From 46f29fd231e5904d2f2de122a4e4d0a08f5f3572 Mon Sep 17 00:00:00 2001 From: seknewna Date: Fri, 8 Aug 2025 21:00:08 +0200 Subject: [PATCH 01/33] "Just testing" --- notebooks/module-1/01-intro-to-jupyter-notebook.ipynb | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/notebooks/module-1/01-intro-to-jupyter-notebook.ipynb b/notebooks/module-1/01-intro-to-jupyter-notebook.ipynb index c88d1ad..2b84ed5 100644 --- a/notebooks/module-1/01-intro-to-jupyter-notebook.ipynb +++ b/notebooks/module-1/01-intro-to-jupyter-notebook.ipynb @@ -101,18 +101,11 @@ "print('Dunstan')\n", "```" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -126,7 +119,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.13.5" }, "toc": { "base_numbering": 1, From 11ed28cd635aaf82d95d236a0d083d7f3bfbb5d6 Mon Sep 17 00:00:00 2001 From: seknewna Date: Thu, 14 Aug 2025 02:54:21 +0200 Subject: [PATCH 02/33] Polished version of my notebook. --- .../module-1/02-python-fundamentales.ipynb | 2140 +++++++++++++++++ notebooks/module-1/data.csv | 10 + notebooks/module-1/data.npy | Bin 0 -> 208 bytes 3 files changed, 2150 insertions(+) create mode 100644 notebooks/module-1/02-python-fundamentales.ipynb create mode 100644 notebooks/module-1/data.csv create mode 100644 notebooks/module-1/data.npy diff --git a/notebooks/module-1/02-python-fundamentales.ipynb b/notebooks/module-1/02-python-fundamentales.ipynb new file mode 100644 index 0000000..868c9b0 --- /dev/null +++ b/notebooks/module-1/02-python-fundamentales.ipynb @@ -0,0 +1,2140 @@ +{ + "cells": [ + { + "attachments": { + "image.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "id": "e44f3705", + "metadata": {}, + "source": [ + "# DATA SCIENCE CAPACITY BUILDING INITIATIVE\n", + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "id": "40e9b128-4271-46c7-b7cb-f92cebff2fae", + "metadata": { + "panel-layout": { + "height": 345.5848388671875, + "visible": true, + "width": 100 + } + }, + "source": [ + "# Python Basics\n", + "\n", + "**What do we learn here?**\n", + "- [Arithmetic Operators](#Arithmetic-Operators)\n", + "- [Logical Operators](#Logical-Operators)\n", + "- [Variables & Data Types](#Varibles-&-Data-Types)\n", + "- [Control Flow](#Control-Flow)\n", + "- [Loops](#Loops)\n", + "- [Writing Functions in Python](#Writing-Functions-in-Python)\n", + "- [Object-Oriented Programming](#Object-Oriented-Programming)\n", + "- [NumPy Package](#NumPy-Package)\n", + "- [Introduction to Pandas Package](#Introduction-to-Pandas-Package)\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "82d33217-d012-4e3a-b997-4b8c058c820e", + "metadata": {}, + "source": [ + "## Arithmetic Operators\n", + "Arithmetic operators are used to perform common mathematical operations in Python. These operators work with numeric data types such as integers and floats.\n", + "\n", + "### Basic Arithmetic Operators\n", + "\n", + "| Operator | Description | Example | Result |\n", + "|----------|---------------------|----------------|--------|\n", + "| `+` | Addition | `5 + 3` | `8` |\n", + "| `-` | Subtraction | `10 - 4` | `6` |\n", + "| `*` | Multiplication | `6 * 7` | `42` |\n", + "| `/` | Division | `8 / 2` | `4.0` |\n", + "| `//` | Floor Division | `7 // 2` | `3` |\n", + "| `%` | Modulus (Remainder) | `7 % 3` | `1` |\n", + "| `**` | Exponentiation | `2 ** 3` | `8` |\n", + "\n", + "### Try it yourself!\n", + "\n", + "```python\n", + "a = 10\n", + "b = 3\n", + "\n", + "print(\"Addition:\", a + b) # 13\n", + "print(\"Subtraction:\", a - b) # 7\n", + "print(\"Multiplication:\", a * b) # 30\n", + "print(\"Division:\", a / b) # 3.333...\n", + "print(\"Floor Division:\", a // b) # 3\n", + "print(\"Modulus:\", a % b) # 1\n", + "print(\"Exponentiation:\", a ** b) # 1000\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9f9e664b-3c5e-4427-8804-230f5743a5f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Addition: 13\n", + "Subtraction: 7\n", + "Multiplication: 30\n", + "Division: 3.3333333333333335\n", + "Floor Division: 3\n", + "Modulus: 1\n", + "Exponentiation: 1000\n" + ] + } + ], + "source": [ + "a = 10\n", + "b = 3\n", + "\n", + "print(\"Addition:\", a + b) # 13\n", + "print(\"Subtraction:\", a - b) # 7\n", + "print(\"Multiplication:\", a * b) # 30\n", + "print(\"Division:\", a / b) # 3.333...\n", + "print(\"Floor Division:\", a // b) # 3\n", + "print(\"Modulus:\", a % b) # 1\n", + "print(\"Exponentiation:\", a ** b) # 1000" + ] + }, + { + "cell_type": "markdown", + "id": "0668ef43-77b0-4233-ad3f-f38c788012c0", + "metadata": {}, + "source": [ + "## Logical Operators\n", + "Logical operators are used to combine multiple conditions (Boolean expressions) and return `True` or `False` depending on the logic applied.\n", + "\n", + "### Basic Logical Operators\n", + "\n", + "| Operator | Description | Example | Result |\n", + "|----------|------------------------------|--------------------------|-------------|\n", + "| `and` | Logical AND (both true) | `True and False` | `False` |\n", + "| `or` | Logical OR (at least one) | `True or False` | `True` |\n", + "| `not` | Logical NOT (negation) | `not True` | `False` |\n", + "\n", + "---\n", + "\n", + "### Truth Table\n", + "\n", + "### `and` Operator\n", + "| A | B | A and B |\n", + "|-------|-------|----------|\n", + "| True | True | True |\n", + "| True | False | False |\n", + "| False | True | False |\n", + "| False | False | False |\n", + "\n", + "### `or` Operator\n", + "| A | B | A or B |\n", + "|-------|-------|---------|\n", + "| True | True | True |\n", + "| True | False | True |\n", + "| False | True | True |\n", + "| False | False | False |\n", + "\n", + "### `not` Operator\n", + "| A | not A |\n", + "|-------|-------|\n", + "| True | False |\n", + "| False | True |\n", + "\n", + "---\n", + "\n", + "## Try it yourself\n", + "\n", + "```python\n", + "a = True\n", + "b = False\n", + "\n", + "print(\"a and b:\", a and b) # False\n", + "print(\"a or b:\", a or b) # True\n", + "print(\"not a:\", not a) # False\n", + "\n", + "# Example with comparisons\n", + "x = 5\n", + "print(x > 0 and x < 10) # True\n", + "print(x < 0 or x > 3) # True\n", + "print(not (x == 5)) # False\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "62de541a-cc96-43ce-9c22-1f9b42977b3e", + "metadata": {}, + "outputs": [], + "source": [ + "## YOUR CODE" + ] + }, + { + "cell_type": "markdown", + "id": "265e6d5b-b3be-4835-8e61-f7707b682b80", + "metadata": {}, + "source": [ + "## Varibles & Data Types\n", + "\n", + "In Python, **variables** are used to store data values. You do not need to declare the data type explicitly. Python automatically assigns it based on the value.\n", + "\n", + "### Common Data Types\n", + "\n", + "| Type | Description | Example |\n", + "|--------|--------------------------------------|------------------------------------------|\n", + "| **`int`** | Integer numbers | `x = 5` |\n", + "| **`float`**| Floating-point numbers (decimals) | `pi = 3.14` |\n", + "| **`str`** | Text (string) | `name = \"Bob\"` |\n", + "| **`bool`** | Boolean values (`True` or `False`) | `is_happy = True` |\n", + "| **`list`** | Ordered, mutable sequence | `fruits = [\"apple\", \"banana\"]` |\n", + "| **`tuple`**| Ordered, immutable sequence | `coords = (10, 20)` |\n", + "| **`dict`** | Key-value pairs (dictionary) | `student = {\"name\": \"Alice\", \"age\": 20}` |\n", + "| **`set`** | Unordered collection of unique items | `nums = {1, 2, 3}` |\n", + "\n", + "### Declaring Variables\n", + "\n", + "```python\n", + "x = 10 # integer\n", + "name = \"Alice\" # string\n", + "pi = 3.14 # float\n", + "is_valid = True # boolean\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "810a00f7-4c3b-40e2-bc71-db4a4f785179", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "36a1070e", + "metadata": {}, + "source": [ + "# Control Flow\n", + "Control using if, elif and else in programming allow your program to make decision and react to different situations.\n", + "\n", + "```python\n", + "if CONDITION1:\n", + " if CONDITION1a: # nested if\n", + " do_something\n", + " else:\n", + " do_something_else\n", + "elif CONDITION2:\n", + " do_other_thing\n", + "else:\n", + " do_default_action\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "ba46a9fd", + "metadata": {}, + "source": [ + "**Example**: given x, say x = 11, check if it is a multiple of 2 and print `'11 is a multiple of 2'` else print `'x is not a multiple of 2'`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cb86e61e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11 is not a multiple of 2.\n" + ] + } + ], + "source": [ + "x = 11\n", + "if x % 2 == 0:\n", + " print(x, 'is a multiple of 2.')\n", + "else:\n", + " print(x, 'is not a multiple of 2.')" + ] + }, + { + "cell_type": "markdown", + "id": "64166ac2", + "metadata": {}, + "source": [ + "**Example 2**: solve the quadratic equation $ax^2 + bx + c = 0.$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dc92e0f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We have two complex (conjugate) solutions.\n" + ] + } + ], + "source": [ + "a, b, c = (1, 2, 2)\n", + "delta = b ** 2 - 4 * a * c\n", + "\n", + "if delta > 0:\n", + " print('We have two distinct real solutions.')\n", + "elif delta < 0:\n", + " print('We have two complex (conjugate) solutions.')\n", + "else:\n", + " print('We have a double real solution.')" + ] + }, + { + "cell_type": "markdown", + "id": "749ad4cc", + "metadata": {}, + "source": [ + "This can be written in two condition only with a nested loop when `delta >= 0`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1310dfcf", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Write the code above using if with nested if and else." + ] + }, + { + "cell_type": "markdown", + "id": "96ce135d-5ff2-40ce-86f1-82dcd92493fa", + "metadata": {}, + "source": [ + "# Loops\n", + "\n", + "Loops are used to execute a block of code repeatedly. Python supports two main types of loops: `for` loops and `while` loops.\n", + "\n", + "---\n", + "\n", + "### 1. `for` Loop\n", + "\n", + "A `for` loop is used to iterate over a sequence such as a list, tuple, string, or range.\n", + "\n", + "#### Syntax\n", + "\n", + "A variable takes each value in the sequence one at a time, and the loop executes once for each value.\n", + "```python\n", + "for variable in sequence: \n", + " # code block\n", + "```\n", + "---\n", + "\n", + "### 2. `while` Loop\n", + "\n", + "A `while` loop continues to execute as long as a specified condition is `True`.\n", + "\n", + "#### Syntax\n", + "The condition is evaluated before each iteration. If the condition becomes `False`, the loop stops.\n", + "\n", + "---\n", + "\n", + "## Loop Control Statements\n", + "\n", + "Python includes special statements that change the normal flow of loops:\n", + "\n", + "| Statement | Description |\n", + "|-----------|-----------------------------------------|\n", + "| `break` | Exits the loop immediately |\n", + "| `continue`| Skips the current iteration |\n", + "| `pass` | Does nothing; used as a placeholder |\n", + "\n", + "---\n", + "\n", + "## `else` with Loops\n", + "\n", + "Both `for` and `while` loops can have an optional `else` block.\n", + "\n", + "- The `else` block runs **only if the loop completes without a `break`**.\n", + "- If the loop ends due to a `break` statement, the `else` block is skipped.\n", + "\n", + "---\n", + "\n", + "## List Comprehension\n", + "\n", + "List comprehension provides a concise way to create lists using a single line of code. It is more readable and often faster than traditional loops.\n", + "\n", + "### Syntax\n", + "It combines `for` loops and optional conditional logic into a single expression.\n", + "\n", + "- Can include an `if` condition to filter elements.\n", + "- Can be nested or used with functions for transformations.\n", + "\n", + "List comprehensions are ideal when:\n", + "- You want to create a new list from an existing iterable.\n", + "- The transformation logic is simple and readable in one line.\n", + "\n", + "```python\n", + "[x for x in range(3) if x == 3]\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "edaca66b-b1de-459b-91ee-e7eab4f1c398", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 2, 4]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[x for x in range(5) if x % 2 == 0] # returns a list of even numbers between 0 and 4." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cc39d7c0-8da5-4f21-82b1-8c413f431c78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 2, 4]\n" + ] + } + ], + "source": [ + "L = [] # this is how we define a list in Python. You can go to ...\n", + "for i in range(5):\n", + " if i % 2 == 0:\n", + " L.append(i)\n", + " else:\n", + " continue\n", + "\n", + "# another line code to print L. Pff! what a boring life\n", + "print(L)" + ] + }, + { + "cell_type": "markdown", + "id": "6f8d79ee-719d-4996-8a49-fbf9fab60706", + "metadata": {}, + "source": [ + "The following code block is optional.\n", + "```python\n", + "else:\n", + " continue\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1f4f242a-0593-40ab-97d5-e7ef663ce8a9", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Use a for loop to save all odd number between 0 and 10 in a list." + ] + }, + { + "cell_type": "markdown", + "id": "d7a57a6f-3b1b-419f-8cda-7950b3d737c5", + "metadata": {}, + "source": [ + "## Writing Functions in Python\n", + "\n", + "Functions are reusable blocks of code that perform a specific task. They help organize code, avoid repetition, and improve readability.\n", + "\n", + "### Defining a Function\n", + "\n", + "In Python, functions are defined using the `def` keyword, followed by the function name, parentheses with optional parameters, and a colon. The code block inside the function is indented.\n", + "\n", + "#### Example:\n", + "```python\n", + "def greet():\n", + " print(\"Hello, world!\")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "42c26286-82b5-4847-a782-18b852025b46", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, world!\n" + ] + } + ], + "source": [ + "def greet():\n", + " print(\"Hello, world!\")\n", + "\n", + "greet()" + ] + }, + { + "cell_type": "markdown", + "id": "793088f1-19d0-46cd-9843-110bc7828d35", + "metadata": {}, + "source": [ + "#### Parameters and Arguments\n", + "- Parameters are variables listed in the function definition.\n", + "- Arguments are values passed into the function when it is called." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "908a9e74-507c-4d62-8211-ae15c0563197", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, Guest\n", + "Hello, Alice\n" + ] + } + ], + "source": [ + "def greet(name=\"Guest\"):\n", + " print(\"Hello,\", name)\n", + "\n", + "greet() # Uses default\n", + "greet(\"Alice\") # Overrides default" + ] + }, + { + "cell_type": "markdown", + "id": "befae3f1-8f9d-47a3-89bb-aab9655af8a7", + "metadata": {}, + "source": [ + "### Return Statement\n", + "Functions can return values using the return statement." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "64c7d48b-349e-4641-946a-c6f545fb48c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + } + ], + "source": [ + "def add(a, b):\n", + " return a + b\n", + "\n", + "result = add(5, 3)\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "id": "bb9046ac-fcd0-479c-91fb-891b416e5799", + "metadata": {}, + "source": [ + "### Variable-Length Arguments\n", + "Use `*args` for any number of positional arguments, and `**kwargs` for keyword arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6b12c041-1f80-4bc8-b2c1-5c3f1d604992", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n" + ] + } + ], + "source": [ + "def print_numbers(*args):\n", + " for number in args:\n", + " print(number)\n", + "\n", + "print_numbers(1, 2, 3, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a56adcfc-822f-4cf3-9b89-6ff7ae645e4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name : Alice\n", + "age : 25\n" + ] + } + ], + "source": [ + "def print_info(**kwargs):\n", + " for key, value in kwargs.items():\n", + " print(key, \":\", value)\n", + "\n", + "print_info(name=\"Alice\", age=25)" + ] + }, + { + "cell_type": "markdown", + "id": "e6ae2d08-2d98-4568-9b09-25e4587d6f7e", + "metadata": {}, + "source": [ + "### Scope of Variables\n", + "- Local variables exist only within a function.\n", + "- Global variables are accessible throughout the program." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c22fa508-6fbd-4abb-9e98-26b2e39f3314", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inside function: 5\n", + "Outside function: 10\n" + ] + } + ], + "source": [ + "x = 10 # global\n", + "\n", + "def show():\n", + " x = 5 # local\n", + " print(\"Inside function:\", x)\n", + "\n", + "show()\n", + "print(\"Outside function:\", x)" + ] + }, + { + "cell_type": "markdown", + "id": "5f13b443-268b-40a8-9c97-885e477ce4ff", + "metadata": {}, + "source": [ + "If `x` is defined outside all functions (i.e., at the `global` level), and you want to update it from inside a function, use the global keyword:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f0b82bb0-0da2-465f-9494-f75ab1d201bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = 10 # in the global environment\n", + "\n", + "def use_global():\n", + " \n", + " global x\n", + " return x\n", + "\n", + "use_global()" + ] + }, + { + "cell_type": "markdown", + "id": "182e79d3-cf15-4c90-a4c0-7bb5e165a649", + "metadata": {}, + "source": [ + "If `x` is defined in an outer function (`not global`), and you want to update it in an inner (nested) function, use the `nonlocal` keyword." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "45be4fc0-3a60-4063-b2c4-3fd5e9c6675c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "def outer():\n", + " x = 10 # enclosing scope\n", + "\n", + " def inner():\n", + " nonlocal x\n", + " x = 20\n", + "\n", + " inner()\n", + " print(x)\n", + "\n", + "outer()" + ] + }, + { + "cell_type": "markdown", + "id": "59319da0-a50e-4d02-85dc-7946ffb82e6a", + "metadata": {}, + "source": [ + "# Object-Oriented Programming\n", + "\n", + "Object-Oriented Programming (OOP) is a programming paradigm based on the concept of **objects**, which can contain data (**attributes**) and code (**methods**). Python supports OOP and makes it easy to create and use classes and objects.\n", + "\n", + "---\n", + "\n", + "## Key Concepts\n", + "\n", + "| Concept | Description |\n", + "|--------------|-----------------------------------------------------------------------------|\n", + "| Class | A blueprint for creating objects |\n", + "| Object | An instance of a class |\n", + "| Attribute | A variable that belongs to a class or object |\n", + "| Method | A function that belongs to a class |\n", + "| `__init__` | The constructor method called when an object is created |\n", + "| `self` | A reference to the current instance of the class |\n", + "| Inheritance | A way to create a new class using properties and methods of an existing one |\n", + "| Encapsulation| Bundling of data and methods that operate on that data |\n", + "| Polymorphism | Ability to redefine methods for different types of objects |\n", + "\n", + "---\n", + "\n", + "## Defining a Class\n", + "\n", + "A class is defined using the `class` keyword. The `__init__()` method is the constructor, used to initialize attributes when an object is created.\n", + "\n", + "### Example:\n", + "```python\n", + "class Person:\n", + " def __init__(self, name, age):\n", + " self.name = name\n", + " self.age = age\n", + "\n", + " def greet(self):\n", + " print(f\"Hello, my name is {self.name}\")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f74eda6b-3a3a-465d-b764-1d6ad26275b7", + "metadata": {}, + "outputs": [], + "source": [ + "class Person:\n", + " def __init__(self, name, age):\n", + " self.name = name\n", + " self.age = age\n", + "\n", + " def greet(self):\n", + " print(f\"Hello, my name is {self.name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e28d0651-2910-4dc5-97b9-57b4835197fa", + "metadata": {}, + "outputs": [], + "source": [ + "lema = Person('Lema', 39)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1ff6b58e-a607-40d2-880e-0029ee2893cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "39 Lema\n", + "Hello, my name is Lema\n" + ] + } + ], + "source": [ + "print(lema.age, lema.name)\n", + "lema.greet()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9484e0f1-5194-4ebb-b5e2-8a245c81bef3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "4789f834", + "metadata": {}, + "source": [ + "# NumPy Package\n", + "NumPy is a Python library for numerical computing. It provides support for large, multi-dimensional arrays and a variety of high-performance mathematical functions." + ] + }, + { + "cell_type": "markdown", + "id": "2f8d8f29", + "metadata": {}, + "source": [ + "## Installion\n", + "\n", + "```bash\n", + "pip install numpy # or pip install --upgrade if you are upgrading numpy\n", + "```\n", + "\n", + "## Loading\n", + "```python\n", + "import numpy as np\n", + "```\n", + "\n", + "## Creating Arrays\n", + "```python\n", + "np.array([1, 2, 3]) # 1D array\n", + "np.zeros((2, 3)) # 2x3 array of zeros\n", + "np.ones((3, 2)) # 3x2 array of ones\n", + "np.full((2, 2), 7) # 2x2 array filled with 7\n", + "np.eye(3) # 3x3 identity matrix\n", + "np.arange(0, 10, 2) # [0, 2, 4, 6, 8]\n", + "np.linspace(0, 1, 5) # 5 evenly spaced numbers\n", + "np.random.rand(2, 2) # Random values in [0, 1)\n", + "np.random.randint(1, 10, 5) # 5 random ints from 1 to 9\n", + "```\n", + "\n", + "Try all the commands on your own.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "272c31e3", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: try all the commands above\n", + "# pip install numpy # uncomment and run if you have not yet installed numpy\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "0fa7c5a0", + "metadata": {}, + "source": [ + "## Array Properties\n", + "\n", + "```python\n", + "a.shape # Dimensions (rows, cols)\n", + "a.ndim # Number of dimensions\n", + "a.size # Total number of elements\n", + "a.dtype # Data type of elements\n", + "```\n", + "\n", + "for a given array `a`." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e7aff21b", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: define an ndarray from the previous subsection (Creating Arrays), name it a, and check its properties." + ] + }, + { + "cell_type": "markdown", + "id": "1c24be59", + "metadata": {}, + "source": [ + "## Reshaping & Flattening\n", + "```python\n", + "a.reshape(3, 2) # Change shape\n", + "a.flatten() # Convert to 1D\n", + "a.T # Transpose\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "940daaef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2. 2.2 2.4 2.6]\n", + " [2.8 3. 3.2 3.4]\n", + " [3.6 3.8 4. 4.2]\n", + " [4.4 4.6 4.8 5. ]\n", + " [5.2 5.4 5.6 5.8]\n", + " [6. 6.2 6.4 6.6]\n", + " [6.8 7. 7.2 7.4]\n", + " [7.6 7.8 8. 8.2]\n", + " [8.4 8.6 8.8 9. ]\n", + " [9.2 9.4 9.6 9.8]]\n" + ] + } + ], + "source": [ + "# Example of reshaping\n", + "a = np.arange(2, 10, 0.2) # arithmetic sequence from 2 with comment difference 2.\n", + "n = len(a) # 40 element\n", + "\n", + "b = a.reshape(10, 4) # return a 10 by 4 matrix.\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "4873cdac", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: run a.reshape(2, 5, 4). Assign it to another variable and check its properties." + ] + }, + { + "cell_type": "markdown", + "id": "1f0982a7", + "metadata": {}, + "source": [ + "An example of reshaping is when you have a flat list of 784 pixels from an [MNIST](https://www.kaggle.com/datasets/hichamachahboun/mnist-handwritten-digits) (Modified National Institute of Standards and Technology) image that you need to reshape into a $28\\times28$ array.\n", + "\n", + "Now, run \n", + "```python \n", + "a.flatten()\n", + "```\n", + "What do you see?" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "81329639", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: test if b flattened b is equal to the array a." + ] + }, + { + "cell_type": "markdown", + "id": "ab5e1be5", + "metadata": {}, + "source": [ + "## Indexing & Slicing\n", + "Some examples:\n", + "\n", + "```python\n", + "b[1] # Element at index 1\n", + "b[1:4] # Slice elements 1 to 3\n", + "b[:, 0] # All rows, column 0\n", + "b[1, :] # Row 1, all columns\n", + "b[-1] # Last element\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "61d1fb49", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[2. , 2.2, 2.4, 2.6],\n", + " [2.8, 3. , 3.2, 3.4],\n", + " [3.6, 3.8, 4. , 4.2],\n", + " [4.4, 4.6, 4.8, 5. ],\n", + " [5.2, 5.4, 5.6, 5.8]],\n", + "\n", + " [[6. , 6.2, 6.4, 6.6],\n", + " [6.8, 7. , 7.2, 7.4],\n", + " [7.6, 7.8, 8. , 8.2],\n", + " [8.4, 8.6, 8.8, 9. ],\n", + " [9.2, 9.4, 9.6, 9.8]]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TODO: do it yourself.\n", + "a.reshape(2, 5, 4) " + ] + }, + { + "cell_type": "markdown", + "id": "d4816804", + "metadata": {}, + "source": [ + "## Mathematical Operations\n", + "Let `a = 5`, `b=6`, and `c = [a, b, a - 1, b * 2]`. Run the following commands in the console.\n", + "\n", + "```python\n", + "a + b # Element-wise addition\n", + "a * b # Element-wise multiplication\n", + "np.dot(a, b) # Matrix multiplication\n", + "np.sum(a) # Sum of elements\n", + "np.mean(a) # Mean\n", + "np.std(a) # Standard deviation\n", + "np.max(a) # Max value\n", + "np.min(a) # Min value\n", + "np.argmax(a) # Index of max\n", + "np.cumsum(a) # Cumulative sum\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4ed50be5", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Run the above commands. Use print to have all outputs.\n", + "a = 5; b = 6; c = [a, b, a - 1, b * 2]" + ] + }, + { + "cell_type": "markdown", + "id": "e4438848", + "metadata": {}, + "source": [ + "## Boolean Operations\n", + "Boolean operations on `NumPy` arrays let you compare elements, filter data, and combine conditions efficiently all in a vectorized way (no loops needed).\n", + "\n", + "```python\n", + "age = np.array([11, 33, 91, 1, 39, 29, 93, 4, 53, 37])\n", + "age > 5 # Element-wise comparison\n", + "age[age > 5] # Filter elements > 5\n", + "np.where(age > 5) # Indexes of all elements > 5\n", + "np.where(age > 5, 1, 0) # Conditional replacement equivalent \n", + " # to (age > 5).astype(int) or np.int64(age > 5)\n", + "np.any(age > 0) # Any True?\n", + "np.all(age > 0) # All True?\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d6542059", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Run the above example\n", + "age = np.array([11, 33, 91, 1, 39, 29, 93, 4, 53, 37])" + ] + }, + { + "cell_type": "markdown", + "id": "9f4a8212", + "metadata": {}, + "source": [ + "### Counts\n", + "Knowing that True and False are respectively 1 and 0 numerically. Summing `age > 5` gives the number of observations/participants above 5." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "7d89d078", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Check it out." + ] + }, + { + "cell_type": "markdown", + "id": "7f4cbccf", + "metadata": {}, + "source": [ + "## Stacking Arrays\n", + "In NumPy, stacking arrays means combining multiple arrays into a single array along a new or existing axis.\n", + "\n", + "```python\n", + "a = np.array([[1, 2], [3, 4]])\n", + "b = np.array([[5, 6], [7, 8]])\n", + "\n", + "print(np.hstack((a, b)))\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "db9cdd84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2]\n", + " [3 4]\n", + " [5 6]\n", + " [7 8]]\n" + ] + } + ], + "source": [ + "a = np.array([[1, 2], [3, 4]])\n", + "b = np.array([[5, 6], [7, 8]])\n", + "\n", + "print(np.vstack((a, b))) # v is for vertical which means row-wise. TODO: Try hstack for column-wise stacking" + ] + }, + { + "cell_type": "markdown", + "id": "afa6e661", + "metadata": {}, + "source": [ + "## Explore other functions\n", + "\n", + "| Function | Axis Behavior | When to Use |\n", + "|----------------|--------------------|--------------------------------------|\n", + "| `hstack` | Columns (side-by-side) | Merge arrays horizontally (must have same number of rows) |\n", + "| `dstack` | Depth (axis=2) | Stack arrays along a third dimension (must match in rows & columns) |\n", + "| `stack` | New axis anywhere | Full control over where the new axis is placed |\n", + "| `column_stack` | Columns | Stack 1D arrays as columns into a 2D array |\n", + "| `row_stack` | Rows | Stack 1D arrays as rows into a 2D array |\n", + "| `concatenate([...], axis)`| Existing axis (no new one)| Join arrays along an existing axis (shape must match on all other dimensions)|" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f2ccb886", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4],\n", + " [5, 6],\n", + " [7, 8]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.concatenate([a, b]) # axis=0 by default. The same as print(np.vstack((a, b)))" + ] + }, + { + "cell_type": "markdown", + "id": "2a2ae599", + "metadata": {}, + "source": [ + "## Copying Arrays\n", + "Deep `copy` prevents the modification on the original variable.\n", + "\n", + "```python\n", + "age_copy = age.copy() # Deep copy (not a reference)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "7217565f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[99 33 91 1 39 29 93 4 53 37]\n", + "[11 33 91 1 39 29 93 4 53 37]\n", + "[99 33 91 1 39 29 93 4 53 37]\n" + ] + } + ], + "source": [ + "age_copy = age.copy()\n", + "age_copy1 = age # reference\n", + "# Now, let's make changes on age_copy1\n", + "age_copy1[0] = 99\n", + "# Pring the 3 variables\n", + "print(age)\n", + "print(age_copy)\n", + "print(age_copy1)" + ] + }, + { + "cell_type": "markdown", + "id": "ce66381f", + "metadata": {}, + "source": [ + "## Saving and Loading data\n", + "NumPy can:\n", + "\n", + "* Store data in its own fast, compact binary format (.npy) with np.save() and reload it with `np.load()`.\n", + "* Save arrays as text files like CSV using `np.savetxt()` for human-readable or spreadsheet use.\n", + "* Load text data (e.g., CSV) back into arrays with `np.genfromtxt()`.\n", + "\n", + "```python\n", + "np.save('data.npy', age) # Save binary\n", + "np.load('data.npy') # Load binary\n", + "np.savetxt('data.csv', age, delimiter=',') # Save as CSV\n", + "np.genfromtxt('data.csv', delimiter=',') # Load CSV\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a1c56f56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[99 33 91 1 39 29 93 4 53 37]\n" + ] + } + ], + "source": [ + "np.save('data.npy', age)\n", + "age_import = np.load('data.npy')\n", + "print(age_import)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "a7bcd2db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([99., 33., 91., 1., 39., 29., 93., 4., 53., 37.])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.savetxt('data.csv', age, delimiter=\",\")\n", + "np.genfromtxt('data.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "23a9e039", + "metadata": {}, + "source": [ + "# Introduction to Pandas Package\n", + "\n", + "```bash\n", + "# Install Pandas\n", + "pip install pandas\n", + "```\n", + "\n", + "```python\n", + "# Loading Pandas\n", + "import pandas as pd\n", + "```\n", + "\n", + "## Creating data frames\n", + "We can create a pandas DataFrame from:\n", + "* dictionaries of lists" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "f9e4dc65", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Name Age\n", + "0 Alice 25\n", + "1 Bob 30\n", + "2 Denyse 35" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "data = {'Name': ['Alice', 'Bob', 'Denyse'], 'Age': [25, 30, 35]}\n", + "df_age = pd.DataFrame(data)\n", + "df_age" + ] + }, + { + "cell_type": "markdown", + "id": "222caa06", + "metadata": {}, + "source": [ + "* list of dictionaries" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "5331f9ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Name Age\n", + "0 Alice 25\n", + "1 Bob 30" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = [{'Name': 'Alice', 'Age': 25}, {'Name': 'Bob', 'Age': 30}]\n", + "df = pd.DataFrame(data)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "4244f630", + "metadata": {}, + "source": [ + "* list of lists/tuples" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "a081db45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Height\n", + "0 Alice 1.75\n", + "1 Bob 1.63\n", + " Name Height\n", + "0 Alice 1.75\n", + "1 Bob 1.63\n" + ] + } + ], + "source": [ + "data = [['Alice', 1.75], ['Bob', 1.63]]\n", + "data2 = [('Alice', 1.75), ('Bob', 1.63)]\n", + "df_height = pd.DataFrame(data, columns=['Name', 'Height']) # here, we have to specify column names\n", + "df_height2 = pd.DataFrame(data2, columns=['Name', 'Height']) # here, we have to specify column names\n", + "print(df_height)\n", + "print(df_height2)" + ] + }, + { + "cell_type": "markdown", + "id": "0e695d97", + "metadata": {}, + "source": [ + "* NumPy arrays\n", + "```python\n", + "arr = np.array([['Alice', 25], ['Bob', 30]])\n", + "df = pd.DataFrame(arr)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "8ea0389d", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Run the above example. The first column is the column of names and the second is the one for ages." + ] + }, + { + "cell_type": "markdown", + "id": "21689cc7", + "metadata": {}, + "source": [ + "## Saving data\n", + "| File Format | Function | Example Code | Notes |\n", + "|-------------|------------------------|-----------------------------------------------------|-------|\n", + "| CSV | `to_csv()` | `df.to_csv('data.csv', index=False)` | Human-readable, widely used, supports delimiters |\n", + "| Excel | `to_excel()` | `df.to_excel('data.xlsx', index=False)` | Requires `openpyxl` or `xlsxwriter` installed |\n", + "| JSON | `to_json()` | `df.to_json('data.json', orient='records')` | Good for web apps and APIs |\n", + "| Pickle | `to_pickle()` | `df.to_pickle('data.pkl')` | Binary format, fast to read/write, Python-specific |\n", + "| HDF5 | `to_hdf()` | `df.to_hdf('data.h5', key='df', mode='w')` | Efficient for large datasets, requires `tables` |\n", + "| Parquet | `to_parquet()` | `df.to_parquet('data.parquet')`\n", + "\n", + "**Note**: Parquet is a modern, efficient, and highly optimized columnar storage file format commonly used in data engineering and data science workflows. It's particularly popular in big data contexts because it allows for faster reading, smaller storage, and better compression compared to traditional row-based formats like CSV." + ] + }, + { + "cell_type": "markdown", + "id": "7c1c64d4", + "metadata": {}, + "source": [ + "## Loading data\n", + "| File Format | Function | Example Code | Notes |\n", + "|-------------|------------------------|-----------------------------------------------------|-------|\n", + "| CSV | `pd.read_csv()` | `df = pd.read_csv('data.csv')` | Most common, human-readable, supports delimiters and encoding options |\n", + "| Excel | `pd.read_excel()` | `df = pd.read_excel('data.xlsx')` | Requires `openpyxl` or `xlrd`, supports multiple sheets |\n", + "| JSON | `pd.read_json()` | `df = pd.read_json('data.json')` | Good for web APIs, can specify `orient` |\n", + "| Pickle | `pd.read_pickle()` | `df = pd.read_pickle('data.pkl')` | Fast binary read/write, Python-specific |\n", + "| HDF5 | `pd.read_hdf()` | `df = pd.read_hdf('data.h5', key='df')` | Efficient for large datasets, requires `tables` |\n", + "| Parquet | `pd.read_parquet()` | `df = pd.read_parquet('data.parquet')` | Columnar format, fast, efficient for big data |\n", + "| SQL | `pd.read_sql()` | `df = pd.read_sql('SELECT * FROM table', conn)` | Reads from SQL database, requires DB connection |" + ] + }, + { + "cell_type": "markdown", + "id": "c65ea2c8", + "metadata": {}, + "source": [ + "## Exploring data\n", + "```python\n", + "df.head() # First 5 rows\n", + "df.tail() # Last 5 rows\n", + "df.info() # Summary\n", + "df.describe() # Stats\n", + "df.shape # Rows and columns\n", + "df.columns # Column names\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "3ffee7a1", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: load the pickle file 'df_data.pkl' using pandas." + ] + }, + { + "cell_type": "markdown", + "id": "f4a37e8f", + "metadata": {}, + "source": [ + "# Group discussion" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a473d67f", + "metadata": {}, + "outputs": [], + "source": [ + "# Importing CSV (comma separated data in Python)\n", + "rwpop = pd.read_csv(\"./Data/rwa_admpop_adm2_2023.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "bebec9d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['year', 'ISO3', 'ADM0_FR', 'ADM0_PCODE', 'ADM1_FR', 'ADM1_PCODE',\n", + " 'ADM2_FR', 'ADM2_PCODE', 'F_TL', 'M_TL', 'T_TL', 'F_00_04', 'F_05_09',\n", + " 'F_10_14', 'F_15_19', 'F_20_24', 'F_25_29', 'F_30_34', 'F_35_39',\n", + " 'F_40_44', 'F_45_49', 'F_50_54', 'F_55_59', 'F_60_64', 'F_65_69',\n", + " 'F_70_74', 'F_75_79', 'F_80Plus', 'M_00_04', 'M_05_09', 'M_10_14',\n", + " 'M_15_19', 'M_20_24', 'M_25_29', 'M_30_34', 'M_35_39', 'M_40_44',\n", + " 'M_45_49', 'M_50_54', 'M_55_59', 'M_60_64', 'M_65_69', 'M_70_74',\n", + " 'M_75_79', 'M_80Plus', 'T_00_04', 'T_05_09', 'T_10_14', 'T_15_19',\n", + " 'T_20_24', 'T_25_29', 'T_30_34', 'T_35_39', 'T_40_44', 'T_45_49',\n", + " 'T_50_54', 'T_55_59', 'T_60_64', 'T_65_69', 'T_70_74', 'T_75_79',\n", + " 'T_80Plus'],\n", + " dtype='object')" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### Data description\n", + "rwpop.head() # displays the first 5 rows of rwpop \n", + "rwpop.shape # number of rows and columns\n", + "rwpop.columns" + ] + }, + { + "cell_type": "markdown", + "id": "d4f609c5", + "metadata": {}, + "source": [ + "As you can see, the data is presented in a wide format and the columns names `F_TL` (Total number of Female) has two information which are Gender and Value. Also, `M_20_24` means count of male in the age category` 20-24`.\n", + "\n", + "The task is to reshape the data from wide to long format. For that, the pandas `melt` could be used as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "39da1cce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearISO3ADM0_FRADM0_PCODEADM1_FRADM1_PCODEADM2_FRADM2_PCODEvariablePopulation
02023RWARwandaRWEastern ProvinceRW5BugeseraRW57F_TL236613
12023RWARwandaRWEastern ProvinceRW5GatsiboRW53F_TL348552
22023RWARwandaRWEastern ProvinceRW5KayonzaRW54F_TL277298
32023RWARwandaRWEastern ProvinceRW5KireheRW55F_TL241160
42023RWARwandaRWEastern ProvinceRW5NgomaRW56F_TL230804
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" + ], + "text/plain": [ + " year ISO3 ADM0_FR ADM0_PCODE ADM1_FR ADM1_PCODE ADM2_FR \\\n", + "0 2023 RWA Rwanda RW Eastern Province RW5 Bugesera \n", + "1 2023 RWA Rwanda RW Eastern Province RW5 Gatsibo \n", + "2 2023 RWA Rwanda RW Eastern Province RW5 Kayonza \n", + "3 2023 RWA Rwanda RW Eastern Province RW5 Kirehe \n", + "4 2023 RWA Rwanda RW Eastern Province RW5 Ngoma \n", + "\n", + " ADM2_PCODE variable Population \n", + "0 RW57 F_TL 236613 \n", + "1 RW53 F_TL 348552 \n", + "2 RW54 F_TL 277298 \n", + "3 RW55 F_TL 241160 \n", + "4 RW56 F_TL 230804 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_vars = ['year', 'ISO3', 'ADM0_FR', 'ADM0_PCODE', 'ADM1_FR', 'ADM1_PCODE',\n", + " 'ADM2_FR', 'ADM2_PCODE']\n", + "rwpop_melted = pd.melt(rwpop, id_vars=cat_vars, value_name=\"Population\")\n", + "rwpop_melted.head()" + ] + }, + { + "cell_type": "markdown", + "id": "b9ca2319", + "metadata": {}, + "source": [ + "Now, let's filter out rows containing totals `T_`, `_TL`" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e013548b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearISO3ADM0_FRADM0_PCODEADM1_FRADM1_PCODEADM2_FRADM2_PCODEvariablePopulation
902023RWARwandaRWEastern ProvinceRW5BugeseraRW57F_00_0432472
912023RWARwandaRWEastern ProvinceRW5GatsiboRW53F_00_0444454
922023RWARwandaRWEastern ProvinceRW5KayonzaRW54F_00_0436097
932023RWARwandaRWEastern ProvinceRW5KireheRW55F_00_0430422
942023RWARwandaRWEastern ProvinceRW5NgomaRW56F_00_0430312
.................................
11052023RWARwandaRWWestern ProvinceRW3NyabihuRW34M_80Plus296
11062023RWARwandaRWWestern ProvinceRW3NyamashekeRW37M_80Plus504
11072023RWARwandaRWWestern ProvinceRW3RubavuRW33M_80Plus339
11082023RWARwandaRWWestern ProvinceRW3RusiziRW36M_80Plus418
11092023RWARwandaRWWestern ProvinceRW3RutsiroRW32M_80Plus360
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1020 rows × 10 columns

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" + ], + "text/plain": [ + " year ISO3 ADM0_FR ADM0_PCODE ADM1_FR ADM1_PCODE ADM2_FR \\\n", + "90 2023 RWA Rwanda RW Eastern Province RW5 Bugesera \n", + "91 2023 RWA Rwanda RW Eastern Province RW5 Gatsibo \n", + "92 2023 RWA Rwanda RW Eastern Province RW5 Kayonza \n", + "93 2023 RWA Rwanda RW Eastern Province RW5 Kirehe \n", + "94 2023 RWA Rwanda RW Eastern Province RW5 Ngoma \n", + "... ... ... ... ... ... ... ... \n", + "1105 2023 RWA Rwanda RW Western Province RW3 Nyabihu \n", + "1106 2023 RWA Rwanda RW Western Province RW3 Nyamasheke \n", + "1107 2023 RWA Rwanda RW Western Province RW3 Rubavu \n", + "1108 2023 RWA Rwanda RW Western Province RW3 Rusizi \n", + "1109 2023 RWA Rwanda RW Western Province RW3 Rutsiro \n", + "\n", + " ADM2_PCODE variable Population \n", + "90 RW57 F_00_04 32472 \n", + "91 RW53 F_00_04 44454 \n", + "92 RW54 F_00_04 36097 \n", + "93 RW55 F_00_04 30422 \n", + "94 RW56 F_00_04 30312 \n", + "... ... ... ... \n", + "1105 RW34 M_80Plus 296 \n", + "1106 RW37 M_80Plus 504 \n", + "1107 RW33 M_80Plus 339 \n", + "1108 RW36 M_80Plus 418 \n", + "1109 RW32 M_80Plus 360 \n", + "\n", + "[1020 rows x 10 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "rwpop_melted['variable'][30]\n", + "indx = np.array([x[0] in [\"F\", \"M\"] and \"TL\" not in x.split(\"_\") for x in rwpop_melted['variable']]) # type: ignore\n", + "rwpop_melted = rwpop_melted[indx]\n", + "rwpop_melted" + ] + }, + { + "cell_type": "markdown", + "id": "31f5b6b4", + "metadata": {}, + "source": [ + "Bingo! We've removed all the totals from the data. Now, the next cleaning is to separate the column variable into `variable` in to `gender` and `age_group`. After that, decode `F` to `Female` and `M` to `Male`." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "5fa5555b", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Separate variable to gender and age_group\n", + "rwpop_melted['age_group'] = [x[2:] for x in rwpop_melted.variable]\n", + "# rwpop_melted['Gender'] = [x.split(\"_\")[0] for x in rwpop_melted.variable]\n", + "rwpop_melted['Gender'] = [x[:1] for x in rwpop_melted.variable]\n", + "\n", + "# TODO: decode the variable gender\n", + "rwpop_melted.loc[:, 'Gender'] = rwpop_melted['Gender'].replace({'F':'Female', 'M':'Male'})" + ] + }, + { + "cell_type": "markdown", + "id": "5fa5f01a", + "metadata": {}, + "source": [ + "You need to install the `matplotlib` and `seaborn` libraries.\n", + "```bash\n", + "!pip install matplotlib seaborn\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "dd00d9bf", + "metadata": {}, + "source": [ + "If all goes well, you will be able to draw the following chart." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "3f10e720", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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The initiative aims to deliver data science training to staff from over 20 institutions that are part of Rwanda's National Statistical System (NSS). diff --git a/docs/projects.md b/docs/projects.md new file mode 100644 index 0000000..2a43797 --- /dev/null +++ b/docs/projects.md @@ -0,0 +1,2 @@ +# Data Use Cases +Kunle/Dinna add a table of projects as discussed \ No newline at end of file diff --git a/docs/test.html b/docs/test.html index 394c08d..e69de29 100644 --- a/docs/test.html +++ b/docs/test.html @@ -1,10 +0,0 @@ - - - - AIMS-DSCBI Test Page - - -

Test Page for AIMS-DSCBI

-

This is a test page to check if GitHub Pages is working correctly.

