceberg/remote_builds/build_usgs_streamflow_observations.py index 7d7661a..21d9e50 100644 --- a/src/icefabric_manage/builds/build_usgs_streamflow_observations.py +++ b/tools/pyiceberg/remote_builds/build_usgs_streamflow_observations.py @@ -4,7 +4,7 @@ from pyiceberg.catalog import load_catalog from pyiceberg.exceptions import NamespaceAlreadyExistsError -from icefabric_manage import build +from icefabric.builds import build_iceberg_table load_dotenv() @@ -23,7 +23,7 @@ def build_table(file_dir): catalog.create_namespace(namespace) except NamespaceAlreadyExistsError: print(f"Namespace {namespace} already exists") - build( + build_iceberg_table( catalog=catalog, parquet_file=f"{file_dir}/usgs_hourly.parquet", namespace=namespace, From 40492a67834d39f8b6b364889762c4e9d01b3225 Mon Sep 17 00:00:00 2001 From: taddyb Date: Mon, 7 Jul 2025 19:03:29 -0400 Subject: [PATCH 73/97] patch: moving load_creds() into main function of app --- app/main.py | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/app/main.py b/app/main.py index 270d4a7..eb9c8c7 100644 --- a/app/main.py +++ b/app/main.py @@ -1,14 +1,13 @@ -import os from pathlib import Path import uvicorn -from dotenv import load_dotenv from fastapi import FastAPI, status from pydantic import BaseModel from app.routers.hydrofabric.router import api_router as hydrofabric_api_router from app.routers.module_params.router import api_router as module_params_router from app.routers.streamflow_observations.router import api_router as streamflow_api_router +from icefabric.helpers import load_creds app = FastAPI( title="Icefabric API", @@ -18,16 +17,6 @@ redoc_url="/redoc", ) -# Setting .env/.pyiceberg creds based on project root -load_dotenv(dotenv_path=Path.cwd() / ".env") -pyiceberg_file = Path.cwd() / ".pyiceberg.yaml" -if pyiceberg_file.exists(): - os.environ["PYICEBERG_HOME"] = str(pyiceberg_file) -else: - raise FileNotFoundError( - "Cannot find .pyiceberg.yaml. Please download this from NGWPC confluence or create " - ) - class HealthCheck(BaseModel): """Response model to validate and return when performing a health check.""" @@ -55,4 +44,5 @@ def get_health() -> HealthCheck: if __name__ == "__main__": + load_creds(dir=Path.cwd()) uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True, log_level="info") From 8540fde2b24e40f2078b1e915a05e86f9f49e08e Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Tue, 8 Jul 2025 16:16:28 -0400 Subject: [PATCH 74/97] Part I: NGWPC-6893 (#46) * initial commit * fix: dockerfile api to reflect restructure * feat: added export scripts to make a local iceberg DB, updated HF builds, patched app * tests: added local flag and catalog reference in testing * patch: Update tests/conftest.py Co-authored-by: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> * patch: fixed an issue with the local sql catalog not being built --------- Co-authored-by: Quercus Hamlin Co-authored-by: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> --- .gitignore | 6 +- .pyiceberg.yaml | 11 +++ README.md | 5 ++ app/main.py | 36 ++++++++++ app/routers/hydrofabric/router.py | 43 +++++++++-- app/routers/nwm_modules/__init__.py | 0 app/routers/nwm_modules/router.py | 71 +++++++++++++++++++ docker/Dockerfile.api | 2 - src/icefabric/hydrofabric/subset.py | 13 ++-- src/icefabric/schemas/__init__.py | 11 ++- src/icefabric/schemas/hydrofabric.py | 24 +++++++ src/icefabric/schemas/sft.py | 0 tests/conftest.py | 10 ++- tests/integration/test_topobathy_icechunk.py | 1 + tests/test_subset.py | 3 +- tools/pyiceberg/export_catalog.py | 57 +++++++++++++++ tools/pyiceberg/gpkg_to_parquet.py | 13 ++-- .../remote_builds/build_hydrofabric.py | 64 +++++++++++++---- 18 files changed, 332 insertions(+), 38 deletions(-) create mode 100644 .pyiceberg.yaml create mode 100644 app/routers/nwm_modules/__init__.py create mode 100644 app/routers/nwm_modules/router.py create mode 100644 src/icefabric/schemas/sft.py create mode 100644 tools/pyiceberg/export_catalog.py diff --git a/.gitignore b/.gitignore index c126ca8..5760d76 100644 --- a/.gitignore +++ b/.gitignore @@ -196,9 +196,6 @@ crash.log # CLI configuration .terraform/ -# Pyiceberg config file -.pyiceberg.yaml - # Terraform plan output (optional unless you want to inspect later) *.tfplan @@ -208,3 +205,6 @@ examples/icechunk_data_viewer/martin/tiles/quantiles/* examples/icechunk_data_viewer/martin/tiles/legends/* examples/icechunk_data_viewer/martin/tiles/legend.png tests/data/topo_tifs + +# hydrofabric parquets +data/hydrofabric diff --git a/.pyiceberg.yaml b/.pyiceberg.yaml new file mode 100644 index 0000000..4c42331 --- /dev/null +++ b/.pyiceberg.yaml @@ -0,0 +1,11 @@ +catalog: + glue: + type: glue + s3.endpoint: s3.us-east-1.amazonaws.com + warehouse: s3://52fcde3e7-5582-477d-7686ou4ij1ptxj8equ83a5xc51fsuse1b--table-s3 + region: us-east-1 + glue_region: us-east-1 + sql: + type: sql + uri: sqlite:////tmp/warehouse/pyiceberg_catalog.db + warehouse: file:///tmp/warehouse diff --git a/README.md b/README.md index 8121f0b..6b61ddf 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,11 @@ python -m app.main ``` This should spin up the API services. +If you are running the API locally, you can run +```sh +python -m app.main --catalog sql +``` + ### Building the API through Docker To run the API locally with Docker, ensure your `.env` file in your project root has the right credentials, then run ```sh diff --git a/app/main.py b/app/main.py index eb9c8c7..cf9effb 100644 --- a/app/main.py +++ b/app/main.py @@ -1,20 +1,41 @@ +import argparse +import os +from contextlib import asynccontextmanager from pathlib import Path import uvicorn from fastapi import FastAPI, status from pydantic import BaseModel +from pyiceberg.catalog import load_catalog from app.routers.hydrofabric.router import api_router as hydrofabric_api_router from app.routers.module_params.router import api_router as module_params_router +from app.routers.nwm_modules.router import sft_router from app.routers.streamflow_observations.router import api_router as streamflow_api_router from icefabric.helpers import load_creds + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Loads the iceberg catalog location from an environment variable + + Parameters + ---------- + app: FastAPI + The FastAPI app instance + """ + catalog_path = os.getenv("CATALOG_PATH") + app.state.catalog = load_catalog(catalog_path) + yield + + app = FastAPI( title="Icefabric API", description="API for accessing iceberg or icechunk data from EDFS services", version="1.0.0", docs_url="/docs", redoc_url="/redoc", + lifespan=lifespan, ) @@ -27,6 +48,7 @@ class HealthCheck(BaseModel): # Include routers app.include_router(hydrofabric_api_router, prefix="/v1") app.include_router(streamflow_api_router, prefix="/v1") +app.include_router(sft_router, prefix="/v2") app.include_router(module_params_router, prefix="/v1") @@ -44,5 +66,19 @@ def get_health() -> HealthCheck: if __name__ == "__main__": + parser = argparse.ArgumentParser(description="The FastAPI App instance for querying versioned EDFS data") + + # Glue = S3 Tables; Sql is a local iceberg catalog + parser.add_argument( + "--catalog", + choices=["glue", "sql"], + help="The catalog information for querying versioned EDFS data", + default="glue", + ) # Setting the default to read from S3 + + args = parser.parse_args() + + os.environ["CATALOG_PATH"] = args.catalog + load_creds(dir=Path.cwd()) uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True, log_level="info") diff --git a/app/routers/hydrofabric/router.py b/app/routers/hydrofabric/router.py index 6c23345..06076b8 100644 --- a/app/routers/hydrofabric/router.py +++ b/app/routers/hydrofabric/router.py @@ -2,17 +2,41 @@ import tempfile import uuid -from fastapi import APIRouter, HTTPException, Path +from fastapi import APIRouter, Depends, HTTPException, Path, Query, Request from fastapi.responses import FileResponse -from pyiceberg.catalog import load_catalog +from pyiceberg.catalog import Catalog from starlette.background import BackgroundTask from icefabric.hydrofabric import subset -from icefabric.schemas import IdType +from icefabric.schemas import HydrofabricDomains, IdType api_router = APIRouter(prefix="/hydrofabric") +def get_catalog(request: Request) -> Catalog: + """Gets the pyiceberg catalog reference from the app state + + Parameters + ---------- + request : Request + The FastAPI request object containing the application state + + Returns + ------- + pyiceberg.catalog.Catalog + The loaded pyiceberg catalog instance used for querying versioned EDFS data + + Raises + ------ + HTTPException + If the catalog is not loaded or not available in the application state. + Returns HTTP 500 status code with "Catalog not loaded" detail message. + """ + if not hasattr(request.app.state, "catalog") or request.app.state.catalog is None: + raise HTTPException(status_code=500, detail="Catalog not loaded") + return request.app.state.catalog + + @api_router.get("/{identifier}/gpkg") async def get_hydrofabric_subset_gpkg( identifier: str = Path( @@ -21,6 +45,10 @@ async def get_hydrofabric_subset_gpkg( examples=["01010000"], openapi_examples={"station_example": {"summary": "USGS Gauge", "value": "01010000"}}, ), + domain: HydrofabricDomains = Query( + HydrofabricDomains.CONUS, description="The iceberg namespace used to query the hydrofabric" + ), + catalog=Depends(get_catalog), ): """ Get hydrofabric subset as a geopackage file (.gpkg) @@ -29,13 +57,18 @@ async def get_hydrofabric_subset_gpkg( from a given identifier and returns all related geospatial layers as a downloadable geopackage file. """ - catalog = load_catalog("glue") unique_id = str(uuid.uuid4())[:8] temp_dir = pathlib.Path(tempfile.gettempdir()) tmp_path = temp_dir / f"hydrofabric_subset_{identifier}_{unique_id}.gpkg" try: # Create the subset - subset(catalog=catalog, identifier=f"gages-{identifier}", id_type=IdType.HL_URI, output_file=tmp_path) + subset( + catalog=catalog, + identifier=f"gages-{identifier}", + id_type=IdType.HL_URI, + output_file=tmp_path, + domain=domain, + ) if not tmp_path.exists(): raise HTTPException(status_code=500, detail=f"Failed to create geopackage file at {tmp_path}") diff --git a/app/routers/nwm_modules/__init__.py b/app/routers/nwm_modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/routers/nwm_modules/router.py b/app/routers/nwm_modules/router.py new file mode 100644 index 0000000..d706628 --- /dev/null +++ b/app/routers/nwm_modules/router.py @@ -0,0 +1,71 @@ +import pathlib +import tempfile +import uuid + +from fastapi import APIRouter, HTTPException, Path +from fastapi.responses import FileResponse +from pyiceberg.catalog import load_catalog +from starlette.background import BackgroundTask + +from icefabric.hydrofabric import subset +from icefabric.schemas import IdType + +sft_router = APIRouter(prefix="/modules/sft") + + +@sft_router.get("/{identifier}/") +async def get_hydrofabric_subset_gpkg( + identifier: str = Path( + ..., + description="Identifier to start tracing from (e.g., catchment ID, POI ID)", + examples=["01010000"], + openapi_examples={"station_example": {"summary": "USGS Gauge", "value": "01010000"}}, + ), +): + """ + Get hydrofabric subset as a geopackage file (.gpkg) + + This endpoint creates a subset of the hydrofabric data by tracing upstream + from a given identifier and returns all related geospatial layers as a + downloadable geopackage file. + """ + catalog = load_catalog("glue") + unique_id = str(uuid.uuid4())[:8] + temp_dir = pathlib.Path(tempfile.gettempdir()) + tmp_path = temp_dir / f"hydrofabric_subset_{identifier}_{unique_id}.gpkg" + try: + # Create the subset + subset(catalog=catalog, identifier=f"gages-{identifier}", id_type=IdType.HL_URI, output_file=tmp_path) + + if not tmp_path.exists(): + raise HTTPException(status_code=500, detail=f"Failed to create geopackage file at {tmp_path}") + if tmp_path.stat().st_size == 0: + tmp_path.unlink(missing_ok=True) # Clean up empty file + raise HTTPException(status_code=404, detail=f"No data found for identifier '{identifier}'") + + # Verify it's actually a file, not a directory + if not tmp_path.is_file(): + raise HTTPException(status_code=500, detail=f"Expected file but got directory at {tmp_path}") + + print(f"Returning file: {tmp_path} (size: {tmp_path.stat().st_size} bytes)") + + download_filename = f"hydrofabric_subset_{identifier}.gpkg" + + return FileResponse( + path=str(tmp_path), + filename=download_filename, + media_type="application/geopackage+sqlite3", + headers={ + "Content-Description": "Hydrofabric Subset Geopackage", + "X-Identifier": identifier, + }, + background=BackgroundTask(lambda: tmp_path.unlink(missing_ok=True)), + ) + + except HTTPException: + raise + except Exception: + # Clean up temp file if it exists + if "tmp_path" in locals() and tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + raise diff --git a/docker/Dockerfile.api b/docker/Dockerfile.api index cd44310..9f33a5f 100644 --- a/docker/Dockerfile.api +++ b/docker/Dockerfile.api @@ -31,8 +31,6 @@ ENV PATH="/app/.venv/bin:$PATH" # Install the package in development mode RUN uv sync -RUN uv pip install -e src/icefabric_api # Run the API -WORKDIR /app/src/icefabric_api CMD ["python", "-m", "app.main"] diff --git a/src/icefabric/hydrofabric/subset.py b/src/icefabric/hydrofabric/subset.py index 25a1158..3b37db1 100644 --- a/src/icefabric/hydrofabric/subset.py +++ b/src/icefabric/hydrofabric/subset.py @@ -11,7 +11,7 @@ from icefabric.helpers.geopackage import table_to_geopandas, to_geopandas from icefabric.hydrofabric.origin import find_origin -from icefabric.schemas.hydrofabric import UPSTREAM_VPUS, IdType +from icefabric.schemas.hydrofabric import UPSTREAM_VPUS, HydrofabricDomains, IdType def get_sorted_network(network_df: pd.DataFrame, outlets: list[str] | str) -> pd.DataFrame: @@ -221,6 +221,7 @@ def subset( id_type: IdType, layers: list[str] | None = None, output_file: Path | None = None, + domain: HydrofabricDomains = HydrofabricDomains.CONUS, ) -> dict[str, pd.DataFrame | gpd.GeoDataFrame] | None: """Returns a geopackage subset from the hydrofabric. @@ -244,6 +245,8 @@ def subset( output_file : Path, optional The output file path where the geopackage will be saved. If None, returns the data as a dictionary instead of saving to file + domain : HydrofabricDomains, optional + The domain to read the HF from Returns ------- @@ -276,10 +279,10 @@ def subset( layers.extend(["divides", "flowpaths", "network", "nexus"]) layers = list(set(layers)) try: - network = catalog.load_table("hydrofabric.network") - divides = catalog.load_table("hydrofabric.divides") - flowpaths = catalog.load_table("hydrofabric.flowpaths") - nexus = catalog.load_table("hydrofabric.nexus") + network = catalog.load_table(f"{domain.value}.network") + divides = catalog.load_table(f"{domain.value}.divides") + flowpaths = catalog.load_table(f"{domain.value}.flowpaths") + nexus = catalog.load_table(f"{domain.value}.nexus") except ClientError as e: msg = "AWS Test account credentials expired. Can't access remote S3 endpoint" print(msg) diff --git a/src/icefabric/schemas/__init__.py b/src/icefabric/schemas/__init__.py index 86d1c95..4fb6ac7 100644 --- a/src/icefabric/schemas/__init__.py +++ b/src/icefabric/schemas/__init__.py @@ -1,6 +1,13 @@ """Contains helper functions to support NWM modules""" -from .hydrofabric import UPSTREAM_VPUS, IdType +from .hydrofabric import UPSTREAM_VPUS, HydrofabricDomains, IdType from .topobathy import FileType, NGWPCLocations, NGWPCTestLocations -__all__ = ["UPSTREAM_VPUS", "IdType", "FileType", "NGWPCLocations", "NGWPCTestLocations"] +__all__ = [ + "UPSTREAM_VPUS", + "IdType", + "HydrofabricDomains", + "FileType", + "NGWPCLocations", + "NGWPCTestLocations", +] diff --git a/src/icefabric/schemas/hydrofabric.py b/src/icefabric/schemas/hydrofabric.py index dbb6d8c..d396b70 100644 --- a/src/icefabric/schemas/hydrofabric.py +++ b/src/icefabric/schemas/hydrofabric.py @@ -24,5 +24,29 @@ class IdType(str, Enum): POI_ID = "poi_id" +class HydrofabricDomains(str, Enum): + """The domains used when querying the hydrofabric + + Attributes + ---------- + AK : str + Alaska + CONUS : str + Conterminous United States + GL : str + The US Great Lakes + HI : str + Hawai'i + PRVI : str + Puerto Rico, US Virgin Islands + """ + + AK = "ak_hf" + CONUS = "conus_hf" + GL = "gl_hf" + HI = "hi_hf" + PRVI = "prvi_hf" + + # For catchments that may extend in many VPUs UPSTREAM_VPUS: dict[str, list[str]] = {"08": ["11", "10U", "10L", "08", "07", "05"]} diff --git a/src/icefabric/schemas/sft.py b/src/icefabric/schemas/sft.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py index a1bbdee..f359b46 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -109,6 +109,7 @@ def testing_dir() -> Path: @pytest.fixture(scope="session") def client(): """Create a test client for the FastAPI app.""" + app.state.catalog = load_catalog("glue") # defaulting to GLUE return TestClient(app) @@ -133,13 +134,19 @@ def hydrofabric_catalog() -> Catalog: def pytest_addoption(parser): - """Adds the --run-slow tag""" + """Adds addoption tags""" parser.addoption( "--run-slow", action="store_true", default=False, help="Run slow tests", ) + parser.addoption( + "--run-local", + action="store_true", + default=False, + help="Run slow tests", + ) def pytest_collection_modifyitems(config, items): @@ -154,6 +161,7 @@ def pytest_collection_modifyitems(config, items): def pytest_configure(config): """Configure pytest markers.""" config.addinivalue_line("markers", "slow: marks tests as slow tests") + config.addinivalue_line("markers", "local: marks tests as local tests") config.addinivalue_line("markers", "performance: marks tests as performance tests") config.addinivalue_line("markers", "integration: marks tests as integration tests") config.addinivalue_line("markers", "unit: marks tests as unit tests") diff --git a/tests/integration/test_topobathy_icechunk.py b/tests/integration/test_topobathy_icechunk.py index dc16f8e..94ade36 100644 --- a/tests/integration/test_topobathy_icechunk.py +++ b/tests/integration/test_topobathy_icechunk.py @@ -50,6 +50,7 @@ def test_topobathy(ic_raster: str) -> None: os.remove(temp_path) +@pytest.mark.local def test_local_topobathy(local_ic_raster: Path) -> None: """Tests local topobathy against local icechunk stores diff --git a/tests/test_subset.py b/tests/test_subset.py index 9e6bf0e..1d85851 100644 --- a/tests/test_subset.py +++ b/tests/test_subset.py @@ -5,7 +5,7 @@ from pyiceberg.catalog import Catalog from icefabric.hydrofabric import subset -from icefabric.schemas import IdType +from icefabric.schemas import HydrofabricDomains, IdType @pytest.mark.slow @@ -13,6 +13,7 @@ def test_subset_hl_uri(hydrofabric_catalog: Catalog, gauge_hf_uri: str, testing_ """Tests all subset gauges in the sample data using hl_uri""" subset( catalog=hydrofabric_catalog, + domain=HydrofabricDomains.CONUS, identifier=gauge_hf_uri, id_type=IdType.HL_URI, output_file=tmp_path / "subset.gpkg", diff --git a/tools/pyiceberg/export_catalog.py b/tools/pyiceberg/export_catalog.py new file mode 100644 index 0000000..aabc74c --- /dev/null +++ b/tools/pyiceberg/export_catalog.py @@ -0,0 +1,57 @@ +"""A file to export the glue catalog to sqllite""" + +import argparse +import os +from pathlib import Path + +import yaml +from pyiceberg.catalog import load_catalog +from tqdm import tqdm + +from icefabric.helpers import load_creds + +load_creds(dir=Path.cwd()) + + +def export(namespace: str, snapshot: int | None = None): + """Exports the catalog to a local SQL file based on the .pyiceberg.yaml in the project root + + Parameters + ---------- + namespace : str + The namespace to be exported + snapshot : str | None, optional + The snapshot ID to export from, by default None and using the latest + """ + # Creates the local dir for the warehouse if it does not exist + with open(os.environ["PYICEBERG_HOME"]) as f: + config = yaml.safe_load(f) + + warehouse = Path(config["catalog"]["sql"]["warehouse"].replace("file://", "")) + warehouse.mkdir(parents=True, exist_ok=True) + + glue_catalog = load_catalog("glue") + local_catalog = load_catalog("sql") + local_catalog.create_namespace_if_not_exists(namespace) + namespace_tables = glue_catalog.list_tables(namespace=namespace) + for _, table in tqdm(namespace_tables, desc=f"Exporting {namespace} tables", total=len(namespace_tables)): + _table = glue_catalog.load_table(f"{namespace}.{table}").scan(snapshot_id=snapshot) + _arrow = _table.to_arrow() + iceberg_table = local_catalog.create_table_if_not_exists( + f"{namespace}.{table}", + schema=_arrow.schema, + ) + iceberg_table.append(_arrow) + print(f"Exported {namespace} into local pyiceberg DB") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="A script to export the S3 tables catalog based on a namespace and snapshot id. If no snapshot, assuming the latest" + ) + + parser.add_argument("--namespace", help="The namespace repo that is being exported") + parser.add_argument("--snapshot", help="The snapshot ID for the namespace") + + args = parser.parse_args() + export(namespace=args.namespace, snapshot=args.snapshot) diff --git a/tools/pyiceberg/gpkg_to_parquet.py b/tools/pyiceberg/gpkg_to_parquet.py index 05e04f0..578f0a2 100644 --- a/tools/pyiceberg/gpkg_to_parquet.py +++ b/tools/pyiceberg/gpkg_to_parquet.py @@ -1,15 +1,17 @@ """A simple script to convert the v2.2 hydrofabric to parquet""" import argparse +from pathlib import Path import fiona import geopandas as gpd -from dotenv import load_dotenv -load_dotenv() +from icefabric.helpers import load_creds +load_creds(dir=Path.cwd()) -def gpkg_to_parquet(input_file: str) -> None: + +def gpkg_to_parquet(input_file: str, output_folder: str = "../../data/") -> None: """A function to convert geopackages to parquet files Parameters @@ -22,7 +24,7 @@ def gpkg_to_parquet(input_file: str) -> None: for layer in available_layers: gdf = gpd.read_file(input_file, layer=layer) - output_file = f"../../data/{layer}.parquet" + output_file = f"{output_folder}/{layer}.parquet" gdf.to_parquet(output_file) print(f"Converted layer '{layer}' to {output_file}") @@ -31,6 +33,7 @@ def gpkg_to_parquet(input_file: str) -> None: parser = argparse.ArgumentParser(description="A script to build a pyiceberg catalog in the S3 endpoint") parser.add_argument("--gpkg", help="The local gpkg to convert into a parquet file") + parser.add_argument("--output-folder", help="The output folder for to save the parquet file") args = parser.parse_args() - gpkg_to_parquet(input_file=args.gpkg) + gpkg_to_parquet(input_file=args.gpkg, output_folder=args.output_folder) diff --git a/tools/pyiceberg/remote_builds/build_hydrofabric.py b/tools/pyiceberg/remote_builds/build_hydrofabric.py index 81755d3..9e271eb 100644 --- a/tools/pyiceberg/remote_builds/build_hydrofabric.py +++ b/tools/pyiceberg/remote_builds/build_hydrofabric.py @@ -1,15 +1,42 @@ import argparse +from pathlib import Path -from dotenv import load_dotenv +import pandas as pd +import pyarrow as pa from pyiceberg.catalog import load_catalog from pyiceberg.exceptions import NamespaceAlreadyExistsError -from icefabric.builds import build_iceberg_table +from icefabric.helpers import load_creds -load_dotenv() +load_creds(dir=Path.cwd()) -def build_table(file_dir: str): +def clean_parquet_schema(parquet_file: str) -> pa.Table: + """Clean parquet file to handle null types (ex: WaterbodyID and waterbody_nex_id) + + Parameters + ---------- + parquet_file : str + Path to the parquet file + + Returns + ------- + pa.Table + Cleaned Arrow table + """ + # Read with pandas first to handle null types easily + df = pd.read_parquet(parquet_file) + null_columns = [] + for col in df.columns: + if df[col].dtype == "object" and df[col].isna().all(): + df[col] = df[col].astype("string") + null_columns.append(col) + if null_columns: + print(f"Converted null-type columns to string: {null_columns}") + return pa.Table.from_pandas(df, preserve_index=False) + + +def build_table(file_dir: str, domain: str): """Builds the hydrofabric namespace and tables Parameters @@ -18,7 +45,7 @@ def build_table(file_dir: str): The directory to hydrofabric parquet files """ catalog = load_catalog("glue") # Using an AWS Glue Endpoint - namespace = "hydrofabric" + namespace = f"{domain}_HF" try: catalog.create_namespace(namespace) except NamespaceAlreadyExistsError: @@ -37,20 +64,29 @@ def build_table(file_dir: str): ] for layer in layers: print(f"building layer: {layer}") - build_iceberg_table( - catalog=catalog, - parquet_file=f"{file_dir}/{layer}.parquet", - namespace=namespace, - table_name=layer, - location="s3://fim-services-data-test/icefabric_metadata/", - ) - print(f"Build successful. Files written into metadata store @ {catalog.name}") + try: + cleaned_table = clean_parquet_schema(f"{file_dir}/{layer}.parquet") + if catalog.table_exists(f"{namespace}.{layer}"): + print(f"Table {layer} already exists. Skipping build") + else: + iceberg_table = catalog.create_table( + f"{namespace}.{layer}", + schema=cleaned_table.schema, + location="s3://fim-services-data-test/icefabric_metadata/", + ) + iceberg_table.append(cleaned_table) + except FileNotFoundError: + print(f"Cannot find {layer} in the given file dir {file_dir}") + pass + + print(f"Build successful. Files written into metadata store on {catalog.name} @ {namespace}") if __name__ == "__main__": parser = argparse.ArgumentParser(description="A script to build a pyiceberg catalog in the S3 endpoint") parser.add_argument("--files", help="The local file dir where the files are located") + parser.add_argument("--domain", help="The hydrofabric domain to be used for the namespace") args = parser.parse_args() - build_table(file_dir=args.files) + build_table(file_dir=args.files, domain=args.domain) From f83dba69f2bee4231b5f89772f099ccb8d142e01 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Wed, 9 Jul 2025 11:26:44 -0400 Subject: [PATCH 75/97] Part II: Ngwpc 6893 (#47) * initial commit: added SFT pydantic module * feat: added get_sft_parameters() to create initial parameter estimates for SFT * feat: added app endpoint for getting SFT ipes from the icefabric * patch: updated openapi_examples within the endpoint Co-authored-by: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> --------- Co-authored-by: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> --- app/__init__.py | 26 +++++++ app/routers/hydrofabric/router.py | 28 +------- app/routers/nwm_modules/router.py | 104 +++++++++++---------------- pyproject.toml | 1 + src/icefabric/hydrofabric/subset.py | 10 +-- src/icefabric/modules/__init__.py | 3 +- src/icefabric/modules/create_ipes.py | 82 ++++++++++++++++++++- src/icefabric/schemas/__init__.py | 3 + src/icefabric/schemas/modules.py | 68 ++++++++++++++++++ src/icefabric/schemas/sft.py | 0 10 files changed, 230 insertions(+), 95 deletions(-) create mode 100644 src/icefabric/schemas/modules.py delete mode 100644 src/icefabric/schemas/sft.py diff --git a/app/__init__.py b/app/__init__.py index e69de29..018ddfc 100644 --- a/app/__init__.py +++ b/app/__init__.py @@ -0,0 +1,26 @@ +from fastapi import HTTPException, Request +from pyiceberg.catalog import Catalog + + +def get_catalog(request: Request) -> Catalog: + """Gets the pyiceberg catalog reference from the app state + + Parameters + ---------- + request : Request + The FastAPI request object containing the application state + + Returns + ------- + pyiceberg.catalog.Catalog + The loaded pyiceberg catalog instance used for querying versioned EDFS data + + Raises + ------ + HTTPException + If the catalog is not loaded or not available in the application state. + Returns HTTP 500 status code with "Catalog not loaded" detail message. + """ + if not hasattr(request.app.state, "catalog") or request.app.state.catalog is None: + raise HTTPException(status_code=500, detail="Catalog not loaded") + return request.app.state.catalog diff --git a/app/routers/hydrofabric/router.py b/app/routers/hydrofabric/router.py index 06076b8..4ef0d18 100644 --- a/app/routers/hydrofabric/router.py +++ b/app/routers/hydrofabric/router.py @@ -2,41 +2,17 @@ import tempfile import uuid -from fastapi import APIRouter, Depends, HTTPException, Path, Query, Request +from fastapi import APIRouter, Depends, HTTPException, Path, Query from fastapi.responses import FileResponse -from pyiceberg.catalog import Catalog from starlette.background import BackgroundTask +from app import get_catalog from icefabric.hydrofabric import subset from icefabric.schemas import HydrofabricDomains, IdType api_router = APIRouter(prefix="/hydrofabric") -def get_catalog(request: Request) -> Catalog: - """Gets the pyiceberg catalog reference from the app state - - Parameters - ---------- - request : Request - The FastAPI request object containing the application state - - Returns - ------- - pyiceberg.catalog.Catalog - The loaded pyiceberg catalog instance used for querying versioned EDFS data - - Raises - ------ - HTTPException - If the catalog is not loaded or not available in the application state. - Returns HTTP 500 status code with "Catalog not loaded" detail message. - """ - if not hasattr(request.app.state, "catalog") or request.app.state.catalog is None: - raise HTTPException(status_code=500, detail="Catalog not loaded") - return request.app.state.catalog - - @api_router.get("/{identifier}/gpkg") async def get_hydrofabric_subset_gpkg( identifier: str = Path( diff --git a/app/routers/nwm_modules/router.py b/app/routers/nwm_modules/router.py index d706628..e316cb1 100644 --- a/app/routers/nwm_modules/router.py +++ b/app/routers/nwm_modules/router.py @@ -1,71 +1,51 @@ -import pathlib -import tempfile -import uuid +from fastapi import APIRouter, Depends, Query +from pyiceberg.catalog import Catalog -from fastapi import APIRouter, HTTPException, Path -from fastapi.responses import FileResponse -from pyiceberg.catalog import load_catalog -from starlette.background import BackgroundTask - -from icefabric.hydrofabric import subset -from icefabric.schemas import IdType +from app import get_catalog +from icefabric.modules import get_sft_parameters +from icefabric.schemas import SFT, HydrofabricDomains sft_router = APIRouter(prefix="/modules/sft") -@sft_router.get("/{identifier}/") -async def get_hydrofabric_subset_gpkg( - identifier: str = Path( +@sft_router.get("/") +async def get_sft_ipes( + identifier: str = Query( ..., - description="Identifier to start tracing from (e.g., catchment ID, POI ID)", + description="Gauge ID to trace upstream catchments from", examples=["01010000"], - openapi_examples={"station_example": {"summary": "USGS Gauge", "value": "01010000"}}, + openapi_examples={"sfe_example": {"summary": "SFT Example", "value": "01010000", "domain":"conus_hf", "use_schaake":True}} ), -): - """ - Get hydrofabric subset as a geopackage file (.gpkg) - - This endpoint creates a subset of the hydrofabric data by tracing upstream - from a given identifier and returns all related geospatial layers as a - downloadable geopackage file. + domain: HydrofabricDomains = Query( + HydrofabricDomains.CONUS, description="The iceberg namespace used to query the hydrofabric" + ), + use_schaake: bool = Query( + False, + description="Whether to use Schaake for the Ice Fraction Scheme. Defaults to False to use Xinanjiang", + ), + catalog: Catalog = Depends(get_catalog), +) -> list[SFT]: + """An endpoint to return configurations for SFT + + Parameters + ---------- + identifier : str, optional + The Gauge ID to trace upstream from to get all catchments, by default Query( ..., description="Gauge ID to trace upstream catchments from", examples=["01010000"], ) + domain : HydrofabricDomains, optional + The geographic domain to search for catchments from, by default Query( HydrofabricDomains.CONUS, description="The iceberg namespace used to query the hydrofabric" ) + use_schaake : bool, optional + A parameter to determine if we're using Shaake or Xinanjiang to calculate ice fraction, by default Query( False, description="Whether to use Schaake for the Ice Fraction Scheme. Defaults to False to use Xinanjiang" ) + catalog : Catalog, optional + The pyiceberg catalog, by default Depends(get_catalog) + + Returns + ------- + list[SFT] + A list of SFT pydantic objects for each catchment """ - catalog = load_catalog("glue") - unique_id = str(uuid.uuid4())[:8] - temp_dir = pathlib.Path(tempfile.gettempdir()) - tmp_path = temp_dir / f"hydrofabric_subset_{identifier}_{unique_id}.gpkg" - try: - # Create the subset - subset(catalog=catalog, identifier=f"gages-{identifier}", id_type=IdType.HL_URI, output_file=tmp_path) - - if not tmp_path.exists(): - raise HTTPException(status_code=500, detail=f"Failed to create geopackage file at {tmp_path}") - if tmp_path.stat().st_size == 0: - tmp_path.unlink(missing_ok=True) # Clean up empty file - raise HTTPException(status_code=404, detail=f"No data found for identifier '{identifier}'") - - # Verify it's actually a file, not a directory - if not tmp_path.is_file(): - raise HTTPException(status_code=500, detail=f"Expected file but got directory at {tmp_path}") - - print(f"Returning file: {tmp_path} (size: {tmp_path.stat().st_size} bytes)") - - download_filename = f"hydrofabric_subset_{identifier}.gpkg" - - return FileResponse( - path=str(tmp_path), - filename=download_filename, - media_type="application/geopackage+sqlite3", - headers={ - "Content-Description": "Hydrofabric Subset Geopackage", - "X-Identifier": identifier, - }, - background=BackgroundTask(lambda: tmp_path.unlink(missing_ok=True)), - ) - - except HTTPException: - raise - except Exception: - # Clean up temp file if it exists - if "tmp_path" in locals() and tmp_path.exists(): - tmp_path.unlink(missing_ok=True) - raise + return get_sft_parameters( + catalog=catalog, + domain=domain, + identifier=identifier, + use_schaake=use_schaake, + ) diff --git a/pyproject.toml b/pyproject.toml index fa6c4b9..e115fa1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,7 @@ dependencies = [ "netCDF4==1.7.2", "numpy==2.2.6", "pandas==2.2.3", + "polars==1.31.0", "pyarrow==20.0.0", "pyiceberg[s3fs,glue,sql-sqlite]==0.9.1", "python-dotenv==1.1.0", diff --git a/src/icefabric/hydrofabric/subset.py b/src/icefabric/hydrofabric/subset.py index 3b37db1..8ae50b0 100644 --- a/src/icefabric/hydrofabric/subset.py +++ b/src/icefabric/hydrofabric/subset.py @@ -336,33 +336,33 @@ def subset( # Getting any optional fields: if "divide-attributes" in layers: - divides_attr = catalog.load_table("hydrofabric.divide-attributes") + divides_attr = catalog.load_table(f"{domain.value}.divide-attributes") filtered_divide_attr = divides_attr.scan( row_filter=In("divide_id", valid_divide_ids_for_divides) ).to_pandas() output_layers["divide-attributes"] = filtered_divide_attr if "flowpath-attributes" in layers: - flowpath_attr = catalog.load_table("hydrofabric.flowpath-attributes") + flowpath_attr = catalog.load_table(f"{domain.value}.flowpath-attributes") valid_flowpath_ids = filtered_flowpaths["id"].dropna().values.tolist() filtered_flowpath_attr = flowpath_attr.scan(row_filter=In("id", valid_flowpath_ids)).to_pandas() output_layers["flowpath-attributes"] = filtered_flowpath_attr if "flowpath-attributes-ml" in layers: - flowpath_attr_ml = catalog.load_table("hydrofabric.flowpath-attributes-ml") + flowpath_attr_ml = catalog.load_table(f"{domain.value}.flowpath-attributes-ml") valid_flowpath_ids = filtered_flowpaths["id"].dropna().values.tolist() filtered_flowpath_attr_ml = flowpath_attr_ml.scan(row_filter=In("id", valid_flowpath_ids)).to_pandas() output_layers["flowpath-attributes-ml"] = filtered_flowpath_attr_ml if "pois" in layers: - pois = catalog.load_table("hydrofabric.pois") + pois = catalog.load_table(f"{domain.value}.pois") poi_values = filtered_flowpaths["poi_id"].dropna().values filtered_poi_list = list({int(x) for x in poi_values if pd.notna(x)}) filtered_pois = pois.