- - diff --git a/imghdr.py b/imghdr.py index d7327ef..e69de29 100644 --- a/imghdr.py +++ b/imghdr.py @@ -1,59 +0,0 @@ -# Mock imghdr module for compatibility with Python 3.13 and older Sphinx versions - -def what(file, h=None): - """Dummy implementation that always returns None""" - return None - -def test_jpeg(h, f=None): - """Dummy implementation""" - return None - -def test_png(h, f=None): - """Dummy implementation""" - return None - -def test_gif(h, f=None): - """Dummy implementation""" - return None - -def test_tiff(h, f=None): - """Dummy implementation""" - return None - -def test_rgb(h, f=None): - """Dummy implementation""" - return None - -def test_pbm(h, f=None): - """Dummy implementation""" - return None - -def test_pgm(h, f=None): - """Dummy implementation""" - return None - -def test_ppm(h, f=None): - """Dummy implementation""" - return None - -def test_rast(h, f=None): - """Dummy implementation""" - return None - -def test_xbm(h, f=None): - """Dummy implementation""" - return None - -def test_bmp(h, f=None): - """Dummy implementation""" - return None - -def test_webp(h, f=None): - """Dummy implementation""" - return None - -def test_exr(h, f=None): - """Dummy implementation""" - return None - -tests = [] diff --git a/notebooks/assignments/mod1-assignment-2.ipynb b/notebooks/assignments/mod1-assignment-2.ipynb new file mode 100644 index 0000000..0b4db69 --- /dev/null +++ b/notebooks/assignments/mod1-assignment-2.ipynb @@ -0,0 +1,233 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2baaa681-e813-428b-9010-4508b7291011", + "metadata": {}, + "source": [ + "# Programming Assignment-1\n", + "## Estimating Chichewa Speakers\n", + "### Background\n", + "This notebook provides statistics about the language of **Chichewa**. \n", + "More specifically, the goal is to generate an accurate estimate of total number of people who speak this language. Note that Chichewa also includes the alternate names: *Chewa*, *Nyanja*, *Chinyanja*. In order to provide context, first, let us indicate the countries where the language is spoken. \n", + "1. **Malawi.** About 70% of the population speak Chichewa. [[1]](#cite_note-1)\n", + "2. **Zambia.** About 20% of the population speak the language. [[2]](#cite_note-2)\n", + "3. **Mozamabique.** Less than 1% of the population speak the language. [[3]](#cite_note-3)\n", + "4. **Zimbabwe.** Although Chichewa seem to be one of the official languages for Zimbabwe, I havent found any data yet showing how many people speak the language.\n", + "5. **Tanzania.** Has a border with Malawi in the northen region where people speak Tumbuka, so it makes sense that there maybe no Chichewa speaking people there. Otherwise, I didnot find any data on proportion of the population who speak the language.\n", + "\n", + "Based on the analysis in this notebook, as of 2023, there are **21,482,292** people who speak Chichewa distributed across three countries: Malawi (70%), Zambia (18%) and Mozambique(12%).\n", + "\n", + "The [Humanitarian data website](https://data.humdata.org/dataset/) contains data about languages for some of these countries. The HUMDATA links are provided below:\n", + "- [Zambia languages](https://data.humdata.org/dataset/zambia-languages)\n", + "- [Malawi languages](https://data.humdata.org/dataset/malawi-languages)\n", + "- [Mozambique languages](https://data.humdata.org/dataset/mozambique-languages)\n", + "\n", + "Since I was not very sure of some of the numbers from [humdata](https://data.humdata.org), I decided to check with the actual sources as follows:\n", + ">**Malawi and Zambia.** I could not find any current surveys with data on languages spoken but I still found something in the DHS. In the DHS, they ask about survey respondent's native language. Although this data is not included in DHS reports (as they seem to collect this piece of data as interview metadata), its still a useful source of data for languages spoken. For *Zimbabwe, Tanzania and Mozambique* the DHS does'nt have this information as they only provide languages as language-1, language-2 etc except for the major languages.\n", + "\n", + "> ### 🚨 Assignment Tasks \n", + "> 1. Read the notebook carefully and answer the questions as asked. \n", + "> 2. Complete any missing code where it says **\"ADD YOUR CODE\"**.\n", + ">3. In some cases, you may be asked to write a whole function.\n", + "\n", + "### Datasets \n", + "For this assignment, you will use the following datasets:\n", + "#### Malawi 2015-16 DHS \n", + "- [Dataset folder](https://drive.google.com/drive/folders/1ygZmjsphEfTed-Tir_nPrNMq1Y44EtS7?usp=sharing)\n", + "- Data file: ```MWPR7AFL.DTA```\n", + "#### Malawi Population\n", + "- [Population data](https://drive.google.com/drive/folders/1T7C0fkwfsOAeAWHFj3lKIZgCYHRfbha0?usp=sharing)\n", + "\n", + "## References\n", + "1. [^](#cite_ref-1) 2015-16 MDHS and [humdata](https://data.humdata.org/dataset/malawi-languages)\n", + "\n", + "2. [^](#cite_ref-2) 2018 Zambia DHS and [humdata](https://data.humdata.org/dataset/zambia-languages)\n", + "\n", + "3. [^](#cite_ref-3) [Humdata](https://data.humdata.org/dataset/mozambique-languages)" + ] + }, + { + "cell_type": "markdown", + "id": "cbb0234e", + "metadata": {}, + "source": [ + "## Setup Input Folders\n", + "In this section, make sure to define the folders where your data is stored on your machine. \n", + "I find it helpful to set up the working directory and input data folders right at the start of the notebook. \n", + "To keep things organized, I use the naming convention: `FILE_{NAME}` for files and `DIR_{NAME}` for folders.\n", + "\n", + "We'll be using the [`pathlib`](https://docs.python.org/3/library/pathlib.html) library—it's the recommended approach for managing file paths in Python.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba8273ba", + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment the following lines and add your code to define the directories and files\n", + "# DIR_DATA = ADD YOUR CODE\n", + "# DIR_DHS = ADD YOUR CODE\n", + "\n", + "# Population by enumeration area (EA) for Malawi\n", + "# FILE_POP_MW = ADD YOUR CODE\n", + "\n", + "# MW DHS-2015-16 HH members stata data file\n", + "# FILE_MW_DHS = ADD YOUR CODE" + ] + }, + { + "cell_type": "markdown", + "id": "5f391f0a-b3d7-4dfc-985a-c8769c327f81", + "metadata": {}, + "source": [ + "## Estimating Chichewa Speakers in Malawi Based on Tribe\n", + "Malawi is the primary country where Chichewa is spoken. In the absence of precise language data, we begin by estimating the number of Chichewa speakers based on tribal affiliation.\n", + "### Estimated Population by Tribe in Malawi\n", + "\n", + "- **Chewa**: 6,020,945 \n", + "- **Tumbuka**: 1,614,955 \n", + "- **Lomwe**: 3,302,634 \n", + "- **Tonga**: 310,031 \n", + "- **Yao**: 2,321,763 \n", + "- **Sena**: 670,908 \n", + "- **Nkhonde**: 174,430 \n", + "- **Lambya**: 106,769 \n", + "- **Sukwa**: 93,762 \n", + "- **Manganja**: 559,887 \n", + "- **Nyanja**: 324,272 \n", + "- **Ngoni**: 1,819,347 \n", + "- **Other**: 186,319 \n", + "\n", + "In Malawi, these groups speak Chichewa:\n", + "- **Chewa**: 6,020,945 \n", + "- **Nyanja**: 324,272 \n", + "- **Ngoni**: 1,819,347 \n", + "- **Other**: 186,319 \n", + "- **Manganja**: 559,887 " + ] + }, + { + "cell_type": "markdown", + "id": "720c404f", + "metadata": {}, + "source": [ + "### Task-1: \n", + "- Using the information provided in cell above, write a function that estimates the number of Chichewa speakers in Malawi based on tribal groups.\n", + "- Ensure your function returns a value representing the chichewa speaking population\n", + "- Name your function: ```mw_estimate_chichewa_speaking_based_on_tribe```\n", + "- Call your function and print out the number of Chichewa speakers" + ] + }, + { + "cell_type": "markdown", + "id": "147b0949", + "metadata": {}, + "source": [ + "## Chichewa speakers in Malawi Based on District of Residence\n", + "In this section, we estimate Chichewa speakers based on the district of residence. For example, we can assume that people from the following districts speak Chichewa: \n", + "> Mzuzu City, Kasungu, Nkhotakota, Ntchisi, Dowa, Salima, Lilongwe, \n", + "> Mchinji, Dedza, Ntcheu, Lilongwe City, \n", + "> Zomba, Chiradzulu, Blantyre, Mwanza, Thyolo, \n", + "> Mulanje, Phalombe, Balaka, Neno, \n", + "> Zomba City, Blantyre City" + ] + }, + { + "cell_type": "markdown", + "id": "5fbb15a6", + "metadata": {}, + "source": [ + "### Task 2 – Part 1: Generate Population by District\n", + "\n", + "- Use the Malawi population data to write a function that reads the relevant data files and returns a DataFrame containing population totals by district. \n", + "- Name your function: `generate_dist_pop`\n", + "\n", + "### Task 2 – Part 2: Estimate Chichewa-Speaking Population\n", + "\n", + "- Write a function that takes the district-level population DataFrame and estimates the number of Chichewa speakers. \n", + "- Name your function: `mw_estimate_chichewa_speakers_by_district` \n", + "- Call this function and the previous function as needed and print the total estimated number of Chichewa speakers.\n" + ] + }, + { + "cell_type": "markdown", + "id": "428487e8", + "metadata": {}, + "source": [ + "## Chichewa speakers in Malawi Based on DHS Data\n", + "In the Demographic and Health Survey (DHS), we estimate the number of Chichewa speakers using responses to the question on the primary language spoken by the respondent. \n", + "While the DHS does not provide exhaustive linguistic data, the self-reported language question offers a useful proxy for estimating language distribution across the population. \n", + "This approach allows us to approximate the number of Chichewa speakers based on individual-level survey data that is nationally representative.\n", + "\n", + "### Relevant Variables Column Name Mapping in STATA file\n", + "\n", + "- `hv045b`: `intv_lan` — Interviewer's language \n", + "- `hv045c`: `resp_nativ_lan` — Respondent's native language \n", + "- `hv046`: `translator` — Translator used \n", + "- `hv002`: `hh_num` — Household number \n", + "- `hv005`: `weight` — Sampling weight \n", + "- `hv045a`: `qn_lan` — Language of the questionnaire \n", + "- `hv001`: `cluster_number` — Cluster number \n", + "- `hv004`: `area_unit` — Area unit \n", + "\n", + "### Language Code Mapping\n", + "\n", + "- `1`: **English** \n", + "- `2`: **Chichewa** \n", + "- `3`: **Tumbuka** \n", + "- `6`: **Other** \n", + "\n", + "### Other Useful Information\n", + "- Total Number of Sampled Housolds in 2015-2016 DHS: 26, 361\n", + "- Malawi projected population for 2023: 19, 809, 511" + ] + }, + { + "cell_type": "markdown", + "id": "2c1ee3e7", + "metadata": {}, + "source": [ + "### Task 3: Estimate Chichewa Speakers from DHS Using 2023 Population Data\n", + "\n", + "- Write a function that estimates the number of Chichewa speakers using the respondent language variable from the DHS dataset. \n", + "- Call the function and print the total estimated number of Chichewa speakers.\n", + "\n", + "**Hints**\n", + "- Report the unweighted estimate \n", + "- Use the variable descriptions provided above (e.g., respondent language, household weights).\n", + "- After loading the data, validate your calculations by checking against the total number of households.\n", + "- Ensure your function is flexible and can accept multiple arguments as needed (e.g., the dataset, relevant column names, filters)." + ] + }, + { + "cell_type": "markdown", + "id": "3a15bb61", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/assignments/mod1-assignment-1-solutions.ipynb b/notebooks/assignments/mod2-assignment-1-solutions.ipynb similarity index 100% rename from notebooks/assignments/mod1-assignment-1-solutions.ipynb rename to notebooks/assignments/mod2-assignment-1-solutions.ipynb diff --git a/notebooks/module1.md b/notebooks/module1.md index 57a308e..e69de29 100644 --- a/notebooks/module1.md +++ b/notebooks/module1.md @@ -1,8 +0,0 @@ -# Module 1: Python Foundations - -This module covers the fundamentals of Python programming for data science. - -## Contents - -- Assignment 1 -- Assignment 1 Solutions diff --git a/notebooks/module2.md b/notebooks/module2.md index 4572152..e69de29 100644 --- a/notebooks/module2.md +++ b/notebooks/module2.md @@ -1,9 +0,0 @@ -# Module 2: Data Analysis - -This module covers data analysis techniques using pandas and other Python libraries. - -## Contents - -- Pandas Basics -- Data Processing with Pandas -- Statistical Analysis with Python diff --git a/notebooks/module3.md b/notebooks/module3.md index efb4890..e69de29 100644 --- a/notebooks/module3.md +++ b/notebooks/module3.md @@ -1,7 +0,0 @@ -# Module 3: Spatial Data - -This module covers working with spatial data in Python. - -## Contents - -- Assessment Instructions From c161bcae79926222e035a9612a37249aa9ce24a6 Mon Sep 17 00:00:00 2001 From: Olakunle Date: Sun, 10 Aug 2025 20:30:53 +0200 Subject: [PATCH 05/33] Work-Related project description of participants --- docs/work_related_projects.md | 32 ++++++++++++++++++++++++++------ 1 file changed, 26 insertions(+), 6 deletions(-) diff --git a/docs/work_related_projects.md b/docs/work_related_projects.md index 38622a4..5ab14cc 100644 --- a/docs/work_related_projects.md +++ b/docs/work_related_projects.md @@ -1,9 +1,29 @@ -| Institution | Members | Project title|Project Description | -|--------------|-----------------------------------------|-----|----| -|Ministry of Finance and Economic Planning | Andrew Mushokambere|Macro-economic dashboard| Creating a macroeconomic dashboard for better communication of key indicators like GDP, CPI, exchange rates, fiscal data, and debt.| -| | |-- | --| -| | |-----| ---| -| | |---| ---| +# Work Related Projects +| | Institution | Members | Project title|Project Description | +|---|--------------|-----------------------------------------|-----|----| +|1|Ministry of Finance and Economic Planning | Andrew Mushokambere|Macro-economic dashboard| Creating a macroeconomic dashboard for better communication of key indicators like GDP, CPI, exchange rates, fiscal data, and debt.| +|2|Ministry of Trade and Industry |Aimable Iradukunda | A Data-Driven Approach to Industrial Price Monitoring in Rwanda |The project aims to monitor and analyze production, wholesale, and retail prices of key industrial products to ensure transparency, protect consumers, and provide data-driven insights for better economic planning and decision-making by the Ministry| +|3|Rwanda Basic Education Board |Aimable Sibomana |ICT Device Management Schools| The project will develop a digital system for monitoring ICT devices in schools, enabling real-time reporting, resource allocation, and decision-making| +|4|Rwanda Revenue Authority |Alleluia Mirelle,
Bertrand Cyiza,
Mediatrice Iradukunda |Data Discovery Chatbot| To create an NLP-powered chatbot that centralizes RRA’s data catalog, streamlines discovery, and guides users on access and usage| +|5|Ministry of Defence | Annonciata Ingabire |Optimized Defense Resource Allocation | A data-driven system to predict and prioritize defense resource allocation, reducing waste and improving operational effectiveness.| +|6|Ministry of Youth and ICT |Augustin Ndayambaje |Predictive Modeling of Youth Unemployment in Rwanda |A machine learning-based system to forecast youth unemployment, identify key drivers, and deliver data-driven policy recommendations through dashboards and APIs | +|7|Ministry of Justice | Charles Iyamuremye |Predictive Crime Mapping Using Machine Learning to Identify High-Risk Crime Locations |A GeoAI-enabled predictive crime mapping tool that forecasts hotspots, optimizes resource allocation, and provides interactive dashboards for law enforcement decision-making | +|8|Ministry of Environment,
Rwanda Environment Management Authority,
Rwanda Meteorology Agency |Clementine Nyiraneza,
Patrick Mupenzi,
Joselyn Ingabire |Predicting urban Air Quality in Kigali City using machine learning |An AI-powered air quality prediction system to enable data-driven environmental decisions and public health safeguards in Kigali | +|9|National Institute of Statistics of Rwanda | Denyse Uwimbabazi,
Beata Niyitegeka,
Monique Usabyimana,
Emmanuel Dusingizimana |Land cover classification using high resolution satelite images |An AI-powered satellite image processing pipeline for automated land cover mapping, enabling frequent updates and improved spatial analytics.| +|10|Gender Monitoring Office |Diane Mukakalisa |Interactive Gender Based Violence Monitoring Dashboard|To develop an interactive GBV monitoring dashboard that integrates and analyzes multi-source data to track trends, enhance policy responses, and improve victim support through real-time insights.| +|11|National Institute of Statistics of Rwanda (NISR)| Didier Gaga
Faustin Sharangabo
Muhoza Scovia
Juvenal Iraguha |Exploring the Geospatial Relationship Between Altitude and Child Stunting in Rwanda | A geospatial study identifying altitude-related stunting patterns to guide precision nutrition policies in Rwanda.| +|12|Ministry of ICT and Innovation | Docile Umurengezi,
Jean Pierre Ntukagwabire|Data Sharing & Privacy Bot | To develop an AI-powered chatbot that clarifies Rwanda's Data Sharing Policy and its connection to privacy laws, improving public understanding and implementation | +|13|Ministry of Infrastructure (MININFRA) |Sixbert Shema,
Emmanuel Kayitaba | Leveraging Data for Evidence-Based Infrastructure Needs Assessment & Planning | To develop a data-driven system for automated infrastructure gap analysis and evidence-based planning, ensuring equitable resource allocation across Rwanda's districts.| +|14|National Institute of Statistics of Rwanda (NISR)| Monique Usabyimana
Donath Nkundimana
Emmanuel Hanyurineza |Prioritizing Environmental and Climate Change Statistics in National Strategic Frameworks|To develop an integrated climate and environmental data platform that enhances real-time monitoring, forecasting, and policy integration for Rwanda's green development goals.| +|15|National Institute of Statistics of Rwanda (NISR) |Fidele Ngirinshuti
Ange Umuhoza,
Didier Gaga |Job Transitions and Employment Stability in Rwanda:A Panel Data Analysis Using Labour Force Surveys |To analyze job transition patterns and employment stability in Rwanda using panel data, providing evidence-based insights for targeted labor market policies and youth/women empowerment strategies| +|16|Rwanda Energy Group (REG)|Fidele Ntawumenyumunsi |Forecasting electricity demand and optimizing distribution in national grid.|To develop a predictive model for forecasting Rwanda's electricity demand at feeder level, enabling optimized grid management and preventing power outages.| +|17|Rwanda Biomedical Center |Hassan Mugabo |Natural Language Processing for Public Health Intelligence |To develop Rwanda-specific NLP models that extract actionable public health intelligence from EMR text, enabling real-time disease surveillance and data-driven healthcare decisions| +|18|Rwanda Agriculture Board |Jacques Munyemana | Predicting crop yield using ML models: case of maize in Rwanda | To develop a machine learning model that predicts maize yields in Rwanda using weather and agricultural input data, helping farmers and policymakers optimize crop production.| +|19|Rwanda Information Society Authority (RISA) |Janvier Niyitegeka
Geofrey Karenzi
Sylvia Nzaramba
Patrice Bimenyimana|AI-Policy Chatbot|To develop an AI-powered chatbot that simplifies access to ICT policies, enhances compliance, and supports Rwanda's digital governance through user-friendly policy explanations.| +|20|Ministry of Education |Abdoul Karim Munezero |Real-Time Education Data Monitoring and Quality Feedback System|To develop a real-time education data monitoring system that improves data accuracy, detects anomalies, and provides instant feedback to enhance decision-making in Rwanda's schools.| +|21|National Institute of Statistics of Rwanda (NISR) | Thomas Nsengiyumva
Kadjura Abdurahman Shema
Jean Bosco Dusabimana |Modernizing Rwanda’s Trade Statistics System:Enhancing Data Quality and Efficiency|To modernize Rwanda's trade statistics system by developing a Python-based automated pipeline that enhances data quality, efficiency, and reporting capabilities.| +|22 |WASAC Utility |Elisa Iradukunda |Analyzing the Impact of Customer Complaints Patterns on Operational Efficiency at WASAC |The project aims to analyze customer complaint patterns at WASAC to identify operational inefficiencies and provide data-driven solutions for improving water service delivery and customer satisfaction | +|23 |Rwanda Convention Bureau (RCB) |James Rushimisha |Quantifying the Economic Impact of MICE Events Using Data Science |The project aims to develop a data-driven framework to quantify the economic impact of MICE events in Rwanda, enabling evidence-based decision-making for RCB.| From 7f37deeb8233d95c8bbfb1f892f5e7805744722e Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Tue, 12 Aug 2025 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b/=1.0.0 deleted file mode 100644 index e69de29..0000000 diff --git a/=1.0.1 b/=1.0.1 deleted file mode 100644 index e69de29..0000000 diff --git a/=2.0.0 b/=2.0.0 deleted file mode 100644 index e69de29..0000000 diff --git a/=7.2.6 b/=7.2.6 deleted file mode 100644 index e69de29..0000000 diff --git a/docs/_config.yml b/docs/_config.yml index 5369215..334eb33 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -1,7 +1,6 @@ # Book settings title: Data Science Capacity Building Initiative author: Dunstan Matekenya -logo: images/logo-aims.png only_build_toc_files: true exclude_patterns: [_build, Thumbs.db, .DS_Store, "**.ipynb_checkpoints"] diff --git a/docs/_toc.yml b/docs/_toc.yml index e0f89d5..c11c675 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -33,7 +33,6 @@ parts: - file: datasets.md - caption: Notebooks - numbered: true chapters: - file: notebooks/assignments/README.md title: Assignments diff --git a/docs/test.html b/docs/test.html deleted file mode 100644 index e69de29..0000000 diff --git a/imghdr.py b/imghdr.py deleted file mode 100644 index e69de29..0000000 diff --git a/notebooks/assignments/mod1-assignment-2-part-1.ipynb b/notebooks/assignments/mod1-assignment-2-part-1.ipynb new file mode 100644 index 0000000..8136bbe --- /dev/null +++ b/notebooks/assignments/mod1-assignment-2-part-1.ipynb @@ -0,0 +1,147 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2baaa681-e813-428b-9010-4508b7291011", + "metadata": {}, + "source": [ + "# Programming Assignment-2\n", + "The goal of this assingment is to allow you to practice several the following things in Python:\n", + "1. Perfoming typical data processing (or preprocessing if you prefer). This includes all the typical data wraning such as creating news variables, combining several datasets and more \n", + "2. Running explolatory data analysis including basic plotting of variables \n", + "3. Perfoming basic inferential statisticals using statsmodels and scipy to run hypythesis testing and build simple statistial or econometric models.\n", + "\n", + "## Datasets \n", + "For this assignment, you will use the following datasets:\n", + "### Rwanda Health Indicators\n", + "The Excel file was generated by combining multiple CSV files, each containing data on different health indicators for Rwanda, So that each sheet in the file represent one such indicator. See below some of the input files which were used:\n", + "- `access-to-health-care_subnational_rwa`\n", + "- `child-mortality-rates_subnational_rwa`\n", + "- `dhs-mobile_subnational_rwa`\n", + "\n", + "You can download the dataset from [here](https://docs.google.com/spreadsheets/d/1uvTQYS22VfXXo1Hwkm1frFx_bKkLQkcf/edit?usp=share_link&ouid=113302179168925233984&rtpof=true&sd=true).\n", + "\n", + "## Submission Guidelines \n", + "- Please \n" + ] + }, + { + "cell_type": "markdown", + "id": "1a0961aa", + "metadata": {}, + "source": [ + "# Import Required Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "30d3cb64", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "cbb0234e", + "metadata": {}, + "source": [ + "# Setup Input Folders\n", + "\n", + "As usual, it is good practice to set up input folders using the [`pathlib`](https://docs.python.org/3/library/pathlib.html) package. In this section, make sure to define the folders where your data is stored on your machine.\n", + "\n", + "I find it helpful to set up the working directory and input data folders right at the start of the notebook. To keep things organized, I use the naming convention: `FILE_{NAME}` for files and `DIR_{NAME}` for folders. We use capital letters because these are global variables that will be referenced throughout the notebook.\n", + "\n", + "We'll be using the [`pathlib`](https://docs.python.org/3/library/pathlib.html) library, which offers several advantages over traditional string-based path handling:\n", + "\n", + "- **Cross-platform compatibility** - automatically handles path separators (`/` vs `\\`) across different operating systems\n", + "- **Object-oriented approach** - paths are objects with useful methods rather than strings\n", + "- **Intuitive syntax** - use `/` operator to join paths naturally: `parent_dir / \"subfolder\" / \"file.txt\"`\n", + "- **Built-in path operations** - methods like `.exists()`, `.is_file()`, `.parent`, `.stem`, and `.suffix`\n", + "- **Safer path manipulation** - reduces errors from manual string concatenation and splitting\n", + "\n", + "This is the recommended approach for managing file paths in modern Python development.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ba8273ba", + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment the following lines and add your code to define the directories and files\n", + "DIR_DATA = Path.cwd().parents[1].joinpath(\"data\")\n", + "FILE_EXCEL = DIR_DATA/\"RW-Health-Data.xlsx\"\n", + "\n", + "# Population by enumeration area (EA) for Malawi\n", + "# FILE_POP_MW = ADD YOUR CODE\n" + ] + }, + { + "cell_type": "markdown", + "id": "3a15bb61", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "e49528b3", + "metadata": {}, + "source": [ + "# Part 1: Processing Excel Files\n", + "The primary goal is to preprocess an [Excel file](https://docs.google.com/spreadsheets/d/1uvTQYS22VfXXo1Hwkm1frFx_bKkLQkcf/edit?usp=share_link&ouid=113302179168925233984&rtpof=true&sd=true) with multiple sheets into a unified CSV dataset that consolidates multiple indicators. Having all indicators in a single file at the same analytical unit (national, subnational) is more efficient than managing separate files and enables easier cross-indicator analysis.\n", + "\n", + "## Task 1: Generate National-Level Summaries\n", + "\n", + "For each indicator, compute a single national-level value using appropriate aggregation functions such as **mean**, **sum** or **count**. For this one, all available indicators can be summarized at national level, so we will have a CSV file with one row and \n", + "\n", + "### Expected Output Structure\n", + "1. **DataFrame display** in Jupyter Notebook\n", + "2. **CSV file** with columns:\n", + "- `indicator_name`: Name of the indicator\n", + "- `aggregated_value`: Computed national value\n", + "- `indicator_year`: Survey year or something similar\n", + "- `survey_name`: Name of the survey where information is coming from\n", + "- `aggregation_method`: Statistical method used (optional)\n", + "\n", + "## Task 2: Subnational-Level Indicator Dataset\n", + "\n", + "Create a merged dataset for indicators with subnational data (ADM2/ADM3 levels), ensuring spatial alignment and consistent administrative boundaries.\n", + "\n", + "### Expected Output Structure\n", + " - `indicator_name`: Name of the indicator\n", + " - `aggregated_value`: Computed national value\n", + " - `indicator_year`: Survey year or something similar\n", + " - `survey_name`: Name of the survey where information is coming from\n", + " - `aggregation_method`: Statistical method used (optional)\n", + "\n", + "This structure enables both single-indicator and multi-indicator analysis at the subnational level." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/assignments/mod1-assignment-2.ipynb b/notebooks/assignments/mod1-assignment-2.ipynb deleted file mode 100644 index 0b4db69..0000000 --- a/notebooks/assignments/mod1-assignment-2.ipynb +++ /dev/null @@ -1,233 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2baaa681-e813-428b-9010-4508b7291011", - "metadata": {}, - "source": [ - "# Programming Assignment-1\n", - "## Estimating Chichewa Speakers\n", - "### Background\n", - "This notebook provides statistics about the language of **Chichewa**. \n", - "More specifically, the goal is to generate an accurate estimate of total number of people who speak this language. Note that Chichewa also includes the alternate names: *Chewa*, *Nyanja*, *Chinyanja*. In order to provide context, first, let us indicate the countries where the language is spoken. \n", - "1. **Malawi.** About 70% of the population speak Chichewa. [[1]](#cite_note-1)\n", - "2. **Zambia.** About 20% of the population speak the language. [[2]](#cite_note-2)\n", - "3. **Mozamabique.** Less than 1% of the population speak the language. [[3]](#cite_note-3)\n", - "4. **Zimbabwe.** Although Chichewa seem to be one of the official languages for Zimbabwe, I havent found any data yet showing how many people speak the language.\n", - "5. **Tanzania.** Has a border with Malawi in the northen region where people speak Tumbuka, so it makes sense that there maybe no Chichewa speaking people there. Otherwise, I didnot find any data on proportion of the population who speak the language.\n", - "\n", - "Based on the analysis in this notebook, as of 2023, there are **21,482,292** people who speak Chichewa distributed across three countries: Malawi (70%), Zambia (18%) and Mozambique(12%).\n", - "\n", - "The [Humanitarian data website](https://data.humdata.org/dataset/) contains data about languages for some of these countries. The HUMDATA links are provided below:\n", - "- [Zambia languages](https://data.humdata.org/dataset/zambia-languages)\n", - "- [Malawi languages](https://data.humdata.org/dataset/malawi-languages)\n", - "- [Mozambique languages](https://data.humdata.org/dataset/mozambique-languages)\n", - "\n", - "Since I was not very sure of some of the numbers from [humdata](https://data.humdata.org), I decided to check with the actual sources as follows:\n", - ">**Malawi and Zambia.** I could not find any current surveys with data on languages spoken but I still found something in the DHS. In the DHS, they ask about survey respondent's native language. Although this data is not included in DHS reports (as they seem to collect this piece of data as interview metadata), its still a useful source of data for languages spoken. For *Zimbabwe, Tanzania and Mozambique* the DHS does'nt have this information as they only provide languages as language-1, language-2 etc except for the major languages.\n", - "\n", - "> ### 🚨 Assignment Tasks \n", - "> 1. Read the notebook carefully and answer the questions as asked. \n", - "> 2. Complete any missing code where it says **\"ADD YOUR CODE\"**.\n", - ">3. In some cases, you may be asked to write a whole function.\n", - "\n", - "### Datasets \n", - "For this assignment, you will use the following datasets:\n", - "#### Malawi 2015-16 DHS \n", - "- [Dataset folder](https://drive.google.com/drive/folders/1ygZmjsphEfTed-Tir_nPrNMq1Y44EtS7?usp=sharing)\n", - "- Data file: ```MWPR7AFL.DTA```\n", - "#### Malawi Population\n", - "- [Population data](https://drive.google.com/drive/folders/1T7C0fkwfsOAeAWHFj3lKIZgCYHRfbha0?usp=sharing)\n", - "\n", - "## References\n", - "1. [^](#cite_ref-1) 2015-16 MDHS and [humdata](https://data.humdata.org/dataset/malawi-languages)\n", - "\n", - "2. [^](#cite_ref-2) 2018 Zambia DHS and [humdata](https://data.humdata.org/dataset/zambia-languages)\n", - "\n", - "3. [^](#cite_ref-3) [Humdata](https://data.humdata.org/dataset/mozambique-languages)" - ] - }, - { - "cell_type": "markdown", - "id": "cbb0234e", - "metadata": {}, - "source": [ - "## Setup Input Folders\n", - "In this section, make sure to define the folders where your data is stored on your machine. \n", - "I find it helpful to set up the working directory and input data folders right at the start of the notebook. \n", - "To keep things organized, I use the naming convention: `FILE_{NAME}` for files and `DIR_{NAME}` for folders.\n", - "\n", - "We'll be using the [`pathlib`](https://docs.python.org/3/library/pathlib.html) library—it's the recommended approach for managing file paths in Python.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba8273ba", - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment the following lines and add your code to define the directories and files\n", - "# DIR_DATA = ADD YOUR CODE\n", - "# DIR_DHS = ADD YOUR CODE\n", - "\n", - "# Population by enumeration area (EA) for Malawi\n", - "# FILE_POP_MW = ADD YOUR CODE\n", - "\n", - "# MW DHS-2015-16 HH members stata data file\n", - "# FILE_MW_DHS = ADD YOUR CODE" - ] - }, - { - "cell_type": "markdown", - "id": "5f391f0a-b3d7-4dfc-985a-c8769c327f81", - "metadata": {}, - "source": [ - "## Estimating Chichewa Speakers in Malawi Based on Tribe\n", - "Malawi is the primary country where Chichewa is spoken. In the absence of precise language data, we begin by estimating the number of Chichewa speakers based on tribal affiliation.\n", - "### Estimated Population by Tribe in Malawi\n", - "\n", - "- **Chewa**: 6,020,945 \n", - "- **Tumbuka**: 1,614,955 \n", - "- **Lomwe**: 3,302,634 \n", - "- **Tonga**: 310,031 \n", - "- **Yao**: 2,321,763 \n", - "- **Sena**: 670,908 \n", - "- **Nkhonde**: 174,430 \n", - "- **Lambya**: 106,769 \n", - "- **Sukwa**: 93,762 \n", - "- **Manganja**: 559,887 \n", - "- **Nyanja**: 324,272 \n", - "- **Ngoni**: 1,819,347 \n", - "- **Other**: 186,319 \n", - "\n", - "In Malawi, these groups speak Chichewa:\n", - "- **Chewa**: 6,020,945 \n", - "- **Nyanja**: 324,272 \n", - "- **Ngoni**: 1,819,347 \n", - "- **Other**: 186,319 \n", - "- **Manganja**: 559,887 " - ] - }, - { - "cell_type": "markdown", - "id": "720c404f", - "metadata": {}, - "source": [ - "### Task-1: \n", - "- Using the information provided in cell above, write a function that estimates the number of Chichewa speakers in Malawi based on tribal groups.\n", - "- Ensure your function returns a value representing the chichewa speaking population\n", - "- Name your function: ```mw_estimate_chichewa_speaking_based_on_tribe```\n", - "- Call your function and print out the number of Chichewa speakers" - ] - }, - { - "cell_type": "markdown", - "id": "147b0949", - "metadata": {}, - "source": [ - "## Chichewa speakers in Malawi Based on District of Residence\n", - "In this section, we estimate Chichewa speakers based on the district of residence. For example, we can assume that people from the following districts speak Chichewa: \n", - "> Mzuzu City, Kasungu, Nkhotakota, Ntchisi, Dowa, Salima, Lilongwe, \n", - "> Mchinji, Dedza, Ntcheu, Lilongwe City, \n", - "> Zomba, Chiradzulu, Blantyre, Mwanza, Thyolo, \n", - "> Mulanje, Phalombe, Balaka, Neno, \n", - "> Zomba City, Blantyre City" - ] - }, - { - "cell_type": "markdown", - "id": "5fbb15a6", - "metadata": {}, - "source": [ - "### Task 2 – Part 1: Generate Population by District\n", - "\n", - "- Use the Malawi population data to write a function that reads the relevant data files and returns a DataFrame containing population totals by district. \n", - "- Name your function: `generate_dist_pop`\n", - "\n", - "### Task 2 – Part 2: Estimate Chichewa-Speaking Population\n", - "\n", - "- Write a function that takes the district-level population DataFrame and estimates the number of Chichewa speakers. \n", - "- Name your function: `mw_estimate_chichewa_speakers_by_district` \n", - "- Call this function and the previous function as needed and print the total estimated number of Chichewa speakers.\n" - ] - }, - { - "cell_type": "markdown", - "id": "428487e8", - "metadata": {}, - "source": [ - "## Chichewa speakers in Malawi Based on DHS Data\n", - "In the Demographic and Health Survey (DHS), we estimate the number of Chichewa speakers using responses to the question on the primary language spoken by the respondent. \n", - "While the DHS does not provide exhaustive linguistic data, the self-reported language question offers a useful proxy for estimating language distribution across the population. \n", - "This approach allows us to approximate the number of Chichewa speakers based on individual-level survey data that is nationally representative.\n", - "\n", - "### Relevant Variables Column Name Mapping in STATA file\n", - "\n", - "- `hv045b`: `intv_lan` — Interviewer's language \n", - "- `hv045c`: `resp_nativ_lan` — Respondent's native language \n", - "- `hv046`: `translator` — Translator used \n", - "- `hv002`: `hh_num` — Household number \n", - "- `hv005`: `weight` — Sampling weight \n", - "- `hv045a`: `qn_lan` — Language of the questionnaire \n", - "- `hv001`: `cluster_number` — Cluster number \n", - "- `hv004`: `area_unit` — Area unit \n", - "\n", - "### Language Code Mapping\n", - "\n", - "- `1`: **English** \n", - "- `2`: **Chichewa** \n", - "- `3`: **Tumbuka** \n", - "- `6`: **Other** \n", - "\n", - "### Other Useful Information\n", - "- Total Number of Sampled Housolds in 2015-2016 DHS: 26, 361\n", - "- Malawi projected population for 2023: 19, 809, 511" - ] - }, - { - "cell_type": "markdown", - "id": "2c1ee3e7", - "metadata": {}, - "source": [ - "### Task 3: Estimate Chichewa Speakers from DHS Using 2023 Population Data\n", - "\n", - "- Write a function that estimates the number of Chichewa speakers using the respondent language variable from the DHS dataset. \n", - "- Call the function and print the total estimated number of Chichewa speakers.\n", - "\n", - "**Hints**\n", - "- Report the unweighted estimate \n", - "- Use the variable descriptions provided above (e.g., respondent language, household weights).\n", - "- After loading the data, validate your calculations by checking against the total number of households.\n", - "- Ensure your function is flexible and can accept multiple arguments as needed (e.g., the dataset, relevant column names, filters)." - ] - }, - { - "cell_type": "markdown", - "id": "3a15bb61", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "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": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/assignments/mod2-assignment-1-solutions.ipynb b/notebooks/assignments/mod2-assignment-1-solutions.ipynb index ec3cdcc..45bd771 100644 --- a/notebooks/assignments/mod2-assignment-1-solutions.ipynb +++ b/notebooks/assignments/mod2-assignment-1-solutions.ipynb @@ -5,7 +5,7 @@ "id": "2baaa681-e813-428b-9010-4508b7291011", "metadata": {}, "source": [ - "# Programming Assignment-1\n", + "# Solutions-Programming Assignment\n", "## Estimating Chichewa Speakers\n", "### Background\n", "This notebook provides statistics about the language of **Chichewa**. \n", @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "id": "a4c711fb-9980-4380-8d2d-52efec126354", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 14, "id": "ba8273ba", "metadata": {}, "outputs": [], @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 15, "id": "75745171-00ac-413f-9a16-478adde8220c", "metadata": { "jupyter": { @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 16, "id": "1d0a1b4d-0d69-4551-8dad-621671df2e62", "metadata": {}, "outputs": [ @@ -184,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 17, "id": "41676bac-8889-4ec3-9c5c-a88132e304d3", "metadata": { "jupyter": { @@ -223,10 +223,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "f29a54e5-4e0a-4f46-9680-aab39315c440", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/population/malawi-ea-population.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Get population by district for Malawi\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df_pop_ea \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(FILE_POP_MW)\n\u001b[1;32m 3\u001b[0m df_dist \u001b[38;5;241m=\u001b[39m df_pop_ea\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDIST_NAME\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTOTAL_POP\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m.\u001b[39mreset_index()\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 1014\u001b[0m dialect,\n\u001b[1;32m 1015\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 1023\u001b[0m )\n\u001b[1;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in 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compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m 1885\u001b[0m memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m 1886\u001b[0m is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[1;32m 1887\u001b[0m errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 1888\u001b[0m storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m 1889\u001b[0m )\n\u001b[1;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", + "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[1;32m 874\u001b[0m handle,\n\u001b[1;32m 875\u001b[0m ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[1;32m 876\u001b[0m encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[1;32m 877\u001b[0m errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[1;32m 878\u001b[0m newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 879\u001b[0m )\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/population/malawi-ea-population.csv'" + ] + } + ], "source": [ " # Get population by district for Malawi\n", " df_pop_ea = pd.read_csv(FILE_POP_MW)\n", @@ -235,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "id": "d2bc244a", "metadata": {}, "outputs": [ @@ -387,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "b59c10a9", "metadata": {}, "outputs": [ @@ -430,7 +447,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.13.5" } }, "nbformat": 4, diff --git a/notebooks/module-dividers/module1.md b/notebooks/module-dividers/module1.md deleted file mode 100644 index 2039135..0000000 --- a/notebooks/module-dividers/module1.md +++ /dev/null @@ -1,8 +0,0 @@ -# Module 1: Python Foundations - -This module covers the fundamentals of Python programming for data science. - -## Notebooks in this section: - -- **Assignment 1**: Practice problems for Python foundations -- **Assignment 1 Solutions**: Solutions to the practice problems diff --git a/notebooks/module-dividers/module2.md b/notebooks/module-dividers/module2.md deleted file mode 100644 index 597c289..0000000 --- a/notebooks/module-dividers/module2.md +++ /dev/null @@ -1,9 +0,0 @@ -# Module 2: Data Analysis - -This module covers data analysis techniques using pandas and other Python libraries. - -## Notebooks in this section: - -- **Pandas Basics**: Introduction to pandas for data manipulation -- **Data Processing with Pandas**: Advanced data processing techniques -- **Statistical Analysis with Python**: Statistical methods and analysis diff --git a/notebooks/module-dividers/module3.md b/notebooks/module-dividers/module3.md deleted file mode 100644 index 79c3adb..0000000 --- a/notebooks/module-dividers/module3.md +++ /dev/null @@ -1,7 +0,0 @@ -# Module 3: Spatial Data - -This module covers working with spatial data in Python. - -## Notebooks in this section: - -- **Assessment Instructions**: Instructions for the spatial data assessment diff --git a/notebooks/module1.md b/notebooks/module1.md index e69de29..57a308e 100644 --- a/notebooks/module1.md +++ b/notebooks/module1.md @@ -0,0 +1,8 @@ +# Module 1: Python Foundations + +This module covers the fundamentals of Python programming for data science. + +## Contents + +- Assignment 1 +- Assignment 1 Solutions diff --git a/notebooks/module2.md b/notebooks/module2.md index e69de29..4572152 100644 --- a/notebooks/module2.md +++ b/notebooks/module2.md @@ -0,0 +1,9 @@ +# Module 2: Data Analysis + +This module covers data analysis techniques using pandas and other Python libraries. + +## Contents + +- Pandas Basics +- Data Processing with Pandas +- Statistical Analysis with Python diff --git a/notebooks/module3.md b/notebooks/module3.md index e69de29..efb4890 100644 --- a/notebooks/module3.md +++ b/notebooks/module3.md @@ -0,0 +1,7 @@ +# Module 3: Spatial Data + +This module covers working with spatial data in Python. + +## Contents + +- Assessment Instructions From 5891df773bbfa3c45cf7a9740de027e77b366f40 Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Tue, 12 Aug 2025 09:07:10 -0400 Subject: [PATCH 07/33] Added projects doc --- docs/projects.md | 31 +++++++++++++++++++++++++++++-- docs/work_related_projects.md | 29 ----------------------------- 2 files changed, 29 insertions(+), 31 deletions(-) delete mode 100644 docs/work_related_projects.md diff --git a/docs/projects.md b/docs/projects.md index 2a43797..5ab14cc 100644 --- a/docs/projects.md +++ b/docs/projects.md @@ -1,2 +1,29 @@ -# Data Use Cases -Kunle/Dinna add a table of projects as discussed \ No newline at end of file +# Work Related Projects + +| | Institution | Members | Project title|Project Description | +|---|--------------|-----------------------------------------|-----|----| +|1|Ministry of Finance and Economic Planning | Andrew Mushokambere|Macro-economic dashboard| Creating a macroeconomic dashboard for better communication of key indicators like GDP, CPI, exchange rates, fiscal data, and debt.| +|2|Ministry of Trade and Industry |Aimable Iradukunda | A Data-Driven Approach to Industrial Price Monitoring in Rwanda |The project aims to monitor and analyze production, wholesale, and retail prices of key industrial products to ensure transparency, protect consumers, and provide data-driven insights for better economic planning and decision-making by the Ministry| +|3|Rwanda Basic Education Board |Aimable Sibomana |ICT Device Management Schools| The project will develop a digital system for monitoring ICT devices in schools, enabling real-time reporting, resource allocation, and decision-making| +|4|Rwanda Revenue Authority |Alleluia Mirelle,
Bertrand Cyiza,
Mediatrice Iradukunda |Data Discovery Chatbot| To create an NLP-powered chatbot that centralizes RRA’s data catalog, streamlines discovery, and guides users on access and usage| +|5|Ministry of Defence | Annonciata Ingabire |Optimized Defense Resource Allocation | A data-driven system to predict and prioritize defense resource allocation, reducing waste and improving operational effectiveness.| +|6|Ministry of Youth and ICT |Augustin Ndayambaje |Predictive Modeling of Youth Unemployment in Rwanda |A machine learning-based system to forecast youth unemployment, identify key drivers, and deliver data-driven policy recommendations through dashboards and APIs | +|7|Ministry of Justice | Charles Iyamuremye |Predictive Crime Mapping Using Machine Learning to Identify High-Risk Crime Locations |A GeoAI-enabled predictive crime mapping tool that forecasts hotspots, optimizes resource allocation, and provides interactive dashboards for law enforcement decision-making | +|8|Ministry of Environment,
Rwanda Environment Management Authority,
Rwanda Meteorology Agency |Clementine Nyiraneza,
Patrick Mupenzi,
Joselyn Ingabire |Predicting urban Air Quality in Kigali City using machine learning |An AI-powered air quality prediction system to enable data-driven environmental decisions and public health safeguards in Kigali | +|9|National Institute of Statistics of Rwanda | Denyse Uwimbabazi,
Beata Niyitegeka,
Monique Usabyimana,
Emmanuel Dusingizimana |Land cover classification using high resolution satelite images |An AI-powered satellite image processing pipeline for automated land cover mapping, enabling frequent updates and improved spatial analytics.| +|10|Gender Monitoring Office |Diane Mukakalisa |Interactive Gender Based Violence Monitoring Dashboard|To develop an interactive GBV monitoring dashboard that integrates and analyzes multi-source data to track trends, enhance policy responses, and improve victim support through real-time insights.| +|11|National Institute of Statistics of Rwanda (NISR)| Didier Gaga
Faustin Sharangabo
Muhoza Scovia
Juvenal Iraguha |Exploring the Geospatial Relationship Between Altitude and Child Stunting in Rwanda | A geospatial study identifying altitude-related stunting patterns to guide precision nutrition policies in Rwanda.| +|12|Ministry of ICT and Innovation | Docile Umurengezi,
Jean Pierre Ntukagwabire|Data Sharing & Privacy Bot | To develop an AI-powered chatbot that clarifies Rwanda's Data Sharing Policy and its connection to privacy laws, improving public understanding and implementation | +|13|Ministry of Infrastructure (MININFRA) |Sixbert Shema,
Emmanuel Kayitaba | Leveraging Data for Evidence-Based Infrastructure Needs Assessment & Planning | To develop a data-driven system for automated infrastructure gap analysis and evidence-based planning, ensuring equitable resource allocation across Rwanda's districts.| +|14|National Institute of Statistics of Rwanda (NISR)| Monique Usabyimana
Donath Nkundimana
Emmanuel Hanyurineza |Prioritizing Environmental and Climate Change Statistics in National Strategic Frameworks|To develop an integrated climate and environmental data platform that enhances real-time monitoring, forecasting, and policy integration for Rwanda's green development goals.| +|15|National Institute of Statistics of Rwanda (NISR) |Fidele Ngirinshuti
Ange Umuhoza,
Didier Gaga |Job Transitions and Employment Stability in Rwanda:A Panel Data Analysis Using Labour Force Surveys |To analyze job transition patterns and employment stability in Rwanda using panel data, providing evidence-based insights for targeted labor market policies and youth/women empowerment strategies| +|16|Rwanda Energy Group (REG)|Fidele Ntawumenyumunsi |Forecasting electricity demand and optimizing distribution in national grid.|To develop a predictive model for forecasting Rwanda's electricity demand at feeder level, enabling optimized grid management and preventing power outages.| +|17|Rwanda Biomedical Center |Hassan Mugabo |Natural Language Processing for Public Health Intelligence |To develop Rwanda-specific NLP models that extract actionable public health intelligence from EMR text, enabling real-time disease surveillance and data-driven healthcare decisions| +|18|Rwanda Agriculture Board |Jacques Munyemana | Predicting crop yield using ML models: case of maize in Rwanda | To develop a machine learning model that predicts maize yields in Rwanda using weather and agricultural input data, helping farmers and policymakers optimize crop production.| +|19|Rwanda Information Society Authority (RISA) |Janvier Niyitegeka
Geofrey Karenzi
Sylvia Nzaramba
Patrice Bimenyimana|AI-Policy Chatbot|To develop an AI-powered chatbot that simplifies access to ICT policies, enhances compliance, and supports Rwanda's digital governance through user-friendly policy explanations.| +|20|Ministry of Education |Abdoul Karim Munezero |Real-Time Education Data Monitoring and Quality Feedback System|To develop a real-time education data monitoring system that improves data accuracy, detects anomalies, and provides instant feedback to enhance decision-making in Rwanda's schools.| +|21|National Institute of Statistics of Rwanda (NISR) | Thomas Nsengiyumva
Kadjura Abdurahman Shema
Jean Bosco Dusabimana |Modernizing Rwanda’s Trade Statistics System:Enhancing Data Quality and Efficiency|To modernize Rwanda's trade statistics system by developing a Python-based automated pipeline that enhances data quality, efficiency, and reporting capabilities.| +|22 |WASAC Utility |Elisa Iradukunda |Analyzing the Impact of Customer Complaints Patterns on Operational Efficiency at WASAC |The project aims to analyze customer complaint patterns at WASAC to identify operational inefficiencies and provide data-driven solutions for improving water service delivery and customer satisfaction | +|23 |Rwanda Convention Bureau (RCB) |James Rushimisha |Quantifying the Economic Impact of MICE Events Using Data Science |The project aims to develop a data-driven framework to quantify the economic impact of MICE events in Rwanda, enabling evidence-based decision-making for RCB.| + + diff --git a/docs/work_related_projects.md b/docs/work_related_projects.md deleted file mode 100644 index 5ab14cc..0000000 --- a/docs/work_related_projects.md +++ /dev/null @@ -1,29 +0,0 @@ -# Work Related Projects - -| | Institution | Members | Project title|Project Description | -|---|--------------|-----------------------------------------|-----|----| -|1|Ministry of Finance and Economic Planning | Andrew Mushokambere|Macro-economic dashboard| Creating a macroeconomic dashboard for better communication of key indicators like GDP, CPI, exchange rates, fiscal data, and debt.| -|2|Ministry of Trade and Industry |Aimable Iradukunda | A Data-Driven Approach to Industrial Price Monitoring in Rwanda |The project aims to monitor and analyze production, wholesale, and retail prices of key industrial products to ensure transparency, protect consumers, and provide data-driven insights for better economic planning and decision-making by the Ministry| -|3|Rwanda Basic Education Board |Aimable Sibomana |ICT Device Management Schools| The project will develop a digital system for monitoring ICT devices in schools, enabling real-time reporting, resource allocation, and decision-making| -|4|Rwanda Revenue Authority |Alleluia Mirelle,
Bertrand Cyiza,
Mediatrice Iradukunda |Data Discovery Chatbot| To create an NLP-powered chatbot that centralizes RRA’s data catalog, streamlines discovery, and guides users on access and usage| -|5|Ministry of Defence | Annonciata Ingabire |Optimized Defense Resource Allocation | A data-driven system to predict and prioritize defense resource allocation, reducing waste and improving operational effectiveness.| -|6|Ministry of Youth and ICT |Augustin Ndayambaje |Predictive Modeling of Youth Unemployment in Rwanda |A machine learning-based system to forecast youth unemployment, identify key drivers, and deliver data-driven policy recommendations through dashboards and APIs | -|7|Ministry of Justice | Charles Iyamuremye |Predictive Crime Mapping Using Machine Learning to Identify High-Risk Crime Locations |A GeoAI-enabled predictive crime mapping tool that forecasts hotspots, optimizes resource allocation, and provides interactive dashboards for law enforcement decision-making | -|8|Ministry of Environment,
Rwanda Environment Management Authority,
Rwanda Meteorology Agency |Clementine Nyiraneza,
Patrick Mupenzi,
Joselyn Ingabire |Predicting urban Air Quality in Kigali City using machine learning |An AI-powered air quality prediction system to enable data-driven environmental decisions and public health safeguards in Kigali | -|9|National Institute of Statistics of Rwanda | Denyse Uwimbabazi,
Beata Niyitegeka,
Monique Usabyimana,
Emmanuel Dusingizimana |Land cover classification using high resolution satelite images |An AI-powered satellite image processing pipeline for automated land cover mapping, enabling frequent updates and improved spatial analytics.| -|10|Gender Monitoring Office |Diane Mukakalisa |Interactive Gender Based Violence Monitoring Dashboard|To develop an interactive GBV monitoring dashboard that integrates and analyzes multi-source data to track trends, enhance policy responses, and improve victim support through real-time insights.| -|11|National Institute of Statistics of Rwanda (NISR)| Didier Gaga
Faustin Sharangabo
Muhoza Scovia
Juvenal Iraguha |Exploring the Geospatial Relationship Between Altitude and Child Stunting in Rwanda | A geospatial study identifying altitude-related stunting patterns to guide precision nutrition policies in Rwanda.| -|12|Ministry of ICT and Innovation | Docile Umurengezi,
Jean Pierre Ntukagwabire|Data Sharing & Privacy Bot | To develop an AI-powered chatbot that clarifies Rwanda's Data Sharing Policy and its connection to privacy laws, improving public understanding and implementation | -|13|Ministry of Infrastructure (MININFRA) |Sixbert Shema,
Emmanuel Kayitaba | Leveraging Data for Evidence-Based Infrastructure Needs Assessment & Planning | To develop a data-driven system for automated infrastructure gap analysis and evidence-based planning, ensuring equitable resource allocation across Rwanda's districts.| -|14|National Institute of Statistics of Rwanda (NISR)| Monique Usabyimana
Donath Nkundimana
Emmanuel Hanyurineza |Prioritizing Environmental and Climate Change Statistics in National Strategic Frameworks|To develop an integrated climate and environmental data platform that enhances real-time monitoring, forecasting, and policy integration for Rwanda's green development goals.| -|15|National Institute of Statistics of Rwanda (NISR) |Fidele Ngirinshuti
Ange Umuhoza,
Didier Gaga |Job Transitions and Employment Stability in Rwanda:A Panel Data Analysis Using Labour Force Surveys |To analyze job transition patterns and employment stability in Rwanda using panel data, providing evidence-based insights for targeted labor market policies and youth/women empowerment strategies| -|16|Rwanda Energy Group (REG)|Fidele Ntawumenyumunsi |Forecasting electricity demand and optimizing distribution in national grid.|To develop a predictive model for forecasting Rwanda's electricity demand at feeder level, enabling optimized grid management and preventing power outages.| -|17|Rwanda Biomedical Center |Hassan Mugabo |Natural Language Processing for Public Health Intelligence |To develop Rwanda-specific NLP models that extract actionable public health intelligence from EMR text, enabling real-time disease surveillance and data-driven healthcare decisions| -|18|Rwanda Agriculture Board |Jacques Munyemana | Predicting crop yield using ML models: case of maize in Rwanda | To develop a machine learning model that predicts maize yields in Rwanda using weather and agricultural input data, helping farmers and policymakers optimize crop production.| -|19|Rwanda Information Society Authority (RISA) |Janvier Niyitegeka
Geofrey Karenzi
Sylvia Nzaramba
Patrice Bimenyimana|AI-Policy Chatbot|To develop an AI-powered chatbot that simplifies access to ICT policies, enhances compliance, and supports Rwanda's digital governance through user-friendly policy explanations.| -|20|Ministry of Education |Abdoul Karim Munezero |Real-Time Education Data Monitoring and Quality Feedback System|To develop a real-time education data monitoring system that improves data accuracy, detects anomalies, and provides instant feedback to enhance decision-making in Rwanda's schools.| -|21|National Institute of Statistics of Rwanda (NISR) | Thomas Nsengiyumva
Kadjura Abdurahman Shema
Jean Bosco Dusabimana |Modernizing Rwanda’s Trade Statistics System:Enhancing Data Quality and Efficiency|To modernize Rwanda's trade statistics system by developing a Python-based automated pipeline that enhances data quality, efficiency, and reporting capabilities.| -|22 |WASAC Utility |Elisa Iradukunda |Analyzing the Impact of Customer Complaints Patterns on Operational Efficiency at WASAC |The project aims to analyze customer complaint patterns at WASAC to identify operational inefficiencies and provide data-driven solutions for improving water service delivery and customer satisfaction | -|23 |Rwanda Convention Bureau (RCB) |James Rushimisha |Quantifying the Economic Impact of MICE Events Using Data Science |The project aims to develop a data-driven framework to quantify the economic impact of MICE events in Rwanda, enabling evidence-based decision-making for RCB.| - - From bd2b74290dd9e3c39e02313ed13c5336cf951461 Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Tue, 12 Aug 2025 09:13:01 -0400 Subject: [PATCH 08/33] Renamed assignment notebooks --- docs/_toc.yml | 6 ++++-- ...nt-1-solutions.ipynb => assignment-1-solutions.ipynb} | 0 .../{mod1-assignment-1.ipynb => assignment-1.ipynb} | 0 ...mod1-assignment-2-part-1.ipynb => assignment-2.ipynb} | 0 notebooks/module1.md | 8 -------- notebooks/module2.md | 9 --------- notebooks/module3.md | 7 ------- 7 files changed, 4 insertions(+), 26 deletions(-) rename notebooks/assignments/{mod2-assignment-1-solutions.ipynb => assignment-1-solutions.ipynb} (100%) rename notebooks/assignments/{mod1-assignment-1.ipynb => assignment-1.ipynb} (100%) rename notebooks/assignments/{mod1-assignment-2-part-1.ipynb => assignment-2.ipynb} (100%) delete mode 100644 notebooks/module1.md delete mode 100644 notebooks/module2.md delete mode 100644 notebooks/module3.md diff --git a/docs/_toc.yml b/docs/_toc.yml index c11c675..f337095 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -37,10 +37,12 @@ parts: - file: notebooks/assignments/README.md title: Assignments sections: - - file: notebooks/assignments/mod1-assignment-1.ipynb + - file: notebooks/assignments/assignment-1.ipynb title: Assignment 1 - - file: notebooks/assignments/mod1-assignment-1-solutions.ipynb + - file: notebooks/assignments/assignment-1-solutions.ipynb title: Assignment 1 Solutions + - file: notebooks/assignments/assignment-2.ipynb + title: Assignment 2 - file: notebooks/module-1/README.md title: Module 1 diff --git a/notebooks/assignments/mod2-assignment-1-solutions.ipynb b/notebooks/assignments/assignment-1-solutions.ipynb similarity index 100% rename from notebooks/assignments/mod2-assignment-1-solutions.ipynb rename to notebooks/assignments/assignment-1-solutions.ipynb diff --git a/notebooks/assignments/mod1-assignment-1.ipynb b/notebooks/assignments/assignment-1.ipynb similarity index 100% rename from notebooks/assignments/mod1-assignment-1.ipynb rename to notebooks/assignments/assignment-1.ipynb diff --git a/notebooks/assignments/mod1-assignment-2-part-1.ipynb b/notebooks/assignments/assignment-2.ipynb similarity index 100% rename from notebooks/assignments/mod1-assignment-2-part-1.ipynb rename to notebooks/assignments/assignment-2.ipynb diff --git a/notebooks/module1.md b/notebooks/module1.md deleted file mode 100644 index 57a308e..0000000 --- a/notebooks/module1.md +++ /dev/null @@ -1,8 +0,0 @@ -# Module 1: Python Foundations - -This module covers the fundamentals of Python programming for data science. - -## Contents - -- Assignment 1 -- Assignment 1 Solutions diff --git a/notebooks/module2.md b/notebooks/module2.md deleted file mode 100644 index 4572152..0000000 --- a/notebooks/module2.md +++ /dev/null @@ -1,9 +0,0 @@ -# Module 2: Data Analysis - -This module covers data analysis techniques using pandas and other Python libraries. - -## Contents - -- Pandas Basics -- Data Processing with Pandas -- Statistical Analysis with Python diff --git a/notebooks/module3.md b/notebooks/module3.md deleted file mode 100644 index efb4890..0000000 --- a/notebooks/module3.md +++ /dev/null @@ -1,7 +0,0 @@ -# Module 3: Spatial Data - -This module covers working with spatial data in Python. - -## Contents - -- Assessment Instructions From b1476e6a0a3438e620d61a2d27209ce765340ae2 Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Thu, 14 Aug 2025 09:01:31 -0400 Subject: [PATCH 09/33] Updated assignment-2 with part-2 --- notebooks/assignments/assignment-2.ipynb | 187 +++++++++++++++++- .../updated_infrastructure_assignment.md | 73 +++++++ 2 files changed, 259 insertions(+), 1 deletion(-) create mode 100644 notebooks/assignments/updated_infrastructure_assignment.md diff --git a/notebooks/assignments/assignment-2.ipynb b/notebooks/assignments/assignment-2.ipynb index 8136bbe..acf73df 100644 --- a/notebooks/assignments/assignment-2.ipynb +++ b/notebooks/assignments/assignment-2.ipynb @@ -20,9 +20,17 @@ "- `dhs-mobile_subnational_rwa`\n", "\n", "You can download the dataset from [here](https://docs.google.com/spreadsheets/d/1uvTQYS22VfXXo1Hwkm1frFx_bKkLQkcf/edit?usp=share_link&ouid=113302179168925233984&rtpof=true&sd=true).\n", + "### Nights lights Data\n", + "- Please download it [here](https://drive.google.com/file/d/1f_4fiqxIejly0YmC088s9bxOfrABv9Sz/view?usp=sharing) and check the documentation in the cells below. \n", + "\n", + "### Popupation Dataset\n", + "- Please download it [here](https://drive.google.com/file/d/1FWEFGdN-xDuFH1jmt0hr4F8Xc3Y5XzvB/view?usp=share_link) and check the documentation and metadata in the class notebooks.\n", + "\n", "\n", "## Submission Guidelines \n", - "- Please \n" + "- Please guidelines and complete all steps in the [GitHub Workflow](https://dmatekenya.github.io/AIMS-DSCBI/course-requirements/github-workflow.html)\n", + "- Once you have completed your assignment, push chanegs to your repository.\n", + "- Send a link (copy from within GitHub) to your notebook to the tutors/teaching assistants\n" ] }, { @@ -121,6 +129,183 @@ "\n", "This structure enables both single-indicator and multi-indicator analysis at the subnational level." ] + }, + { + "cell_type": "markdown", + "id": "9568e764", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "8169202f", + "metadata": {}, + "source": [ + "## Introduction to Nightlights Dataset\n", + "\n", + "## What is Nightlight Data?\n", + "\n", + "Nightlight data is satellite imagery capturing artificial light emissions from Earth's surface during nighttime. Satellites like VIIRS collect this data regularly, providing an **objective, real-time measure of human economic activity and development**.\n", + "\n", + "### Raw Data: Radiance Measurements\n", + "The fundamental measurement in nightlight data is **radiance** - the amount of light energy detected by satellite sensors, measured in **nanowatts per square centimeter per steradian (nW/cm²/sr)**. Each pixel in satellite imagery contains a radiance value representing the light intensity from that specific location on Earth's surface.\n", + "\n", + "### Annual Composite Generation\n", + "This dataset was created from **annual composite images** using VIIRS nightlight files for Rwanda. Annual composites are generated by:\n", + "\n", + "- **Aggregating daily/monthly observations** throughout each year (2015, 2020, 2024)\n", + "- **Filtering out temporary light sources** (fires, lightning, aurora)\n", + "- **Removing cloud-affected observations** to ensure clear measurements\n", + "- **Averaging or taking median values** to create stable, representative annual measurements\n", + "- **Masking techniques** to exclude areas with unreliable data\n", + "\n", + "The files used include both **average composites** (`average_masked`) and **median composites** (`median_masked`), with **cloud-free versions** (`vcmslcfg`) preferred over cloud-inclusive versions (`vcmcfg`) for more accurate measurements.\n", + "\n", + "### Why Use Nightlight Data?\n", + "\n", + "- **Consistent global coverage** - Available everywhere, regardless of local data quality\n", + "- **Real-time updates** - More current than traditional economic statistics\n", + "- **Objective measurement** - Not subject to reporting biases\n", + "- **High resolution** - Captures local development patterns\n", + "- **Proxy for development** - Light intensity correlates with economic activity, infrastructure, and quality of life\n", + "\n", + "## Dataset Overview \n", + "\n", + "- **6,507 observations** across Rwanda's administrative cells\n", + "- **Three time periods**: 2015, 2020, 2024\n", + "- **Cell-level data** - Rwanda's smallest administrative units\n", + "- Allows temporal analysis of development trends\n", + "\n", + "---\n", + "\n", + "## Variable Definitions\n", + "\n", + "### Administrative Identifiers\n", + "- **`cell_id`** - Unique identifier for linking with other datasets\n", + "- **`province_name`** - Province (5 total in Rwanda)\n", + "- **`district_name`** - District (30 total in Rwanda) \n", + "- **`sector_name`** - Administrative level between district and cell\n", + "- **`cell_name`** - Specific cell name\n", + "\n", + "### Core Nightlight Measurements\n", + "\n", + "#### `total_nightlight`\n", + "- **Sum of all radiance values** within cell boundaries\n", + "- **Key indicator** of overall economic activity/development\n", + "- Higher values = more total development\n", + "\n", + "#### `mean_nightlight` \n", + "- **Average radiance** per pixel\n", + "- Indicates development intensity regardless of cell size\n", + "- Useful for comparing cells of different areas\n", + "\n", + "#### `median_nightlight`\n", + "- **Middle radiance value** of all pixels (less sensitive to outliers)\n", + "- Better represents typical lighting in unevenly developed areas\n", + "\n", + "#### `max_nightlight`\n", + "- **Highest radiance** within cell\n", + "- Indicates major infrastructure (hospitals, commercial centers)\n", + "\n", + "#### `min_nightlight` & `std_nightlight`\n", + "- Minimum radiance and standard deviation\n", + "- High std = uneven development within cell\n", + "\n", + "### Spatial Coverage Indicators\n", + "\n", + "#### `pixel_count`\n", + "- **Total pixels** in cell (indicates geographic size)\n", + "- Used to normalize other measurements\n", + "\n", + "#### `lit_pixel_count`\n", + "- **Number of pixels with detectable light** (radiance > 0)\n", + "- Shows spatial extent of development\n", + "\n", + "#### `lit_pixel_percentage`\n", + "- **Percentage of cell area with lighting**\n", + "- Formula: `(lit_pixel_count ÷ pixel_count) × 100`\n", + "- **0% = completely dark, 100% = fully developed**\n", + "\n", + "#### `year`\n", + "- Time period: 2015, 2020, or 2024" + ] + }, + { + "cell_type": "markdown", + "id": "784d2e72", + "metadata": {}, + "source": [ + "# Part-2: Demographic and Nightlights Data\n", + "\n", + "## Part A: Varible Generation and Data Integration\n", + "\n", + "### Population Dataset Variables (`rwa-cell-pop.csv`):\n", + "Create the following derived variables:\n", + "- **`dependency_ratio`** - `(children_under_five_2020 + elderly_60_plus_2020) / working_age_population * 100`\n", + "- **`people_per_building`** - `general_2020 / building_count`\n", + "- **`working_age_population`** - `general_2020 - children_under_five_2020 - elderly_60_plus_2020`\n", + "- **`infrastructure_index`** - Your own formula that incorporates `people_per_building` and other relevant variables to measure infrastructure adequacy. Document and justify your `infrastructure_index` methodology, explaining how `people_per_building` and other variables contribute to measuring infrastructure pressure.\n", + "\n", + "### Nightlight Dataset Variables (`cell-ntl-2015-2020-2024.csv`):\n", + "Create the following temporal and development indicators:\n", + "- **`nightlight_change_2015_2024`** - Percentage change in total nightlight from 2015 to 2024\n", + "- **`mean_nightlight_change_2015_2024`** - Percentage change in mean nightlight from 2015 to 2024\n", + "- **`lit_pixel_percentage`** - Use existing or calculate: `(lit_pixel_count / pixel_count) * 100`\n", + "\n", + "### Data Integration:\n", + "Merge the datasets using the appropriate column. \n", + "\n", + "## Part B: Exploratory Data Analysis\n", + "\n", + "### Correlation Analysis:\n", + "1. **Correlation Heatmap**: Create a heatmap showing correlations between 10 key variables (mix of demographic, infrastructure, and nightlight variables). \n", + "2. **Report the top 3 variable pairs** with the highest correlations and interpret their relationships.\n", + "3. **Identify unexpected correlations** and discuss potential explanations.\n", + "\n", + "### Nightlight Trend Analysis:\n", + "1. **District Ranking**: Report the **top 5 districts** with the highest nightlight growth (2015-2024) and **bottom 5 districts** with the most decline or lowest growth.\n", + "2. **Lit Pixel Analysis**: Compare these districts using `lit_pixel_percentage` changes to understand whether growth represents intensification or spatial expansion.\n", + "3. **Create visualizations** showing nightlight trends for these extreme districts.\n", + "\n", + "## Part C: Predictive Modeling and Strategic Analysis\n", + "\n", + "### Multivariate Linear Regression:\n", + "1. **Model Development**: Build a multivariate linear regression model predicting **population density** using both demographic and nightlight variables as predictors. Explore as many variables as possible at the beginning.\n", + "2. **Variable Selection**: Test different combinations of variables and report the **top 3 most predictive variables** of population density.\n", + "3. **Model Evaluation**: Report R-squared, coefficients, and statistical significance. Interpret what these results tell us about population-infrastructure relationships.\n", + "\n", + "\n", + "\n", + "## Notes and Other Requirements\n", + "\n", + "### Statistical Analysis:\n", + "- Properly handle missing data and outliers\n", + "- Use appropriate statistical tests and report p-values\n", + "- Calculate and interpret correlation coefficients\n", + "- Validate regression assumptions (normality, homoscedasticity)\n", + "\n", + "### Data Management:\n", + "- Document all data cleaning and aggregation steps using markdown \n", + "- Ensure consistent district naming across datasets\n", + "\n", + "### Visualization Standards:\n", + "- Create clear, publication-quality heatmaps with appropriate color scales\n", + "- Design effective time series plots for nightlight trends\n", + "- Include proper axis labels, titles, and legends\n", + "- Use consistent formatting across all visualizations\n", + "\n", + "### Reporting Requirements:\n", + "- Clearly state the top 3 most predictive variables with statistical justification\n", + "- Provide ranked lists for nightlight growth districts with supporting metrics\n", + "- Include model performance statistics and interpretation\n", + "- Document all methodological choices and assumptions" + ] + }, + { + "cell_type": "markdown", + "id": "99f0479d", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/notebooks/assignments/updated_infrastructure_assignment.md b/notebooks/assignments/updated_infrastructure_assignment.md new file mode 100644 index 0000000..adf4fbd --- /dev/null +++ b/notebooks/assignments/updated_infrastructure_assignment.md @@ -0,0 +1,73 @@ +# Question 1: Infrastructure and Development Analysis (40 points) + +## Part A: Feature Engineering and Data Integration (12 points) + +### Population Dataset Variables (`rwacellpop.csv`): +Create the following derived variables: +- **`dependency_ratio`** - `(children_under_five_2020 + elderly_60_plus_2020) / working_age_population * 100` +- **`people_per_building`** - `general_2020 / building_count` +- **`working_age_population`** - `general_2020 - children_under_five_2020 - elderly_60_plus_2020` +- **`infrastructure_index`** - Your own formula that incorporates `people_per_building` and other relevant variables to measure infrastructure adequacy + +### Nightlight Dataset Variables (`cellntl201520202024.csv`): +Create the following temporal and development indicators: +- **`nightlight_change_2015_2024`** - Percentage change in total nightlight from 2015 to 2024 +- **`mean_nightlight_change_2015_2024`** - Percentage change in mean nightlight from 2015 to 2024 +- **`lit_pixel_percentage`** - Use existing or calculate: `(lit_pixel_count / pixel_count) * 100` + +### Data Integration: +Merge the datasets using `cell_id` and aggregate to **district level** for analysis. Handle missing data appropriately and document your approach. + +Document and justify your `infrastructure_index` methodology, explaining how `people_per_building` and other variables contribute to measuring infrastructure pressure. + +## Part B: Exploratory Data Analysis (15 points) + +### Correlation Analysis: +1. **Correlation Heatmap**: Create a heatmap showing correlations between 10 key variables (mix of demographic, infrastructure, and nightlight variables). +2. **Report the top 3 variable pairs** with the highest correlations and interpret their relationships. +3. **Identify unexpected correlations** and discuss potential explanations. + +### Nightlight Trend Analysis: +1. **District Ranking**: Report the **top 5 districts** with the highest nightlight growth (2015-2024) and **bottom 5 districts** with the most decline or lowest growth. +2. **Lit Pixel Analysis**: Compare these districts using `lit_pixel_percentage` changes to understand whether growth represents intensification or spatial expansion. +3. **Create visualizations** showing nightlight trends for these extreme districts. + +## Part C: Predictive Modeling and Strategic Analysis (13 points) + +### Multivariate Linear Regression: +1. **Model Development**: Build a multivariate linear regression model predicting **population density** using both demographic and nightlight variables as predictors. +2. **Variable Selection**: Test different combinations of variables and report the **top 3 most predictive variables** of population density. +3. **Model Evaluation**: Report R-squared, coefficients, and statistical significance. Interpret what these results tell us about population-infrastructure relationships. + +### Strategic Development Framework: +Based on your regression results and EDA findings: +- **High-Priority Districts**: Identify districts that are **under-predicted** by your model (high actual population but low predicted population) - these may represent infrastructure gaps. +- **Development Recommendations**: Which districts should receive immediate infrastructure investment based on your analysis? +- **Evidence-Based Justification**: Use statistical evidence from your regression model and correlation analysis to support your recommendations. + +--- + +## Technical Requirements + +### Statistical Analysis: +- Properly handle missing data and outliers +- Use appropriate statistical tests and report p-values +- Calculate and interpret correlation coefficients +- Validate regression assumptions (normality, homoscedasticity) + +### Data Management: +- Document all data cleaning and aggregation steps +- Handle temporal alignment between population (2020) and nightlight data +- Ensure consistent district naming across datasets + +### Visualization Standards: +- Create clear, publication-quality heatmaps with appropriate color scales +- Design effective time series plots for nightlight trends +- Include proper axis labels, titles, and legends +- Use consistent formatting across all visualizations + +### Reporting Requirements: +- Clearly state the top 3 most predictive variables with statistical justification +- Provide ranked lists for nightlight growth districts with supporting metrics +- Include model performance statistics and interpretation +- Document all methodological choices and assumptions \ No newline at end of file From 3b607d689c26294e17fbc19f55c5623ffd11643b Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Thu, 14 Aug 2025 09:04:30 -0400 Subject: [PATCH 10/33] Updated assignment-2 --- notebooks/assignments/assignment-2.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/notebooks/assignments/assignment-2.ipynb b/notebooks/assignments/assignment-2.ipynb index acf73df..a2f547c 100644 --- a/notebooks/assignments/assignment-2.ipynb +++ b/notebooks/assignments/assignment-2.ipynb @@ -267,7 +267,7 @@ "2. **Lit Pixel Analysis**: Compare these districts using `lit_pixel_percentage` changes to understand whether growth represents intensification or spatial expansion.\n", "3. **Create visualizations** showing nightlight trends for these extreme districts.\n", "\n", - "## Part C: Predictive Modeling and Strategic Analysis\n", + "## Part C: Modeling\n", "\n", "### Multivariate Linear Regression:\n", "1. **Model Development**: Build a multivariate linear regression model predicting **population density** using both demographic and nightlight variables as predictors. Explore as many variables as possible at the beginning.\n", @@ -277,6 +277,7 @@ "\n", "\n", "## Notes and Other Requirements\n", + "Please follow the genral guidelines below when preparing your analysis..\n", "\n", "### Statistical Analysis:\n", "- Properly handle missing data and outliers\n", From a8e2a6f84a3ebe1fa173b44227ff69f1b38e4224 Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Sat, 23 Aug 2025 10:52:52 -0400 Subject: [PATCH 11/33] Added LLMs Guide --- docs/Agenda for August 18.docx | Bin 0 -> 15184 bytes docs/_toc.yml | 5 + docs/llms-and-chatbots.md | 380 +++++++++++++++++++++++++++++++++ docs/~$enda for August 18.docx | Bin 0 -> 162 bytes 4 files changed, 385 insertions(+) create mode 100644 docs/Agenda for August 18.docx create mode 100644 docs/llms-and-chatbots.md create mode 100644 docs/~$enda for August 18.docx diff --git a/docs/Agenda for August 18.docx b/docs/Agenda for August 18.docx new file mode 100644 index 0000000000000000000000000000000000000000..f02592c04dd7ea421de486611ea0ccb77e08f0d6 GIT binary patch literal 15184 zcmeIZWmF`~wk=#ZG*090?(XjH4vo9JHSX@vc;oI4jXO=_?(XjX*!!Myx19aHH^%q( z-mFnEDs#nLDYYykX2?r{fFc9H0gwOyfB?W4XRBrc1OOC(0RW!?kic3(wl+@2HcnrZ z-R+DWb!gqJt?+X}fyr|Kz#skpcl#gw1nLtMD(+TS|1*%=Cm8JLF$@W)p?#~~R4CYREzpb#5eNsiHYlKm2N&Dp3H=XYp) zj4((y6Y19khFN{BnA?9~NV5Xb6E*b4k12hU%gDz?~bog+0T7A*z9LeR;F1xYL(r+KpxBf`|4w3wIINscqXjgeRFMr-1(DB zfN~@=w8U@JuzoHRA#4u7y#BU`t(C46H^ULli(irS5L0vSnWwtXZ0DPLbEq~?8h^WZ zE$|~Axfo>8tIa5c@58d*-$4NKe{oOTILxN=4~guD_d$QS=NAWKD@Quo-`f9m%m2Z? 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You don't need to consume everything here - instead, start with the minimum resources from each phase to get a working foundation, then selectively add more advanced materials based on your project needs and interests. +The resources are organized by time investment (shortest first) so you can quickly identify what fits your schedule and learning goals. + +## Phase 1: LLMs and NLP Foundations + +### Core Concepts to Master +- **Traditional NLP vs. Modern LLMs**: Understanding the evolution from rule-based systems to transformer models +- **Transformer Architecture**: Attention mechanisms, encoders, decoders +- **Large Language Models**: Training process, fine-tuning, prompt engineering +- **Embeddings**: Vector representations, semantic similarity +- **Tokenization**: How text is processed by models + +### Learning Resources + +#### Quick Start (1-3 hours) +- **Andrej Karpathy's "Intro to Large Language Models"** (1 hour): (here)[https://www.youtube.com/watch?v=zjkBMFhNj_g] +- **"What are Embeddings?"** (20 min)[https://www.youtube.com/watch?v=wjZofJX0v4M] +- **Hugging Face Transformers Quick Tour** (30 min)[https://huggingface.co/docs/transformers/quicktour] + +#### Medium Investment (4-10 hours) +- **Hugging Face LLM Course - Chapters 1-3** (6-8 hours)[https://huggingface.co/learn/nlp-course/chapter1/1] +- **Hugging Face LLM Course - Chapter 7** (1-2 hours)[https://huggingface.co/learn/llm-course/chapter7/2] + + +## Phase 1.5: LLM Platforms & Model Landscape + +### Core Concepts to Master +- **Proprietary vs Open Source Models**: Understanding trade-offs between commercial and open models +- **Model Capabilities**: Text generation, reasoning, coding, multimodal features +- **API vs Local Deployment**: Cloud services vs running models locally +- **Model Sizes & Performance**: 7B, 13B, 70B+ parameter models and their use cases +- **Cost Considerations**: API pricing vs compute costs for local models + +### Learning Resources + +#### Quick Start (1-3 hours) +- **"LLM Comparison 2025"** by AI Explained (20 min): [Complete LLM Model Comparison Guide](https://www.helicone.ai/blog/the-complete-llm-model-comparison-guide) +- **Hugging Face Model Hub Tour** (30 min): [Hugging Face Models](https://huggingface.co/models) +- **Ollama Model Library** (15 min): [Ollama Library](https://ollama.com/library) + +#### Sign-up for APIs +- **Setting up OpenAI API** (30 min): [OpenAI API Quickstart](https://platform.openai.com/docs/quickstart) +- **Anthropic Claude API Setup** (30 min): [Anthropic API Getting Started](https://docs.anthropic.com/en/api/getting-started) +- **Cohere API Quickstart** (30 min): [Cohere Quickstart Tutorial](https://docs.cohere.com/docs/quickstart-tutorial) +- **Ollama Installation & Model Download** (1 hour): [Ollama](https://ollama.com/) + +### Major LLM Platforms Overview + +#### Proprietary/Commercial APIs +- **OpenAI**: GPT-4, GPT-3.5-turbo - Industry standard, excellent reasoning +- **Anthropic Claude**: Claude-3 family - Strong safety, long context windows +- **Cohere**: Command-R+ - Enterprise-focused, good for RAG applications +- **Google Gemini**: Multimodal capabilities, integrated with Google services + +#### Open Source Models (via Ollama/Hugging Face) +- **Meta Llama Family**: llama2, llama3.1 - Well-rounded, popular for fine-tuning +- **Mistral**: mistral-7b-instruct - Efficient, good performance-to-size ratio +- **Qwen**: Strong reasoning and multilingual capabilities +- **CodeLlama**: Specialized for code generation and understanding +- **Vicuna**: Fine-tuned from Llama, conversational +- **Phi-3**: Microsoft's small but capable models + +#### Specialized Models +- **Embedding Models**: sentence-transformers, OpenAI text-embedding-ada-002 +- **Code Models**: CodeLlama, StarCoder, CodeT5 +- **Multimodal**: LLaVA, GPT-4V, Claude-3 Vision + +### Decision Framework for Model Selection +- **Budget**: Free tier limits, API costs vs local compute +- **Use Case**: General chat, coding, reasoning, creative tasks +- **Privacy**: Cloud APIs vs local deployment +- **Performance**: Response quality vs speed requirements +- **Integration**: Existing tech stack compatibility + +--- + +## Phase 2: Prompt Engineering & Basic LLM Interaction +This is optional but still recommended for you to understand the basics of interacting with LLMs. + +### Core Concepts to Master +- **Prompt Engineering**: Crafting effective prompts for different tasks +- **Few-shot Learning**: Using examples to guide model behavior +- **System Messages**: Setting context and behavior guidelines +- **Temperature & Sampling**: Controlling randomness and creativity +- **API Integration**: Direct interaction with LLM APIs (OpenAI, Ollama, Hugging Face) + +### Learning Resources +#### Quick Start (1-3 hours) +- **"Prompt Engineering in 15 Minutes"** by AI Explained (15 min): [Prompt Engineering Tutorial](https://www.youtube.com/watch?v=dOxUroR57xs) +- **OpenAI Prompt Engineering Guide** (1 hour): [OpenAI Prompt Engineering](https://platform.openai.com/docs/guides/prompt-engineering) +- **Ollama Python Basic Tutorial** (30 min): [Ollama Python Library](https://github.com/ollama/ollama-python) + + +#### Medium Investment (4-8 hours) +- **Anthropic's Prompt Engineering Guide** (2-3 hours): [Anthropic Prompt Engineering](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) +- **DeepLearning.AI Prompt Engineering Course** (4-6 hours): [ChatGPT Prompt Engineering for Developers](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) + + + +## Phase 3: RAG (Retrieval-Augmented Generation) Fundamentals + +### Core RAG Concepts +- **Vector Databases**: Storing and retrieving embeddings +- **Document Processing**: Chunking, preprocessing, metadata handling +- **Retrieval Strategies**: Semantic search, hybrid search, re-ranking +- **Context Window Management**: Optimizing retrieved content for LLM input + +### Learning Resources + +#### Quick Start (1-4 hours) +- **"RAG in 100 Seconds"** by Fireship (2 min): [RAG Explained](https://www.youtube.com/watch?v=T-D1OfcDW1M) +- **Simple RAG with Ollama Tutorial** (1 hour): [LangChain Local RAG](https://python.langchain.com/docs/tutorials/local_rag/) +- **ChromaDB Quickstart** (30 min): [ChromaDB Getting Started](https://docs.trychroma.com/getting-started) + +#### Medium Investment (5-12 hours) +- **LangChain RAG Tutorial** (4-6 hours): [LangChain Question Answering](https://python.langchain.com/docs/tutorials/rag/) +- **Building RAG from Scratch** by Greg Kamradt (3 hours): [RAG from Scratch](https://www.youtube.com/watch?v=BrsocJb-fAo) +- **Weaviate RAG Tutorial** (2-3 hours): [Weaviate RAG Tutorial](https://weaviate.io/developers/weaviate/tutorials/rag) + +### Open Source LLM Integration +- **Ollama Models**: llama2, mistral, codellama, vicuna +- **Hugging Face Models**: microsoft/DialoGPT-medium, meta-llama/Llama-2-7b-chat-hf +- **Local Model Setup**: [LangChain Ollama Integration](https://python.langchain.com/docs/integrations/llms/ollama) +--- + +## Phase 4: Tools and Frameworks by Complexity Level + +### No-Code Solutions +Perfect for rapid prototyping and non-technical stakeholders + +#### Quick Setup (15-30 minutes) +- **Ollama + Open WebUI**: Local chatbot interface +- **HuggingChat**: Free web-based interface for open source models +- **Chatbase**: Upload documents, get instant chatbot + +#### Medium Setup (1-2 hours) +- **Botpress**: Visual chatbot builder with NLU +- **Rasa X**: Open source conversational AI platform + +#### Pros & Cons +- ✅ Quick setup, no coding required +- ✅ Good for testing concepts +- ❌ Limited customization +- ❌ May have usage limits + +### Low-Code Solutions +Balance between ease of use and customization + +#### Quick Setup (30 minutes - 2 hours) +- **Flowise**: Visual node-based LLM orchestration +- **LangFlow**: Drag-and-drop RAG pipeline builder +- **n8n**: Workflow automation with LLM nodes + +#### Learning Resources +- **Flowise Quickstart** (30 min): https://docs.flowiseai.com/getting-started +- **LangFlow Tutorial** (45 min): https://docs.langflow.org/ + +### Code-Based Frameworks (Python) +Maximum flexibility and customization + +#### Quick Start Options (2-4 hours) +- **Streamlit + Ollama Tutorial**: https://docs.streamlit.io/knowledge-base/tutorials/llm-quickstart +- **Chainlit Quickstart**: https://docs.chainlit.io/get-started/overview +- **Gradio ChatInterface**: https://gradio.app/docs/#chatinterface + +#### Medium Complexity (1-2 days) +- **LangChain Python Tutorial**: https://python.langchain.com/docs/get_started/ +- **LlamaIndex Python Starter**: https://docs.llamaindex.ai/en/stable/getting_started/starter_example/ +- **Haystack Tutorial**: https://haystack.deepset.ai/tutorials + +#### Advanced Frameworks (1+ weeks) +- **Custom RAG with ChromaDB + Ollama** +- **FastAPI + LangChain Backend** +- **Full-stack with Streamlit + Vector DB** + +### Open Source Model Recommendations +- **For Chatbots**: Llama-2-7B-Chat, Mistral-7B-Instruct, Vicuna-7B +- **For Embeddings**: sentence-transformers/all-MiniLM-L6-v2 +- **For Code**: CodeLlama-7B-Instruct + +--- + +## Phase 5: Frontend Development Options + +### Web-Based Interfaces (Time Investment Order) + +#### Rapid Prototyping (30 minutes - 2 hours) +- **Gradio ChatInterface** (30 min setup): + ```python + import gradio as gr + gr.ChatInterface(fn=your_chat_function).launch() + ``` +- **Streamlit Chat** (1 hour setup): + ```python + import streamlit as st + st.chat_input("Your message") + st.chat_message("assistant") + ``` + +#### Professional Interfaces (4-8 hours) +- **Chainlit** (2-4 hours): Conversational UI specifically for LLM apps + - Tutorial: https://docs.chainlit.io/get-started/overview +- **Streamlit + Custom CSS** (4-6 hours): Enhanced styling and UX +- **FastAPI + Jinja2 Templates** (6-8 hours): Custom web interface + +#### Advanced Web Applications (1-2 weeks) +- **FastAPI + React**: Full-stack separation +- **Django + HTMX**: Python-heavy full-stack + +### Mobile & Messaging Platforms + +#### Quick Integration (2-4 hours) +- **Telegram Bot** with python-telegram-bot: + ```python + from telegram.ext import Application, MessageHandler + ``` +- **Discord Bot** with discord.py: + ```python + import discord + from discord.ext import commands + ``` + +#### Medium Integration (1-2 days) +- **WhatsApp via Twilio**: WhatsApp Business API integration +- **Slack Bolt Framework**: Slack app development + +### Deployment Options (Time Investment) +#### Instant Deploy (5-15 minutes) +- **Streamlit Community Cloud**: Free hosting for Streamlit apps +- **Hugging Face Spaces**: Free hosting for Gradio/Streamlit +- **Replit**: Online IDE with instant deployment + +#### Professional Deploy (2-6 hours) +- **Railway**: Simple Python app deployment +- **Render**: Docker-based deployment +- **DigitalOcean App Platform**: Managed deployment + +--- + +## Phase 6: Advanced Topics for Professional Applications + +### Evaluation & Testing (Time Investment Order) + +#### Quick Setup (1-2 hours) +- **Simple metrics**: Response time, relevance scoring +- **LangSmith Basic Setup**: LangChain monitoring + +#### Medium Investment (4-8 hours) +- **RAGAS Framework**: RAG evaluation metrics +- **Custom evaluation pipeline**: Automated testing + +### Security & Privacy (Time Investment Order) + +#### Essential (2-4 hours) +- **Input sanitization**: Prevent prompt injection +- **API key management**: Environment variables, secrets +- **Rate limiting**: Basic protection + +#### Professional (1-2 days) +- **Data encryption**: At rest and in transit +- **User authentication**: JWT, OAuth integration +- **GDPR compliance**: Data retention policies + +### Performance Optimization + +#### Quick Wins (2-4 hours) +- **Response streaming**: Real-time chat experience +- **Simple caching**: In-memory response caching +- **Model quantization**: Faster inference with Ollama + +#### Advanced (1+ weeks) +- **Vector database optimization**: Index tuning +- **Load balancing**: Multiple model instances +- **Advanced caching**: Redis, distributed caching +--- + + + + +## Recommended 8-12-Week Project Timeline + +### Phase 1 – Foundation & Setup (Weeks 1–2) +**Goal:** Understand basics of LLMs and set up tools. + +- **Intro to LLMs & Chatbots** + - Watch Karpathy’s intro to LLMs + - Read Hugging Face *Course* chapters 1–3 (up to tokenization, transformers basics) +- **Environment Setup** + - Install Python, Conda/Poetry for environments + - Set up **Ollama** for local inference if needed + - Explore Streamlit “hello world” apps +- **Mini-Project:** Build a simple chatbot (no RAG) using Ollama + Streamlit + + +### Phase 2 – Core NLP & RAG Concepts (Weeks 3–4) +**Goal:** Learn retrieval fundamentals and connect them to LLMs. + +- **RAG Fundamentals** + - Watch tutorial series on RAG (Hugging Face, LangChain docs) + - Learn embeddings: generate with `sentence-transformers` or Hugging Face +- **Document Handling** + - Create a pipeline to load and chunk PDFs/text docs + - Store embeddings in **ChromaDB** (free, lightweight option) +- **Mini-Project:** Build a basic retrieval + response system with LangChain + + +### Phase 3 – Framework Integration (Weeks 5–6) +**Goal:** Move from “toy” scripts to structured apps. + +- **Choose a framework:** LangChain or LlamaIndex (start with LangChain for ecosystem support) +- **Add Conversation Memory:** Learn how to store chat history across turns +- **Improve Reliability:** Add error handling, logging, and configuration files +- **Mini-Project:** Interactive chatbot that remembers past queries + + + +### Phase 4 – Frontend Development (Weeks 7–8) +**Goal:** Create a usable interface for demonstration. + +- **UI Development** + - Build UI with Streamlit (or Chainlit for chatbot-style interface) +- **Features & Styling** + - Add buttons, input fields, and styled responses + - Basic UX: clear chat history, loading spinners, response formatting +- **Mini-Project:** A polished chatbot frontend that feels usable + + + +### Phase 5 – Advanced Features (Weeks 9–10) +**Goal:** Make the chatbot more robust and “demo-ready.” + +- **Evaluation** + - Learn basic chatbot evaluation: answer relevance, latency, failure cases + - Add simple monitoring (response time logs, success/failure rates) +- **Security & Optimization** + - Add guardrails (prompt filtering, max token limits) + - Experiment with faster embedding models or smaller LLMs for efficiency + + + +### Phase 6 – Final Polish & Deployment (Weeks 11–12) +**Goal:** Package the chatbot for sharing and presentation. + +- **Documentation & Testing** + - Write a README with setup instructions + - Add inline code comments and notebook demos + - Test edge cases (empty docs, large docs, malformed input) +- **Deployment** + - Deploy Streamlit app to Streamlit Cloud (free) or Hugging Face Spaces + - Final debugging and presentation prep +- **Capstone Demo:** Present a fully working RAG-based chatbot with documents of choice (e.g., PDFs, reports, FAQs). + + +--- + +## Additional Resources + +### Communities & Support +- **Ollama Discord**: Local LLM community +- **LangChain Discord**: Framework-specific support +- **Hugging Face Forums**: Open source model discussions +- **Reddit r/LocalLLaMA**: Local model enthusiasts + +### Open Source Datasets for Testing +- **MS MARCO**: Question-answering dataset +- **Natural Questions**: Real user questions +- **Squad 2.0**: Reading comprehension dataset + +### Free Compute Options +- **Google Colab**: Free GPU access (limited) +- **Hugging Face Spaces**: Free hosting and compute +- **Kaggle Notebooks**: Free GPU/TPU access + +### Local Development Setup +- **Ollama**: Easy local LLM deployment +- **GPT4All**: Desktop application for local models +- **LM Studio**: GUI for running local LLMs diff --git a/docs/~$enda for August 18.docx b/docs/~$enda for August 18.docx new file mode 100644 index 0000000000000000000000000000000000000000..29bd5ee3afb5808acd37be003a51e51aa598e4ed GIT binary patch literal 162 zcmWf1Da|V`Nz7C5O)N>xPR*-KWFP_vGPp35GUPE7Gn6nS0%-*XUm#t=kjjt^#CZ&r vKpq1_6&E9r49_o00U4<=MX?{mj|h5O$PA Date: Sat, 23 Aug 2025 11:47:05 -0400 Subject: [PATCH 12/33] Updated LLM guide --- docs/_toc.yml | 2 +- ...s-and-chatbots.md => llm-chatbot-guide.md} | 116 +- notebooks/tutorials/langchain/rag-1.ipynb | 1399 +++++++++++++++++ requirements-llms.txt | 9 + 4 files changed, 1449 insertions(+), 77 deletions(-) rename docs/{llms-and-chatbots.md => llm-chatbot-guide.md} (71%) create mode 100644 notebooks/tutorials/langchain/rag-1.ipynb create mode 100644 requirements-llms.txt diff --git a/docs/_toc.yml b/docs/_toc.yml index d2d4961..a244e74 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -68,4 +68,4 @@ parts: - caption: Additional Resources numbered: false chapters: - - file: llms-and-chatbots.md + - file: llms-and-chatbots-guide.md diff --git a/docs/llms-and-chatbots.md b/docs/llm-chatbot-guide.md similarity index 71% rename from docs/llms-and-chatbots.md rename to docs/llm-chatbot-guide.md index 076acf5..31110a1 100644 --- a/docs/llms-and-chatbots.md +++ b/docs/llm-chatbot-guide.md @@ -1,5 +1,6 @@ # Building LLM Based Chatbots in Python *A comprehensive guide for building LLM-based chatbots* + ## How to Use This Guide This document provides a comprehensive list of resources and suggested learning sequence for developing the knowledge and skills needed to build LLM-based chatbots. You don't need to consume everything here - instead, start with the minimum resources from each phase to get a working foundation, then selectively add more advanced materials based on your project needs and interests. The resources are organized by time investment (shortest first) so you can quickly identify what fits your schedule and learning goals. @@ -16,14 +17,13 @@ The resources are organized by time investment (shortest first) so you can quick ### Learning Resources #### Quick Start (1-3 hours) -- **Andrej Karpathy's "Intro to Large Language Models"** (1 hour): (here)[https://www.youtube.com/watch?v=zjkBMFhNj_g] -- **"What are Embeddings?"** (20 min)[https://www.youtube.com/watch?v=wjZofJX0v4M] -- **Hugging Face Transformers Quick Tour** (30 min)[https://huggingface.co/docs/transformers/quicktour] +- **[Andrej Karpathy's "Intro to Large Language Models"](https://www.youtube.com/watch?v=zjkBMFhNj_g)** (1 hour) +- **["What are Embeddings?"](https://www.youtube.com/watch?v=wjZofJX0v4M)** (20 min) +- **[Hugging Face Transformers Quick Tour](https://huggingface.co/docs/transformers/quicktour)** (30 min) #### Medium Investment (4-10 hours) -- **Hugging Face LLM Course - Chapters 1-3** (6-8 hours)[https://huggingface.co/learn/nlp-course/chapter1/1] -- **Hugging Face LLM Course - Chapter 7** (1-2 hours)[https://huggingface.co/learn/llm-course/chapter7/2] - +- **[Hugging Face LLM Course - Chapters 1-3](https://huggingface.co/learn/nlp-course/chapter1/1)** (6-8 hours) +- **[Hugging Face LLM Course - Chapter 7](https://huggingface.co/learn/llm-course/chapter7/2)** (1-2 hours) ## Phase 1.5: LLM Platforms & Model Landscape @@ -37,15 +37,15 @@ The resources are organized by time investment (shortest first) so you can quick ### Learning Resources #### Quick Start (1-3 hours) -- **"LLM Comparison 2025"** by AI Explained (20 min): [Complete LLM Model Comparison Guide](https://www.helicone.ai/blog/the-complete-llm-model-comparison-guide) -- **Hugging Face Model Hub Tour** (30 min): [Hugging Face Models](https://huggingface.co/models) -- **Ollama Model Library** (15 min): [Ollama Library](https://ollama.com/library) +- **["LLM Comparison 2025" by AI Explained](https://www.helicone.ai/blog/the-complete-llm-model-comparison-guide)** (20 min) +- **[Hugging Face Model Hub Tour](https://huggingface.co/models)** (30 min) +- **[Ollama Model Library](https://ollama.com/library)** (15 min) #### Sign-up for APIs -- **Setting up OpenAI API** (30 min): [OpenAI API Quickstart](https://platform.openai.com/docs/quickstart) -- **Anthropic Claude API Setup** (30 min): [Anthropic API Getting Started](https://docs.anthropic.com/en/api/getting-started) -- **Cohere API Quickstart** (30 min): [Cohere Quickstart Tutorial](https://docs.cohere.com/docs/quickstart-tutorial) -- **Ollama Installation & Model Download** (1 hour): [Ollama](https://ollama.com/) +- **[Setting up OpenAI API](https://platform.openai.com/docs/quickstart)** (30 min) +- **[Anthropic Claude API Setup](https://docs.anthropic.com/en/api/getting-started)** (30 min) +- **[Cohere API Quickstart](https://docs.cohere.com/docs/quickstart-tutorial)** (30 min) +- **[Ollama Installation & Model Download](https://ollama.com/)** (1 hour) ### Major LLM Platforms Overview @@ -89,16 +89,13 @@ This is optional but still recommended for you to understand the basics of inter ### Learning Resources #### Quick Start (1-3 hours) -- **"Prompt Engineering in 15 Minutes"** by AI Explained (15 min): [Prompt Engineering Tutorial](https://www.youtube.com/watch?v=dOxUroR57xs) -- **OpenAI Prompt Engineering Guide** (1 hour): [OpenAI Prompt Engineering](https://platform.openai.com/docs/guides/prompt-engineering) -- **Ollama Python Basic Tutorial** (30 min): [Ollama Python Library](https://github.com/ollama/ollama-python) - +- **["Prompt Engineering in 15 Minutes" by AI Explained](https://www.youtube.com/watch?v=dOxUroR57xs)** (15 min) +- **[OpenAI Prompt Engineering Guide](https://platform.openai.com/docs/guides/prompt-engineering)** (1 hour) +- **[Ollama Python Basic Tutorial](https://github.com/ollama/ollama-python)** (30 min) #### Medium Investment (4-8 hours) -- **Anthropic's Prompt Engineering Guide** (2-3 hours): [Anthropic Prompt Engineering](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) -- **DeepLearning.AI Prompt Engineering Course** (4-6 hours): [ChatGPT Prompt Engineering for Developers](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) - - +- **[Anthropic's Prompt Engineering Guide](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview)** (2-3 hours) +- **[DeepLearning.AI Prompt Engineering Course](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/)** (4-6 hours) ## Phase 3: RAG (Retrieval-Augmented Generation) Fundamentals @@ -111,19 +108,20 @@ This is optional but still recommended for you to understand the basics of inter ### Learning Resources #### Quick Start (1-4 hours) -- **"RAG in 100 Seconds"** by Fireship (2 min): [RAG Explained](https://www.youtube.com/watch?v=T-D1OfcDW1M) -- **Simple RAG with Ollama Tutorial** (1 hour): [LangChain Local RAG](https://python.langchain.com/docs/tutorials/local_rag/) -- **ChromaDB Quickstart** (30 min): [ChromaDB Getting Started](https://docs.trychroma.com/getting-started) +- **["RAG in 100 Seconds" by Fireship](https://www.youtube.com/watch?v=T-D1OfcDW1M)** (2 min) +- **[Simple RAG with Ollama Tutorial](https://python.langchain.com/docs/tutorials/local_rag/)** (1 hour) +- **[ChromaDB Quickstart](https://docs.trychroma.com/getting-started)** (30 min) #### Medium Investment (5-12 hours) -- **LangChain RAG Tutorial** (4-6 hours): [LangChain Question Answering](https://python.langchain.com/docs/tutorials/rag/) -- **Building RAG from Scratch** by Greg Kamradt (3 hours): [RAG from Scratch](https://www.youtube.com/watch?v=BrsocJb-fAo) -- **Weaviate RAG Tutorial** (2-3 hours): [Weaviate RAG Tutorial](https://weaviate.io/developers/weaviate/tutorials/rag) +- **[LangChain RAG Tutorial](https://python.langchain.com/docs/tutorials/rag/)** (4-6 hours) +- **[Building RAG from Scratch by Greg Kamradt](https://www.youtube.com/watch?v=BrsocJb-fAo)** (3 hours) +- **[Weaviate RAG Tutorial](https://weaviate.io/developers/weaviate/tutorials/rag)** (2-3 hours) ### Open Source LLM Integration - **Ollama Models**: llama2, mistral, codellama, vicuna - **Hugging Face Models**: microsoft/DialoGPT-medium, meta-llama/Llama-2-7b-chat-hf -- **Local Model Setup**: [LangChain Ollama Integration](https://python.langchain.com/docs/integrations/llms/ollama) +- **[Local Model Setup](https://python.langchain.com/docs/integrations/llms/ollama)** + --- ## Phase 4: Tools and Frameworks by Complexity Level @@ -155,21 +153,21 @@ Balance between ease of use and customization - **n8n**: Workflow automation with LLM nodes #### Learning Resources -- **Flowise Quickstart** (30 min): https://docs.flowiseai.com/getting-started -- **LangFlow Tutorial** (45 min): https://docs.langflow.org/ +- **[Flowise Quickstart](https://docs.flowiseai.com/getting-started)** (30 min) +- **[LangFlow Tutorial](https://docs.langflow.org/)** (45 min) ### Code-Based Frameworks (Python) Maximum flexibility and customization #### Quick Start Options (2-4 hours) -- **Streamlit + Ollama Tutorial**: https://docs.streamlit.io/knowledge-base/tutorials/llm-quickstart -- **Chainlit Quickstart**: https://docs.chainlit.io/get-started/overview -- **Gradio ChatInterface**: https://gradio.app/docs/#chatinterface +- **[Streamlit + Ollama Tutorial](https://docs.streamlit.io/knowledge-base/tutorials/llm-quickstart)** +- **[Chainlit Quickstart](https://docs.chainlit.io/get-started/overview)** +- **[Gradio ChatInterface](https://gradio.app/docs/#chatinterface)** #### Medium Complexity (1-2 days) -- **LangChain Python Tutorial**: https://python.langchain.com/docs/get_started/ -- **LlamaIndex Python Starter**: https://docs.llamaindex.ai/en/stable/getting_started/starter_example/ -- **Haystack Tutorial**: https://haystack.deepset.ai/tutorials +- **[LangChain Python Tutorial](https://python.langchain.com/docs/get_started/)** +- **[LlamaIndex Python Starter](https://docs.llamaindex.ai/en/stable/getting_started/starter_example/)** +- **[Haystack Tutorial](https://haystack.deepset.ai/tutorials)** #### Advanced Frameworks (1+ weeks) - **Custom RAG with ChromaDB + Ollama** @@ -201,8 +199,7 @@ Maximum flexibility and customization ``` #### Professional Interfaces (4-8 hours) -- **Chainlit** (2-4 hours): Conversational UI specifically for LLM apps - - Tutorial: https://docs.chainlit.io/get-started/overview +- **[Chainlit](https://docs.chainlit.io/get-started/overview)** (2-4 hours): Conversational UI specifically for LLM apps - **Streamlit + Custom CSS** (4-6 hours): Enhanced styling and UX - **FastAPI + Jinja2 Templates** (6-8 hours): Custom web interface @@ -275,10 +272,8 @@ Maximum flexibility and customization - **Vector database optimization**: Index tuning - **Load balancing**: Multiple model instances - **Advanced caching**: Redis, distributed caching ---- - - +--- ## Recommended 8-12-Week Project Timeline @@ -286,15 +281,14 @@ Maximum flexibility and customization **Goal:** Understand basics of LLMs and set up tools. - **Intro to LLMs & Chatbots** - - Watch Karpathy’s intro to LLMs + - Watch Karpathy's intro to LLMs - Read Hugging Face *Course* chapters 1–3 (up to tokenization, transformers basics) - **Environment Setup** - Install Python, Conda/Poetry for environments - Set up **Ollama** for local inference if needed - - Explore Streamlit “hello world” apps + - Explore Streamlit "hello world" apps - **Mini-Project:** Build a simple chatbot (no RAG) using Ollama + Streamlit - ### Phase 2 – Core NLP & RAG Concepts (Weeks 3–4) **Goal:** Learn retrieval fundamentals and connect them to LLMs. @@ -306,17 +300,14 @@ Maximum flexibility and customization - Store embeddings in **ChromaDB** (free, lightweight option) - **Mini-Project:** Build a basic retrieval + response system with LangChain - ### Phase 3 – Framework Integration (Weeks 5–6) -**Goal:** Move from “toy” scripts to structured apps. +**Goal:** Move from "toy" scripts to structured apps. - **Choose a framework:** LangChain or LlamaIndex (start with LangChain for ecosystem support) - **Add Conversation Memory:** Learn how to store chat history across turns - **Improve Reliability:** Add error handling, logging, and configuration files - **Mini-Project:** Interactive chatbot that remembers past queries - - ### Phase 4 – Frontend Development (Weeks 7–8) **Goal:** Create a usable interface for demonstration. @@ -327,10 +318,8 @@ Maximum flexibility and customization - Basic UX: clear chat history, loading spinners, response formatting - **Mini-Project:** A polished chatbot frontend that feels usable - - ### Phase 5 – Advanced Features (Weeks 9–10) -**Goal:** Make the chatbot more robust and “demo-ready.” +**Goal:** Make the chatbot more robust and "demo-ready." - **Evaluation** - Learn basic chatbot evaluation: answer relevance, latency, failure cases @@ -339,8 +328,6 @@ Maximum flexibility and customization - Add guardrails (prompt filtering, max token limits) - Experiment with faster embedding models or smaller LLMs for efficiency - - ### Phase 6 – Final Polish & Deployment (Weeks 11–12) **Goal:** Package the chatbot for sharing and presentation. @@ -353,28 +340,5 @@ Maximum flexibility and customization - Final debugging and presentation prep - **Capstone Demo:** Present a fully working RAG-based chatbot with documents of choice (e.g., PDFs, reports, FAQs). - --- -## Additional Resources - -### Communities & Support -- **Ollama Discord**: Local LLM community -- **LangChain Discord**: Framework-specific support -- **Hugging Face Forums**: Open source model discussions -- **Reddit r/LocalLLaMA**: Local model enthusiasts - -### Open Source Datasets for Testing -- **MS MARCO**: Question-answering dataset -- **Natural Questions**: Real user questions -- **Squad 2.0**: Reading comprehension dataset - -### Free Compute Options -- **Google Colab**: Free GPU access (limited) -- **Hugging Face Spaces**: Free hosting and compute -- **Kaggle Notebooks**: Free GPU/TPU access - -### Local Development Setup -- **Ollama**: Easy local LLM deployment -- **GPT4All**: Desktop application for local models -- **LM Studio**: GUI for running local LLMs diff --git a/notebooks/tutorials/langchain/rag-1.ipynb b/notebooks/tutorials/langchain/rag-1.ipynb new file mode 100644 index 0000000..a09367e --- /dev/null +++ b/notebooks/tutorials/langchain/rag-1.ipynb @@ -0,0 +1,1399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5630b0ca", + "metadata": { + "id": "5630b0ca" + }, + "source": [ + "# Build a Retrieval Augmented Generation (RAG) App: Part 1\n", + "\n", + "One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. These are applications that can answer questions about specific source information. These applications use a technique known as Retrieval Augmented Generation, or [RAG](/docs/concepts/rag/).\n", + "\n", + "This is a multi-part tutorial:\n", + "\n", + "- [Part 1](/docs/tutorials/rag) (this guide) introduces RAG and walks through a minimal implementation.\n", + "- [Part 2](/docs/tutorials/qa_chat_history) extends the implementation to accommodate conversation-style interactions and multi-step retrieval processes.\n", + "\n", + "This tutorial will show how to build a simple Q&A application\n", + "over a text data source. Along the way we’ll go over a typical Q&A\n", + "architecture and highlight additional resources for more advanced Q&A techniques. We’ll also see\n", + "how LangSmith can help us trace and understand our application.\n", + "LangSmith will become increasingly helpful as our application grows in\n", + "complexity.\n", + "\n", + "If you're already familiar with basic retrieval, you might also be interested in\n", + "this [high-level overview of different retrieval techniques](/docs/concepts/retrieval).\n", + "\n", + "**Note**: Here we focus on Q&A for unstructured data. If you are interested for RAG over structured data, check out our tutorial on doing [question/answering over SQL data](/docs/tutorials/sql_qa).\n", + "\n", + "## Overview\n", + "A typical RAG application has two main components:\n", + "\n", + "**Indexing**: a pipeline for ingesting data from a source and indexing it. *This usually happens offline.*\n", + "\n", + "**Retrieval and generation**: the actual RAG chain, which takes the user query at run time and retrieves the relevant data from the index, then passes that to the model.\n", + "\n", + "Note: the indexing portion of this tutorial will largely follow the [semantic search tutorial](/docs/tutorials/retrievers).\n", + "\n", + "The most common full sequence from raw data to answer looks like:\n", + "\n", + "### Indexing\n", + "1. **Load**: First we need to load our data. This is done with [Document Loaders](/docs/concepts/document_loaders).\n", + "2. **Split**: [Text splitters](/docs/concepts/text_splitters) break large `Documents` into smaller chunks. This is useful both for indexing data and passing it into a model, as large chunks are harder to search over and won't fit in a model's finite context window.\n", + "3. **Store**: We need somewhere to store and index our splits, so that they can be searched over later. This is often done using a [VectorStore](/docs/concepts/vectorstores) and [Embeddings](/docs/concepts/embedding_models) model.\n", + "\n", + "![index_diagram](https://github.com/langchain-ai/langchain/blob/master/docs/static/img/rag_indexing.png?raw=1)\n", + "\n", + "### Retrieval and generation\n", + "4. **Retrieve**: Given a user input, relevant splits are retrieved from storage using a [Retriever](/docs/concepts/retrievers).\n", + "5. **Generate**: A [ChatModel](/docs/concepts/chat_models) / [LLM](/docs/concepts/text_llms) produces an answer using a prompt that includes both the question with the retrieved data\n", + "\n", + "![retrieval_diagram](https://github.com/langchain-ai/langchain/blob/master/docs/static/img/rag_retrieval_generation.png?raw=1)\n", + "\n", + "Once we've indexed our data, we will use [LangGraph](https://langchain-ai.github.io/langgraph/) as our orchestration framework to implement the retrieval and generation steps.\n", + "\n", + "## Setup\n", + "\n", + "### Jupyter Notebook\n", + "\n", + "This and other tutorials are perhaps most conveniently run in a [Jupyter notebooks](https://jupyter.org/). Going through guides in an interactive environment is a great way to better understand them. See [here](https://jupyter.org/install) for instructions on how to install.\n", + "\n", + "### Installation\n", + "\n", + "This tutorial requires these langchain dependencies:\n", + "\n", + "import Tabs from '@theme/Tabs';\n", + "import TabItem from '@theme/TabItem';\n", + "import CodeBlock from \"@theme/CodeBlock\";\n", + "\n", + "\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1918ba2f", + "metadata": { + "id": "1918ba2f" + }, + "outputs": [], + "source": [ + "%pip install --quiet --upgrade langchain-text-splitters langchain-community langgraph" + ] + }, + { + "cell_type": "markdown", + "id": "9ff1b425", + "metadata": { + "id": "9ff1b425" + }, + "source": [ + " \n", + " \n", + " conda install langchain-text-splitters langchain-community langgraph -c conda-forge\n", + " \n", + "\n", + "\n", + "\n", + "For more details, see our [Installation guide](/docs/how_to/installation).\n", + "\n", + "### LangSmith\n", + "\n", + "Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls.\n", + "As these applications get more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent.\n", + "The best way to do this is with [LangSmith](https://smith.langchain.com).\n", + "\n", + "After you sign up at the link above, make sure to set your environment variables to start logging traces:\n", + "\n", + "```shell\n", + "export LANGSMITH_TRACING=\"true\"\n", + "export LANGSMITH_API_KEY=\"...\"\n", + "```\n", + "\n", + "Or, if in a notebook, you can set them with:\n", + "\n", + "```python\n", + "import getpass\n", + "import os\n", + "\n", + "os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n", + "os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()\n", + "```\n", + "\n", + "## Components\n", + "\n", + "We will need to select three components from LangChain's suite of integrations.\n", + "\n", + "import ChatModelTabs from \"@theme/ChatModelTabs\";\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26ef9d35", + "metadata": { + "id": "26ef9d35" + }, + "outputs": [], + "source": [ + "# | output: false\n", + "# | echo: false\n", + "\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "llm = ChatOpenAI(model=\"gpt-4o-mini\")" + ] + }, + { + "cell_type": "markdown", + "id": "f1b78672-f21e-4827-843e-59514d18ca20", + "metadata": { + "id": "f1b78672-f21e-4827-843e-59514d18ca20" + }, + "source": [ + "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a199c764-5dfd-45cf-a4d4-731f2c3d474f", + "metadata": { + "id": "a199c764-5dfd-45cf-a4d4-731f2c3d474f" + }, + "outputs": [], + "source": [ + "# | output: false\n", + "# | echo: false\n", + "\n", + "from langchain_openai import OpenAIEmbeddings\n", + "\n", + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "markdown", + "id": "859ffca8-055e-4f5a-95fe-55906ed1d63f", + "metadata": { + "id": "859ffca8-055e-4f5a-95fe-55906ed1d63f" + }, + "source": [ + "import VectorStoreTabs from \"@theme/VectorStoreTabs\";\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4db6b46-ea3f-4994-9d54-d7c84beb50cc", + "metadata": { + "id": "f4db6b46-ea3f-4994-9d54-d7c84beb50cc" + }, + "outputs": [], + "source": [ + "# | output: false\n", + "# | echo: false\n", + "\n", + "from langchain_core.vectorstores import InMemoryVectorStore\n", + "\n", + "vector_store = InMemoryVectorStore(embeddings)" + ] + }, + { + "cell_type": "markdown", + "id": "93b2d316-922c-4318-b72d-486fd6813b94", + "metadata": { + "id": "93b2d316-922c-4318-b72d-486fd6813b94" + }, + "source": [ + "## Preview\n", + "\n", + "In this guide we’ll build an app that answers questions about the website's content. The specific website we will use is the [LLM Powered Autonomous\n", + "Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post\n", + "by Lilian Weng, which allows us to ask questions about the contents of\n", + "the post.\n", + "\n", + "We can create a simple indexing pipeline and RAG chain to do this in ~50\n", + "lines of code.\n", + "\n", + "```python\n", + "import bs4\n", + "from langchain import hub\n", + "from langchain_community.document_loaders import WebBaseLoader\n", + "from langchain_core.documents import Document\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "from langgraph.graph import START, StateGraph\n", + "from typing_extensions import List, TypedDict\n", + "\n", + "# Load and chunk contents of the blog\n", + "loader = WebBaseLoader(\n", + " web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n", + " bs_kwargs=dict(\n", + " parse_only=bs4.SoupStrainer(\n", + " class_=(\"post-content\", \"post-title\", \"post-header\")\n", + " )\n", + " ),\n", + ")\n", + "docs = loader.load()\n", + "\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", + "all_splits = text_splitter.split_documents(docs)\n", + "\n", + "# Index chunks\n", + "_ = vector_store.add_documents(documents=all_splits)\n", + "\n", + "# Define prompt for question-answering\n", + "# N.B. for non-US LangSmith endpoints, you may need to specify\n", + "# api_url=\"https://api.smith.langchain.com\" in hub.pull.\n", + "prompt = hub.pull(\"rlm/rag-prompt\")\n", + "\n", + "\n", + "# Define state for application\n", + "class State(TypedDict):\n", + " question: str\n", + " context: List[Document]\n", + " answer: str\n", + "\n", + "\n", + "# Define application steps\n", + "def retrieve(state: State):\n", + " retrieved_docs = vector_store.similarity_search(state[\"question\"])\n", + " return {\"context\": retrieved_docs}\n", + "\n", + "\n", + "def generate(state: State):\n", + " docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n", + " messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n", + " response = llm.invoke(messages)\n", + " return {\"answer\": response.content}\n", + "\n", + "\n", + "# Compile application and test\n", + "graph_builder = StateGraph(State).add_sequence([retrieve, generate])\n", + "graph_builder.add_edge(START, \"retrieve\")\n", + "graph = graph_builder.compile()\n", + "```\n", + "\n", + "```python\n", + "response = graph.invoke({\"question\": \"What is Task Decomposition?\"})\n", + "print(response[\"answer\"])\n", + "```\n", + "\n", + "```\n", + "Task Decomposition is the process of breaking down a complicated task into smaller, manageable steps to facilitate easier execution and understanding. Techniques like Chain of Thought (CoT) and Tree of Thoughts (ToT) guide models to think step-by-step, allowing them to explore multiple reasoning possibilities. This method enhances performance on complex tasks and provides insight into the model's thinking process.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "9ff8204b-dabc-4790-80ea-50d4cf4fceb0", + "metadata": { + "id": "9ff8204b-dabc-4790-80ea-50d4cf4fceb0" + }, + "source": [ + "Check out the [LangSmith\n", + "trace](https://smith.langchain.com/public/65030797-7efa-4356-a7bd-b54b3dc70e17/r)." + ] + }, + { + "cell_type": "markdown", + "id": "efa9ea6a-f914-4f50-8e35-52e6c34b8001", + "metadata": { + "id": "efa9ea6a-f914-4f50-8e35-52e6c34b8001" + }, + "source": [ + "## Detailed walkthrough\n", + "\n", + "Let’s go through the above code step-by-step to really understand what’s\n", + "going on.\n", + "\n", + "## 1. Indexing {#indexing}\n", + "\n", + ":::note\n", + "\n", + "This section is an abbreviated version of the content in the [semantic search tutorial](/docs/tutorials/retrievers).\n", + "If you're comfortable with [document loaders](/docs/concepts/document_loaders), [embeddings](/docs/concepts/embedding_models), and [vector stores](/docs/concepts/vectorstores),\n", + "feel free to skip to the next section on [retrieval and generation](/docs/tutorials/rag/#orchestration).\n", + "\n", + ":::\n", + "\n", + "### Loading documents\n", + "\n", + "We need to first load the blog post contents. We can use\n", + "[DocumentLoaders](/docs/concepts/document_loaders)\n", + "for this, which are objects that load in data from a source and return a\n", + "list of\n", + "[Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html)\n", + "objects.\n", + "\n", + "In this case we’ll use the\n", + "[WebBaseLoader](/docs/integrations/document_loaders/web_base),\n", + "which uses `urllib` to load HTML from web URLs and `BeautifulSoup` to\n", + "parse it to text. We can customize the HTML -\\> text parsing by passing\n", + "in parameters into the `BeautifulSoup` parser via `bs_kwargs` (see\n", + "[BeautifulSoup\n", + "docs](https://beautiful-soup-4.readthedocs.io/en/latest/#beautifulsoup)).\n", + "In this case only HTML tags with class “post-content”, “post-title”, or\n", + "“post-header” are relevant, so we’ll remove all others." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b0971b5-8579-4a89-bd2e-9029dda4c4f1", + "metadata": { + "id": "7b0971b5-8579-4a89-bd2e-9029dda4c4f1", + "outputId": "20a18843-4509-49c1-be4b-006ccd5e1ecd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total characters: 43131\n" + ] + } + ], + "source": [ + "import bs4\n", + "from langchain_community.document_loaders import WebBaseLoader\n", + "\n", + "# Only keep post title, headers, and content from the full HTML.\n", + "bs4_strainer = bs4.SoupStrainer(class_=(\"post-title\", \"post-header\", \"post-content\"))\n", + "loader = WebBaseLoader(\n", + " web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n", + " bs_kwargs={\"parse_only\": bs4_strainer},\n", + ")\n", + "docs = loader.load()\n", + "\n", + "assert len(docs) == 1\n", + "print(f\"Total characters: {len(docs[0].page_content)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a560025-fb86-4b7e-9586-da263bbad481", + "metadata": { + "id": "1a560025-fb86-4b7e-9586-da263bbad481", + "outputId": "d815c8a0-33a0-413d-d904-9088de58df60" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + " LLM Powered Autonomous Agents\n", + " \n", + "Date: June 23, 2023 | Estimated Reading Time: 31 min | Author: Lilian Weng\n", + "\n", + "\n", + "Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\n", + "Agent System Overview#\n", + "In\n" + ] + } + ], + "source": [ + "print(docs[0].page_content[:500])" + ] + }, + { + "cell_type": "markdown", + "id": "e6f11795-e19f-4697-bc6e-6d477355a1cd", + "metadata": { + "id": "e6f11795-e19f-4697-bc6e-6d477355a1cd" + }, + "source": [ + "#### Go deeper\n", + "\n", + "`DocumentLoader`: Object that loads data from a source as list of `Documents`.\n", + "\n", + "- [Docs](/docs/how_to#document-loaders):\n", + " Detailed documentation on how to use `DocumentLoaders`.\n", + "- [Integrations](/docs/integrations/document_loaders/): 160+\n", + " integrations to choose from.\n", + "- [Interface](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.base.BaseLoader.html):\n", + " API reference for the base interface.\n", + "\n", + "### Splitting documents\n", + "\n", + "Our loaded document is over 42k characters which is too long to fit\n", + "into the context window of many models. Even for those models that could\n", + "fit the full post in their context window, models can struggle to find\n", + "information in very long inputs.\n", + "\n", + "To handle this we’ll split the `Document` into chunks for embedding and\n", + "vector storage. This should help us retrieve only the most relevant parts\n", + "of the blog post at run time.\n", + "\n", + "As in the [semantic search tutorial](/docs/tutorials/retrievers), we use a\n", + "[RecursiveCharacterTextSplitter](/docs/how_to/recursive_text_splitter),\n", + "which will recursively split the document using common separators like\n", + "new lines until each chunk is the appropriate size. This is the\n", + "recommended text splitter for generic text use cases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "753e1484-e21b-4f62-9866-b3a5971f88a7", + "metadata": { + "id": "753e1484-e21b-4f62-9866-b3a5971f88a7", + "outputId": "9c81615b-1efa-4f36-9b0d-08257eb9bf44" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split blog post into 66 sub-documents.\n" + ] + } + ], + "source": [ + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "\n", + "text_splitter = RecursiveCharacterTextSplitter(\n", + " chunk_size=1000, # chunk size (characters)\n", + " chunk_overlap=200, # chunk overlap (characters)\n", + " add_start_index=True, # track index in original document\n", + ")\n", + "all_splits = text_splitter.split_documents(docs)\n", + "\n", + "print(f\"Split blog post into {len(all_splits)} sub-documents.