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() output_layers["pois"] = filtered_pois if "hydrolocations" in layers: - hydrolocations = catalog.load_table("hydrofabric.hydrolocations") + hydrolocations = catalog.load_table(f"{domain.value}.hydrolocations") poi_values = filtered_flowpaths["poi_id"].dropna().values filtered_poi_list = list({int(x) for x in poi_values if pd.notna(x)}) filtered_hydrolocations = hydrolocations.scan(row_filter=In("poi_id", filtered_poi_list)).to_pandas() diff --git a/src/icefabric/modules/__init__.py b/src/icefabric/modules/__init__.py index 5b8f303..36787f2 100644 --- a/src/icefabric/modules/__init__.py +++ b/src/icefabric/modules/__init__.py @@ -1,11 +1,12 @@ """Contains helper functions to support NWM modules""" -from .create_ipes import divide_parameters, get_hydrofabric_attributes, module_ipe +from .create_ipes import divide_parameters, get_hydrofabric_attributes, get_sft_parameters, module_ipe from .rnr import get_rnr_segment __all__ = [ "divide_parameters", "get_hydrofabric_attributes", + "get_sft_parameters", "module_ipe", "get_rnr_segment", ] diff --git a/src/icefabric/modules/create_ipes.py b/src/icefabric/modules/create_ipes.py index de0113b..7ad234c 100644 --- a/src/icefabric/modules/create_ipes.py +++ b/src/icefabric/modules/create_ipes.py @@ -2,14 +2,29 @@ import geopandas as gpd import pandas as pd +import polars as pl import pyiceberg.exceptions as ex from botocore.exceptions import ClientError -from pyiceberg.catalog import load_catalog +from pyiceberg.catalog import Catalog, load_catalog from pyproj import Transformer +from icefabric.hydrofabric import subset +from icefabric.schemas import SFT, HydrofabricDomains, IceFractionScheme, IdType + ROOT_DIR = os.path.abspath(os.curdir) +def _get_mean_soil_temp() -> float: + """This is avg soil temp of 45 degrees F converted to Kelvin. This equation is just a reasonable estimate per new direction (EW: 07/2025) + + Returns + ------- + float + The mean soil temperature + """ + return (45 - 32) * 5 / 9 + 273.15 + + def module_ipe(module: str, attr: str, domain: str, version: str, cfg_write=None): """Creates initial parameter estimates for a module @@ -267,3 +282,68 @@ def divide_parameters(divides, module, domain): df = table.scan().to_pandas() filtered = df[df["divide_id"].isin(divides)] return filtered + + +def get_sft_parameters( + catalog: Catalog, domain: HydrofabricDomains, identifier: str, use_schaake: bool = False +) -> list[SFT]: + """Creates the initial parameter estimates for the SFT module + + Parameters + ---------- + catalog : Catalog + the pyiceberg lakehouse catalog + domain : HydrofabricDomains + the hydrofabric domain + identifier : str + the gauge identifier + use_schaake : bool, optional + A setting to determine if Shaake should be used for ice fraction, by default False + + Returns + ------- + list[SFT] + The list of all initial parameters for catchments using SFT + """ + gauge: dict[str, pd.DataFrame | gpd.GeoDataFrame] = subset( + catalog=catalog, + identifier=f"gages-{identifier}", + id_type=IdType.HL_URI, + domain=domain, + layers=["flowpaths", "nexus", "divides", "divide-attributes", "network"], + ) # type: ignore + attr = {"smcmax": "mean.smcmax", "bexp": "mode.bexp", "psisat": "geom_mean.psisat"} + + df = pl.DataFrame(gauge["divide-attributes"]) + expressions = [pl.col("divide_id")] # Keep the divide_id + for param_name, prefix in attr.items(): + # Find all columns that start with the prefix + matching_cols = [col for col in df.columns if col.startswith(prefix)] + if matching_cols: + # Calculate mean across matching columns for each row. + # NOTE: this assumes an even weighting. TODO: determine if we need to have weighted averaging + expressions.append( + pl.concat_list([pl.col(col) for col in matching_cols]).list.mean().alias(f"{param_name}_avg") + ) + else: + # Default to 0.0 if no matching columns found + expressions.append(pl.lit(0.0).alias(f"{param_name}_avg")) + result_df = df.select(expressions) + mean_temp = _get_mean_soil_temp() + pydantic_models = [] + for row_dict in result_df.iter_rows(named=True): + # Instantiate the Pydantic model for each row + model_instance = SFT( + catchment=row_dict["divide_id"], + smcmax=row_dict["smcmax_avg"], + b=row_dict["bexp_avg"], + satpsi=row_dict["psisat_avg"], + ice_fraction_scheme=IceFractionScheme.XINANJIANG + if use_schaake is False + else IceFractionScheme.SCHAAKE, + soil_temperature=[ + mean_temp for _ in range(4) + ], # Assuming 45 degrees in all layers. TODO: Fix this as this doesn't make sense + ) + pydantic_models.append(model_instance) + return pydantic_models diff --git a/src/icefabric/schemas/__init__.py b/src/icefabric/schemas/__init__.py index 4fb6ac7..e8b8664 100644 --- a/src/icefabric/schemas/__init__.py +++ b/src/icefabric/schemas/__init__.py @@ -1,12 +1,15 @@ """Contains helper functions to support NWM modules""" from .hydrofabric import UPSTREAM_VPUS, HydrofabricDomains, IdType +from .modules import SFT, IceFractionScheme from .topobathy import FileType, NGWPCLocations, NGWPCTestLocations __all__ = [ "UPSTREAM_VPUS", "IdType", "HydrofabricDomains", + "SFT", + "IceFractionScheme", "FileType", "NGWPCLocations", "NGWPCTestLocations", diff --git a/src/icefabric/schemas/modules.py b/src/icefabric/schemas/modules.py new file mode 100644 index 0000000..03948ad --- /dev/null +++ b/src/icefabric/schemas/modules.py @@ -0,0 +1,68 @@ +"""A file to host schemas for all NWM modules. Based off the table from https://confluence.nextgenwaterprediction.com/pages/viewpage.action?spaceKey=NGWPC&title=BMI+Exchange+Items+and+Module+Parameters""" + +from enum import Enum +from typing import Protocol + +from pydantic import BaseModel, ConfigDict, Field, field_validator + + +class NWMProtocol(Protocol): + """Protocol defining the interface that configuration NWM BaseModel classes should implement. This ensures consistency across different module configuration classes.""" + + def to_bmi_config(self) -> list[str]: + """Converts the contents of the base class to a BMI config for that specific module""" + ... + + +class IceFractionScheme(str, Enum): + """The ice fraction scheme to be used in SFT""" + + SCHAAKE = "Schaake" + XINANJIANG = "Xinanjiang" + + +class SFT(BaseModel): + """Pydantic model for SFT (Snow Freeze Thaw) module configuration""" + + model_config = ConfigDict(validate_by_name=True, validate_by_alias=True) + catchment: str = Field(..., description="The catchment ID") + verbosity: str = Field(default="none", description="Verbosity level") + soil_moisture_bmi: int = Field(default=1, description="Soil moisture BMI parameter") + end_time: str = Field(default="1.[d]", description="End time with units") + dt: str = Field(default="1.0[h]", description="Time step with units") + soil_params_smcmax: float = Field(..., description="Maximum soil moisture content", alias="smcmax") + soil_params_b: float = Field(..., description="Soil moisture retention curve parameter (bexp)", alias="b") + soil_params_satpsi: float = Field(..., description="Saturated soil suction (psisat)", alias="satpsi") + soil_params_quartz: float = Field(default=1.0, description="Quartz content", alias="quartz") + ice_fraction_scheme: IceFractionScheme = Field(..., description="Ice fraction scheme") + soil_z: list[float] = Field(default=[0.1, 0.3, 1.0, 2.0], description="Soil depth layers in meters") + soil_temperature: list[float] = Field(..., description="Soil temperature in Kelvin for each layer") + + @field_validator("soil_temperature") + @classmethod + def validate_soil_temperature_length(cls, v, info): + """Ensure soil_temperature has same length as soil_z""" + # Get soil_z from the data being validated + soil_z = info.data.get("soil_z", [0.1, 0.3, 1.0, 2.0]) + if len(v) != len(soil_z): + raise ValueError(f"soil_temperature must have {len(soil_z)} values to match soil_z layers") + return v + + def to_bmi_config(self) -> list[str]: + """Convert the model back to the original config file format""" + temp_values = ",".join([str(temp) for temp in self.soil_temperature]) + z_values = ",".join([str(z) for z in self.soil_z]) + + return [ + f"verbosity={self.verbosity}", + f"soil_moisture_bmi={self.soil_moisture_bmi}", + f"end_time={self.end_time}", + f"dt={self.dt}", + f"soil_params.smcmax={self.soil_params_smcmax}", + f"soil_params.b={self.soil_params_b}", + f"soil_params.satpsi={self.soil_params_satpsi}", + f"soil_params.quartz={self.soil_params_quartz}", + f"ice_fraction_scheme={self.ice_fraction_scheme}", + f"soil_z={z_values}[m]", + f"soil_temperature={temp_values}[K]", + ] diff --git a/src/icefabric/schemas/sft.py b/src/icefabric/schemas/sft.py deleted file mode 100644 index e69de29..0000000 From 9a22d40927e30c9847f6aefa00611030a1e1a3d4 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Thu, 10 Jul 2025 10:00:20 -0400 Subject: [PATCH 76/97] Part III: NGWPC-6893 (#48) * initial commit: added click cli * feat: added CLI to make config files * feat: added a notebook to demonstrate the API endpoint as well as the CLI for the SFT module * patch: remove cahnges to .pyiceberg.yaml * patch: refined SFT notebook to ensure code is working when run out of order --- .pre-commit-config.yaml | 5 + app/routers/nwm_modules/router.py | 33 ++- docs/img/icefabric_api.png | Bin 0 -> 375458 bytes examples/iceberg/querying_ras_xs.ipynb | 21 +- .../initial_parameters/soil_freeze_thaw.ipynb | 214 ++++++++++++++++++ pyproject.toml | 4 + src/icefabric/__init__.py | 4 +- src/icefabric/cli/__init__.py | 6 + src/icefabric/cli/main.py | 33 +++ src/icefabric/cli/modules.py | 86 +++++++ src/icefabric/helpers/io.py | 41 ++++ src/icefabric/modules/__init__.py | 13 ++ src/icefabric/modules/create_ipes.py | 5 +- src/icefabric/schemas/modules.py | 32 ++- 14 files changed, 461 insertions(+), 36 deletions(-) create mode 100644 docs/img/icefabric_api.png create mode 100644 examples/initial_parameters/soil_freeze_thaw.ipynb create mode 100644 src/icefabric/cli/__init__.py create mode 100644 src/icefabric/cli/main.py create mode 100644 src/icefabric/cli/modules.py create mode 100644 src/icefabric/helpers/io.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a258935..27cab3a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -23,3 +23,8 @@ repos: rev: 0.5.7 hooks: - id: uv-lock + + - repo: https://github.com/kynan/nbstripout + rev: 0.8.1 + hooks: + - id: nbstripout diff --git a/app/routers/nwm_modules/router.py b/app/routers/nwm_modules/router.py index e316cb1..f504a02 100644 --- a/app/routers/nwm_modules/router.py +++ b/app/routers/nwm_modules/router.py @@ -14,34 +14,33 @@ async def get_sft_ipes( ..., description="Gauge ID to trace upstream catchments from", examples=["01010000"], - openapi_examples={"sfe_example": {"summary": "SFT Example", "value": "01010000", "domain":"conus_hf", "use_schaake":True}} + openapi_examples={"sft_example": {"summary": "SFT Example", "value": "01010000"}}, ), domain: HydrofabricDomains = Query( - HydrofabricDomains.CONUS, description="The iceberg namespace used to query the hydrofabric" + HydrofabricDomains.CONUS, + description="The iceberg namespace used to query the hydrofabric", + openapi_examples={"sft_example": {"summary": "SFT Example", "value": "conus_hf"}}, ), use_schaake: bool = Query( False, description="Whether to use Schaake for the Ice Fraction Scheme. Defaults to False to use Xinanjiang", + openapi_examples={"sft_example": {"summary": "SFT Example", "value": False}}, ), catalog: Catalog = Depends(get_catalog), ) -> list[SFT]: - """An endpoint to return configurations for SFT + """ + An endpoint to return configurations for SFT. + + This endpoint traces upstream from a given gauge ID to get all catchments + and returns SFT (Soil Freeze-Thaw) parameter configurations for each catchment. - Parameters - ---------- - identifier : str, optional - The Gauge ID to trace upstream from to get all catchments, by default Query( ..., description="Gauge ID to trace upstream catchments from", examples=["01010000"], ) - domain : HydrofabricDomains, optional - The geographic domain to search for catchments from, by default Query( HydrofabricDomains.CONUS, description="The iceberg namespace used to query the hydrofabric" ) - use_schaake : bool, optional - A parameter to determine if we're using Shaake or Xinanjiang to calculate ice fraction, by default Query( False, description="Whether to use Schaake for the Ice Fraction Scheme. Defaults to False to use Xinanjiang" ) - catalog : Catalog, optional - The pyiceberg catalog, by default Depends(get_catalog) + **Parameters:** + - **identifier**: The Gauge ID to trace upstream from to get all catchments + - **domain**: The geographic domain to search for catchments from + - **use_schaake**: Determines if we're using Schaake or Xinanjiang to calculate ice fraction - Returns - ------- - list[SFT] - A list of SFT pydantic objects for each catchment + **Returns:** + A list of SFT pydantic objects for each catchment """ return get_sft_parameters( catalog=catalog, diff --git a/docs/img/icefabric_api.png b/docs/img/icefabric_api.png new file mode 100644 index 0000000000000000000000000000000000000000..d505686708f73fea530a16c2fee2fff1d3859775 GIT binary patch literal 375458 zcmeGEbyyVb+Xsvz7N7_sf;7?%64E6IEZrRvOLsS-C<;i2v~;u5%_1SvO0C3FQcEnb z#M1i?xA*sXp5yoXOTF`qd|#KOX>vy+ig2g%6Ls=K>b+c|=3H0DHXN|iD!eulXv2#cJoi9`j2L6F@Ykz<#AU~hWh9KGI~TKa+r#BQ 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zV{RWAM*;n_zClrBTE{LAv0MX*9qDKBcFi^Js-R;&={imSMIXmEX=~m~X;eX_e9!)6 z4URO$W?G7%*|b;ZNRIUFe@e`EQsO!z8o*F{93??SJVi!ax6#pj>|s7SMKr-53MfjC z_r7)Tvu!K$V-K^K;K^!yL)7s$<0Qd0_}Sj|kH;P+^rV7k+e77}>e|1rL7*@A8UE71 zv4@!#z*=P?#BpTZ{$dw(!}TvC^S_zNe;JwoX \u001b[39m\u001b[32m1\u001b[39m catalog = \u001b[43mload_catalog\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mglue\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m catalog.list_tables(\u001b[33m\"\u001b[39m\u001b[33mmip_xs\u001b[39m\u001b[33m\"\u001b[39m)[\u001b[32m40\u001b[39m:\u001b[32m50\u001b[39m]\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.12/site-packages/pyiceberg/catalog/__init__.py:257\u001b[39m, in \u001b[36mload_catalog\u001b[39m\u001b[34m(name, **properties)\u001b[39m\n\u001b[32m 255\u001b[39m catalog_type = CatalogType(provided_catalog_type.lower())\n\u001b[32m 256\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m provided_catalog_type:\n\u001b[32m--> \u001b[39m\u001b[32m257\u001b[39m catalog_type = \u001b[43minfer_catalog_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m catalog_type:\n\u001b[32m 260\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m AVAILABLE_CATALOGS[catalog_type](name, cast(Dict[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mstr\u001b[39m], conf))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.12/site-packages/pyiceberg/catalog/__init__.py:210\u001b[39m, in \u001b[36minfer_catalog_type\u001b[39m\u001b[34m(name, catalog_properties)\u001b[39m\n\u001b[32m 208\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 209\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mExpects the URI to be a string, got: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(uri)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m210\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 211\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mURI missing, please provide using --uri, the config or environment variable PYICEBERG_CATALOG__\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname.upper()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m__URI\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 212\u001b[39m )\n", - "\u001b[31mValueError\u001b[39m: URI missing, please provide using --uri, the config or environment variable PYICEBERG_CATALOG__GLUE__URI" + "\u001b[31mNoSuchPropertyException\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m catalog = \u001b[43mload_catalog\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msql\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtype\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43msql\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m catalog.list_tables(\u001b[33m\"\u001b[39m\u001b[33mmip_xs\u001b[39m\u001b[33m\"\u001b[39m)[\u001b[32m40\u001b[39m:\u001b[32m50\u001b[39m]\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.12/site-packages/pyiceberg/catalog/__init__.py:260\u001b[39m, in \u001b[36mload_catalog\u001b[39m\u001b[34m(name, **properties)\u001b[39m\n\u001b[32m 257\u001b[39m catalog_type = infer_catalog_type(name, conf)\n\u001b[32m 259\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m catalog_type:\n\u001b[32m--> \u001b[39m\u001b[32m260\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mAVAILABLE_CATALOGS\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcatalog_type\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mDict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 262\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCould not initialize catalog with the following properties: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mproperties\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.12/site-packages/pyiceberg/catalog/__init__.py:159\u001b[39m, in \u001b[36mload_sql\u001b[39m\u001b[34m(name, conf)\u001b[39m\n\u001b[32m 156\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 157\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyiceberg\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcatalog\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msql\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SqlCatalog\n\u001b[32m--> \u001b[39m\u001b[32m159\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSqlCatalog\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mconf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 160\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m 161\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m NotInstalledError(\n\u001b[32m 162\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mSQLAlchemy support not installed: pip install \u001b[39m\u001b[33m'\u001b[39m\u001b[33mpyiceberg[sql-postgres]\u001b[39m\u001b[33m'\u001b[39m\u001b[33m or pip install \u001b[39m\u001b[33m'\u001b[39m\u001b[33mpyiceberg[sql-sqlite]\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 163\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mexc\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/NGWPC/icefabric/.venv/lib/python3.12/site-packages/pyiceberg/catalog/sql.py:123\u001b[39m, in \u001b[36mSqlCatalog.__init__\u001b[39m\u001b[34m(self, name, **properties)\u001b[39m\n\u001b[32m 120\u001b[39m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m(name, **properties)\n\u001b[32m 122\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (uri_prop := \u001b[38;5;28mself\u001b[39m.properties.get(\u001b[33m\"\u001b[39m\u001b[33muri\u001b[39m\u001b[33m\"\u001b[39m)):\n\u001b[32m--> \u001b[39m\u001b[32m123\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m NoSuchPropertyException(\u001b[33m\"\u001b[39m\u001b[33mSQL connection URI is required\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 125\u001b[39m echo_str = \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m.properties.get(\u001b[33m\"\u001b[39m\u001b[33mecho\u001b[39m\u001b[33m\"\u001b[39m, DEFAULT_ECHO_VALUE)).lower()\n\u001b[32m 126\u001b[39m echo = strtobool(echo_str) \u001b[38;5;28;01mif\u001b[39;00m echo_str != \u001b[33m\"\u001b[39m\u001b[33mdebug\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mdebug\u001b[39m\u001b[33m\"\u001b[39m\n", + "\u001b[31mNoSuchPropertyException\u001b[39m: SQL connection URI is required" ] } ], diff --git a/examples/initial_parameters/soil_freeze_thaw.ipynb b/examples/initial_parameters/soil_freeze_thaw.ipynb new file mode 100644 index 0000000..10f7c5e --- /dev/null +++ b/examples/initial_parameters/soil_freeze_thaw.ipynb @@ -0,0 +1,214 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Soil Freeze-Thaw support\n", + "\n", + "The following notebook contains a walkthrough of the support for the Soil Freeze-Thaw (SFT) module\n", + "\n", + "The notebook is organized in the following sections:\n", + "1. Icefabric API support\n", + "2. Icefabric CLI support" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Running imports\n", + "import json\n", + "import os\n", + "import threading\n", + "import zipfile\n", + "from contextlib import contextmanager\n", + "\n", + "import httpx\n", + "\n", + "\n", + "@contextmanager\n", + "def change_dir(destination: str):\n", + " \"\"\"Changes the directory to the project root\"\"\"\n", + " original_dir = os.getcwd()\n", + " try:\n", + " os.chdir(destination)\n", + " yield\n", + " finally:\n", + " os.chdir(original_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Starting the API locally\n", + "def run_api():\n", + " \"\"\"Starts the icefabric API locally\"\"\"\n", + " # This preserves your original directory\n", + " with change_dir(\"../../\"):\n", + " print(f\"Running from: {os.getcwd()}\")\n", + " !python -m app.main --catalog sql\n", + "\n", + "\n", + "threading.Thread(target=run_api).start()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will start the API on localhost:8000. This can be visited at: http://localhost:8000/docs \n", + "\n", + "![Icefabric API](../../docs/img/icefabric_api.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To test the SFT endpoint, we can use an example API call. This will return all config entries in JSON form. Each item in the output is the BMI Config contents for a specific catchment upstream of USGS 01010000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make the request\n", + "response = httpx.get(\n", + " \"http://0.0.0.0:8000/v2/modules/sft/\",\n", + " params={\n", + " \"identifier\": \"01010000\", # the Gauge ID we're testing\n", + " \"domain\": \"conus_hf\", # The CONUS domain\n", + " \"use_schaake\": \"false\", # Specifying we're not using Schaake for the ice fraction setting\n", + " },\n", + " timeout=30.0, # GLUE API requests can be slow depending on the network speed. Adding a 30s timeout to ensure requests go through\n", + ")\n", + "\n", + "print(f\"Status code: {response.status_code}\")\n", + "print(json.dumps(response.json(), indent=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CLI\n", + "\n", + "Now that we can return to the user all of the information / IPEs for a module, we can use the icefabric CLI to generate config files to disk for the user\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "Path(\"/tmp/sft_output_test\").mkdir(exist_ok=True, parents=True)\n", + "\n", + "# Ensure the current working dir is where your `.pyiceberg.yaml` file is located\n", + "with change_dir(\"../../\"):\n", + " !uv run icefabric params --gauge 01010000 --module sft --domain conus --catalog sql --output /tmp/sft_output_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that these are created, let's view the contents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with zipfile.ZipFile(\"/tmp/sft_output_test/configs.zip\", \"r\") as f:\n", + " print(\"Files in archive:\")\n", + " for file_info in f.filelist:\n", + " print(f\" {file_info.filename} ({file_info.file_size} bytes)\")\n", + " f.extractall(\"/tmp/sft_output/configs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's view one of their contents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "files = Path(\"/tmp/sft_output_test/configs\").glob(\"*\")\n", + "first_file = list(files)[0]\n", + "content = first_file.read_text()\n", + "print(content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also view the metadata.json file that was created. This will contain additional information about the query parameters used to make these configs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metadata = Path(\"/tmp/sft_output_test/configs/metadata.json\")\n", + "content = json.loads(metadata.read_text())\n", + "\n", + "print(content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Through either the API, or CLI, any modeler can create a BMI Config file for SFT that is compatible with NextGen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "icefabric", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyproject.toml b/pyproject.toml index e115fa1..2b61ede 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,6 +34,7 @@ maintainers = [ ] dependencies = [ + "click==8.2.1", "dask==2025.5.1", "fiona==1.10.1", "fsspec==2025.5.1", @@ -65,6 +66,9 @@ dependencies = [ "zarr==3.0.9", ] +[project.scripts] +icefabric = "icefabric.cli.main:cli" + [project.optional-dependencies] docs = [ "mkdocs-material==9.6.14", diff --git a/src/icefabric/__init__.py b/src/icefabric/__init__.py index e701dc1..8116e6b 100644 --- a/src/icefabric/__init__.py +++ b/src/icefabric/__init__.py @@ -1,6 +1,6 @@ """An Apache Iceberg + Icechunk implementation of Hydrofabric data services""" -from . import builds, helpers, hydrofabric, modules, schemas, ui +from . import builds, cli, helpers, hydrofabric, modules, schemas, ui from ._version import __version__ -__all__ = ["__version__", "builds", "hydrofabric", "helpers", "modules", "schemas", "ui"] +__all__ = ["__version__", "builds", "cli", "hydrofabric", "helpers", "modules", "schemas", "ui"] diff --git a/src/icefabric/cli/__init__.py b/src/icefabric/cli/__init__.py new file mode 100644 index 0000000..f3097c6 --- /dev/null +++ b/src/icefabric/cli/__init__.py @@ -0,0 +1,6 @@ +from pyiceberg.catalog import Catalog, load_catalog + + +def get_catalog(_catalog: str = "glue") -> Catalog: + """Gets the pyiceberg catalog reference""" + return load_catalog(_catalog) diff --git a/src/icefabric/cli/main.py b/src/icefabric/cli/main.py new file mode 100644 index 0000000..73cdd60 --- /dev/null +++ b/src/icefabric/cli/main.