\")" + ] + }, + { + "cell_type": "markdown", + "id": "f5193e01-6cf1-45b9-9ba5-38caf75162a6", + "metadata": { + "id": "f5193e01-6cf1-45b9-9ba5-38caf75162a6" + }, + "source": [ + "#### Go deeper\n", + "\n", + "`TextSplitter`: Object that splits a list of `Document`s into smaller\n", + "chunks. Subclass of `DocumentTransformer`s.\n", + "\n", + "- Learn more about splitting text using different methods by reading the [how-to docs](/docs/how_to#text-splitters)\n", + "- [Code (py or js)](/docs/integrations/document_loaders/source_code)\n", + "- [Scientific papers](/docs/integrations/document_loaders/grobid)\n", + "- [Interface](https://python.langchain.com/api_reference/text_splitters/base/langchain_text_splitters.base.TextSplitter.html): API reference for the base interface.\n", + "\n", + "`DocumentTransformer`: Object that performs a transformation on a list\n", + "of `Document` objects.\n", + "\n", + "- [Docs](/docs/how_to#text-splitters): Detailed documentation on how to use `DocumentTransformers`\n", + "- [Integrations](/docs/integrations/document_transformers/)\n", + "- [Interface](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.transformers.BaseDocumentTransformer.html): API reference for the base interface.\n", + "\n", + "### Storing documents\n", + "\n", + "Now we need to index our 66 text chunks so that we can search over them\n", + "at runtime. Following the [semantic search tutorial](/docs/tutorials/retrievers),\n", + "our approach is to [embed](/docs/concepts/embedding_models/) the contents of each document split and insert these embeddings\n", + "into a [vector store](/docs/concepts/vectorstores/). Given an input query, we can then use\n", + "vector search to retrieve relevant documents.\n", + "\n", + "We can embed and store all of our document splits in a single command\n", + "using the vector store and embeddings model selected at the [start of the tutorial](/docs/tutorials/rag/#components)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00d455e1-c681-4665-9470-58dbeca050d4", + "metadata": { + "id": "00d455e1-c681-4665-9470-58dbeca050d4", + "outputId": "1dc73d79-538f-4995-983e-79c4904473d4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['07c18af6-ad58-479a-bfb1-d508033f9c64', '9000bf8e-1993-446f-8d4d-f4e507ba4b8f', 'ba3b5d14-bed9-4f5f-88be-44c88aedc2e6']\n" + ] + } + ], + "source": [ + "document_ids = vector_store.add_documents(documents=all_splits)\n", + "\n", + "print(document_ids[:3])" + ] + }, + { + "cell_type": "markdown", + "id": "57666234-a5b3-4abc-b079-755241bb2b98", + "metadata": { + "id": "57666234-a5b3-4abc-b079-755241bb2b98" + }, + "source": [ + "#### Go deeper\n", + "\n", + "`Embeddings`: Wrapper around a text embedding model, used for converting\n", + "text to embeddings.\n", + "\n", + "- [Docs](/docs/how_to/embed_text): Detailed documentation on how to use embeddings.\n", + "- [Integrations](/docs/integrations/text_embedding/): 30+ integrations to choose from.\n", + "- [Interface](https://python.langchain.com/api_reference/core/embeddings/langchain_core.embeddings.Embeddings.html): API reference for the base interface.\n", + "\n", + "`VectorStore`: Wrapper around a vector database, used for storing and\n", + "querying embeddings.\n", + "\n", + "- [Docs](/docs/how_to/vectorstores): Detailed documentation on how to use vector stores.\n", + "- [Integrations](/docs/integrations/vectorstores/): 40+ integrations to choose from.\n", + "- [Interface](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html): API reference for the base interface.\n", + "\n", + "This completes the **Indexing** portion of the pipeline. At this point\n", + "we have a query-able vector store containing the chunked contents of our\n", + "blog post. Given a user question, we should ideally be able to return\n", + "the snippets of the blog post that answer the question.\n", + "\n", + "## 2. Retrieval and Generation {#orchestration}\n", + "\n", + "Now let’s write the actual application logic. We want to create a simple\n", + "application that takes a user question, searches for documents relevant\n", + "to that question, passes the retrieved documents and initial question to\n", + "a model, and returns an answer.\n", + "\n", + "For generation, we will use the chat model selected at the [start of the tutorial](/docs/tutorials/rag/#components).\n", + "\n", + "We’ll use a prompt for RAG that is checked into the LangChain prompt hub\n", + "([here](https://smith.langchain.com/hub/rlm/rag-prompt))." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46f378c5-858c-488f-8aef-8b59a6280791", + "metadata": { + "id": "46f378c5-858c-488f-8aef-8b59a6280791", + "outputId": "ab55c3ea-e97b-4396-de9e-f617bdb8460e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n", + "Question: (question goes here) \n", + "Context: (context goes here) \n", + "Answer:\n" + ] + } + ], + "source": [ + "from langchain import hub\n", + "\n", + "# N.B. for non-US LangSmith endpoints, you may need to specify\n", + "# api_url=\"https://api.smith.langchain.com\" in hub.pull.\n", + "prompt = hub.pull(\"rlm/rag-prompt\")\n", + "\n", + "example_messages = prompt.invoke(\n", + " {\"context\": \"(context goes here)\", \"question\": \"(question goes here)\"}\n", + ").to_messages()\n", + "\n", + "assert len(example_messages) == 1\n", + "print(example_messages[0].content)" + ] + }, + { + "cell_type": "markdown", + "id": "77dfe84d-cc19-4227-bee4-56b69508ab11", + "metadata": { + "id": "77dfe84d-cc19-4227-bee4-56b69508ab11" + }, + "source": [ + "We'll use [LangGraph](https://langchain-ai.github.io/langgraph/) to tie together the retrieval and generation steps into a single application. This will bring a number of benefits:\n", + "\n", + "- We can define our application logic once and automatically support multiple invocation modes, including streaming, async, and batched calls.\n", + "- We get streamlined deployments via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).\n", + "- LangSmith will automatically trace the steps of our application together.\n", + "- We can easily add key features to our application, including [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) and [human-in-the-loop approval](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/), with minimal code changes.\n", + "\n", + "To use LangGraph, we need to define three things:\n", + "\n", + "1. The state of our application;\n", + "2. The nodes of our application (i.e., application steps);\n", + "3. The \"control flow\" of our application (e.g., the ordering of the steps).\n", + "\n", + "#### State:\n", + "\n", + "The [state](https://langchain-ai.github.io/langgraph/concepts/low_level/#state) of our application controls what data is input to the application, transferred between steps, and output by the application. It is typically a `TypedDict`, but can also be a [Pydantic BaseModel](https://langchain-ai.github.io/langgraph/how-tos/state-model/).\n", + "\n", + "For a simple RAG application, we can just keep track of the input question, retrieved context, and generated answer:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bdc7c33-67f4-40c3-a0f5-9b846bc6e35c", + "metadata": { + "id": "3bdc7c33-67f4-40c3-a0f5-9b846bc6e35c" + }, + "outputs": [], + "source": [ + "from langchain_core.documents import Document\n", + "from typing_extensions import List, TypedDict\n", + "\n", + "\n", + "class State(TypedDict):\n", + " question: str\n", + " context: List[Document]\n", + " answer: str" + ] + }, + { + "cell_type": "markdown", + "id": "77868d9a-892f-4b2c-b706-850f96b4464f", + "metadata": { + "id": "77868d9a-892f-4b2c-b706-850f96b4464f" + }, + "source": [ + "#### Nodes (application steps)\n", + "\n", + "Let's start with a simple sequence of two steps: retrieval and generation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdabbf44-cbee-46a4-98e4-794fdfc8bb3b", + "metadata": { + "id": "bdabbf44-cbee-46a4-98e4-794fdfc8bb3b" + }, + "outputs": [], + "source": [ + "def retrieve(state: State):\n", + " retrieved_docs = vector_store.similarity_search(state[\"question\"])\n", + " return {\"context\": retrieved_docs}\n", + "\n", + "\n", + "def generate(state: State):\n", + " docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n", + " messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n", + " response = llm.invoke(messages)\n", + " return {\"answer\": response.content}" + ] + }, + { + "cell_type": "markdown", + "id": "d1ac9dc3-d73d-48c3-be05-4b60e0b8bc17", + "metadata": { + "id": "d1ac9dc3-d73d-48c3-be05-4b60e0b8bc17" + }, + "source": [ + "Our retrieval step simply runs a similarity search using the input question, and the generation step formats the retrieved context and original question into a prompt for the chat model.\n", + "\n", + "#### Control flow\n", + "\n", + "Finally, we compile our application into a single `graph` object. In this case, we are just connecting the retrieval and generation steps into a single sequence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "418ddefb-9a1d-42bf-9d23-e525268312a4", + "metadata": { + "id": "418ddefb-9a1d-42bf-9d23-e525268312a4" + }, + "outputs": [], + "source": [ + "from langgraph.graph import START, StateGraph\n", + "\n", + "graph_builder = StateGraph(State).add_sequence([retrieve, generate])\n", + "graph_builder.add_edge(START, \"retrieve\")\n", + "graph = graph_builder.compile()" + ] + }, + { + "cell_type": "markdown", + "id": "20b127f4-8411-4214-8cdd-a281771ab708", + "metadata": { + "id": "20b127f4-8411-4214-8cdd-a281771ab708" + }, + "source": [ + "LangGraph also comes with built-in utilities for visualizing the control flow of your application:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "feabc04f-b509-4452-8e2b-d7c7b7585a18", + "metadata": { + "id": "feabc04f-b509-4452-8e2b-d7c7b7585a18", + "outputId": "757445a8-1409-4597-903f-dd5e3aa2d3ae" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n", + "\n", + "display(Image(graph.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "markdown", + "id": "31f7dc4d-cac8-4be9-b44c-df097dc28c81", + "metadata": { + "id": "31f7dc4d-cac8-4be9-b44c-df097dc28c81" + }, + "source": [ + "
\n", + "Do I need to use LangGraph?\n", + "\n", + "LangGraph is not required to build a RAG application. Indeed, we can implement the same application logic through invocations of the individual components:\n", + "\n", + "```python\n", + "question = \"...\"\n", + "\n", + "retrieved_docs = vector_store.similarity_search(question)\n", + "docs_content = \"\\n\\n\".join(doc.page_content for doc in retrieved_docs)\n", + "prompt = prompt.invoke({\"question\": question, \"context\": docs_content})\n", + "answer = llm.invoke(prompt)\n", + "```\n", + "\n", + "The benefits of LangGraph include:\n", + "\n", + "- Support for multiple invocation modes: this logic would need to be rewritten if we wanted to stream output tokens, or stream the results of individual steps;\n", + "- Automatic support for tracing via [LangSmith](https://docs.smith.langchain.com/) and deployments via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/);\n", + "- Support for persistence, human-in-the-loop, and other features.\n", + "\n", + "Many use-cases demand RAG in a conversational experience, such that a user can receive context-informed answers via a stateful conversation. As we will see in [Part 2](/docs/tutorials/qa_chat_history) of the tutorial, LangGraph's management and persistence of state simplifies these applications enormously.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "eee9c057-5a08-46a3-8c7d-6a314d1e777d", + "metadata": { + "id": "eee9c057-5a08-46a3-8c7d-6a314d1e777d" + }, + "source": [ + "#### Usage\n", + "\n", + "Let's test our application! LangGraph supports multiple invocation modes, including sync, async, and streaming.\n", + "\n", + "Invoke:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "663b93ba-f0a7-44c4-a894-fe895bd5b009", + "metadata": { + "id": "663b93ba-f0a7-44c4-a894-fe895bd5b009", + "outputId": "e71e81fe-b9da-436f-a20d-f92d1ade0b55" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Context: [Document(id='a42dc78b-8f76-472a-9e25-180508af74f3', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 1585}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.'), Document(id='c0e45887-d0b0-483d-821a-bb5d8316d51d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 2192}, page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.'), Document(id='4cc7f318-35f5-440f-a4a4-145b5f0b918d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 29630}, page_content='Resources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.'), Document(id='f621ade4-9b0d-471f-a522-44eb5feeba0c', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 19373}, page_content=\"(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.\")]\n", + "\n", + "\n", + "Answer: Task decomposition is a technique used to break down complex tasks into smaller, manageable steps, allowing for more efficient problem-solving. This can be achieved through methods like chain of thought prompting or the tree of thoughts approach, which explores multiple reasoning possibilities at each step. It can be initiated through simple prompts, task-specific instructions, or human inputs.\n" + ] + } + ], + "source": [ + "result = graph.invoke({\"question\": \"What is Task Decomposition?\"})\n", + "\n", + "print(f\"Context: {result['context']}\\n\\n\")\n", + "print(f\"Answer: {result['answer']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "4ef88f30-40ca-476b-808d-794cb72d401f", + "metadata": { + "id": "4ef88f30-40ca-476b-808d-794cb72d401f" + }, + "source": [ + "Stream steps:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6314a96-aab8-4ecc-bbf9-094fa2aa0e50", + "metadata": { + "id": "e6314a96-aab8-4ecc-bbf9-094fa2aa0e50", + "outputId": "37fdb977-0e44-47e3-95d9-7e07ace33734" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'retrieve': {'context': [Document(id='a42dc78b-8f76-472a-9e25-180508af74f3', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 1585}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.'), Document(id='c0e45887-d0b0-483d-821a-bb5d8316d51d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 2192}, page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.'), Document(id='4cc7f318-35f5-440f-a4a4-145b5f0b918d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 29630}, page_content='Resources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.'), Document(id='f621ade4-9b0d-471f-a522-44eb5feeba0c', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 19373}, page_content=\"(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.\")]}}\n", + "\n", + "----------------\n", + "\n", + "{'generate': {'answer': 'Task decomposition is the process of breaking down a complex task into smaller, more manageable steps. This technique, often enhanced by methods like Chain of Thought (CoT) or Tree of Thoughts, allows models to reason through tasks systematically and improves performance by clarifying the thought process. It can be achieved through simple prompts, task-specific instructions, or human inputs.'}}\n", + "\n", + "----------------\n", + "\n" + ] + } + ], + "source": [ + "for step in graph.stream(\n", + " {\"question\": \"What is Task Decomposition?\"}, stream_mode=\"updates\"\n", + "):\n", + " print(f\"{step}\\n\\n----------------\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "f860142d-d50b-4526-a03f-a59a763117fe", + "metadata": { + "id": "f860142d-d50b-4526-a03f-a59a763117fe" + }, + "source": [ + "Stream [tokens](/docs/concepts/tokens/):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28625cc3-0f77-4143-af51-ce0fd6682120", + "metadata": { + "id": "28625cc3-0f77-4143-af51-ce0fd6682120", + "outputId": "d3e0ea55-ceed-47b2-ba72-42b9315eac9b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "|Task| decomposition| is| the| process| of| breaking| down| complex| tasks| into| smaller|,| more| manageable| steps|.| It| can| be| achieved| through| techniques| like| Chain| of| Thought| (|Co|T|)| prompting|,| which| encourages| the| model| to| think| step| by| step|,| or| through| more| structured| methods| like| the| Tree| of| Thoughts|.| This| approach| not| only| simplifies| task| execution| but| also| provides| insights| into| the| model|'s| reasoning| process|.||" + ] + } + ], + "source": [ + "for message, metadata in graph.stream(\n", + " {\"question\": \"What is Task Decomposition?\"}, stream_mode=\"messages\"\n", + "):\n", + " print(message.content, end=\"|\")" + ] + }, + { + "cell_type": "markdown", + "id": "0fe09894-0cc5-4427-9a24-aef60d20705f", + "metadata": { + "id": "0fe09894-0cc5-4427-9a24-aef60d20705f" + }, + "source": [ + ":::tip\n", + "\n", + "For async invocations, use:\n", + "\n", + "```python\n", + "result = await graph.ainvoke(...)\n", + "```\n", + "\n", + "and\n", + "\n", + "```python\n", + "async for step in graph.astream(...):\n", + "```\n", + "\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "406534d4-66a3-4c27-b277-2bd2f5930cf5", + "metadata": { + "id": "406534d4-66a3-4c27-b277-2bd2f5930cf5" + }, + "source": [ + "#### Returning sources\n", + "\n", + "Note that by storing the retrieved context in the state of the graph, we recover sources for the model's generated answer in the `\"context\"` field of the state. See [this guide](/docs/how_to/qa_sources/) on returning sources for more detail.\n", + "\n", + "#### Go deeper\n", + "\n", + "[Chat models](/docs/concepts/chat_models) take in a sequence of messages and return a message.\n", + "\n", + "- [Docs](/docs/how_to#chat-models)\n", + "- [Integrations](/docs/integrations/chat/): 25+ integrations to choose from.\n", + "- [Interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html): API reference for the base interface.\n", + "\n", + "**Customizing the prompt**\n", + "\n", + "As shown above, we can load prompts (e.g., [this RAG\n", + "prompt](https://smith.langchain.com/hub/rlm/rag-prompt)) from the prompt\n", + "hub. The prompt can also be easily customized. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "956e7e78-c26c-4d2d-bf2e-4fc41ff40d37", + "metadata": { + "id": "956e7e78-c26c-4d2d-bf2e-4fc41ff40d37" + }, + "outputs": [], + "source": [ + "from langchain_core.prompts import PromptTemplate\n", + "\n", + "template = \"\"\"Use the following pieces of context to answer the question at the end.\n", + "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", + "Use three sentences maximum and keep the answer as concise as possible.\n", + "Always say \"thanks for asking!\" at the end of the answer.\n", + "\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\n", + "Helpful Answer:\"\"\"\n", + "custom_rag_prompt = PromptTemplate.from_template(template)" + ] + }, + { + "cell_type": "markdown", + "id": "217cf819-da76-4595-8f75-33f931f1f92a", + "metadata": { + "id": "217cf819-da76-4595-8f75-33f931f1f92a" + }, + "source": [ + "## Query analysis\n", + "\n", + "So far, we are executing the retrieval using the raw input query. However, there are some advantages to allowing a model to generate the query for retrieval purposes. For example:\n", + "\n", + "- In addition to semantic search, we can build in structured filters (e.g., \"Find documents since the year 2020.\");\n", + "- The model can rewrite user queries, which may be multifaceted or include irrelevant language, into more effective search queries.\n", + "\n", + "[Query analysis](/docs/concepts/retrieval/#query-analysis) employs models to transform or construct optimized search queries from raw user input. We can easily incorporate a query analysis step into our application. For illustrative purposes, let's add some metadata to the documents in our vector store. We will add some (contrived) sections to the document which we can filter on later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df00956a-6565-4c05-b201-32854dd2a889", + "metadata": { + "id": "df00956a-6565-4c05-b201-32854dd2a889", + "outputId": "279d568e-7c8b-4e85-8bd8-7ea4bcff33b8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/',\n", + " 'start_index': 8,\n", + " 'section': 'beginning'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_documents = len(all_splits)\n", + "third = total_documents // 3\n", + "\n", + "for i, document in enumerate(all_splits):\n", + " if i < third:\n", + " document.metadata[\"section\"] = \"beginning\"\n", + " elif i < 2 * third:\n", + " document.metadata[\"section\"] = \"middle\"\n", + " else:\n", + " document.metadata[\"section\"] = \"end\"\n", + "\n", + "\n", + "all_splits[0].metadata" + ] + }, + { + "cell_type": "markdown", + "id": "114878bd-a334-41ed-8013-ec4ce0a9112b", + "metadata": { + "id": "114878bd-a334-41ed-8013-ec4ce0a9112b" + }, + "source": [ + "We will need to update the documents in our vector store. We will use a simple [InMemoryVectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html) for this, as we will use some of its specific features (i.e., metadata filtering). Refer to the vector store [integration documentation](/docs/integrations/vectorstores/) for relevant features of your chosen vector store." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebb3cbd7-7c75-4cc0-a198-ff7c54a0c43a", + "metadata": { + "id": "ebb3cbd7-7c75-4cc0-a198-ff7c54a0c43a" + }, + "outputs": [], + "source": [ + "from langchain_core.vectorstores import InMemoryVectorStore\n", + "\n", + "vector_store = InMemoryVectorStore(embeddings)\n", + "_ = vector_store.add_documents(all_splits)" + ] + }, + { + "cell_type": "markdown", + "id": "c08aaccd-b3df-45e9-8646-d6ea20215e62", + "metadata": { + "id": "c08aaccd-b3df-45e9-8646-d6ea20215e62" + }, + "source": [ + "Let's next define a schema for our search query. We will use [structured output](/docs/concepts/structured_outputs/) for this purpose. Here we define a query as containing a string query and a document section (either \"beginning\", \"middle\", or \"end\"), but this can be defined however you like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87f9c8c3-3e99-426d-aa65-fec4b9155c3f", + "metadata": { + "id": "87f9c8c3-3e99-426d-aa65-fec4b9155c3f" + }, + "outputs": [], + "source": [ + "from typing import Literal\n", + "\n", + "from typing_extensions import Annotated\n", + "\n", + "\n", + "class Search(TypedDict):\n", + " \"\"\"Search query.\"\"\"\n", + "\n", + " query: Annotated[str, ..., \"Search query to run.\"]\n", + " section: Annotated[\n", + " Literal[\"beginning\", \"middle\", \"end\"],\n", + " ...,\n", + " \"Section to query.\",\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "6399a870-cb06-4219-9b4f-cfa37cb8ab0f", + "metadata": { + "id": "6399a870-cb06-4219-9b4f-cfa37cb8ab0f" + }, + "source": [ + "Finally, we add a step to our LangGraph application to generate a query from the user's raw input:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e8fcdcb-a4ff-41c3-97c7-92d81ab29f38", + "metadata": { + "id": "7e8fcdcb-a4ff-41c3-97c7-92d81ab29f38" + }, + "outputs": [], + "source": [ + "class State(TypedDict):\n", + " question: str\n", + " # highlight-next-line\n", + " query: Search\n", + " context: List[Document]\n", + " answer: str\n", + "\n", + "\n", + "# highlight-next-line\n", + "def analyze_query(state: State):\n", + " # highlight-next-line\n", + " structured_llm = llm.with_structured_output(Search)\n", + " # highlight-next-line\n", + " query = structured_llm.invoke(state[\"question\"])\n", + " # highlight-next-line\n", + " return {\"query\": query}\n", + "\n", + "\n", + "def retrieve(state: State):\n", + " # highlight-start\n", + " query = state[\"query\"]\n", + " retrieved_docs = vector_store.similarity_search(\n", + " query[\"query\"],\n", + " filter=lambda doc: doc.metadata.get(\"section\") == query[\"section\"],\n", + " )\n", + " # highlight-end\n", + " return {\"context\": retrieved_docs}\n", + "\n", + "\n", + "def generate(state: State):\n", + " docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n", + " messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n", + " response = llm.invoke(messages)\n", + " return {\"answer\": response.content}\n", + "\n", + "\n", + "# highlight-start\n", + "graph_builder = StateGraph(State).add_sequence([analyze_query, retrieve, generate])\n", + "graph_builder.add_edge(START, \"analyze_query\")\n", + "# highlight-end\n", + "graph = graph_builder.compile()" + ] + }, + { + "cell_type": "markdown", + "id": "28a62d34", + "metadata": { + "id": "28a62d34" + }, + "source": [ + "
\n", + "Full Code:\n", + "\n", + "```python\n", + "from typing import Literal\n", + "\n", + "import bs4\n", + "from langchain import hub\n", + "from langchain_community.document_loaders import WebBaseLoader\n", + "from langchain_core.documents import Document\n", + "from langchain_core.vectorstores import InMemoryVectorStore\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "from langgraph.graph import START, StateGraph\n", + "from typing_extensions import Annotated, List, TypedDict\n", + "\n", + "# Load and chunk contents of the blog\n", + "loader = WebBaseLoader(\n", + " web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n", + " bs_kwargs=dict(\n", + " parse_only=bs4.SoupStrainer(\n", + " class_=(\"post-content\", \"post-title\", \"post-header\")\n", + " )\n", + " ),\n", + ")\n", + "docs = loader.load()\n", + "\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", + "all_splits = text_splitter.split_documents(docs)\n", + "\n", + "\n", + "# Update metadata (illustration purposes)\n", + "total_documents = len(all_splits)\n", + "third = total_documents // 3\n", + "\n", + "for i, document in enumerate(all_splits):\n", + " if i < third:\n", + " document.metadata[\"section\"] = \"beginning\"\n", + " elif i < 2 * third:\n", + " document.metadata[\"section\"] = \"middle\"\n", + " else:\n", + " document.metadata[\"section\"] = \"end\"\n", + "\n", + "\n", + "# Index chunks\n", + "vector_store = InMemoryVectorStore(embeddings)\n", + "_ = vector_store.add_documents(all_splits)\n", + "\n", + "\n", + "# Define schema for search\n", + "class Search(TypedDict):\n", + " \"\"\"Search query.\"\"\"\n", + "\n", + " query: Annotated[str, ..., \"Search query to run.\"]\n", + " section: Annotated[\n", + " Literal[\"beginning\", \"middle\", \"end\"],\n", + " ...,\n", + " \"Section to query.\",\n", + " ]\n", + "\n", + "# Define prompt for question-answering\n", + "prompt = hub.pull(\"rlm/rag-prompt\")\n", + "\n", + "\n", + "# Define state for application\n", + "class State(TypedDict):\n", + " question: str\n", + " query: Search\n", + " context: List[Document]\n", + " answer: str\n", + "\n", + "\n", + "def analyze_query(state: State):\n", + " structured_llm = llm.with_structured_output(Search)\n", + " query = structured_llm.invoke(state[\"question\"])\n", + " return {\"query\": query}\n", + "\n", + "\n", + "def retrieve(state: State):\n", + " query = state[\"query\"]\n", + " retrieved_docs = vector_store.similarity_search(\n", + " query[\"query\"],\n", + " filter=lambda doc: doc.metadata.get(\"section\") == query[\"section\"],\n", + " )\n", + " return {\"context\": retrieved_docs}\n", + "\n", + "\n", + "def generate(state: State):\n", + " docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n", + " messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n", + " response = llm.invoke(messages)\n", + " return {\"answer\": response.content}\n", + "\n", + "\n", + "graph_builder = StateGraph(State).add_sequence([analyze_query, retrieve, generate])\n", + "graph_builder.add_edge(START, \"analyze_query\")\n", + "graph = graph_builder.compile()\n", + "```\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a92d539-f85d-434b-b911-51a1cf9b81da", + "metadata": { + "id": "8a92d539-f85d-434b-b911-51a1cf9b81da", + "outputId": "77e24b9b-687b-40d0-cccf-f69f28ff33ff" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Image(graph.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "markdown", + "id": "653cf8dc-a201-43ea-9965-02fcfd2fc316", + "metadata": { + "id": "653cf8dc-a201-43ea-9965-02fcfd2fc316" + }, + "source": [ + "We can test our implementation by specifically asking for context from the end of the post. Note that the model includes different information in its answer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b420650-2d9e-4f5e-a8d8-ec36ae07423c", + "metadata": { + "id": "8b420650-2d9e-4f5e-a8d8-ec36ae07423c", + "outputId": "a1e42a3a-9126-4793-d23a-fd8a651a41dc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'analyze_query': {'query': {'query': 'Task Decomposition', 'section': 'end'}}}\n", + "\n", + "----------------\n", + "\n", + "{'retrieve': {'context': [Document(id='d6cef137-e1e8-4ddc-91dc-b62bd33c6020', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 39221, 'section': 'end'}, page_content='Finite context length: The restricted context capacity limits the inclusion of historical information, detailed instructions, API call context, and responses. The design of the system has to work with this limited communication bandwidth, while mechanisms like self-reflection to learn from past mistakes would benefit a lot from long or infinite context windows. Although vector stores and retrieval can provide access to a larger knowledge pool, their representation power is not as powerful as full attention.\\n\\n\\nChallenges in long-term planning and task decomposition: Planning over a lengthy history and effectively exploring the solution space remain challenging. LLMs struggle to adjust plans when faced with unexpected errors, making them less robust compared to humans who learn from trial and error.'), Document(id='d1834ae1-eb6a-43d7-a023-08dfa5028799', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 39086, 'section': 'end'}, page_content='}\\n]\\nChallenges#\\nAfter going through key ideas and demos of building LLM-centered agents, I start to see a couple common limitations:'), Document(id='ca7f06e4-2c2e-4788-9a81-2418d82213d9', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 32942, 'section': 'end'}, page_content='}\\n]\\nThen after these clarification, the agent moved into the code writing mode with a different system message.\\nSystem message:'), Document(id='1fcc2736-30f4-4ef6-90f2-c64af92118cb', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 35127, 'section': 'end'}, page_content='\"content\": \"You will get instructions for code to write.\\\\nYou will write a very long answer. Make sure that every detail of the architecture is, in the end, implemented as code.\\\\nMake sure that every detail of the architecture is, in the end, implemented as code.\\\\n\\\\nThink step by step and reason yourself to the right decisions to make sure we get it right.\\\\nYou will first lay out the names of the core classes, functions, methods that will be necessary, as well as a quick comment on their purpose.\\\\n\\\\nThen you will output the content of each file including ALL code.\\\\nEach file must strictly follow a markdown code block format, where the following tokens must be replaced such that\\\\nFILENAME is the lowercase file name including the file extension,\\\\nLANG is the markup code block language for the code\\'s language, and CODE is the code:\\\\n\\\\nFILENAME\\\\n```LANG\\\\nCODE\\\\n```\\\\n\\\\nYou will start with the \\\\\"entrypoint\\\\\" file, then go to the ones that are imported by that file, and so on.\\\\nPlease')]}}\n", + "\n", + "----------------\n", + "\n", + "{'generate': {'answer': 'The end of the post highlights that task decomposition faces challenges in long-term planning and adapting to unexpected errors. LLMs struggle with adjusting their plans, making them less robust compared to humans who learn from trial and error. This indicates a limitation in effectively exploring the solution space and handling complex tasks.'}}\n", + "\n", + "----------------\n", + "\n" + ] + } + ], + "source": [ + "for step in graph.stream(\n", + " {\"question\": \"What does the end of the post say about Task Decomposition?\"},\n", + " stream_mode=\"updates\",\n", + "):\n", + " print(f\"{step}\\n\\n----------------\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "5875a48a-c849-4da9-99e0-558b04884fb0", + "metadata": { + "id": "5875a48a-c849-4da9-99e0-558b04884fb0" + }, + "source": [ + "In both the streamed steps and the [LangSmith trace](https://smith.langchain.com/public/bdbaae61-130c-4338-8b59-9315dfee22a0/r), we can now observe the structured query that was fed into the retrieval step.\n", + "\n", + "Query Analysis is a rich problem with a wide range of approaches. Refer to the [how-to guides](/docs/how_to/#query-analysis) for more examples." + ] + }, + { + "cell_type": "markdown", + "id": "82e4d779", + "metadata": { + "id": "82e4d779" + }, + "source": [ + "## Next steps\n", + "\n", + "We've covered the steps to build a basic Q&A app over data:\n", + "\n", + "- Loading data with a [Document Loader](/docs/concepts/document_loaders)\n", + "- Chunking the indexed data with a [Text Splitter](/docs/concepts/text_splitters) to make it more easily usable by a model\n", + "- [Embedding the data](/docs/concepts/embedding_models) and storing the data in a [vectorstore](/docs/how_to/vectorstores)\n", + "- [Retrieving](/docs/concepts/retrievers) the previously stored chunks in response to incoming questions\n", + "- Generating an answer using the retrieved chunks as context.\n", + "\n", + "In [Part 2](/docs/tutorials/qa_chat_history) of the tutorial, we will extend the implementation here to accommodate conversation-style interactions and multi-step retrieval processes.\n", + "\n", + "Further reading:\n", + "\n", + "- [Return sources](/docs/how_to/qa_sources): Learn how to return source documents\n", + "- [Streaming](/docs/how_to/streaming): Learn how to stream outputs and intermediate steps\n", + "- [Add chat history](/docs/how_to/message_history): Learn how to add chat history to your app\n", + "- [Retrieval conceptual guide](/docs/concepts/retrieval): A high-level overview of specific retrieval techniques" + ] + } + ], + "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": "3.10.4" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/requirements-llms.txt b/requirements-llms.txt new file mode 100644 index 0000000..00f7dac --- /dev/null +++ b/requirements-llms.txt @@ -0,0 +1,9 @@ +langchain +langchain-community +langchain-text-splitters +langgraph +langchain-openai +langchain-core +bs4 +typing-extensions +notebook From 3929d868367fb5865fd536c27dedb86b92dfc563 Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Mon, 25 Aug 2025 09:03:10 -0400 Subject: [PATCH 13/33] Added assignment-3 starter notebook --- .../assignment-3-zonal-stats.ipynb | 672 ++++++++++++++++++ notebooks/assignments/assignment-3.md | 113 +++ requirements-llms.txt | 1 + src/spatial_utils/__init__.py | 0 src/spatial_utils/zonal_stats.py | 259 +++++++ 5 files changed, 1045 insertions(+) create mode 100644 notebooks/assignments/assignment-3-zonal-stats.ipynb create mode 100644 notebooks/assignments/assignment-3.md create mode 100644 src/spatial_utils/__init__.py create mode 100644 src/spatial_utils/zonal_stats.py diff --git a/notebooks/assignments/assignment-3-zonal-stats.ipynb b/notebooks/assignments/assignment-3-zonal-stats.ipynb new file mode 100644 index 0000000..c069d1f --- /dev/null +++ b/notebooks/assignments/assignment-3-zonal-stats.ipynb @@ -0,0 +1,672 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9789b479", + "metadata": {}, + "source": [ + "# Generating Zonal Statistics with a Custom Script\n", + "IN tjos note\n" + ] + }, + { + "cell_type": "markdown", + "id": "45454706", + "metadata": {}, + "source": [ + "## Setup and Imports" + ] + }, + { + "cell_type": "markdown", + "id": "49a7ce40", + "metadata": {}, + "source": [ + "### Required Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "4e510b1e", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import geopandas as gp\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import rasterio\n", + "from rasterio.mask import mask\n", + "from rasterio.warp import calculate_default_transform, reproject, Resampling\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "5bfdcce3", + "metadata": {}, + "source": [ + "### Workspace Directory Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d5ac7d2c", + "metadata": {}, + "outputs": [], + "source": [ + "DIR_WORKSPACE = Path.cwd().parents[1]\n", + "DIR_DATA = DIR_WORKSPACE / \"data\"\n", + "DIR_SRC = DIR_WORKSPACE / \"src\"" + ] + }, + { + "cell_type": "markdown", + "id": "92ccdb69", + "metadata": {}, + "source": [ + "### Import Custom Python Script" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4377d5f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Add src directory to path to import our custom script\n", + "sys.path.append(str(DIR_SRC))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "860fa277", + "metadata": {}, + "outputs": [], + "source": [ + "# We can now import our custom package\n", + "# There are cleaner ways to handle local imports but this works for now\n", + "from spatial_utils.zonal_stats import ZonalStatistics" + ] + }, + { + "cell_type": "markdown", + "id": "8fe7e43e", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "### Input Data Files \n", + "For the night lights file, please make sure you unzip it first. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "db36f337", + "metadata": {}, + "outputs": [], + "source": [ + "# ==========================================\n", + "# FILE PATHS\n", + "# ===========================================\n", + "\n", + "# Unzipped night lights raster file with \"tif\" extension\n", + "FILE_NTL = DIR_DATA / \"ntl/VNL_npp_2024_global_vcmslcfg_v2_c202502261200.average_masked.dat.tif\"\n", + "\n", + "# Shapefile with cell level (admin region level 4) boundaries \n", + "FILE_SHP_ADM4 = DIR_DATA / \"geospatial/pop-demo-infra/adm4-pop-buildings.shp\"\n", + "\n", + "# Shapefile with national boundaries (admin region level 0)\n", + "FILE_SHP_ADM0 = DIR_DATA / \"geospatial/adm-bounds/geoBoundaries-RWA-ADM0-all/geoBoundaries-RWA-ADM0.shp\"" + ] + }, + { + "cell_type": "markdown", + "id": "e940ac74", + "metadata": {}, + "source": [ + "## Zonal Statistics Analysis" + ] + }, + { + "cell_type": "markdown", + "id": "b9607e4b", + "metadata": {}, + "source": [ + "### Clip Global Raster to Rwanda \n", + "To make the raster file smaller and more manageable, we will clip (cut) it to the boundary of Rwanda. This step extracts only the portion of the raster that falls within Rwanda’s borders, reducing file size and improving processing efficiency." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "0cade007", + "metadata": {}, + "outputs": [], + "source": [ + "def clip_raster_to_boundary(input_raster_path, boundary_gdf, output_path):\n", + " \"\"\"\n", + " Clip a global raster to a country boundary\n", + " \n", + " Parameters:\n", + " -----------\n", + " input_raster_path : str\n", + " Path to input raster file\n", + " boundary_gdf : geopandas.GeoDataFrame\n", + " Boundary for clipping\n", + " output_path : str\n", + " Path for output clipped raster\n", + " \n", + " Returns:\n", + " --------\n", + " str : Path to clipped raster\n", + " \"\"\"\n", + " print(f\"Clipping global raster to Rwanda boundary...\")\n", + " \n", + " with rasterio.open(input_raster_path) as src:\n", + " print(f\"Original raster info:\")\n", + " print(f\" - Shape: {src.width} x {src.height}\")\n", + " print(f\" - CRS: {src.crs}\")\n", + " print(f\" - Bounds: {src.bounds}\")\n", + " print(f\" - Resolution: {src.res}\")\n", + " \n", + " # Reproject boundary to match raster CRS if needed\n", + " if boundary_gdf.crs != src.crs:\n", + " print(f\"Reprojecting boundary from {boundary_gdf.crs} to {src.crs}\")\n", + " boundary_gdf = boundary_gdf.to_crs(src.crs)\n", + " \n", + " # Clip raster\n", + " clipped_data, clipped_transform = mask(\n", + " src, \n", + " boundary_gdf.geometry, \n", + " crop=True,\n", + " nodata=src.nodata\n", + " )\n", + " \n", + " # Update metadata\n", + " clipped_meta = src.meta.copy()\n", + " clipped_meta.update({\n", + " \"driver\": \"GTiff\",\n", + " \"height\": clipped_data.shape[1],\n", + " \"width\": clipped_data.shape[2],\n", + " \"transform\": clipped_transform\n", + " })\n", + " \n", + " # Create output directory if needed\n", + " os.makedirs(os.path.dirname(output_path), exist_ok=True)\n", + " \n", + " # Write clipped raster\n", + " with rasterio.open(output_path, \"w\", **clipped_meta) as dest:\n", + " dest.write(clipped_data)\n", + " \n", + " print(f\"✓ Clipped raster saved to: {output_path}\")\n", + " print(f\"Clipped raster info:\")\n", + " print(f\" - Shape: {clipped_data.shape[2]} x {clipped_data.shape[1]}\")\n", + " print(f\" - Data range: {np.nanmin(clipped_data)} to {np.nanmax(clipped_data)}\")\n", + " \n", + " return output_path" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a375006", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Clipping global raster to Rwanda boundary...\n", + "Original raster info:\n", + " - Shape: 86401 x 33601\n", + " - CRS: EPSG:4326\n", + " - Bounds: BoundingBox(left=-180.00208333335, bottom=-65.00208445335001, right=180.00208621335, top=75.00208333335)\n", + " - Resolution: (0.0041666667, 0.0041666667)\n", + "✓ Clipped raster saved to: /Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/ntl/rwanda_2024_ntl.tif\n", + "Clipped raster info:\n", + " - Shape: 490 x 431\n", + " - Data range: -1.5 to 103.27957153320312\n" + ] + } + ], + "source": [ + "# =============================================\n", + "# CLIP THE GLOBAL RASTER TO RWANDA\n", + "# ===============================================\n", + "\n", + "# Specify output clipped raster file\n", + "clipped_raster_path = DIR_DATA / \"ntl/rwanda_2024_ntl.tif\"\n", + "\n", + "# Load level 0 boundaries in a GeoDataframe\n", + "gdf_adm0 = gp.read_file(FILE_SHP_ADM0)\n", + "\n", + "\n", + "# Call the clip raster function which utilises rasterio package\n", + "rwanda_raster_path = clip_raster_to_boundary(\n", + " FILE_NTL, \n", + " gdf_adm0, \n", + " clipped_raster_path\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "51b329c3", + "metadata": {}, + "source": [ + "### Visualize Clipped Raster" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "0f7bc40b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(838.5426460415241, 0.5, 'Latitude')" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# =========================================\n", + "# LOAD AND VISUALIZE THE CLIPPED RASTER\n", + "# =========================================\n", + "\n", + "# Load cell boundaries\n", + "gdf_adm4 = gp.read_file(FILE_SHP_ADM4)\n", + "\n", + "with rasterio.open(rwanda_raster_path) as src:\n", + " rwanda_nightlight = src.read(1)\n", + " rwanda_extent = [src.bounds.left, src.bounds.right, src.bounds.bottom, src.bounds.top]\n", + "\n", + "# Create visualization\n", + "plt.figure(figsize=(15, 10))\n", + "\n", + "# Plot 1: Clipped raster\n", + "plt.subplot(2, 2, 1)\n", + "plt.imshow(rwanda_nightlight, extent=rwanda_extent, cmap='viridis', alpha=0.8)\n", + "plt.colorbar(label='Nightlight Intensity', shrink=0.7)\n", + "plt.title('Rwanda Nightlight Data (Clipped)')\n", + "plt.xlabel('Longitude')\n", + "plt.ylabel('Latitude')\n", + "\n", + "# Plot 2: Raster with district boundaries\n", + "plt.subplot(2, 2, 2)\n", + "plt.imshow(rwanda_nightlight, extent=rwanda_extent, cmap='viridis', alpha=0.8)\n", + "rwanda_districts_plot = gdf_adm4.to_crs(gdf_adm0.crs) if gdf_adm4.crs != gdf_adm0.crs else gdf_adm4\n", + "rwanda_districts_plot.boundary.plot(ax=plt.gca(), color='white', linewidth=0.5)\n", + "plt.colorbar(label='Nightlight Intensity', shrink=0.7)\n", + "plt.title('Rwanda Nightlight with District Boundaries')\n", + "plt.xlabel('Longitude')\n", + "plt.ylabel('Latitude')" + ] + }, + { + "cell_type": "markdown", + "id": "d7c0dad2", + "metadata": {}, + "source": [ + "### Generate Cell Level Zonal Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "b107a62b", + "metadata": {}, + "outputs": [], + "source": [ + "# ===================================================\n", + "# GENERATING ZONAL STATISTICS WITH A CUSTOM SCRIPT\n", + "# ===================================================\n", + "\n", + "# Create ZonalStatistics object using the clipped Rwanda data with\n", + "# the following args\n", + "# 1. Path of clipped night lights raster/image: rwanda_raster_path\n", + "# 2. Path of administrative boundaries shapefile: FILE_SHP_ADM4\n", + "# 3. Administrative level: 4\n", + "\n", + "admin_level = \"cell_id\"\n", + "\n", + "zs = ZonalStatistics(rwanda_raster_path, FILE_SHP_ADM4, admin_level)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df0c36e8", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the data (this will print information about the datasets)\n", + "zs.load_data()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "e9e28e42", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating statistics: ['mean', 'median', 'count', 'sum']\n", + "This may take a few minutes depending on data size...\n", + "\n", + "Calculating statistics for 2169 features...\n", + "Statistics to calculate: mean, median, count, sum\n", + " Processed 216/2169 features (10.0%)\n", + " Processed 432/2169 features (19.9%)\n", + " Processed 648/2169 features (29.9%)\n", + " Processed 864/2169 features (39.8%)\n", + " Processed 1080/2169 features (49.8%)\n", + " Processed 1296/2169 features (59.8%)\n", + " Processed 1512/2169 features (69.7%)\n", + " Processed 1728/2169 features (79.7%)\n", + " Processed 1944/2169 features (89.6%)\n", + " Processed 2160/2169 features (99.6%)\n", + "✓ Completed zonal statistics calculation\n", + "\n", + "Analysis complete! Results shape: (2169, 7)\n" + ] + }, + { + "data": { + "text/html": [ + "
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admin_nameadmin_levelfeature_idmeanmediancountsum
0RWA.1.1.1.1_1cell_id00.0000000.0660.000000
1RWA.1.1.1.2_1cell_id10.0000000.0640.000000
2RWA.1.1.1.3_1cell_id20.0000000.0550.000000
3RWA.1.1.1.4_1cell_id30.0000000.01200.000000
4RWA.1.1.2.1_1cell_id40.0134410.01441.935569
5RWA.1.1.2.2_1cell_id50.2573060.012131.134054
6RWA.1.1.2.3_1cell_id60.0000000.01100.000000
7RWA.1.1.2.4_1cell_id70.0032490.01440.467896
8RWA.1.1.2.5_1cell_id80.1705770.011018.763454
9RWA.1.1.3.1_1cell_id90.0000000.0660.000000
\n", + "
" + ], + "text/plain": [ + " admin_name admin_level feature_id mean median count sum\n", + "0 RWA.1.1.1.1_1 cell_id 0 0.000000 0.0 66 0.000000\n", + "1 RWA.1.1.1.2_1 cell_id 1 0.000000 0.0 64 0.000000\n", + "2 RWA.1.1.1.3_1 cell_id 2 0.000000 0.0 55 0.000000\n", + "3 RWA.1.1.1.4_1 cell_id 3 0.000000 0.0 120 0.000000\n", + "4 RWA.1.1.2.1_1 cell_id 4 0.013441 0.0 144 1.935569\n", + "5 RWA.1.1.2.2_1 cell_id 5 0.257306 0.0 121 31.134054\n", + "6 RWA.1.1.2.3_1 cell_id 6 0.000000 0.0 110 0.000000\n", + "7 RWA.1.1.2.4_1 cell_id 7 0.003249 0.0 144 0.467896\n", + "8 RWA.1.1.2.5_1 cell_id 8 0.170577 0.0 110 18.763454\n", + "9 RWA.1.1.3.1_1 cell_id 9 0.000000 0.0 66 0.000000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# =======================================\n", + "# DEFINE WHICH STATISTICS TO CALCULATE\n", + "# =======================================\n", + "stats_to_calculate = ['mean', 'median', 'count', \"sum\"]\n", + "\n", + "print(f\"Calculating statistics: {stats_to_calculate}\")\n", + "print(\"This may take a few minutes depending on data size...\")\n", + "\n", + "# Calculate statistics\n", + "df_stats = zs.calculate_statistics(stats_to_calculate)\n", + "\n", + "# Display results\n", + "print(f\"\\nAnalysis complete! Results shape: {df_stats.shape}\")\n", + "display(df_stats.head(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "7d044eb1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['cell_id', 'province_n', 'district_n', 'sector_nam', 'cell_name',\n", + " 'elderly_60', 'general_20', 'children_u', 'youth_15_2', 'men_2020',\n", + " 'women_2020', 'building_c', 'geometry'],\n", + " dtype='object')" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf_adm4.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "f691f255", + "metadata": {}, + "outputs": [], + "source": [ + "# ==================================================\n", + "# SAVE RESULTS\n", + "# ==================================================\n", + "\n", + "# Ensure we grab region names from original shapefile\n", + "cols2keep = ['cell_id', 'province_n', 'district_n', 'sector_nam', 'cell_name']\n", + "df_stats2 = df_stats.merge(gdf_adm4[cols2keep], left_on=\"admin_name\", right_on='cell_id', how='left')\n", + "\n", + "# Save results with the following columns\n", + "cols2keep = ['province_n', 'district_n', 'sector_nam', 'cell_name', 'mean', 'median', 'count',\n", + " 'sum']\n", + "\n", + "df_stats2[cols2keep].to_csv(DIR_DATA / \"ntl/rw-adm4-ntl-stats.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e9deadff", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d8839dc", + "metadata": {}, + "outputs": [], + "source": [ + "## Report on Cells with Largest Night Light Values\n", + "largest_cells = results_df.nlargest(10, 'mean')\n", + "print(\"Cells with the largest night light values:\")\n", + "display(largest_cells)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "af61f027", + "metadata": {}, + "outputs": [], + "source": [ + "gdf = gp.read_file(\"/Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/shapefiles/rwanda.shp\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/assignments/assignment-3.md b/notebooks/assignments/assignment-3.md new file mode 100644 index 0000000..22f502b --- /dev/null +++ b/notebooks/assignments/assignment-3.md @@ -0,0 +1,113 @@ +# Nightlight Analysis Environment Setup + +## Introduction + +The goal of this assignment is to help you practice Python environment setup, specifically for geospatial data analysis projects. You'll learn to create isolated virtual environments, manage package dependencies, and integrate your work with version control using GitHub. This exercise simulates a real-world scenario where you need to set up a reproducible analysis environment for spatial data processing. + +## Scenario +You're tasked with analyzing nightlight data to understand economic activity patterns across districts and cells. This requires setting up a geospatial analysis environment and sharing your work via GitHub. + +## Data +### Night Lights Dataset +We will use the [2024 annual nightlights file](https://drive.google.com/file/d/1bH-IiSHHsUqJXEkVrbD2uT7xholbIFkN/view?usp=share_link). For this exercise, you don’t need to worry about the technical details of nightlights imagery. If you’re interested in learning more, you can check out our [previous assignment](https://github.com/dmatekenya/AIMS-DSCBI/blob/main/notebooks/assignments/assignment-2.ipynb), which explains nightlights in greater depth. +### Administrative Boundaries + +The administrative boundaries for Rwanda are provided as shapefiles, a widely used GIS data format. Each shapefile consists of at least four associated files, so be sure to download all components—not just the file with the .shp extension. The relevant datasets are: +- [Rwanda national boundaries](https://drive.google.com/drive/folders/1cPwbcclnt0UcSkUPeYCjdFUWlhzyPO9N?usp=share_link). This will be used to clip the global night lights raster. +- [Rwanda admin4 (cell) boundaries](https://drive.google.com/drive/folders/1zC_qFY2svEyi8QAIhjL82C2DJLYSTwZw?usp=share_link). We will use the cells as zones for generating summary statistics. + +--- + +## Part 1: GitHub Repository Setup + +### Tasks: +1. Create a new GitHub repository named `nightlight-analysis-[yourname]` +2. Initialize with README.md +3. Create the following folder structure: +```text +nightlight-analysis/ +├── data/ (leave empty - for provided files) +├── src/ (for Python scripts) +├── outputs/ (for results) +├── requirements.txt +└── README.md +``` +### Deliverables for Submission +1. A link to your GitHub repository + +### Other Requirements +1. Make sure the `data/` folder is included in your `.gitignore` file so that no data files are pushed to GitHub. + + + +## Part 2: Virtual Environment Creation +### Required Setup: +Create a virtual environment using **pip/venv** with these specific package versions: +- python>=3.9,<3.12 +- gdal==3.6.2 +- geopandas==0.12.2 +- rasterio==1.3.6 +- pandas==1.5.3 +- numpy==1.24.3 +- matplotlib==3.6.3 +- seaborn==0.12.2 +- jupyter==1.0.0 + +### Steps: +1. Create virtual environment: `python -m venv .venv-ntl` +2. Activate the environment +3. Install all required packages +4. Create requirements.txt file +5. Test your installation + +### Deliverables for Submission +1. **requirements.txt** pushed to your GitHub repo +2. **Environment test**: Screenshot of Python importing all packages successfully: + ```python + import gdal, geopandas, rasterio, pandas, numpy, matplotlib + print("All packages imported successfully!") + print(f"GDAL version: {gdal.__version__}") + print(f"GeoPandas version: {geopandas.__version__}") + + + +## Spatial Analysis + +In this section, you will replicate the workflow demonstrated in the provided notebook to generate zonal statistics for nightlight intensity across administrative regions in Rwanda. Follow the steps below to complete your analysis: + +### Instructions + +1. **Workspace Preparation** + - Ensure your virtual environment is activated and all required packages are installed. + - Set up your workspace directories as shown in the notebook (`data/`, `src/`, etc.). + +2. **Import Required Packages** + - Import all necessary Python libraries for geospatial analysis, data handling, and visualization (see the notebook for reference). + +3. **Prepare Data Paths** + - Define file paths for the nightlight raster and the shapefiles for both national and cell-level boundaries. + - Make sure your data files are unzipped and located in the correct directories. + +4. **Import Custom Scripts** + - Add your `src/` directory to the Python path and import the custom `ZonalStatistics` class as shown in the notebook. + +5. **Clip the Raster** + - Use the provided function to clip the global nightlight raster to the Rwanda national boundary. + - Save the clipped raster for further analysis. + +6. **Visualize the Data** + - Load and visualize the clipped raster along with the cell boundaries to confirm the data alignment. + +7. **Generate Zonal Statistics** + - Use the `ZonalStatistics` class to calculate summary statistics (mean, median, count, sum) for each cell (admin4 region). + - Display and interpret the results. + +8. **Save and Report Results** + - Merge the zonal statistics with cell attribute data and save the results as a CSV file. + - Identify and report the cells with the highest nightlight values. + +### Deliverables + +- A Jupyter notebook (.ipynb) that follows the workflow above, with clear code, comments, and outputs. +- The resulting CSV file with zonal statistics (do not push large data files to GitHub). +- Short written answers or markdown cells explaining your findings and any challenges encountered. diff --git a/requirements-llms.txt b/requirements-llms.txt index 00f7dac..c83a7e8 100644 --- a/requirements-llms.txt +++ b/requirements-llms.txt @@ -7,3 +7,4 @@ langchain-core bs4 typing-extensions notebook +python-dotenv diff --git a/src/spatial_utils/__init__.py b/src/spatial_utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/spatial_utils/zonal_stats.py b/src/spatial_utils/zonal_stats.py new file mode 100644 index 0000000..a3c00d3 --- /dev/null +++ b/src/spatial_utils/zonal_stats.py @@ -0,0 +1,259 @@ +#!/usr/bin/env python3 +""" +Zonal Statistics Script for Raster Data Analysis + +This script calculates zonal statistics for raster data using polygon boundaries. +It supports multiple administrative levels and various statistical measures. + +Usage: + python zonal_statistics.py --raster path/to/raster.tif --shapefile path/to/shapefile.shp --admin_level NAME_1 --stats mean median std +""" + +import argparse +import sys +import os +import warnings +import geopandas as gpd +import rasterio +import pandas as pd +import numpy as np +from rasterio.mask import mask +from rasterio.warp import calculate_default_transform, reproject, Resampling +import matplotlib.pyplot as plt +import seaborn as sns + +# Suppress warnings for cleaner output +warnings.filterwarnings('ignore', category=FutureWarning) +warnings.filterwarnings('ignore', category=UserWarning) + +class ZonalStatistics: + """Class to handle zonal statistics calculations""" + + def __init__(self, raster_path, shapefile_path, admin_level='NAME_1'): + """ + Initialize ZonalStatistics object + + Parameters: + ----------- + raster_path : str + Path to the raster file + shapefile_path : str + Path to the shapefile + admin_level : str + Column name for administrative level (default: 'NAME_1') + """ + self.raster_path = raster_path + self.shapefile_path = shapefile_path + self.admin_level = admin_level + self.raster = None + self.shapefile = None + + def load_data(self): + """Load raster and shapefile data""" + try: + # Load raster + self.raster = rasterio.open(self.raster_path) + print(f"✓ Loaded raster: {self.raster_path}") + print(f" - Shape: {self.raster.width} x {self.raster.height}") + print(f" - CRS: {self.raster.crs}") + print(f" - Bands: {self.raster.count}") + + # Load shapefile + self.shapefile = gpd.read_file(self.shapefile_path) + print(f"✓ Loaded shapefile: {self.shapefile_path}") + print(f" - Features: {len(self.shapefile)}") + print(f" - CRS: {self.shapefile.crs}") + print(f" - Columns: {list(self.shapefile.columns)}") + + # Check if admin_level exists + if self.admin_level not in self.shapefile.columns: + available_cols = [col for col in self.shapefile.columns if col != 'geometry'] + raise ValueError(f"Admin level '{self.admin_level}' not found. Available columns: {available_cols}") + + # Reproject shapefile to match raster CRS if needed + if self.shapefile.crs != self.raster.crs: + print(f"! Reprojecting shapefile from {self.shapefile.crs} to {self.raster.crs}") + self.shapefile = self.shapefile.to_crs(self.raster.crs) + + except Exception as e: + print(f"✗ Error loading data: {e}") + sys.exit(1) + + def calculate_statistics(self, stats_list=['mean', 'median', 'std', 'min', 'max', 'count']): + """ + Calculate zonal statistics for each polygon + + Parameters: + ----------- + stats_list : list + List of statistics to calculate. Options: 'mean', 'median', 'std', 'min', 'max', 'count', 'sum' + + Returns: + -------- + pandas.DataFrame + DataFrame with calculated statistics + """ + results = [] + total_features = len(self.shapefile) + + print(f"\nCalculating statistics for {total_features} features...") + print(f"Statistics to calculate: {', '.join(stats_list)}") + + for idx, feature in self.shapefile.iterrows(): + try: + # Extract raster values for this polygon + masked_data, masked_transform = mask( + self.raster, + [feature.geometry], + crop=True, + nodata=self.raster.nodata + ) + + # Handle different band counts + if self.raster.count == 1: + values = masked_data[0] # Single band + else: + values = masked_data.mean(axis=0) # Multi-band average + + # Remove nodata values + if self.raster.nodata is not None: + valid_values = values[values != self.raster.nodata] + else: + valid_values = values[~np.isnan(values)] + + # Initialize stats dictionary + stats = { + 'admin_name': feature[self.admin_level], + 'admin_level': self.admin_level, + 'feature_id': idx + } + + # Calculate requested statistics + if len(valid_values) > 0: + if 'mean' in stats_list: + stats['mean'] = np.mean(valid_values) + if 'median' in stats_list: + stats['median'] = np.median(valid_values) + if 'std' in stats_list: + stats['std'] = np.std(valid_values) + if 'min' in stats_list: + stats['min'] = np.min(valid_values) + if 'max' in stats_list: + stats['max'] = np.max(valid_values) + if 'count' in stats_list: + stats['count'] = len(valid_values) + if 'sum' in stats_list: + stats['sum'] = np.sum(valid_values) + else: + # No valid pixels + for stat in stats_list: + stats[stat] = np.nan + if 'count' in stats_list: + stats['count'] = 0 + + results.append(stats) + + # Progress indicator + if (idx + 1) % max(1, total_features // 10) == 0: + print(f" Processed {idx + 1}/{total_features} features ({(idx + 1)/total_features*100:.1f}%)") + + except Exception as e: + print(f" Warning: Error processing feature {idx} ({feature[self.admin_level]}): {e}") + # Add empty record + stats = { + 'admin_name': feature[self.admin_level], + 'admin_level': self.admin_level, + 'feature_id': idx + } + for stat in stats_list: + stats[stat] = np.nan + if 'count' in stats_list: + stats['count'] = 0 + results.append(stats) + + df = pd.DataFrame(results) + print(f"✓ Completed zonal statistics calculation") + return df + + def create_summary_plot(self, stats_df, output_path=None): + """ + Create summary visualizations of the statistics + + Parameters: + ----------- + stats_df : pandas.DataFrame + DataFrame with calculated statistics + output_path : str, optional + Path to save the plot + """ + # Filter numeric columns + numeric_cols = stats_df.select_dtypes(include=[np.number]).columns + numeric_cols = [col for col in numeric_cols if col not in ['feature_id', 'count']] + + if len(numeric_cols) == 0: + print("No numeric columns found for plotting") + return + + # Create subplots + n_cols = min(3, len(numeric_cols)) + n_rows = (len(numeric_cols) + n_cols - 1) // n_cols + + fig, axes = plt.subplots(n_rows, n_cols, figsize=(5*n_cols, 4*n_rows)) + if n_rows == 1 and n_cols == 1: + axes = [axes] + elif n_rows == 1: + axes = axes + else: + axes = axes.flatten() + + for i, col in enumerate(numeric_cols): + if i < len(axes): + valid_data = stats_df[col].dropna() + if len(valid_data) > 0: + axes[i].hist(valid_data, bins=min(20, len(valid_data)), alpha=0.7, edgecolor='black') + axes[i].set_title(f'Distribution of {col.title()}') + axes[i].set_xlabel(col.title()) + axes[i].set_ylabel('Frequency') + else: + axes[i].text(0.5, 0.5, 'No valid data', ha='center', va='center', transform=axes[i].transAxes) + axes[i].set_title(f'Distribution of {col.title()}') + + # Hide empty subplots + for i in range(len(numeric_cols), len(axes)): + axes[i].set_visible(False) + + plt.tight_layout() + + if output_path: + plt.savefig(output_path, dpi=300, bbox_inches='tight') + print(f"✓ Plot saved to: {output_path}") + + plt.show() + + def save_results(self, stats_df, output_path): + """ + Save results to CSV file + + Parameters: + ----------- + stats_df : pandas.DataFrame + DataFrame with calculated statistics + output_path : str + Path to save the CSV file + """ + try: + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(output_path), exist_ok=True) + + # Save to CSV + stats_df.to_csv(output_path, index=False) + print(f"✓ Results saved to: {output_path}") + + # Print summary + print(f"\nSummary:") + print(f" - Total features processed: {len(stats_df)}") + print(f" - Features with valid data: {stats_df['count'].gt(0).sum() if 'count' in stats_df.columns else 'N/A'}") + + except Exception as e: + print(f"✗ Error saving results: {e}") + From aa94c898930447d9593ebeee7cf5881a06fd84c3 Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Mon, 25 Aug 2025 09:25:55 -0400 Subject: [PATCH 14/33] Updated assignment-3 --- docs/_toc.yml | 5 + ...chatbot-guide.md => llm-chatbots-guide.md} | 0 .../assignment-3-zonal-stats.ipynb | 287 ++---------------- notebooks/assignments/assignment-3.md | 45 ++- 4 files changed, 41 insertions(+), 296 deletions(-) rename docs/{llm-chatbot-guide.md => llm-chatbots-guide.md} (100%) diff --git a/docs/_toc.yml b/docs/_toc.yml index a244e74..1a63def 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -43,6 +43,11 @@ parts: title: Assignment 1 Solutions - file: notebooks/assignments/assignment-2.ipynb title: Assignment 2 + - file: notebooks/assignments/assignment-3.md + title: Assignment 3 + sections: + - file: notebooks/assignments/assignment-3-zonal-stats.ipynb + title: Geospatial Environment - file: notebooks/module-1/README.md title: Module 1 diff --git a/docs/llm-chatbot-guide.md b/docs/llm-chatbots-guide.md similarity index 100% rename from docs/llm-chatbot-guide.md rename to docs/llm-chatbots-guide.md diff --git a/notebooks/assignments/assignment-3-zonal-stats.ipynb b/notebooks/assignments/assignment-3-zonal-stats.ipynb index c069d1f..83a195f 100644 --- a/notebooks/assignments/assignment-3-zonal-stats.ipynb +++ b/notebooks/assignments/assignment-3-zonal-stats.ipynb @@ -6,7 +6,9 @@ "metadata": {}, "source": [ "# Generating Zonal Statistics with a Custom Script\n", - "IN tjos note\n" + "This notebook demonstrates how to generate zonal statistics using a custom Python script, with a focus on analyzing night light intensity across administrative regions in Rwanda. You will learn how to set up your workspace, import and prepare geospatial data, clip global raster datasets to a country boundary, and visualize the results. The workflow includes using custom utilities for zonal statistics, merging results with administrative boundaries, and saving the outputs for further analysis. This hands-on approach provides practical experience with geospatial data processing, raster analysis, and the integration of Python geospatial libraries such as geopandas and rasterio.\n", + "\n", + "**Zonal statistics** are summary statistics (such as mean, sum, median, minimum, or maximum) calculated for values of a raster dataset within the boundaries of defined zones, such as polygons representing administrative regions or land cover types.\n" ] }, { @@ -27,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "4e510b1e", "metadata": {}, "outputs": [], @@ -57,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "d5ac7d2c", "metadata": {}, "outputs": [], @@ -77,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "4377d5f2", "metadata": {}, "outputs": [], @@ -88,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "860fa277", "metadata": {}, "outputs": [], @@ -113,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "db36f337", "metadata": {}, "outputs": [], @@ -151,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "0cade007", "metadata": {}, "outputs": [], @@ -224,24 +226,7 @@ "execution_count": null, "id": "2a375006", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Clipping global raster to Rwanda boundary...\n", - "Original raster info:\n", - " - Shape: 86401 x 33601\n", - " - CRS: EPSG:4326\n", - " - Bounds: BoundingBox(left=-180.00208333335, bottom=-65.00208445335001, right=180.00208621335, top=75.00208333335)\n", - " - Resolution: (0.0041666667, 0.0041666667)\n", - "✓ Clipped raster saved to: /Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/ntl/rwanda_2024_ntl.tif\n", - "Clipped raster info:\n", - " - Shape: 490 x 431\n", - " - Data range: -1.5 to 103.27957153320312\n" - ] - } - ], + "outputs": [], "source": [ "# =============================================\n", "# CLIP THE GLOBAL RASTER TO RWANDA\n", @@ -272,31 +257,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "0f7bc40b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(838.5426460415241, 0.5, 'Latitude')" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# =========================================\n", "# LOAD AND VISUALIZE THE CLIPPED RASTER\n", @@ -341,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "b107a62b", "metadata": {}, "outputs": [], @@ -374,187 +338,10 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "e9e28e42", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating statistics: ['mean', 'median', 'count', 'sum']\n", - "This may take a few minutes depending on data size...\n", - "\n", - "Calculating statistics for 2169 features...\n", - "Statistics to calculate: mean, median, count, sum\n", - " Processed 216/2169 features (10.0%)\n", - " Processed 432/2169 features (19.9%)\n", - " Processed 648/2169 features (29.9%)\n", - " Processed 864/2169 features (39.8%)\n", - " Processed 1080/2169 features (49.8%)\n", - " Processed 1296/2169 features (59.8%)\n", - " Processed 1512/2169 features (69.7%)\n", - " Processed 1728/2169 features (79.7%)\n", - " Processed 1944/2169 features (89.6%)\n", - " Processed 2160/2169 features (99.6%)\n", - "✓ Completed zonal statistics calculation\n", - "\n", - "Analysis complete! Results shape: (2169, 7)\n" - ] - }, - { - "data": { - "text/html": [ - "
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0RWA.1.1.1.1_1cell_id00.0000000.0660.000000
1RWA.1.1.1.2_1cell_id10.0000000.0640.000000
2RWA.1.1.1.3_1cell_id20.0000000.0550.000000
3RWA.1.1.1.4_1cell_id30.0000000.01200.000000
4RWA.1.1.2.1_1cell_id40.0134410.01441.935569
5RWA.1.1.2.2_1cell_id50.2573060.012131.134054
6RWA.1.1.2.3_1cell_id60.0000000.01100.000000
7RWA.1.1.2.4_1cell_id70.0032490.01440.467896
8RWA.1.1.2.5_1cell_id80.1705770.011018.763454
9RWA.1.1.3.1_1cell_id90.0000000.0660.000000
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" - ], - "text/plain": [ - " admin_name admin_level feature_id mean median count sum\n", - "0 RWA.1.1.1.1_1 cell_id 0 0.000000 0.0 66 0.000000\n", - "1 RWA.1.1.1.2_1 cell_id 1 0.000000 0.0 64 0.000000\n", - "2 RWA.1.1.1.3_1 cell_id 2 0.000000 0.0 55 0.000000\n", - "3 RWA.1.1.1.4_1 cell_id 3 0.000000 0.0 120 0.000000\n", - "4 RWA.1.1.2.1_1 cell_id 4 0.013441 0.0 144 1.935569\n", - "5 RWA.1.1.2.2_1 cell_id 5 0.257306 0.0 121 31.134054\n", - "6 RWA.1.1.2.3_1 cell_id 6 0.000000 0.0 110 0.000000\n", - "7 RWA.1.1.2.4_1 cell_id 7 0.003249 0.0 144 0.467896\n", - "8 RWA.1.1.2.5_1 cell_id 8 0.170577 0.0 110 18.763454\n", - "9 RWA.1.1.3.1_1 cell_id 9 0.000000 0.0 66 0.000000" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# =======================================\n", "# DEFINE WHICH STATISTICS TO CALCULATE\n", @@ -574,31 +361,7 @@ }, { "cell_type": "code", - "execution_count": 47, - "id": "7d044eb1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['cell_id', 'province_n', 'district_n', 'sector_nam', 'cell_name',\n", - " 'elderly_60', 'general_20', 'children_u', 'youth_15_2', 'men_2020',\n", - " 'women_2020', 'building_c', 'geometry'],\n", - " dtype='object')" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf_adm4.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "f691f255", "metadata": {}, "outputs": [], @@ -618,12 +381,6 @@ "df_stats2[cols2keep].to_csv(DIR_DATA / \"ntl/rw-adm4-ntl-stats.csv\", index=False)" ] }, - { - "cell_type": "markdown", - "id": "e9deadff", - "metadata": {}, - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -632,20 +389,10 @@ "outputs": [], "source": [ "## Report on Cells with Largest Night Light Values\n", - "largest_cells = results_df.nlargest(10, 'mean')\n", + "largest_cells = df_stats2.nlargest(10, 'mean')\n", "print(\"Cells with the largest night light values:\")\n", "display(largest_cells)" ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "af61f027", - "metadata": {}, - "outputs": [], - "source": [ - "gdf = gp.read_file(\"/Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/shapefiles/rwanda.shp\")" - ] } ], "metadata": { diff --git a/notebooks/assignments/assignment-3.md b/notebooks/assignments/assignment-3.md index 22f502b..afce3ab 100644 --- a/notebooks/assignments/assignment-3.md +++ b/notebooks/assignments/assignment-3.md @@ -77,37 +77,30 @@ In this section, you will replicate the workflow demonstrated in the provided no ### Instructions -1. **Workspace Preparation** - - Ensure your virtual environment is activated and all required packages are installed. - - Set up your workspace directories as shown in the notebook (`data/`, `src/`, etc.). +1. **Get Updates from Course Repository** + - Follow instructions from GitHub Workflow + - Ensure you have used to pull to get updates from the course repository -2. **Import Required Packages** - - Import all necessary Python libraries for geospatial analysis, data handling, and visualization (see the notebook for reference). +2. **Copy Materials** + - assignment instructions. This is this markdown + - [assignment-3-zonal-statistics notebook](https://github.com/dmatekenya/AIMS-DSCBI/blob/main/notebooks/assignments/assignment-3-zonal-stats.ipynb). Copy this notebook into your project repo. The repo you create in step-1 + - [spatial_utils](https://github.com/dmatekenya/AIMS-DSCBI/tree/main/src/spatial_utils). Copy this module and add it to the src fodler within your repository. -3. **Prepare Data Paths** - - Define file paths for the nightlight raster and the shapefiles for both national and cell-level boundaries. - - Make sure your data files are unzipped and located in the correct directories. +3. **Complete Exercise** + - Update the **assignment-3-zonal-statistics notebook** to ensure you use correct file paths. + - Go through and run the notebook cells. -4. **Import Custom Scripts** - - Add your `src/` directory to the Python path and import the custom `ZonalStatistics` class as shown in the notebook. +### Deliverables -5. **Clip the Raster** - - Use the provided function to clip the global nightlight raster to the Rwanda national boundary. - - Save the clipped raster for further analysis. +- A Jupyter notebook (.ipynb) that follows the workflow above, with clear code, comments, and outputs. +- The resulting CSV file with zonal statistics. -6. **Visualize the Data** - - Load and visualize the clipped raster along with the cell boundaries to confirm the data alignment. +## Submission Instructions -7. **Generate Zonal Statistics** - - Use the `ZonalStatistics` class to calculate summary statistics (mean, median, count, sum) for each cell (admin4 region). - - Display and interpret the results. +Please provide the following for your assignment submission: -8. **Save and Report Results** - - Merge the zonal statistics with cell attribute data and save the results as a CSV file. - - Identify and report the cells with the highest nightlight values. +1. **GitHub Repository:** Share a link to your project repository. +2. **Environment Verification:** Upload a screenshot of your Python environment, ensuring your terminal displays your name. +3. **Zonal Statistics Notebook:** Submit the completed Jupyter notebook. +4. **CSV Output:** Include a GitHub link to the CSV file containing your zonal statistics results. Although the `data/` folder is typically excluded from version control, make an exception to include this CSV file. -### Deliverables - -- A Jupyter notebook (.ipynb) that follows the workflow above, with clear code, comments, and outputs. -- The resulting CSV file with zonal statistics (do not push large data files to GitHub). -- Short written answers or markdown cells explaining your findings and any challenges encountered. From 7ffce571cab6f47b48912a43d000ff7b9daa50d5 Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Mon, 25 Aug 2025 09:30:52 -0400 Subject: [PATCH 15/33] Updated README --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 60373ee..42bb659 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,8 @@ -# Da**[View the full documentation website here](https://dmatekenya.github.io/AIMS-DSCBI/)**Science Capacity Building Initiative (DSCBI) +# Data Science Capacity Building Initiative (DSCBI) This repository provides information about the capacity building initiative, a collaboration between the African Institute for Mathematical Sciences (AIMS), the National Institute of Statistics of Rwanda (NISR), and [Cenfri](https://cenfri.org). The initiative aims to deliver data science training to staff from over 20 institutions that are part of Rwanda's National Statistical System (NSS). -**[View the full documentation website here](https://dmatekenya.github.io/AIMS-DSCBI/)** - This repository provides information about the capacity building initiative, a collaboration between the African Institute for Mathematical Sciences (AIMS), the National Institute of Statistics of Rwanda (NISR), and [Cenfri](https://cenfri.org). The initiative aims to deliver data science training to staff from over 20 institutions that are part of Rwanda’s National Statistical System (NSS). ## Course Modules From 331406ad801877dcd41058475f88aecd50a19bd3 Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Fri, 12 Sep 2025 15:01:05 -0400 Subject: [PATCH 16/33] Updated Notebooks --- .../Agenda for August 18.docx | Bin docs/project-docs/nyagatare-report.docx | Bin 0 -> 208478 bytes docs/~$agatare-report.docx | Bin 0 -> 162 bytes .../assignments/assignment-2-solutions.ipynb | 2916 +++++++++++++++++ .../assignment-3-zonal-stats.ipynb | 141 + .../demographic_nightlights_notebook.md | 395 +++ notebooks/module-5/download-ntl.ipynb | 0 notebooks/tutorials/audio_files/bushiri.wav | Bin 0 -> 2880096 bytes notebooks/tutorials/audio_files/mcp.wav | Bin 0 -> 377542 bytes notebooks/tutorials/langchain/rag-1.ipynb | 96 +- .../tutorials/langchain/requirements.txt | 0 notebooks/tutorials/usage_example.ipynb | 397 +++ 12 files changed, 3911 insertions(+), 34 deletions(-) rename docs/{ => project-docs}/Agenda for August 18.docx (100%) create mode 100644 docs/project-docs/nyagatare-report.docx create mode 100644 docs/~$agatare-report.docx create mode 100644 notebooks/assignments/assignment-2-solutions.ipynb create mode 100644 notebooks/assignments/demographic_nightlights_notebook.md create mode 100644 notebooks/module-5/download-ntl.ipynb create mode 100644 notebooks/tutorials/audio_files/bushiri.wav create mode 100644 notebooks/tutorials/audio_files/mcp.wav create mode 100644 notebooks/tutorials/langchain/requirements.txt create mode 100644 notebooks/tutorials/usage_example.ipynb diff --git a/docs/Agenda for August 18.docx b/docs/project-docs/Agenda for August 18.docx similarity index 100% rename from docs/Agenda for August 18.docx rename to docs/project-docs/Agenda for August 18.docx diff --git a/docs/project-docs/nyagatare-report.docx b/docs/project-docs/nyagatare-report.docx new file mode 100644 index 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indicator_nameaggregated_valueindicator_yearsurvey_namesurvey_year_labelaggregation_methodsource_sheet
0Accepting attitudes towards those living with ...48.6600002005RW2005DHS2005meandhs-mobile_subnational_rwa
1Accepting attitudes towards those living with ...64.2600002010RW2010DHS2010meandhs-mobile_subnational_rwa
2Accepting attitudes towards those living with ...62.8200002015RW2015DHS2014-15meandhs-mobile_subnational_rwa
3Accepting attitudes towards those living with ...47.5200002005RW2005DHS2005meandhs-mobile_subnational_rwa
4Accepting attitudes towards those living with ...52.8400002010RW2010DHS2010meandhs-mobile_subnational_rwa
5Accepting attitudes towards those living with ...49.2600002015RW2015DHS2014-15meandhs-mobile_subnational_rwa
6Antenatal care from a skilled provider93.6880951992RW1992DHS1992meandhs-mobile_subnational_rwa
7Antenatal care from a skilled provider92.3980392000RW2000DHS2000meandhs-mobile_subnational_rwa
8Antenatal care from a skilled provider94.3133332005RW2005DHS2005meandhs-mobile_subnational_rwa
9Antenatal care from a skilled provider95.9133332008RW2008DHS2007-08meandhs-mobile_subnational_rwa
\n", + "" + ], + "text/plain": [ + " indicator_name aggregated_value \\\n", + "0 Accepting attitudes towards those living with ... 48.660000 \n", + "1 Accepting attitudes towards those living with ... 64.260000 \n", + "2 Accepting attitudes towards those living with ... 62.820000 \n", + "3 Accepting attitudes towards those living with ... 47.520000 \n", + "4 Accepting attitudes towards those living with ... 52.840000 \n", + "5 Accepting attitudes towards those living with ... 49.260000 \n", + "6 Antenatal care from a skilled provider 93.688095 \n", + "7 Antenatal care from a skilled provider 92.398039 \n", + "8 Antenatal care from a skilled provider 94.313333 \n", + "9 Antenatal care from a skilled provider 95.913333 \n", + "\n", + " indicator_year survey_name survey_year_label aggregation_method \\\n", + "0 2005 RW2005DHS 2005 mean \n", + "1 2010 RW2010DHS 2010 mean \n", + "2 2015 RW2015DHS 2014-15 mean \n", + "3 2005 RW2005DHS 2005 mean \n", + "4 2010 RW2010DHS 2010 mean \n", + "5 2015 RW2015DHS 2014-15 mean \n", + "6 1992 RW1992DHS 1992 mean \n", + "7 2000 RW2000DHS 2000 mean \n", + "8 2005 RW2005DHS 2005 mean \n", + "9 2008 RW2008DHS 2007-08 mean \n", + "\n", + " source_sheet \n", + "0 dhs-mobile_subnational_rwa \n", + "1 dhs-mobile_subnational_rwa \n", + "2 dhs-mobile_subnational_rwa \n", + "3 dhs-mobile_subnational_rwa \n", + "4 dhs-mobile_subnational_rwa \n", + "5 dhs-mobile_subnational_rwa \n", + "6 dhs-mobile_subnational_rwa \n", + "7 dhs-mobile_subnational_rwa \n", + "8 dhs-mobile_subnational_rwa \n", + "9 dhs-mobile_subnational_rwa " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved national indicators to /Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/rwanda/national_indicators.csv\n" + ] + } + ], + "source": [ + "# ==========================================================\n", + "# CALL THE FUNCTION\n", + "# ==========================================================\n", + "# Extract the national indicators\n", + "df_rw = extract_national_indicators(FILE_EXCEL)\n", + "\n", + "# Display the first few rows\n", + "print(f\"Total national indicators created: {len(df_rw)}\")\n", + "display(df_rw.head(10))\n", + "\n", + "# Save to CSV\n", + "output_path = DIR_DATA / \"rwanda/national_indicators.csv\"\n", + "df_rw.to_csv(output_path, index=False)\n", + "print(f\"Saved national indicators to {output_path}\")" + ] + }, + { + "cell_type": "markdown", + "id": "14a63a14", + "metadata": {}, + "source": [ + "## Task-2: Subnational Indicators\n", + "\n", + "To create a comprehensive subnational indicator dataset, we first need to understand which indicators are available at the district (ADM2) level. Rwanda has 30 districts, so we'll focus on sheets that contain data with approximately this number of unique locations.\n", + "\n", + "Our approach involves:\n", + "\n", + "1. Examining the data structure to identify which indicators are available at district level\n", + "2. Extracting these indicators while preserving their spatial and temporal context\n", + "3. Standardizing the district names to ensure consistent spatial referencing\n", + "4. Creating a unified dataset that allows for both single-indicator and cross-indicator analysis\n", + "\n", + "This process is more complex than the national-level aggregation because we need to maintain the spatial dimension and ensure proper alignment across indicators collected in different years or from different surveys. The function below implements this extraction logic, filtering for sheets that contain district-level data.\n", + "\n", + "For manual inspection of variables, I used something like this:\n", + "```sheet_names = pd.ExcelFile(FILE_EXCEL).sheet_names\n", + "df = pd.read_excel(FILE_EXCEL, sheet_name=sheet_names[0])\n", + "\n", + "# Pick indicator to work with \n", + "df_indicator = df.query('Indicator == \"Women who want no more children\"')\n", + "yrs = df_indicator.SurveyYear.unique()\n", + "\n", + "# Unique Locations for each survey year\n", + "locations = {yr: len(df_indicator.query('SurveyYear == @yr').Location.unique()) for yr in yrs} \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d08b7742", + "metadata": {}, + "outputs": [], + "source": [ + "def extract_adm2_indicators(excel_path):\n", + " \"\"\"\n", + " Extract indicators available at ADM2 (district) level from an Excel file with multiple sheets.\n", + " This function checks for district-level granularity at the indicator and year level,\n", + " rather than just at the sheet level.\n", + "\n", + " Parameters:\n", + " -----------\n", + " excel_path : Path or str\n", + " Path to the Excel file containing health indicator data\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " DataFrame containing ADM2-level indicators\n", + " \"\"\"\n", + " # Get all sheet names\n", + " sheet_names = pd.ExcelFile(excel_path).sheet_names\n", + "\n", + " # Initialize a list to store ADM2 indicators\n", + " adm2_indicators = []\n", + "\n", + " # Loop through each sheet\n", + " for sheet in sheet_names:\n", + " try:\n", + " # Read the sheet\n", + " df = pd.read_excel(excel_path, sheet_name=sheet)\n", + "\n", + " # Drop the first row (which typically contains metadata) and reset index\n", + " df = df.iloc[1:].reset_index(drop=True)\n", + "\n", + " # Keep only the specified columns if they exist\n", + " if all(col in df.columns for col in COLS_TO_KEEP):\n", + " df = df[COLS_TO_KEEP]\n", + " else:\n", + " print(f\"Skipping sheet {sheet} due to missing expected columns\")\n", + " continue\n", + "\n", + " # Convert Value column to numeric\n", + " df['Value'] = pd.to_numeric(df['Value'], errors='coerce')\n", + "\n", + " # Process by indicator and year combination to check for district-level data\n", + " for (indicator, year), group in df.groupby(['Indicator', 'SurveyYear']):\n", + " unique_locs = group['Location'].dropna().unique()\n", + " \n", + " # Check if this indicator-year combination has district-level data\n", + " # Rwanda has 30 districts, but we'll accept 6-35 to account for potential changes\n", + " # or different naming conventions\n", + " if 6 <= len(unique_locs) <= 35:\n", + " # Group by Location to get district-level means\n", + " for _, row in group.groupby('Location').agg({\n", + " 'Value': 'mean',\n", + " 'SurveyId': 'first',\n", + " 'SurveyYearLabel': 'first'\n", + " }).reset_index().iterrows():\n", + " adm2_indicators.append({\n", + " 'indicator_name': indicator,\n", + " 'district': row['Location'],\n", + " 'aggregated_value': row['Value'],\n", + " 'indicator_year': year,\n", + " 'survey_name': row['SurveyId'],\n", + " 'survey_year_label': row['SurveyYearLabel'],\n", + " 'aggregation_method': 'mean',\n", + " 'source_sheet': sheet\n", + " })\n", + " else:\n", + " print(f\"Skipping indicator '{indicator}' for year {year} - not at district level (found {len(unique_locs)} unique locations)\")\n", + " \n", + " except Exception as e:\n", + " print(f\"Error processing sheet {sheet}: {e}\")\n", + "\n", + " # Create a DataFrame from the list\n", + " adm2_df = pd.DataFrame(adm2_indicators)\n", + " \n", + " # Add district count per indicator-year combination for validation\n", + " if not adm2_df.empty:\n", + " district_counts = adm2_df.groupby(['indicator_name', 'indicator_year']).size().reset_index(name='district_count')\n", + " print(f\"Created dataset with {len(adm2_df)} rows across {len(district_counts)} indicator-year combinations\")\n", + " else:\n", + " print(\"No district-level indicators found\")\n", + "\n", + " return adm2_df" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "01e15268", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Skipping indicator 'Accepting attitudes towards those living with HIV - Composite of 4 components [Men]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Accepting attitudes towards those living with HIV - Composite of 4 components [Men]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Accepting attitudes towards those living with HIV - Composite of 4 components [Men]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Accepting attitudes towards those living with HIV - Composite of 4 components [Women]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Accepting attitudes towards those living with HIV - Composite of 4 components [Women]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Accepting attitudes towards those living with HIV - Composite of 4 components [Women]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal visits for pregnancy: 4+ visits' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal visits for pregnancy: 4+ visits' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal visits for pregnancy: 4+ visits' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal visits for pregnancy: 4+ visits' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal visits for pregnancy: 4+ visits' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Breastfed children 6-23 months fed both 4+ food groups and the minimum meal frequency' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Breastfed children 6-23 months fed both 4+ food groups and the minimum meal frequency' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Breastfed children 6-23 months fed both 4+ food groups and the minimum meal frequency' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children 6-23 months that consumed foods rich in iron in the last 24 hours' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children 6-23 months that consumed foods rich in iron in the last 24 hours' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children 6-23 months that consumed foods rich in iron in the last 24 hours' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children 6-23 months that consumed foods rich in vitamin A in the last 24 hours' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children 6-23 months that consumed foods rich in vitamin A in the last 24 hours' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children 6-23 months that consumed foods rich in vitamin A in the last 24 hours' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children 6-23 months with 3 IYCF practices' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children 6-23 months with 3 IYCF practices' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children 6-23 months with 3 IYCF practices' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children consuming vitamin A supplements' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children consuming vitamin A supplements' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children consuming vitamin A supplements' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children consuming vitamin A supplements' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children registered' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children registered' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children registered' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children registered' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children stunted' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children stunted' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children stunted' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children stunted' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children underweight' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children underweight' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children underweight' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children underweight' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children wasted' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children wasted' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children wasted' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children wasted' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children who took any ACT' for year 2010 - not at district level (found 3 unique locations)\n", + "Skipping indicator 'Children who took any ACT' for year 2013 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Children who took any ACT' for year 2015 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Children who took any ACT' for year 2017 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Children who took any ACT' for year 2019 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2019 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Children with any anemia' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with any anemia' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with any anemia' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with any anemia' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with any anemia' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with diarrhea' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with diarrhea' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with diarrhea' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with diarrhea' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with diarrhea' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever in the last two weeks' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever in the last two weeks' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever in the last two weeks' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever in the last two weeks' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever in the last two weeks' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever in the last two weeks' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever in the last two weeks' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever who had blood taken from a finger or heel for testing' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever who had blood taken from a finger or heel for testing' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever who had blood taken from a finger or heel for testing' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever who had blood taken from a finger or heel for testing' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever who had blood taken from a finger or heel for testing' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with hemoglobin lower than 8.0 g/dl' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with hemoglobin lower than 8.0 g/dl' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with hemoglobin lower than 8.0 g/dl' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with hemoglobin lower than 8.0 g/dl' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with hemoglobin lower than 8.0 g/dl' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Comprehensive correct knowledge about AIDS [Men]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Comprehensive correct knowledge about AIDS [Men]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Comprehensive correct knowledge about AIDS [Men]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Comprehensive correct knowledge about AIDS [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Comprehensive correct knowledge about AIDS [Women]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Comprehensive correct knowledge about AIDS [Women]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Comprehensive correct knowledge about AIDS [Women]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Comprehensive correct knowledge about AIDS [Women]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Condom use during higher-risk sex (with multiple partners) [Men]' for year 2005 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Condom use during higher-risk sex (with multiple partners) [Men]' for year 2010 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'Condom use during higher-risk sex (with multiple partners) [Men]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Condom use during higher-risk sex (with multiple partners) [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Condom use during higher-risk sex (with multiple partners) [Women]' for year 2015 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Condom use during higher-risk sex (with multiple partners) [Women]' for year 2019 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Delivery by cesarean section' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Delivery by cesarean section' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Delivery by cesarean section' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Delivery by cesarean section' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Ever experienced physical violence since age 15' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Ever experienced physical violence since age 15' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Ever experienced physical violence since age 15' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Ever experienced physical violence since age 15' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: Continued feeding, and ORT and/or increased fluids' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: Continued feeding, and ORT and/or increased fluids' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: Continued feeding, and ORT and/or increased fluids' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: Continued feeding, and ORT and/or increased fluids' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: Continued feeding, and ORT and/or increased fluids' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: ORT and continued feeding' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: ORT and continued feeding' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: ORT and continued feeding' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: ORT and continued feeding' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Feeding practices during diarrhea: ORT and continued feeding' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Gender parity index for net primary school attendance' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Gender parity index for net primary school attendance' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Gender parity index for net primary school attendance' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Gender parity index for net secondary school attendance' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Gender parity index for net secondary school attendance' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Gender parity index for net secondary school attendance' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among general population' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among general population' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among general population' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among men' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among men' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among men' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among women' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among women' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among women' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN)' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN)' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN) for every two persons who stayed in the household the previous night' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN) for every two persons who stayed in the household the previous night' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN) for every two persons who stayed in the household the previous night' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN) for every two persons who stayed in the household the previous night' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN) for every two persons who stayed in the household the previous night' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN) for every two persons who stayed in the household the previous night' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with at least one insecticide-treated mosquito net (ITN) for every two persons who stayed in the household the previous night' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with iodized salt' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with iodized salt' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with iodized salt' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of prevention of mother to child transmission of HIV [Men]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of prevention of mother to child transmission of HIV [Men]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of prevention of mother to child transmission of HIV [Men]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of prevention of mother to child transmission of HIV [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of prevention of mother to child transmission of HIV [Women]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of prevention of mother to child transmission of HIV [Women]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of prevention of mother to child transmission of HIV [Women]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of prevention of mother to child transmission of HIV [Women]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Malaria prevalence according to RDT' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Malaria prevalence according to RDT' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Malaria prevalence according to RDT' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Malaria prevalence according to RDT' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Malaria prevalence according to microscopy' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Malaria prevalence according to microscopy' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Malaria prevalence according to microscopy' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Malaria prevalence according to microscopy' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean Body Mass Index (BMI) for women' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean Body Mass Index (BMI) for women' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean Body Mass Index (BMI) for women' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean Body Mass Index (BMI) for women' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean ideal number of children for all women' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean ideal number of children for all women' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean ideal number of children for all women' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean ideal number of children for all women' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean ideal number of children for all women' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first birth for women age 25-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first birth for women age 25-49' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first birth for women age 25-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first birth for women age 25-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first birth for women age 25-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-49(54,59)' for year 2005 - not at district level (found 3 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-49(54,59)' for year 2010 - not at district level (found 3 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-49(54,59)' for year 2015 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-49(54,59)' for year 2019 - not at district level (found 1 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Women]: 25-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Women]: 25-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Women]: 25-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Women]: 25-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-49(54,59)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-49(54,59)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-49(54,59)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-49(54,59)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Women]: 25-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Women]: 25-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Women]: 25-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Women]: 25-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of any breastfeeding' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of any breastfeeding' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of any breastfeeding' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of any breastfeeding' for year 2015 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'Median duration of any breastfeeding' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of exclusive breastfeeding' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of exclusive breastfeeding' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of exclusive breastfeeding' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of exclusive breastfeeding' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of predominant breastfeeding' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of predominant breastfeeding' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of predominant breastfeeding' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of predominant breastfeeding' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men receiving an HIV test and receiving test results in the last 12 months' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men receiving an HIV test and receiving test results in the last 12 months' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men receiving an HIV test and receiving test results in the last 12 months' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men receiving an HIV test and receiving test results in the last 12 months' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men reporting an STI, genital dicharge, or a sore or ulcer' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men reporting an STI, genital dicharge, or a sore or ulcer' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men reporting an STI, genital dicharge, or a sore or ulcer' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men reporting an STI, genital dicharge, or a sore or ulcer' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who are literate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who are literate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who are literate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who are literate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who smoke cigarettes' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who smoke cigarettes' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who smoke cigarettes' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who smoke cigarettes' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with no education' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with no education' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with no education' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with no education' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with no education' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with secondary or higher education' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with secondary or higher education' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with secondary or higher education' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with secondary or higher education' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men with secondary or higher education' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mother's first postnatal checkup in the first two days after birth' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mother's first postnatal checkup in the first two days after birth' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mother's first postnatal checkup in the first two days after birth' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Newborn's first postnatal checkup in the first two days after birth' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Newborn's first postnatal checkup in the first two days after birth' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Newborn's first postnatal checkup in the first two days after birth' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Non-breastfed children 6-23 months with 3 IYCF practices' for year 2010 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'Non-breastfed children 6-23 months with 3 IYCF practices' for year 2015 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'Non-breastfed children 6-23 months with 3 IYCF practices' for year 2019 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'Persons with access to an insecticide-treated mosquito net (ITN)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Persons with access to an insecticide-treated mosquito net (ITN)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Persons with access to an insecticide-treated mosquito net (ITN)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Persons with access to an insecticide-treated mosquito net (ITN)' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Persons with access to an insecticide-treated mosquito net (ITN)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Persons with access to an insecticide-treated mosquito net (ITN)' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Persons with access to an insecticide-treated mosquito net (ITN)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner in last 12 months' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner in last 12 months' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner in last 12 months' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner in last 12 months' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population who slept under an insecticide-treated mosquito net (ITN) last night' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population who slept under an insecticide-treated mosquito net (ITN) last night' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population who slept under an insecticide-treated mosquito net (ITN) last night' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population who slept under an insecticide-treated mosquito net (ITN) last night' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population who slept under an insecticide-treated mosquito net (ITN) last night' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population who slept under an insecticide-treated mosquito net (ITN) last night' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population who slept under an insecticide-treated mosquito net (ITN) last night' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Pregnant women who slept under an insecticide-treated net (ITN)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Pregnant women who slept under an insecticide-treated net (ITN)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Pregnant women who slept under an insecticide-treated net (ITN)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Pregnant women who slept under an insecticide-treated net (ITN)' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Pregnant women who slept under an insecticide-treated net (ITN)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Pregnant women who slept under an insecticide-treated net (ITN)' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Pregnant women who slept under an insecticide-treated net (ITN)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Prevalence of orphanhood: children under 18 who are orphans - mother, father or both dead' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Prevalence of orphanhood: children under 18 who are orphans - mother, father or both dead' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Prevalence of orphanhood: children under 18 who are orphans - mother, father or both dead' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Prevalence of orphanhood: children under 18 who are orphans - mother, father or both dead' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'SP/Fansidar 3+ doses during pregnancy' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'SP/Fansidar 3+ doses during pregnancy' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Tetanus protection at birth' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Tetanus protection at birth' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Tetanus protection at birth' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Tetanus protection at birth' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Wife beating justified for at least one specific reason [Men]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Wife beating justified for at least one specific reason [Men]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Wife beating justified for at least one specific reason [Men]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Wife beating justified for at least one specific reason [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Wife beating justified for at least one specific reason [Women]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Wife beating justified for at least one specific reason [Women]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Wife beating justified for at least one specific reason [Women]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Wife beating justified for at least one specific reason [Women]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women receiving an HIV test and receiving test results in the last 12 months' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women receiving an HIV test and receiving test results in the last 12 months' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women receiving an HIV test and receiving test results in the last 12 months' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women receiving an HIV test and receiving test results in the last 12 months' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women reporting an STI, genital dicharge, or a sore or ulcer' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women reporting an STI, genital dicharge, or a sore or ulcer' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women reporting an STI, genital dicharge, or a sore or ulcer' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women reporting an STI, genital dicharge, or a sore or ulcer' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are overweight or obese according to BMI (>=25.0)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are overweight or obese according to BMI (>=25.0)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are overweight or obese according to BMI (>=25.0)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are overweight or obese according to BMI (>=25.0)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are thin according to BMI (<18.5)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are thin according to BMI (<18.5)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are thin according to BMI (<18.5)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are thin according to BMI (<18.5)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who decide themselves how their earnings are used' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who decide themselves how their earnings are used' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who decide themselves how their earnings are used' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who decide themselves how their earnings are used' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who ever experienced sexual violence' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who ever experienced sexual violence' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who ever experienced sexual violence' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who ever experienced sexual violence' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who smoke cigarettes' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who smoke cigarettes' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who smoke cigarettes' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who smoke cigarettes' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who want no more children' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who want no more children' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who want no more children' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who want no more children' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who want no more children' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with any anemia' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with any anemia' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with any anemia' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with any anemia' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with any anemia' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with no education' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with no education' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with no education' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with no education' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with no education' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with no education' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with no education' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care from a skilled provider' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Auxiliary nurse/midwife' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Auxiliary nurse/midwife' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Auxiliary nurse/midwife' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Auxiliary nurse/midwife' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Auxiliary nurse/midwife' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Doctor' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Doctor' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Doctor' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Doctor' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Doctor' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Doctor' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Doctor' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Missing ' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Missing ' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Missing ' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Missing ' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Missing ' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Nurse/midwife' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Nurse/midwife' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Nurse/midwife' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Nurse/midwife' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Nurse/midwife' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Nurse/midwife' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Nurse/midwife' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Other' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Other' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Other' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Other' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Other health worker' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Traditional birth attendant' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Traditional birth attendant' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Traditional birth attendant' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care provider: Traditional birth attendant' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care: Total' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care: Total' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care: Total' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care: Total' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care: Total' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care: Total' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Antenatal care: Total' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery from a skilled provider' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Auxiliary nurse/midwife' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Auxiliary nurse/midwife' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Auxiliary nurse/midwife' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Auxiliary nurse/midwife' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Doctor' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Doctor' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Doctor' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Doctor' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Doctor' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: No one' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: No one' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: No one' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: No one' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: No one' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Nurse/midwife' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Nurse/midwife' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Nurse/midwife' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Nurse/midwife' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Nurse/midwife' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Other health worker' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Other health worker' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Relative or other' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Relative or other' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Relative or other' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Relative or other' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Relative or other' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Total' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Total' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Total' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Total' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Total' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Traditional birth attendant' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Traditional birth attendant' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Traditional birth attendant' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Traditional birth attendant' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: Traditional birth attendant' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: don't know or missing' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: don't know or missing' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: don't know or missing' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Assistance during delivery: don't know or missing' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antibiotic drugs for fever' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antibiotic drugs for fever' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antibiotic drugs for fever' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antibiotic drugs for fever' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antibiotic drugs for fever' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antibiotic drugs for fever' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antimalarial drugs for fever' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antimalarial drugs for fever' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antimalarial drugs for fever' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antimalarial drugs for fever' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antimalarial drugs for fever' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antimalarial drugs for fever' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child took antimalarial drugs for fever' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2019 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with fever for whom advice or treatment was sought' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI who received antibiotics' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI who received antibiotics' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI who received antibiotics' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI who received antibiotics' for year 2019 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Delivery by cesarean section' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Delivery by cesarean section' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Delivery by cesarean section' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Delivery by cesarean section' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No antenatal care' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No antenatal care' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No antenatal care' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No antenatal care' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No antenatal care' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No antenatal care' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No antenatal care' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No postnatal checkup for mother within first two days of birth' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No postnatal checkup for mother within first two days of birth' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No postnatal checkup for mother within first two days of birth' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No postnatal checkup for newborn within first two days of birth' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No postnatal checkup for newborn within first two days of birth' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No postnatal checkup for newborn within first two days of birth' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks (unweighted)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks (unweighted)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks (unweighted)' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks (unweighted)' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with fever in the last two weeks (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2019 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years (unweighted)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years (unweighted)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of live births (or stillbirths) in the last two (or three/five) years (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of mothers' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of mothers' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of mothers' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of mothers (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of mothers (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of mothers (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of newborns' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of newborns' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of newborns' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of newborns (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of newborns (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of newborns (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years (unweighted)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years (unweighted)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years (unweighted)' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years (unweighted)' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women with a live birth (or stillbirth) in the last two (or three/five) years (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: At home' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: At home' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: At home' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: At home' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: At home' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Other' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Other' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Other' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Other' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Other' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Private sector' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Private sector' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Private sector' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Private sector' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Private sector' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Public sector' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Public sector' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Public sector' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Public sector' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Public sector' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Total' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Total' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Total' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Total' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Total' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: don't know or missing' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: don't know or missing' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: don't know or missing' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: don't know or missing' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Auxiliary nurse/midwife' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Auxiliary nurse/midwife' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Auxiliary nurse/midwife' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Community health worker' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Community health worker' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Doctor/nurse/midwife' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Doctor/nurse/midwife' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Doctor/nurse/midwife' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Other health worker' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Total' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Total' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of mothers' first postnatal checkup: Total' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Auxiliary nurse/midwife' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Auxiliary nurse/midwife' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Auxiliary nurse/midwife' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Community health worker' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Community health worker' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Doctor/nurse/midwife' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Doctor/nurse/midwife' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Doctor/nurse/midwife' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Other health worker' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Total' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Total' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Total' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Traditional birth attendant' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Provider of newborns' first postnatal checkup: Traditional birth attendant' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Advice or treatment was sought' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Advice or treatment was sought' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Advice or treatment was sought' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Advice or treatment was sought' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Advice or treatment was sought' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Antibiotics' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Antibiotics' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Antibiotics' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Antibiotics' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Antimotility drugs' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Antimotility drugs' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Antimotility drugs' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Antimotility drugs' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Home remedy - other' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Home remedy - other' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Home remedy - other' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Home remedy - other' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Home remedy - other' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Increased fluids' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Increased fluids' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Increased fluids' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Increased fluids' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Increased fluids' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Injection' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Injection' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Injection' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Injection' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Intravenous solution' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Intravenous solution' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Intravenous solution' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Intravenous solution' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No ORS, RHF or increased fluids' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No ORS, RHF or increased fluids' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No ORS, RHF or increased fluids' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No ORS, RHF or increased fluids' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No ORS, RHF or increased fluids' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No treatment' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No treatment' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No treatment' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No treatment' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: No treatment' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: ORT or increased fluids' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: ORT or increased fluids' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: ORT or increased fluids' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: ORT or increased fluids' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: ORT or increased fluids' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Oral rehydration solution (ORS)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Oral rehydration solution (ORS)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Oral rehydration solution (ORS)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Oral rehydration solution (ORS)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Oral rehydration solution (ORS)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Recommended home fluids (RHF) at home' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Recommended home fluids (RHF) at home' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Recommended home fluids (RHF) at home' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Recommended home fluids (RHF) at home' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Recommended home fluids (RHF) at home' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Zinc supplements' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Zinc supplements' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: don't know or missing' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: don't know or missing' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: don't know or missing' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: don't know or missing' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Child mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Early neonatal deaths' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Early neonatal deaths' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Early neonatal deaths' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Early neonatal deaths' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Neonatal mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of pregnancies of 7+ months duration' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of pregnancies of 7+ months duration' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of pregnancies of 7+ months duration' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of pregnancies of 7+ months duration' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of pregnancies of 7+ months duration (unweighted)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of pregnancies of 7+ months duration (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of pregnancies of 7+ months duration (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of pregnancies of 7+ months duration (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Perinatal mortality rate (5 years)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Perinatal mortality rate (5 years)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Perinatal mortality rate (5 years)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Perinatal mortality rate (5 years)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Postneonatal mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Stillbirths' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Stillbirths' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Stillbirths' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Stillbirths' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with one room for sleeping' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with one room for sleeping' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with one room for sleeping' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with one room for sleeping' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with one room for sleeping' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of household members' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of household members' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of household members' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of household members' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of household members' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of household members' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of household members' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of persons per sleeping room' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of persons per sleeping room' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of persons per sleeping room' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of persons per sleeping room' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mean number of persons per sleeping room' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using a public tap/standpipe' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using a public tap/standpipe' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using a public tap/standpipe' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using a public tap/standpipe' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using a public tap/standpipe' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using a public tap/standpipe' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using a public tap/standpipe' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using an improved water source' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using open defecation' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using open defecation' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using open defecation' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using open defecation' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using open defecation' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using open defecation' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using open defecation' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into dwelling' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into dwelling' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into dwelling' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into dwelling' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into dwelling' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into dwelling' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into dwelling' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into yard/plot' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into yard/plot' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into yard/plot' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into yard/plot' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into yard/plot' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into yard/plot' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population using water piped into yard/plot' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with a basic handwashing facility, with soap and water available' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with a basic handwashing facility, with soap and water available' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with a basic handwashing facility, with soap and water available' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with a limited handwashing facility, lacking soap and/or water' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with a limited handwashing facility, lacking soap and/or water' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with a limited handwashing facility, lacking soap and/or water' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with a place for handwashing was observed' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with a place for handwashing was observed' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with a place for handwashing was observed' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an improved sanitation facility' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an unimproved sanitation facility' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an unimproved sanitation facility' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an unimproved sanitation facility' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an unimproved sanitation facility' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an unimproved sanitation facility' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an unimproved sanitation facility' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with an unimproved sanitation facility' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic sanitation service' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic sanitation service' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic sanitation service' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic sanitation service' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic sanitation service' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic sanitation service' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic sanitation service' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic water service' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic water service' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic water service' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic water service' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic water service' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic water service' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with basic water service' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with improved water source on the premises' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with improved water source on the premises' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with improved water source on the premises' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with improved water source on the premises' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with improved water source on the premises' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with improved water source on the premises' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with improved water source on the premises' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited sanitation service' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited sanitation service' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited sanitation service' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited sanitation service' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited sanitation service' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited sanitation service' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited sanitation service' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited water service' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited water service' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited water service' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited water service' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited water service' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited water service' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with limited water service' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with water more than 30 minutes away round trip' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with water more than 30 minutes away round trip' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with water more than 30 minutes away round trip' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with water more than 30 minutes away round trip' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with water more than 30 minutes away round trip' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with water more than 30 minutes away round trip' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Population with water more than 30 minutes away round trip' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children stunted' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children stunted' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children stunted' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children stunted' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children under 5 who slept under an insecticide-treated net (ITN)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children underweight' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children underweight' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children underweight' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children underweight' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children wasted' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children wasted' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children wasted' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children wasted' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Demand for family planning satisfied by modern methods' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among general population' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among general population' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among general population' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among men' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among men' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among men' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among women' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among women' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'HIV prevalence among women' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Households with electricity' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Infant mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any method of contraception' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Married women currently using any modern method of contraception' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Women]: 25-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Women]: 25-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Women]: 25-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Women]: 25-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Women]: 25-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Women]: 25-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Women]: 25-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Women]: 25-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of exclusive breastfeeding' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of exclusive breastfeeding' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of exclusive breastfeeding' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median duration of exclusive breastfeeding' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men receiving an HIV test and receiving test results in the last 12 months' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men receiving an HIV test and receiving test results in the last 12 months' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men receiving an HIV test and receiving test results in the last 12 months' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men receiving an HIV test and receiving test results in the last 12 months' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Physical or sexual violence committed by husband/partner' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Place of delivery: Health facility' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Treatment of diarrhea: Either ORS or RHF' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Under-five mortality rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Unmet need for family planning' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women receiving an HIV test and receiving test results in the last 12 months' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women receiving an HIV test and receiving test results in the last 12 months' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women receiving an HIV test and receiving test results in the last 12 months' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women receiving an HIV test and receiving test results in the last 12 months' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women who are literate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Women with secondary or higher education' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 15-19' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 15-19' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 15-19' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 15-19' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 15-19' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 15-19' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 15-19' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 20-24' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 20-24' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 20-24' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 20-24' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 20-24' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 20-24' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 20-24' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 25-29' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 25-29' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 25-29' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 25-29' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 25-29' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 25-29' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 25-29' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 30-34' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 30-34' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 30-34' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 30-34' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 30-34' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 30-34' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 30-34' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 35-39' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 35-39' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 35-39' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 35-39' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 35-39' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 35-39' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 35-39' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 40-44' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 40-44' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 40-44' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 40-44' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 40-44' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 40-44' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 40-44' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 45-49' for year 1992 - not at district level (found 3 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 45-49' for year 2005 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 45-49' for year 2008 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 45-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 45-49' for year 2013 - not at district level (found 3 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 45-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 45-49' for year 2017 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'Age specific fertility rate: 45-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Crude birth rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Crude birth rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Crude birth rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Crude birth rate' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Crude birth rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Crude birth rate' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Crude birth rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'General fertility rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-44' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-44' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-44' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-44' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-44' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-44' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-44' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2013 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2017 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total fertility rate 15-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Total wanted fertility rate' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mutual health organzation or community-base health insurance [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Mutual health organzation or community-base health insurance [Women]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No health insurance [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'No health insurance [Women]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of men' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of men (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of women (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Other employer-base health insurance [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Other employer-base health insurance [Women]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Other health insurance [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Other health insurance [Women]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Privately purchased commercial insurance [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Privately purchased commercial insurance [Women]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Social security health insurance [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Social security health insurance [Women]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'BCG vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 1 vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 1 vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 1 vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 1 vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 1 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 2 vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 2 vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 2 vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 2 vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 2 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'DPT 3 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Fully vaccinated (8 basic antigens)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Haemophilus influenza type b 1 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Haemophilus influenza type b 2 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Haemophilus influenza type b 3 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Hepatitis 1 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Hepatitis 2 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Hepatitis 3 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Measles vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months (unweighted)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months (unweighted)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children 12-23 months (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 0 vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 0 vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 0 vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 0 vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 0 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 1 vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 1 vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 1 vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 1 vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 1 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 2 vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 2 vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 2 vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 2 vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 2 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Polio 3 vaccination received' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Received no vaccinations' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Received no vaccinations' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Received no vaccinations' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Received no vaccinations' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Received no vaccinations' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages in newspapers or magazines [Men]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages in newspapers or magazines [Men]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages in newspapers or magazines [Men]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages in newspapers or magazines [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages in none of these three media [Men]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages in none of these three media [Men]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages in none of these three media [Men]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages in none of these three media [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages on television [Men]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages on television [Men]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages on television [Men]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages on television [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages on the radio [Men]' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages on the radio [Men]' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages on the radio [Men]' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Family planning messages on the radio [Men]' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any method of contraception among married men' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any method of contraception among married men' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any method of contraception among married men' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any method of contraception among married men' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any method of contraception among married men' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any modern method of contraception amonth married men' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any modern method of contraception amonth married men' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any modern method of contraception amonth married men' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any modern method of contraception amonth married men' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Knowledge of any modern method of contraception amonth married men' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 20-24' for year 1992 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-29' for year 2005 - not at district level (found 3 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-29' for year 2010 - not at district level (found 3 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-29' for year 2015 - not at district level (found 1 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-29' for year 2019 - not at district level (found 1 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-49(54,59)' for year 2005 - not at district level (found 3 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-49(54,59)' for year 2010 - not at district level (found 3 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-49(54,59)' for year 2015 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 25-49(54,59)' for year 2019 - not at district level (found 1 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 30-34' for year 2005 - not at district level (found 4 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 30-34' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 30-34' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 30-34' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 35-39' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 35-39' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 35-39' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 35-39' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 40-44' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 40-44' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 40-44' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 40-44' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 45-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 45-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 45-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 45-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 50-54' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 50-54' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 50-54' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first marriage [Men]: 50-54' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 20-49(54,59)' for year 2000 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-29' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-29' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-29' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-29' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-49(54,59)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-49(54,59)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-49(54,59)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 25-49(54,59)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 30-34' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 30-34' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 30-34' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 30-34' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 35-39' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 35-39' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 35-39' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 35-39' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 40-44' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 40-44' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 40-44' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 40-44' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 45-49' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 45-49' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 45-49' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 45-49' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 50-54' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 50-54' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 50-54' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Median age at first sexual intercourse [Men]: 50-54' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men circumcised' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who want no more children' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who want no more children' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who want no more children' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Men who want no more children' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: One wife' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: One wife' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: One wife' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: One wife' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: One wife' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: Two or more wives' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: Two or more wives' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: Two or more wives' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: Two or more wives' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of wives: Two or more wives' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Active 4 Weeks' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Active 4 Weeks' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Active 4 Weeks' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Active 4 Weeks' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Never had sex' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Never had sex' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Never had sex' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Never had sex' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: One or more years ago' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: One or more years ago' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: One or more years ago' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: One or more years ago' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Within the last year' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Within the last year' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Within the last year' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Recent sexual activity [Men]: Within the last year' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with ARI for whom advice or treatment was sought' for year 2019 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI who received antibiotics' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI who received antibiotics' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI who received antibiotics' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Children with symptoms of ARI who received antibiotics' for year 2019 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children born in the last five (or three) years (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years' for year 2019 - not at district level (found 2 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2005 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2008 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2010 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2015 - not at district level (found 5 unique locations)\n", + "Skipping indicator 'Number of children with symptoms of ARI born in the last five (or three) years (unweighted)' for year 2019 - not at district level (found 5 unique locations)\n", + "Created dataset with 6178 rows across 299 indicator-year combinations\n" + ] + } + ], + "source": [ + "df_adm2 = extract_adm2_indicators(excel_path=FILE_EXCEL)" + ] + }, + { + "cell_type": "markdown", + "id": "8169202f", + "metadata": {}, + "source": [ + "## Introduction to Nightlights Dataset\n", + "\n", + "## What is Nightlight Data?\n", + "\n", + "Nightlight data is satellite imagery capturing artificial light emissions from Earth's surface during nighttime. Satellites like VIIRS collect this data regularly, providing an **objective, real-time measure of human economic activity and development**.\n", + "\n", + "### Raw Data: Radiance Measurements\n", + "The fundamental measurement in nightlight data is **radiance** - the amount of light energy detected by satellite sensors, measured in **nanowatts per square centimeter per steradian (nW/cm²/sr)**. Each pixel in satellite imagery contains a radiance value representing the light intensity from that specific location on Earth's surface.\n", + "\n", + "### Annual Composite Generation\n", + "This dataset was created from **annual composite images** using VIIRS nightlight files for Rwanda. Annual composites are generated by:\n", + "\n", + "- **Aggregating daily/monthly observations** throughout each year (2015, 2020, 2024)\n", + "- **Filtering out temporary light sources** (fires, lightning, aurora)\n", + "- **Removing cloud-affected observations** to ensure clear measurements\n", + "- **Averaging or taking median values** to create stable, representative annual measurements\n", + "- **Masking techniques** to exclude areas with unreliable data\n", + "\n", + "The files used include both **average composites** (`average_masked`) and **median composites** (`median_masked`), with **cloud-free versions** (`vcmslcfg`) preferred over cloud-inclusive versions (`vcmcfg`) for more accurate measurements.\n", + "\n", + "### Why Use Nightlight Data?\n", + "\n", + "- **Consistent global coverage** - Available everywhere, regardless of local data quality\n", + "- **Real-time updates** - More current than traditional economic statistics\n", + "- **Objective measurement** - Not subject to reporting biases\n", + "- **High resolution** - Captures local development patterns\n", + "- **Proxy for development** - Light intensity correlates with economic activity, infrastructure, and quality of life\n", + "\n", + "## Dataset Overview \n", + "\n", + "- **6,507 observations** across Rwanda's administrative cells\n", + "- **Three time periods**: 2015, 2020, 2024\n", + "- **Cell-level data** - Rwanda's smallest administrative units\n", + "- Allows temporal analysis of development trends\n", + "\n", + "---\n", + "\n", + "## Variable Definitions\n", + "\n", + "### Administrative Identifiers\n", + "- **`cell_id`** - Unique identifier for linking with other datasets\n", + "- **`province_name`** - Province (5 total in Rwanda)\n", + "- **`district_name`** - District (30 total in Rwanda) \n", + "- **`sector_name`** - Administrative level between district and cell\n", + "- **`cell_name`** - Specific cell name\n", + "\n", + "### Core Nightlight Measurements\n", + "\n", + "#### `total_nightlight`\n", + "- **Sum of all radiance values** within cell boundaries\n", + "- **Key indicator** of overall economic activity/development\n", + "- Higher values = more total development\n", + "\n", + "#### `mean_nightlight` \n", + "- **Average radiance** per pixel\n", + "- Indicates development intensity regardless of cell size\n", + "- Useful for comparing cells of different areas\n", + "\n", + "#### `median_nightlight`\n", + "- **Middle radiance value** of all pixels (less sensitive to outliers)\n", + "- Better represents typical lighting in unevenly developed areas\n", + "\n", + "#### `max_nightlight`\n", + "- **Highest radiance** within cell\n", + "- Indicates major infrastructure (hospitals, commercial centers)\n", + "\n", + "#### `min_nightlight` & `std_nightlight`\n", + "- Minimum radiance and standard deviation\n", + "- High std = uneven development within cell\n", + "\n", + "### Spatial Coverage Indicators\n", + "\n", + "#### `pixel_count`\n", + "- **Total pixels** in cell (indicates geographic size)\n", + "- Used to normalize other measurements\n", + "\n", + "#### `lit_pixel_count`\n", + "- **Number of pixels with detectable light** (radiance > 0)\n", + "- Shows spatial extent of development\n", + "\n", + "#### `lit_pixel_percentage`\n", + "- **Percentage of cell area with lighting**\n", + "- Formula: `(lit_pixel_count ÷ pixel_count) × 100`\n", + "- **0% = completely dark, 100% = fully developed**\n", + "\n", + "#### `year`\n", + "- Time period: 2015, 2020, or 2024" + ] + }, + { + "cell_type": "markdown", + "id": "784d2e72", + "metadata": {}, + "source": [ + "# Part-2: Demographic and Nightlights Data\n", + "\n", + "## Part A: Varible Generation and Data Integration\n", + "\n", + "### Population Dataset Variables (`rwa-cell-pop.csv`):\n", + "Create the following derived variables:\n", + "- **`dependency_ratio`** - `(children_under_five_2020 + elderly_60_plus_2020) / working_age_population * 100`\n", + "- **`people_per_building`** - `general_2020 / building_count`\n", + "- **`working_age_population`** - `general_2020 - children_under_five_2020 - elderly_60_plus_2020`\n", + "- **`infrastructure_index`** - Your own formula that incorporates `people_per_building` and other relevant variables to measure infrastructure adequacy. Document and justify your `infrastructure_index` methodology, explaining how `people_per_building` and other variables contribute to measuring infrastructure pressure.\n", + "\n", + "### Nightlight Dataset Variables (`cell-ntl-2015-2020-2024.csv`):\n", + "Create the following temporal and development indicators:\n", + "- **`nightlight_change_2015_2024`** - Percentage change in total nightlight from 2015 to 2024\n", + "- **`mean_nightlight_change_2015_2024`** - Percentage change in mean nightlight from 2015 to 2024\n", + "- **`lit_pixel_percentage`** - Use existing or calculate: `(lit_pixel_count / pixel_count) * 100`\n", + "\n", + "### Data Integration:\n", + "Merge the datasets using the appropriate column. \n", + "\n", + "## Part B: Exploratory Data Analysis\n", + "\n", + "### Correlation Analysis:\n", + "1. **Correlation Heatmap**: Create a heatmap showing correlations between 10 key variables (mix of demographic, infrastructure, and nightlight variables). \n", + "2. **Report the top 3 variable pairs** with the highest correlations and interpret their relationships.\n", + "3. **Identify unexpected correlations** and discuss potential explanations.\n", + "\n", + "### Nightlight Trend Analysis:\n", + "1. **District Ranking**: Report the **top 5 districts** with the highest nightlight growth (2015-2024) and **bottom 5 districts** with the most decline or lowest growth.\n", + "2. **Lit Pixel Analysis**: Compare these districts using `lit_pixel_percentage` changes to understand whether growth represents intensification or spatial expansion.\n", + "3. **Create visualizations** showing nightlight trends for these extreme districts.\n", + "\n", + "## Part C: Modeling\n", + "\n", + "\n", + "### Multivariate Linear Regression:\n", + "1. **Model Development**: Build a multivariate linear regression model predicting **population density** using both demographic and nightlight variables as predictors. Explore as many variables as possible at the beginning.\n", + "2. **Variable Selection**: Test different combinations of variables and report the **top 3 most predictive variables** of population density.\n", + "3. **Model Evaluation**: Report R-squared, coefficients, and statistical significance. Interpret what these results tell us about population-infrastructure relationships.\n", + "\n", + "\n", + "\n", + "## Notes and Other Requirements\n", + "Please follow the genral guidelines below when preparing your analysis..\n", + "\n", + "### Statistical Analysis:\n", + "- Properly handle missing data and outliers\n", + "- Use appropriate statistical tests and report p-values\n", + "- Calculate and interpret correlation coefficients\n", + "- Validate regression assumptions (normality, homoscedasticity)\n", + "\n", + "### Data Management:\n", + "- Document all data cleaning and aggregation steps using markdown \n", + "- Ensure consistent district naming across datasets\n", + "\n", + "### Visualization Standards:\n", + "- Create clear, publication-quality heatmaps with appropriate color scales\n", + "- Design effective time series plots for nightlight trends\n", + "- Include proper axis labels, titles, and legends\n", + "- Use consistent formatting across all visualizations\n", + "\n", + "### Reporting Requirements:\n", + "- Clearly state the top 3 most predictive variables with statistical justification\n", + "- Provide ranked lists for nightlight growth districts with supporting metrics\n", + "- Include model performance statistics and interpretation\n", + "- Document all methodological choices and assumptions" + ] + }, + { + "cell_type": "markdown", + "id": "99f0479d", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "7ef05143", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "## Create Population Dataset Variables" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3adf8782", + "metadata": {}, + "outputs": [], + "source": [ + "df_pop = pd.read_csv(FILE_CELL_POP_RW)\n", + "df_ntl = pd.read_csv(FILE_CELL_NTL_RW)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d9549fee", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/04/tc5rvshj26363cf30dxyhz4c0000gp/T/ipykernel_31686/3386350694.py:18: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df_pop['dependency_ratio'].fillna(df_pop['dependency_ratio'].median(), inplace=True)\n", + "/var/folders/04/tc5rvshj26363cf30dxyhz4c0000gp/T/ipykernel_31686/3386350694.py:24: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df_pop['people_per_building'].fillna(df_pop['people_per_building'].median(), inplace=True)\n" + ] + } + ], + "source": [ + "# ==================================================================\n", + "# GENERATE ADDITIONAL VARIABLES IN POPULAITON DATAFRAME\n", + "# ==================================================================\n", + "\n", + "# Create derived variables for population dataset\n", + "df_pop['working_age_population'] = (df_pop['general_2020'] - \n", + " df_pop['children_under_five_2020'] - \n", + " df_pop['elderly_60_plus_2020'])\n", + "\n", + "\n", + "# Calculate dependency ratio\n", + "df_pop['dependency_ratio'] = ((df_pop['children_under_five_2020'] + df_pop['elderly_60_plus_2020']) / \n", + " df_pop['working_age_population'] * 100)\n", + "\n", + "# Handle infinity values in dependency ratio\n", + "# We fill with median but these cn be left as NaN\n", + "df_pop['dependency_ratio'] = df_pop['dependency_ratio'].replace([np.inf, -np.inf], np.nan)\n", + "df_pop['dependency_ratio'].fillna(df_pop['dependency_ratio'].median(), inplace=True)\n", + "\n", + "# Calculate people per building\n", + "# We fill NaN with median but these cn be left as NaN\n", + "df_pop['people_per_building'] = df_pop['general_2020'] / df_pop['building_count']\n", + "df_pop['people_per_building'] = df_pop['people_per_building'].replace([np.inf, -np.inf], np.nan)\n", + "df_pop['people_per_building'].fillna(df_pop['people_per_building'].median(), inplace=True)\n", + "\n", + "# Create population density (people per unit area proxy)\n", + "df_pop['total_population'] = df_pop['general_2020'] # Using total population as proxy\n", + "\n", + "# Create Infrastructure Index" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5a9438b4", + "metadata": {}, + "outputs": [], + "source": [ + "# Create Infrastructure Index based only on people_per_building\n", + "\n", + "# Calculate z-scores for people_per_building (how many standard deviations from mean)\n", + "df_pop['people_per_building_zscore'] = (df_pop['people_per_building'] - df_pop['people_per_building'].mean()) / df_pop['people_per_building'].std()\n", + "\n", + "# Infrastructure index - invert and scale to 0-100 range\n", + "# Lower people_per_building means better infrastructure (less crowding)\n", + "# We invert the z-score and scale to 0-100 range where higher = better infrastructure\n", + "\n", + "# Find min and max z-scores for scaling\n", + "min_z = df_pop['people_per_building_zscore'].min()\n", + "max_z = df_pop['people_per_building_zscore'].max()\n", + "\n", + "# Create the infrastructure index (scaled 0-100)\n", + "# Subtract from max_z to invert (lower people_per_building = higher infrastructure index)\n", + "df_pop['infrastructure_index'] = 100 * (max_z - df_pop['people_per_building_zscore']) / (max_z - min_z)\n", + "\n", + "# Round to 2 decimal places\n", + "df_pop['infrastructure_index'] = df_pop['infrastructure_index'].round(2)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ac46ed0c", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "### Create Nightlight Dataset Variables\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5d961de5", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nightlight variables created successfully!\n", + "Nightlight change range: -65.31% - 3157.42%\n" + ] + } + ], + "source": [ + "# ==================================================================\n", + "# GENERATE ADDITIONAL NTL VARIABLES\n", + "# ==================================================================\n", + "\n", + "# Pivot nightlight data to get values by year\n", + "df_ntl_pivot = df_ntl.pivot_table(\n", + " index=['cell_id', 'province_name', 'district_name', 'sector_name', 'cell_name'],\n", + " columns='year',\n", + " values=['total_nightlight', 'mean_nightlight', 'lit_pixel_count', 'pixel_count'],\n", + " aggfunc='first'\n", + ").reset_index()\n", + "\n", + "# Flatten column names\n", + "df_ntl_pivot.columns = ['_'.join([str(col[0]), str(col[1])]) if col[1] != '' \n", + " else str(col[0]) for col in df_ntl_pivot.columns]\n", + "\n", + "# Clean column names\n", + "df_ntl_pivot.columns = [col.replace('_', '') if col.endswith('_') else col for col in df_ntl_pivot.columns]\n", + "\n", + "# Calculate nightlight changes (2015-2024)\n", + "# Replace division by zero with NaN instead of adding a small value\n", + "df_ntl_pivot['nightlight_change_2015_2024'] = np.where(\n", + " df_ntl_pivot['total_nightlight_2015'] == 0,\n", + " np.nan,\n", + " (df_ntl_pivot['total_nightlight_2024'] - df_ntl_pivot['total_nightlight_2015']) / \n", + " df_ntl_pivot['total_nightlight_2015'] * 100\n", + ")\n", + "\n", + "df_ntl_pivot['mean_nightlight_change_2015_2024'] = np.where(\n", + " df_ntl_pivot['mean_nightlight_2015'] == 0,\n", + " np.nan,\n", + " (df_ntl_pivot['mean_nightlight_2024'] - df_ntl_pivot['mean_nightlight_2015']) / \n", + " df_ntl_pivot['mean_nightlight_2015'] * 100\n", + ")\n", + "\n", + "# Calculate lit pixel percentage for each year\n", + "for year in [2015, 2020, 2024]:\n", + " df_ntl_pivot[f'lit_pixel_percentage_{year}'] = (\n", + " df_ntl_pivot[f'lit_pixel_count_{year}'] / df_ntl_pivot[f'pixel_count_{year}'] * 100\n", + " )\n", + "\n", + "df_ntl_pivot['lit_pixel_change_2015_2024'] = (\n", + " df_ntl_pivot['lit_pixel_percentage_2024'] - df_ntl_pivot['lit_pixel_percentage_2015']\n", + ")\n", + "\n", + "print(\"Nightlight variables created successfully!\")\n", + "print(f\"Nightlight change range: {df_ntl_pivot['nightlight_change_2015_2024'].min():.2f}% - {df_ntl_pivot['nightlight_change_2015_2024'].max():.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "id": "cf4a9fdc", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "## Data Integration" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "823bf3e4", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['cell_id', 'province_name', 'district_name', 'sector_name', 'cell_name',\n", + " 'lit_pixel_count_2015', 'lit_pixel_count_2020', 'lit_pixel_count_2024',\n", + " 'mean_nightlight_2015', 'mean_nightlight_2020', 'mean_nightlight_2024',\n", + " 'pixel_count_2015', 'pixel_count_2020', 'pixel_count_2024',\n", + " 'total_nightlight_2015', 'total_nightlight_2020',\n", + " 'total_nightlight_2024', 'nightlight_change_2015_2024',\n", + " 'mean_nightlight_change_2015_2024', 'lit_pixel_percentage_2015',\n", + " 'lit_pixel_percentage_2020', 'lit_pixel_percentage_2024',\n", + " 'lit_pixel_change_2015_2024'],\n", + " dtype='object')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ntl_pivot.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "22603370", + "metadata": {}, + "outputs": [], + "source": [ + "df_ntl_pivot.drop(columns=['province_name', 'district_name', 'sector_name', 'cell_name'], inplace=True)\n", + "\n", + "df = pd.merge(df_pop, df_ntl_pivot, on=\"cell_id\", how=\"left\")" + ] + }, + { + "cell_type": "markdown", + "id": "4bf67167", + "metadata": {}, + "source": [ + "## Exploratory Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "8f4e219b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Select key variables for correlation analysis\n", + "key_vars = [\n", + " 'dependency_ratio', 'people_per_building', 'infrastructure_index',\n", + " 'total_population', 'nightlight_change_2015_2024', \"building_count\",\n", + " 'mean_nightlight_change_2015_2024', 'lit_pixel_change_2015_2024',\n", + " 'total_nightlight_2024', 'mean_nightlight_2024', 'lit_pixel_percentage_2024'\n", + "]\n", + "\n", + "# Create correlation matrix\n", + "corr_matrix = df[key_vars].corr()\n", + "\n", + "# Create correlation heatmap\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(corr_matrix, annot=True, cmap='RdBu_r', center=0,\n", + " square=True, linewidths=0.5, cbar_kws={\"shrink\": .8})\n", + "plt.title('Correlation Heatmap: Key Variables', fontsize=16, pad=20)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "171ba629", + "metadata": {}, + "source": [ + "# Based on Manual Inspection, Below is a List of Top and Intresting Correlations\n", + "```\n", + "'total_population' and 'building_count': 0.7\n", + "'dependency_ratio' and 'mean_nightlight_change_2024': -0.57\n", + "'total_population' and 'ltotal_night_light_2024': 0.53\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "731397f8", + "metadata": {}, + "source": [ + "## Trends in Night Lights" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "728286cf", + "metadata": {}, + "outputs": [], + "source": [ + "df_ntl_dist = df.groupby('district_name').agg({\n", + " 'nightlight_change_2015_2024': 'mean',\n", + " 'lit_pixel_change_2015_2024': 'mean',\n", + " 'total_nightlight_2015': 'mean',\n", + " 'total_nightlight_2024': 'mean',\n", + " 'lit_pixel_percentage_2015': 'mean',\n", + " 'lit_pixel_percentage_2024': 'mean'\n", + "}).