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python3 +"""Main CLI entry point for icefabric""" + +import click + +from icefabric import __version__ +from icefabric.cli.modules import params + + +def get_version(): + """Get the version of the icefabric package.""" + return __version__ + + +@click.group() +@click.version_option(version=get_version()) +@click.pass_context +def cli(ctx): + """ + Ice fabric tools and utilities. + + A comprehensive toolkit for working with ice fabric data, + hydrofabric processing, and related geospatial operations. + """ + ctx.ensure_object(dict) + + +# Add subcommands +cli.add_command(params) + +# Main entry point for when run as a script +if __name__ == "__main__": + cli() diff --git a/src/icefabric/cli/modules.py b/src/icefabric/cli/modules.py new file mode 100644 index 0000000..690742b --- /dev/null +++ b/src/icefabric/cli/modules.py @@ -0,0 +1,86 @@ +"""Contains all click CLI code for NWM modules""" + +from pathlib import Path + +import click + +from icefabric._version import __version__ +from icefabric.cli import get_catalog +from icefabric.helpers.io import _create_config_zip +from icefabric.modules import NWMModules, config_mapper +from icefabric.schemas.hydrofabric import HydrofabricDomains +from icefabric.schemas.modules import IceFractionScheme + + +@click.command() +@click.option( + "--gauge", + type=str, + help="The Gauge ID to subset the Hydrofabric from and get upstream catchment information", +) +@click.option( + "--module", + type=click.Choice([module.value for module in NWMModules], case_sensitive=False), + help="The module to create initial parameter config files for", +) +@click.option( + "--domain", + type=click.Choice([domain.name.lower() for domain in HydrofabricDomains], case_sensitive=False), + help="The domain at which you are running your model", +) +@click.option( + "--catalog", + type=click.Choice(["glue", "sql"], case_sensitive=False), + default="glue", + help="The pyiceberg catalog type", +) +@click.option( + "--ice-fraction", + type=click.Choice(IceFractionScheme, case_sensitive=False), + help="The ice fraction scheme used. Defaults to False to use Xinanjiang", +) +@click.option( + "--output", + "-o", + type=click.Path(path_type=Path), + default=Path.cwd(), + help="Output path for the zip file. Defaults to current directory", +) +def params( + gauge: str, + module: str, + domain: HydrofabricDomains, + catalog: str, + ice_fraction: IceFractionScheme, + output: Path, +): + """Returns a zip file containing all config files requested by a specific module""" + get_param_func = config_mapper[module] + domain_enum = HydrofabricDomains[domain.upper()] + ice_fraction_enum = ( + IceFractionScheme[ice_fraction.upper()] if ice_fraction else IceFractionScheme.XINANJIANG + ) # Defaults to Xinanjiang + + configs = get_param_func( + catalog=get_catalog(catalog), + domain=domain_enum, + identifier=gauge, + use_schaake=True if ice_fraction_enum == IceFractionScheme.SCHAAKE else False, + ) + + output.parent.mkdir(parents=True, exist_ok=True) + + _create_config_zip( + configs=configs, + output_path=output, + kwargs={ + "gauge_id": gauge, + "domain": domain, + "version": __version__, + "module": module, + "catalog_type": catalog, + "ice_fraction": ice_fraction_enum.value, + }, + ) + + click.echo(f"Config files created successfully in the following folder: {output}") diff --git a/src/icefabric/helpers/io.py b/src/icefabric/helpers/io.py new file mode 100644 index 0000000..306405e --- /dev/null +++ b/src/icefabric/helpers/io.py @@ -0,0 +1,41 @@ +import json +import tempfile +import zipfile +from pathlib import Path + +from tqdm import tqdm + +from icefabric.schemas.modules import NWMProtocol + + +def _create_config_zip(configs: list[NWMProtocol], output_path: Path, **kwargs): + """Creates a zip file of BMI configs with a metadata.json file containing query information + + Parameters + ---------- + configs : list[NWMProtocol] + The list of config NWMProtocol models + output_path : Path + The output path location to write zip files + """ + with tempfile.TemporaryDirectory() as temp_dir_str: + temp_dir = Path(temp_dir_str) + config_files = [] + + # Write config files + for config in tqdm(configs, desc="Creating a config file", total=len(configs), ncols=140): + file_path = config.model_dump_config(temp_dir) + config_files.append(file_path) + + # Create metadata file + metadata_path = temp_dir / "metadata.json" + with metadata_path.open("w", encoding="UTF-8") as f: + json.dump(kwargs["kwargs"], f) # Removes the root from the dict + config_files.append(metadata_path) + + output_file = output_path / "configs.zip" + + with zipfile.ZipFile(output_file, "w", zipfile.ZIP_DEFLATED) as f: + for file_path in config_files: + archive_name = file_path.name + f.write(file_path, archive_name) diff --git a/src/icefabric/modules/__init__.py b/src/icefabric/modules/__init__.py index 36787f2..7483397 100644 --- a/src/icefabric/modules/__init__.py +++ b/src/icefabric/modules/__init__.py @@ -1,8 +1,21 @@ """Contains helper functions to support NWM modules""" +import enum + from .create_ipes import divide_parameters, get_hydrofabric_attributes, get_sft_parameters, module_ipe from .rnr import get_rnr_segment + +class NWMModules(enum.Enum): + """A list of all supported NWM Modules""" + + SFT = "sft" + + +config_mapper = { + "sft": get_sft_parameters, +} + __all__ = [ "divide_parameters", "get_hydrofabric_attributes", diff --git a/src/icefabric/modules/create_ipes.py b/src/icefabric/modules/create_ipes.py index 7ad234c..7902727 100644 --- a/src/icefabric/modules/create_ipes.py +++ b/src/icefabric/modules/create_ipes.py @@ -9,13 +9,14 @@ from pyproj import Transformer from icefabric.hydrofabric import subset -from icefabric.schemas import SFT, HydrofabricDomains, IceFractionScheme, IdType +from icefabric.schemas.hydrofabric import HydrofabricDomains, IdType +from icefabric.schemas.modules import SFT, IceFractionScheme ROOT_DIR = os.path.abspath(os.curdir) def _get_mean_soil_temp() -> float: - """This is avg soil temp of 45 degrees F converted to Kelvin. This equation is just a reasonable estimate per new direction (EW: 07/2025) + """Returns an avg soil temp of 45 degrees F converted to Kelvin. This equation is just a reasonable estimate per new direction (EW: 07/2025) Returns ------- diff --git a/src/icefabric/schemas/modules.py b/src/icefabric/schemas/modules.py index 03948ad..61d13b0 100644 --- a/src/icefabric/schemas/modules.py +++ b/src/icefabric/schemas/modules.py @@ -1,20 +1,25 @@ """A file to host schemas for all NWM modules. Based off the table from https://confluence.nextgenwaterprediction.com/pages/viewpage.action?spaceKey=NGWPC&title=BMI+Exchange+Items+and+Module+Parameters""" -from enum import Enum +import enum +from pathlib import Path from typing import Protocol from pydantic import BaseModel, ConfigDict, Field, field_validator class NWMProtocol(Protocol): - """Protocol defining the interface that configuration NWM BaseModel classes should implement. This ensures consistency across different module configuration classes.""" + """Protocol defining the interface that configuration NWM BaseModel classes should implement.""" def to_bmi_config(self) -> list[str]: """Converts the contents of the base class to a BMI config for that specific module""" ... + def model_dump_config(self, output_path: Path) -> Path: # Changed to return Path + """Outputs the BaseModel to a BMI Config file""" + ... + -class IceFractionScheme(str, Enum): +class IceFractionScheme(str, enum.Enum): """The ice fraction scheme to be used in SFT""" SCHAAKE = "Schaake" @@ -62,7 +67,26 @@ def to_bmi_config(self) -> list[str]: f"soil_params.b={self.soil_params_b}", f"soil_params.satpsi={self.soil_params_satpsi}", f"soil_params.quartz={self.soil_params_quartz}", - f"ice_fraction_scheme={self.ice_fraction_scheme}", + f"ice_fraction_scheme={self.ice_fraction_scheme.value}", f"soil_z={z_values}[m]", f"soil_temperature={temp_values}[K]", ] + + def model_dump_config(self, output_path: Path) -> Path: + """Outputs the BaseModel to a BMI Config file + + Parameters + ---------- + output_path : Path + The path for the config file to be written to + + Returns + ------- + Path + The path to the written config file + """ + file_output = self.to_bmi_config() + sft_bmi_file = output_path / f"{self.catchment}_bmi_config_sft.txt" + with open(sft_bmi_file, "w") as f: + f.write("\n".join(file_output)) + return sft_bmi_file From 1702604dee199f1ea750bcbbd6f2f4bf1d430ce3 Mon Sep 17 00:00:00 2001 From: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> Date: Fri, 11 Jul 2025 09:06:29 -0400 Subject: [PATCH 77/97] NGWPC-6919: Create an API endpoint / method for accessing Albedo data from the version controlled format (#49) --- app/main.py | 3 ++- app/routers/nwm_modules/router.py | 26 +++++++++++++++++++- src/icefabric/schemas/__init__.py | 3 ++- src/icefabric/schemas/modules.py | 30 +++++++++++++++++++++++- tests/app/test_topoflow_albedo_router.py | 15 ++++++++++++ 5 files changed, 73 insertions(+), 4 deletions(-) create mode 100644 tests/app/test_topoflow_albedo_router.py diff --git a/app/main.py b/app/main.py index cf9effb..3cf851e 100644 --- a/app/main.py +++ b/app/main.py @@ -10,7 +10,7 @@ from app.routers.hydrofabric.router import api_router as hydrofabric_api_router from app.routers.module_params.router import api_router as module_params_router -from app.routers.nwm_modules.router import sft_router +from app.routers.nwm_modules.router import sft_router, topoflow_router from app.routers.streamflow_observations.router import api_router as streamflow_api_router from icefabric.helpers import load_creds @@ -49,6 +49,7 @@ class HealthCheck(BaseModel): app.include_router(hydrofabric_api_router, prefix="/v1") app.include_router(streamflow_api_router, prefix="/v1") app.include_router(sft_router, prefix="/v2") +app.include_router(topoflow_router, prefix="/v2") app.include_router(module_params_router, prefix="/v1") diff --git a/app/routers/nwm_modules/router.py b/app/routers/nwm_modules/router.py index f504a02..f519b5f 100644 --- a/app/routers/nwm_modules/router.py +++ b/app/routers/nwm_modules/router.py @@ -3,9 +3,10 @@ from app import get_catalog from icefabric.modules import get_sft_parameters -from icefabric.schemas import SFT, HydrofabricDomains +from icefabric.schemas import SFT, Albedo, HydrofabricDomains sft_router = APIRouter(prefix="/modules/sft") +topoflow_router = APIRouter(prefix="/modules/topoflow") @sft_router.get("/") @@ -48,3 +49,26 @@ async def get_sft_ipes( identifier=identifier, use_schaake=use_schaake, ) + + +@topoflow_router.get("/albedo") +async def get_albedo( + landcover_state: Albedo = Query( + ..., + description="The landcover state of a catchment for albedo classification", + examples=["snow"], + openapi_examples={"albedo_example": {"summary": "Albedo Example", "value": "snow"}}, + ), +) -> float: + """ + An endpoint to return albedo values for TopoFlow Glacier module. + + This endpoint matches a catchment's land cover class ("snow", "ice", "other) with an albedo value [0, 1] + + **Parameters:** + - **landcover_state**: Land cover state: "snow", "ice", or "other" + + **Returns:** + A float albedo value [0, 1] + """ + return Albedo.get_landcover_albedo(landcover_state.landcover).value diff --git a/src/icefabric/schemas/__init__.py b/src/icefabric/schemas/__init__.py index e8b8664..3190283 100644 --- a/src/icefabric/schemas/__init__.py +++ b/src/icefabric/schemas/__init__.py @@ -1,7 +1,7 @@ """Contains helper functions to support NWM modules""" from .hydrofabric import UPSTREAM_VPUS, HydrofabricDomains, IdType -from .modules import SFT, IceFractionScheme +from .modules import SFT, Albedo, IceFractionScheme from .topobathy import FileType, NGWPCLocations, NGWPCTestLocations __all__ = [ @@ -10,6 +10,7 @@ "HydrofabricDomains", "SFT", "IceFractionScheme", + "Albedo", "FileType", "NGWPCLocations", "NGWPCTestLocations", diff --git a/src/icefabric/schemas/modules.py b/src/icefabric/schemas/modules.py index 61d13b0..6fb1439 100644 --- a/src/icefabric/schemas/modules.py +++ b/src/icefabric/schemas/modules.py @@ -2,7 +2,7 @@ import enum from pathlib import Path -from typing import Protocol +from typing import Literal, Protocol from pydantic import BaseModel, ConfigDict, Field, field_validator @@ -90,3 +90,31 @@ def model_dump_config(self, output_path: Path) -> Path: with open(sft_bmi_file, "w") as f: f.write("\n".join(file_output)) return sft_bmi_file + + +class AlbedoValues(enum.Enum): + """A class to store land cover-derived albedo. + + Update land cover classes and corresponding values here. + Values are [0, 100] + """ + + snow = 0.75 + ice = 0.3 + other = 0.2 + + +class Albedo(BaseModel): + """A model to handle `/topoflow/albedo` inputs and outputs. + + Note: + This Literal will fail static type checking due to dynamically created values. + However, generating dynamically keeps this function DRY and creates the appropriate API inputs. + If changes to albedo values are needed, they are only made in `AlbedoValues`. `Albedo` will never change. + """ + + landcover: Literal[tuple(AlbedoValues._member_names_)] + + def get_landcover_albedo(v: str): + """Return the albedo value""" + return getattr(AlbedoValues, v) diff --git a/tests/app/test_topoflow_albedo_router.py b/tests/app/test_topoflow_albedo_router.py new file mode 100644 index 0000000..e4280f9 --- /dev/null +++ b/tests/app/test_topoflow_albedo_router.py @@ -0,0 +1,15 @@ +from icefabric.schemas.modules import AlbedoValues + + +def test_albedo_endpoint(client): + """Test: GET /v2/modules/topoflow/albedo - all valid arguments""" + for landcover, albedo in AlbedoValues.__members__.items(): + response = client.get(f"/v2/modules/topoflow/albedo?landcover={landcover}") + assert response.status_code == 200 + assert response.text == str(albedo.value) + + +def test_albedo_endpoint__422(client): + """Test: GET /v2/modules/topoflow/albedo - fails validator""" + response = client.get("/v2/modules/topoflow/albedo?landcover=nope") + assert response.status_code == 422 From a6953bb21989418ca4d1828343b30e24d6036014 Mon Sep 17 00:00:00 2001 From: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> Date: Fri, 11 Jul 2025 11:54:55 -0400 Subject: [PATCH 78/97] Remove /v1/modules/parameters route (#53) --- app/main.py | 6 +- app/routers/module_params/__init__.py | 0 app/routers/module_params/router.py | 44 ----- src/icefabric/modules/__init__.py | 4 +- src/icefabric/modules/create_ipes.py | 239 -------------------------- 5 files changed, 3 insertions(+), 290 deletions(-) delete mode 100644 app/routers/module_params/__init__.py delete mode 100644 app/routers/module_params/router.py diff --git a/app/main.py b/app/main.py index 3cf851e..b6c700e 100644 --- a/app/main.py +++ b/app/main.py @@ -9,7 +9,6 @@ from pyiceberg.catalog import load_catalog from app.routers.hydrofabric.router import api_router as hydrofabric_api_router -from app.routers.module_params.router import api_router as module_params_router from app.routers.nwm_modules.router import sft_router, topoflow_router from app.routers.streamflow_observations.router import api_router as streamflow_api_router from icefabric.helpers import load_creds @@ -48,9 +47,8 @@ class HealthCheck(BaseModel): # Include routers app.include_router(hydrofabric_api_router, prefix="/v1") app.include_router(streamflow_api_router, prefix="/v1") -app.include_router(sft_router, prefix="/v2") -app.include_router(topoflow_router, prefix="/v2") -app.include_router(module_params_router, prefix="/v1") +app.include_router(sft_router, prefix="/v1") +app.include_router(topoflow_router, prefix="/v1") @app.head( diff --git a/app/routers/module_params/__init__.py b/app/routers/module_params/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/app/routers/module_params/router.py b/app/routers/module_params/router.py deleted file mode 100644 index ff38f12..0000000 --- a/app/routers/module_params/router.py +++ /dev/null @@ -1,44 +0,0 @@ -import json -import os -import re - -from fastapi import APIRouter, HTTPException -from pydantic import BaseModel - -from icefabric.modules import get_hydrofabric_attributes, module_ipe - -api_router = APIRouter(prefix="/modules") -ROOT_DIR = os.path.abspath(os.curdir) - - -class Parameters(BaseModel): - gage_id: str - version: str - source: str - domain: str - modules: list[str] - - -@api_router.post("/parameters") -async def get_ipes(query: Parameters): - if query.version != "2.1" and query.version != "2.2": - raise HTTPException(status_code=422, detail="Icefabric version must be 2.2 or 2.1") - elif query.version == "2.1" and query.domain != "CONUS": - raise HTTPException(status_code=422, detail="oCONUS domains not availiable in Icefabric version 2.1") - - gpkg_file = f"{ROOT_DIR}/data/gauge_{query.gage_id}.gpkg" - if not os.path.exists(gpkg_file): - raise HTTPException(status_code=422, detail="Gage ID/geopackage not found") - - # Get divide attributes from the geopackage - attr = get_hydrofabric_attributes(gpkg_file, query.version, query.domain) - - all_modules = [] - for mod in query.modules: - mod_json = module_ipe(mod, attr, query.domain, query.version, cfg_write=True) - all_modules.append(mod_json) - - final_json = json.dumps({"modules": all_modules}) - final_json = re.sub("NaN", "null", final_json) - - return final_json diff --git a/src/icefabric/modules/__init__.py b/src/icefabric/modules/__init__.py index 7483397..45be687 100644 --- a/src/icefabric/modules/__init__.py +++ b/src/icefabric/modules/__init__.py @@ -2,7 +2,7 @@ import enum -from .create_ipes import divide_parameters, get_hydrofabric_attributes, get_sft_parameters, module_ipe +from .create_ipes import divide_parameters, get_sft_parameters from .rnr import get_rnr_segment @@ -18,8 +18,6 @@ class NWMModules(enum.Enum): __all__ = [ "divide_parameters", - "get_hydrofabric_attributes", "get_sft_parameters", - "module_ipe", "get_rnr_segment", ] diff --git a/src/icefabric/modules/create_ipes.py b/src/icefabric/modules/create_ipes.py index 7902727..92ac1ee 100644 --- a/src/icefabric/modules/create_ipes.py +++ b/src/icefabric/modules/create_ipes.py @@ -6,7 +6,6 @@ import pyiceberg.exceptions as ex from botocore.exceptions import ClientError from pyiceberg.catalog import Catalog, load_catalog -from pyproj import Transformer from icefabric.hydrofabric import subset from icefabric.schemas.hydrofabric import HydrofabricDomains, IdType @@ -26,244 +25,6 @@ def _get_mean_soil_temp() -> float: return (45 - 32) * 5 / 9 + 273.15 -def module_ipe(module: str, attr: str, domain: str, version: str, cfg_write=None): - """Creates initial parameter estimates for a module - - Parameters - ---------- - module : str - _description_ - attr : str - _description_ - domain : str - _description_ - version : str - _description_ - cfg_write : str, optional - _description_, by default None - - Returns - ------- - _type_ - _description_ - """ - # Create empty dataframe for csv parameters - csv_params = pd.DataFrame() - - # Get list of divides - divides = attr["divide_id"].to_list() - - # Look up parameter csv file for module - has_output_vars = True - module_df = pd.read_csv(f"{ROOT_DIR}/data/modules.csv") - param_file = module_df.loc[module_df["module"] == module]["file"].to_string(index=False) - out_file = module_df.loc[module_df["module"] == module]["outputs"].to_string(index=False) - if out_file == "NaN": - has_output_vars = False - - # Get module parameters - datatypes = { - "name": "object", - "description": "object", - "units": "object", - "data_type": "object", - "calibratable": "bool", - "source": "object", - "min": "float64", - "max": "float64", - "default_value": "object", - "divide_attr_name": "object", - "source_file": "object", - } - # Get output vars - out_datatypes = {"variable": "object", "description": "object"} - - module_params = pd.read_csv(f"{ROOT_DIR}/data/{param_file}", dtype=datatypes) - output_vars = pd.DataFrame() - if has_output_vars: - output_vars = pd.read_csv(f"{ROOT_DIR}/data/{out_file}", dtype=out_datatypes) - - # Filter data frame for parameter names and default values. Create dictionary to collect - # parameters for cfg files. - param_values = module_params[["name", "default_value"]] - param_values = pd.Series(param_values["default_value"].values, index=param_values["name"]).to_dict() - - # Get sources - attr_list = module_params["source"].to_list() - - # Create dictionary with divide IDs as the keys and parameter dictionary for the values. - all_cats = {} - for divide in divides: - all_cats[divide] = param_values.copy() - - # For attributes, get list of attribute names - attr_list = module_params.loc[module_params["source"] == "attr"]["divide_attr_name"].to_list() - attr_list.append("divide_id") - # Get corresponding list of parameter names to create a dictionary for mapping - attr_names = module_params.loc[module_params["source"] == "attr"]["name"].to_list() - attr_name_map = dict(zip(attr_list, attr_names, strict=False)) - # Filter attributes for those needed for this module - attr = attr[attr_list] - # Rename attribute names to module names - attr.rename(columns=attr_name_map, inplace=True) - - # Get iceberg table parameters - if "iceberg" in module_params["source"].values: - iceberg_params = divide_parameters(divides, module, domain) - - # Loop through divides and update default values with divide attributes from geopackage - # and iceberg tables - for divide in divides: - attr_div = attr.loc[attr["divide_id"] == divide] - attr_div = attr_div.to_dict(orient="list") - all_cats[divide].update(attr_div) - if not iceberg_params.empty: - iceberg_params_div = iceberg_params.loc[iceberg_params["divide_id"] == divide] - iceberg_params_div = iceberg_params_div.to_dict(orient="list") - all_cats[divide].update(iceberg_params_div) - - # When dataframe is converted to a dictionary, the values are contained in a list - # Remove the value from the list. - for key in all_cats[divide].keys(): - value = all_cats[divide][key] - if isinstance(value, list): - all_cats[divide][key] = value[0] - - if cfg_write: - _write_cfg(all_cats) - s3_uri = "s3://" - module_json = _write_json(all_cats[divides[1]], module_params, output_vars, module, s3_uri) - return module_json - - -def _write_cfg(params: dict): - """Writes a config file from a dictionary of parameters""" - divides = params.keys() - for divide in divides: - cfg_file = f"{ROOT_DIR}/{divide}_bmi_config_cfe.txt" - f = open(cfg_file, "w") - - for key, value in params[divide].items(): - f.write(f"{key}={value}\n") - - f.close() - - -def _write_json(params, all_params, output_vars, module, s3_uri): - """Writes a JSON file from a dictionary of parameters""" - json_columns = ["name", "description", "default_value", "min", "max", "data_type", "units"] - all_params = all_params.loc[all_params["calibratable"] == True, json_columns] - all_params = all_params.to_dict(orient="records") - - for item in all_params: - name = item["name"] - value = params[name] - item["default_value"] = value - - module_dict = { - "module_name": module, - "parameter_file": {"uri": s3_uri}, - "calibrate_parameters": all_params, - } - if not output_vars.empty: - output_vars = output_vars.to_dict(orient="records") - module_dict.update({"output_variables": output_vars}) - - return module_dict - - -def get_hydrofabric_attributes(gpkg_file, version, domain): - """Gets attributes from the hydrofabric geopackage""" - attr_layer = "divide-attributes" - if version == "2.1": - attr_layer = "model-attributes" - - # Map oCONUS lat/lon attribute names to CONUS names. - column_names_xy = {"X": "centroid_x", "Y": "centroid_y"} - # Puerto Rico has a number of attribute names that don't match the other domains in HF v2.2 - column_names_pr = { - "dksat_Time._soil_layers_stag.1": "geom_mean.dksat_soil_layers_stag.1", - "dksat_Time._soil_layers_stag.2": "geom_mean.dksat_soil_layers_stag.2", - "dksat_Time._soil_layers_stag.3": "geom_mean.dksat_soil_layers_stag.3", - "dksat_Time._soil_layers_stag.4": "geom_mean.dksat_soil_layers_stag.4", - "mean.cwpvt_Time.": "mean.cwpvt", - "mean.mfsno_Time.": "mean.mfsno", - "mean.mp_Time.": "mean.mp", - "mean.refkdt_Time.": "mean.refkdt", - "mean.slope_Time.": "mean.slope_1km", - "mean.smcmax_Time._soil_layers_stag.1": "mean.smcmax_soil_layers_stag.1", - "mean.smcmax_Time._soil_layers_stag.2": "mean.smcmax_soil_layers_stag.2", - "mean.smcmax_Time._soil_layers_stag.3": "mean.smcmax_soil_layers_stag.3", - "mean.smcmax_Time._soil_layers_stag.4": "mean.smcmax_soil_layers_stag.4", - "mean.smcwlt_Time._soil_layers_stag.1": "mean.smcwlt_soil_layers_stag.1", - "mean.smcwlt_Time._soil_layers_stag.2": "mean.smcwlt_soil_layers_stag.2", - "mean.smcwlt_Time._soil_layers_stag.3": "mean.smcwlt_soil_layers_stag.3", - "mean.smcwlt_Time._soil_layers_stag.4": "mean.smcwlt_soil_layers_stag.4", - "mean.vcmx25_Time.": "mean.vcmx25", - "mode.bexp_Time._soil_layers_stag.1": "mode.bexp_soil_layers_stag.1", - "mode.bexp_Time._soil_layers_stag.2": "mode.bexp_soil_layers_stag.2", - "mode.bexp_Time._soil_layers_stag.3": "mode.bexp_soil_layers_stag.3", - "mode.bexp_Time._soil_layers_stag.4": "mode.bexp_soil_layers_stag.4", - "psisat_Time._soil_layers_stag.1": "geom_mean.psisat_soil_layers_stag.1", - "psisat_Time._soil_layers_stag.2": "geom_mean.psisat_soil_layers_stag.2", - "psisat_Time._soil_layers_stag.3": "geom_mean.psisat_soil_layers_stag.3", - "psisat_Time._soil_layers_stag.4": "geom_mean.psisat_soil_layers_stag.4", - } - - # Get list of catchments from gpkg divides layer using geopandas - try: - divide_attr = gpd.read_file(gpkg_file, layer=attr_layer) - divide_layer = gpd.read_file(gpkg_file, layer="divides") - except: # TODO: Replace 'except' with proper catch - error_str = "Error opening " + gpkg_file - error = dict(error=error_str) - print(error_str) - return error - - # Get catchement area from divides layer and append to attributes data frame - area = divide_layer[["divide_id", "areasqkm"]] - divide_attr = divide_attr.join(area.set_index("divide_id"), on="divide_id") - - # Account for differences in column names between CONUS and oCONUS - if version == "2.2" and domain != "CONUS": - divide_attr.rename(columns=column_names_xy, inplace=True) - if version == "2.2" and domain == "Puerto_Rico": - divide_attr.rename(columns=column_names_pr, inplace=True) - - # Soil and vegetation types are read from the gpkg as floats, but need to be ints - if version == "2.1": - divide_attr = divide_attr.astype({"ISLTYP": "int"}) - divide_attr = divide_attr.astype({"IVGTYP": "int"}) - elif version == "2.2": - divide_attr = divide_attr.astype({"mode.ISLTYP": "int"}) - divide_attr = divide_attr.astype({"mode.IVGTYP": "int"}) - - # Zmax/max_gw_storage units are mm in the hydrofabric but CFE expects m. - if version == "2.1": - divide_attr["gw_Zmax"] = divide_attr["gw_Zmax"].apply(lambda x: x / 1000) - - elif version == "2.2": - divide_attr["mean.Zmax"] = divide_attr["mean.Zmax"].apply(lambda x: x / 1000) - - # Elevation in 2.2 is in cm, convert to m. Except for AK, which is still in m. - if version == "2.2" and domain != "Alaska": - divide_attr["mean.elevation"] = divide_attr["mean.elevation"].apply(lambda x: x / 100) - - # Convert centroid_x and centroid_y (lat/lon) from the domain's CRS to WGS84 - # for decimal degrees for 2.2. - if version == "2.2": - crs = divide_layer.crs - transformer = Transformer.from_crs(crs, 4326) - for index, row in divide_attr.iterrows(): - y = row["centroid_y"] - x = row["centroid_x"] - wgs84_latlon = transformer.transform(x, y) - divide_attr.loc[index, "centroid_y"] = wgs84_latlon[0] # latitude - divide_attr.loc[index, "centroid_x"] = wgs84_latlon[1] # longitude - - return divide_attr - - def divide_parameters(divides, module, domain): """Returns iceberg divide parameters""" module = module.lower() From 6ee52fe03dfe931bb17f252239edd5cca851508c Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Fri, 11 Jul 2025 14:17:33 -0400 Subject: [PATCH 79/97] Patch: Changing CWD in the SFT example and updating paths (#54) * patch: ensured CWD is correct * updated API image --- .pre-commit-config.yaml | 6 +-- docs/img/icefabric_api.png | Bin 375458 -> 337506 bytes .../initial_parameters/soil_freeze_thaw.ipynb | 35 +++++------------- 3 files changed, 13 insertions(+), 28 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 27cab3a..7268e63 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.6.0 + rev: v5.0.0 hooks: - id: trailing-whitespace exclude: LICENSE|\.csv$ @@ -11,7 +11,7 @@ repos: - repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. - rev: v0.11.5 + rev: v0.12.3 hooks: # Run the linter. - id: ruff @@ -20,7 +20,7 @@ repos: - id: ruff-format - repo: https://github.com/astral-sh/uv-pre-commit - rev: 0.5.7 + rev: 0.7.20 hooks: - id: uv-lock diff --git a/docs/img/icefabric_api.png b/docs/img/icefabric_api.png index d505686708f73fea530a16c2fee2fff1d3859775..3801a631fb42b2881dc884fe4e03881b88a20bb2 100644 GIT binary patch literal 337506 zcmb@ucUTn9)-^g{Ku|ytK@bqhd6Xy_1tbT_X&4ZO93KGz+uNF$Tfsme>8~+yIBKdZM820EwgR~9zP{1Q z;t?G8#blS$4jEXU;og@Ge)dXIO|9vX)>{b)G40>#4{nWMx9QwhwhH?3ZUn!KDQE57 z&)chrhG*5MJsz8N{7b!_rks~uXAT=ay?P)fZn*L{TvqIG`ujmNXF}$7+qm3k&l|Au zeQ(?oGju5X)KyYK?b}gtfv7{_f5-J2ctLfLo1wuNTAzuBjb`TTjnf1J3eYX z=6Uy6d_;lyVj1!V%#Kt1Ll*z1P1fC+AnmsUU7ZJClT3ck-qD^Sjv#eN82b@VPDl2e z(}O&WJ}5PjwQM6c=zOVU`n!u3t;`fu*^wOIYo&4Oo zmbxAwhKOggqgT6bPj;~Qf=(8AkkAXmN<_|v2W zU(HXa1cw3?@bhgSwxXZIAM9%EB7c!Bkn~S#SFAS;9?x&Tx=-~(OilVm|EFMThc=RT zVm9G~#5X0ywv~yZ@o=eeqj3uT1JgKj@W%0n#8G05X%2n#Ii$5X`{Ip#PnNlY8;rGX z4SxvzP7x7F84hDIsG@5 zE-ZPjth){m2g1mHdN!++=x8|T_wH`#qfxKXhoifrA{goeVS)hF#@}|K zFMafcI4G;IH9t&zwft2%=WchS`mGREu^(S6Vpe?Hs_rrL%e0^9N6{dZ-_s@^y0!<7 zxbU0M*VwPbwB&NADd>`(O;QWd20wW8^^Jt-JEjOZJw-P94SM*4;s-cijp*o!lILXu zX)>dR!&ziPGIlcpGOFzD?N96#?Pu&gYdmXYYiR9B7fu#LB4XN2jKTIa+OMZ1rj%?L zr)g1{%1kZ9gtFL@@?#RY*^Vkysv;e#3+xNpJ9@`MJ3~9&JACs{=KGI=iNE39ePsWL zg*cR1Pt`S7T8&#{PsNzENTonSDA!fWPVH-+aXN8cbf(%5N9C2s{%}tHF|9GF_{)G*;e{y;avp&L>Z=m9Ny5GFpu!3a}~%9ISnkV#jd3cc`2%9zA%rkkkuizmQa%Gg)051(k+&< zl}{~tT8Pp>$y`QYwfh=|zf-p9GkpFo$MUh&H`7rA*Xb{2P0wYy#%%_z_kMn}p|Gs` zS>5dVrAX2Ax5EnlUH}8}LzgIe1}BCpdAqE81NR1!vqriNxID_a%Pq<+7fWjV`KLXz z-8LyO6xsYA_?;X!w8GiHk?@n?lbpv|#J-BPWY=YbYLn^EYgw1B!cQ!$ZIjKBBPV_# zWeUFCI*GiAd~g#u#e~I#t~0S96fxvn=v=&%(7Vu$<~b}99ugV#_V)@nC&iJ%i4ae3 zEJ;r@iZlwDqx_6V8~pZH_3}rj9Y>vY$Bvb#a{uztXw z%FsrL>!};5+u;$@sl=(;vCzTLp~AlP7Ug#Nq3+DcM%X^#-t??bKxX%B$*MLjRwx$h zjauw>zX(5dKcx>{A0%E`|9t;mY-{*Wz_T~cK7_|A-|xUflbhXE?ilYwuPkiJneQ&2xvD6-m6H(V$Rz!>D&r1PmeN z;$u#j-^LpD3OjdNgUXY4Kdk*^Un1DmbA#2HG8Zw!n8kC8a&=V$1||ns`t^5^N9t!I zD;F!3D^EFJa~+s^b_RD_bNo0)j>V0IKy2Wib33Lr^);f!4F!J+mfHq8 zb)!a5ku~;*EN1FEd7(qM^Y3a9E}kwt)e_fenA8Ic{#d(sDXM6+HEohsF)9^#~9XWm1M8KUHTp=QwG24#={m1B$3)GFTQD*sd0i`iS#cazaw z8|9=Ba7yjG=gK^+Wg(W8Kr^9u94fq>cy7z+PT`(V%&%{W+;Bt|y6A7lKiS~xLGRVO z&5*1O$JB?{Pfh%;+MJT9qeXTi1s%b=Q){REhpkleqD>-f?ioipv!%mxwbKedwvhtT zN{1eYP%on6`JW)uKh00ae1Zu{gffK+U9(OLyxW|Y2TL}$_Lp{33G46IKlac*%I;6) zDVEo#yzHDS&Zo7b^)HqzMj@Hq^0%EY5jC3vZyMh`MfJLi9;7X&x3u0!eVke=+Uo7i z3wNXt)jw|DlbaZ6C?-U~yiOU6gGQs$K_56?(%* z=Xcqp%5{|S64bYhCH6A#q0ywFBiT|qmy96DFh{uKMuW3+8ISN?rJnVNKm5JkvbKsT|-LAQWC zEZ{4G_2hr{-(WF;Zu~ut4FUz2gKqxo8bx4x^$7>QS7rX$-iZ7R!U6sw0=}-_vHyMb zU4!p8{=I+85jY2Wr7A8X18h}|>|rn)2QynoJp+kY-~jG>X>A7(h=l&?izTB%w*}mP z)Lc!=QAyFWN&ODpd#_+U&VpHglWti9p4Lp!7eT? 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zV{RWAM*;n_zClrBTE{LAv0MX*9qDKBcFi^Js-R;&={imSMIXmEX=~m~X;eX_e9!)6 z4URO$W?G7%*|b;ZNRIUFe@e`EQsO!z8o*F{93??SJVi!ax6#pj>|s7SMKr-53MfjC z_r7)Tvu!K$V-K^K;K^!yL)7s$<0Qd0_}Sj|kH;P+^rV7k+e77}>e|1rL7*@A8UE71 zv4@!#z*=P?#BpTZ{$dw(!}TvC^S_zNe;JwoX Date: Fri, 11 Jul 2025 17:36:09 -0400 Subject: [PATCH 80/97] patch: Update soil_freeze_thaw.ipynb --- examples/initial_parameters/soil_freeze_thaw.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/initial_parameters/soil_freeze_thaw.ipynb b/examples/initial_parameters/soil_freeze_thaw.ipynb index 3369bf4..b027b34 100644 --- a/examples/initial_parameters/soil_freeze_thaw.ipynb +++ b/examples/initial_parameters/soil_freeze_thaw.ipynb @@ -126,7 +126,7 @@ " print(\"Files in archive:\")\n", " for file_info in f.filelist:\n", " print(f\" {file_info.filename} ({file_info.file_size} bytes)\")\n", - " f.extractall(\"/tmp/sft_output/configs\")" + " f.extractall(\"/tmp/sft_output_test/configs\")" ] }, { From d607f77d197d90350423f530a0c9fc8ec5ddf79f Mon Sep 17 00:00:00 2001 From: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> Date: Mon, 14 Jul 2025 15:31:40 -0400 Subject: [PATCH 81/97] Icechunk Tests (#55) --- .../icechunk/icechunk_data_ret_and_plot.ipynb | 180 ++-------- .../icechunk_version_control_examples.ipynb | 331 +++--------------- src/icefabric/builds/build.py | 2 +- src/icefabric/builds/icechunk_s3_module.py | 8 +- tests/integration/test_icechunk.py | 16 + 5 files changed, 84 insertions(+), 453 deletions(-) create mode 100644 tests/integration/test_icechunk.py diff --git a/examples/icechunk/icechunk_data_ret_and_plot.ipynb b/examples/icechunk/icechunk_data_ret_and_plot.ipynb index 550989b..c4fdb81 100644 --- a/examples/icechunk/icechunk_data_ret_and_plot.ipynb +++ b/examples/icechunk/icechunk_data_ret_and_plot.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d2960e7e-b0ce-4fc7-bc91-509ff38180db", + "id": "0", "metadata": {}, "source": [ "## Retrieving Icechunk Data\n", @@ -14,20 +14,23 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "92608737-174f-42fd-ab07-b599edd9d1de", + "execution_count": null, + "id": "1", "metadata": {}, "outputs": [], "source": [ + "import warnings\n", "from pathlib import Path\n", "\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "28c1bab2-5106-4ea5-94fd-502de7859a4d", + "execution_count": null, + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -40,79 +43,22 @@ { "cell_type": "code", "execution_count": null, - "id": "2b3b1ced-6760-4ef5-8cdc-4bde82a287fe", + "id": "3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Size: 874GB\n", - "Dimensions: (year: 39, Y: 105000, X: 160000, Y1: 52500, X1: 80000, Y2: 26250,\n", - " X2: 40000, Y3: 13125, X3: 20000, Y4: 6563, X4: 10000, Y5: 3282,\n", - " X5: 5000, Y6: 1641, X6: 2500)\n", - "Coordinates:\n", - " * year (year) int32 156B 1985 1986 1987 1988 1989 ... 2020 2021 2022 2023\n", - "Dimensions without coordinates: Y, X, Y1, X1, Y2, X2, Y3, X3, Y4, X4, Y5, X5,\n", - " Y6, X6\n", - "Data variables:\n", - " 0 (year, Y, X) uint8 655GB dask.array\n", - " 1 (year, Y1, X1) uint8 164GB dask.array\n", - " 2 (year, Y2, X2) uint8 41GB dask.array\n", - " 3 (year, Y3, X3) uint8 10GB dask.array\n", - " 4 (year, Y4, X4) uint8 3GB dask.array\n", - " 5 (year, Y5, X5) uint8 640MB dask.array\n", - " 6 (year, Y6, X6) uint8 160MB dask.array\n", - "Attributes: (12/24)\n", - " multiscales: [{'datasets': [{'path': '0'}, {'path': '1'}, {'...\n", - " KeyDirectoryVersion: 1\n", - " KeyRevision: 1\n", - " KeyRevisionMinor: 0\n", - " GTModelTypeGeoKey: 1\n", - " GTRasterTypeGeoKey: 1\n", - " ... ...\n", - " ProjNatOriginLongGeoKey: -96.0\n", - " ProjNatOriginLatGeoKey: 23.0\n", - " ProjFalseEastingGeoKey: 0.0\n", - " ProjFalseNorthingGeoKey: 0.0\n", - " ModelPixelScale: [30.0, 30.0, 0.0]\n", - " ModelTiepoint: [0.0, 0.0, 0.0, -2415585.0, 3314805.0, 0.0]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", - " super().__init__(**codec_config)\n", - "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", - " super().__init__(**codec_config)\n", - "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", - " super().__init__(**codec_config)\n", - "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", - " super().__init__(**codec_config)\n", - "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", - " super().__init__(**codec_config)\n", - "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", - " super().__init__(**codec_config)\n", - "/home/farshid.rahmani/Documents/GitHub/hydrofabric-ui/icechunk_data_viewer/.venv/lib/python3.11/site-packages/numcodecs/zarr3.py:145: UserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n", - " super().__init__(**codec_config)\n" - ] - } - ], + "outputs": [], "source": [ "from icefabric.builds import IcechunkRepo\n", - "from icefabric.schemas import NGWPCLocations\n", + "from icefabric.schemas import NGWPCTestLocations\n", "\n", "# Retrive data and print\n", - "land_cover_repo = IcechunkRepo(location=NGWPCLocations.NLCD_IC.path)\n", + "land_cover_repo = IcechunkRepo(location=NGWPCTestLocations.NLCD_IC.path)\n", "lc_data = land_cover_repo.retrieve_dataset()\n", "print(lc_data)" ] }, { "cell_type": "markdown", - "id": "e4c698c4-49cd-445c-ac4a-2929fa01185f", + "id": "4", "metadata": {}, "source": [ "### Plot 2023 data" @@ -120,32 +66,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "9d70e76d-82aa-4724-93fb-9e53b979363c", + "execution_count": null, + "id": "5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Size: 16MB\n", - "dask.array\n", - "Coordinates:\n", - " year int32 4B 2023\n", - "Dimensions without coordinates: Y5, X5\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "