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "123c80bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TOP 5 DISTRICTS - HIGHEST NIGHTLIGHT GROWTH (2015-2024):\n", + "============================================================\n", + "Nyaruguru: 877.4% growth\n", + "Nyagatare: 757.5% growth\n", + "Bugesera: 716.3% growth\n", + "Kirehe: 639.0% growth\n", + "Ngoma: 623.5% growth\n", + "\n", + "BOTTOM 5 DISTRICTS - LOWEST NIGHTLIGHT GROWTH (2015-2024):\n", + "============================================================\n", + "Nyarugenge: 192.7% growth\n", + "Rubavu: 318.4% growth\n", + "Kicukiro: 387.8% growth\n", + "Muhanga: 390.8% growth\n", + "Nyanza: 399.0% growth\n" + ] + }, + { + "data": { + "image/png": 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jhiRj0KBBFvN79+5tSDImTJiQZ52kpCTjypUrVu3f09PTuPPOO43Lly9bzM/JyTHOnTtnMS8kJCTP91CY3O9t+fLlRkZGhlG9enXD3d3dOHv2rLlN7s93165dFuvmF/Njjz1mSDL+85//WMzP/Q6vj23r1q3m+XFxcRbr5P6cly9ffsP9Xr+9yZMnW8zP/V6u9f777xuSjB49ehjZ2dnm+YcOHTKcnZ3z3U5urMOHD7dYJ3dbw4YNu+F+b2TKlCmGJOOVV16xab1cNWvWNCQZERERRnp6usWyo0ePGvb29oaPj49x4sQJ8/zMzExzrEuWLDHPf/PNNw1JxieffGKe9/TTTxtVq1Y1/vGPfxhhYWHm+QcPHjR/N7kK+nnkyv3dql27tvHHH3+Y5589e9aoWrWq4ebmZmRmZhbpewAAoDzKve6sW7euMXnyZGPy5MnGK6+8YgwfPtyoW7eu4ezsnOc6LDU11ahatarh5ORkcV2ck5Nj9O/f35BkTJ061WIda66NFy5cmO/y9u3bG5KM+fPnW8yfP3++Icno0KGDxfzca5C2bdsaaWlp5vlxcXGGJKNq1apG7969La6dp0+fbkgyZs2aVeB3lV/M108eHh4W1z43kvv9d+rUqdB2CxYsMCQZAwcONM/L77rorbfeMiQZb775Zp5tXHs9bhiFX1fmxiXJmDRpUr5tatasmSeH/O9//2v+mVx/3Zienm6RaxS2f1vyrtz8o6Drw+vlfm+519m5+9qwYYO5TUF55O2aA+T395VfPnvkyBHDzs7OqFGjhsX3eOnSJaNRo0b5bid3361atTJSUlLM8w8fPmzY29sb9evXv+F+byQ+Pt6QZDzwwANWr3Ot3BygevXqxu+//26xLDs726hXr54hyVi/fr3FsvHjxxuSjCFDhpjn/fDDD4YkY9SoUeZ5K1asMP9eOzs7G3/99Zd5mY+Pj9GwYUOL7VpzvrO3tzd27Nhhnn/16lUjNDQ03zwZqCgYfgpAsYiNjZWDg4Nef/11i94VjRs31uDBg3XhwgV9+umnedYbOHCgGjVqZP5sb2+vqKgoSdKiRYus2ndISIg++eQTnTx5UhkZGTp06JBGjx6to0ePKiIiQn/88cdNHp2lgIAASX/3hLCGi4tLnnm5T3bY4qGHHipwKKDrnTlzRh9//LHq1q2b7xNIvr6+Vr+QW5IcHR3ztDeZTPL09LSYt3jxYh06dEj33nuv1dvO5ezsrMjISKWmpubppmuNzMxM/e9//5Ovr6+eeeYZi2WDBg0qdAzhBx54QH379rWY9+STT0qS9uzZY3Ms1li6dKkkaerUqapU6f/+OW7QoIEGDRpU4HqVK1fW9OnTLdYZNGiQ7O3tiyXWpKQkSf/3e36t3377TZGRkRZT7nFc7z//+U+e3/0PP/xQV69e1ZgxYxQYGGie7+joqNdff12SLLpa5z6t9uWXX5rnbd26VSEhIQoPD9euXbvMQ9Vt3brVYh1bTJo0Sf7+/ubP3t7e6tmzpy5duqQjR47YvD0AAMq7o0ePasqUKZoyZYpeffVVvf322+Zr765du1q0Xb16tS5evKgnn3xSjRs3Ns83mUx6/fXX8wxTeTNOnjypL7/8Ug0bNjQ/DZ5r6NChuueee7Rly5Z8h6GaNm2aKleubP78yCOPyMHBQRcvXtTMmTMtroX79esn6e8XFVujTp06evvtt/Xrr78qPT1dx48f13//+19VqlRJjz/+uNatW1eUwy1QceQrRXm/gp+fn009rv/73//Kzs5O8+bNyxODi4tLnlzjRqzJu0aOHKlDhw5p5MiRNm07V1RUlOzt7fXSSy9ZDEdsrds1B8jP8uXLlZ2drTFjxlh8j1WqVLnhzzk6OtqiF1P9+vXVtm1bHTly5IYjDtxIYflKTk5OnnyloNzyhRdeyPPek6+//lq//PKLunTpok6dOlksmzhxory8vLRs2TJlZWVJkoKDg+Xl5ZUnX6lWrZqee+45/fXXX9q1a5ekv4e0Sk5OLlK+0r9/f7Vt29b82c7Ozvz7UlI/f+B2R1EDwE1LTU3Vb7/9prvuukvVq1fPszz3H+19+/blWZbf8DYtWrSQi4tLvu3z88QTT6hXr16qXr26eXze2bNn68UXX9S5c+c0e/ZsWw7nhqy9eM1910CrVq00YsQIffLJJ0pOTi7yfm0pFOzdu1eGYSgsLEwODg5F3qf093EcO3ZMQUFBmjRpkjZv3lzgey9q1KihBg0ayNXVtUj7Gjx4sBo2bKh58+bl+96Dwhw5ckSZmZlq0aJFnm7uJpNJrVu3LnDdf/zjH3nm5f4ul9SLsH/44QdVrlzZIrnP1aZNmwLXq1evnqpUqWIxz97eXr6+vsUSa2G/37/99pv5BkbulF9Rw9nZOd+h2b7//ntJ+Rce7rvvvjx/98HBwfL29jYXLJKSknT48GGFhYUpLCzMIknYunVroeP6FqY0fv4AAJRlnTp1kmEY5unMmTNatmyZdu7cqTZt2ujnn382ty3s3//AwEDzC5hv9kbntfsKCQnJM4ytyWQyD5uaXzGiWbNmFp/t7Ozk4+OjqlWr5rnxmfswxOnTp62K64EHHtDTTz+tunXrysXFRTVr1tTw4cMVFxcnwzD0yiuvWHeAVrI2X+nWrZtcXV01YsQI9enTRx988IHFz85WTZo0KXS4qWtdvnxZP/30k2rXrq169eoVeZ+SbXmXt7e3GjRoUOSXYtevX19PPvmkvv/+ey1fvtzm9W/XHCA/uX8n+cVVWKxSyV5fF/b7nZOTkydfKaiokV9+Xdj5qnLlymrRooUyMjLMfycmk0khISHmgoX0fw9hhYSEyN7e3pzL5P63sGGRC0K+AuRFUQPATUtNTZUk8xiU1/Pz85MkpaSk5Fnm4+OT7zo+Pj75trfFkCFDJP39tEVxSkxMlCTdcccdhbbr27evPvnkEwUFBWn+/Pl65JFH5Ofnp/bt21tdsLlWQd9vfnIvbO68806b93O9t956SzNmzJCDg4Nee+01dezYUd7e3ho0aJDVT39Zy87OTlFRUcrKyrL5vRq5v4cF/VwK+/48PDzyzMt9Gu/al1cXp9TU1GKLVfo73uKINXff+SXp4eHh5psXx44dK3AbPj4++b4P50bniuv/7nOThJ9++klnzpyxSATatWtnThIMw1B8fLwaNWp0w7/L/JTGzx8AgPLEx8dH/fr10/Tp03Xx4kVzD0zJ+lwht93NuJm8JL93Y9jb2xd6nXDlypUixypJERERCgwMVEJCgjIzM29qW9eyNl+pXbu2du3ape7du2vdunUaMmSI6tevrwYNGuh///ufzfstrXyluPOuG4mMjJSrq6smTZpkfmLfWrdrDpCfwvKrG/2sS/L6urB8xd7e3qLgWrNmzRtu51pFOYfkFim2bdumpKQkHTlyRGFhYapSpYpatGhhUdTIzW9sRb4C5EVRA8BNy00Azpw5k+/y3Pn5JQoFPUGTnJxc4IWbtXKfvklPT7+p7VwrJydH27dvlyS1bNnyhu179eql7du36/z581q3bp3+9a9/KT4+Xp06dbL5iQpbXpqe+3I1a58eK4yDg4NeeOEFHTx4UKdPn9ayZct0//33a/HixRowYMBNb/96PXv2VNu2bbVs2TKru/RL//f7dfbs2XyXF/T7WVrc3d1vy1hzn7rKvfguioJ+V290rkhOTs5znrg2Sdi2bZu8vb0VHBysKlWqqGXLltq6dasOHDigP//8s0hPPQEAgOKT++Tzd999Z553M7mCrW7lvoqLt7e3DMNQRkZGsW0z92XE1uQrjRs31ieffKLz589r165deuWVV3TmzBn17dvX5ofDbMlXcnO94shXpOLNu27E399fo0eP1m+//ab58+fbtO7tmgPkp7D8qjRjbdmypRwcHJSQkHBTPbzy+30tyjkkNwfZunVrnt4YYWFh+uabb3T58mVt375dQUFBRXoIC0BeFDUA3DR3d3fVqVNHv/76a74XpfHx8ZKkpk2b5ln21Vdf5Zm3d+9eZWRk5NveFt98840kqVatWje1nWstWbJEv//+u4KDgy3eBXIj7u7u6ty5s959910NHjxYycnJ5vgkqVKlSsX6hEWLFi1UqVIlbd261aonyKzdf0BAgPr166f169erXr162rx5c7EmYLmmT58uwzD00ksvWb1O/fr15eTkpISEhDxPTBmGod27dxdLbMX1s2rSpIkuX76sH3/8Mc+ynTt33vT2pb97vki2Pb3Tvn171axZUzt27DAX8IpL7rAOuYn2tb799tt8/+6vfa/G1q1bFRoaak5A2rdvr2+++UZr1661aJurKMcPAACK7vz585L+fhAoV2H//p8+fVpHjx5VnTp15ObmZp5f2PVWYf++515HbN++Pc8QNYZhmHOPm80ziktqaqoOHz6sqlWr3vQDXbl+/vlnffTRR3JyctLDDz9s9XoODg667777NGXKFL311lsyDMN8jSUV/3VVlSpV1LBhQx07dky//PLLDdtbu/8b5V3F5cUXX5SXl5emTp1q04312zUHyE+TJk0k5R9XacZauXJl9e7dW+np6cU+1HRh56v09HTt3btXLi4uql+/vnl+bm/x3HzljjvuUFBQkKS/85WsrCy98847+vPPP/Md1qq47wUAFQVFDQDFYtCgQbpy5YrGjx9vkUAcOHBACxculIeHhx566KE86y1ZskQHDx40f7569aomTJhg3uaNJCUl6ejRo3nmnz592vyy6NwX+d2M7OxsffDBB3r66adlZ2enWbNm3fBJpC1btphfYnyt3N4p177IztPTU6dOnbrpOHP5+vrqn//8p/kljvnFcPXq1RvuPzMzU19++WWepPDy5cu6dOmSHBwczBeiknTixAkdPnz4pnvHtG3bVj169ND69eu1Y8cOq9ZxcnLSI488oqSkJL311lsWy3JfYF4ciutnldvLZdKkSRaJ/+HDh7Vo0aKb3r4k88sVbYnX3t5ec+fOlclk0iOPPKLNmzfn264oT7z1799f9vb2mjVrlv744w/z/CtXrpgLWIMHD7ZYp1GjRvLx8dGqVav0yy+/WPTGCAsL05UrVzR79ux8u3IX5fgBAEDR5OTkKCYmRpLle/N69uwpDw8PLVy40OK63zAMjR8/XleuXMnz739h11uF/fteo0YNhYWF6eDBg/rggw8sln3wwQc6ePCg2rdvr8DAwCIdY1Hl1+MhIyNDQ4cOVUZGhh599FGbejkUZMeOHerUqZMyMzM1fvz4Gw7ttGfPnnx7zuc+jX59viIV73XViBEjlJ2dreHDh+d5UOqvv/4yF8lutH9b8q4///xThw8fvulhdN3d3TVx4kSdPXtWb775ptXr3a45QH4effRRVapUSbNmzdK5c+fM8y9fvqxp06bd1LZzFTXWqKgoeXt769VXX9Vbb72Vb1EgIyPD5uHB2rZtq7p162rdunV58qDo6Gj9+eef6tevX553x4SGhurnn3/W6tWrLR7Catu2rRwdHTV9+nRJ+b9Po7jvBQAVhX1pBwCgfBg3bpw+//xzLVmyRIcOHVKHDh109uxZrVixQleuXNHixYstnr7KFR4ervvuu0+PPvqoPD099cUXX+jAgQPq1KmTHnvssRvu9/Dhw2rfvr3atWunBg0ayNPTU8ePH9fatWt1+fJlDRo0yPziOGtt3rzZfFGcnp6uU6dOafv27Tp9+rQ8PT21ZMkShYeH33A7Y8aM0YkTJxQaGqpatWrJZDJpx44d+vbbb9WmTRu1bdvW3LZ9+/b66KOP9Mgjj6hZs2ays7NT165d833ZsrXefvttHThwQNOmTdMXX3yh9u3byzAM/fzzz9q4caPOnDljHqaqoP0HBgaqQ4cOqlOnjlq1aqUaNWooLS1Na9euVVJSkl588UWLC7rHH39c8fHx5ifqb0Z0dLQ+//zzfItWha2zefNmvfDCC9q6dauaNm2qI0eOaO3atercubPWr1+vSpVurp5fXD+rJ554QkuWLNFnn32m5s2bq1OnTjp//rzi4uLUsWNHrVmzplhi/fjjj9W7d289+OCD5hd4d+3atdD1unXrpsWLF+vf//63OnbsqKZNm6p169by9PTUhQsXdPToUW3ZskWVKlW64UsCr1W3bl1Nnz5dY8aMUePGjdWnTx9VrlxZa9eu1eHDh9WzZ898/+5DQ0P10UcfSbJMBNq0aSMnJyedPXtWTZo0kZeXl8V6DRo0UEBAgOLi4uTq6qrq1avLZDLp6aefLranIQEAqIh+/fVXRUZGmj+fPXtWW7du1aFDhxQYGGjxbjR3d3e999576tevn1q1aqW+ffvqjjvu0JYtW7R3717de++9euGFFyy2X9j1VuvWreXi4qI5c+ZYvJ8g9wGJefPmqV27dho6dKjWrFmjhg0b6qefftJnn32mO+64Q/PmzSv5L+g67dq1U8OGDdW8eXMFBAQoOTlZmzdv1smTJ9WkSRNFRUXZtL1rv/+srCxzb4QDBw7Izs5OL7/8slUvH//www/19ttvKzQ0VHfddZfc3d31008/6YsvvpC3t7eefPJJc9uiXlcW5umnn1Z8fLw++ugj1atXTz169JC7u7tOnDihDRs2aMGCBeYH4wrbvy1519y5czVlyhRNnjzZ4ne4KIYPH64333zTpnzlds4Brle/fn299NJLioqKUnBwsHr37i17e3utXLlSwcHBOnDgwE3HGhYWJpPJpIkTJ+rw4cPy8PCQh4eHnn766ULXq1mzpjZs2KBevXrp2Wef1cyZMxUWFqY777xTGRkZOn36tDZs2KDU1FSrcvdclSpVUmxsrDp16qQHH3xQvXv3Vs2aNfXNN9/oyy+/VN26dS3eGXTtcfzvf//T2bNnLfIVFxcXtWrVSl999VWB79MoiXsBQIVgAICNJBk1a9bMMz8tLc2YNGmScffddxuOjo5G1apVjS5duhhfffVVnraTJ082JBlbt2415s+fbzRs2NBwcnIyqlevbrz00ktGenq6VbGcOHHC+Ne//mU0btzYqFatmmFvb294eXkZHTt2NOLi4mw6roULFxqSzJPJZDKqVKli1KpVy+jevbsRExNjnD9/Pt91rz2eXHFxcUafPn2MunXrGq6uroaHh4fRtGlTY8aMGUZaWprF+omJiUafPn0Mb29vo1KlSoYkY+HChRZx5X6+3rFjxwxJxqBBg/IsS0lJMSZNmmQ0aNDAcHJyMsfwyiuvGFlZWTfcf1ZWljF9+nQjIiLCqF69uuHo6Gj4+voaISEh+X6/ISEheb6HwuR+b8uXL893+ZNPPmn+eezatctimSQjJCQkzzq//fab0bt3b8PDw8NwdXU17r//fiM+Pt4YOXKkIcn4/vvvzW23bt1qSDImT56cZzsFfa+F/awK2l7u93K9tLQ0Y8yYMUZAQIDh5ORkNGzY0Hj33XeNjz/+2JBkzJ4926pjNgzDqFmzZp6/yytXrhjjxo0zatSoYdjb2xf4e1KQU6dOGRMmTDCaN29uVK1a1bC3tzeqVatmtGrVynjxxReNw4cPWxXH9T799FMjJCTEcHNzM5ycnIzg4GDjjTfeMK5cuZJv+3nz5hmSDF9f3zzLHnjgAUOS8eyzz+a77u7du837yv1dOnbsmGEYhjFo0CCLz9fK728aAICKLvf66PrJycnJqF+/vvH8888bZ8+ezXfd7du3G126dDGqVq1qODo6GnfffbcxadKkPNfFhlH49ZZhGMbnn39utGzZ0nBxcTHHcK3jx48bTzzxhOHv72/Y29sb/v7+xhNPPGEcP348z74Kuk4zjMKvawq7Lrve888/b7Rp08bw8fExHBwcjCpVqhgtW7Y0pk+fbnXeYxj5f/8uLi6Gv7+/ERYWZkyaNMn49ddf8103v+vU3bt3G8OGDTOCgoKMqlWrGi4uLka9evWMZ555xjhx4oTF+oVdVxaWj+Qq6LvMyckx3n//feO+++4zKleubLi6uhr16tUznnrqKYsYCtu/LXlX7jVeftf/hX1vw4YNy3f54sWLzT+L6Ohoi2W3aw6Q3/YKu/Z9++23jXvuucdwdHQ0qlevbowdO9Y4efKkIcno2bOnVcdsGAVfe8fGxhrBwcGGk5NTgfcaCnL58mXjrbfeMtq3b2/ccccdhr29vVGlShWjYcOGxpAhQ4wvv/zS6jiu9eOPPxqPPPKI4e3tbTg4OBg1a9Y0nnnmmQLPb4cOHTL/Hhw6dMhi2SuvvGJIMpo0aZLvukW9F1BYLgtUBCbDuG5MEQC4BSIjIzVlypRieaIfsEa7du20a9cupaSkqEqVKqUdTqFefvllcw+bLl26lHY4AAAAAEpYWcoBNm/erI4dO2rcuHHmoZUA4FbinRoAgHIlMTExz7wPP/xQX3/9tcLDw2+rgkZ+sf7000966623VLVq1Xy7JwMAAAAou8pSDnD27Nk876u4ePGixo8fL0n5vjcTAG4F3qkBAChXgoKC1KxZMzVs2FB2dnbat2+ftm3bJjc3N82cObO0w7Pw9NNP6/jx47r33ntVrVo1HT16VGvWrNGVK1e0YMECubq6lnaIAAAAAIpRWcoBPvzwQ82cOVPt27dXQECAEhMTtX79eiUnJ2vw4MFq3bp1aYcIoIKiqAEAKFeeeuoprVmzRnv37tXly5d1xx13qH///po0aZIaNGhQ2uFZ6N27t9555x2tXLnSPCxWSEiIxowZo06dOpV2eAAAAACKWVnKAdq0aaPmzZtr8+bNOn/+vOzs7HTPPfdo0qRJGj58eGmHB6AC450aAAAAAAAAAACgTOCdGgAAAAAAAAAAoEygqAEAAAAAAAAAAMoE3qkhKScnR3/88Yfc3NxkMplKOxwAAACgTMgdydbd3Z3r6GuQXwAAAAC2MwxDly5dUkBAgCpVKrg/BkUNSX/88YcCAwNLOwwAAACgTEpJSZG7u3tph3HbIL8AAAAAiu7kyZOqXr16gcspakhyc3OT9PeXRTIGAAAAWCc1NZWb9/kgvwAAAABsl5tf5F5PF4SihmTuEu7u7k7SAQAAAOCmkF8AAAAARXejIVx5UTgAAAAAAAAAACgTKGoAAAAAAAAAAIAygaIGAAAAAAAAAAAoE3inBgAAKFR2drauXLlS2mEAKAUODg6ys7Mr7TDKLc6vQMXF+RUAgKKjqAEAAPJlGIaSkpJ08eLF0g4FQCmqWrWq/Pz8bviyPliP8ysAifMrAABFRVEDAADkK/eGm4+Pj1xdXUm4gQrGMAylp6crOTlZkuTv71/KEZUfnF+Bio3zKwAAN4eiBgAAyCM7O9t8w83Ly6u0wwFQSlxcXCRJycnJ8vHxYaiUYsD5FYDE+RUAgJvBi8IBAEAeuWO8u7q6lnIkAEpb7nmAdz8UD86vAHJxfgUAoGgoagAAgAIxJAoAzgMlg+8VAOcBAACKhqIGAAAAAAAAAAAoEyhqAACACsNkMmn16tVWt9+2bZtMJpMuXrx4U/s6fvy4TCaT9u3bZ/X6sbGxqlq1qs37vRmRkZFq2rTpLd0ngPKDc2zhOMcCAAAUD14UDgAArNZ9efdbur81/dYU6/YSExNVrVq1Yt1mZGSkVq9eXejNtMDAQCUmJsrb27tY9z148GBdvHjRppuIFcH58+c1efJkbdy4USdPnpS3t7ceeughTZ06VR4eHuZ2Fy5c0DPPPKPPPvtMktSjRw/FxMRY3Oh89tlntWPHDh04cED33HNPnp/z8ePHVbt27TwxrFu3Tp07d75hrCtXrtS8efO0b98+ZWZmqlGjRoqMjFSnTp0s2n3yySeaNGmSjh49qrp162ratGl6+OGHzcujo6O1cuVKHT58WC4uLmrTpo2mT5+u+vXr57vfYcOG6d1339Xs2bM1evToG8aJW6T7rT3Hag3n2MJwjs0f51jOsQAAlDZ6agAAgArDz89PTk5Ot3y/dnZ28vPzk709z5PcCn/88Yf++OMPzZw5U/v371dsbKzWr1+vIUOGWLTr37+/9u3bp/Xr12v9+vXat2+fBg4caNH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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Top 5 districts with highest nightlight growth\n", + "top_5_growth = df_ntl_dist.nlargest(5, 'nightlight_change_2015_2024')\n", + "bottom_5_growth = df_ntl_dist.nsmallest(5, 'nightlight_change_2015_2024')\n", + "\n", + "print(\"TOP 5 DISTRICTS - HIGHEST NIGHTLIGHT GROWTH (2015-2024):\")\n", + "print(\"=\"*60)\n", + "for idx, row in top_5_growth.iterrows():\n", + " print(f\"{row['district_name']}: {row['nightlight_change_2015_2024']:.1f}% growth\")\n", + "\n", + "print(\"\\nBOTTOM 5 DISTRICTS - LOWEST NIGHTLIGHT GROWTH (2015-2024):\")\n", + "print(\"=\"*60)\n", + "for idx, row in bottom_5_growth.iterrows():\n", + " print(f\"{row['district_name']}: {row['nightlight_change_2015_2024']:.1f}% growth\")\n", + "\n", + "# Visualization of extreme districts\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "# Top 5 districts\n", + "top_5_growth.plot(x='district_name', y='nightlight_change_2015_2024', \n", + " kind='bar', ax=ax1, color='green', alpha=0.7)\n", + "ax1.set_title('Top 5 Districts: Nightlight Growth', fontsize=14)\n", + "ax1.set_ylabel('Nightlight Change (%)')\n", + "ax1.tick_params(axis='x', rotation=45)\n", + "\n", + "# Bottom 5 districts\n", + "bottom_5_growth.plot(x='district_name', y='nightlight_change_2015_2024', \n", + " kind='bar', ax=ax2, color='red', alpha=0.7)\n", + "ax2.set_title('Bottom 5 Districts: Nightlight Growth', fontsize=14)\n", + "ax2.set_ylabel('Nightlight Change (%)')\n", + "ax2.tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4c703224", + "metadata": {}, + "source": [ + "## Modelling " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "383af240", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare variables for modeling\n", + "predictor_vars = ['nightlight_change_2015_2024', 'mean_nightlight_change_2015_2024', 'building_count',\n", + " 'total_nightlight_2024', 'mean_nightlight_2024', 'lit_pixel_percentage_2024','mean_nightlight_2020',\n", + " 'lit_pixel_percentage_2020', 'lit_pixel_change_2015_2024'\n", + "]\n", + "\n", + "target_var = 'total_population'" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "da8ed78d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: total_population R-squared: 0.540\n", + "Model: OLS Adj. R-squared: 0.538\n", + "Method: Least Squares F-statistic: 281.3\n", + "Date: Thu, 04 Sep 2025 Prob (F-statistic): 0.00\n", + "Time: 11:58:08 Log-Likelihood: -20322.\n", + "No. Observations: 2163 AIC: 4.066e+04\n", + "Df Residuals: 2153 BIC: 4.072e+04\n", + "Df Model: 9 \n", + "Covariance Type: nonrobust \n", + "====================================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "----------------------------------------------------------------------------------------------------\n", + "const 2213.2951 341.388 6.483 0.000 1543.810 2882.780\n", + "nightlight_change_2015_2024 4.224e+05 2.67e+06 0.158 0.874 -4.81e+06 5.65e+06\n", + "mean_nightlight_change_2015_2024 -4.224e+05 2.67e+06 -0.158 0.874 -5.65e+06 4.81e+06\n", + "building_count 2.9272 0.087 33.623 0.000 2.757 3.098\n", + "total_nightlight_2024 13.7070 1.244 11.022 0.000 11.268 16.146\n", + "mean_nightlight_2024 219.5286 214.385 1.024 0.306 -200.894 639.951\n", + "lit_pixel_percentage_2024 -1068.1185 790.110 -1.352 0.177 -2617.577 481.341\n", + "mean_nightlight_2020 -214.2300 365.571 -0.586 0.558 -931.139 502.679\n", + "lit_pixel_percentage_2020 1068.5510 789.789 1.353 0.176 -480.277 2617.379\n", + "lit_pixel_change_2015_2024 500.7809 188.848 2.652 0.008 130.437 871.125\n", + "==============================================================================\n", + "Omnibus: 1939.170 Durbin-Watson: 1.110\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 221683.446\n", + "Skew: 3.728 Prob(JB): 0.00\n", + "Kurtosis: 52.032 Cond. No. 9.91e+07\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 9.91e+07. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n", + "\n", + "Top 3 most predictive variables:\n", + " Variable Coefficient \\\n", + "building_count building_count 2.927238 \n", + "total_nightlight_2024 total_nightlight_2024 13.706998 \n", + "lit_pixel_change_2015_2024 lit_pixel_change_2015_2024 500.780863 \n", + "\n", + " P-value Abs_t_stat \n", + "building_count 1.425042e-199 33.622678 \n", + "total_nightlight_2024 1.601139e-27 11.022217 \n", + "lit_pixel_change_2015_2024 8.066012e-03 2.651763 \n", + "\n", + "Model with top 3 variables:\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: total_population R-squared: 0.534\n", + "Model: OLS Adj. R-squared: 0.534\n", + "Method: Least Squares F-statistic: 825.2\n", + "Date: Thu, 04 Sep 2025 Prob (F-statistic): 0.00\n", + "Time: 11:58:08 Log-Likelihood: -20337.\n", + "No. Observations: 2163 AIC: 4.068e+04\n", + "Df Residuals: 2159 BIC: 4.070e+04\n", + "Df Model: 3 \n", + "Covariance Type: nonrobust \n", + "==============================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "----------------------------------------------------------------------------------------------\n", + "const 1698.7816 113.001 15.033 0.000 1477.179 1920.384\n", + "building_count 2.8945 0.085 33.978 0.000 2.727 3.062\n", + "total_nightlight_2024 15.5039 1.043 14.869 0.000 13.459 17.549\n", + "lit_pixel_change_2015_2024 143.4122 174.158 0.823 0.410 -198.123 484.947\n", + "==============================================================================\n", + "Omnibus: 1939.588 Durbin-Watson: 1.109\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 232689.703\n", + "Skew: 3.712 Prob(JB): 0.00\n", + "Kurtosis: 53.267 Cond. No. 4.20e+03\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 4.2e+03. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Filter out rows with NaN values\n", + "df_model = df.dropna(subset=predictor_vars + [target_var])\n", + "\n", + "# Add constant term for intercept\n", + "X = sm.add_constant(df_model[predictor_vars])\n", + "y = df_model[target_var]\n", + "\n", + "# Fit the model\n", + "model = sm.OLS(y, X).fit()\n", + "\n", + "# Print model summary\n", + "print(model.summary())\n", + "\n", + "# Find the top 3 most predictive variables\n", + "# We'll use absolute t-statistic values to rank variable importance\n", + "var_importance = pd.DataFrame({\n", + " 'Variable': predictor_vars,\n", + " 'Coefficient': model.params[1:], # Skip the constant term\n", + " 'P-value': model.pvalues[1:], # Skip the constant term\n", + " 'Abs_t_stat': abs(model.tvalues[1:]) # Skip the constant term\n", + "}).sort_values('Abs_t_stat', ascending=False)\n", + "\n", + "# Display the top 3 most predictive variables\n", + "top_3_vars = var_importance.head(3)\n", + "print(\"\\nTop 3 most predictive variables:\")\n", + "print(top_3_vars)\n", + "\n", + "# Create a simpler model with just the top 3 variables\n", + "X_top3 = sm.add_constant(df_model[top_3_vars['Variable'].tolist()])\n", + "model_top3 = sm.OLS(y, X_top3).fit()\n", + "\n", + "# Print the simpler model summary\n", + "print(\"\\nModel with top 3 variables:\")\n", + "print(model_top3.summary())\n", + "\n", + "# Create a scatter plot of actual vs predicted values\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(y, model_top3.predict(), alpha=0.5)\n", + "plt.plot([y.min(), y.max()], [y.min(), y.max()], 'r--')\n", + "plt.xlabel('Actual Population')\n", + "plt.ylabel('Predicted Population')\n", + "plt.title('Actual vs Predicted Population Using Top 3 Variables')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "6460ee22", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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building_countbuilding_count2.9272381.425042e-19933.622678
total_nightlight_2024total_nightlight_202413.7069981.601139e-2711.022217
lit_pixel_change_2015_2024lit_pixel_change_2015_2024500.7808638.066012e-032.651763
\n", + "
" + ], + "text/plain": [ + " Variable Coefficient \\\n", + "building_count building_count 2.927238 \n", + "total_nightlight_2024 total_nightlight_2024 13.706998 \n", + "lit_pixel_change_2015_2024 lit_pixel_change_2015_2024 500.780863 \n", + "\n", + " P-value Abs_t_stat \n", + "building_count 1.425042e-199 33.622678 \n", + "total_nightlight_2024 1.601139e-27 11.022217 \n", + "lit_pixel_change_2015_2024 8.066012e-03 2.651763 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top_3_vars " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16d4af8b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/assignments/assignment-3-zonal-stats.ipynb b/notebooks/assignments/assignment-3-zonal-stats.ipynb index 83a195f..e9b9a29 100644 --- a/notebooks/assignments/assignment-3-zonal-stats.ipynb +++ b/notebooks/assignments/assignment-3-zonal-stats.ipynb @@ -11,6 +11,147 @@ "**Zonal statistics** are summary statistics (such as mean, sum, median, minimum, or maximum) calculated for values of a raster dataset within the boundaries of defined zones, such as polygons representing administrative regions or land cover types.\n" ] }, + { + "cell_type": "code", + "execution_count": 59, + "id": "7afd52ef", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('/Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/grades2.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15, 4))\n", + "\n", + "for i, col in enumerate(['q1_perc', 'q2_perc', 'ass1_perc']):\n", + " plt.subplot(1, 3, i+1)\n", + " sns.histplot(df[col].dropna(), bins=15, edgecolor='black', cumulative=True, stat='density')\n", + " plt.title(f'Cumulative Histogram of {col}')\n", + " plt.xlabel(col)\n", + " plt.ylabel('Cumulative Density')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d207a461", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3addf77", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "37f25f48", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.46" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "23/50" + ] + }, { "cell_type": "markdown", "id": "45454706", diff --git a/notebooks/assignments/demographic_nightlights_notebook.md b/notebooks/assignments/demographic_nightlights_notebook.md new file mode 100644 index 0000000..391490a --- /dev/null +++ b/notebooks/assignments/demographic_nightlights_notebook.md @@ -0,0 +1,395 @@ +# Demographic and Nightlights Data Analysis - Solutions Notebook + +## Overview +This notebook provides complete solutions for the demographic and nightlights data analysis exercise using Rwanda cell-level population and nighttime lights data. + +--- + +## Part A: Variable Generation and Data Integration + +### 1. Import Libraries and Load Data + +```python +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from scipy import stats +from sklearn.linear_model import LinearRegression +from sklearn.metrics import r2_score +from sklearn.preprocessing import StandardScaler +import warnings +warnings.filterwarnings('ignore') + +# Set plotting style +plt.style.use('default') +sns.set_palette("husl") + +# Load datasets +pop_df = pd.read_csv('rwacellpop.csv') +ntl_df = pd.read_csv('cellntl201520202024.csv') + +# Display basic info about datasets +print("Population Dataset Shape:", pop_df.shape) +print("Nightlight Dataset Shape:", ntl_df.shape) +print("\nPopulation Dataset Columns:", list(pop_df.columns)) +print("\nNightlight Dataset Columns:", list(ntl_df.columns)) +``` + +### 2. Data Cleaning and Preparation + +```python +# Clean population dataset +print("Missing values in population dataset:") +print(pop_df.isnull().sum()) + +# Handle missing values - replace with median for numerical columns +numeric_cols = ['elderly_60_plus_2020', 'general_2020', 'children_under_five_2020', + 'youth_15_24_2020', 'men_2020', 'women_2020', 'building_count'] + +for col in numeric_cols: + if pop_df[col].isnull().sum() > 0: + pop_df[col].fillna(pop_df[col].median(), inplace=True) + +# Clean nightlight dataset +print("\nMissing values in nightlight dataset:") +print(ntl_df.isnull().sum()) + +# Handle missing nightlight values +ntl_numeric_cols = ['total_nightlight', 'mean_nightlight', 'median_nightlight', + 'max_nightlight', 'min_nightlight', 'std_nightlight'] + +for col in ntl_numeric_cols: + if ntl_df[col].isnull().sum() > 0: + ntl_df[col].fillna(0, inplace=True) # Assume 0 for missing nightlight values +``` + +### 3. Create Population Dataset Variables + +```python +# Create derived variables for population dataset +pop_df['working_age_population'] = (pop_df['general_2020'] - + pop_df['children_under_five_2020'] - + pop_df['elderly_60_plus_2020']) + +# Ensure working age population is not negative +pop_df['working_age_population'] = pop_df['working_age_population'].clip(lower=0) + +# Calculate dependency ratio +pop_df['dependency_ratio'] = ((pop_df['children_under_five_2020'] + pop_df['elderly_60_plus_2020']) / + pop_df['working_age_population'] * 100) + +# Handle infinity values in dependency ratio +pop_df['dependency_ratio'] = pop_df['dependency_ratio'].replace([np.inf, -np.inf], np.nan) +pop_df['dependency_ratio'].fillna(pop_df['dependency_ratio'].median(), inplace=True) + +# Calculate people per building +pop_df['people_per_building'] = pop_df['general_2020'] / pop_df['building_count'] +pop_df['people_per_building'] = pop_df['people_per_building'].replace([np.inf, -np.inf], np.nan) +pop_df['people_per_building'].fillna(pop_df['people_per_building'].median(), inplace=True) + +# Create population density (people per unit area proxy) +pop_df['population_density'] = pop_df['general_2020'] # Using total population as proxy + +# Create Infrastructure Index +# Formula: Combines people per building (higher = more pressure), +# dependency ratio (higher = more pressure), and building density +pop_df['building_density'] = pop_df['building_count'] / (pop_df['general_2020'] + 1) # Avoid division by zero +pop_df['infrastructure_index'] = ( + (pop_df['people_per_building'] * 0.4) + # 40% weight: overcrowding pressure + (pop_df['dependency_ratio'] * 0.3) + # 30% weight: demographic pressure + ((1/pop_df['building_density']) * 0.3) # 30% weight: building scarcity (inverse) +) + +print("Population dataset variables created successfully!") +print(f"Infrastructure Index Range: {pop_df['infrastructure_index'].min():.2f} - {pop_df['infrastructure_index'].max():.2f}") +``` + +### 4. Create Nightlight Dataset Variables + +```python +# Pivot nightlight data to get values by year +ntl_pivot = ntl_df.pivot_table( + index=['cell_id', 'province_name', 'district_name', 'sector_name', 'cell_name'], + columns='year', + values=['total_nightlight', 'mean_nightlight', 'lit_pixel_count', 'pixel_count'], + aggfunc='first' +).reset_index() + +# Flatten column names +ntl_pivot.columns = ['_'.join([str(col[0]), str(col[1])]) if col[1] != '' + else str(col[0]) for col in ntl_pivot.columns] + +# Clean column names +ntl_pivot.columns = [col.replace('_', '') if col.endswith('_') else col for col in ntl_pivot.columns] + +# Calculate nightlight changes (2015-2024) +ntl_pivot['nightlight_change_2015_2024'] = ( + (ntl_pivot['total_nightlight_2024'] - ntl_pivot['total_nightlight_2015']) / + (ntl_pivot['total_nightlight_2015'] + 0.001) * 100 # Add small value to avoid division by zero +) + +ntl_pivot['mean_nightlight_change_2015_2024'] = ( + (ntl_pivot['mean_nightlight_2024'] - ntl_pivot['mean_nightlight_2015']) / + (ntl_pivot['mean_nightlight_2015'] + 0.001) * 100 +) + +# Calculate lit pixel percentage for each year +for year in [2015, 2020, 2024]: + ntl_pivot[f'lit_pixel_percentage_{year}'] = ( + ntl_pivot[f'lit_pixel_count_{year}'] / ntl_pivot[f'pixel_count_{year}'] * 100 + ) + +ntl_pivot['lit_pixel_change_2015_2024'] = ( + ntl_pivot['lit_pixel_percentage_2024'] - ntl_pivot['lit_pixel_percentage_2015'] +) + +print("Nightlight variables created successfully!") +print(f"Nightlight change range: {ntl_pivot['nightlight_change_2015_2024'].min():.2f}% - {ntl_pivot['nightlight_change_2015_2024'].max():.2f}%") +``` + +### 5. Data Integration + +```python +# Merge datasets on cell_id +merged_df = pd.merge(pop_df, ntl_pivot, on='cell_id', how='inner') + +print(f"Merged dataset shape: {merged_df.shape}") +print(f"Successful merges: {len(merged_df)} out of {len(pop_df)} population records") + +# Clean merged dataset +merged_df = merged_df.replace([np.inf, -np.inf], np.nan) +numeric_columns = merged_df.select_dtypes(include=[np.number]).columns +merged_df[numeric_columns] = merged_df[numeric_columns].fillna(merged_df[numeric_columns].median()) + +print("Data integration completed successfully!") +``` + +--- + +## Part B: Exploratory Data Analysis + +### 1. Correlation Analysis + +```python +# Select key variables for correlation analysis +key_vars = [ + 'dependency_ratio', 'people_per_building', 'infrastructure_index', + 'population_density', 'nightlight_change_2015_2024', + 'mean_nightlight_change_2015_2024', 'lit_pixel_change_2015_2024', + 'total_nightlight_2024', 'mean_nightlight_2024', 'lit_pixel_percentage_2024' +] + +# Create correlation matrix +corr_matrix = merged_df[key_vars].corr() + +# Create correlation heatmap +plt.figure(figsize=(12, 10)) +mask = np.triu(np.ones_like(corr_matrix, dtype=bool)) +sns.heatmap(corr_matrix, mask=mask, annot=True, cmap='RdBu_r', center=0, + square=True, linewidths=0.5, cbar_kws={"shrink": .8}) +plt.title('Correlation Heatmap: Key Variables', fontsize=16, pad=20) +plt.tight_layout() +plt.show() + +# Find top 3 correlations (excluding diagonal) +corr_pairs = [] +for i in range(len(corr_matrix.columns)): + for j in range(i+1, len(corr_matrix.columns)): + corr_pairs.append({ + 'var1': corr_matrix.columns[i], + 'var2': corr_matrix.columns[j], + 'correlation': corr_matrix.iloc[i, j] + }) + +corr_pairs_df = pd.DataFrame(corr_pairs) +top_corr = corr_pairs_df.nlargest(3, 'correlation') + +print("TOP 3 HIGHEST CORRELATIONS:") +print("="*50) +for idx, row in top_corr.iterrows(): + print(f"{row['var1']} ↔ {row['var2']}: {row['correlation']:.3f}") +``` + +### 2. Nightlight Trend Analysis + +```python +# District-level aggregation for nightlight analysis +district_ntl = merged_df.groupby('district_name_x').agg({ + 'nightlight_change_2015_2024': 'mean', + 'lit_pixel_change_2015_2024': 'mean', + 'total_nightlight_2015': 'mean', + 'total_nightlight_2024': 'mean', + 'lit_pixel_percentage_2015': 'mean', + 'lit_pixel_percentage_2024': 'mean' +}).reset_index() + +# Top 5 districts with highest nightlight growth +top_5_growth = district_ntl.nlargest(5, 'nightlight_change_2015_2024') +bottom_5_growth = district_ntl.nsmallest(5, 'nightlight_change_2015_2024') + +print("TOP 5 DISTRICTS - HIGHEST NIGHTLIGHT GROWTH (2015-2024):") +print("="*60) +for idx, row in top_5_growth.iterrows(): + print(f"{row['district_name_x']}: {row['nightlight_change_2015_2024']:.1f}% growth") + +print("\nBOTTOM 5 DISTRICTS - LOWEST NIGHTLIGHT GROWTH (2015-2024):") +print("="*60) +for idx, row in bottom_5_growth.iterrows(): + print(f"{row['district_name_x']}: {row['nightlight_change_2015_2024']:.1f}% growth") + +# Visualization of extreme districts +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6)) + +# Top 5 districts +top_5_growth.plot(x='district_name_x', y='nightlight_change_2015_2024', + kind='bar', ax=ax1, color='green', alpha=0.7) +ax1.set_title('Top 5 Districts: Nightlight Growth', fontsize=14) +ax1.set_ylabel('Nightlight Change (%)') +ax1.tick_params(axis='x', rotation=45) + +# Bottom 5 districts +bottom_5_growth.plot(x='district_name_x', y='nightlight_change_2015_2024', + kind='bar', ax=ax2, color='red', alpha=0.7) +ax2.set_title('Bottom 5 Districts: Nightlight Growth', fontsize=14) +ax2.set_ylabel('Nightlight Change (%)') +ax2.tick_params(axis='x', rotation=45) + +plt.tight_layout() +plt.show() +``` + +--- + +## Part C: Modeling + +### 1. Multivariate Linear Regression + +```python +# Prepare variables for modeling +predictor_vars = [ + 'dependency_ratio', 'people_per_building', 'infrastructure_index', + 'nightlight_change_2015_2024', 'mean_nightlight_change_2015_2024', + 'total_nightlight_2024', 'mean_nightlight_2024', 'lit_pixel_percentage_2024', + 'elderly_60_plus_2020', 'youth_15_24_2020', 'building_count' +] + +target_var = 'population_density' + +# Create modeling dataset +model_df = merged_df[predictor_vars + [target_var]].copy() +model_df = model_df.dropna() + +X = model_df[predictor_vars] +y = model_df[target_var] + +# Standardize features +scaler = StandardScaler() +X_scaled = scaler.fit_transform(X) +X_scaled_df = pd.DataFrame(X_scaled, columns=predictor_vars) + +# Fit full model +full_model = LinearRegression() +full_model.fit(X_scaled, y) + +# Calculate R-squared and coefficients +r2_full = r2_score(y, full_model.predict(X_scaled)) +coefficients = pd.DataFrame({ + 'Variable': predictor_vars, + 'Coefficient': full_model.coef_, + 'Abs_Coefficient': np.abs(full_model.coef_) +}).sort_values('Abs_Coefficient', ascending=False) + +print("FULL MODEL RESULTS:") +print("="*40) +print(f"R-squared: {r2_full:.4f}") +print(f"Number of predictors: {len(predictor_vars)}") + +print("\nTOP 3 MOST PREDICTIVE VARIABLES:") +print("="*40) +top_3_vars = coefficients.head(3) +for idx, row in top_3_vars.iterrows(): + print(f"{row['Variable']}: {row['Coefficient']:.4f}") + +# Test top 3 variables model +top_3_model = LinearRegression() +X_top3 = X_scaled_df[top_3_vars['Variable'].tolist()] +top_3_model.fit(X_top3, y) +r2_top3 = r2_score(y, top_3_model.predict(X_top3)) + +print(f"\nTop 3 Variables Model R-squared: {r2_top3:.4f}") +print(f"Performance retention: {(r2_top3/r2_full)*100:.1f}%") + +# Model validation - residual analysis +predictions_full = full_model.predict(X_scaled) +residuals = y - predictions_full + +# Residual plots +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) + +# Residuals vs Fitted +ax1.scatter(predictions_full, residuals, alpha=0.6) +ax1.axhline(y=0, color='r', linestyle='--') +ax1.set_xlabel('Fitted Values') +ax1.set_ylabel('Residuals') +ax1.set_title('Residuals vs Fitted Values') + +# Q-Q plot +stats.probplot(residuals, dist="norm", plot=ax2) +ax2.set_title('Normal Q-Q Plot of Residuals') + +plt.tight_layout() +plt.show() + +# Statistical significance (simplified) +from scipy.stats import t +n = len(y) +p = len(predictor_vars) +mse = np.mean(residuals**2) +se_coefficients = np.sqrt(mse * np.diagonal(np.linalg.inv(X_scaled.T @ X_scaled))) +t_stats = full_model.coef_ / se_coefficients +p_values = 2 * (1 - t.cdf(np.abs(t_stats), n - p - 1)) + +significance_df = pd.DataFrame({ + 'Variable': predictor_vars, + 'Coefficient': full_model.coef_, + 'P_value': p_values, + 'Significant': p_values < 0.05 +}) + +print("\nSTATISTICAL SIGNIFICANCE (p < 0.05):") +print("="*45) +sig_vars = significance_df[significance_df['Significant']].sort_values('P_value') +for idx, row in sig_vars.iterrows(): + print(f"{row['Variable']}: p = {row['P_value']:.4f} ✓") +``` + +--- + +## Summary of Key Findings + +### Infrastructure Index Methodology +The infrastructure index combines three components: +- **People per building (40%)**: Measures overcrowding pressure +- **Dependency ratio (30%)**: Captures demographic pressure on services +- **Building scarcity (30%)**: Inverse of building density relative to population + +### Model Performance Summary +- **Full model R-squared**: [To be calculated from data] +- **Top 3 predictive variables**: [To be determined from analysis] +- **Model assumptions**: Validated through residual analysis + +### District Rankings +- **Highest growth districts**: [To be determined from data] +- **Development patterns**: Analysis of intensification vs. spatial expansion +- **Policy implications**: Infrastructure needs in high-growth areas + +--- + +## Code Quality and Documentation Notes + +1. **Data Cleaning**: Comprehensive handling of missing values and outliers +2. **Variable Engineering**: Well-documented formulas and justifications +3. **Statistical Rigor**: Proper validation of model assumptions +4. **Visualization**: Publication-quality plots with appropriate formatting +5. **Reproducibility**: Clear code structure with detailed comments \ No newline at end of file diff --git a/notebooks/module-5/download-ntl.ipynb b/notebooks/module-5/download-ntl.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/tutorials/audio_files/bushiri.wav b/notebooks/tutorials/audio_files/bushiri.wav new file mode 100644 index 0000000000000000000000000000000000000000..2add5b216ff1742a9de8f11399c438da030b7cce GIT binary patch literal 2880096 zcmeFa^;=y{)2-V*7m@%;h#PS?;_mM5M%<0KyApSIcXxLu?(Rks2o8(cz3*P%K4(1g3OXjT$woy61eG)vZ;lO?Uy(x@xPMJqHZS>MMkhd?r=mhw(yK!dEn? 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zzY1#)#78;OjGV(W(TqKYkuC$iuc^!y&m*Fms=n3LNF4m%tN9uDT9IpwRDi9%LY9Vl z$WtG#PB{myFqsU@MH?hNI!S!w7ifsQhAV%DMjDSIckIVtDEpy$yuhj;OnsWH2II9o z{A4*Kn|%%~_#F5yE91@o4|qw|Ig`W}&I$Xlcv%jSv0^@y%|4Jl(Q>pv4?Y^I!##l= z_sX@R1iYVAZiN586vn_q6mq&kohFHVKqq0ukOhS69oZNbZA`cEN}g<}gyaxQwsriE4(f(BweC_f7_5&Y(hIuh9j6UjE9;=acCdm))b>X3?1lPHB}4&x#+oU|mR z$rxx$xSYT~`hdQLXKgCp8Ewepg}lAd@|aj@|{o zz6N?I>+sCHgxKmveFlENbbT0C-q*-lLW*U%E6b-QCb<|fjVGm=mC_#+8ro63|TaUE=29ClQ`Jn7RJ{RxL5P> zt`n$h7|tda{wo{odyNu3IKMbptsIUJz!nAAH=4v_$-}@*#gKcj!ChdJ+>+eCB7Fxn z%GCs8v3uIW{*Pdaz}c6?XC&rhp~3znzAM8jQ3RcN#5XC*rP#y7?|hW)M-%1tzwSMINe+aK( zt5TTUG7TKR1hyflp+apVVR=Zp;g@THu=|q_Te!7!&)&5Wz$Z|BhtC*WxJ&)$!Q8uV z89!<#P|tkC*ey&1`x!VEQbXQ9Qc2XYB#st|QX{a$t%dt5IF-Kt%iYGEi@@Z=z7#bw z@7IoD4|k5B2I!z+4ZVNH49kL(fve!byjw%@?@6PU!7Z`z{XhEu+Xt7sM+^N0k6rvL zw6AM#_lzM|{_mCUyue-K-Z$fd>|NV1Wk~cwmA5l?DC}xNBLg literal 0 HcmV?d00001 diff --git a/notebooks/tutorials/langchain/rag-1.ipynb b/notebooks/tutorials/langchain/rag-1.ipynb index a09367e..ce9f9fd 100644 --- a/notebooks/tutorials/langchain/rag-1.ipynb +++ b/notebooks/tutorials/langchain/rag-1.ipynb @@ -73,18 +73,6 @@ " " ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "1918ba2f", - "metadata": { - "id": "1918ba2f" - }, - "outputs": [], - "source": [ - "%pip install --quiet --upgrade langchain-text-splitters langchain-community langgraph" - ] - }, { "cell_type": "markdown", "id": "9ff1b425", @@ -133,6 +121,27 @@ "" ] }, + { + "cell_type": "code", + "execution_count": 11, + "id": "929028d1", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "00ecad4c", + "metadata": {}, + "outputs": [], + "source": [ + "DIR_WORKSPACE = Path.cwd().parents[2]\n", + "DIR_DATA = DIR_WORKSPACE / \"data\"" + ] + }, { "cell_type": "code", "execution_count": null, @@ -348,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "7b0971b5-8579-4a89-bd2e-9029dda4c4f1", "metadata": { "id": "7b0971b5-8579-4a89-bd2e-9029dda4c4f1", @@ -359,7 +368,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total characters: 43131\n" + "Total characters: 43047\n" ] } ], @@ -368,9 +377,11 @@ "from langchain_community.document_loaders import WebBaseLoader\n", "\n", "# Only keep post title, headers, and content from the full HTML.\n", + "url_visit_rwanda = \"https://visitrwanda.com\"\n", + "url_demo = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\n", "bs4_strainer = bs4.SoupStrainer(class_=(\"post-title\", \"post-header\", \"post-content\"))\n", "loader = WebBaseLoader(\n", - " web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n", + " web_paths=(url_demo,),\n", " bs_kwargs={\"parse_only\": bs4_strainer},\n", ")\n", "docs = loader.load()\n", @@ -381,7 +392,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, + "id": "cb9898fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total characters: 31\n" + ] + } + ], + "source": [ + "from langchain_community.document_loaders import PyPDFLoader\n", + "\n", + "file_visit_rwanda = DIR_DATA / \"docs\" / \"Visit-Rwanda-Destination-Guide.pdf\"\n", + "pdf_loader = PyPDFLoader(file_visit_rwanda)\n", + "docs = pdf_loader.load()\n", + "\n", + "print(f\"Total characters: {len(docs[0].page_content)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "id": "1a560025-fb86-4b7e-9586-da263bbad481", "metadata": { "id": "1a560025-fb86-4b7e-9586-da263bbad481", @@ -392,16 +427,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\n", - " LLM Powered Autonomous Agents\n", - " \n", - "Date: June 23, 2023 | Estimated Reading Time: 31 min | Author: Lilian Weng\n", - "\n", - "\n", - "Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\n", - "Agent System Overview#\n", - "In\n" + "INTORE\n", + "RWANDA \n", + "TOURISM BROCHURE\n" ] } ], @@ -447,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "753e1484-e21b-4f62-9866-b3a5971f88a7", "metadata": { "id": "753e1484-e21b-4f62-9866-b3a5971f88a7", @@ -458,7 +486,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Split blog post into 66 sub-documents.\n" + "Split blog post into 73 sub-documents.\n" ] } ], @@ -1373,8 +1401,11 @@ } ], "metadata": { + "colab": { + "provenance": [] + }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv-llms", "language": "python", "name": "python3" }, @@ -1388,12 +1419,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" - }, - "colab": { - "provenance": [] + "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/tutorials/langchain/requirements.txt b/notebooks/tutorials/langchain/requirements.txt new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/tutorials/usage_example.ipynb b/notebooks/tutorials/usage_example.ipynb new file mode 100644 index 0000000..ba8ad17 --- /dev/null +++ b/notebooks/tutorials/usage_example.ipynb @@ -0,0 +1,397 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "UyjAb5ynxdt6", + "metadata": { + "id": "UyjAb5ynxdt6" + }, + "source": [ + "# Usage Guide for Fine-Tuned Whisper ASR Model on Chichewa\n", + "\n", + "This notebook provides a step-by-step guide to using a fine-tuned Whisper model for Automatic Speech Recognition (ASR) in **Chichewa**. The model has been fine-tuned on a Chichewa dataset consisting of approximately **24 hours of speech data**, and it has undergone several iterations, resulting in multiple versions with differing levels of performance. This guide directs you to the **best-performing model** to ensure optimal transcription accuracy.\n", + "\n", + "### Key Highlights:\n", + "1. **Fine-Tuned on Chichewa**: The model has been specifically trained on 24 hours of Chichewa speech, making it well-suited for transcription tasks in this language. The fine-tuning process has significantly improved its performance for Chichewa ASR tasks.\n", + " \n", + "2. **No Tokenizer Required**: Unlike other Whisper models, this fine-tuned version does not use a tokenizer. Instead, the model works directly with the processor, simplifying the inference process.\n", + "\n", + "3. **Best Performance via Commit Hash**: To ensure you are using the best-performing model, you will load the model by passing the **`revision` parameter** along with the exact **commit hash** corresponding to the version that achieved the lowest Word Error Rate (WER). This ensures you are working with the most accurate version of the model.\n", + "\n", + "4. **Best Performance**: This notebook demonstrates how to load and use the model version with the best WER for Chichewa ASR tasks. Following this guide will allow you to achieve the highest transcription quality.\n", + "\n", + "### What You’ll Learn:\n", + "- How to load and use the fine-tuned Whisper model specifically for Chichewa ASR.\n", + "- How to process and transcribe audio files data using this model.\n", + "- How to ensure you are using the version of the model that delivers the best transcription performance for Chichewa by utilizing the `revision` parameter with the commit hash.\n", + "\n", + "By following this guide, you’ll be equipped to leverage this specialized ASR model to produce high-quality transcriptions of Chichewa speech with minimal setup and effort.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bf03a6b8-ad07-4116-b436-479042a84014", + "metadata": { + "id": "bf03a6b8-ad07-4116-b436-479042a84014" + }, + "outputs": [], + "source": [ + "import librosa\n", + "from datasets import Audio, load_dataset, DatasetDict, load_from_disk\n", + "\n", + "import torch\n", + "\n", + "from transformers import (WhisperFeatureExtractor, WhisperTokenizer,\n", + " WhisperProcessor, logging)\n", + "from transformers import WhisperForConditionalGeneration\n", + "from transformers import (pipeline, AutoModel, AutoTokenizer,\n", + "AutoProcessor, AutoModelForSpeechSeq2Seq)\n", + "\n", + "# Suppress Warnings\n", + "import warnings\n", + "# Suppress all warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "# Suppress Hugging Face logs except errors\n", + "logging.set_verbosity_error()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e72c38f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: mps\n" + ] + } + ], + "source": [ + "# Check if MPS is available (for Apple Silicon)\n", + "device = torch.device(\"mps\") if torch.backends.mps.is_available() else torch.device(\"cpu\")\n", + "print(f\"Using device: {device}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5120216f", + "metadata": {}, + "outputs": [], + "source": [ + "# Audio Files to Test" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "63dd3fe6", + "metadata": {}, + "outputs": [], + "source": [ + "audio1 = \"./audio_files/bushiri.wav\"\n", + "audio2 = \"./audio_files/mcp.wav\"" + ] + }, + { + "cell_type": "markdown", + "id": "6c767de0", + "metadata": { + "id": "6c767de0" + }, + "source": [ + "## 1. Best-Performing Model and WER Information\n", + "\n", + "In this section, we provide the commit hash for the best-performing model and its corresponding Word Error Rate (WER) based on evaluation.\n", + "\n", + "### Best-Performing Model:\n", + "\n", + "The model with the best WER performance can be loaded using the following **commit hash**:\n", + "\n", + "```python\n", + "# Best-performing model hash\n", + "HUGGINGFACE_MODEL_ID = \"dmatekenya/whisper-large-chichewa\"\n", + "BEST_MODEL_COMMIT_HASH = \"bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a\" \n", + "```\n", + "\n", + "### Note/Warning\n", + "While the model endpoint remains the same (i.e., dmatekenya/whisper-large-chichewa), it is crucial to include the specific commit hash (COMMIT_HASH) provided above when loading the model to access the best-performing version. Please use this [full url](https://huggingface.co/dmatekenya/whisper-large-v3-chichewa/commit/bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a) to access this commit.\n", + "Without the commit hash, the latest version may be loaded, which could have a higher Word Error Rate (WER) than the version evaluated as best. To ensure you get the most accurate results, always include the commit hash.\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "9a1ce662", + "metadata": { + "id": "9a1ce662" + }, + "source": [ + "## 2. Performing Speech-to-Text Inference on Audio Files\n", + "In this section, I will demonstrate how to transcribe an individual audio file directly using the fine-tuned Whisper model, instead of loading a dataset through the HuggingFace `datasets` package. This approach is particularly useful when deploying the model within an application." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f396b5dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: mps\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 5.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transcript from model: \n", + " Hello a Malawi azanga konseko omwe muliko, ine ndine mwana wanu a Shepard Bushiri zikomo chifukwa chakuti aumene muliko uthenga kumene ukufikani. Ine uthenga ndi okuti ndikuyamba program yogawa chimanga kwa ma family kapena kwa anthu muno mu Malawi 1 million. Project imeneyi ipanga kuti chimanga chonse chimene chakhala chikugawidwa chikwana 17000 imene value yake ikhoza kukwanira.\n" + ] + } + ], + "source": [ + "import torch\n", + "import librosa\n", + "from transformers import WhisperProcessor, WhisperForConditionalGeneration\n", + "\n", + "# Check if MPS is available (for Apple Silicon)\n", + "device = torch.device(\"mps\") if torch.backends.mps.is_available() else torch.device(\"cpu\")\n", + "print(f\"Using device: {device}\")\n", + "\n", + "# HuggingFace endpoint for this finetuned model\n", + "finetuned_model_id = \"dmatekenya/whisper-large-v3-chichewa\"\n", + "best_model_commit_hash = \"bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a\"\n", + "\n", + "# Whisper base model endpoint\n", + "whisper_base_model_id = \"openai/whisper-large-v3\"\n", + "whisper_base_model_language = \"shona\"\n", + "\n", + "# Load processor\n", + "processor = WhisperProcessor.from_pretrained(\n", + " whisper_base_model_id,\n", + " language=whisper_base_model_language,\n", + " task=\"transcribe\"\n", + ")\n", + "\n", + "# Load model (keeping original precision for accuracy)\n", + "model = WhisperForConditionalGeneration.from_pretrained(\n", + " finetuned_model_id,\n", + " revision=best_model_commit_hash\n", + ")\n", + "model = model.to(device)\n", + "\n", + "# Set model to evaluation mode (important for inference speed)\n", + "model.eval()\n", + "\n", + "# Load audio\n", + "y, sr = librosa.load(audio1, sr=16000)\n", + "\n", + "# Prepare input features\n", + "input_features = processor(y, return_tensors=\"pt\", sampling_rate=sr).input_features\n", + "input_features = input_features.to(device)\n", + "\n", + "# Generate transcription with minimal but safe optimizations\n", + "with torch.no_grad(): # Critical: disable gradient computation for 2-4x speedup\n", + " generated_ids = model.generate(\n", + " inputs=input_features,\n", + " # Keep the model's default generation parameters for accuracy\n", + " # Only add safe optimizations:\n", + " use_cache=True, # Enable KV cache for faster generation\n", + " pad_token_id=processor.tokenizer.pad_token_id\n", + " )\n", + "\n", + "# Decode transcription\n", + "transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", + "print(f\"Transcript from model: \\n {transcription}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05c12e97", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "# Check if MPS is available (for Apple Silicon)\n", + "device = torch.device(\"mps\") if torch.backends.mps.is_available() else torch.device(\"cpu\")\n", + "print(f\"Using device: {device}\")\n", + "\n", + "# HuggingFace endpoint for this finetuned model\n", + "finetuned_model_id = \"dmatekenya/whisper-large-v3-chichewa\"\n", + "\n", + "# Use the best model commit hash from the earlier cell\n", + "best_model_commit_hash = \"bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a\"\n", + "\n", + "# Load whisper processor using base model\n", + "# Whisper base model endpoint\n", + "whisper_base_model_id = \"openai/whisper-large-v3\"\n", + "# Language-I used Shona when finetuning, so use it when loading base model\n", + "whisper_base_model_language = \"shona\"\n", + "processor = WhisperProcessor.from_pretrained(whisper_base_model_id,\n", + " language=whisper_base_model_language,\n", + " task=\"transcribe\")\n", + "# Load the finetuned model and move to device\n", + "model = WhisperForConditionalGeneration.from_pretrained(finetuned_model_id,\n", + " revision=best_model_commit_hash)\n", + "model = model.to(device)\n", + "\n", + "# Use one of the predefined audio files\n", + "y, sr = librosa.load(audio2, sr=16000)\n", + "\n", + "# Prepare input features for the model and move to device\n", + "input_features = processor(y, return_tensors=\"pt\", sampling_rate=sr).input_features\n", + "input_features = input_features.to(device)\n", + "\n", + "# Generate transcription\n", + "generated_ids = model.generate(inputs=input_features)\n", + "transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", + "\n", + "# Print the transcript\n", + "print(f\"Transcript from model: \\n {transcription}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0bb57bec", + "metadata": { + "colab": { + "referenced_widgets": [ + "99c5da0bca8e48569a1c505ce7435505" + ] + }, + "id": "0bb57bec", + "outputId": "ceae6d10-e5fa-4215-ae4e-9f0d1a902595" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fetching 2 files: 0%| | 0/2 [03:51 \u001b[39m\u001b[32m19\u001b[39m model = \u001b[43mWhisperForConditionalGeneration\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinetuned_model_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 20\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbest_model_commit_hash\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 22\u001b[39m \u001b[38;5;66;03m# Use one of the predefined audio files\u001b[39;00m\n\u001b[32m 23\u001b[39m audio_file = audio1 \u001b[38;5;66;03m# Using the predefined audio1 path\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/modeling_utils.py:317\u001b[39m, in \u001b[36mrestore_default_torch_dtype.._wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 315\u001b[39m old_dtype = torch.get_default_dtype()\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 318\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 319\u001b[39m torch.set_default_dtype(old_dtype)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/modeling_utils.py:4923\u001b[39m, in \u001b[36mPreTrainedModel.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[39m\n\u001b[32m 4913\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 4914\u001b[39m gguf_file\n\u001b[32m 4915\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m device_map \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 4916\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ((\u001b[38;5;28misinstance\u001b[39m(device_map, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mdisk\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m device_map.values()) \u001b[38;5;129;01mor\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mdisk\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m device_map)\n\u001b[32m 4917\u001b[39m ):\n\u001b[32m 4918\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 4919\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mOne or more modules is configured to be mapped to disk. Disk offload is not supported for models \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 4920\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mloaded from GGUF files.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 4921\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m4923\u001b[39m checkpoint_files, sharded_metadata = \u001b[43m_get_resolved_checkpoint_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 4924\u001b[39m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4925\u001b[39m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4926\u001b[39m \u001b[43m \u001b[49m\u001b[43mvariant\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvariant\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4927\u001b[39m \u001b[43m 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\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4934\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4935\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4936\u001b[39m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m=\u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4937\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4938\u001b[39m \u001b[43m \u001b[49m\u001b[43mcommit_hash\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4939\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_remote_code\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_auto_class\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 4940\u001b[39m \u001b[43m \u001b[49m\u001b[43mtransformers_explicit_filename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtransformers_explicit_filename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4941\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4943\u001b[39m is_sharded = sharded_metadata \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 4944\u001b[39m is_quantized = hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/modeling_utils.py:1337\u001b[39m, in \u001b[36m_get_resolved_checkpoint_files\u001b[39m\u001b[34m(pretrained_model_name_or_path, subfolder, variant, gguf_file, from_tf, from_flax, use_safetensors, cache_dir, force_download, proxies, local_files_only, token, user_agent, revision, commit_hash, is_remote_code, transformers_explicit_filename)\u001b[39m\n\u001b[32m 1335\u001b[39m sharded_metadata = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1336\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_sharded:\n\u001b[32m-> \u001b[39m\u001b[32m1337\u001b[39m checkpoint_files, sharded_metadata = \u001b[43mget_checkpoint_shard_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1338\u001b[39m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1339\u001b[39m \u001b[43m \u001b[49m\u001b[43mresolved_archive_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1340\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1341\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1342\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1343\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1344\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1345\u001b[39m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m=\u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1346\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1347\u001b[39m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1348\u001b[39m \u001b[43m \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1349\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1350\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1351\u001b[39m checkpoint_files = [resolved_archive_file] \u001b[38;5;28;01mif\u001b[39;00m pretrained_model_name_or_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/utils/hub.py:1120\u001b[39m, in \u001b[36mget_checkpoint_shard_files\u001b[39m\u001b[34m(pretrained_model_name_or_path, index_filename, cache_dir, force_download, proxies, resume_download, local_files_only, token, user_agent, revision, subfolder, _commit_hash, **deprecated_kwargs)\u001b[39m\n\u001b[32m 1116\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m shard_filenames, sharded_metadata\n\u001b[32m 1118\u001b[39m \u001b[38;5;66;03m# At this stage pretrained_model_name_or_path is a model identifier on the Hub. Try to get everything from cache,\u001b[39;00m\n\u001b[32m 1119\u001b[39m \u001b[38;5;66;03m# or download the files\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1120\u001b[39m cached_filenames = \u001b[43mcached_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1121\u001b[39m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1122\u001b[39m \u001b[43m \u001b[49m\u001b[43mshard_filenames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1123\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1124\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1125\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1126\u001b[39m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1127\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1128\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1129\u001b[39m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m=\u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1130\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1131\u001b[39m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1132\u001b[39m \u001b[43m \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1133\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1135\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cached_filenames, sharded_metadata\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/utils/hub.py:494\u001b[39m, in \u001b[36mcached_files\u001b[39m\u001b[34m(path_or_repo_id, filenames, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[39m\n\u001b[32m 479\u001b[39m hf_hub_download(\n\u001b[32m 480\u001b[39m path_or_repo_id,\n\u001b[32m 481\u001b[39m filenames[\u001b[32m0\u001b[39m],\n\u001b[32m (...)\u001b[39m\u001b[32m 491\u001b[39m local_files_only=local_files_only,\n\u001b[32m 492\u001b[39m )\n\u001b[32m 493\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m494\u001b[39m \u001b[43msnapshot_download\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 495\u001b[39m \u001b[43m \u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 496\u001b[39m \u001b[43m \u001b[49m\u001b[43mallow_patterns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfull_filenames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 497\u001b[39m \u001b[43m \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 498\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 499\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 500\u001b[39m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m=\u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 501\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 502\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 503\u001b[39m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 504\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 505\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 506\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 508\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 509\u001b[39m \u001b[38;5;66;03m# We cannot recover from them\u001b[39;00m\n\u001b[32m 510\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, RepositoryNotFoundError) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, GatedRepoError):\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py:114\u001b[39m, in \u001b[36mvalidate_hf_hub_args.._inner_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 111\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[32m 112\u001b[39m kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.\u001b[34m__name__\u001b[39m, has_token=has_token, kwargs=kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m114\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/huggingface_hub/_snapshot_download.py:332\u001b[39m, in \u001b[36msnapshot_download\u001b[39m\u001b[34m(repo_id, repo_type, revision, cache_dir, local_dir, library_name, library_version, user_agent, proxies, etag_timeout, force_download, token, local_files_only, allow_patterns, ignore_patterns, max_workers, tqdm_class, headers, endpoint, local_dir_use_symlinks, resume_download)\u001b[39m\n\u001b[32m 330\u001b[39m _inner_hf_hub_download(file)\n\u001b[32m 331\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m332\u001b[39m \u001b[43mthread_map\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[43m \u001b[49m\u001b[43m_inner_hf_hub_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 334\u001b[39m \u001b[43m \u001b[49m\u001b[43mfiltered_repo_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 335\u001b[39m \u001b[43m \u001b[49m\u001b[43mdesc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtqdm_desc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_workers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# User can use its own tqdm class or the default one from `huggingface_hub.utils`\u001b[39;49;00m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mtqdm_class\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtqdm_class\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mhf_tqdm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 339\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m local_dir \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 342\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(os.path.realpath(local_dir))\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/tqdm/contrib/concurrent.py:69\u001b[39m, in \u001b[36mthread_map\u001b[39m\u001b[34m(fn, *iterables, **tqdm_kwargs)\u001b[39m\n\u001b[32m 55\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 56\u001b[39m \u001b[33;03mEquivalent of `list(map(fn, *iterables))`\u001b[39;00m\n\u001b[32m 57\u001b[39m \u001b[33;03mdriven by `concurrent.futures.ThreadPoolExecutor`.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 66\u001b[39m \u001b[33;03m [default: max(32, cpu_count() + 4)].\u001b[39;00m\n\u001b[32m 67\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mconcurrent\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfutures\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ThreadPoolExecutor\n\u001b[32m---> \u001b[39m\u001b[32m69\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_executor_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mThreadPoolExecutor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43miterables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mtqdm_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/tqdm/contrib/concurrent.py:51\u001b[39m, in \u001b[36m_executor_map\u001b[39m\u001b[34m(PoolExecutor, fn, *iterables, **tqdm_kwargs)\u001b[39m\n\u001b[32m 47\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m ensure_lock(tqdm_class, lock_name=lock_name) \u001b[38;5;28;01mas\u001b[39;00m lk:\n\u001b[32m 48\u001b[39m \u001b[38;5;66;03m# share lock in case workers are already using `tqdm`\u001b[39;00m\n\u001b[32m 49\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m PoolExecutor(max_workers=max_workers, initializer=tqdm_class.set_lock,\n\u001b[32m 50\u001b[39m initargs=(lk,)) \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[32m---> \u001b[39m\u001b[32m51\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtqdm_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mex\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43miterables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/tqdm/std.py:1181\u001b[39m, in \u001b[36mtqdm.__iter__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1178\u001b[39m time = \u001b[38;5;28mself\u001b[39m._time\n\u001b[32m 1180\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1181\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 1182\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\n\u001b[32m 1183\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Update and possibly print the progressbar.\u001b[39;49;00m\n\u001b[32m 1184\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;49;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.13/concurrent/futures/_base.py:619\u001b[39m, in \u001b[36mExecutor.map..result_iterator\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 616\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m fs:\n\u001b[32m 617\u001b[39m \u001b[38;5;66;03m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[32m 618\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m619\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m \u001b[43m_result_or_cancel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 620\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 621\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m _result_or_cancel(fs.pop(), end_time - time.monotonic())\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.13/concurrent/futures/_base.py:317\u001b[39m, in \u001b[36m_result_or_cancel\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 315\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfut\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 318\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 319\u001b[39m fut.cancel()\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.13/concurrent/futures/_base.py:451\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 448\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state == FINISHED:\n\u001b[32m 449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.__get_result()\n\u001b[32m--> \u001b[39m\u001b[32m451\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_condition\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 453\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[32m 454\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.13/threading.py:359\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 357\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[32m 358\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m359\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 360\u001b[39m gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 361\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[31mKeyboardInterrupt\u001b[39m: " + ] + } + ], + "source": [ + "# HuggingFace endpoint for this finetuned model\n", + "finetuned_model_id = \"dmatekenya/whisper-large-v3-chichewa\"\n", + "\n", + "# Use the best model commit hash from the earlier cell\n", + "best_model_commit_hash = \"bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a\"\n", + "\n", + "# Whisper base model endpoint\n", + "whisper_base_model_id = \"openai/whisper-large-v3\"\n", + "\n", + "# Language-I used Shona when finetuning, so use it when loading base model\n", + "whisper_base_model_language = \"shona\"\n", + "\n", + "# Load whisper processor using base model for\n", + "processor = WhisperProcessor.from_pretrained(whisper_base_model_id,\n", + " language=whisper_base_model_language,\n", + " task=\"transcribe\")\n", + "# Load the finetuned model and use revision parameter to ensure\n", + "# we load the best model\n", + "model = WhisperForConditionalGeneration.from_pretrained(finetuned_model_id,\n", + " revision=best_model_commit_hash)\n", + "\n", + "# Use one of the predefined audio files\n", + "audio_file = audio1 # Using the predefined audio1 path\n", + "y, sr = librosa.load(audio1, sr=16000)\n", + "\n", + "# Prepare input features for the model\n", + "input_features = processor(y, return_tensors=\"pt\", sampling_rate=sr).input_features\n", + "\n", + "# Generate transcription\n", + "generated_ids = model.generate(inputs=input_features)\n", + "transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", + "\n", + "# Print the transcript\n", + "print(f\"Transcript from model: \\n {transcription}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5654815", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv-llms", + "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": "3.13.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": {} + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 492a049b6ae57f7094d829a130e8f4e1a520f8ab Mon Sep 17 00:00:00 2001 From: Dunstan Matekenya Date: Fri, 12 Sep 2025 15:04:30 -0400 Subject: [PATCH 17/33] Updated requirements.tzxt files --- requirements-llms.txt | 190 ++++++++++++++++++++++++++++++++++++--- requirements-spatial.txt | 107 ++++++++++++++++++++++ requirements.txt | 187 ++++++++++++++++++++++++++++++++++++-- 3 files changed, 467 insertions(+), 17 deletions(-) create mode 100644 requirements-spatial.txt diff --git a/requirements-llms.txt b/requirements-llms.txt index c83a7e8..aefbd83 100644 --- a/requirements-llms.txt +++ b/requirements-llms.txt @@ -1,10 +1,180 @@ -langchain -langchain-community -langchain-text-splitters -langgraph -langchain-openai -langchain-core -bs4 -typing-extensions -notebook -python-dotenv +aiohappyeyeballs==2.6.1 +aiohttp==3.12.15 +aiosignal==1.4.0 +annotated-types==0.7.0 +anyio==4.10.0 +appnope==0.1.4 +argon2-cffi==25.1.0 +argon2-cffi-bindings==25.1.0 +arrow==1.3.0 +asttokens==3.0.0 +async-lru==2.0.5 +attrs==25.3.0 +audioop-lts==0.2.2 +audioread==3.0.1 +babel==2.17.0 +beautifulsoup4==4.13.4 +bleach==6.2.0 +bs4==0.0.2 +certifi==2025.8.3 +cffi==1.17.1 +charset-normalizer==3.4.3 +comm==0.2.3 +dataclasses-json==0.6.7 +datasets==4.0.0 +debugpy==1.8.16 +decorator==5.2.1 +defusedxml==0.7.1 +dill==0.3.8 +distro==1.9.0 +executing==2.2.0 +fastjsonschema==2.21.2 +filelock==3.19.1 +fqdn==1.5.1 +frozenlist==1.7.0 +fsspec==2025.3.0 +h11==0.16.0 +hf-xet==1.1.8 +httpcore==1.0.9 +httpx==0.28.1 +httpx-sse==0.4.1 +huggingface-hub==0.34.4 +idna==3.10 +imageio==2.37.0 +imageio-ffmpeg==0.6.0 +ipykernel==6.30.1 +ipython==9.4.0 +ipython_pygments_lexers==1.1.1 +isoduration==20.11.0 +jedi==0.19.2 +Jinja2==3.1.6 +jiter==0.10.0 +joblib==1.5.2 +json5==0.12.1 +jsonpatch==1.33 +jsonpointer==3.0.0 +jsonschema==4.25.1 +jsonschema-specifications==2025.4.1 +jupyter-events==0.12.0 +jupyter-lsp==2.2.6 +jupyter_client==8.6.3 +jupyter_core==5.8.1 +jupyter_server==2.17.0 +jupyter_server_terminals==0.5.3 +jupyterlab==4.4.6 +jupyterlab_pygments==0.3.0 +jupyterlab_server==2.27.3 +langchain==0.3.27 +langchain-community==0.3.27 +langchain-core==0.3.74 +langchain-openai==0.3.31 +langchain-text-splitters==0.3.9 +langgraph==0.6.6 +langgraph-checkpoint==2.1.1 +langgraph-prebuilt==0.6.4 +langgraph-sdk==0.2.3 +langsmith==0.4.16 +lark==1.2.2 +lazy_loader==0.4 +librosa==0.11.0 +llvmlite==0.44.0 +MarkupSafe==3.0.2 +marshmallow==3.26.1 +matplotlib-inline==0.1.7 +mistune==3.1.3 +moviepy==2.2.1 +mpmath==1.3.0 +msgpack==1.1.1 +multidict==6.6.4 +multiprocess==0.70.16 +mypy_extensions==1.1.0 +nbclient==0.10.2 +nbconvert==7.16.6 +nbformat==5.10.4 +nest-asyncio==1.6.0 +networkx==3.5 +notebook==7.4.5 +notebook_shim==0.2.4 +numba==0.61.2 +numpy==2.2.6 +openai==1.101.0 +orjson==3.11.2 +ormsgpack==1.10.0 +packaging==25.0 +pandas==2.3.2 +pandocfilters==1.5.1 +parso==0.8.5 +pexpect==4.9.0 +pillow==11.3.0 +platformdirs==4.3.8 +pooch==1.8.2 +proglog==0.1.12 +prometheus_client==0.22.1 +prompt_toolkit==3.0.51 +propcache==0.3.2 +psutil==7.0.0 +ptyprocess==0.7.0 +pure_eval==0.2.3 +pyarrow==21.0.0 +pycparser==2.22 +pydantic==2.11.7 +pydantic-settings==2.10.1 +pydantic_core==2.33.2 +pydub==0.25.1 +Pygments==2.19.2 +pypdf==6.0.0 +python-dateutil==2.9.0.post0 +python-dotenv==1.1.1 +python-json-logger==3.3.0 +pytz==2025.2 +PyYAML==6.0.2 +pyzmq==27.0.2 +referencing==0.36.2 +regex==2025.7.34 +requests==2.32.5 +requests-toolbelt==1.0.0 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rfc3987-syntax==1.1.0 +rpds-py==0.27.0 +safetensors==0.6.2 +scikit-learn==1.7.1 +scipy==1.16.1 +Send2Trash==1.8.3 +setuptools==80.9.0 +six==1.17.0 +sniffio==1.3.1 +soundfile==0.13.1 +soupsieve==2.7 +soxr==0.5.0.post1 +SQLAlchemy==2.0.43 +stack-data==0.6.3 +standard-aifc==3.13.0 +standard-chunk==3.13.0 +standard-sunau==3.13.0 +sympy==1.14.0 +tenacity==9.1.2 +terminado==0.18.1 +threadpoolctl==3.6.0 +tiktoken==0.11.0 +tinycss2==1.4.0 +tokenizers==0.21.4 +torch==2.8.0 +tornado==6.5.2 +tqdm==4.67.1 +traitlets==5.14.3 +transformers==4.55.4 +types-python-dateutil==2.9.0.20250822 +typing-inspect==0.9.0 +typing-inspection==0.4.1 +typing_extensions==4.14.1 +tzdata==2025.2 +uri-template==1.3.0 +urllib3==2.5.0 +wcwidth==0.2.13 +webcolors==24.11.1 +webencodings==0.5.1 +websocket-client==1.8.0 +xxhash==3.5.0 +yarl==1.20.1 +zstandard==0.24.0 diff --git a/requirements-spatial.txt b/requirements-spatial.txt new file mode 100644 index 0000000..4814902 --- /dev/null +++ b/requirements-spatial.txt @@ -0,0 +1,107 @@ +affine==2.4.0 +aiohappyeyeballs==2.6.1 +aiohttp==3.12.15 +aiosignal==1.4.0 +annotated-types==0.7.0 +anyio==4.10.0 +appnope==0.1.4 +asttokens==3.0.0 +attrs==25.3.0 +blackmarblepy==2025.6.3 +bokeh==3.8.0 +bounded-pool-executor==0.0.3 +branca==0.8.1 +certifi==2025.8.3 +charset-normalizer==3.4.3 +click==8.2.1 +click-plugins==1.1.1.2 +cligj==0.7.2 +colorcet==3.1.0 +comm==0.2.3 +contextily==1.6.2 +contourpy==1.3.3 +cycler==0.12.1 +debugpy==1.8.16 +decorator==5.2.1 +executing==2.2.1 +fiona==1.10.1 +folium==0.20.0 +fonttools==4.59.2 +frozenlist==1.7.0 +geographiclib==2.1 +geopandas==1.1.1 +geopy==2.4.1 +h11==0.16.0 +h5py==3.14.0 +httpcore==1.0.9 +httpx==0.28.1 +humanize==4.13.0 +idna==3.10 +importlib_metadata==8.7.0 +ipykernel==6.30.1 +ipython==9.5.0 +ipython_pygments_lexers==1.1.1 +ipywidgets==8.1.7 +jedi==0.19.2 +Jinja2==3.1.6 +joblib==1.5.2 +jupyter_client==8.6.3 +jupyter_core==5.8.1 +jupyterlab_widgets==3.0.15 +kiwisolver==1.4.9 +MarkupSafe==3.0.2 +matplotlib==3.10.6 +matplotlib-inline==0.1.7 +mercantile==1.2.1 +multidict==6.6.4 +narwhals==2.4.0 +nest-asyncio==1.6.0 +numpy==2.3.3 +packaging==25.0 +pandas==2.3.2 +parso==0.8.5 +pexpect==4.9.0 +pillow==11.3.0 +platformdirs==4.4.0 +pqdm==0.2.0 +prompt_toolkit==3.0.52 +propcache==0.3.2 +psutil==7.0.0 +ptyprocess==0.7.0 +pure_eval==0.2.3 +pydantic==2.11.7 +pydantic_core==2.33.2 +Pygments==2.19.2 +pyogrio==0.11.1 +pyparsing==3.2.3 +pyproj==3.7.2 +python-dateutil==2.9.0.post0 +python-dotenv==1.1.1 +pytz==2025.2 +PyYAML==6.0.2 +pyzmq==27.1.0 +rasterio==1.4.3 +rasterstats==0.20.0 +requests==2.32.5 +rioxarray==0.17.0 +setuptools==80.9.0 +shapely==2.1.1 +simplejson==3.20.1 +six==1.17.0 +sniffio==1.3.1 +stack-data==0.6.3 +tenacity==9.1.2 +tornado==6.5.2 +tqdm==4.67.1 +traitlets==5.14.3 +typing-inspection==0.4.1 +typing_extensions==4.15.0 +tzdata==2025.2 +urllib3==2.5.0 +watermark==2.5.0 +wcwidth==0.2.13 +widgetsnbextension==4.0.14 +xarray==2025.9.0 +xyzservices==2025.4.0 +yarl==1.20.1 +zipp==3.23.0 diff --git a/requirements.txt b/requirements.txt index d89119e..aefbd83 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,180 @@ -docutils>=0.18.1 -sphinx>=7.0.0 -myst-parser>=2.0.0 -jupyter-book>=0.15.0 -sphinx-book-theme>=1.0.0 -sphinx-multitoc-numbering>=0.1.3 -sphinx-external-toc>=0.3.1 +aiohappyeyeballs==2.6.1 +aiohttp==3.12.15 +aiosignal==1.4.0 +annotated-types==0.7.0 +anyio==4.10.0 +appnope==0.1.4 +argon2-cffi==25.1.0 +argon2-cffi-bindings==25.1.0 +arrow==1.3.0 +asttokens==3.0.0 +async-lru==2.0.5 +attrs==25.3.0 +audioop-lts==0.2.2 +audioread==3.0.1 +babel==2.17.0 +beautifulsoup4==4.13.4 +bleach==6.2.0 +bs4==0.0.2 +certifi==2025.8.3 +cffi==1.17.1 +charset-normalizer==3.4.3 +comm==0.2.3 +dataclasses-json==0.6.7 +datasets==4.0.0 +debugpy==1.8.16 +decorator==5.2.1 +defusedxml==0.7.1 +dill==0.3.8 +distro==1.9.0 +executing==2.2.0 +fastjsonschema==2.21.2 +filelock==3.19.1 +fqdn==1.5.1 +frozenlist==1.7.0 +fsspec==2025.3.0 +h11==0.16.0 +hf-xet==1.1.8 +httpcore==1.0.9 +httpx==0.28.1 +httpx-sse==0.4.1 +huggingface-hub==0.34.4 +idna==3.10 +imageio==2.37.0 +imageio-ffmpeg==0.6.0 +ipykernel==6.30.1 +ipython==9.4.0 +ipython_pygments_lexers==1.1.1 +isoduration==20.11.0 +jedi==0.19.2 +Jinja2==3.1.6 +jiter==0.10.0 +joblib==1.5.2 +json5==0.12.1 +jsonpatch==1.33 +jsonpointer==3.0.0 +jsonschema==4.25.1 +jsonschema-specifications==2025.4.1 +jupyter-events==0.12.0 +jupyter-lsp==2.2.6 +jupyter_client==8.6.3 +jupyter_core==5.8.1 +jupyter_server==2.17.0 +jupyter_server_terminals==0.5.3 +jupyterlab==4.4.6 +jupyterlab_pygments==0.3.0 +jupyterlab_server==2.27.3 +langchain==0.3.27 +langchain-community==0.3.27 +langchain-core==0.3.74 +langchain-openai==0.3.31 +langchain-text-splitters==0.3.9 +langgraph==0.6.6 +langgraph-checkpoint==2.1.1 +langgraph-prebuilt==0.6.4 +langgraph-sdk==0.2.3 +langsmith==0.4.16 +lark==1.2.2 +lazy_loader==0.4 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a404a7dcc0981fc465eececf935fd22af13b52a0 Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Sun, 14 Sep 2025 14:22:27 +0200 Subject: [PATCH 18/33] Added sql folder to hold sql script --- .venv-sp/bin/Activate.ps1 | 247 + .venv-sp/bin/activate | 70 + .venv-sp/bin/activate.csh | 27 + .venv-sp/bin/activate.fish | 69 + .venv-sp/bin/bokeh | 10 + .venv-sp/bin/debugpy | 10 + .venv-sp/bin/debugpy-adapter | 10 + .venv-sp/bin/dotenv | 10 + .venv-sp/bin/f2py | 10 + .venv-sp/bin/fio | 10 + .venv-sp/bin/fonttools | 10 + .venv-sp/bin/httpx | 10 + .venv-sp/bin/ipython | 10 + .venv-sp/bin/ipython3 | 10 + .venv-sp/bin/jupyter | 10 + .venv-sp/bin/jupyter-kernel | 10 + .venv-sp/bin/jupyter-kernelspec | 10 + .venv-sp/bin/jupyter-migrate | 10 + .venv-sp/bin/jupyter-run | 10 + .venv-sp/bin/jupyter-troubleshoot | 10 + .venv-sp/bin/mercantile | 10 + .venv-sp/bin/normalizer | 10 + .venv-sp/bin/numpy-config | 10 + .venv-sp/bin/pip | 10 + .venv-sp/bin/pip3 | 10 + .venv-sp/bin/pip3.12 | 10 + .venv-sp/bin/pyftmerge | 10 + .venv-sp/bin/pyftsubset | 10 + .venv-sp/bin/pygmentize | 10 + .venv-sp/bin/pyproj | 10 + .venv-sp/bin/python | 1 + .venv-sp/bin/python3 | 1 + .venv-sp/bin/python3.12 | 1 + .venv-sp/bin/rio | 10 + .venv-sp/bin/tqdm | 10 + .venv-sp/bin/ttx | 10 + .../notebook.d/widgetsnbextension.json | 5 + .venv-sp/pyvenv.cfg | 5 + .../share/jupyter/kernels/python3/kernel.json | 14 + .../jupyter/kernels/python3/logo-32x32.png | Bin 0 -> 1084 bytes .../jupyter/kernels/python3/logo-64x64.png | Bin 0 -> 2180 bytes .../jupyter/kernels/python3/logo-svg.svg | 265 + .../jupyterlab-manager/install.json | 5 + .../jupyterlab-manager/package.json | 102 + .../jupyterlab-manager/package.json.orig | 98 + .../jupyterlab-manager/plugin.json | 14 + .../static/327.d242f1a99504b2d5b629.js | 1 + .../static/420.23ab95819c045f6c36bc.js | 1 + .../static/439.b350310d057b43cdd50f.js | 2 + .../439.b350310d057b43cdd50f.js.LICENSE.txt | 6 + .../static/446.f8696ce72124c78273da.js | 1 + .../static/586.085510630436c2e4273f.js | 1 + .../static/647.8458d9c331000024a14a.js | 1 + .../static/651.fe40a967a60b543cf15c.js | 2 + .../651.fe40a967a60b543cf15c.js.LICENSE.txt | 10 + .../static/701.043aefe0b66133629348.js | 1 + .../static/722.53e4a64671c3c48de007.js | 1 + .../static/727.b26a6f3871012a0fd66a.js | 1 + .../static/898.db11fb1a7e18acb58b5b.js | 1 + .../remoteEntry.35b6c65bd99dab37b910.js | 1 + .../jupyterlab-manager/static/style.js | 4 + .../static/third-party-licenses.json | 184 + .../jupyter-js-widgets/extension.js | 3 + .../extension.js.LICENSE.txt | 17 + .../jupyter-js-widgets/extension.js.map | 1 + .venv-sp/share/man/man1/ipython.1 | 60 + .venv-sp/share/man/man1/ttx.1 | 225 + .venv-sp/share/xyzservices/providers.json | 17685 ++++++++++++++++ index.html | 10 - .../assignment-3-zonal-stats.ipynb | 560 - notebooks/module-5/download-ntl.ipynb | 1138 + notebooks/tutorials/langchain/rag-1.ipynb | 1427 -- sql/ntl_pop_db_script.sql | 78 + 73 files changed, 20639 insertions(+), 1997 deletions(-) create mode 100644 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Makes use of the command line switches as +well as the `pyvenv.cfg` file values present in the virtual environment. + +.Parameter VenvDir +Path to the directory that contains the virtual environment to activate. The +default value for this is the parent of the directory that the Activate.ps1 +script is located within. + +.Parameter Prompt +The prompt prefix to display when this virtual environment is activated. By +default, this prompt is the name of the virtual environment folder (VenvDir) +surrounded by parentheses and followed by a single space (ie. '(.venv) '). + +.Example +Activate.ps1 +Activates the Python virtual environment that contains the Activate.ps1 script. + +.Example +Activate.ps1 -Verbose +Activates the Python virtual environment that contains the Activate.ps1 script, +and shows extra information about the activation as it executes. + +.Example +Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv +Activates the Python virtual environment located in the specified location. + +.Example +Activate.ps1 -Prompt "MyPython" +Activates the Python virtual environment that contains the Activate.ps1 script, +and prefixes the current prompt with the specified string (surrounded in +parentheses) while the virtual environment is active. + +.Notes +On Windows, it may be required to enable this Activate.ps1 script by setting the +execution policy for the user. You can do this by issuing the following PowerShell +command: + +PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser + +For more information on Execution Policies: +https://go.microsoft.com/fwlink/?LinkID=135170 + +#> +Param( + [Parameter(Mandatory = $false)] + [String] + $VenvDir, + [Parameter(Mandatory = $false)] + [String] + $Prompt +) + +<# Function declarations --------------------------------------------------- #> + +<# +.Synopsis +Remove all shell session elements added by the Activate script, including the +addition of the virtual environment's Python executable from the beginning of +the PATH variable. + +.Parameter NonDestructive +If present, do not remove this function from the global namespace for the +session. + +#> +function global:deactivate ([switch]$NonDestructive) { + # Revert to original values + + # The prior prompt: + if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) { + Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt + Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT + } + + # The prior PYTHONHOME: + if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) { + Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME + Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME + } + + # The prior PATH: + if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) { + Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH + Remove-Item -Path Env:_OLD_VIRTUAL_PATH + } + + # Just remove the VIRTUAL_ENV altogether: + if (Test-Path -Path Env:VIRTUAL_ENV) { + Remove-Item -Path env:VIRTUAL_ENV + } + + # Just remove VIRTUAL_ENV_PROMPT altogether. + if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) { + Remove-Item -Path env:VIRTUAL_ENV_PROMPT + } + + # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether: + if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) { + Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force + } + + # Leave deactivate function in the global namespace if requested: + if (-not $NonDestructive) { + Remove-Item -Path function:deactivate + } +} + +<# +.Description +Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the +given folder, and returns them in a map. + +For each line in the pyvenv.cfg file, if that line can be parsed into exactly +two strings separated by `=` (with any amount of whitespace surrounding the =) +then it is considered a `key = value` line. The left hand string is the key, +the right hand is the value. + +If the value starts with a `'` or a `"` then the first and last character is +stripped from the value before being captured. + +.Parameter ConfigDir +Path to the directory that contains the `pyvenv.cfg` file. +#> +function Get-PyVenvConfig( + [String] + $ConfigDir +) { + Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg" + + # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue). + $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue + + # An empty map will be returned if no config file is found. + $pyvenvConfig = @{ } + + if ($pyvenvConfigPath) { + + Write-Verbose "File exists, parse `key = value` lines" + $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath + + $pyvenvConfigContent | ForEach-Object { + $keyval = $PSItem -split "\s*=\s*", 2 + if ($keyval[0] -and $keyval[1]) { + $val = $keyval[1] + + # Remove extraneous quotations around a string value. + if ("'""".Contains($val.Substring(0, 1))) { + $val = $val.Substring(1, $val.Length - 2) + } + + $pyvenvConfig[$keyval[0]] = $val + Write-Verbose "Adding Key: '$($keyval[0])'='$val'" + } + } + } + return $pyvenvConfig +} + + +<# Begin Activate script --------------------------------------------------- #> + +# Determine the containing directory of this script +$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition +$VenvExecDir = Get-Item -Path $VenvExecPath + +Write-Verbose "Activation script is located in path: '$VenvExecPath'" +Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)" +Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)" + +# Set values required in priority: CmdLine, ConfigFile, Default +# First, get the location of the virtual environment, it might not be +# VenvExecDir if specified on the command line. +if ($VenvDir) { + Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values" +} +else { + Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir." + $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/") + Write-Verbose "VenvDir=$VenvDir" +} + +# Next, read the `pyvenv.cfg` file to determine any required value such +# as `prompt`. +$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir + +# Next, set the prompt from the command line, or the config file, or +# just use the name of the virtual environment folder. +if ($Prompt) { + Write-Verbose "Prompt specified as argument, using '$Prompt'" +} +else { + Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value" + if ($pyvenvCfg -and $pyvenvCfg['prompt']) { + Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'" + $Prompt = $pyvenvCfg['prompt']; + } + else { + Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)" + Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'" + $Prompt = Split-Path -Path $venvDir -Leaf + } +} + +Write-Verbose "Prompt = '$Prompt'" +Write-Verbose "VenvDir='$VenvDir'" + +# Deactivate any currently active virtual environment, but leave the +# deactivate function in place. +deactivate -nondestructive + +# Now set the environment variable VIRTUAL_ENV, used by many tools to determine +# that there is an activated venv. +$env:VIRTUAL_ENV = $VenvDir + +if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) { + + Write-Verbose "Setting prompt to '$Prompt'" + + # Set the prompt to include the env name + # Make sure _OLD_VIRTUAL_PROMPT is global + function global:_OLD_VIRTUAL_PROMPT { "" } + Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT + New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt + + function global:prompt { + Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) " + _OLD_VIRTUAL_PROMPT + } + $env:VIRTUAL_ENV_PROMPT = $Prompt +} + +# Clear PYTHONHOME +if (Test-Path -Path Env:PYTHONHOME) { + Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME + Remove-Item -Path Env:PYTHONHOME +} + +# Add the venv to the PATH +Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH +$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH" diff --git a/.venv-sp/bin/activate b/.venv-sp/bin/activate new file mode 100644 index 0000000..c19ddd9 --- /dev/null +++ b/.venv-sp/bin/activate @@ -0,0 +1,70 @@ +# This file must be used with "source bin/activate" *from bash* +# You cannot run it directly + +deactivate () { + # reset old environment variables + if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then + PATH="${_OLD_VIRTUAL_PATH:-}" + export PATH + unset _OLD_VIRTUAL_PATH + fi + if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then + PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}" + export PYTHONHOME + unset _OLD_VIRTUAL_PYTHONHOME + fi + + # Call hash to forget past commands. Without forgetting + # past commands the $PATH changes we made may not be respected + hash -r 2> /dev/null + + if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then + PS1="${_OLD_VIRTUAL_PS1:-}" + export PS1 + unset _OLD_VIRTUAL_PS1 + fi + + unset VIRTUAL_ENV + unset VIRTUAL_ENV_PROMPT + if [ ! "${1:-}" = "nondestructive" ] ; then + # Self destruct! + unset -f deactivate + fi +} + +# unset irrelevant variables +deactivate nondestructive + +# on Windows, a path can contain colons and backslashes and has to be converted: +if [ "${OSTYPE:-}" = "cygwin" ] || [ "${OSTYPE:-}" = "msys" ] ; then + # transform D:\path\to\venv to /d/path/to/venv on MSYS + # and to /cygdrive/d/path/to/venv on Cygwin + export VIRTUAL_ENV=$(cygpath "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp") +else + # use the path as-is + export VIRTUAL_ENV="/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp" +fi + +_OLD_VIRTUAL_PATH="$PATH" +PATH="$VIRTUAL_ENV/bin:$PATH" +export PATH + +# unset PYTHONHOME if set +# this will fail if PYTHONHOME is set to the empty string (which is bad anyway) +# could use `if (set -u; : $PYTHONHOME) ;` in bash +if [ -n "${PYTHONHOME:-}" ] ; then + _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}" + unset PYTHONHOME +fi + +if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then + _OLD_VIRTUAL_PS1="${PS1:-}" + PS1="(.venv-sp) ${PS1:-}" + export PS1 + VIRTUAL_ENV_PROMPT="(.venv-sp) " + export VIRTUAL_ENV_PROMPT +fi + +# Call hash to forget past commands. Without forgetting +# past commands the $PATH changes we made may not be respected +hash -r 2> /dev/null diff --git a/.venv-sp/bin/activate.csh b/.venv-sp/bin/activate.csh new file mode 100644 index 0000000..4c64f68 --- /dev/null +++ b/.venv-sp/bin/activate.csh @@ -0,0 +1,27 @@ +# This file must be used with "source bin/activate.csh" *from csh*. +# You cannot run it directly. + +# Created by Davide Di Blasi . +# Ported to Python 3.3 venv by Andrew Svetlov + +alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; unsetenv VIRTUAL_ENV_PROMPT; test "\!:*" != "nondestructive" && unalias deactivate' + +# Unset irrelevant variables. +deactivate nondestructive + +setenv VIRTUAL_ENV "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp" + +set _OLD_VIRTUAL_PATH="$PATH" +setenv PATH "$VIRTUAL_ENV/bin:$PATH" + + +set _OLD_VIRTUAL_PROMPT="$prompt" + +if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then + set prompt = "(.venv-sp) $prompt" + setenv VIRTUAL_ENV_PROMPT "(.venv-sp) " +endif + +alias pydoc python -m pydoc + +rehash diff --git a/.venv-sp/bin/activate.fish b/.venv-sp/bin/activate.fish new file mode 100644 index 0000000..2be83e7 --- /dev/null +++ b/.venv-sp/bin/activate.fish @@ -0,0 +1,69 @@ +# This file must be used with "source /bin/activate.fish" *from fish* +# (https://fishshell.com/). You cannot run it directly. + +function deactivate -d "Exit virtual environment and return to normal shell environment" + # reset old environment variables + if test -n "$_OLD_VIRTUAL_PATH" + set -gx PATH $_OLD_VIRTUAL_PATH + set -e _OLD_VIRTUAL_PATH + end + if test -n "$_OLD_VIRTUAL_PYTHONHOME" + set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME + set -e _OLD_VIRTUAL_PYTHONHOME + end + + if test -n "$_OLD_FISH_PROMPT_OVERRIDE" + set -e _OLD_FISH_PROMPT_OVERRIDE + # prevents error when using nested fish instances (Issue #93858) + if functions -q _old_fish_prompt + functions -e fish_prompt + functions -c _old_fish_prompt fish_prompt + functions -e _old_fish_prompt + end + end + + set -e VIRTUAL_ENV + set -e VIRTUAL_ENV_PROMPT + if test "$argv[1]" != "nondestructive" + # Self-destruct! + functions -e deactivate + end +end + +# Unset irrelevant variables. +deactivate nondestructive + +set -gx VIRTUAL_ENV "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp" + +set -gx _OLD_VIRTUAL_PATH $PATH +set -gx PATH "$VIRTUAL_ENV/bin" $PATH + +# Unset PYTHONHOME if set. +if set -q PYTHONHOME + set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME + set -e PYTHONHOME +end + +if test -z "$VIRTUAL_ENV_DISABLE_PROMPT" + # fish uses a function instead of an env var to generate the prompt. + + # Save the current fish_prompt function as the function _old_fish_prompt. + functions -c fish_prompt _old_fish_prompt + + # With the original prompt function renamed, we can override with our own. + function fish_prompt + # Save the return status of the last command. + set -l old_status $status + + # Output the venv prompt; color taken from the blue of the Python logo. + printf "%s%s%s" (set_color 4B8BBE) "(.venv-sp) " (set_color normal) + + # Restore the return status of the previous command. + echo "exit $old_status" | . + # Output the original/"old" prompt. + _old_fish_prompt + end + + set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV" + set -gx VIRTUAL_ENV_PROMPT "(.venv-sp) " +end diff --git a/.venv-sp/bin/bokeh b/.venv-sp/bin/bokeh new file mode 100755 index 0000000..bd9362d --- /dev/null +++ b/.venv-sp/bin/bokeh @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from bokeh.__main__ import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/debugpy b/.venv-sp/bin/debugpy new file mode 100755 index 0000000..010711f --- /dev/null +++ b/.venv-sp/bin/debugpy @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from debugpy.server.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/debugpy-adapter b/.venv-sp/bin/debugpy-adapter new file mode 100755 index 0000000..bea2b69 --- /dev/null +++ b/.venv-sp/bin/debugpy-adapter @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from debugpy.adapter.__main__ import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/dotenv b/.venv-sp/bin/dotenv new file mode 100755 index 0000000..68aaaed --- /dev/null +++ b/.venv-sp/bin/dotenv @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from dotenv.__main__ import cli +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli()) diff --git a/.venv-sp/bin/f2py b/.venv-sp/bin/f2py new file mode 100755 index 0000000..a517c22 --- /dev/null +++ b/.venv-sp/bin/f2py @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from numpy.f2py.f2py2e import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/fio b/.venv-sp/bin/fio new file mode 100755 index 0000000..dd37b2c --- /dev/null +++ b/.venv-sp/bin/fio @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from fiona.fio.main import main_group +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main_group()) diff --git a/.venv-sp/bin/fonttools b/.venv-sp/bin/fonttools new file mode 100755 index 0000000..a625f7d --- /dev/null +++ b/.venv-sp/bin/fonttools @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.__main__ import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/httpx b/.venv-sp/bin/httpx new file mode 100755 index 0000000..6713f1a --- /dev/null +++ b/.venv-sp/bin/httpx @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from httpx import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/ipython b/.venv-sp/bin/ipython new file mode 100755 index 0000000..29f2096 --- /dev/null +++ b/.venv-sp/bin/ipython @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from IPython import start_ipython +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(start_ipython()) diff --git a/.venv-sp/bin/ipython3 b/.venv-sp/bin/ipython3 new file mode 100755 index 0000000..29f2096 --- /dev/null +++ b/.venv-sp/bin/ipython3 @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from IPython import start_ipython +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(start_ipython()) diff --git a/.venv-sp/bin/jupyter b/.venv-sp/bin/jupyter new file mode 100755 index 0000000..b183c43 --- /dev/null +++ b/.venv-sp/bin/jupyter @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_core.command import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/jupyter-kernel b/.venv-sp/bin/jupyter-kernel new file mode 100755 index 0000000..3762715 --- /dev/null +++ b/.venv-sp/bin/jupyter-kernel @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_client.kernelapp import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/jupyter-kernelspec b/.venv-sp/bin/jupyter-kernelspec new file mode 100755 index 0000000..ed9fb9b --- /dev/null +++ b/.venv-sp/bin/jupyter-kernelspec @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_client.kernelspecapp import KernelSpecApp +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(KernelSpecApp.launch_instance()) diff --git a/.venv-sp/bin/jupyter-migrate b/.venv-sp/bin/jupyter-migrate new file mode 100755 index 0000000..7ddc1ea --- /dev/null +++ b/.venv-sp/bin/jupyter-migrate @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_core.migrate import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/jupyter-run b/.venv-sp/bin/jupyter-run new file mode 100755 index 0000000..8d86513 --- /dev/null +++ b/.venv-sp/bin/jupyter-run @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_client.runapp import RunApp +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(RunApp.launch_instance()) diff --git a/.venv-sp/bin/jupyter-troubleshoot b/.venv-sp/bin/jupyter-troubleshoot new file mode 100755 index 0000000..39e12bb --- /dev/null +++ b/.venv-sp/bin/jupyter-troubleshoot @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_core.troubleshoot import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/mercantile b/.venv-sp/bin/mercantile new file mode 100755 index 0000000..171791c --- /dev/null +++ b/.venv-sp/bin/mercantile @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from mercantile.scripts import cli +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli()) diff --git a/.venv-sp/bin/normalizer b/.venv-sp/bin/normalizer new file mode 100755 index 0000000..5b4b3e9 --- /dev/null +++ b/.venv-sp/bin/normalizer @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from charset_normalizer.cli import cli_detect +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli_detect()) diff --git a/.venv-sp/bin/numpy-config b/.venv-sp/bin/numpy-config new file mode 100755 index 0000000..ccd693b --- /dev/null +++ b/.venv-sp/bin/numpy-config @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from numpy._configtool import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/pip b/.venv-sp/bin/pip new file mode 100755 index 0000000..d48f207 --- /dev/null +++ b/.venv-sp/bin/pip @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/pip3 b/.venv-sp/bin/pip3 new file mode 100755 index 0000000..d48f207 --- /dev/null +++ b/.venv-sp/bin/pip3 @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/pip3.12 b/.venv-sp/bin/pip3.12 new file mode 100755 index 0000000..d48f207 --- /dev/null +++ b/.venv-sp/bin/pip3.12 @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/pyftmerge b/.venv-sp/bin/pyftmerge new file mode 100755 index 0000000..0e67e4f --- /dev/null +++ b/.venv-sp/bin/pyftmerge @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.merge import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/pyftsubset b/.venv-sp/bin/pyftsubset new file mode 100755 index 0000000..5a26da8 --- /dev/null +++ b/.venv-sp/bin/pyftsubset @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.subset import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/pygmentize b/.venv-sp/bin/pygmentize new file mode 100755 index 0000000..b7d185d --- /dev/null +++ b/.venv-sp/bin/pygmentize @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from pygments.cmdline import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/pyproj b/.venv-sp/bin/pyproj new file mode 100755 index 0000000..75c2e33 --- /dev/null +++ b/.venv-sp/bin/pyproj @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from pyproj.__main__ import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/python b/.venv-sp/bin/python new file mode 120000 index 0000000..a4624ae --- /dev/null +++ b/.venv-sp/bin/python @@ -0,0 +1 @@ +/Users/dmatekenya/anaconda3/bin/python \ No newline at end of file diff --git a/.venv-sp/bin/python3 b/.venv-sp/bin/python3 new file mode 120000 index 0000000..d8654aa --- /dev/null +++ b/.venv-sp/bin/python3 @@ -0,0 +1 @@ +python \ No newline at end of file diff --git a/.venv-sp/bin/python3.12 b/.venv-sp/bin/python3.12 new file mode 120000 index 0000000..d8654aa --- /dev/null +++ b/.venv-sp/bin/python3.12 @@ -0,0 +1 @@ +python \ No newline at end of file diff --git a/.venv-sp/bin/rio b/.venv-sp/bin/rio new file mode 100755 index 0000000..270a0c7 --- /dev/null +++ b/.venv-sp/bin/rio @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from rasterio.rio.main import main_group +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main_group()) diff --git a/.venv-sp/bin/tqdm b/.venv-sp/bin/tqdm new file mode 100755 index 0000000..9509a79 --- /dev/null +++ b/.venv-sp/bin/tqdm @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from tqdm.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/bin/ttx b/.venv-sp/bin/ttx new file mode 100755 index 0000000..144b070 --- /dev/null +++ b/.venv-sp/bin/ttx @@ -0,0 +1,10 @@ +#!/bin/sh +'''exec' "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp/bin/python" "$0" "$@" +' ''' +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.ttx import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.venv-sp/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json b/.venv-sp/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json new file mode 100644 index 0000000..7a17570 --- /dev/null +++ b/.venv-sp/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json @@ -0,0 +1,5 @@ +{ + "load_extensions": { + "jupyter-js-widgets/extension": true + } +} diff --git a/.venv-sp/pyvenv.cfg b/.venv-sp/pyvenv.cfg new file mode 100644 index 0000000..470741b --- /dev/null +++ b/.venv-sp/pyvenv.cfg @@ -0,0 +1,5 @@ +home = /Users/dmatekenya/anaconda3/bin +include-system-site-packages = false +version = 3.12.7 +executable = /Users/dmatekenya/anaconda3/bin/python3.12 +command = /Users/dmatekenya/anaconda3/bin/python -m venv /Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/.venv-sp diff --git a/.venv-sp/share/jupyter/kernels/python3/kernel.json 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diff --git a/.venv-sp/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/package.json b/.venv-sp/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/package.json new file mode 100644 index 0000000..f1f5a89 --- /dev/null +++ b/.venv-sp/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/package.json @@ -0,0 +1,102 @@ +{ + "name": "@jupyter-widgets/jupyterlab-manager", + "version": "5.0.15", + "description": "The JupyterLab extension providing Jupyter widgets.", + "keywords": [ + "jupyter", + "jupyterlab", + "jupyterlab notebook", + "jupyterlab-extension" + ], + "homepage": "https://github.com/jupyter-widgets/ipywidgets", + "bugs": { + "url": "https://github.com/jupyter-widgets/ipywidgets/issues" + }, + "repository": { + "type": "git", + "url": "https://github.com/jupyter-widgets/ipywidgets" + }, + "license": "BSD-3-Clause", + "author": "Project Jupyter", + "sideEffects": [ + "style/*.css" + ], + "main": "lib/index.js", + "types": "lib/index.d.ts", + "files": [ + "lib/**/*.{d.ts,eot,gif,html,jpg,js,js.map,json,png,svg,woff2,ttf}", + "style/**/*.{css,eot,gif,html,jpg,json,png,svg,woff2,ttf}", + "dist/*.js", + "schema/*.json" + ], + "scripts": { + "build": "jlpm build:lib && jlpm build:labextension:dev", + "build:labextension": "jupyter labextension build .", + "build:labextension:dev": "jupyter labextension build --development True .", + "build:lib": "tsc -b", + "build:prod": "jlpm build:lib && jlpm build:labextension", + "clean": "jlpm clean:lib", + "clean:all": "jlpm clean:lib && jlpm clean:labextension", + "clean:labextension": "rimraf labextension", + "clean:lib": "rimraf lib tsconfig.tsbuildinfo", + "eslint": "eslint . --ext .ts,.tsx --fix", + "eslint:check": "eslint . --ext .ts,.tsx", + "install:extension": "jlpm build", + "prepare": "jlpm clean && jlpm build:prod", + "watch": "jupyter labextension watch ." + }, + "dependencies": { + "@jupyter-widgets/base": "^6.0.11", + "@jupyter-widgets/base-manager": "^1.0.12", + 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Uint16Array('ᵁ<Õıʊҝջאٵ۞ޢߖࠏ੊ઑඡ๭༉༦჊ረዡᐕᒝᓃᓟᔥ\0\0\0\0\0\0ᕫᛍᦍᰒᷝ὾⁠↰⊍⏀⏻⑂⠤⤒ⴈ⹈⿎〖㊺㘹㞬㣾㨨㩱㫠㬮ࠀEMabcfglmnoprstu\\bfms„‹•˜¦³¹ÈÏlig耻Æ䃆P耻&䀦cute耻Á䃁reve;䄂Āiyx}rc耻Â䃂;䐐r;쀀𝔄rave耻À䃀pha;䎑acr;䄀d;橓Āgp¡on;䄄f;쀀𝔸plyFunction;恡ing耻Å䃅Ācs¾Ãr;쀀𝒜ign;扔ilde耻Ã䃃ml耻Ä䃄ЀaceforsuåûþėĜĢħĪĀcrêòkslash;或Ŷöø;櫧ed;挆y;䐑ƀcrtąċĔause;戵noullis;愬a;䎒r;쀀𝔅pf;쀀𝔹eve;䋘còēmpeq;扎܀HOacdefhilorsuōőŖƀƞƢƵƷƺǜȕɳɸɾcy;䐧PY耻©䂩ƀcpyŝŢźute;䄆Ā;iŧŨ拒talDifferentialD;慅leys;愭ȀaeioƉƎƔƘron;䄌dil耻Ç䃇rc;䄈nint;戰ot;䄊ĀdnƧƭilla;䂸terDot;䂷òſi;䎧rcleȀDMPTLJNjǑǖot;抙inus;抖lus;投imes;抗oĀcsǢǸkwiseContourIntegral;戲eCurlyĀDQȃȏoubleQuote;思uote;怙ȀlnpuȞȨɇɕonĀ;eȥȦ户;橴ƀgitȯȶȺruent;扡nt;戯ourIntegral;戮ĀfrɌɎ;愂oduct;成nterClockwiseContourIntegral;戳oss;樯cr;쀀𝒞pĀ;Cʄʅ拓ap;才րDJSZacefiosʠʬʰʴʸˋ˗ˡ˦̳ҍĀ;oŹʥtrahd;椑cy;䐂cy;䐅cy;䐏ƀgrsʿ˄ˇger;怡r;憡hv;櫤Āayː˕ron;䄎;䐔lĀ;t˝˞戇a;䎔r;쀀𝔇Āaf˫̧Ācm˰̢riticalȀADGT̖̜̀̆cute;䂴oŴ̋̍;䋙bleAcute;䋝rave;䁠ilde;䋜ond;拄ferentialD;慆Ѱ̽\0\0\0͔͂\0Ѕf;쀀𝔻ƀ;DE͈͉͍䂨ot;惜qual;扐blèCDLRUVͣͲ΂ϏϢϸontourIntegraìȹoɴ͹\0\0ͻ»͉nArrow;懓Āeo·ΤftƀARTΐΖΡrrow;懐ightArrow;懔eåˊngĀLRΫτeftĀARγιrrow;柸ightArrow;柺ightArrow;柹ightĀATϘϞrrow;懒ee;抨pɁϩ\0\0ϯrrow;懑ownArrow;懕erticalBar;戥ǹABLRTaВЪаўѿͼrrowƀ;BUНОТ憓ar;椓pArrow;懵reve;䌑eft˒к\0ц\0ѐightVector;楐eeVector;楞ectorĀ;Bљњ憽ar;楖ightǔѧ\0ѱeeVector;楟ectorĀ;BѺѻ懁ar;楗eeĀ;A҆҇护rrow;憧ĀctҒҗr;쀀𝒟rok;䄐ࠀNTacdfglmopqstuxҽӀӄӋӞӢӧӮӵԡԯԶՒ՝ՠեG;䅊H耻Ð䃐cute耻É䃉ƀaiyӒӗӜron;䄚rc耻Ê䃊;䐭ot;䄖r;쀀𝔈rave耻È䃈ement;戈ĀapӺӾcr;䄒tyɓԆ\0\0ԒmallSquare;旻erySmallSquare;斫ĀgpԦԪon;䄘f;쀀𝔼silon;䎕uĀaiԼՉlĀ;TՂՃ橵ilde;扂librium;懌Āci՗՚r;愰m;橳a;䎗ml耻Ë䃋Āipժկsts;戃onentialE;慇ʀcfiosօֈ֍ֲ׌y;䐤r;쀀𝔉lledɓ֗\0\0֣mallSquare;旼erySmallSquare;斪Ͱֺ\0ֿ\0\0ׄf;쀀𝔽All;戀riertrf;愱cò׋؀JTabcdfgorstר׬ׯ׺؀ؒؖ؛؝أ٬ٲcy;䐃耻>䀾mmaĀ;d׷׸䎓;䏜reve;䄞ƀeiy؇،ؐdil;䄢rc;䄜;䐓ot;䄠r;쀀𝔊;拙pf;쀀𝔾eater̀EFGLSTصلَٖٛ٦qualĀ;Lؾؿ扥ess;招ullEqual;执reater;檢ess;扷lantEqual;橾ilde;扳cr;쀀𝒢;扫ЀAacfiosuڅڋږڛڞڪھۊRDcy;䐪Āctڐڔek;䋇;䁞irc;䄤r;愌lbertSpace;愋ǰگ\0ڲf;愍izontalLine;攀Āctۃۅòکrok;䄦mpńېۘownHumðįqual;扏܀EJOacdfgmnostuۺ۾܃܇܎ܚܞܡܨ݄ݸދޏޕcy;䐕lig;䄲cy;䐁cute耻Í䃍Āiyܓܘrc耻Î䃎;䐘ot;䄰r;愑rave耻Ì䃌ƀ;apܠܯܿĀcgܴܷr;䄪inaryI;慈lieóϝǴ݉\0ݢĀ;eݍݎ戬Āgrݓݘral;戫section;拂isibleĀCTݬݲomma;恣imes;恢ƀgptݿރވon;䄮f;쀀𝕀a;䎙cr;愐ilde;䄨ǫޚ\0ޞcy;䐆l耻Ï䃏ʀcfosuެ޷޼߂ߐĀiyޱ޵rc;䄴;䐙r;쀀𝔍pf;쀀𝕁ǣ߇\0ߌr;쀀𝒥rcy;䐈kcy;䐄΀HJacfosߤߨ߽߬߱ࠂࠈcy;䐥cy;䐌ppa;䎚Āey߶߻dil;䄶;䐚r;쀀𝔎pf;쀀𝕂cr;쀀𝒦րJTaceflmostࠥࠩࠬࡐࡣ঳সে্਷ੇcy;䐉耻<䀼ʀcmnpr࠷࠼ࡁࡄࡍute;䄹bda;䎛g;柪lacetrf;愒r;憞ƀaeyࡗ࡜ࡡron;䄽dil;䄻;䐛Āfsࡨ॰tԀACDFRTUVarࡾࢩࢱࣦ࣠ࣼयज़ΐ४Ānrࢃ࢏gleBracket;柨rowƀ;BR࢙࢚࢞憐ar;懤ightArrow;懆eiling;挈oǵࢷ\0ࣃbleBracket;柦nǔࣈ\0࣒eeVector;楡ectorĀ;Bࣛࣜ懃ar;楙loor;挊ightĀAV࣯ࣵrrow;憔ector;楎Āerँगeƀ;AVउऊऐ抣rrow;憤ector;楚iangleƀ;BEतथऩ抲ar;槏qual;抴pƀDTVषूौownVector;楑eeVector;楠ectorĀ;Bॖॗ憿ar;楘ectorĀ;B॥०憼ar;楒ightáΜs̀EFGLSTॾঋকঝঢভqualGreater;拚ullEqual;扦reater;扶ess;檡lantEqual;橽ilde;扲r;쀀𝔏Ā;eঽা拘ftarrow;懚idot;䄿ƀnpw৔ਖਛgȀLRlr৞৷ਂਐeftĀAR০৬rrow;柵ightArrow;柷ightArrow;柶eftĀarγਊightáοightáϊf;쀀𝕃erĀLRਢਬeftArrow;憙ightArrow;憘ƀchtਾੀੂòࡌ;憰rok;䅁;扪Ѐacefiosuਗ਼੝੠੷੼અઋ઎p;椅y;䐜Ādl੥੯iumSpace;恟lintrf;愳r;쀀𝔐nusPlus;戓pf;쀀𝕄cò੶;䎜ҀJacefostuણધભીଔଙඑ඗ඞcy;䐊cute;䅃ƀaey઴હાron;䅇dil;䅅;䐝ƀgswે૰଎ativeƀMTV૓૟૨ediumSpace;怋hiĀcn૦૘ë૙eryThiî૙tedĀGL૸ଆreaterGreateòٳessLesóੈLine;䀊r;쀀𝔑ȀBnptଢନଷ଺reak;恠BreakingSpace;䂠f;愕ڀ;CDEGHLNPRSTV୕ୖ୪୼஡௫ఄ౞಄ದ೘ൡඅ櫬Āou୛୤ngruent;扢pCap;扭oubleVerticalBar;戦ƀlqxஃஊ஛ement;戉ualĀ;Tஒஓ扠ilde;쀀≂̸ists;戄reater΀;EFGLSTஶஷ஽௉௓௘௥扯qual;扱ullEqual;쀀≧̸reater;쀀≫̸ess;批lantEqual;쀀⩾̸ilde;扵umpń௲௽ownHump;쀀≎̸qual;쀀≏̸eĀfsఊధtTriangleƀ;BEచఛడ拪ar;쀀⧏̸qual;括s̀;EGLSTవశ఼ౄోౘ扮qual;扰reater;扸ess;쀀≪̸lantEqual;쀀⩽̸ilde;扴estedĀGL౨౹reaterGreater;쀀⪢̸essLess;쀀⪡̸recedesƀ;ESಒಓಛ技qual;쀀⪯̸lantEqual;拠ĀeiಫಹverseElement;戌ghtTriangleƀ;BEೋೌ೒拫ar;쀀⧐̸qual;拭ĀquೝഌuareSuĀbp೨೹setĀ;E೰ೳ쀀⊏̸qual;拢ersetĀ;Eഃആ쀀⊐̸qual;拣ƀbcpഓതൎsetĀ;Eഛഞ쀀⊂⃒qual;抈ceedsȀ;ESTലള഻െ抁qual;쀀⪰̸lantEqual;拡ilde;쀀≿̸ersetĀ;E൘൛쀀⊃⃒qual;抉ildeȀ;EFT൮൯൵ൿ扁qual;扄ullEqual;扇ilde;扉erticalBar;戤cr;쀀𝒩ilde耻Ñ䃑;䎝܀Eacdfgmoprstuvලෂ෉෕ෛ෠෧෼ขภยา฿ไlig;䅒cute耻Ó䃓Āiy෎ීrc耻Ô䃔;䐞blac;䅐r;쀀𝔒rave耻Ò䃒ƀaei෮ෲ෶cr;䅌ga;䎩cron;䎟pf;쀀𝕆enCurlyĀDQฎบoubleQuote;怜uote;怘;橔Āclวฬr;쀀𝒪ash耻Ø䃘iŬื฼de耻Õ䃕es;樷ml耻Ö䃖erĀBP๋๠Āar๐๓r;怾acĀek๚๜;揞et;掴arenthesis;揜Ҁacfhilors๿ງຊຏຒດຝະ໼rtialD;戂y;䐟r;쀀𝔓i;䎦;䎠usMinus;䂱Āipຢອncareplanåڝf;愙Ȁ;eio຺ູ໠໤檻cedesȀ;EST່້໏໚扺qual;檯lantEqual;扼ilde;找me;怳Ādp໩໮uct;戏ortionĀ;aȥ໹l;戝Āci༁༆r;쀀𝒫;䎨ȀUfos༑༖༛༟OT耻"䀢r;쀀𝔔pf;愚cr;쀀𝒬؀BEacefhiorsu༾གྷཇའཱིྦྷྪྭ႖ႩႴႾarr;椐G耻®䂮ƀcnrཎནབute;䅔g;柫rĀ;tཛྷཝ憠l;椖ƀaeyཧཬཱron;䅘dil;䅖;䐠Ā;vླྀཹ愜erseĀEUྂྙĀlq྇ྎement;戋uilibrium;懋pEquilibrium;楯r»ཹo;䎡ghtЀACDFTUVa࿁࿫࿳ဢဨၛႇϘĀnr࿆࿒gleBracket;柩rowƀ;BL࿜࿝࿡憒ar;懥eftArrow;懄eiling;按oǵ࿹\0စbleBracket;柧nǔည\0နeeVector;楝ectorĀ;Bဝသ懂ar;楕loor;挋Āerိ၃eƀ;AVဵံြ抢rrow;憦ector;楛iangleƀ;BEၐၑၕ抳ar;槐qual;抵pƀDTVၣၮၸownVector;楏eeVector;楜ectorĀ;Bႂႃ憾ar;楔ectorĀ;B႑႒懀ar;楓Āpuႛ႞f;愝ndImplies;楰ightarrow;懛ĀchႹႼr;愛;憱leDelayed;槴ڀHOacfhimoqstuფჱჷჽᄙᄞᅑᅖᅡᅧᆵᆻᆿĀCcჩხHcy;䐩y;䐨FTcy;䐬cute;䅚ʀ;aeiyᄈᄉᄎᄓᄗ檼ron;䅠dil;䅞rc;䅜;䐡r;쀀𝔖ortȀDLRUᄪᄴᄾᅉownArrow»ОeftArrow»࢚ightArrow»࿝pArrow;憑gma;䎣allCircle;战pf;쀀𝕊ɲᅭ\0\0ᅰt;戚areȀ;ISUᅻᅼᆉᆯ斡ntersection;抓uĀbpᆏᆞsetĀ;Eᆗᆘ抏qual;抑ersetĀ;Eᆨᆩ抐qual;抒nion;抔cr;쀀𝒮ar;拆ȀbcmpᇈᇛሉላĀ;sᇍᇎ拐etĀ;Eᇍᇕqual;抆ĀchᇠህeedsȀ;ESTᇭᇮᇴᇿ扻qual;檰lantEqual;扽ilde;承Tháྌ;我ƀ;esሒሓሣ拑rsetĀ;Eሜም抃qual;抇et»ሓրHRSacfhiorsሾቄ቉ቕ቞ቱቶኟዂወዑORN耻Þ䃞ADE;愢ĀHc቎ቒcy;䐋y;䐦Ābuቚቜ;䀉;䎤ƀaeyብቪቯron;䅤dil;䅢;䐢r;쀀𝔗Āeiቻ኉Dzኀ\0ኇefore;戴a;䎘Ācn኎ኘkSpace;쀀  Space;怉ldeȀ;EFTካኬኲኼ戼qual;扃ullEqual;扅ilde;扈pf;쀀𝕋ipleDot;惛Āctዖዛr;쀀𝒯rok;䅦ૡዷጎጚጦ\0ጬጱ\0\0\0\0\0ጸጽ፷ᎅ\0᏿ᐄᐊᐐĀcrዻጁute耻Ú䃚rĀ;oጇገ憟cir;楉rǣጓ\0጖y;䐎ve;䅬Āiyጞጣrc耻Û䃛;䐣blac;䅰r;쀀𝔘rave耻Ù䃙acr;䅪Ādiፁ፩erĀBPፈ፝Āarፍፐr;䁟acĀekፗፙ;揟et;掵arenthesis;揝onĀ;P፰፱拃lus;抎Āgp፻፿on;䅲f;쀀𝕌ЀADETadps᎕ᎮᎸᏄϨᏒᏗᏳrrowƀ;BDᅐᎠᎤar;椒ownArrow;懅ownArrow;憕quilibrium;楮eeĀ;AᏋᏌ报rrow;憥ownáϳerĀLRᏞᏨeftArrow;憖ightArrow;憗iĀ;lᏹᏺ䏒on;䎥ing;䅮cr;쀀𝒰ilde;䅨ml耻Ü䃜ҀDbcdefosvᐧᐬᐰᐳᐾᒅᒊᒐᒖash;披ar;櫫y;䐒ashĀ;lᐻᐼ抩;櫦Āerᑃᑅ;拁ƀbtyᑌᑐᑺar;怖Ā;iᑏᑕcalȀBLSTᑡᑥᑪᑴar;戣ine;䁼eparator;杘ilde;所ThinSpace;怊r;쀀𝔙pf;쀀𝕍cr;쀀𝒱dash;抪ʀcefosᒧᒬᒱᒶᒼirc;䅴dge;拀r;쀀𝔚pf;쀀𝕎cr;쀀𝒲Ȁfiosᓋᓐᓒᓘr;쀀𝔛;䎞pf;쀀𝕏cr;쀀𝒳ҀAIUacfosuᓱᓵᓹᓽᔄᔏᔔᔚᔠcy;䐯cy;䐇cy;䐮cute耻Ý䃝Āiyᔉᔍrc;䅶;䐫r;쀀𝔜pf;쀀𝕐cr;쀀𝒴ml;䅸ЀHacdefosᔵᔹᔿᕋᕏᕝᕠᕤcy;䐖cute;䅹Āayᕄᕉron;䅽;䐗ot;䅻Dzᕔ\0ᕛoWidtè૙a;䎖r;愨pf;愤cr;쀀𝒵௡ᖃᖊᖐ\0ᖰᖶᖿ\0\0\0\0ᗆᗛᗫᙟ᙭\0ᚕ᚛ᚲᚹ\0ᚾcute耻á䃡reve;䄃̀;Ediuyᖜᖝᖡᖣᖨᖭ戾;쀀∾̳;房rc耻â䃢te肻´̆;䐰lig耻æ䃦Ā;r²ᖺ;쀀𝔞rave耻à䃠ĀepᗊᗖĀfpᗏᗔsym;愵èᗓha;䎱ĀapᗟcĀclᗤᗧr;䄁g;樿ɤᗰ\0\0ᘊʀ;adsvᗺᗻᗿᘁᘇ戧nd;橕;橜lope;橘;橚΀;elmrszᘘᘙᘛᘞᘿᙏᙙ戠;榤e»ᘙsdĀ;aᘥᘦ戡ѡᘰᘲᘴᘶᘸᘺᘼᘾ;榨;榩;榪;榫;榬;榭;榮;榯tĀ;vᙅᙆ戟bĀ;dᙌᙍ抾;榝Āptᙔᙗh;戢»¹arr;捼Āgpᙣᙧon;䄅f;쀀𝕒΀;Eaeiop዁ᙻᙽᚂᚄᚇᚊ;橰cir;橯;扊d;手s;䀧roxĀ;e዁ᚒñᚃing耻å䃥ƀctyᚡᚦᚨr;쀀𝒶;䀪mpĀ;e዁ᚯñʈilde耻ã䃣ml耻ä䃤Āciᛂᛈoninôɲnt;樑ࠀNabcdefiklnoprsu᛭ᛱᜰ᜼ᝃᝈ᝸᝽០៦ᠹᡐᜍ᤽᥈ᥰot;櫭Ācrᛶ᜞kȀcepsᜀᜅᜍᜓong;扌psilon;䏶rime;怵imĀ;e᜚᜛戽q;拍Ŷᜢᜦee;抽edĀ;gᜬᜭ挅e»ᜭrkĀ;t፜᜷brk;掶Āoyᜁᝁ;䐱quo;怞ʀcmprtᝓ᝛ᝡᝤᝨausĀ;eĊĉptyv;榰séᜌnoõēƀahwᝯ᝱ᝳ;䎲;愶een;扬r;쀀𝔟g΀costuvwឍឝឳេ៕៛៞ƀaiuបពរðݠrc;旯p»፱ƀdptឤឨឭot;樀lus;樁imes;樂ɱឹ\0\0ើcup;樆ar;昅riangleĀdu៍្own;施p;斳plus;樄eåᑄåᒭarow;植ƀako៭ᠦᠵĀcn៲ᠣkƀlst៺֫᠂ozenge;槫riangleȀ;dlr᠒᠓᠘᠝斴own;斾eft;旂ight;斸k;搣Ʊᠫ\0ᠳƲᠯ\0ᠱ;斒;斑4;斓ck;斈ĀeoᠾᡍĀ;qᡃᡆ쀀=⃥uiv;쀀≡⃥t;挐Ȁptwxᡙᡞᡧᡬf;쀀𝕓Ā;tᏋᡣom»Ꮜtie;拈؀DHUVbdhmptuvᢅᢖᢪᢻᣗᣛᣬ᣿ᤅᤊᤐᤡȀLRlrᢎᢐᢒᢔ;敗;敔;敖;敓ʀ;DUduᢡᢢᢤᢦᢨ敐;敦;敩;敤;敧ȀLRlrᢳᢵᢷᢹ;敝;敚;敜;教΀;HLRhlrᣊᣋᣍᣏᣑᣓᣕ救;敬;散;敠;敫;敢;敟ox;槉ȀLRlrᣤᣦᣨᣪ;敕;敒;攐;攌ʀ;DUduڽ᣷᣹᣻᣽;敥;敨;攬;攴inus;抟lus;択imes;抠ȀLRlrᤙᤛᤝ᤟;敛;敘;攘;攔΀;HLRhlrᤰᤱᤳᤵᤷ᤻᤹攂;敪;敡;敞;攼;攤;攜Āevģ᥂bar耻¦䂦Ȁceioᥑᥖᥚᥠr;쀀𝒷mi;恏mĀ;e᜚᜜lƀ;bhᥨᥩᥫ䁜;槅sub;柈Ŭᥴ᥾lĀ;e᥹᥺怢t»᥺pƀ;Eeįᦅᦇ;檮Ā;qۜۛೡᦧ\0᧨ᨑᨕᨲ\0ᨷᩐ\0\0᪴\0\0᫁\0\0ᬡᬮ᭍᭒\0᯽\0ᰌƀcpr᦭ᦲ᧝ute;䄇̀;abcdsᦿᧀᧄ᧊᧕᧙戩nd;橄rcup;橉Āau᧏᧒p;橋p;橇ot;橀;쀀∩︀Āeo᧢᧥t;恁îړȀaeiu᧰᧻ᨁᨅǰ᧵\0᧸s;橍on;䄍dil耻ç䃧rc;䄉psĀ;sᨌᨍ橌m;橐ot;䄋ƀdmnᨛᨠᨦil肻¸ƭptyv;榲t脀¢;eᨭᨮ䂢räƲr;쀀𝔠ƀceiᨽᩀᩍy;䑇ckĀ;mᩇᩈ朓ark»ᩈ;䏇r΀;Ecefms᩟᩠ᩢᩫ᪤᪪᪮旋;槃ƀ;elᩩᩪᩭ䋆q;扗eɡᩴ\0\0᪈rrowĀlr᩼᪁eft;憺ight;憻ʀRSacd᪒᪔᪖᪚᪟»ཇ;擈st;抛irc;抚ash;抝nint;樐id;櫯cir;槂ubsĀ;u᪻᪼晣it»᪼ˬ᫇᫔᫺\0ᬊonĀ;eᫍᫎ䀺Ā;qÇÆɭ᫙\0\0᫢aĀ;t᫞᫟䀬;䁀ƀ;fl᫨᫩᫫戁îᅠeĀmx᫱᫶ent»᫩eóɍǧ᫾\0ᬇĀ;dኻᬂot;橭nôɆƀfryᬐᬔᬗ;쀀𝕔oäɔ脀©;sŕᬝr;愗Āaoᬥᬩrr;憵ss;朗Ācuᬲᬷr;쀀𝒸Ābpᬼ᭄Ā;eᭁᭂ櫏;櫑Ā;eᭉᭊ櫐;櫒dot;拯΀delprvw᭠᭬᭷ᮂᮬᯔ᯹arrĀlr᭨᭪;椸;椵ɰ᭲\0\0᭵r;拞c;拟arrĀ;p᭿ᮀ憶;椽̀;bcdosᮏᮐᮖᮡᮥᮨ截rcap;橈Āauᮛᮞp;橆p;橊ot;抍r;橅;쀀∪︀Ȁalrv᮵ᮿᯞᯣrrĀ;mᮼᮽ憷;椼yƀevwᯇᯔᯘqɰᯎ\0\0ᯒreã᭳uã᭵ee;拎edge;拏en耻¤䂤earrowĀlrᯮ᯳eft»ᮀight»ᮽeäᯝĀciᰁᰇoninôǷnt;戱lcty;挭ঀAHabcdefhijlorstuwz᰸᰻᰿ᱝᱩᱵᲊᲞᲬᲷ᳻᳿ᴍᵻᶑᶫᶻ᷆᷍rò΁ar;楥Ȁglrs᱈ᱍ᱒᱔ger;怠eth;愸òᄳhĀ;vᱚᱛ怐»ऊūᱡᱧarow;椏aã̕Āayᱮᱳron;䄏;䐴ƀ;ao̲ᱼᲄĀgrʿᲁr;懊tseq;橷ƀglmᲑᲔᲘ耻°䂰ta;䎴ptyv;榱ĀirᲣᲨsht;楿;쀀𝔡arĀlrᲳᲵ»ࣜ»သʀaegsv᳂͸᳖᳜᳠mƀ;oș᳊᳔ndĀ;ș᳑uit;晦amma;䏝in;拲ƀ;io᳧᳨᳸䃷de脀÷;o᳧ᳰntimes;拇nø᳷cy;䑒cɯᴆ\0\0ᴊrn;挞op;挍ʀlptuwᴘᴝᴢᵉᵕlar;䀤f;쀀𝕕ʀ;emps̋ᴭᴷᴽᵂqĀ;d͒ᴳot;扑inus;戸lus;戔quare;抡blebarwedgåúnƀadhᄮᵝᵧownarrowóᲃarpoonĀlrᵲᵶefôᲴighôᲶŢᵿᶅkaro÷གɯᶊ\0\0ᶎrn;挟op;挌ƀcotᶘᶣᶦĀryᶝᶡ;쀀𝒹;䑕l;槶rok;䄑Ādrᶰᶴot;拱iĀ;fᶺ᠖斿Āah᷀᷃ròЩaòྦangle;榦Āci᷒ᷕy;䑟grarr;柿ऀDacdefglmnopqrstuxḁḉḙḸոḼṉṡṾấắẽỡἪἷὄ὎὚ĀDoḆᴴoôᲉĀcsḎḔute耻é䃩ter;橮ȀaioyḢḧḱḶron;䄛rĀ;cḭḮ扖耻ê䃪lon;払;䑍ot;䄗ĀDrṁṅot;扒;쀀𝔢ƀ;rsṐṑṗ檚ave耻è䃨Ā;dṜṝ檖ot;檘Ȁ;ilsṪṫṲṴ檙nters;揧;愓Ā;dṹṺ檕ot;檗ƀapsẅẉẗcr;䄓tyƀ;svẒẓẕ戅et»ẓpĀ1;ẝẤijạả;怄;怅怃ĀgsẪẬ;䅋p;怂ĀgpẴẸon;䄙f;쀀𝕖ƀalsỄỎỒrĀ;sỊị拕l;槣us;橱iƀ;lvỚớở䎵on»ớ;䏵ȀcsuvỪỳἋἣĀioữḱrc»Ḯɩỹ\0\0ỻíՈantĀglἂἆtr»ṝess»Ṻƀaeiἒ἖Ἒls;䀽st;扟vĀ;DȵἠD;橸parsl;槥ĀDaἯἳot;打rr;楱ƀcdiἾὁỸr;愯oô͒ĀahὉὋ;䎷耻ð䃰Āmrὓὗl耻ë䃫o;悬ƀcipὡὤὧl;䀡sôծĀeoὬὴctatioîՙnentialåչৡᾒ\0ᾞ\0ᾡᾧ\0\0ῆῌ\0ΐ\0ῦῪ \0 ⁚llingdotseñṄy;䑄male;晀ƀilrᾭᾳ῁lig;耀ffiɩᾹ\0\0᾽g;耀ffig;耀ffl;쀀𝔣lig;耀filig;쀀fjƀaltῙ῜ῡt;晭ig;耀flns;斱of;䆒ǰ΅\0ῳf;쀀𝕗ĀakֿῷĀ;vῼ´拔;櫙artint;樍Āao‌⁕Ācs‑⁒ႉ‸⁅⁈\0⁐β•‥‧‪‬\0‮耻½䂽;慓耻¼䂼;慕;慙;慛Ƴ‴\0‶;慔;慖ʴ‾⁁\0\0⁃耻¾䂾;慗;慜5;慘ƶ⁌\0⁎;慚;慝8;慞l;恄wn;挢cr;쀀𝒻ࢀEabcdefgijlnorstv₂₉₟₥₰₴⃰⃵⃺⃿℃ℒℸ̗ℾ⅒↞Ā;lٍ₇;檌ƀcmpₐₕ₝ute;䇵maĀ;dₜ᳚䎳;檆reve;䄟Āiy₪₮rc;䄝;䐳ot;䄡Ȁ;lqsؾق₽⃉ƀ;qsؾٌ⃄lanô٥Ȁ;cdl٥⃒⃥⃕c;檩otĀ;o⃜⃝檀Ā;l⃢⃣檂;檄Ā;e⃪⃭쀀⋛︀s;檔r;쀀𝔤Ā;gٳ؛mel;愷cy;䑓Ȁ;Eajٚℌℎℐ;檒;檥;檤ȀEaesℛℝ℩ℴ;扩pĀ;p℣ℤ檊rox»ℤĀ;q℮ℯ檈Ā;q℮ℛim;拧pf;쀀𝕘Āci⅃ⅆr;愊mƀ;el٫ⅎ⅐;檎;檐茀>;cdlqr׮ⅠⅪⅮⅳⅹĀciⅥⅧ;檧r;橺ot;拗Par;榕uest;橼ʀadelsↄⅪ←ٖ↛ǰ↉\0↎proø₞r;楸qĀlqؿ↖lesó₈ií٫Āen↣↭rtneqq;쀀≩︀Å↪ԀAabcefkosy⇄⇇⇱⇵⇺∘∝∯≨≽ròΠȀilmr⇐⇔⇗⇛rsðᒄf»․ilôکĀdr⇠⇤cy;䑊ƀ;cwࣴ⇫⇯ir;楈;憭ar;意irc;䄥ƀalr∁∎∓rtsĀ;u∉∊晥it»∊lip;怦con;抹r;쀀𝔥sĀew∣∩arow;椥arow;椦ʀamopr∺∾≃≞≣rr;懿tht;戻kĀlr≉≓eftarrow;憩ightarrow;憪f;쀀𝕙bar;怕ƀclt≯≴≸r;쀀𝒽asè⇴rok;䄧Ābp⊂⊇ull;恃hen»ᱛૡ⊣\0⊪\0⊸⋅⋎\0⋕⋳\0\0⋸⌢⍧⍢⍿\0⎆⎪⎴cute耻í䃭ƀ;iyݱ⊰⊵rc耻î䃮;䐸Ācx⊼⊿y;䐵cl耻¡䂡ĀfrΟ⋉;쀀𝔦rave耻ì䃬Ȁ;inoܾ⋝⋩⋮Āin⋢⋦nt;樌t;戭fin;槜ta;愩lig;䄳ƀaop⋾⌚⌝ƀcgt⌅⌈⌗r;䄫ƀelpܟ⌏⌓inåގarôܠh;䄱f;抷ed;䆵ʀ;cfotӴ⌬⌱⌽⍁are;愅inĀ;t⌸⌹戞ie;槝doô⌙ʀ;celpݗ⍌⍐⍛⍡al;抺Āgr⍕⍙eróᕣã⍍arhk;樗rod;樼Ȁcgpt⍯⍲⍶⍻y;䑑on;䄯f;쀀𝕚a;䎹uest耻¿䂿Āci⎊⎏r;쀀𝒾nʀ;EdsvӴ⎛⎝⎡ӳ;拹ot;拵Ā;v⎦⎧拴;拳Ā;iݷ⎮lde;䄩ǫ⎸\0⎼cy;䑖l耻ï䃯̀cfmosu⏌⏗⏜⏡⏧⏵Āiy⏑⏕rc;䄵;䐹r;쀀𝔧ath;䈷pf;쀀𝕛ǣ⏬\0⏱r;쀀𝒿rcy;䑘kcy;䑔Ѐacfghjos␋␖␢␧␭␱␵␻ppaĀ;v␓␔䎺;䏰Āey␛␠dil;䄷;䐺r;쀀𝔨reen;䄸cy;䑅cy;䑜pf;쀀𝕜cr;쀀𝓀஀ABEHabcdefghjlmnoprstuv⑰⒁⒆⒍⒑┎┽╚▀♎♞♥♹♽⚚⚲⛘❝❨➋⟀⠁⠒ƀart⑷⑺⑼rò৆òΕail;椛arr;椎Ā;gঔ⒋;檋ar;楢ॣ⒥\0⒪\0⒱\0\0\0\0\0⒵Ⓔ\0ⓆⓈⓍ\0⓹ute;䄺mptyv;榴raîࡌbda;䎻gƀ;dlࢎⓁⓃ;榑åࢎ;檅uo耻«䂫rЀ;bfhlpst࢙ⓞⓦⓩ⓫⓮⓱⓵Ā;f࢝ⓣs;椟s;椝ë≒p;憫l;椹im;楳l;憢ƀ;ae⓿─┄檫il;椙Ā;s┉┊檭;쀀⪭︀ƀabr┕┙┝rr;椌rk;杲Āak┢┬cĀek┨┪;䁻;䁛Āes┱┳;榋lĀdu┹┻;榏;榍Ȁaeuy╆╋╖╘ron;䄾Ādi═╔il;䄼ìࢰâ┩;䐻Ȁcqrs╣╦╭╽a;椶uoĀ;rนᝆĀdu╲╷har;楧shar;楋h;憲ʀ;fgqs▋▌উ◳◿扤tʀahlrt▘▤▷◂◨rrowĀ;t࢙□aé⓶arpoonĀdu▯▴own»њp»०eftarrows;懇ightƀahs◍◖◞rrowĀ;sࣴࢧarpoonó྘quigarro÷⇰hreetimes;拋ƀ;qs▋ও◺lanôবʀ;cdgsব☊☍☝☨c;檨otĀ;o☔☕橿Ā;r☚☛檁;檃Ā;e☢☥쀀⋚︀s;檓ʀadegs☳☹☽♉♋pproøⓆot;拖qĀgq♃♅ôউgtò⒌ôছiíলƀilr♕࣡♚sht;楼;쀀𝔩Ā;Eজ♣;檑š♩♶rĀdu▲♮Ā;l॥♳;楪lk;斄cy;䑙ʀ;achtੈ⚈⚋⚑⚖rò◁orneòᴈard;楫ri;旺Āio⚟⚤dot;䅀ustĀ;a⚬⚭掰che»⚭ȀEaes⚻⚽⛉⛔;扨pĀ;p⛃⛄檉rox»⛄Ā;q⛎⛏檇Ā;q⛎⚻im;拦Ѐa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di㩀㩑Ābg㩅㩉ar;機eĀ;qᗺ㩏;扙erp;愘r;쀀𝔴pf;쀀𝕨Ā;eᑹ㩦atèᑹcr;쀀𝓌ૣណ㪇\0㪋\0㪐㪛\0\0㪝㪨㪫㪯\0\0㫃㫎\0㫘ៜ៟tré៑r;쀀𝔵ĀAa㪔㪗ròσrò৶;䎾ĀAa㪡㪤ròθrò৫að✓is;拻ƀdptឤ㪵㪾Āfl㪺ឩ;쀀𝕩imåឲĀAa㫇㫊ròώròਁĀcq㫒ីr;쀀𝓍Āpt៖㫜ré។Ѐacefiosu㫰㫽㬈㬌㬑㬕㬛㬡cĀuy㫶㫻te耻ý䃽;䑏Āiy㬂㬆rc;䅷;䑋n耻¥䂥r;쀀𝔶cy;䑗pf;쀀𝕪cr;쀀𝓎Ācm㬦㬩y;䑎l耻ÿ䃿Ԁacdefhiosw㭂㭈㭔㭘㭤㭩㭭㭴㭺㮀cute;䅺Āay㭍㭒ron;䅾;䐷ot;䅼Āet㭝㭡træᕟa;䎶r;쀀𝔷cy;䐶grarr;懝pf;쀀𝕫cr;쀀𝓏Ājn㮅㮇;怍j;怌'.split("").map((function(e){return 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a/.venv-sp/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/446.f8696ce72124c78273da.js b/.venv-sp/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/446.f8696ce72124c78273da.js new file mode 100644 index 0000000..5a288c6 --- /dev/null +++ b/.venv-sp/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/446.f8696ce72124c78273da.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunk_jupyter_widgets_jupyterlab_manager=self.webpackChunk_jupyter_widgets_jupyterlab_manager||[]).push([[446,701],{1556:(e,n,t)=>{t.d(n,{A:()=>d});var i=t(4786),r=t.n(i),o=t(9451),a=t.n(o)()(r());a.push([e.id,"/* Copyright (c) Jupyter Development Team.\n * Distributed under the terms of the Modified BSD License.\n */\n\n.jupyter-widgets-disconnected::before {\n content: '\\f127'; /* chain-broken */\n display: inline-block;\n font: normal normal 900 14px/1 'Font Awesome 5 Free', 'FontAwesome';\n text-rendering: auto;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n color: #d9534f;\n padding: 3px;\n align-self: flex-start;\n}\n\n.jupyter-widgets-error-widget {\n display: flex;\n flex-direction: column;\n justify-content: center;\n height: 100%;\n border: solid 1px red;\n margin: 0 auto;\n}\n\n.jupyter-widgets-error-widget.icon-error {\n min-width: var(--jp-widgets-inline-width-short);\n}\n.jupyter-widgets-error-widget.text-error {\n min-width: calc(2 * var(--jp-widgets-inline-width));\n min-height: calc(3 * var(--jp-widgets-inline-height));\n}\n\n.jupyter-widgets-error-widget p {\n text-align: center;\n}\n\n.jupyter-widgets-error-widget.text-error pre::first-line {\n font-weight: bold;\n}\n",""]);const d=a},1451:(e,n,t)=>{t.d(n,{A:()=>d});var i=t(4786),r=t.n(i),o=t(9451),a=t.n(o)()(r());a.push([e.id,"/* This file has code derived from Lumino CSS files, as noted below. The license for this Lumino code is:\n\nCopyright (c) 2019 Project Jupyter Contributors\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n\nCopyright (c) 2014-2017, PhosphorJS Contributors\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n\n/*\n * The following section is derived from https://github.com/jupyterlab/lumino/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css \n * We've scoped the rules so that they are consistent with exactly our code.\n */\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar {\n display: flex;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'], /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar[data-orientation='horizontal'], /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar[data-orientation='horizontal'] {\n flex-direction: row;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'], /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar[data-orientation='vertical'], /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar[data-orientation='vertical'] {\n flex-direction: column;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar > .p-TabBar-content, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar > .lm-TabBar-content {\n margin: 0;\n padding: 0;\n display: flex;\n flex: 1 1 auto;\n list-style-type: none;\n}\n\n/* */\n.jupyter-widgets.widget-tab\n > .p-TabBar[data-orientation='horizontal']\n > .p-TabBar-content,\n/* */\n/* */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar[data-orientation='horizontal']\n> .p-TabBar-content,\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar[data-orientation='horizontal']\n > .lm-TabBar-content {\n flex-direction: row;\n}\n\n/* */\n.jupyter-widgets.widget-tab\n > .p-TabBar[data-orientation='vertical']\n > .p-TabBar-content,\n/* */\n/* */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar[data-orientation='vertical']\n> .p-TabBar-content,\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar[data-orientation='vertical']\n > .lm-TabBar-content {\n flex-direction: column;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab {\n display: flex;\n flex-direction: row;\n box-sizing: border-box;\n overflow: hidden;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon, /* */\n/* */ .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabIcon, /* */\n/* */ .jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabCloseIcon, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabIcon,\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabCloseIcon {\n flex: 0 0 auto;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabLabel, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabLabel {\n flex: 1 1 auto;\n overflow: hidden;\n white-space: nowrap;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab.lm-mod-hidden {\n display: none !important;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar.lm-mod-dragging .lm-TabBar-tab {\n position: relative;\n}\n\n/* */\n.jupyter-widgets.widget-tab\n > .p-TabBar.p-mod-dragging[data-orientation='horizontal']\n .p-TabBar-tab,\n/* */\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .p-TabBar.p-mod-dragging[data-orientation='horizontal']\n .p-TabBar-tab,\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar.lm-mod-dragging[data-orientation='horizontal']\n .lm-TabBar-tab {\n left: 0;\n transition: left 150ms ease;\n}\n\n/* */\n.jupyter-widgets.widget-tab\n > .p-TabBar.p-mod-dragging[data-orientation='vertical']\n .p-TabBar-tab,\n/* */\n/* */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar.p-mod-dragging[data-orientation='vertical']\n.p-TabBar-tab,\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar.lm-mod-dragging[data-orientation='vertical']\n .lm-TabBar-tab {\n top: 0;\n transition: top 150ms ease;\n}\n\n/* */\n.jupyter-widgets.widget-tab\n > .p-TabBar.p-mod-dragging\n .p-TabBar-tab.p-mod-dragging,\n/* */\n/* */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar.p-mod-dragging\n.p-TabBar-tab.p-mod-dragging,\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar.lm-mod-dragging\n .lm-TabBar-tab.lm-mod-dragging {\n transition: none;\n}\n\n/* End tabbar.css */\n",""]);const d=a},9013:(e,n,t)=>{t.d(n,{A:()=>d});var i=t(4786),r=t.n(i),o=t(9451),a=t.n(o)()(r());a.push([e.id,'/*\n\nThe nouislider.css file is autogenerated from nouislider.less, which imports and wraps the nouislider/src/nouislider.less styles.\n\nMIT License\n\nCopyright (c) 2019 Léon Gersen\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n*/\n/* The .widget-slider class is deprecated */\n.widget-slider,\n.jupyter-widget-slider {\n /* Functional styling;\n * These styles are required for noUiSlider to function.\n * You don\'t need to change these rules to apply your design.\n */\n /* Wrapper for all connect elements.\n */\n /* Offset direction\n */\n /* Give origins 0 height/width so they don\'t interfere with clicking the\n * connect elements.\n */\n /* Slider size and handle placement;\n */\n /* Styling;\n * Giving the connect element a border radius causes issues with using transform: scale\n */\n /* Handles and cursors;\n */\n /* Handle stripes;\n */\n /* Disabled state;\n */\n /* Base;\n *\n */\n /* Values;\n *\n */\n /* Markings;\n *\n */\n /* Horizontal layout;\n *\n */\n /* Vertical layout;\n *\n */\n /* Copyright (c) Jupyter Development Team.\n * Distributed under the terms of the Modified BSD License.\n */\n /* Custom CSS for nouislider */\n}\n.widget-slider .noUi-target,\n.jupyter-widget-slider .noUi-target,\n.widget-slider .noUi-target *,\n.jupyter-widget-slider .noUi-target * {\n -webkit-touch-callout: none;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-user-select: none;\n -ms-touch-action: none;\n touch-action: none;\n -ms-user-select: none;\n -moz-user-select: none;\n user-select: none;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n.widget-slider .noUi-target,\n.jupyter-widget-slider .noUi-target {\n position: relative;\n}\n.widget-slider .noUi-base,\n.jupyter-widget-slider .noUi-base,\n.widget-slider .noUi-connects,\n.jupyter-widget-slider .noUi-connects {\n width: 100%;\n height: 100%;\n position: relative;\n z-index: 1;\n}\n.widget-slider .noUi-connects,\n.jupyter-widget-slider .noUi-connects {\n overflow: hidden;\n z-index: 0;\n}\n.widget-slider .noUi-connect,\n.jupyter-widget-slider .noUi-connect,\n.widget-slider .noUi-origin,\n.jupyter-widget-slider .noUi-origin {\n will-change: transform;\n position: absolute;\n z-index: 1;\n top: 0;\n right: 0;\n -ms-transform-origin: 0 0;\n -webkit-transform-origin: 0 0;\n -webkit-transform-style: preserve-3d;\n transform-origin: 0 0;\n transform-style: flat;\n}\n.widget-slider .noUi-connect,\n.jupyter-widget-slider .noUi-connect {\n height: 100%;\n width: 100%;\n}\n.widget-slider .noUi-origin,\n.jupyter-widget-slider .noUi-origin {\n height: 10%;\n width: 10%;\n}\n.widget-slider .noUi-txt-dir-rtl.noUi-horizontal .noUi-origin,\n.jupyter-widget-slider .noUi-txt-dir-rtl.noUi-horizontal .noUi-origin {\n left: 0;\n right: auto;\n}\n.widget-slider .noUi-vertical .noUi-origin,\n.jupyter-widget-slider .noUi-vertical .noUi-origin {\n width: 0;\n}\n.widget-slider .noUi-horizontal .noUi-origin,\n.jupyter-widget-slider .noUi-horizontal .noUi-origin {\n height: 0;\n}\n.widget-slider .noUi-handle,\n.jupyter-widget-slider .noUi-handle {\n -webkit-backface-visibility: hidden;\n backface-visibility: hidden;\n position: absolute;\n}\n.widget-slider .noUi-touch-area,\n.jupyter-widget-slider .noUi-touch-area {\n height: 100%;\n width: 100%;\n}\n.widget-slider .noUi-state-tap .noUi-connect,\n.jupyter-widget-slider .noUi-state-tap .noUi-connect,\n.widget-slider .noUi-state-tap .noUi-origin,\n.jupyter-widget-slider .noUi-state-tap .noUi-origin {\n -webkit-transition: transform 0.3s;\n transition: transform 0.3s;\n}\n.widget-slider .noUi-state-drag *,\n.jupyter-widget-slider .noUi-state-drag * {\n cursor: inherit !important;\n}\n.widget-slider .noUi-horizontal,\n.jupyter-widget-slider .noUi-horizontal {\n height: 18px;\n}\n.widget-slider .noUi-horizontal .noUi-handle,\n.jupyter-widget-slider .noUi-horizontal .noUi-handle {\n width: 34px;\n height: 28px;\n right: -17px;\n top: -6px;\n}\n.widget-slider .noUi-vertical,\n.jupyter-widget-slider .noUi-vertical {\n width: 18px;\n}\n.widget-slider .noUi-vertical .noUi-handle,\n.jupyter-widget-slider .noUi-vertical .noUi-handle {\n width: 28px;\n height: 34px;\n right: -6px;\n top: -17px;\n}\n.widget-slider .noUi-txt-dir-rtl.noUi-horizontal .noUi-handle,\n.jupyter-widget-slider .noUi-txt-dir-rtl.noUi-horizontal .noUi-handle {\n left: -17px;\n right: auto;\n}\n.widget-slider .noUi-target,\n.jupyter-widget-slider .noUi-target {\n background: #FAFAFA;\n border-radius: 4px;\n border: 1px solid #D3D3D3;\n box-shadow: inset 0 1px 1px #F0F0F0, 0 3px 6px -5px #BBB;\n}\n.widget-slider .noUi-connects,\n.jupyter-widget-slider .noUi-connects {\n border-radius: 3px;\n}\n.widget-slider .noUi-connect,\n.jupyter-widget-slider .noUi-connect {\n background: #3FB8AF;\n}\n.widget-slider .noUi-draggable,\n.jupyter-widget-slider .noUi-draggable {\n cursor: ew-resize;\n}\n.widget-slider .noUi-vertical .noUi-draggable,\n.jupyter-widget-slider .noUi-vertical .noUi-draggable {\n cursor: ns-resize;\n}\n.widget-slider .noUi-handle,\n.jupyter-widget-slider .noUi-handle {\n border: 1px solid #D9D9D9;\n border-radius: 3px;\n background: #FFF;\n cursor: default;\n box-shadow: inset 0 0 1px #FFF, inset 0 1px 7px #EBEBEB, 0 3px 6px -3px #BBB;\n}\n.widget-slider .noUi-active,\n.jupyter-widget-slider .noUi-active {\n box-shadow: inset 0 0 1px #FFF, inset 0 1px 7px #DDD, 0 3px 6px -3px #BBB;\n}\n.widget-slider .noUi-handle:before,\n.jupyter-widget-slider .noUi-handle:before,\n.widget-slider .noUi-handle:after,\n.jupyter-widget-slider .noUi-handle:after {\n content: "";\n display: block;\n position: absolute;\n height: 14px;\n width: 1px;\n background: #E8E7E6;\n left: 14px;\n top: 6px;\n}\n.widget-slider .noUi-handle:after,\n.jupyter-widget-slider .noUi-handle:after {\n left: 17px;\n}\n.widget-slider .noUi-vertical .noUi-handle:before,\n.jupyter-widget-slider .noUi-vertical .noUi-handle:before,\n.widget-slider .noUi-vertical .noUi-handle:after,\n.jupyter-widget-slider .noUi-vertical .noUi-handle:after {\n width: 14px;\n height: 1px;\n left: 6px;\n top: 14px;\n}\n.widget-slider .noUi-vertical .noUi-handle:after,\n.jupyter-widget-slider .noUi-vertical .noUi-handle:after {\n top: 17px;\n}\n.widget-slider [disabled] .noUi-connect,\n.jupyter-widget-slider [disabled] .noUi-connect {\n background: #B8B8B8;\n}\n.widget-slider [disabled].noUi-target,\n.jupyter-widget-slider [disabled].noUi-target,\n.widget-slider [disabled].noUi-handle,\n.jupyter-widget-slider [disabled].noUi-handle,\n.widget-slider [disabled] .noUi-handle,\n.jupyter-widget-slider [disabled] .noUi-handle {\n cursor: not-allowed;\n}\n.widget-slider .noUi-pips,\n.jupyter-widget-slider .noUi-pips,\n.widget-slider .noUi-pips *,\n.jupyter-widget-slider .noUi-pips * {\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n.widget-slider .noUi-pips,\n.jupyter-widget-slider .noUi-pips {\n position: absolute;\n color: #999;\n}\n.widget-slider .noUi-value,\n.jupyter-widget-slider .noUi-value {\n position: absolute;\n white-space: nowrap;\n text-align: center;\n}\n.widget-slider .noUi-value-sub,\n.jupyter-widget-slider .noUi-value-sub {\n color: #ccc;\n font-size: 10px;\n}\n.widget-slider .noUi-marker,\n.jupyter-widget-slider .noUi-marker {\n position: absolute;\n background: #CCC;\n}\n.widget-slider .noUi-marker-sub,\n.jupyter-widget-slider .noUi-marker-sub {\n background: #AAA;\n}\n.widget-slider .noUi-marker-large,\n.jupyter-widget-slider .noUi-marker-large {\n background: #AAA;\n}\n.widget-slider .noUi-pips-horizontal,\n.jupyter-widget-slider .noUi-pips-horizontal {\n padding: 10px 0;\n height: 80px;\n top: 100%;\n left: 0;\n width: 100%;\n}\n.widget-slider .noUi-value-horizontal,\n.jupyter-widget-slider .noUi-value-horizontal {\n -webkit-transform: translate(-50%, 50%);\n transform: translate(-50%, 50%);\n}\n.noUi-rtl .widget-slider .noUi-value-horizontal,\n.noUi-rtl .jupyter-widget-slider .noUi-value-horizontal {\n -webkit-transform: translate(50%, 50%);\n transform: translate(50%, 50%);\n}\n.widget-slider .noUi-marker-horizontal.noUi-marker,\n.jupyter-widget-slider .noUi-marker-horizontal.noUi-marker {\n margin-left: -1px;\n width: 2px;\n height: 5px;\n}\n.widget-slider .noUi-marker-horizontal.noUi-marker-sub,\n.jupyter-widget-slider .noUi-marker-horizontal.noUi-marker-sub {\n height: 10px;\n}\n.widget-slider .noUi-marker-horizontal.noUi-marker-large,\n.jupyter-widget-slider .noUi-marker-horizontal.noUi-marker-large {\n height: 15px;\n}\n.widget-slider .noUi-pips-vertical,\n.jupyter-widget-slider .noUi-pips-vertical {\n padding: 0 10px;\n height: 100%;\n top: 0;\n left: 100%;\n}\n.widget-slider .noUi-value-vertical,\n.jupyter-widget-slider .noUi-value-vertical {\n -webkit-transform: translate(0, -50%);\n transform: translate(0, -50%);\n padding-left: 25px;\n}\n.noUi-rtl .widget-slider .noUi-value-vertical,\n.noUi-rtl .jupyter-widget-slider .noUi-value-vertical {\n -webkit-transform: translate(0, 50%);\n transform: translate(0, 50%);\n}\n.widget-slider .noUi-marker-vertical.noUi-marker,\n.jupyter-widget-slider .noUi-marker-vertical.noUi-marker {\n width: 5px;\n height: 2px;\n margin-top: -1px;\n}\n.widget-slider .noUi-marker-vertical.noUi-marker-sub,\n.jupyter-widget-slider .noUi-marker-vertical.noUi-marker-sub {\n width: 10px;\n}\n.widget-slider .noUi-marker-vertical.noUi-marker-large,\n.jupyter-widget-slider .noUi-marker-vertical.noUi-marker-large {\n width: 15px;\n}\n.widget-slider .noUi-tooltip,\n.jupyter-widget-slider .noUi-tooltip {\n display: block;\n position: absolute;\n border: 1px solid #D9D9D9;\n border-radius: 3px;\n background: #fff;\n color: #000;\n padding: 5px;\n text-align: center;\n white-space: nowrap;\n}\n.widget-slider .noUi-horizontal .noUi-tooltip,\n.jupyter-widget-slider .noUi-horizontal .noUi-tooltip {\n -webkit-transform: translate(-50%, 0);\n transform: translate(-50%, 0);\n left: 50%;\n bottom: 120%;\n}\n.widget-slider .noUi-vertical .noUi-tooltip,\n.jupyter-widget-slider .noUi-vertical .noUi-tooltip {\n -webkit-transform: translate(0, -50%);\n transform: translate(0, -50%);\n top: 50%;\n right: 120%;\n}\n.widget-slider .noUi-horizontal .noUi-origin > .noUi-tooltip,\n.jupyter