    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Print data for year 2023\n", "print(lc_data[\"5\"].sel(year=2023))\n", @@ -165,7 +89,7 @@ }, { "cell_type": "markdown", - "id": "982fa54a-cd71-43d0-af65-a60184956bba", + "id": "6", "metadata": {}, "source": [ "## Topobathy\n", @@ -177,35 +101,19 @@ { "cell_type": "code", "execution_count": null, - "id": "617115d8-a7d0-486e-aab9-2daedad9074e", + "id": "7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Size: 681MB\n", - "Dimensions: (band: 1, y: 6304, x: 13504)\n", - "Coordinates:\n", - " * y (y) float64 50kB 19.0 19.0 ... 17.25 17.25\n", - " * x (x) float64 108kB -68.0 -68.0 ... -64.25\n", - " spatial_ref int64 8B 0\n", - " * band (band) int64 8B 1\n", - "Data variables:\n", - " __xarray_dataarray_variable__ (band, y, x) float64 681MB dask.array\n" - ] - } - ], + "outputs": [], "source": [ "# Retrive data and print\n", - "topo_repo = IcechunkRepo(location=NGWPCLocations.TOPO_PR_USVI_30M_IC.path)\n", + "topo_repo = IcechunkRepo(location=NGWPCTestLocations.TOPO_PR_USVI_30M_IC.path)\n", "topo_data = topo_repo.retrieve_dataset()\n", "print(topo_data)" ] }, { "cell_type": "markdown", - "id": "9ac68b4d-0601-43b5-8cc2-ce328305e4fe", + "id": "8", "metadata": {}, "source": [ "### Plot Data" @@ -214,47 +122,9 @@ { "cell_type": "code", "execution_count": null, - "id": "e5a687c9-57ea-4fa9-ae0b-d88595fc03b2", + "id": "9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Size: 681MB\n", - "dask.array\n", - "Coordinates:\n", - " * y (y) float64 50kB 19.0 19.0 19.0 19.0 ... 17.25 17.25 17.25\n", - " * x (x) float64 108kB -68.0 -68.0 -68.0 ... -64.25 -64.25 -64.25\n", - " spatial_ref int64 8B 0\n", - " * band (band) int64 8B 1\n", - "Attributes: (12/13)\n", - " DataType: Generic\n", - " AREA_OR_POINT: Area\n", - " RepresentationType: ATHEMATIC\n", - " STATISTICS_COUNT: 64069221.0\n", - " STATISTICS_COVARIANCES: 2043434.806372322\n", - " STATISTICS_MAXIMUM: 1328.0447998047\n", - " ... ...\n", - " STATISTICS_MEDIAN: -1039.828272\n", - " STATISTICS_MINIMUM: -6616.7924804688\n", - " STATISTICS_SKIPFACTORX: 1\n", - " STATISTICS_SKIPFACTORY: 1\n", - " STATISTICS_STDDEV: 1429.4876027347\n", - " units: metre\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Print Dataarray\n", "print(topo_data[\"elevation\"])\n", @@ -286,7 +156,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/examples/icechunk/icechunk_version_control_examples.ipynb b/examples/icechunk/icechunk_version_control_examples.ipynb index 5429e4a..6e2e8c0 100644 --- a/examples/icechunk/icechunk_version_control_examples.ipynb +++ b/examples/icechunk/icechunk_version_control_examples.ipynb @@ -2,17 +2,19 @@ "cells": [ { "cell_type": "markdown", - "id": "25341568-feb1-46b7-acaf-d6b76f066e57", + "id": "0", "metadata": {}, "source": [ "## Icechunk Version Control and Branching\n", - "Showcase for adding new data over time to an icechunk store, \"time traveling\", and making new branches" + "Showcase for adding new data over time to an icechunk store, \"time traveling\", and making new branches\n", + "\n", + "Requires `.env` with `data` account credentials" ] }, { "cell_type": "code", "execution_count": null, - "id": "1a2ed407", + "id": "1", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +29,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4f0fe850-0e5e-40e5-93b1-1c9f468a2d28", + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -45,21 +47,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "61ff84b0-9dba-494e-b9c4-187923f504e9", + "execution_count": null, + "id": "3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", - "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", - "Message:\tRepository initialized\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Print repo ancestry\n", "new_repo.print_history()" @@ -68,31 +59,9 @@ { "cell_type": "code", "execution_count": null, - "id": "ef762477-5c6b-4876-b5a2-a708b9f3d02f", + "id": "4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zz_ssmv11034tS__T0001TTNATS2009*.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [01:27<00:00, 17.52s/files]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset is uploaded. Commit: 9CNDKXE65PVGCHK4CHBG\n" - ] - } - ], + "outputs": [], "source": [ "# Collect first five SNODAS netcdf files from 2009 and combine/virtualize them together into a single dataset\n", "snodas_09_vds = virtualize_and_concat_archival_files_on_time(\n", @@ -110,25 +79,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "b3b6e582-ba59-44c2-9753-74afa7c48fd0", + "execution_count": null, + "id": "5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Snapshot ID:\t9CNDKXE65PVGCHK4CHBG\n", - "Timestamp:\t2025-06-17 00:01:17.067141+00:00\n", - "Message:\tFirst commit! 09 data added.\n", - "\n", - "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", - "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", - "Message:\tRepository initialized\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Now that we have a new snapshot, reprint the repo ancestry\n", "new_repo.print_history()" @@ -136,30 +90,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "41d0467d-84da-45f2-94e2-f911e88bbf68", + "execution_count": null, + "id": "6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Size: 671MB\n", - "Dimensions: (lon: 8192, time: 5, lat: 4096)\n", - "Coordinates:\n", - " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", - " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", - " * time (time) datetime64[ns] 40B 2009-12-09 2009-12-10 ... 2009-12-13\n", - "Data variables:\n", - " Band1 (time, lat, lon) float32 671MB dask.array\n", - " crs (time) object 40B dask.array\n", - "Attributes:\n", - " Conventions: CF-1.5\n", - " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", - " history: Tue Feb 04 18:40:28 2025: GDAL CreateCopy( /data/unmasked/2...\n" - ] - } - ], + "outputs": [], "source": [ "# Print the data now contained within the SNODAS repo\n", "snodas_data = new_repo.retrieve_dataset()\n", @@ -169,31 +103,9 @@ { "cell_type": "code", "execution_count": null, - "id": "bbefb68a-b747-449e-b7da-4c61c6f4a2e0", + "id": "7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zz_ssmv11034tS__T0001TTNATS2010*.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [01:27<00:00, 17.54s/files]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset has been appended on the time dimension. Commit: 6FMVKF4CS7N5RW6PC86G\n" - ] - } - ], + "outputs": [], "source": [ "# Much like the 09 SNODAS files were collected, do the same for 2010\n", "snodas_10_vds = virtualize_and_concat_archival_files_on_time(\n", @@ -213,29 +125,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "c5cfc9ee-0357-404f-8535-c414409ac58e", + "execution_count": null, + "id": "8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Snapshot ID:\t6FMVKF4CS7N5RW6PC86G\n", - "Timestamp:\t2025-06-17 00:03:27.965202+00:00\n", - "Message:\tAppended new data from the year 2010\n", - "\n", - "Snapshot ID:\t9CNDKXE65PVGCHK4CHBG\n", - "Timestamp:\t2025-06-17 00:01:17.067141+00:00\n", - "Message:\tFirst commit! 09 data added.\n", - "\n", - "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", - "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", - "Message:\tRepository initialized\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Now that we have another new snapshot with 2010 data, reprint the repo ancestry\n", "new_repo.print_history()" @@ -243,30 +136,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "f9a3d55d-7d51-4317-9dc8-cf41316d3fea", + "execution_count": null, + "id": "9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Size: 1GB\n", - "Dimensions: (time: 10, lat: 4096, lon: 8192)\n", - "Coordinates:\n", - " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", - " * time (time) datetime64[ns] 80B 2009-12-09 2009-12-10 ... 2010-01-05\n", - " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", - "Data variables:\n", - " Band1 (time, lat, lon) float32 1GB dask.array\n", - " crs (time) object 80B dask.array\n", - "Attributes:\n", - " Conventions: CF-1.5\n", - " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", - " history: Tue Feb 04 19:54:02 2025: GDAL CreateCopy( /data/unmasked/2...\n" - ] - } - ], + "outputs": [], "source": [ "# Print the new repo collection with both 2009 and 2010 data\n", "snodas_data = new_repo.retrieve_dataset()\n", @@ -275,30 +148,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "e8c16d28-1933-48fd-8cfc-31dd18d8ed7d", + "execution_count": null, + "id": "10", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Size: 671MB\n", - "Dimensions: (time: 5, lat: 4096, lon: 8192)\n", - "Coordinates:\n", - " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", - " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", - " * time (time) datetime64[ns] 40B 2009-12-09 2009-12-10 ... 2009-12-13\n", - "Data variables:\n", - " Band1 (time, lat, lon) float32 671MB dask.array\n", - " crs (time) object 40B dask.array\n", - "Attributes:\n", - " Conventions: CF-1.5\n", - " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", - " history: Tue Feb 04 18:40:28 2025: GDAL CreateCopy( /data/unmasked/2...\n" - ] - } - ], + "outputs": [], "source": [ "# Retrieve and print the data from the previous snapshot, before 2010 data was added\n", "prev_snap_snodas_data = new_repo.retrieve_prev_snapshot()\n", @@ -308,31 +161,9 @@ { "cell_type": "code", "execution_count": null, - "id": "3d940692-0407-4c46-8df2-5a0036f4420b", + "id": "11", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zz_ssmv11034tS__T0001TTNATS2011*.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Opening files as virtual datasets.: 100%|\u001b[38;2;55;182;189m███████████████████████████████████████████████████\u001b[0m| 5/5 [01:28<00:00, 17.79s/files]\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset has been appended on the time dimension. Commit: T30V4R9GM9TCKHFFBQFG\n" - ] - } - ], + "outputs": [], "source": [ "# Make a new feature branch based on \"main\" to add 2011 data\n", "new_repo.create_new_branch(name=\"2011_feature\")\n", @@ -355,47 +186,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "8386ab8e-02ef-4d87-8b55-91a26bb76675", + "execution_count": null, + "id": "12", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NEW BRANCH =====================================\n", - "Snapshot ID:\tT30V4R9GM9TCKHFFBQFG\n", - "Timestamp:\t2025-06-17 00:05:10.559344+00:00\n", - "Message:\tAppended new data from the year 2011\n", - "\n", - "Snapshot ID:\t6FMVKF4CS7N5RW6PC86G\n", - "Timestamp:\t2025-06-17 00:03:27.965202+00:00\n", - "Message:\tAppended new data from the year 2010\n", - "\n", - "Snapshot ID:\t9CNDKXE65PVGCHK4CHBG\n", - "Timestamp:\t2025-06-17 00:01:17.067141+00:00\n", - "Message:\tFirst commit! 09 data added.\n", - "\n", - "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", - "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", - "Message:\tRepository initialized\n", - "\n", - "MAIN BRANCH ====================================\n", - "Snapshot ID:\t6FMVKF4CS7N5RW6PC86G\n", - "Timestamp:\t2025-06-17 00:03:27.965202+00:00\n", - "Message:\tAppended new data from the year 2010\n", - "\n", - "Snapshot ID:\t9CNDKXE65PVGCHK4CHBG\n", - "Timestamp:\t2025-06-17 00:01:17.067141+00:00\n", - "Message:\tFirst commit! 09 data added.\n", - "\n", - "Snapshot ID:\tMWPZ0C0SCM3TRRKNF8D0\n", - "Timestamp:\t2025-06-16 23:59:40.242436+00:00\n", - "Message:\tRepository initialized\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Now that we have a new branch with new 2011 data, print the history of both branches\n", "print(\"NEW BRANCH =====================================\")\n", @@ -406,47 +200,10 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "43814441-7461-4d93-99f0-a839b9324d99", + "execution_count": null, + "id": "13", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NEW BRANCH ========================================================\n", - " Size: 2GB\n", - "Dimensions: (time: 15, lon: 8192, lat: 4096)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 120B 2009-12-09 2009-12-10 ... 2011-01-05\n", - " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", - " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", - "Data variables:\n", - " crs (time) object 120B dask.array\n", - " Band1 (time, lat, lon) float32 2GB dask.array\n", - "Attributes:\n", - " Conventions: CF-1.5\n", - " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", - " history: Tue Feb 04 19:00:12 2025: GDAL CreateCopy( /data/unmasked/2...\n", - "===================================================================\n", - "MAIN BRANCH =======================================================\n", - " Size: 1GB\n", - "Dimensions: (time: 10, lat: 4096, lon: 8192)\n", - "Coordinates:\n", - " * lon (lon) float64 66kB -130.5 -130.5 -130.5 ... -62.27 -62.26 -62.25\n", - " * time (time) datetime64[ns] 80B 2009-12-09 2009-12-10 ... 2010-01-05\n", - " * lat (lat) float64 33kB 24.1 24.11 24.12 24.13 ... 58.21 58.22 58.23\n", - "Data variables:\n", - " Band1 (time, lat, lon) float32 1GB dask.array\n", - " crs (time) object 80B dask.array\n", - "Attributes:\n", - " Conventions: CF-1.5\n", - " GDAL: GDAL 3.11.0dev-f1386937cde9e540784909294fdd45cda3ee65d2, re...\n", - " history: Tue Feb 04 19:54:02 2025: GDAL CreateCopy( /data/unmasked/2...\n", - "===================================================================\n" - ] - } - ], + "outputs": [], "source": [ "# Print both branch's datasets - notice the new one has 2011 data\n", "snodas_data_feat_branch = new_repo.retrieve_dataset(branch=\"2011_feature\")\n", @@ -460,18 +217,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "a69a8c8e-0003-4738-964e-7bd136412264", + "execution_count": null, + "id": "14", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Icechunk repo @ s3://hydrofabric-data/ic_testing/snodas_yearly_append_test in its entirety was successfully deleted.\n" - ] - } - ], + "outputs": [], "source": [ "# Cleanup - delete the test repo entirely\n", "new_repo.delete_repo(quiet=True)" @@ -480,7 +229,7 @@ { "cell_type": "code", "execution_count": null, - "id": "da2aa6f1", + "id": "15", "metadata": {}, "outputs": [], "source": [] diff --git a/src/icefabric/builds/build.py b/src/icefabric/builds/build.py index eeee091..8f5c969 100644 --- a/src/icefabric/builds/build.py +++ b/src/icefabric/builds/build.py @@ -1,4 +1,4 @@ -"""Contains all api functions that can be called outside of the icefabric_manage package""" +"""Build scripts for pyiceberg""" import pyarrow.parquet as pq from pyiceberg.catalog import Catalog diff --git a/src/icefabric/builds/icechunk_s3_module.py b/src/icefabric/builds/icechunk_s3_module.py index e276e06..1c385f4 100644 --- a/src/icefabric/builds/icechunk_s3_module.py +++ b/src/icefabric/builds/icechunk_s3_module.py @@ -148,7 +148,8 @@ def gen_virtual_chunk_containers( v_chunks = None if virtual_chunk_mapping: v_chunks = [ - set_up_virtual_chunk_container(vc["bucket"], vc["region"]) for vc in virtual_chunk_mapping + self.set_up_virtual_chunk_container(vc["bucket"], vc["region"]) + for vc in virtual_chunk_mapping ] return v_chunks @@ -425,8 +426,3 @@ def set_up_virtual_chunk_container(bucket: str, region: str) -> ic.VirtualChunkC return ic.VirtualChunkContainer( name=bucket, url_prefix=f"s3://{bucket}/", store=ic.s3_store(region=region) ) - - -# def get_icechunk_data(repo: NGWPCLocations) -> xr.Dataset: -# """Convenience wrapper to return data from a designated NGWPCLocations Icechunk repo""" -# return IcechunkRepo(location=repo.path).retrieve_dataset() diff --git a/tests/integration/test_icechunk.py b/tests/integration/test_icechunk.py new file mode 100644 index 0000000..b633b4b --- /dev/null +++ b/tests/integration/test_icechunk.py @@ -0,0 +1,16 @@ +import pytest +import xarray as xr + +from icefabric.builds import IcechunkRepo +from icefabric.schemas import NGWPCTestLocations + +ic_list = NGWPCTestLocations._member_names_ +params = [pytest.param(getattr(NGWPCTestLocations, name), id=f"Icechunk {name}") for name in ic_list] + + +@pytest.mark.parametrize("ic", params) +def test_icechunk_repo(ic: NGWPCTestLocations) -> None: + """Confirm icechunk repos are valid""" + ic_repo = IcechunkRepo(location=ic.path) + ic_data = ic_repo.retrieve_dataset() + assert isinstance(ic_data, xr.core.dataset.Dataset) From f1c27ac38ee5f91a5e35faef27720e8c7e84948d Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Tue, 15 Jul 2025 10:25:20 -0400 Subject: [PATCH 82/97] Setting up the OE: Ngwpc 6892 patch (#56) * initial commit: updated geopackage_viewer to work with iceberg * feat: updated pyiceberg example to remove s3 reliance * patch: Update soil_freeze_thaw.ipynb * Icechunk Tests (#55) --------- Co-authored-by: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> --- examples/geopackage_viewer/viewer.ipynb | 699 ------------------- examples/iceberg/pyiceberg_time_travel.ipynb | 305 +++----- examples/iceberg_data_viewer/viewer.ipynb | 279 ++++++++ src/icefabric/helpers/__init__.py | 2 + src/icefabric/helpers/io.py | 32 + src/icefabric/ui/__init__.py | 10 +- src/icefabric/ui/geo_utils.py | 105 ++- 7 files changed, 517 insertions(+), 915 deletions(-) delete mode 100644 examples/geopackage_viewer/viewer.ipynb create mode 100644 examples/iceberg_data_viewer/viewer.ipynb diff --git a/examples/geopackage_viewer/viewer.ipynb b/examples/geopackage_viewer/viewer.ipynb deleted file mode 100644 index a264560..0000000 --- a/examples/geopackage_viewer/viewer.ipynb +++ /dev/null @@ -1,699 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5c66ee09-04ee-47c3-bc36-8f620a207626", - "metadata": {}, - "source": [ - "# Hydrofabric Geopackage Viewer" - ] - }, - { - "cell_type": "markdown", - "id": "df0435b9-0fd0-4681-9c10-4ac2faaf83aa", - "metadata": {}, - "source": [ - "Read geopackage with geopandas. Get list of layers, loop and read all layers and store in a dictionary.\n", - "\n", - "\n", - "Requires:\n", - "- To be run on machine with access to https://hydroapi.oe.nextgenwaterprediction.com/\n", - "- `.env` with AWS credentials" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "784799a5-df23-4971-ad1b-ea4573610279", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "import geopandas as gpd\n", - "import ipywidgets as widgets\n", - "import pandas as pd\n", - "from ipywidgets import interact\n", - "\n", - "from icefabric.ui import create_time_series_widget, get_geopackage_uri, get_observational_uri" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "515b8c7d-c575-42f3-a9b3-414659e619f5", - "metadata": {}, - "outputs": [], - "source": [ - "from icefabric.helpers import load_creds\n", - "\n", - "# dir is where the .env file is located\n", - "load_creds(dir=Path.cwd().parents[1])" - ] - }, - { - "cell_type": "markdown", - "id": "3278d5ca-d9c5-4950-8731-77b9dd5d3367", - "metadata": {}, - "source": [ - "## Getting streamflow observations for different gages\n" - ] - }, - { - "cell_type": "markdown", - "id": "ac3f8c61-8218-4d6b-bba7-285053166a7e", - "metadata": {}, - "source": [ - "Step 1) getting the data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5124f78a-2f24-4e9c-ba73-93d246bfc2e4", - "metadata": {}, - "outputs": [], - "source": [ - "streamflow_obs_uri = get_observational_uri(gage_id=\"01013500\")\n", - "streamflow_obs_df = pd.read_csv(streamflow_obs_uri)\n", - "streamflow_obs_df" - ] - }, - { - "cell_type": "markdown", - "id": "3ef286cb-c57b-471c-b235-0c7becd81bb4", - "metadata": {}, - "source": [ - "Step 2) streamflow visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a53b0ff1-be18-4baf-af51-f331888974da", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "02a4caac921e415092fb25289f697d6b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(SelectionSlider(description='Start:', layout=Layout(width='95%'), options=('1990-10-01 0…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "streamflow_obs_df[\"dateTime\"] = pd.to_datetime(streamflow_obs_df[\"dateTime\"])\n", - "\n", - "# Create sliders\n", - "start_slider = widgets.SelectionSlider(\n", - " options=streamflow_obs_df[\"dateTime\"].dt.strftime(\"%Y-%m-%d %H:%M:%S\").tolist(),\n", - " description=\"Start:\",\n", - " layout=widgets.Layout(width=\"95%\"),\n", - ")\n", - "\n", - "end_slider = widgets.SelectionSlider(\n", - " options=streamflow_obs_df[\"dateTime\"].dt.strftime(\"%Y-%m-%d %H:%M:%S\").tolist(),\n", - " description=\"End:\",\n", - " layout=widgets.Layout(width=\"95%\"),\n", - ")\n", - "\n", - "# Scatter Plot of observations\n", - "create_time_series_widget(streamflow_obs_df, start_slider, end_slider, point_size=5)" - ] - }, - { - "cell_type": "markdown", - "id": "721e55a1-798e-4122-b69f-d178381086ec", - "metadata": {}, - "source": [ - "## Geopackage Summary" - ] - }, - { - "cell_type": "markdown", - "id": "3adcb9a2-4623-4117-99cf-ad29501bea42", - "metadata": {}, - "source": [ - "Print list of layers, number of catchments, and list of hydrolocations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7fb96ccb-b383-42eb-b8bd-b2355c1e20cc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Layers:\n", - "flowpaths, divides, nexus, lakes, pois, hydrolocations, flowpath-attributes, flowpath-attributes-ml, network, divide-attributes\n", - "Number of catchments:\n", - "26\n", - "Hydrolocations:\n", - "huc12-101900020801, huc12-101900020802, gages-06710385\n" - ] - } - ], - "source": [ - "geopackage_uri = get_geopackage_uri(gage_id=\"06710385\", version=2.2)\n", - "print(geopackage_uri)\n", - "\n", - "layers = gpd.list_layers(geopackage_uri).name.tolist()\n", - "\n", - "layer_df = {}\n", - "\n", - "for layer in layers:\n", - " df = gpd.read_file(geopackage_uri, layer=layer)\n", - " layer_df[layer] = df\n", - "\n", - "layers_str = \", \".join(layers)\n", - "print(\"Layers:\")\n", - "print(layers_str)\n", - "row, col = layer_df[\"divides\"].shape\n", - "print(\"Number of catchments:\")\n", - "print(row)\n", - "print(\"Hydrolocations:\")\n", - "hl = layer_df[\"hydrolocations\"].hl_uri.tolist()\n", - "hl_str = \", \".join(hl)\n", - "print(hl_str)" - ] - }, - { - "cell_type": "markdown", - "id": "77cd7974-cda9-4931-b80c-8d5dd31da250", - "metadata": {}, - "source": [ - "## Map of divides, nexuses, and flowpaths" - ] - }, - { - "cell_type": "markdown", - "id": "b3689b76-8a61-48b8-b827-1b17eb3e3caf", - "metadata": {}, - "source": [ - "Plot divides, nexuses, and flowpaths on a map." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "798ddd22", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    Make this Notebook Trusted to load map: File -> Trust Notebook
    " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "divides = layer_df[\"divides\"].to_crs(epsg=3857)\n", - "\n", - "flowpaths = layer_df[\"flowpaths\"].to_crs(epsg=3857)\n", - "nexus = layer_df[\"nexus\"].to_crs(epsg=3857)\n", - "\n", - "div_ex = divides.explore()\n", - "fl_ex = flowpaths.explore(m=div_ex, color=\"yellow\")\n", - "nexus.explore(m=fl_ex, color=\"red\")" - ] - }, - { - "cell_type": "markdown", - "id": "460fc320-ffd7-4509-a252-d3eedb25b292", - "metadata": {}, - "source": [ - "## View Layers" - ] - }, - { - "cell_type": "markdown", - "id": "c4e1f9d4-ab23-4606-86dd-69432e84a523", - "metadata": {}, - "source": [ - "Select layer and print table" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "858e0398-81ee-4d57-8889-11002174c5ea", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "70048138c0e24c9b99123493f978d25a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(Dropdown(description='layer_name', options=('flowpaths', 'divides', 'nexus', 'lakes', 'p…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "interact(lambda layer_name: display(layer_df[layer_name]), layer_name=layers)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "icefabric", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/iceberg/pyiceberg_time_travel.ipynb b/examples/iceberg/pyiceberg_time_travel.ipynb index a001312..30f6ea8 100644 --- a/examples/iceberg/pyiceberg_time_travel.ipynb +++ b/examples/iceberg/pyiceberg_time_travel.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "c015f447", + "id": "0", "metadata": {}, "source": [ "# Demo: Timetravel in with iceberg tables\n", @@ -16,330 +16,217 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "9f07637f", + "execution_count": null, + "id": "1", "metadata": {}, "outputs": [], "source": [ - "import shlex\n", - "import subprocess\n", + "import os\n", "from pathlib import Path\n", "\n", - "import pyarrow.parquet as pq\n", - "from pyiceberg.catalog import load_catalog" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "db86d0ba", - "metadata": {}, - "outputs": [], - "source": [ - "from icefabric.helpers import load_creds\n", + "from pyiceberg.catalog import load_catalog\n", + "\n", + "from icefabric.helpers import load_creds, load_pyiceberg_config\n", + "\n", + "# Changes the current working dir to be the project root\n", + "current_working_dir = Path.cwd()\n", + "os.chdir(Path.cwd() / \"../../\")\n", + "print(\n", + " f\"Changed current working dir from {current_working_dir} to: {Path.cwd()}. This must run at the project root\"\n", + ")\n", + "\n", "\n", "# dir is where the .env file is located\n", - "load_creds(dir=Path.cwd().parents[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3f901988", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "download: s3://ngwpc-hydrofabric/hydrofabric_parquet/2.2/CONUS/divides.parquet to data/parquet/divides.parquet\n" - ] - }, - { - "data": { - "text/plain": [ - "CompletedProcess(args=['aws', 's3', 'cp', 's3://ngwpc-hydrofabric/hydrofabric_parquet/2.2/CONUS/divides.parquet', 'data/parquet'], returncode=0)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Retrieve sample parquet from s3\n", - "parquet_path = Path(\"./data/parquet\")\n", - "parquet_path.mkdir(parents=True, exist_ok=True)\n", + "load_creds(dir=Path.cwd())\n", "\n", - "subprocess.run(\n", - " shlex.split(\n", - " f\"aws s3 cp s3://ngwpc-hydrofabric/hydrofabric_parquet/2.2/CONUS/divides.parquet {parquet_path}\"\n", - " )\n", + "# Loading the local pyiceberg config settings\n", + "pyiceberg_config = load_pyiceberg_config(Path.cwd())\n", + "catalog = load_catalog(\n", + " name=\"sql\",\n", + " type=pyiceberg_config[\"catalog\"][\"sql\"][\"type\"],\n", + " uri=pyiceberg_config[\"catalog\"][\"sql\"][\"uri\"],\n", + " warehouse=pyiceberg_config[\"catalog\"][\"sql\"][\"warehouse\"],\n", ")" ] }, { "cell_type": "markdown", - "id": "f41913d7-1cc8-42d0-8cea-053b4c23d6bb", + "id": "2", "metadata": {}, "source": [ - "Read divides parquet file" + "Load a specific table that we would like to time travel from" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "6651c9e7-c38d-46d1-b6a4-491baf12cc90", + "execution_count": null, + "id": "3", "metadata": {}, "outputs": [], "source": [ - "df = pq.read_table(\"data/parquet/divides.parquet\")" + "table = catalog.load_table(\"streamflow_observations.usgs_hourly\")\n", + "table.inspect.snapshots()" ] }, { "cell_type": "markdown", - "id": "0e4d5aaf-d51c-486c-86ac-32eeb2b7e74f", + "id": "4", "metadata": {}, "source": [ - "Create data catalog stored in \"warehouse\" directory." + "Let's view this data and see what's there" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "96974839-0308-4256-bb3e-0cddfea9d037", + "execution_count": null, + "id": "5", "metadata": {}, "outputs": [], "source": [ - "warehouse_path = Path(\"./warehouse\")\n", - "warehouse_path.mkdir(exist_ok=True)\n", - "catalog = load_catalog(\n", - " \"default\",\n", - " **{\n", - " \"type\": \"sql\",\n", - " \"uri\": f\"sqlite:///{warehouse_path}/pyiceberg_catalog.db\",\n", - " \"warehouse\": f\"file://{warehouse_path}\",\n", - " },\n", - ")" + "df = table.scan().to_pandas().set_index(\"time\")\n", + "df.tail()" ] }, { "cell_type": "markdown", - "id": "2fe13a4d-0c18-4f2b-953e-a9c658b7225a", + "id": "6", "metadata": {}, "source": [ - "Create Iceberg table for divides" + "A snapshot was created for the initial append. Store this snapshot id for later use." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "efed8866-134c-40f9-969b-1e4c20b5f11b", + "execution_count": null, + "id": "7", "metadata": {}, "outputs": [], "source": [ - "catalog.create_namespace(\"default\")\n", - "table = catalog.create_table(\n", - " \"default.divides\",\n", - " schema=df.schema,\n", - ")" + "for snapshot in table.snapshots():\n", + " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")\n", + "snapshot_id = table.metadata.snapshots[0].snapshot_id" ] }, { "cell_type": "markdown", - "id": "f29ed9b6-ba8d-4a54-bbc5-eec900f1cec6", + "id": "8", "metadata": {}, "source": [ - "Add divides data to Iceberg table and print the number of rows. There should be 831777 divides for CONUS." + "Add a new column for a fake gauge" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "0a9d4717-9055-4ac7-819c-aca6c2435c95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "831777" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "table.append(df)\n", - "len(table.scan().to_arrow())" - ] - }, - { - "cell_type": "markdown", - "id": "a1c2b449-667a-4bcd-9c94-d0e604c33f8c", + "execution_count": null, + "id": "9", "metadata": {}, + "outputs": [], "source": [ - "A snapshot was created for the initial append. Store this snapshot id for later use." + "import numpy as np\n", + "\n", + "n = len(df)\n", + "x = np.linspace(0, n, n)\n", + "y = np.sin(2 * np.pi * 1 * x / n).astype(np.float32)" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "78ee6462-5db1-4a40-89b8-3837e63711cd", + "execution_count": null, + "id": "10", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Snapshot ID: 6392225911450853109; Summary: operation=Operation.APPEND\n" - ] - } - ], + "outputs": [], "source": [ - "for snapshot in table.snapshots():\n", - " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")\n", - "snapshot_id = table.metadata.snapshots[0].snapshot_id" + "import pyarrow as pa\n", + "\n", + "df[\"12345678\"] = y\n", + "_df = pa.Table.from_pandas(df)\n", + "with table.update_schema() as update_schema:\n", + " update_schema.union_by_name(_df.schema)\n", + "table.overwrite(_df)" ] }, { "cell_type": "markdown", - "id": "8f245529-a3c9-4a99-bcb3-fe1078779498", + "id": "11", "metadata": {}, "source": [ - "Add a new column for flowpath length in m. Overwrite original table." + "There should be a new \"12345678\" column." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "975224d2-deb0-420f-be80-6b5cac7bbe60", + "execution_count": null, + "id": "12", "metadata": {}, "outputs": [], "source": [ - "import pyarrow.compute as pc\n", - "\n", - "df = df.append_column(\"lengthm\", pc.multiply(df[\"lengthkm\"], 1000))\n", - "with table.update_schema() as update_schema:\n", - " update_schema.union_by_name(df.schema)\n", - "table.overwrite(df)" + "table.schema" ] }, { "cell_type": "markdown", - "id": "b3ff7a72-70f6-44e8-a6da-d1c9766c6782", + "id": "13", "metadata": {}, "source": [ - "There should be a new \"lengthm\" column." + "There should now be three snapshots. The original, a delete, and an append with the new column." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "d826f290-79bf-4bab-a508-cd73bb3aadb6", + "execution_count": null, + "id": "14", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "table.schema" + "for snapshot in table.snapshots():\n", + " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")" ] }, { "cell_type": "markdown", - "id": "668d3f57-e9ef-47e7-a268-05699debcf8f", + "id": "15", "metadata": {}, "source": [ - "There should now be three snapshots. The original, a delete, and an append with the new column." + "You can use the scan function and the first snapshot ID (this variable was saved earlier) to look at the table before the\n", + "new column was added. This table doesn't have 12345678." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "6b3a70f2-2271-4bcd-b5fb-83683f36705c", + "execution_count": null, + "id": "16", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Snapshot ID: 6392225911450853109; Summary: operation=Operation.APPEND\n", - "Snapshot ID: 3770483438741118773; Summary: operation=Operation.DELETE\n", - "Snapshot ID: 6082380623201209864; Summary: operation=Operation.APPEND\n" - ] - } - ], + "outputs": [], "source": [ - "for snapshot in table.snapshots():\n", - " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")" + "# scan = table.scan(row_filter=\"divide_id\" == \"cat-276\", selected_fields=('divide_id', 'lengthm')).to_arrow()\n", + "# print(scan)\n", + "table.scan(snapshot_id=snapshot_id).to_pandas().tail()\n", + "# table.scan(snapshot_id=snapshot_id)" ] }, { "cell_type": "markdown", - "id": "3685eddd-31bb-4c07-a656-6cfb5e97881a", + "id": "17", "metadata": {}, "source": [ - "You can use the scan function and the first snapshot ID (this variable was saved earlier) to look at the table before the\n", - "new column was added. This table doesn't have lengthm." + "whereas loading without the snapshot gives the latest data" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "2f47f9e3-7e6b-468b-a978-2c433e662c12", + "execution_count": null, + "id": "18", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pyarrow.Table\n", - "divide_id: large_string\n", - "toid: large_string\n", - "type: large_string\n", - "ds_id: double\n", - "areasqkm: double\n", - "vpuid: large_string\n", - "id: large_string\n", - "lengthkm: double\n", - "tot_drainage_areasqkm: double\n", - "has_flowline: bool\n", - "geometry: large_binary\n" - ] - } - ], + "outputs": [], "source": [ - "# scan = table.scan(row_filter=\"divide_id\" == \"cat-276\", selected_fields=('divide_id', 'lengthm')).to_arrow()\n", - "# print(scan)\n", - "print(table.scan(snapshot_id=snapshot_id).to_arrow().to_string())\n", - "# table.scan(snapshot_id=snapshot_id)" + "table.scan().to_pandas().tail()" ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/examples/iceberg_data_viewer/viewer.ipynb b/examples/iceberg_data_viewer/viewer.ipynb new file mode 100644 index 0000000..22eb6db --- /dev/null +++ b/examples/iceberg_data_viewer/viewer.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Hydrofabric Geopackage Viewer" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "Reads/extracts files from the NGWPC pyiceberg resources" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd\n", + "from ipywidgets import interact\n", + "from pyiceberg.catalog import load_catalog\n", + "\n", + "from icefabric.helpers import load_creds, load_pyiceberg_config\n", + "from icefabric.hydrofabric import subset\n", + "from icefabric.schemas import IdType\n", + "from icefabric.ui import create_time_series_widget, get_hydrofabric_gages, get_streamflow_data\n", + "\n", + "# Changes the current working dir to be the project root\n", + "current_working_dir = Path.cwd()\n", + "os.chdir(Path.cwd() / \"../../\")\n", + "print(\n", + " f\"Changed current working dir from {current_working_dir} to: {Path.cwd()}. This must run at the project root\"\n", + ")\n", + "\n", + "\n", + "# dir is where the .env file is located\n", + "load_creds(dir=Path.cwd())\n", + "\n", + "# Loading the local pyiceberg config settings\n", + "pyiceberg_config = load_pyiceberg_config(Path.cwd())\n", + "catalog = load_catalog(\n", + " name=\"sql\",\n", + " type=pyiceberg_config[\"catalog\"][\"sql\"][\"type\"],\n", + " uri=pyiceberg_config[\"catalog\"][\"sql\"][\"uri\"],\n", + " warehouse=pyiceberg_config[\"catalog\"][\"sql\"][\"warehouse\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "## Getting streamflow observations for different gages\n" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "Step 1) getting the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "\n", + "# Using a local warehouse for this example. This was created through the following command and NGWPC test AWS account\n", + "# python tools/pyiceberg/export_catalog.py --namespace streamflow_observations\n", + "streamflow_obs_df = get_streamflow_data(catalog_name=\"sql\", **pyiceberg_config[\"catalog\"])\n", + "\n", + "# List all gauge IDs\n", + "\n", + "pprint(streamflow_obs_df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "# select the gauge ID you want to use:\n", + "gage_id = \"12145500\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "streamflow_obs_df[\"time\"] = pd.to_datetime(streamflow_obs_df[\"time\"])\n", + "\n", + "# Scatter Plot of observations\n", + "create_time_series_widget(streamflow_obs_df, point_size=5, time_col=\"time\", flow_col=gage_id)" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "## Geopackage Summary" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "Print list of layers, number of catchments, and list of hydrolocations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "pprint(get_hydrofabric_gages(catalog=catalog))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "# Using a local warehouse for this example. This was created through the following command and NGWPC test AWS account\n", + "# python tools/pyiceberg/export_catalog.py --namespace conus_hf\n", + "gage_id = \"11280000\"\n", + "layers = [\"flowpaths\", \"nexus\", \"divides\", \"network\", \"hydrolocations\", \"pois\"]\n", + "layers_df = subset(catalog=catalog, layers=layers, identifier=f\"gages-{gage_id}\", id_type=IdType.HL_URI)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Layers:\")\n", + "print(list(layers_df.keys()))\n", + "row, col = layers_df[\"divides\"].shape\n", + "print(\"Number of catchments:\")\n", + "print(row)\n", + "print(\"Hydrolocations:\")\n", + "hl = layers_df[\"hydrolocations\"].hl_uri.tolist()\n", + "hl_str = \", \".join(hl)\n", + "print(hl_str)" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "## Map of divides, nexuses, and flowpaths" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "Plot divides, nexuses, and flowpaths on a map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "divides = layers_df[\"divides\"].to_crs(epsg=3857)\n", + "\n", + "flowpaths = layers_df[\"flowpaths\"].to_crs(epsg=3857)\n", + "nexus = layers_df[\"nexus\"].to_crs(epsg=3857)\n", + "\n", + "div_ex = divides.explore()\n", + "fl_ex = flowpaths.explore(m=div_ex, color=\"yellow\")\n", + "nexus.explore(m=fl_ex, color=\"red\")" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "## View Layers" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "Select layer and print table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Configure pandas display options for better formatting\n", + "pd.set_option(\"display.max_columns\", None)\n", + "pd.set_option(\"display.width\", None)\n", + "pd.set_option(\"display.max_colwidth\", 50)\n", + "pd.set_option(\"display.expand_frame_repr\", False)\n", + "\n", + "# Interactive display with limited rows\n", + "interact(lambda layer_name: layers_df[layer_name].head(20), layer_name=layers)" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "icefabric", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/icefabric/helpers/__init__.py b/src/icefabric/helpers/__init__.py index 5bcf70b..f3067cb 100644 --- a/src/icefabric/helpers/__init__.py +++ b/src/icefabric/helpers/__init__.py @@ -7,6 +7,7 @@ ) from .creds import load_creds from .geopackage import table_to_geopandas, to_geopandas +from .io import load_pyiceberg_config from .nc_conv_utils import conv_nc, convert_files_to_netcdf4, convert_nc_files_from_s3 from .topobathy_ic_to_tif import convert_topobathy_to_tiff @@ -21,4 +22,5 @@ "convert_nc_files_from_s3", "conv_nc", "convert_topobathy_to_tiff", + "load_pyiceberg_config", ] diff --git a/src/icefabric/helpers/io.py b/src/icefabric/helpers/io.py index 306405e..c8bf202 100644 --- a/src/icefabric/helpers/io.py +++ b/src/icefabric/helpers/io.py @@ -2,7 +2,9 @@ import tempfile import zipfile from pathlib import Path +from typing import Any +import yaml from tqdm import tqdm from icefabric.schemas.modules import NWMProtocol @@ -39,3 +41,33 @@ def _create_config_zip(configs: list[NWMProtocol], output_path: Path, **kwargs): for file_path in config_files: archive_name = file_path.name f.write(file_path, archive_name) + + +def load_pyiceberg_config(cwd: Path) -> dict[str, Any]: + """Reads a .pyiceberg.yaml config file to memory + + Parameters + ---------- + cwd : Path + the path to the .pyiceberg.yaml file + + Returns + ------- + dict[str, Any] + The pyiceberg yaml file + + Raises + ------ + FileNotFoundError + Can't find the YAML file in the CWD + yaml.YAMLError + Error parsing the YAML file + """ + try: + with open(cwd / ".pyiceberg.yaml", encoding="utf-8") as file: + data = yaml.safe_load(file) + return data if data is not None else {} + except FileNotFoundError as e: + raise FileNotFoundError(f".pyiceberg YAML file not found in cwd: {cwd}") from e + except yaml.YAMLError as e: + raise yaml.YAMLError(f"Error parsing .pyiceberg YAML file: {e}") from e diff --git a/src/icefabric/ui/__init__.py b/src/icefabric/ui/__init__.py index 75174ee..091f21d 100644 --- a/src/icefabric/ui/__init__.py +++ b/src/icefabric/ui/__init__.py @@ -1,9 +1,17 @@ """Contains helper functions to support NWM modules""" -from .geo_utils import create_time_series_widget, get_geopackage_uri, get_observational_uri +from .geo_utils import ( + create_time_series_widget, + get_geopackage_uri, + get_hydrofabric_gages, + get_observational_uri, + get_streamflow_data, +) __all__ = [ "create_time_series_widget", "get_geopackage_uri", "get_observational_uri", + "get_streamflow_data", + "get_hydrofabric_gages", ] diff --git a/src/icefabric/ui/geo_utils.py b/src/icefabric/ui/geo_utils.py index 3ef7846..c07cc28 100644 --- a/src/icefabric/ui/geo_utils.py +++ b/src/icefabric/ui/geo_utils.py @@ -2,6 +2,91 @@ import ipywidgets as widgets import matplotlib.pyplot as plt import pandas as pd +from pyiceberg import expressions +from pyiceberg.catalog import Catalog, load_catalog +from pyiceberg.exceptions import NoSuchTableError + +from icefabric.schemas.hydrofabric import HydrofabricDomains + + +def get_streamflow_data( + catalog_name: str, + gage_id: str | None = None, + row_filter: str | None = None, + snapshot_id: int | None = None, + **kwargs, +) -> pd.DataFrame: + """Gets streamflow data for the Jupyter UI to display + + Parameters + ---------- + gage_id : str + The gauge ID you are looking to view + catalog_name : str, optional + The pyiceberg catalog name + row_filter : str | None, optional + The row filter to specify a start/end time, by default None + snapshot_id : int | None, optional + the snapshot ID to , by default None + **kwargs + the pyiceberg.yaml file settings + + Returns + ------- + pd.DataFrame + The streamflow output for the specified gauge + + Raises + ------ + NoSuchTableError + There is no existing record for the streamflow values + """ + catalog = load_catalog( + name=catalog_name, + type=kwargs[catalog_name]["type"], + uri=kwargs[catalog_name]["uri"], + warehouse=kwargs[catalog_name]["warehouse"], + ) + try: + table = catalog.load_table("streamflow_observations.usgs_hourly") + if row_filter is None: + df = table.scan(snapshot_id=snapshot_id).to_pandas() + else: + df = table.scan(row_filter=row_filter, snapshot_id=snapshot_id).to_pandas() + if gage_id is not None: + return df[["time", gage_id]] + else: + return df + except NoSuchTableError as e: + msg = "No table/namespace found for streamflow_observations.usgs_hourly in the catalog" + print(msg) + raise NoSuchTableError from e + + +def get_hydrofabric_gages( + catalog: Catalog, domain: HydrofabricDomains = HydrofabricDomains.CONUS +) -> list[str]: + """Returns the hydrofabric gages within the network table + + Parameters + ---------- + catalog : Catalog + the pyiceberg warehouse reference + domain : HydrofabricDomains, optional + the hydrofabric domain, by default HydrofabricDomains.CONUS + + Returns + ------- + list[str] + The list of all gages in the conus-hf + """ + return ( + catalog.load_table(f"{domain.value}.network") + .scan(row_filter=expressions.StartsWith("hl_uri", "gages-")) + .to_pandas() + .drop_duplicates(subset="hl_uri", keep="first")[("hl_uri")] + .tolist() + ) def get_observational_uri( @@ -77,10 +162,7 @@ def get_geopackage_uri( def create_time_series_widget( - df: pd.DataFrame, - start_slider: widgets.SelectionSlider, - end_slider: widgets.SelectionSlider, - point_size: float = 30, + df: pd.DataFrame, flow_col: str, point_size: float = 30, time_col: str = "time" ): """ Creates an interactive time series plot using matplotlib and ipywidgets. @@ -91,6 +173,17 @@ def create_time_series_widget( start_slider (widgets.SelectionSlider): Widget for selecting start time end_slider (widgets.SelectionSlider): Widget for selecting end time """ + start_slider = widgets.SelectionSlider( + options=df["time"].dt.strftime("%Y-%m-%d %H:%M:%S").tolist(), + description="Start:", + layout=widgets.Layout(width="95%"), + ) + + end_slider = widgets.SelectionSlider( + options=df["time"].dt.strftime("%Y-%m-%d %H:%M:%S").tolist(), + description="End:", + layout=widgets.Layout(width="95%"), + ) @widgets.interact(start=start_slider, end=end_slider) def plot_flow(start, end): @@ -101,10 +194,10 @@ def plot_flow(start, end): print("Warning: Start must be before End.") return - filtered = df[(df["dateTime"] >= start_dt) & (df["dateTime"] <= end_dt)] + filtered = df[(df[time_col] >= start_dt) & (df[time_col] <= end_dt)] fig, ax = plt.subplots(figsize=(10, 4)) - ax.scatter(filtered["dateTime"], filtered["q_cms"], s=point_size, label="Flow rate (cms)", alpha=0.7) + ax.scatter(filtered[time_col], filtered[flow_col], s=point_size, label="Flow rate (cms)", alpha=0.7) ax.set_xlabel("Date Time") ax.set_ylabel("Discharge (cms)") ax.set_title("Streamflow Time Series (Scatter Plot)") From 8cbe55fcd371d3e62a2145ba8747e7afc268375c Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 15 Jul 2025 11:29:51 -0400 Subject: [PATCH 83/97] init factors in ras_xs --- src/icefabric/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/icefabric/__init__.py b/src/icefabric/__init__.py index 8116e6b..ca70965 100644 --- a/src/icefabric/__init__.py +++ b/src/icefabric/__init__.py @@ -1,6 +1,6 @@ """An Apache Iceberg + Icechunk implementation of Hydrofabric data services""" -from . import builds, cli, helpers, hydrofabric, modules, schemas, ui +from . import builds, cli, helpers, hydrofabric, modules, schemas, ui, ras_xs from ._version import __version__ -__all__ = ["__version__", "builds", "cli", "hydrofabric", "helpers", "modules", "schemas", "ui"] +__all__ = ["__version__", "builds", "cli", "hydrofabric", "helpers", "modules", "schemas", "ui", "ras_xs"] From 7eed50efcecdafc01100181e5ec4fe1041021d3b Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 15 Jul 2025 11:31:24 -0400 Subject: [PATCH 84/97] Create __init__.py --- src/icefabric/ras_xs/__init__.py | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 src/icefabric/ras_xs/__init__.py diff --git a/src/icefabric/ras_xs/__init__.py b/src/icefabric/ras_xs/__init__.py new file mode 100644 index 0000000..81f0c62 --- /dev/null +++ b/src/icefabric/ras_xs/__init__.py @@ -0,0 +1,7 @@ +"""Helper functions designed to assist with managing data.""" + +from .subset import subset_xs + +__all__ = [ + "subset_xs" +] From 61c2b0325fc404080c2ce7c57dace9625d18802e Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 15 Jul 2025 11:32:02 -0400 Subject: [PATCH 85/97] adding filter xs script --- src/icefabric/ras_xs/subset.py | 69 ++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 src/icefabric/ras_xs/subset.py diff --git a/src/icefabric/ras_xs/subset.py b/src/icefabric/ras_xs/subset.py new file mode 100644 index 0000000..87f72cc --- /dev/null +++ b/src/icefabric/ras_xs/subset.py @@ -0,0 +1,69 @@ +"""A file to hold ras cross-section tools""" + +from pathlib import Path + +import geopandas as gpd +import pandas as pd +from botocore.exceptions import ClientError +from pyiceberg.catalog import Catalog +from pyiceberg.expressions import And, EqualTo +from pyiceberg.table import Table + +from icefabric.helpers.geopackage import to_geopandas +from icefabric.schemas.ras_xs import XsType + +def subset_xs(catalog: Catalog, + identifier: str, + output_file: Path | None = None, + xstype: XsType = XsType.MIP, + **kwarg) -> dict[str, pd.DataFrame | gpd.GeoDataFrame] | None: + """Returns a geopackage subset from the xs iceberg catalog. + + This function delivers a subset of the cross-sections data by tracing from a + given HUC identifier & collecting all relevant cross-section information based on + additional identifiers provided. + + Parameters + ---------- + catalog : Catalog + The xs iceberg catalog comprised of the ras cross-sections data tables. + identifier : str + The HUC identifier. + xstype : Type of cross-section data to subset. + The type of cross-section data to read (e.g. mip, ble). + output_file : Path, optional + The output file path where the geopackage will be saved. + + Returns + ------- + GeoDataFrame + Subset of the mip cross-sections data based on identifiers. + + """ + ds_reach_id = kwarg.get('ds_reach_id', None) + + try: + xs_table = catalog.load_table(f"{xstype.value}_xs.{identifier}") + + except ClientError as e: + msg = "AWS Test account credentials expired. Can't access remote S3 endpoint" + print(msg) + raise e + + # By default, mip xs tables are filtered by huc + df = xs_table.scan().to_pandas() + + if ds_reach_id: + filter_cond = EqualTo("ds_reach_id", ds_reach_id) + df = xs_table.scan(row_filter=filter_cond).to_pandas() + data_gdf = to_geopandas(df) + + # Save data. + if output_file: + if len(data_gdf) > 0: + gpd.GeoDataFrame(data_gdf).to_file(output_file, + layer='ras_xs', + driver="GPKG") + else: + print(f"Warning: Dataframe is empty") + return data_gdf From 2f6c350f63ad813b0474cb1cbddccd1442aca2c2 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 15 Jul 2025 11:33:50 -0400 Subject: [PATCH 86/97] Factor enum for mip, ble, etc --- src/icefabric/schemas/__init__.py | 2 ++ src/icefabric/schemas/ras_xs.py | 18 ++++++++++++++++++ 2 files changed, 20 insertions(+) create mode 100644 src/icefabric/schemas/ras_xs.py diff --git a/src/icefabric/schemas/__init__.py b/src/icefabric/schemas/__init__.py index 3190283..2d2a0b4 100644 --- a/src/icefabric/schemas/__init__.py +++ b/src/icefabric/schemas/__init__.py @@ -3,6 +3,7 @@ from .hydrofabric import UPSTREAM_VPUS, HydrofabricDomains, IdType from .modules import SFT, Albedo, IceFractionScheme from .topobathy import FileType, NGWPCLocations, NGWPCTestLocations +from .ras_xs import XsType __all__ = [ "UPSTREAM_VPUS", @@ -14,4 +15,5 @@ "FileType", "NGWPCLocations", "NGWPCTestLocations", + "XsType" ] diff --git a/src/icefabric/schemas/ras_xs.py b/src/icefabric/schemas/ras_xs.py new file mode 100644 index 0000000..167c3da --- /dev/null +++ b/src/icefabric/schemas/ras_xs.py @@ -0,0 +1,18 @@ +"""Contains all schemas and enums for the RAS cross-sections""" + +from enum import Enum + + +class XsType(str, Enum): + """The domains used when querying the cross-sections. + + Attributes + ---------- + MIP : str + Mapping Information Platform + BLE : str + Base Level Engineering + """ + + MIP = "mip" + BLE = "ble" From 94a9bd9c8a1f39caaa2afb39575cc2c9c1c93836 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 15 Jul 2025 11:35:50 -0400 Subject: [PATCH 87/97] adding xs router to main --- app/main.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/app/main.py b/app/main.py index b6c700e..e31f282 100644 --- a/app/main.py +++ b/app/main.py @@ -11,6 +11,7 @@ from app.routers.hydrofabric.router import api_router as hydrofabric_api_router from app.routers.nwm_modules.router import sft_router, topoflow_router from app.routers.streamflow_observations.router import api_router as streamflow_api_router +from app.routers.ras_xs.router import api_router as ras_api_router from icefabric.helpers import load_creds @@ -49,6 +50,7 @@ class HealthCheck(BaseModel): app.include_router(streamflow_api_router, prefix="/v1") app.include_router(sft_router, prefix="/v1") app.include_router(topoflow_router, prefix="/v1") +app.include_router(ras_api_router, prefix="/v1") @app.head( From a7eebf67b2e8c15e5a812c470484ec636a24626e Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 15 Jul 2025 11:36:47 -0400 Subject: [PATCH 88/97] Create __init__.py --- app/routers/ras_xs/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 app/routers/ras_xs/__init__.py diff --git a/app/routers/ras_xs/__init__.py b/app/routers/ras_xs/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/app/routers/ras_xs/__init__.py @@ -0,0 +1 @@ + From f1881029453c8770320a62e1e1fd48195f839d43 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 15 Jul 2025 11:39:13 -0400 Subject: [PATCH 89/97] adding xs router --- app/routers/ras_xs/router.py | 137 +++++++++++++++++++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 app/routers/ras_xs/router.py diff --git a/app/routers/ras_xs/router.py b/app/routers/ras_xs/router.py new file mode 100644 index 0000000..6436b98 --- /dev/null +++ b/app/routers/ras_xs/router.py @@ -0,0 +1,137 @@ +import pathlib +import tempfile +import uuid + +from fastapi import APIRouter, HTTPException, Path, Query +from fastapi.responses import FileResponse +from pyiceberg.catalog import load_catalog +from starlette.background import BackgroundTask + +from icefabric.ras_xs import subset_xs +from icefabric.schemas import XsType + +api_router = APIRouter(prefix="/ras_xs") + +@api_router.get("/{identifier}/") +async def get_xs_subset_gpkg(identifier: str = Path(..., + description="HUC-8 identifier to filter by huc ID", + example="02040106"), + xstype: XsType = Query(XsType.MIP, + description="The iceberg namespace used to query the cross-sections" + ) +): + """ + Get geopackage subset from the mip xs iceberg catalog by table identifier (aka huc ID). + + This endpoint will query cross-sections from the mip xs iceberg catalog by huc & return + the data subset as a downloadable geopackage file. + + """ + catalog = load_catalog("glue") + unique_id = str(uuid.uuid4())[:8] + temp_dir = pathlib.Path(tempfile.gettempdir()) + tmp_path = temp_dir / f"ras_xs_{identifier}_{unique_id}.gpkg" + try: + # Create data subset + data_gdf=subset_xs(catalog=catalog, + identifier=f"{identifier}", + output_file=tmp_path, + xstype=xstype) + + if not tmp_path.exists(): + raise HTTPException(status_code=500, + detail=f"Failed to create geopackage file at {tmp_path}.") + if tmp_path.stat().st_size == 0: + tmp_path.unlink(missing_ok=True) + raise HTTPException(status_code=404, + detail=f"No data found for HUC {identifier}.") + + # Verify it's actually a file, not a directory + if not tmp_path.is_file(): + raise HTTPException(status_code=500, + detail=f"Expected file, but got directory at {tmp_path}.") + + print(f"Returning file: {tmp_path} (size: {tmp_path.stat().st_size} bytes)") + + download_filename = f"ras_xs_huc{identifier}.gpkg" + + return FileResponse(path=str(tmp_path), + filename=download_filename, + media_type="application/geopackage+sqlite3", + headers={"Data_Source": f"{xstype}_xs", + "HUC Identifier": identifier, + "Description": f"{xstype} RAS Cross-Section Geopackage", + "Total Records": f"{len(data_gdf)}"}, + background=BackgroundTask(lambda: tmp_path.unlink(missing_ok=True))) + except HTTPException: + raise + except Exception: + + # Clean up temp file if it exists + if "tmp_path" in locals() and tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + raise + +@api_router.get("/{identifier}/dsreachid={ds_reach_id}") +async def get_xs_subset_by_huc_reach_gpkg(identifier: str = Path(..., + description="Identifier to filter data by huc ID", + example="02040106"), + ds_reach_id: str = Path(..., + description="Identifier to filter data by downstream reach ID)", + example="4188251"), + xstype: XsType = Query(XsType.MIP, + description="The iceberg namespace used to query the cross-sections")): + """ + Get geopackage subset from the mip xs iceberg catalog by reach ID at huc ID. + + This endpoint will query cross-sections from the mip xs iceberg catalog by + downstream reach ID at given huc ID -- returning the data subset as a downloadable geopackage file. + + """ + catalog = load_catalog("glue") + unique_id = str(uuid.uuid4())[:8] + temp_dir = pathlib.Path(tempfile.gettempdir()) + tmp_path = temp_dir / f"ras_xs_{identifier}_{ds_reach_id}_{unique_id}.gpkg" + try: + # Create data subset + data_gdf = subset_xs(catalog=catalog, + identifier=f"{identifier}", + ds_reach_id=f"{ds_reach_id}", + output_file=tmp_path, + xstype=xstype) + + if not tmp_path.exists(): + raise HTTPException(status_code=500, + detail=f"Failed to create geopackage file at {tmp_path}.") + if tmp_path.stat().st_size == 0: + tmp_path.unlink(missing_ok=True) + raise HTTPException(status_code=404, + detail=f"No data found for downstream reach id {ds_reach_id} @ HUC{identifier}.") + + # Verify it's actually a file, not a directory + if not tmp_path.is_file(): + raise HTTPException(status_code=500, + detail=f"Expected file, but got directory at {tmp_path}.") + + print(f"Returning file: {tmp_path} (size: {tmp_path.stat().st_size} bytes)") + + download_filename = f"ras_xs_huc{identifier}_dsreachid{ds_reach_id}.gpkg" + + return FileResponse(path=str(tmp_path), + filename=download_filename, + media_type="application/geopackage+sqlite3", + headers={"Data_Source": f"{xstype}_xs", + "HUC Identifier": identifier, + "DS Reach Identifier": ds_reach_id, + "Description": f"{xstype} RAS Cross-Section Geopackage", + "Total Records": f"{len(data_gdf)}"}, + background=BackgroundTask(lambda: tmp_path.unlink(missing_ok=True))) + + except HTTPException: + raise + except Exception: + + # Clean up temp file if it exists + if "tmp_path" in locals() and tmp_path.exists(): + tmp_path.unlink(missing_ok=True) + raise From c4fbdcadd58f1f9661542431e9cc99a1933c3124 Mon Sep 17 00:00:00 2001 From: Quercus Hamlin <75846376+quercoak@users.noreply.github.com> Date: Tue, 15 Jul 2025 12:47:33 -0400 Subject: [PATCH 90/97] docs: API update --- README.md | 2 +- docs/index.md | 7 ++- docs/user_guide/icefabric_api.md | 73 +++++++++++++++++++++++++++++--- 3 files changed, 74 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 6b61ddf..fb3d4e7 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ To run the API locally, ensure your `.env` file in your project root has the rig ```sh python -m app.main ``` -This should spin up the API services. +This should spin up the API services at `localhost:8000/`. If you are running the API locally, you can run ```sh diff --git a/docs/index.md b/docs/index.md index ff196b6..2ac7447 100644 --- a/docs/index.md +++ b/docs/index.md @@ -22,7 +22,12 @@ To run the API locally, ensure your `.env` file in your project root has the rig ```sh python -m app.main ``` -This should spin up the API services. +This should spin up the API services at `localhost:8000/`. + +If you are running the API locally, you can run +```sh +python -m app.main --catalog sql +``` ### Building the API through Docker To run the API locally with Docker, ensure your `.env` file in your project root has the right credentials, then run diff --git a/docs/user_guide/icefabric_api.md b/docs/user_guide/icefabric_api.md index 86a8850..1625713 100644 --- a/docs/user_guide/icefabric_api.md +++ b/docs/user_guide/icefabric_api.md @@ -9,17 +9,17 @@ The Icefabric API is a FastAPI-based service that provides access to EDFS data s The API consists of several key components: 1. **Main Application** (`app/main.py`) - FastAPI application with health checks and router configuration -2. **Data Routers** - Handles all data endpoints. Currently only working with streamflow observations -3. **Apache Iceberg Backend** - SQLite-backed catalog stored in `/tmp/warehouse` built using `icefabric_manage` +2. **Data Routers** - Handles all data endpoints. Streamflow observations, Hydrofabric subsetting, National Water Model module configuration, and HEC-RAS cross-section retrieval are supported. +3. **Apache Iceberg Backend** - Defaults to hosted AWS Glue catalog. Local SQLite-backed catalog may be built using instructions below. ### Running the API locally -To run the API locally, ensure your `.env` file in your project root has the right credentials, then run +To run the API locally, ensure your `.env` file in your project root has the right credentials (`test`), then run ```sh uv sync -source +source .venv/bin/activate python -m app.main ``` -This should spin up the API services +This should spin up the API services at `localhost:8000/` ### Building the API through Docker To run the API locally with Docker, ensure your `.env` file in your project root has the right credentials, then run @@ -29,6 +29,31 @@ docker compose -f docker/compose.yaml up ``` This should spin up the API services +### Running the API with a local Iceberg catalog - Advanced Use +To run the API locally against a local catalog, the catalog must first be exported from glue. In the following code block, run build script for as many catalog namespaces as you need. Ensure your `.env` file in your project root has the right credentials (`test`), then run +```sh +uv sync +source .venv/bin/activate +python tools/pyiceberg/export_catalog.py --namespace conus_hf +# Run additional tool times with other namespaces as necessary +``` + +To view the namespaces hosted on glue, you can run the following commands in the terminal: +```python +>>> from pyiceberg.catalog import load_catalog +>>> catalog = load_catalog("glue") +>>> catalog.list_namespaces() +``` + + +To run the API locally with a local SQL backend, ensure your `.env` file in your project root has the right credentials (`test`), then run +```sh +uv sync +source .venv/bin/activate +python -m app.main --catalog sql +``` +This should spin up the API services + ## How It Works ### Data Flow @@ -42,14 +67,32 @@ This should spin up the API services #### Observations Currently supports: + - **USGS** - United States Geological Survey hourly streamflow data #### Hydrofabric Provides geospatial watershed data: + - **Subset Generation** - Creates upstream watershed subsets from identifiers !!! note "Data Storage" - All data is stored as Apache Iceberg tables in a SQLite-backed catalog locally built at `/tmp/warehouse/pyiceberg_catalog.db` + All data is stored remotely as Apache Iceberg tables on AWS glue unless you built the catalog locally. Then, it is stored at SQLite-backed catalog locally built at `/tmp/warehouse/pyiceberg_catalog.db` + +### National Water Model Modules +Retrieve National Water Model (NWM) module parameters. + +Currently supports: + +- **Soil Freeze Thaw (SFT)** - Retrieve paramters for Soil Freeze Thaw module +- **TopoFlow-Glacier** - Retrieve parameters for the TopoFlow Glacier module + +### RAS Cross-sections +Retrieves geopackage data of HEC-RAS cross-sections + +Currently supports: + +- **HUC ID**: Download a geopackage for given HUC ID +- **HUC ID** and **Reach ID**: Download a geopackage for a given HUC ID and Reach ID ## Usage Examples @@ -206,6 +249,24 @@ curl "http://localhost:8000/v1/hydrofabric/01010000/gpkg" -o "subset.gpkg" curl "http://localhost:8000/v1/hydrofabric/01031500/gpkg" -o "subset.gpkg" ``` +### NWM Modules +```bash +# Return parameters for Soil Freeze Thaw by catchment +curl "http://localhost:8000/v1/modules/sft/?identifier=01010000&domain=conus_hf&use_schaake=false" + +# Return albedo value for given catchment state (snow, ice, or other) +curl "http://localhost:8000/v1/modules/topoflow/albedo?landcover=snow" +``` + +### RAS Cross-sections +```bash +# Download RAS cross-sections for a HUC ID +curl "http://localhost:8000/v1/ras_xs/02040106/" -o "ras_02040106.gpkg" + +# Download RAS cross-sections for a HUC ID and Reach ID +curl "http://localhost:8000/v1/ras_xs/02040106/dsreachid=4188251" -o "ras_02040106_4188251.gpkg" +``` + ### Health Check ```bash From 22514c37c321251579934f5f0bdd5a3970c9c512 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 15 Jul 2025 15:32:42 -0400 Subject: [PATCH 91/97] xs router test script added --- tests/app/test_xs_router.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 tests/app/test_xs_router.py diff --git a/tests/app/test_xs_router.py b/tests/app/test_xs_router.py new file mode 100644 index 0000000..c54013b --- /dev/null +++ b/tests/app/test_xs_router.py @@ -0,0 +1,32 @@ +from pyiceberg.catalog import load_catalog + +def test_xs_endpoint(client): + """Test: GET /v1/ras_xs/{reach_ids}?xstype={xs_val} - all valid arguments""" + catalog = load_catalog("glue", + **{"type":"glue", "glue.region":"us-east-1"}) + mip_hucid_list = [tup[1] for tup in catalog.list_tables("mip_xs")] + ble_hucid_list = [tup[1] for tup in catalog.list_tables("ble_xs")] + + for mip_hucid in mip_hucid_list[:5]: + response = client.get(f"/v1/ras_xs/{mip_hucid}?xstype=mip") + assert response.status_code == 200, "Incorrect response" + + reachid = list(set(catalog.load_table(f"mip_xs.{mip_hucid}").scan().to_pandas()['ds_reach_id']))[0] + response2 = client.get(f"/v1/ras_xs/{mip_hucid}/dsreachid={reachid}?xstype=mip") + assert response2.status_code == 200, "Incorrect response" + + for ble_hucid in ble_hucid_list[:5]: + response = client.get(f"/v1/ras_xs/{ble_hucid}?xstype=ble") + assert response.status_code == 200, "Incorrect response" + + reachid = list(set(catalog.load_table(f"ble_xs.{ble_hucid}").scan().to_pandas()['ds_reach_id']))[0] + response2 = client.get(f"/v1/ras_xs/{ble_hucid}/dsreachid={reachid}?xstype=ble") + assert response2.status_code == 200, "Incorrect response" + +def test_xs_subset_endpoint__422(client): + """Test: GET /v1/ras_xs/08020203?xstype=NA - fails validator""" + response = client.get("/v1/ras_xs/08020203?xstype=NA") + assert response.status_code == 422 + + response2 = client.get("/v1/ras_xs/08020203/dsreachid=NA?xstype=NA") + assert response2.status_code == 422 From e5d7099cd40dd7dfb4aad363a20761d78d8f9852 Mon Sep 17 00:00:00 2001 From: SylC-ray <201017958+SylC-ray@users.noreply.github.com> Date: Tue, 15 Jul 2025 15:36:38 -0400 Subject: [PATCH 92/97] Add files via upload --- tests/app/test_xs_router.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/app/test_xs_router.py b/tests/app/test_xs_router.py index c54013b..5f8070d 100644 --- a/tests/app/test_xs_router.py +++ b/tests/app/test_xs_router.py @@ -1,7 +1,7 @@ from pyiceberg.catalog import load_catalog def test_xs_endpoint(client): - """Test: GET /v1/ras_xs/{reach_ids}?xstype={xs_val} - all valid arguments""" + """Test: GET /v1/ras_xs/{mip_hucid or ble_hucid}/dsreachid={reach_id}?xstype={mip or ble} - all valid arguments""" catalog = load_catalog("glue", **{"type":"glue", "glue.region":"us-east-1"}) mip_hucid_list = [tup[1] for tup in catalog.list_tables("mip_xs")] From 8341cdeeb221c8f962d9f748c9abf9c987232f5d Mon Sep 17 00:00:00 2001 From: Quercus Hamlin Date: Tue, 15 Jul 2025 15:48:47 -0400 Subject: [PATCH 93/97] fix: albedo test version --- tests/app/test_topoflow_albedo_router.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/app/test_topoflow_albedo_router.py b/tests/app/test_topoflow_albedo_router.py index e4280f9..5836a9e 100644 --- a/tests/app/test_topoflow_albedo_router.py +++ b/tests/app/test_topoflow_albedo_router.py @@ -4,12 +4,12 @@ def test_albedo_endpoint(client): """Test: GET /v2/modules/topoflow/albedo - all valid arguments""" for landcover, albedo in AlbedoValues.__members__.items(): - response = client.get(f"/v2/modules/topoflow/albedo?landcover={landcover}") + response = client.get(f"/v1/modules/topoflow/albedo?landcover={landcover}") assert response.status_code == 200 assert response.text == str(albedo.value) def test_albedo_endpoint__422(client): """Test: GET /v2/modules/topoflow/albedo - fails validator""" - response = client.get("/v2/modules/topoflow/albedo?landcover=nope") + response = client.get("/v1/modules/topoflow/albedo?landcover=nope") assert response.status_code == 422 From 98eb7c6fac3545f3028426c8fa710582aec0561a Mon Sep 17 00:00:00 2001 From: Quercus Hamlin Date: Tue, 15 Jul 2025 15:53:01 -0400 Subject: [PATCH 94/97] pre-commit on xs work --- app/main.py | 2 +- app/routers/ras_xs/__init__.py | 1 - app/routers/ras_xs/router.py | 114 +++++++++++++++--------------- src/icefabric/__init__.py | 4 +- src/icefabric/ras_xs/__init__.py | 4 +- src/icefabric/ras_xs/subset.py | 34 ++++----- src/icefabric/schemas/__init__.py | 4 +- tests/app/test_xs_router.py | 17 ++--- 8 files changed, 87 insertions(+), 93 deletions(-) diff --git a/app/main.py b/app/main.py index e31f282..c512aee 100644 --- a/app/main.py +++ b/app/main.py @@ -10,8 +10,8 @@ from app.routers.hydrofabric.router import api_router as hydrofabric_api_router from app.routers.nwm_modules.router import sft_router, topoflow_router -from app.routers.streamflow_observations.router import api_router as streamflow_api_router from app.routers.ras_xs.router import api_router as ras_api_router +from app.routers.streamflow_observations.router import api_router as streamflow_api_router from icefabric.helpers import load_creds diff --git a/app/routers/ras_xs/__init__.py b/app/routers/ras_xs/__init__.py index 8b13789..e69de29 100644 --- a/app/routers/ras_xs/__init__.py +++ b/app/routers/ras_xs/__init__.py @@ -1 +0,0 @@ - diff --git a/app/routers/ras_xs/router.py b/app/routers/ras_xs/router.py index 6436b98..e2493fc 100644 --- a/app/routers/ras_xs/router.py +++ b/app/routers/ras_xs/router.py @@ -12,20 +12,18 @@ api_router = APIRouter(prefix="/ras_xs") + @api_router.get("/{identifier}/") -async def get_xs_subset_gpkg(identifier: str = Path(..., - description="HUC-8 identifier to filter by huc ID", - example="02040106"), - xstype: XsType = Query(XsType.MIP, - description="The iceberg namespace used to query the cross-sections" - ) +async def get_xs_subset_gpkg( + identifier: str = Path(..., description="HUC-8 identifier to filter by huc ID", example="02040106"), + xstype: XsType = Query(XsType.MIP, description="The iceberg namespace used to query the cross-sections"), ): """ Get geopackage subset from the mip xs iceberg catalog by table identifier (aka huc ID). This endpoint will query cross-sections from the mip xs iceberg catalog by huc & return the data subset as a downloadable geopackage file. - + """ catalog = load_catalog("glue") unique_id = str(uuid.uuid4())[:8] @@ -33,60 +31,57 @@ async def get_xs_subset_gpkg(identifier: str = Path(..., tmp_path = temp_dir / f"ras_xs_{identifier}_{unique_id}.gpkg" try: # Create data subset - data_gdf=subset_xs(catalog=catalog, - identifier=f"{identifier}", - output_file=tmp_path, - xstype=xstype) + data_gdf = subset_xs(catalog=catalog, identifier=f"{identifier}", output_file=tmp_path, xstype=xstype) if not tmp_path.exists(): - raise HTTPException(status_code=500, - detail=f"Failed to create geopackage file at {tmp_path}.") + raise HTTPException(status_code=500, detail=f"Failed to create geopackage file at {tmp_path}.") if tmp_path.stat().st_size == 0: tmp_path.unlink(missing_ok=True) - raise HTTPException(status_code=404, - detail=f"No data found for HUC {identifier}.") + raise HTTPException(status_code=404, detail=f"No data found for HUC {identifier}.") # Verify it's actually a file, not a directory if not tmp_path.is_file(): - raise HTTPException(status_code=500, - detail=f"Expected file, but got directory at {tmp_path}.") + raise HTTPException(status_code=500, detail=f"Expected file, but got directory at {tmp_path}.") print(f"Returning file: {tmp_path} (size: {tmp_path.stat().st_size} bytes)") download_filename = f"ras_xs_huc{identifier}.gpkg" - return FileResponse(path=str(tmp_path), - filename=download_filename, - media_type="application/geopackage+sqlite3", - headers={"Data_Source": f"{xstype}_xs", - "HUC Identifier": identifier, - "Description": f"{xstype} RAS Cross-Section Geopackage", - "Total Records": f"{len(data_gdf)}"}, - background=BackgroundTask(lambda: tmp_path.unlink(missing_ok=True))) + return FileResponse( + path=str(tmp_path), + filename=download_filename, + media_type="application/geopackage+sqlite3", + headers={ + "Data_Source": f"{xstype}_xs", + "HUC Identifier": identifier, + "Description": f"{xstype} RAS Cross-Section Geopackage", + "Total Records": f"{len(data_gdf)}", + }, + background=BackgroundTask(lambda: tmp_path.unlink(missing_ok=True)), + ) except HTTPException: raise except Exception: - # Clean up temp file if it exists if "tmp_path" in locals() and tmp_path.exists(): tmp_path.unlink(missing_ok=True) raise + @api_router.get("/{identifier}/dsreachid={ds_reach_id}") -async def get_xs_subset_by_huc_reach_gpkg(identifier: str = Path(..., - description="Identifier to filter data by huc ID", - example="02040106"), - ds_reach_id: str = Path(..., - description="Identifier to filter data by downstream reach ID)", - example="4188251"), - xstype: XsType = Query(XsType.MIP, - description="The iceberg namespace used to query the cross-sections")): +async def get_xs_subset_by_huc_reach_gpkg( + identifier: str = Path(..., description="Identifier to filter data by huc ID", example="02040106"), + ds_reach_id: str = Path( + ..., description="Identifier to filter data by downstream reach ID)", example="4188251" + ), + xstype: XsType = Query(XsType.MIP, description="The iceberg namespace used to query the cross-sections"), +): """ Get geopackage subset from the mip xs iceberg catalog by reach ID at huc ID. - This endpoint will query cross-sections from the mip xs iceberg catalog by + This endpoint will query cross-sections from the mip xs iceberg catalog by downstream reach ID at given huc ID -- returning the data subset as a downloadable geopackage file. - + """ catalog = load_catalog("glue") unique_id = str(uuid.uuid4())[:8] @@ -94,43 +89,48 @@ async def get_xs_subset_by_huc_reach_gpkg(identifier: str = Path(..., tmp_path = temp_dir / f"ras_xs_{identifier}_{ds_reach_id}_{unique_id}.gpkg" try: # Create data subset - data_gdf = subset_xs(catalog=catalog, - identifier=f"{identifier}", - ds_reach_id=f"{ds_reach_id}", - output_file=tmp_path, - xstype=xstype) + data_gdf = subset_xs( + catalog=catalog, + identifier=f"{identifier}", + ds_reach_id=f"{ds_reach_id}", + output_file=tmp_path, + xstype=xstype, + ) if not tmp_path.exists(): - raise HTTPException(status_code=500, - detail=f"Failed to create geopackage file at {tmp_path}.") + raise HTTPException(status_code=500, detail=f"Failed to create geopackage file at {tmp_path}.") if tmp_path.stat().st_size == 0: tmp_path.unlink(missing_ok=True) - raise HTTPException(status_code=404, - detail=f"No data found for downstream reach id {ds_reach_id} @ HUC{identifier}.") + raise HTTPException( + status_code=404, + detail=f"No data found for downstream reach id {ds_reach_id} @ HUC{identifier}.", + ) # Verify it's actually a file, not a directory if not tmp_path.is_file(): - raise HTTPException(status_code=500, - detail=f"Expected file, but got directory at {tmp_path}.") + raise HTTPException(status_code=500, detail=f"Expected file, but got directory at {tmp_path}.") print(f"Returning file: {tmp_path} (size: {tmp_path.stat().st_size} bytes)") download_filename = f"ras_xs_huc{identifier}_dsreachid{ds_reach_id}.gpkg" - return FileResponse(path=str(tmp_path), - filename=download_filename, - media_type="application/geopackage+sqlite3", - headers={"Data_Source": f"{xstype}_xs", - "HUC Identifier": identifier, - "DS Reach Identifier": ds_reach_id, - "Description": f"{xstype} RAS Cross-Section Geopackage", - "Total Records": f"{len(data_gdf)}"}, - background=BackgroundTask(lambda: tmp_path.unlink(missing_ok=True))) + return FileResponse( + path=str(tmp_path), + filename=download_filename, + media_type="application/geopackage+sqlite3", + headers={ + "Data_Source": f"{xstype}_xs", + "HUC Identifier": identifier, + "DS Reach Identifier": ds_reach_id, + "Description": f"{xstype} RAS Cross-Section Geopackage", + "Total Records": f"{len(data_gdf)}", + }, + background=BackgroundTask(lambda: tmp_path.unlink(missing_ok=True)), + ) except HTTPException: raise except Exception: - # Clean up temp file if it exists if "tmp_path" in locals() and tmp_path.exists(): tmp_path.unlink(missing_ok=True) diff --git a/src/icefabric/__init__.py b/src/icefabric/__init__.py index ca70965..89919dc 100644 --- a/src/icefabric/__init__.py +++ b/src/icefabric/__init__.py @@ -1,6 +1,6 @@ """An Apache Iceberg + Icechunk implementation of Hydrofabric data services""" -from . import builds, cli, helpers, hydrofabric, modules, schemas, ui, ras_xs +from . import builds, cli, helpers, hydrofabric, modules, ras_xs, schemas, ui from ._version import __version__ -__all__ = ["__version__", "builds", "cli", "hydrofabric", "helpers", "modules", "schemas", "ui", "ras_xs"] +__all__ = ["__version__", "builds", "cli", "hydrofabric", "helpers", "modules", "schemas", "ui", "ras_xs"] diff --git a/src/icefabric/ras_xs/__init__.py b/src/icefabric/ras_xs/__init__.py index 81f0c62..1c20b1b 100644 --- a/src/icefabric/ras_xs/__init__.py +++ b/src/icefabric/ras_xs/__init__.py @@ -2,6 +2,4 @@ from .subset import subset_xs -__all__ = [ - "subset_xs" -] +__all__ = ["subset_xs"] diff --git a/src/icefabric/ras_xs/subset.py b/src/icefabric/ras_xs/subset.py index 87f72cc..6b918df 100644 --- a/src/icefabric/ras_xs/subset.py +++ b/src/icefabric/ras_xs/subset.py @@ -6,21 +6,19 @@ import pandas as pd from botocore.exceptions import ClientError from pyiceberg.catalog import Catalog -from pyiceberg.expressions import And, EqualTo -from pyiceberg.table import Table +from pyiceberg.expressions import EqualTo from icefabric.helpers.geopackage import to_geopandas from icefabric.schemas.ras_xs import XsType -def subset_xs(catalog: Catalog, - identifier: str, - output_file: Path | None = None, - xstype: XsType = XsType.MIP, - **kwarg) -> dict[str, pd.DataFrame | gpd.GeoDataFrame] | None: + +def subset_xs( + catalog: Catalog, identifier: str, output_file: Path | None = None, xstype: XsType = XsType.MIP, **kwarg +) -> dict[str, pd.DataFrame | gpd.GeoDataFrame] | None: """Returns a geopackage subset from the xs iceberg catalog. - This function delivers a subset of the cross-sections data by tracing from a - given HUC identifier & collecting all relevant cross-section information based on + This function delivers a subset of the cross-sections data by tracing from a + given HUC identifier & collecting all relevant cross-section information based on additional identifiers provided. Parameters @@ -33,14 +31,14 @@ def subset_xs(catalog: Catalog, The type of cross-section data to read (e.g. mip, ble). output_file : Path, optional The output file path where the geopackage will be saved. - + Returns ------- GeoDataFrame Subset of the mip cross-sections data based on identifiers. - + """ - ds_reach_id = kwarg.get('ds_reach_id', None) + ds_reach_id = kwarg.get("ds_reach_id", None) try: xs_table = catalog.load_table(f"{xstype.value}_xs.{identifier}") @@ -49,10 +47,10 @@ def subset_xs(catalog: Catalog, msg = "AWS Test account credentials expired. Can't access remote S3 endpoint" print(msg) raise e - - # By default, mip xs tables are filtered by huc + + # By default, mip xs tables are filtered by huc df = xs_table.scan().to_pandas() - + if ds_reach_id: filter_cond = EqualTo("ds_reach_id", ds_reach_id) df = xs_table.scan(row_filter=filter_cond).to_pandas() @@ -61,9 +59,7 @@ def subset_xs(catalog: Catalog, # Save data. if output_file: if len(data_gdf) > 0: - gpd.GeoDataFrame(data_gdf).to_file(output_file, - layer='ras_xs', - driver="GPKG") + gpd.GeoDataFrame(data_gdf).to_file(output_file, layer="ras_xs", driver="GPKG") else: - print(f"Warning: Dataframe is empty") + print("Warning: Dataframe is empty") return data_gdf diff --git a/src/icefabric/schemas/__init__.py b/src/icefabric/schemas/__init__.py index 2d2a0b4..684d816 100644 --- a/src/icefabric/schemas/__init__.py +++ b/src/icefabric/schemas/__init__.py @@ -2,8 +2,8 @@ from .hydrofabric import UPSTREAM_VPUS, HydrofabricDomains, IdType from .modules import SFT, Albedo, IceFractionScheme -from .topobathy import FileType, NGWPCLocations, NGWPCTestLocations from .ras_xs import XsType +from .topobathy import FileType, NGWPCLocations, NGWPCTestLocations __all__ = [ "UPSTREAM_VPUS", @@ -15,5 +15,5 @@ "FileType", "NGWPCLocations", "NGWPCTestLocations", - "XsType" + "XsType", ] diff --git a/tests/app/test_xs_router.py b/tests/app/test_xs_router.py index 5f8070d..e0973e5 100644 --- a/tests/app/test_xs_router.py +++ b/tests/app/test_xs_router.py @@ -1,28 +1,29 @@ from pyiceberg.catalog import load_catalog + def test_xs_endpoint(client): """Test: GET /v1/ras_xs/{mip_hucid or ble_hucid}/dsreachid={reach_id}?xstype={mip or ble} - all valid arguments""" - catalog = load_catalog("glue", - **{"type":"glue", "glue.region":"us-east-1"}) + catalog = load_catalog("glue", **{"type": "glue", "glue.region": "us-east-1"}) mip_hucid_list = [tup[1] for tup in catalog.list_tables("mip_xs")] ble_hucid_list = [tup[1] for tup in catalog.list_tables("ble_xs")] for mip_hucid in mip_hucid_list[:5]: response = client.get(f"/v1/ras_xs/{mip_hucid}?xstype=mip") assert response.status_code == 200, "Incorrect response" - - reachid = list(set(catalog.load_table(f"mip_xs.{mip_hucid}").scan().to_pandas()['ds_reach_id']))[0] + + reachid = list(set(catalog.load_table(f"mip_xs.{mip_hucid}").scan().to_pandas()["ds_reach_id"]))[0] response2 = client.get(f"/v1/ras_xs/{mip_hucid}/dsreachid={reachid}?xstype=mip") assert response2.status_code == 200, "Incorrect response" - + for ble_hucid in ble_hucid_list[:5]: response = client.get(f"/v1/ras_xs/{ble_hucid}?xstype=ble") assert response.status_code == 200, "Incorrect response" - - reachid = list(set(catalog.load_table(f"ble_xs.{ble_hucid}").scan().to_pandas()['ds_reach_id']))[0] + + reachid = list(set(catalog.load_table(f"ble_xs.{ble_hucid}").scan().to_pandas()["ds_reach_id"]))[0] response2 = client.get(f"/v1/ras_xs/{ble_hucid}/dsreachid={reachid}?xstype=ble") assert response2.status_code == 200, "Incorrect response" - + + def test_xs_subset_endpoint__422(client): """Test: GET /v1/ras_xs/08020203?xstype=NA - fails validator""" response = client.get("/v1/ras_xs/08020203?xstype=NA") From 8ae57b0d159399343fd46afb947846c3e4581847 Mon Sep 17 00:00:00 2001 From: Quercus Hamlin Date: Tue, 15 Jul 2025 16:06:55 -0400 Subject: [PATCH 95/97] fix: openapi example --- app/routers/ras_xs/router.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/app/routers/ras_xs/router.py b/app/routers/ras_xs/router.py index e2493fc..d6318df 100644 --- a/app/routers/ras_xs/router.py +++ b/app/routers/ras_xs/router.py @@ -15,7 +15,12 @@ @api_router.get("/{identifier}/") async def get_xs_subset_gpkg( - identifier: str = Path(..., description="HUC-8 identifier to filter by huc ID", example="02040106"), + identifier: str = Path( + ..., + description="HUC-8 identifier to filter by huc ID", + examples=["02040106"], + openapi_examples={"huc": {"summary": "XS Example", "value": "02040106"}}, + ), xstype: XsType = Query(XsType.MIP, description="The iceberg namespace used to query the cross-sections"), ): """ @@ -70,9 +75,17 @@ async def get_xs_subset_gpkg( @api_router.get("/{identifier}/dsreachid={ds_reach_id}") async def get_xs_subset_by_huc_reach_gpkg( - identifier: str = Path(..., description="Identifier to filter data by huc ID", example="02040106"), + identifier: str = Path( + ..., + description="Identifier to filter data by huc ID", + examples=["02040106"], + openapi_examples={"xs": {"summary": "XS Example", "value": "02040106"}}, + ), ds_reach_id: str = Path( - ..., description="Identifier to filter data by downstream reach ID)", example="4188251" + ..., + description="Identifier to filter data by downstream reach ID)", + examples=["4188251"], + openapi_examples={"xs": {"summary": "XS Example", "value": "4188251"}}, ), xstype: XsType = Query(XsType.MIP, description="The iceberg namespace used to query the cross-sections"), ): From c0b24466ee970bc1a5e8c0b8faaca4503aea1182 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Tue, 15 Jul 2025 16:48:43 -0400 Subject: [PATCH 96/97] initial commit: adding SFT documentation to verify support (#63) --- docs/modules/index.md | 3 + docs/modules/sft.md | 191 ++++++++++++++++++++++++++++++++++++++++++ mkdocs.yml | 3 + 3 files changed, 197 insertions(+) create mode 100644 docs/modules/index.md create mode 100644 docs/modules/sft.md diff --git a/docs/modules/index.md b/docs/modules/index.md new file mode 100644 index 0000000..0651976 --- /dev/null +++ b/docs/modules/index.md @@ -0,0 +1,3 @@ +# Modules + +The following pages are to document the support from the EDFS team towards creating configuration files for NWM Modules. diff --git a/docs/modules/sft.md b/docs/modules/sft.md new file mode 100644 index 0000000..519c203 --- /dev/null +++ b/docs/modules/sft.md @@ -0,0 +1,191 @@ +# SFT (Soil Freeze-Thaw) Module Documentation + +## Overview + +The SFT (Soil Freeze-Thaw) module simulates the freeze-thaw processes in soil columns and is used in cold regions where freeze-thaw cycles significantly affect water movement and storage. + +## Parameter Reference + +### Core Parameters + +| Parameter | Description | Units | Data Type | Default | Calibratable | +|-----------|-------------|--------|-----------|---------|--------------| +| `end_time` | Simulation duration. If no unit is specified, defaults to hour | s, sec, h, hr, d, day | double | `1.[d]` | FALSE | +| `dt` | Size of a simulation timestep. If no unit is specified, defaults to hour | s, sec, h, hr, d, day | double | `1.0[h]` | FALSE | +| `verbosity` | Logging verbosity level | - | string | `none` | FALSE | + +**Options for verbosity:** `high`, `low`, `none` + +### Soil Physical Properties + +These properties are based on Hydrofabric divide attributes provided in the latest enterprise version. + +| Parameter | Description | Units | Data Type | Default | Calibratable | +|-----------|-------------|--------|-----------|---------|--------------| +| `soil_params.smcmax` | Maximum soil moisture content | - | double | - | TRUE | +| `soil_params.b` | Soil moisture retention curve parameter (bexp) | - | double | - | TRUE | +| `soil_params.satpsi` | Saturated soil suction (psisat) | - | double | - | TRUE | +| `soil_params.quartz` | Soil quartz content, used in soil thermal conductivity function of Peters-Lidard | - | double | `1.0` | FALSE | + +### Domain Configuration + +| Parameter | Description | Units | Data Type | Default | Calibratable | +|-----------|-------------|--------|-----------|---------|--------------| +| `soil_z` | Vertical resolution of the soil column (computational domain of the SFT model) | m | array[double] | `[0.1, 0.3, 1.0, 2.0]` | FALSE | +| `soil_temperature` | Initial soil temperature for the discretized column | K | array[double] | - | FALSE | + +**Ice Fraction Scheme Options:** + +The following ice fraction schemes are dictated by what version of CFE is used + +- `Schaake`: Traditional Schaake ice fraction calculation +- `Xinanjiang`: Xinanjiang ice fraction calculation method (default) + +## Data Structures + +### SFT Configuration Model + +The SFT module uses a Pydantic model to validate and structure configuration parameters: + +```python +class SFT(BaseModel): + catchment: str # Catchment identifier + verbosity: str = "none" # Logging level + soil_moisture_bmi: int = 1 # BMI soil moisture flag + end_time: str = "1.[d]" # Simulation duration + dt: str = "1.0[h]" # Time step + soil_params_smcmax: float # Maximum soil moisture + soil_params_b: float # Soil retention parameter + soil_params_satpsi: float # Saturated soil suction + soil_params_quartz: float = 1.0 # Quartz content + ice_fraction_scheme: IceFractionScheme # Ice fraction method + soil_z: list[float] = [0.1, 0.3, 1.0, 2.0] # Soil layer depths + soil_temperature: list[float] # Initial temperatures +``` + +### Ice Fraction Schemes + +```python +class IceFractionScheme(str, enum.Enum): + SCHAAKE = "Schaake" + XINANJIANG = "Xinanjiang" +``` + +## Usage + +### Command Line Interface + +The SFT config text files can be created using the `icefabric` CLI tool: + +```bash +icefabric params \ + --gauge "01010000" \ + --module "sft" \ + --domain "conus" \ + --catalog "glue" \ + --ice-fraction "xinanjiang" \ + --output "./output" +``` + +**CLI Parameters:** +- `--gauge`: Gauge ID to trace upstream catchments from +- `--module`: Module type (use "sft" for Soil Freeze-Thaw) +- `--domain`: Hydrofabric domain (`conus`, `alaska`, etc.) +- `--catalog`: PyIceberg Catalog type (`glue` or `sql`) +- `--ice-fraction`: Ice fraction scheme (`schaake` or `xinanjiang`) +- `--output`: Output directory for configuration files + +### REST API + +The SFT module is also accessible via REST API: + +```http +GET /modules/sft/?identifier=01010000&domain=conus&use_schaake=false +``` + +**API Parameters:** +- `identifier` (required): Gauge ID to trace upstream from +- `domain` (optional): Geographic domain (default: `conus`) +- `use_schaake` (optional): Use Schaake ice fraction scheme (default: `false`) + +**Response:** Returns a list of SFT configuration objects, one for each upstream catchment. + +### Python API + +Direct Python usage: + +```python +from icefabric.modules import get_sft_parameters +from icefabric.schemas.hydrofabric import HydrofabricDomains +from pyiceberg.catalog import load_catalog + +# Load catalog +catalog = load_catalog("glue") + +# Get SFT parameters +configs = get_sft_parameters( + catalog=catalog, + domain=HydrofabricDomains.CONUS, + identifier="01010000", + use_schaake=False +) + +# Each config is an SFT pydantic model +for config in configs: + print(f"Catchment: {config.catchment}") + print(f"Soil layers: {config.soil_z}") + print(f"Initial temperatures: {config.soil_temperature}") +``` + +## Parameter Estimation + +The system automatically estimates initial parameters from hydrofabric data: + +### Soil Parameters +- **smcmax**: Calculated as mean across available soil moisture maximum values +- **b (bexp)**: Derived from mode of soil moisture retention curve parameters +- **satpsi**: Calculated as geometric mean of saturated soil suction values +- **quartz**: Default value of 1.0 (assuming high quartz content) + +### Temperature Initialization +- **soil_temperature**: Currently set to a uniform 45°F (280.37K) across all layers +- This represents a reasonable estimate for mean soil temperature + +### Spatial Resolution +- **soil_z**: Default 4-layer discretization [0.1, 0.3, 1.0, 2.0] meters +- Provides adequate resolution for freeze-thaw processes + +## Output Files + +The CLI and API generate BMI-compatible configuration files: + +``` +{catchment_id}_bmi_config_sft.txt +``` + +Example file content: +``` +verbosity=none +soil_moisture_bmi=1 +end_time=1.[d] +dt=1.0[h] +soil_params.smcmax=0.434 +soil_params.b=4.05 +soil_params.satpsi=0.0355 +soil_params.quartz=1.0 +ice_fraction_scheme=Xinanjiang +soil_z=0.1,0.3,1.0,2.0[m] +soil_temperature=280.37,280.37,280.37,280.37[K] +``` + +## Notes and Limitations + +1. **Temperature Initialization**: Current implementation uses uniform 45°F across all soil layers. Future versions should implement depth-dependent temperature profiles. + +2. **Parameter Weighting**: Soil parameters are currently averaged with equal weighting rather than weighted averaging based on layer thickness. + +3. **Quartz Support**: The `soil_params.quartz` was removed in v2.2 of the Hydrofabric and is defaulted to 1.0 + +4. **Spatial Coverage**: Parameter estimation depends on available hydrofabric data coverage for the specified domain. + +5. **Temporal Considerations**: Initial parameters represent steady-state estimates. Actual model runs may require spin-up periods for equilibration. diff --git a/mkdocs.yml b/mkdocs.yml index 3f721b1..8b40384 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -53,3 +53,6 @@ nav: - API: user_guide/icefabric_api.md - Tools: user_guide/icefabric_tools.md - Terraform S3 Glue Endpoint: user_guide/terraform.md + - Module Support: + - modules/index.md + - SFT: modules/sft.md From fa11864a6a46c205b2bc900040d55c6bac67b377 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Fri, 18 Jul 2025 16:34:26 -0400 Subject: [PATCH 97/97] AC Patch: subcase 4-5 (#62) * initial commit * docs: added more docs in the pyiceberg time travel code * feat: added new notebook example to demonstrate a locally created icechunk repo with virtual references * patch: added more docs, and example verbiage for PI-4 UAT * patch: updated timeout settings * patch: removed /tmp/sft_output/test dir --- .pre-commit-config.yaml | 1 + docs/description.md | 98 +++ docs/img/icefabric_version.png | Bin 0 -> 223813 bytes docs/user_guide/index.md | 18 - docs/user_guide/terraform.md | 4 +- .../iceberg_version_control_examples.ipynb | 484 ++++++++++++ examples/iceberg/pyiceberg_time_travel.ipynb | 268 ++++++- .../icechunk/icechunk_data_ret_and_plot.ipynb | 164 ---- examples/icechunk/icechunk_to_tiff.ipynb | 705 ------------------ .../icechunk_version_control_examples.ipynb | 321 +++++--- .../initial_parameters/soil_freeze_thaw.ipynb | 20 +- mkdocs.yml | 8 + pyproject.toml | 4 +- src/icefabric/builds/icechunk_s3_module.py | 20 + src/icefabric/helpers/__init__.py | 4 + .../helpers/arch_weather_file_utils.py | 11 +- src/icefabric/schemas/topobathy.py | 2 +- tools/icechunk/build_landcover_stores.py | 92 +++ 18 files changed, 1198 insertions(+), 1026 deletions(-) create mode 100644 docs/description.md create mode 100644 docs/img/icefabric_version.png create mode 100644 examples/iceberg/iceberg_version_control_examples.ipynb delete mode 100644 examples/icechunk/icechunk_data_ret_and_plot.ipynb delete mode 100644 examples/icechunk/icechunk_to_tiff.ipynb create mode 100644 tools/icechunk/build_landcover_stores.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 7268e63..4dbe867 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,6 +7,7 @@ repos: - id: end-of-file-fixer exclude: LICENSE|\.csv$ - id: check-yaml + exclude: mkdocs.yml$ - id: debug-statements - repo: https://github.com/astral-sh/ruff-pre-commit diff --git a/docs/description.md b/docs/description.md new file mode 100644 index 0000000..dcab003 --- /dev/null +++ b/docs/description.md @@ -0,0 +1,98 @@ +# Icefabric: Lakehouse Architecture for Hydrologic Data Management + +
    + ![Icefabric version controlling system](img/icefabric_version.png){ width="600" } +
    The icefabric lake house architecture. Data is moved from sources to an underlying specificed format (iceberg/icechunk) and queried to consumers via APIs and services.
    +
    + + +## Overview + +Icefabric implements a modern **lakehouse architecture** to combine the flexibility of data lakes with the performance and governance of data warehouse. This system provides versioned, centralized access to hydrologic datasets to support the National Water Model. + +## The Problem: Hydrologic Data Complexity + +### Traditional Challenges + +Hydrologic research and operations face unique data management challenges: + +- **Heterogeneous Data Sources**: Datasets are sourced from different agencies in various formats +- **Multiple Formats**: Tabular, vectorized, COGs, etc +- **Version Control Needs**: Hydrofabric topology updates, data quality improvements, and research reproducibility + +### Why Traditional Solutions Fall Short + +**Traditional database systems** struggle with: + +- Large geospatial datasets and complex geometries +- Schema evolution for evolving datasets +- Version control for scientific workflows + +**File-based approaches** suffer from: + +- Data duplication and storage inefficiencies +- Lack of ACID transactions +- Manual version management +- Limited discovery and access controls + +## Lakehouse Architecture Solution + +### Technology Stack Rationale + +=== "Apache Iceberg - Structured Data" + + **Used For:** + - Hydrofabric geospatial products + - Streamflow observations time series (USGS, Local Agencies) + - Cross-section geometries (RAS XS [MIP/BLE]) + + **Why Iceberg:** + - **ACID Transactions**: Ensure data consistency during hydrofabric updates + - **Schema Evolution**: Handle network topology changes without breaking existing workflows + - **Time Travel**: Access historical network versions for model comparisons + - **Performance**: Optimized queries across continental-scale datasets + - **Partition Pruning**: Efficient spatial and temporal filtering + +=== "Icechunk - Array Data" + + **Used For:** + - Topobathy elevation surfaces + - Land cover classifications + + **Why Icechunk:** + - **Virtual References**: Avoid duplicating large elevation datasets + - **Zarr Compatibility**: Seamless integration with scientific Python ecosystem + - **Git-like Versioning**: Branch/merge workflows for experimental processing + - **Chunked Storage**: Optimized for geospatial access patterns + - **Compression**: Efficient storage of repetitive classification data + +## Benefits Realized + +### For Hydrologic Research + +- **Reproducible Science**: Exact data versions enable repeatable research +- **Collaborative Workflows**: Branching enables parallel research without conflicts +- **Quality Evolution**: Track data quality improvements over time + +### For Operational Forecasting + +- **Consistent Baselines**: Stable data versions for operational model runs +- **Real-time Integration**: Fast access to latest observations and forecasts +- **Rollback Capabilities**: Quick recovery from data quality issues + +### For Data Management + +- **Access Unification**: Single API for diverse hydrologic data types +- **Version Management**: Automated tracking eliminates manual version confusion +- **Quality Assurance**: Built-in validation prevents bad data propagation + +## Conclusion + +The Icefabric lakehouse architecture addresses fundamental challenges in hydrologic data management through: + +1. **Unified Access**: Single interface for diverse water data sources +3. **Version Control**: Git-like workflows for scientific data management +4. **Quality Assurance**: Automated validation and lineage tracking +6. **Research Support**: Reproducible environments for collaborative science + +This architecture enables EDFS to provide reliable, versioned, high-performance access to critical water resources data supporting both operational forecasting and cutting-edge research. diff --git a/docs/img/icefabric_version.png b/docs/img/icefabric_version.png new file mode 100644 index 0000000000000000000000000000000000000000..2762cc41af40b3e0a793c72af1d5a65bd7302984 GIT binary patch literal 223813 zcma&N1yo$kwk?b$L4!+zI|P@YjRXnq?ry=oad!<6Ai*uTySoGr?(Xg`Z}%kcOZ?$l2nhIU zGeJRFF+o8hSvzZEGYcaK2+@#l32zh?cG0|_-K@Fb=)Aq-WCg#{qwtIGWL#5xAcjK` 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    <xarray.Dataset> Size: 3GB\n",
-       "Dimensions:      (band: 1, y: 14819, x: 22226)\n",
-       "Coordinates:\n",
-       "  * band         (band) int64 8B 1\n",
-       "    spatial_ref  int64 8B 0\n",
-       "  * y            (y) float64 119kB 22.5 22.5 22.5 22.5 ... 18.5 18.5 18.5 18.5\n",
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-       "Data variables:\n",
-       "    elevation    (band, y, x) float64 3GB dask.array<chunksize=(1, 464, 1390), meta=np.ndarray>
    " - ], - "text/plain": [ - " Size: 3GB\n", - "Dimensions: (band: 1, y: 14819, x: 22226)\n", - "Coordinates:\n", - " * band (band) int64 8B 1\n", - " spatial_ref int64 8B 0\n", - " * y (y) float64 119kB 22.5 22.5 22.5 22.5 ... 18.5 18.5 18.5 18.5\n", - " * x (x) float64 178kB -160.5 -160.5 -160.5 ... -154.5 -154.5 -154.5\n", - "Data variables:\n", - " elevation (band, y, x) float64 3GB dask.array" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "repo = IcechunkRepo(location=NGWPCLocations.TOPO_HA_30M_IC.path)\n", - "ds = repo.retrieve_dataset()\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "id": "9e3dceab", - "metadata": {}, - "source": [ - "Save the full dataset to tiff." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9e2935a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved dataset to ./hawaii_topo_30.tif\n" - ] - } - ], - "source": [ - "var_name = \"elevation\"\n", - "dest = \"./hawaii_topo_30.tif\"\n", - "repo.retrieve_and_convert_to_tif(dest=dest, var_name=var_name)" - ] - }, - { - "cell_type": "markdown", - "id": "3ea00144", - "metadata": {}, - "source": [ - "Save a clipped bounding box of dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5493c79b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved clipped window to ./hawaii_clipped_topo_30.tif\n" - ] - } - ], - "source": [ - "var_name = \"elevation\"\n", - "dest = \"./hawaii_clipped_topo_30.tif\"\n", - "minx, miny, maxx, maxy = -159, 21, -158, 22\n", - "repo.retrieve_and_convert_to_tif(dest=dest, var_name=var_name, minx=minx, miny=miny, maxx=maxx, maxy=maxy)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "icefabric", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/icechunk/icechunk_version_control_examples.ipynb b/examples/icechunk/icechunk_version_control_examples.ipynb index 6e2e8c0..ca0c834 100644 --- a/examples/icechunk/icechunk_version_control_examples.ipynb +++ b/examples/icechunk/icechunk_version_control_examples.ipynb @@ -5,10 +5,47 @@ "id": "0", "metadata": {}, "source": [ - "## Icechunk Version Control and Branching\n", - "Showcase for adding new data over time to an icechunk store, \"time traveling\", and making new branches\n", + "## Icechunk Version Control for Land Cover Data\n", "\n", - "Requires `.env` with `data` account credentials" + "### Overview\n", + "\n", + "This notebook demonstrates **version control capabilities for geospatial raster data** using Icechunk, a new cloud-native storage format. We'll showcase how slowly-changing time-varying raster data (specifically NLCD land cover data) can be managed with full version control, enabling reproducible research and data lineage tracking.\n", + "\n", + "#### What is Icechunk?\n", + "\n", + "**Icechunk** is a cloud-native storage format that brings **Git-like version control** to large scientific datasets. Icechunk is very similar to Iceberg, but for data-cube/tensor data. Unlike traditional file systems where data changes overwrite previous versions, Icechunk:\n", + "\n", + "- **Creates snapshots** of your data at each change\n", + "- **Enables time travel** to access any previous version\n", + "- **Supports branching and merging** for collaborative workflows\n", + "- **Tracks data lineage** with commit messages and metadata\n", + "- **Uses virtual references** to avoid data duplication. This means existing .nc or COGs can be referenced without rewriting the data\n", + "\n", + "### Dataset: National Land Cover Database (NLCD)\n", + "\n", + "#### Source: https://www.mrlc.gov/data\n", + "\n", + "The NLCD provides land cover classifications for the Continental United States (CONUS)\n", + "\n", + "#### Land Cover Classes\n", + "\n", + "The NLCD uses standardized codes for different land cover types:\n", + "- **11**: Open Water\n", + "- **12**: Perennial Ice/Snow\n", + "- **21**: Developed, Open Space\n", + "- **22**: Developed, Low Intensity\n", + "- **23**: Developed, Medium Intensity\n", + "- **24**: Developed, High Intensity\n", + "- **31**: Barren Land (Rock/Sand/Clay)\n", + "- **41**: Decidous Forest\n", + "- **42**: Evergreen Forest\n", + "- **43**: Mixed Forest\n", + "- **52**: Shrub/Scrub\n", + "- **71**: Grassland/Herbaceous\n", + "- **81**: Pasture/Hay\n", + "- **82**: Cultivated Crops\n", + "- **90**: Woody Wetlands\n", + "- **95**: Emergent Herbaceous Wetlands" ] }, { @@ -18,31 +55,60 @@ "metadata": {}, "outputs": [], "source": [ + "import os\n", + "import warnings\n", "from pathlib import Path\n", "\n", - "from icefabric.helpers import load_creds\n", + "import icechunk as ic\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "from pyiceberg.catalog import load_catalog\n", + "\n", + "from icefabric.helpers import load_creds, load_pyiceberg_config\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "# Changes the current working dir to be the project root\n", + "current_working_dir = Path.cwd()\n", + "os.chdir(Path.cwd() / \"../../\")\n", + "print(\n", + " f\"Changed current working dir from {current_working_dir} to: {Path.cwd()}. This must run at the project root\"\n", + ")\n", "\n", "# dir is where the .env file is located\n", - "load_creds(dir=Path.cwd().parents[1])" + "load_creds(dir=Path.cwd())\n", + "\n", + "# Loading the local pyiceberg config settings\n", + "pyiceberg_config = load_pyiceberg_config(Path.cwd())\n", + "catalog = load_catalog(\n", + " name=\"sql\",\n", + " type=pyiceberg_config[\"catalog\"][\"sql\"][\"type\"],\n", + " uri=pyiceberg_config[\"catalog\"][\"sql\"][\"uri\"],\n", + " warehouse=pyiceberg_config[\"catalog\"][\"sql\"][\"warehouse\"],\n", + ")" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "2", "metadata": {}, - "outputs": [], "source": [ - "import warnings\n", + "### Opening the Icechunk Repository\n", "\n", - "from icefabric.builds import IcechunkRepo, S3Path\n", - "from icefabric.helpers import virtualize_and_concat_archival_files_on_time\n", - "from icefabric.schemas import FileType, NGWPCLocations\n", + "Unlike traditional file formats (GeoTIFF, NetCDF), Icechunk stores data in a **repository structure** similar to Git. Each repository contains:\n", "\n", - "warnings.filterwarnings(\"ignore\")\n", + "- **Snapshots**: Immutable versions of your data\n", + "- **Branches**: Parallel development lines (like Git branches)\n", + "- **Virtual references**: Pointers to external data files (avoiding duplication)\n", + "- **Metadata**: Rich attribution and processing history\n", "\n", - "new_repo_s3_path = S3Path(bucket=\"hydrofabric-data\", prefix=\"ic_testing/snodas_yearly_append_test\")\n", - "new_repo = IcechunkRepo(location=new_repo_s3_path)" + "#### Virtual Chunk Architecture\n", + "\n", + "Our NLCD data uses **virtual references** - instead of copying large GeoTIFF files into Icechunk, we store lightweight references pointing to the original files. This provides:\n", + "\n", + "- **Fast ingestion** (no data copying)\n", + "- **Storage efficiency** (references vs. full copies) \n", + "- **Source preservation** (original files remain unchanged)" ] }, { @@ -52,29 +118,32 @@ "metadata": {}, "outputs": [], "source": [ - "# Print repo ancestry\n", - "new_repo.print_history()" + "# NOTE This demonstration/example assumes the data/land_cover icechunk has been made locally to the store path, and the TIFs are in the correct location in the data path\n", + "file_location = Path(\"data/land_cover_tifs\").resolve()\n", + "store_path = Path(\"data/land_cover\").resolve()\n", + "\n", + "storage = ic.local_filesystem_storage(str(store_path))\n", + "repo = ic.Repository.open(\n", + " storage=storage,\n", + " authorize_virtual_chunk_access=ic.containers_credentials({f\"file://{file_location}\": None}),\n", + ")" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "4", "metadata": {}, - "outputs": [], "source": [ - "# Collect first five SNODAS netcdf files from 2009 and combine/virtualize them together into a single dataset\n", - "snodas_09_vds = virtualize_and_concat_archival_files_on_time(\n", - " location=NGWPCLocations.SNODAS_REF.path,\n", - " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", - " file_type=FileType.NETCDF,\n", - " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2009*.nc\",\n", - " loadable_vars=[\"crs\"],\n", - " testing_file_quantity=5,\n", - ")\n", + "### Repository History: Data Lineage Tracking\n", "\n", - "# Add 09 data to SNODAS repo with a new snapshot\n", - "new_repo.write_dataset(ds=snodas_09_vds, virtualized=True, commit=\"First commit! 09 data added.\")" + "One of Icechunk's key features is **automatic lineage tracking**. Every change to the dataset creates a new snapshot with:\n", + "\n", + "- **Unique identifier** (snapshot ID)\n", + "- **Timestamp** of the change\n", + "- **Commit message** describing what changed\n", + "- **Parent relationships** showing data evolution\n", + "\n", + "This provides complete **audit trails** for scientific reproducibility.\n" ] }, { @@ -84,20 +153,21 @@ "metadata": {}, "outputs": [], "source": [ - "# Now that we have a new snapshot, reprint the repo ancestry\n", - "new_repo.print_history()" + "# Print repo ancestry\n", + "for ancestor in repo.ancestry(branch=\"main\"):\n", + " print(f\"Snapshot ID:\\t{ancestor.id}\")\n", + " print(f\"Timestamp:\\t{ancestor.written_at}\")\n", + " print(f\"Message:\\t{ancestor.message}\\n\")" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "6", "metadata": {}, - "outputs": [], "source": [ - "# Print the data now contained within the SNODAS repo\n", - "snodas_data = new_repo.retrieve_dataset()\n", - "print(snodas_data)" + "### Accessing Current Data\n", + "\n", + "The data appears as a standard Xarray Dataset, but with version control underneath." ] }, { @@ -107,20 +177,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Much like the 09 SNODAS files were collected, do the same for 2010\n", - "snodas_10_vds = virtualize_and_concat_archival_files_on_time(\n", - " location=NGWPCLocations.SNODAS_REF.path,\n", - " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", - " file_type=FileType.NETCDF,\n", - " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2010*.nc\",\n", - " loadable_vars=[\"crs\"],\n", - " testing_file_quantity=5,\n", - ")\n", - "\n", - "# Append 2010 data to SNODAS repo with a new snapshot\n", - "new_repo.append_virt_data_to_store(\n", - " vds=snodas_10_vds, append_dim=\"time\", commit=\"Appended new data from the year 2010\"\n", - ")" + "session = repo.readonly_session(branch=\"main\")\n", + "ds = xr.open_zarr(session.store, consolidated=False)\n", + "ds" ] }, { @@ -130,20 +189,36 @@ "metadata": {}, "outputs": [], "source": [ - "# Now that we have another new snapshot with 2010 data, reprint the repo ancestry\n", - "new_repo.print_history()" + "# Set up plot for 1990 land cover\n", + "ds[\"5\"].sel(year=1990).plot(x=\"X5\", y=\"Y5\")\n", + "\n", + "# Invert the y-axis to show the CONUS region correctly\n", + "plt.gca().invert_yaxis()\n", + "\n", + "# Add labels and show the plot\n", + "plt.xlabel(\"LON\")\n", + "plt.ylabel(\"LAT\")\n", + "plt.title(\"1990 CONUS Land Cover\")\n", + "plt.show()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "9", "metadata": {}, - "outputs": [], "source": [ - "# Print the new repo collection with both 2009 and 2010 data\n", - "snodas_data = new_repo.retrieve_dataset()\n", - "print(snodas_data)" + "### Demonstrating Version Control: Adding Metadata\n", + "\n", + "Now we'll demonstrate Icechunk's version control by **adding metadata** to our dataset\n", + "\n", + "#### The Version Control Process\n", + "\n", + "1. **Create a writable session** (like checking out code for editing)\n", + "2. **Modify the dataset** (add/update attributes, data, etc.)\n", + "3. **Commit changes** with descriptive message\n", + "4. **New snapshot created** automatically\n", + "\n", + "**Important**: The original data remains **completely unchanged** and accessible.\n" ] }, { @@ -153,9 +228,10 @@ "metadata": {}, "outputs": [], "source": [ - "# Retrieve and print the data from the previous snapshot, before 2010 data was added\n", - "prev_snap_snodas_data = new_repo.retrieve_prev_snapshot()\n", - "print(prev_snap_snodas_data)" + "session = repo.writable_session(\"main\")\n", + "ds.attrs[\"sample_attr\"] = \"sample_attr\"\n", + "ds2 = ds.copy()\n", + "ds2" ] }, { @@ -165,23 +241,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Make a new feature branch based on \"main\" to add 2011 data\n", - "new_repo.create_new_branch(name=\"2011_feature\")\n", - "\n", - "# Much like the 09 SNODAS files were collected, do the same for 2010\n", - "snodas_11_vds = virtualize_and_concat_archival_files_on_time(\n", - " location=NGWPCLocations.SNODAS_REF.path,\n", - " file_date_pattern=\"zz_ssmv11034tS__T0001TTNATS*05HP001.nc\",\n", - " file_type=FileType.NETCDF,\n", - " manual_file_pattern=\"zz_ssmv11034tS__T0001TTNATS2011*.nc\",\n", - " loadable_vars=[\"crs\"],\n", - " testing_file_quantity=5,\n", - ")\n", - "\n", - "# Append 2011 data to SNODAS repo's new branch with a new snapshot\n", - "new_repo.append_virt_data_to_store(\n", - " vds=snodas_11_vds, append_dim=\"time\", commit=\"Appended new data from the year 2011\", branch=\"2011_feature\"\n", - ")" + "session.store.sync_clear() # Clears the store, but preserves snapshots and references to the data" ] }, { @@ -191,47 +251,110 @@ "metadata": {}, "outputs": [], "source": [ - "# Now that we have a new branch with new 2011 data, print the history of both branches\n", - "print(\"NEW BRANCH =====================================\")\n", - "new_repo.print_history(branch=\"2011_feature\")\n", - "print(\"MAIN BRANCH ====================================\")\n", - "new_repo.print_history(branch=\"main\")" + "# NOTE This may take 8-10 minutes\n", + "ds2.virtualize.to_icechunk(session.store)\n", + "print(session.commit(\"Added a sample attribute\"))" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "### Verifying Version History\n", + "\n", + "Let's examine the repository history again. Notice how we now have **two snapshots**:\n", + "\n", + "1. **Original dataset** (initial commit)\n", + "2. **Dataset with metadata** (our recent addition)\n", + "\n", + "This demonstrates **non-destructive updates** - both versions coexist and remain accessible." ] }, { "cell_type": "code", "execution_count": null, - "id": "13", + "id": "14", "metadata": {}, "outputs": [], "source": [ - "# Print both branch's datasets - notice the new one has 2011 data\n", - "snodas_data_feat_branch = new_repo.retrieve_dataset(branch=\"2011_feature\")\n", - "print(\"NEW BRANCH ========================================================\")\n", - "print(snodas_data_feat_branch)\n", - "print(\"===================================================================\")\n", - "print(\"MAIN BRANCH =======================================================\")\n", - "print(snodas_data)\n", - "print(\"===================================================================\")" + "# Print repo ancestry\n", + "for ancestor in repo.ancestry(branch=\"main\"):\n", + " print(f\"Snapshot ID:\\t{ancestor.id}\")\n", + " print(f\"Timestamp:\\t{ancestor.written_at}\")\n", + " print(f\"Message:\\t{ancestor.message}\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "### Time Travel: Accessing Previous Versions\n", + "\n", + "One of Icechunk's most powerful features is **time travel** - the ability to access any previous version of your data using its snapshot ID.\n", + "\n", + "#### Use Cases for Time Travel:\n", + "\n", + "- **Reproducing analyses** from specific points in time\n", + "- **Debugging** when something goes wrong\n", + "- **Comparing versions** to understand changes\n", + "- **Rolling back** to previous states\n", + "- **Auditing** data processing workflows\n", + "\n", + "Below, we access the **original version** (before we added metadata):\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "14", + "id": "16", "metadata": {}, "outputs": [], "source": [ - "# Cleanup - delete the test repo entirely\n", - "new_repo.delete_repo(quiet=True)" + "snapshot_id = list(repo.ancestry(branch=\"main\"))[1].id\n", + "print(f\"Snapshot ID:\\t{snapshot_id}\")\n", + "\n", + "session = repo.readonly_session(snapshot_id=snapshot_id)\n", + "_ds = xr.open_zarr(session.store, consolidated=False)\n", + "_ds" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "Notice how the **original version lacks the `sample_attr`** we added. This proves that the data is versioned and preserved" ] }, { "cell_type": "code", "execution_count": null, - "id": "15", + "id": "18", "metadata": {}, "outputs": [], + "source": [ + "snapshot_id = list(repo.ancestry(branch=\"main\"))[0].id # Latest\n", + "print(f\"Snapshot ID:\\t{snapshot_id}\")\n", + "\n", + "session = repo.readonly_session(snapshot_id=snapshot_id)\n", + "latest_ds = xr.open_zarr(session.store, consolidated=False)\n", + "latest_ds" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "This demonstrates how Icechunk enables robust version control for geospatial data, meeting enterprise requirements for data governance, reproducibility, and collaborative research workflows (FAIR)" + ] + }, + { + "cell_type": "markdown", + "id": "20", + "metadata": {}, "source": [] } ], diff --git a/examples/initial_parameters/soil_freeze_thaw.ipynb b/examples/initial_parameters/soil_freeze_thaw.ipynb index b027b34..ac59a1f 100644 --- a/examples/initial_parameters/soil_freeze_thaw.ipynb +++ b/examples/initial_parameters/soil_freeze_thaw.ipynb @@ -81,7 +81,7 @@ " \"domain\": \"conus_hf\", # The CONUS domain\n", " \"use_schaake\": \"false\", # Specifying we're not using Schaake for the ice fraction setting\n", " },\n", - " timeout=30.0, # GLUE API requests can be slow depending on the network speed. Adding a 30s timeout to ensure requests go through\n", + " timeout=60.0, # GLUE API requests can be slow depending on the network speed. Adding a 30s timeout to ensure requests go through\n", ")\n", "\n", "print(f\"Status code: {response.status_code}\")\n", @@ -171,8 +171,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Through either the API, or CLI, any modeler can create a BMI Config file for SFT that is compatible with NextGen" + "Through either the API, or CLI, any modeler can create a BMI Config file for SFT that is compatible with NextGen. Now, let's clean up the `/tmp/` dir" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!rm -rf /tmp/sft_output_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/mkdocs.yml b/mkdocs.yml index 8b40384..1037c57 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -32,6 +32,13 @@ markdown_extensions: generic: true - pymdownx.highlight: anchor_linenums: true + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + - pymdownx.tabbed: + alternate_style: true - pymdownx.inlinehilite - pymdownx.snippets - admonition @@ -48,6 +55,7 @@ plugins: nav: - Home: - index.md + - Description: description.md - User Guide: - user_guide/index.md - API: user_guide/icefabric_api.md diff --git a/pyproject.toml b/pyproject.toml index 2b61ede..c4f6729 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,7 @@ dependencies = [ "geopandas==1.0.1", "h5netcdf==1.6.1", "h5py==3.13.0", - "icechunk==0.2.18", + "icechunk==1.0.0", "ipywidgets==8.1.6", "jupytergis==0.5.0", "jupyterlab==4.4.3", @@ -63,7 +63,7 @@ dependencies = [ "tqdm==4.67.1", "virtualizarr==1.3.2", "xarray==2025.4.0", - "zarr==3.0.9", + "zarr==3.1.0", ] [project.scripts] diff --git a/src/icefabric/builds/icechunk_s3_module.py b/src/icefabric/builds/icechunk_s3_module.py index 1c385f4..4aeae8a 100644 --- a/src/icefabric/builds/icechunk_s3_module.py +++ b/src/icefabric/builds/icechunk_s3_module.py @@ -402,6 +402,26 @@ def _infer_var_name_for_geotiff(self, variable_list: list) -> str: ) return var_name + @staticmethod + def create_local_virtual_chunk_container(path: str) -> ic.VirtualChunkContainer: + """ + Create a virtual chunk container from a mapping for local files. + + Parameters + ---------- + path : str + The local path to the files which need to be virtualized + + Returns + ------- + ic.VirtualChunkContainer + A definition of a virtual chunk that the icechunk repo + uses to define access to virtualized data. + """ + abs_path = str(Path(path).resolve()) + store_config = ic.local_filesystem_store(abs_path) + return ic.VirtualChunkContainer(f"file://{abs_path}", store_config) + @staticmethod def set_up_virtual_chunk_container(bucket: str, region: str) -> ic.VirtualChunkContainer: """ diff --git a/src/icefabric/helpers/__init__.py b/src/icefabric/helpers/__init__.py index f3067cb..dce7b64 100644 --- a/src/icefabric/helpers/__init__.py +++ b/src/icefabric/helpers/__init__.py @@ -1,6 +1,8 @@ """Helper functions designed to assist with managing data. Similar to util functions""" from .arch_weather_file_utils import ( + add_time_dim_to_datasets, + extract_dates_from_archival_files, get_archival_weather_files, load_tiff_file, virtualize_and_concat_archival_files_on_time, @@ -15,6 +17,8 @@ "get_archival_weather_files", "load_tiff_file", "virtualize_and_concat_archival_files_on_time", + "extract_dates_from_archival_files", + "add_time_dim_to_datasets", "load_creds", "table_to_geopandas", "to_geopandas", diff --git a/src/icefabric/helpers/arch_weather_file_utils.py b/src/icefabric/helpers/arch_weather_file_utils.py index dfd300b..49d948f 100644 --- a/src/icefabric/helpers/arch_weather_file_utils.py +++ b/src/icefabric/helpers/arch_weather_file_utils.py @@ -6,6 +6,7 @@ """ import os +from pathlib import Path import fsspec import pandas as pd @@ -100,8 +101,8 @@ def get_archival_weather_files( return files -def _extract_dates_from_archival_files( - file_paths: list[str], file_pattern: str, just_year: bool | None = False +def extract_dates_from_archival_files( + file_paths: list[str] | list[Path], file_pattern: str, just_year: bool | None = False ) -> list[pd.DatetimeIndex] | list[int]: """ Pull dates out of list of file names @@ -178,7 +179,7 @@ def _virtualize_datasets( return v_datasets -def _add_time_dim_to_datasets( +def add_time_dim_to_datasets( timeless_datasets: list[xr.Dataset], datetimes: list[pd.DatetimeIndex], just_year: bool | None = False, @@ -269,9 +270,9 @@ def virtualize_and_concat_archival_files_on_time( arch_files = get_archival_weather_files( loc=location, file_type=file_type, manual_file_pattern=manual_file_pattern ) - datetimes = _extract_dates_from_archival_files(arch_files, file_date_pattern, just_year=just_year) + datetimes = extract_dates_from_archival_files(arch_files, file_date_pattern, just_year=just_year) timeless_datasets = _virtualize_datasets(arch_files, loadable_vars, testing_file_quantity) - time_added_datasets = _add_time_dim_to_datasets( + time_added_datasets = add_time_dim_to_datasets( timeless_datasets, datetimes, just_year, testing_file_quantity ) concat_dim = "year" if just_year else "time" diff --git a/src/icefabric/schemas/topobathy.py b/src/icefabric/schemas/topobathy.py index 25ed9b6..752f042 100644 --- a/src/icefabric/schemas/topobathy.py +++ b/src/icefabric/schemas/topobathy.py @@ -61,7 +61,7 @@ class NGWPCTestLocations(Enum): stores. """ - SNODAS_IC = ("edfs-data", "forcing/snodas") + # SNODAS_IC = ("edfs-data", "forcing/snodas") # commenting out until data can be moved from data to test bucket NLCD_IC = ("edfs-data", "land-cover/NLCD-Land-Cover") TOPO_AK_10M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_alaska_10m") TOPO_AK_30M_IC = ("edfs-data", f"{TOPO_BP}/tbdem_alaska_30m") diff --git a/tools/icechunk/build_landcover_stores.py b/tools/icechunk/build_landcover_stores.py new file mode 100644 index 0000000..fa58edf --- /dev/null +++ b/tools/icechunk/build_landcover_stores.py @@ -0,0 +1,92 @@ +"""A file to build the landcover stores using Icechunk""" + +import argparse +from pathlib import Path + +import icechunk as ic +import xarray as xr +from tqdm import tqdm +from virtualizarr import open_virtual_dataset + +from icefabric.helpers import ( + add_time_dim_to_datasets, + extract_dates_from_archival_files, +) + + +def build_landcover_store(virtual_files: str, output_path) -> None: + """Creates a landcover store based on the NLCD data + + Parameters + ---------- + virtual_files : str + The path to where the virtual files live + output_path : _type_ + _description_ + """ + abs_path = Path(virtual_files).resolve() + + # Create virtual chunk container + store_config = ic.local_filesystem_store(str(abs_path)) + container = ic.VirtualChunkContainer(f"file://{abs_path}", store_config) + + # Set up credentials + credentials = ic.containers_credentials({f"file://{abs_path}": None}) + + # Create config and add container + config = ic.RepositoryConfig.default() + config.set_virtual_chunk_container(container) + + # Create storage for the repo + storage = ic.local_filesystem_storage(str(Path(output_path).resolve())) + + # Create/open repository with correct class name + nlcd_repo = ic.Repository.open_or_create( + storage=storage, + config=config, + authorize_virtual_chunk_access=credentials, + ) + + # Get Files + files = sorted([str(f) for f in abs_path.glob("*")]) + datetimes = extract_dates_from_archival_files(files, "Annual_NLCD_LndCov_*_CU_C1V0.tif", just_year=True) + + # Virtualize Data + datasets = [] + for i in tqdm( + range(len(files)), + desc="Opening files as Virtual Datasets", + unit="files", + ncols=125, + colour="#37B6BD", + ): + datasets.append( + open_virtual_dataset( + filepath=files[i], + indexes={}, + ) + ) + time_added_datasets = add_time_dim_to_datasets(datasets, datetimes, just_year=True) + ds = xr.concat( + time_added_datasets, dim="year", coords="minimal", compat="override", combine_attrs="override" + ) + + # Write to icechunk + session = nlcd_repo.writable_session("main") + store = session.store # A zarr store + ds.virtualize.to_icechunk(store) + _ = session.commit("Initial Commit: Building landcover store") + + print("Successfully wrote to icechunk") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="A script to build a pyiceberg catalog in the S3 endpoint") + + parser.add_argument( + "virtual_files", type=str, help="The Path to the files we're virtualizing into icechunk" + ) + parser.add_argument("output_path", type=str, help="The Path to where the repo should be created") + + args = parser.parse_args() + build_landcover_store(args.virtual_files, args.output_path)

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ffByfz#M_>xF^!L}hO|^+!5{hCD!1}wjC}qNi@u~J literal 0 HcmV?d00001 diff --git a/docs/user_guide/index.md b/docs/user_guide/index.md index 3e66a0a..e38398a 100644 --- a/docs/user_guide/index.md +++ b/docs/user_guide/index.md @@ -2,22 +2,4 @@ ## The mission -![A diagram describing the icefabric mission](../img/icefabric_mission.png) - The idea for the icefabric came from the need to version control datasets for the National Water Model 4.0. There were many different file formats, and hydrofabric versions, but the need for an [Apache Iceberg](https://iceberg.apache.org/) style backend was realized. The name itself, icefabric, is a reference to this. - -Thus, this repo is a workspace for all icefabric assembly, tranformation, and dissemination. - -The repo is structured in the following workspace packages: - -*Icefabric Manage:* - -The repo for building a local version of the icefabric and updating cloud stores - -*Icefabric Tools:* - -The repo for all compute services built on the icefabric - -*Icefabric API:* - -The interface between the icefabric and outside entities diff --git a/docs/user_guide/terraform.md b/docs/user_guide/terraform.md index 43c052a..8249e71 100644 --- a/docs/user_guide/terraform.md +++ b/docs/user_guide/terraform.md @@ -1,8 +1,8 @@ +# AWS S3 Tables with Apache Iceberg - Terraform Implementation + !!! note These docs are taken from `src/icefabric_manage/terraform/README.md` -# AWS S3 Tables with Apache Iceberg - Terraform Implementation - This directory contains PoC Terraform IaC for deploying Apache Iceberg tables using the AWS S3 Tables service with AWS Glue catalog integration. It also contains a basic demo / test python script used to verify things. ## Architecture Overview diff --git a/examples/iceberg/iceberg_version_control_examples.ipynb b/examples/iceberg/iceberg_version_control_examples.ipynb new file mode 100644 index 0000000..9b6ea74 --- /dev/null +++ b/examples/iceberg/iceberg_version_control_examples.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Apache Iceberg Version Control for Hydrofabric and Streamflow Data\n", + "\n", + "### Overview\n", + "\n", + "This notebook demonstrates **enterprise-grade version control capabilities** for hydrological datasets using Apache Iceberg. We'll showcase how the hydrofabric and streamflow observations can be managed with full version control.\n", + "\n", + "#### What is Apache Iceberg?\n", + "\n", + "**Apache Iceberg** is a high-performance table format designed for large-scale data lakes. Unlike traditional file formats, Iceberg provides:\n", + "\n", + "- **Automatic snapshots** of every data change\n", + "- **Time travel queries** to access historical versions\n", + "- **ACID transactions** for data consistency\n", + "- **Schema evolution** without breaking existing queries\n", + "- **Query performance** through advanced indexing and pruning\n", + "- **Complete audit trails** for regulatory compliance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import all necessary modules\n", + "import os\n", + "from pathlib import Path\n", + "\n", + "import pyarrow as pa\n", + "from pyiceberg.catalog import load_catalog\n", + "\n", + "from icefabric.helpers import load_creds, load_pyiceberg_config\n", + "\n", + "# Changes the current working dir to be the project root\n", + "current_working_dir = Path.cwd()\n", + "os.chdir(Path.cwd() / \"../../\")\n", + "print(\n", + " f\"Changed current working dir from {current_working_dir} to: {Path.cwd()}. This must run at the project root\"\n", + ")\n", + "\n", + "\n", + "# dir is where the .env file is located\n", + "load_creds(dir=Path.cwd())\n", + "\n", + "# Loading the local pyiceberg config settings\n", + "pyiceberg_config = load_pyiceberg_config(Path.cwd())\n", + "catalog = load_catalog(\n", + " name=\"sql\",\n", + " type=pyiceberg_config[\"catalog\"][\"sql\"][\"type\"],\n", + " uri=pyiceberg_config[\"catalog\"][\"sql\"][\"uri\"],\n", + " warehouse=pyiceberg_config[\"catalog\"][\"sql\"][\"warehouse\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploring the Data Catalog\n", + "\n", + "Apache Iceberg organizes data into **catalogs**, **namespaces**, and **tables** - similar to databases, schemas, and tables in traditional systems. However, each table maintains complete version history automatically.\n", + "\n", + "#### Hydrofabric Tables\n", + "\n", + "The `conus_hf` namespace contains hydrofabric layers associated with the CONUS-based geopackage\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "catalog.list_tables(\"conus_hf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's examine the **hydrolocations** table and make some versioned additions. Below we'll see both the snapshots from the hydrolocations table, and actual geopackage layer exported to a pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table = catalog.load_table(\"conus_hf.hydrolocations\")\n", + "table.inspect.snapshots()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = table.scan().to_pandas()\n", + "df.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Snapshot Analysis: Understanding Version History\n", + "\n", + "Each snapshot in Iceberg contains:\n", + "- **Unique identifier** (snapshot_id)\n", + "- **Summary metadata** describing the operation\n", + "- **Timestamp** of the change\n", + "- **File manifests** pointing to data files\n", + "- **Schema information** at that point in time\n", + "\n", + "This enables **complete traceability** of how data evolved over time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for snapshot in table.snapshots():\n", + " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")\n", + "snapshot_id = table.metadata.snapshots[0].snapshot_id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Demonstrating Version Control: Adding New Monitoring Location\n", + "\n", + "Now we'll demonstrate Iceberg's version control by adding a **new hydrologic monitoring location**\n", + "\n", + "#### The Version Control Process:\n", + "\n", + "1. **Modify data** (add new monitoring location)\n", + "2. **Overwrite table** (creates new snapshot automatically)\n", + "3. **Preserve history** (all previous versions remain accessible)\n", + "4. **Track changes** (complete audit trail maintained)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_df = df.copy()\n", + "new_df.loc[len(new_df)] = {\n", + " \"poi_id\": 99999,\n", + " \"id\": \"wb-0\",\n", + " \"nex_id\": \"tnx-0\",\n", + " \"hf_id\": 999999,\n", + " \"hl_link\": \"Testing\",\n", + " \"hl_reference\": \"testing\",\n", + " \"hl_uri\": \"testing\",\n", + " \"hl_source\": \"testing\",\n", + " \"hl_x\": -1.952088e06,\n", + " \"hl_y\": 1.283884e06,\n", + " \"vpu_id\": 18,\n", + "}\n", + "new_df.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Writing Changes: Automatic Snapshot Creation\n", + "\n", + "When we write changes to an Iceberg table:\n", + "\n", + "1. **Schema validation** ensures data compatibility\n", + "2. **New snapshot created** automatically with unique ID\n", + "3. **Previous snapshots preserved** for time travel\n", + "4. **Metadata updated** with operation summary\n", + "5. **ACID guarantees** ensure consistency\n", + "\n", + "This happens **atomically** - either the entire operation succeeds or fails, with no partial states.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "_df = pa.Table.from_pandas(new_df, preserve_index=False)\n", + "with table.update_schema() as update_schema:\n", + " update_schema.union_by_name(_df.schema)\n", + "table.overwrite(_df)\n", + "table.scan().to_pandas().tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Verifying New Snapshot Creation\n", + "\n", + "Let's examine the updated snapshot history. Notice how we now have **multiple snapshots**:\n", + "\n", + "1. **Original data** (initial snapshot)\n", + "2. **Data with new location** (our recent addition)\n", + "\n", + "Each snapshot is **completely independent** and can be accessed separately for different analyses or rollback scenarios.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for snapshot in table.snapshots():\n", + " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Iceberg's **time travel capability** allows querying any previous snapshot using its ID\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "snapshot_id = table.metadata.snapshots[0].snapshot_id\n", + "snapshot_id_latest = table.metadata.snapshots[-1].snapshot_id\n", + "table.scan(snapshot_id=snapshot_id).to_pandas().tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table.scan(snapshot_id=snapshot_id_latest).to_pandas().tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparing Versions: Before and After\n", + "\n", + "Notice the difference between snapshots:\n", + "- **Original snapshot**: Contains original monitoring locations\n", + "- **Latest snapshot**: Includes our new test location (poi_id: 99999)\n", + "\n", + "This demonstrates **non-destructive updates** - both versions coexist and remain queryable.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Streamflow Observations: Time Series Version Control\n", + "\n", + "Now let's examine **streamflow observations** - time series data that requires different version control considerations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table = catalog.load_table(\"streamflow_observations.usgs_hourly\")\n", + "table.inspect.snapshots()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = table.scan().to_pandas().set_index(\"time\")\n", + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for snapshot in table.snapshots():\n", + " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")\n", + "snapshot_id = table.metadata.snapshots[0].snapshot_id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding Time Series Data: Simulating Real-Time Updates\n", + "\n", + "We'll now add a new streamflow observation to demonstrate version control for time series data\n", + "\n", + "The process maintains **historical context** while adding new information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_streamflow_df = df.copy()\n", + "new_streamflow_df.loc[len(new_df)] = 0.1\n", + "new_streamflow_df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "_df = pa.Table.from_pandas(new_streamflow_df)\n", + "with table.update_schema() as update_schema:\n", + " update_schema.union_by_name(_df.schema)\n", + "table.overwrite(_df)\n", + "table.scan().to_pandas().tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for snapshot in table.snapshots():\n", + " print(f\"Snapshot ID: {snapshot.snapshot_id}; Summary: {snapshot.summary}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time Travel with Time Series Data\n", + "\n", + "Comparing different snapshots of time series data reveals:\n", + "\n", + "#### Original Snapshot (Baseline Data):\n", + "- Contains original observational record\n", + "- Represents specific quality control state\n", + "- Suitable for historical analysis\n", + "\n", + "#### Latest Snapshot (Updated Data): \n", + "- Includes new observations\n", + "- Represents current operational state\n", + "- Suitable for real-time applications" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "snapshot_id = table.metadata.snapshots[0].snapshot_id\n", + "snapshot_id_latest = table.metadata.snapshots[-1].snapshot_id\n", + "table.scan(snapshot_id=snapshot_id).to_pandas().tail().set_index(\"time\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = table.scan(snapshot_id=snapshot_id).to_pandas()\n", + "_df = pa.Table.from_pandas(df)\n", + "with table.update_schema() as update_schema:\n", + " update_schema.union_by_name(_df.schema)\n", + "table.overwrite(_df)\n", + "table.scan().to_pandas().tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table.scan(snapshot_id=snapshot_id_latest).to_pandas().tail().set_index(\"time\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Demonstration Cleanup: Reverting Changes\n", + "\n", + "To maintain data integrity, we'll now **revert our test changes** by removing the added records. This demonstrates:\n", + "\n", + "- **Controlled rollback** procedures\n", + "- **Data management** best practices \n", + "- **Cleanup workflows** for testing environments\n", + "\n", + "**Important**: Even these cleanup operations create new snapshots, maintaining complete audit trails of all activities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cleaning up hydrofabric changes\n", + "table = catalog.load_table(\"conus_hf.hydrolocations\")\n", + "new_df = new_df.drop(new_df.index[-1])\n", + "_df = pa.Table.from_pandas(new_df, preserve_index=False)\n", + "with table.update_schema() as update_schema:\n", + " update_schema.union_by_name(_df.schema)\n", + "table.overwrite(_df)\n", + "catalog.load_table(\"conus_hf.hydrolocations\").scan().to_pandas().tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cleaning up Streamflow Observation changes\n", + "table = catalog.load_table(\"streamflow_observations.usgs_hourly\")\n", + "new_streamflow_df = new_streamflow_df.drop(new_streamflow_df.index[-1])\n", + "_df = pa.Table.from_pandas(new_streamflow_df)\n", + "with table.update_schema() as update_schema:\n", + " update_schema.union_by_name(_df.schema)\n", + "table.overwrite(_df)\n", + "catalog.load_table(\"streamflow_observations.usgs_hourly\").scan().to_pandas().tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**This demonstration showcases Apache Iceberg's capability to provide version control for water resources data, enabling both reliability and reproducibility for large-scale hydrological modeling systems.**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "icefabric", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/iceberg/pyiceberg_time_travel.ipynb b/examples/iceberg/pyiceberg_time_travel.ipynb index 30f6ea8..d99689d 100644 --- a/examples/iceberg/pyiceberg_time_travel.ipynb +++ b/examples/iceberg/pyiceberg_time_travel.ipynb @@ -5,13 +5,27 @@ "id": "0", "metadata": {}, "source": [ - "# Demo: Timetravel in with iceberg tables\n", + "# Demo: Time Travel with Iceberg Tables - CRUD Operations & Version Control\n", "\n", - "Create a demo catalog, make changes, and see the changes with \"snapshot\" history.\n", + "## Overview\n", + "This notebook demonstrates **Create, Read, Update, and Delete (CRUD) operations** on version-controlled data using Apache Iceberg tables. The notebook showcases how Iceberg's snapshot-based architecture enables time travel capabilities and maintains a complete history of all data modifications.\n", "\n", - "Requires:\n", - "- pyiceberg[sql-sqlite] installed\n", - "- `.env` with your AWS credentials" + "## Key Features Demonstrated:\n", + "- **CREATE**: Creating new tables and adding data\n", + "- **READ**: Querying current and historical data snapshots\n", + "- **UPDATE**: Modifying table schemas and data\n", + "- **DELETE**: Removing columns and dropping tables\n", + "- **VERSION CONTROL**: Time travel through snapshots to view historical states\n", + "\n", + "## Prerequisites:\n", + "- a local pyiceberg catalog spun up and referenced through .pyiceberg.yaml\n", + "\n", + "## Objectives:\n", + "By the end of this notebook, you will understand how to:\n", + "1. Perform all CRUD operations on Iceberg tables\n", + "2. Leverage version control to access historical data states\n", + "3. Create and manage table snapshots\n", + "4. Navigate between different versions of your data" ] }, { @@ -54,7 +68,9 @@ "id": "2", "metadata": {}, "source": [ - "Load a specific table that we would like to time travel from" + "### READ Operation: Loading and Inspecting Existing Data\n", + "\n", + "We begin by demonstrating the **READ** operation by loading an existing table and examining its version history. This shows how Iceberg maintains complete metadata about all snapshots (versions) of the data.\n" ] }, { @@ -73,7 +89,7 @@ "id": "4", "metadata": {}, "source": [ - "Let's view this data and see what's there" + "Let's examine the current data in the table. This represents the latest version of our dataset. Notice how we can easily convert Iceberg tables to pandas DataFrames for analysis.\n" ] }, { @@ -92,7 +108,9 @@ "id": "6", "metadata": {}, "source": [ - "A snapshot was created for the initial append. Store this snapshot id for later use." + "### Version Control: Capturing Initial State\n", + "\n", + "**Version Control Feature**: Every operation in Iceberg creates a snapshot with a unique ID. We're capturing the initial snapshot ID here so we can demonstrate time travel capabilities later. This snapshot represents the baseline state of our data before any modifications.\n" ] }, { @@ -112,7 +130,12 @@ "id": "8", "metadata": {}, "source": [ - "Add a new column for a fake gauge" + "### UPDATE Operation: Schema Evolution and Data Modification\n", + " \n", + "Now we'll demonstrate the **UPDATE** operation by adding a new column to our existing table. This involves:\n", + "1. Creating synthetic data for the new column\n", + "2. Updating the table schema to accommodate the new column\n", + "3. Overwriting the table with the updated data\n" ] }, { @@ -139,6 +162,16 @@ "import pyarrow as pa\n", "\n", "df[\"12345678\"] = y\n", + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ "_df = pa.Table.from_pandas(df)\n", "with table.update_schema() as update_schema:\n", " update_schema.union_by_name(_df.schema)\n", @@ -147,34 +180,41 @@ }, { "cell_type": "markdown", - "id": "11", + "id": "12", "metadata": {}, "source": [ - "There should be a new \"12345678\" column." + "After our UPDATE operation, we can verify that the schema has been modified. The new column \"12345678\" should now be part of the table structure.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "12", + "id": "13", "metadata": {}, "outputs": [], "source": [ - "table.schema" + "table.schema().fields[-1]" ] }, { "cell_type": "markdown", - "id": "13", + "id": "14", "metadata": {}, "source": [ - "There should now be three snapshots. The original, a delete, and an append with the new column." + "### Version Control: Tracking All Changes\n", + "\n", + "**Version Control Feature**: Notice how Iceberg has automatically created new snapshots for our UPDATE operation. The snapshot history now shows:\n", + "- Original data snapshot\n", + "- Delete operation snapshot (part of overwrite)\n", + "- New append operation snapshot (with the new column)\n", + "\n", + "This complete audit trail is essential for data governance and debugging." ] }, { "cell_type": "code", "execution_count": null, - "id": "14", + "id": "15", "metadata": {}, "outputs": [], "source": [ @@ -184,38 +224,34 @@ }, { "cell_type": "markdown", - "id": "15", + "id": "16", "metadata": {}, "source": [ - "You can use the scan function and the first snapshot ID (this variable was saved earlier) to look at the table before the\n", - "new column was added. This table doesn't have 12345678." + "**Time Travel Feature**: Using the snapshot ID we captured earlier, we can query the table as it existed before our UPDATE operation. This demonstrates Iceberg's powerful time travel capabilities - you can access any historical state of your data.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "16", + "id": "17", "metadata": {}, "outputs": [], "source": [ - "# scan = table.scan(row_filter=\"divide_id\" == \"cat-276\", selected_fields=('divide_id', 'lengthm')).to_arrow()\n", - "# print(scan)\n", - "table.scan(snapshot_id=snapshot_id).to_pandas().tail()\n", - "# table.scan(snapshot_id=snapshot_id)" + "table.scan(snapshot_id=snapshot_id).to_pandas().tail()" ] }, { "cell_type": "markdown", - "id": "17", + "id": "18", "metadata": {}, "source": [ - "whereas loading without the snapshot gives the latest data" + "Comparing the current state (with the new column) versus the historical state (without the column) demonstrates how version control preserves all data states while allowing easy access to current data.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "18", + "id": "19", "metadata": {}, "outputs": [], "source": [ @@ -224,7 +260,183 @@ }, { "cell_type": "markdown", - "id": "19", + "id": "20", + "metadata": {}, + "source": [ + "Now we'll demonstrate another **UPDATE** operation by removing the column we just added. This shows how Iceberg handles schema evolution in both directions (adding and removing columns).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": {}, + "outputs": [], + "source": [ + "with table.update_schema() as update_schema:\n", + " update_schema.delete_column(\"12345678\")\n", + "\n", + "df = df.drop(\"12345678\", axis=1)\n", + "_df = pa.Table.from_pandas(df)\n", + "table.overwrite(_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [], + "source": [ + "table.schema().fields[-1]" + ] + }, + { + "cell_type": "markdown", + "id": "23", + "metadata": {}, + "source": [ + "### CREATE Operation: Building New Tables\n", + "\n", + "Now we'll demonstrate the **CREATE** operation by building an entirely new table from scratch. This shows how to:\n", + "1. Prepare data for a new table\n", + "2. Create the table structure in the catalog\n", + "3. Populate the table with initial data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": {}, + "outputs": [], + "source": [ + "__df = df.copy()\n", + "__df[\"12345678\"] = y\n", + "subset_df = __df[[\"12345678\"]].copy()\n", + "subset_df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": {}, + "outputs": [], + "source": [ + "namespace = \"streamflow_observations\"\n", + "table_name = \"testing_hourly\"\n", + "arrow_table = pa.Table.from_pandas(subset_df)\n", + "iceberg_table = catalog.create_table(\n", + " f\"{namespace}.{table_name}\",\n", + " schema=arrow_table.schema,\n", + ")\n", + "iceberg_table.append(arrow_table)" + ] + }, + { + "cell_type": "markdown", + "id": "26", + "metadata": {}, + "source": [ + "### READ Operation: Verifying New Table Creation \n", + "\n", + "After our **CREATE** operation, we can verify that the new table exists in our namespace and examine its initial snapshot. Every new table starts with its first snapshot upon creation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27", + "metadata": {}, + "outputs": [], + "source": [ + "catalog.list_tables(namespace)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28", + "metadata": {}, + "outputs": [], + "source": [ + "table = catalog.load_table(f\"{namespace}.{table_name}\")\n", + "table.inspect.snapshots()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29", + "metadata": {}, + "outputs": [], + "source": [ + "table.scan().to_pandas().tail()" + ] + }, + { + "cell_type": "markdown", + "id": "30", + "metadata": {}, + "source": [ + "### DELETE Operation: Table Removal\n", + "\n", + "Finally, we demonstrate the **DELETE** operation by completely removing the table we just created. This shows how to clean up resources and manage table lifecycle.\n", + "\n", + "**Important**: Unlike column deletion (which is reversible through time travel), table deletion is permanent and removes all snapshots and data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31", + "metadata": {}, + "outputs": [], + "source": [ + "catalog.drop_table(f\"{namespace}.{table_name}\")\n", + "catalog.list_tables(namespace)" + ] + }, + { + "cell_type": "markdown", + "id": "32", + "metadata": {}, + "source": [ + "### Summary: CRUD Operations and Version Control Demonstrated\n", + " \n", + "This notebook has successfully demonstrated all required CRUD operations with version-controlled data:\n", + " \n", + "#### CREATE Operations:\n", + "- Created new tables with `catalog.create_table()`\n", + "- Added new columns to existing tables\n", + "- Populated tables with initial data using `append()`\n", + "\n", + "#### READ Operations:\n", + "- Loaded existing tables with `catalog.load_table()`\n", + "- Queried current data states with `table.scan()`\n", + "- Accessed historical data states using snapshot IDs\n", + "- Inspected table schemas and metadata\n", + " \n", + "#### UPDATE Operations:\n", + "- Modified table schemas by adding columns\n", + "- Updated data through `overwrite()` operations\n", + "- Removed columns from existing tables\n", + "\n", + "#### DELETE Operations:\n", + "- Deleted columns from table schemas\n", + "- Removed entire tables with `catalog.drop_table()`\n", + "\n", + "#### Version Control Features:\n", + "- **Snapshot Management**: Every operation creates tracked snapshots\n", + "- **Time Travel**: Access any historical state using snapshot IDs\n", + "- **Audit Trail**: Complete history of all table modifications\n", + "- **Schema Evolution**: Track changes to table structure over time\n" + ] + }, + { + "cell_type": "markdown", + "id": "33", "metadata": {}, "source": [] } diff --git a/examples/icechunk/icechunk_data_ret_and_plot.ipynb b/examples/icechunk/icechunk_data_ret_and_plot.ipynb deleted file mode 100644 index c4fdb81..0000000 --- a/examples/icechunk/icechunk_data_ret_and_plot.ipynb +++ /dev/null @@ -1,164 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0", - "metadata": {}, - "source": [ - "## Retrieving Icechunk Data\n", - "Intantiate and retrieve the data from the NLCD Land Cover Icechunk repo. Print and plot the data.\n", - "\n", - "Requires:\n", - "- `.env` with your AWS credentials" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1", - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "from pathlib import Path\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2", - "metadata": {}, - "outputs": [], - "source": [ - "from icefabric.helpers import load_creds\n", - "\n", - "# dir is where the .env file is located\n", - "load_creds(dir=Path.cwd().parents[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3", - "metadata": {}, - "outputs": [], - "source": [ - "from icefabric.builds import IcechunkRepo\n", - "from icefabric.schemas import NGWPCTestLocations\n", - "\n", - "# Retrive data and print\n", - "land_cover_repo = IcechunkRepo(location=NGWPCTestLocations.NLCD_IC.path)\n", - "lc_data = land_cover_repo.retrieve_dataset()\n", - "print(lc_data)" - ] - }, - { - "cell_type": "markdown", - "id": "4", - "metadata": {}, - "source": [ - "### Plot 2023 data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5", - "metadata": {}, - "outputs": [], - "source": [ - "# Print data for year 2023\n", - "print(lc_data[\"5\"].sel(year=2023))\n", - "\n", - "# Set up plot for 2023 land cover\n", - "lc_data[\"5\"].sel(year=2023).plot(x=\"X5\", y=\"Y5\")\n", - "\n", - "# Invert the y-axis to show the CONUS region correctly\n", - "plt.gca().invert_yaxis()\n", - "\n", - "# Add labels and show the plot\n", - "plt.xlabel(\"LON\")\n", - "plt.ylabel(\"LAT\")\n", - "plt.title(\"CONUS Land Cover\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6", - "metadata": {}, - "source": [ - "## Topobathy\n", - "Intantiate and retrieve the data from one Topobathy Icechunk repo. Print and plot the data.\n", - "\n", - "### Retrieve Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7", - "metadata": {}, - "outputs": [], - "source": [ - "# Retrive data and print\n", - "topo_repo = IcechunkRepo(location=NGWPCTestLocations.TOPO_PR_USVI_30M_IC.path)\n", - "topo_data = topo_repo.retrieve_dataset()\n", - "print(topo_data)" - ] - }, - { - "cell_type": "markdown", - "id": "8", - "metadata": {}, - "source": [ - "### Plot Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9", - "metadata": {}, - "outputs": [], - "source": [ - "# Print Dataarray\n", - "print(topo_data[\"elevation\"])\n", - "\n", - "# Set up plot\n", - "topo_data[\"elevation\"].plot(x=\"x\", y=\"y\")\n", - "\n", - "# Add labels and show the plot\n", - "plt.xlabel(\"LON\")\n", - "plt.ylabel(\"LAT\")\n", - "plt.title(\"Puerto Rico & USVI 30M Topobathy\")\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "icefabric", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/icechunk/icechunk_to_tiff.ipynb b/examples/icechunk/icechunk_to_tiff.ipynb deleted file mode 100644 index e752390..0000000 --- a/examples/icechunk/icechunk_to_tiff.ipynb +++ /dev/null @@ -1,705 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "42d13343", - "metadata": {}, - "source": [ - "# Demo: Export icechunk raster data to geotiff\n", - "\n", - "Raster topobathymetry data made into icechunk stores is not inherently visibile with traditional GIS methods. Use this method to retrieve a dataset and export to geotiff. \n", - "\n", - "Requires:\n", - "- `.env` with AWS credentials" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7cba65ef", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "from icefabric.builds import IcechunkRepo\n", - "from icefabric.schemas import NGWPCLocations" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d479759f", - "metadata": {}, - "outputs": [], - "source": [ - "from icefabric.helpers import load_creds\n", - "\n", - "# dir is where the .env file is located\n", - "load_creds(dir=Path.cwd().parents[1])" - ] - }, - { - "cell_type": "markdown", - "id": "bcebeab5", - "metadata": {}, - "source": [ - "Open the dataset to check information and variable name." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7df8b014", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "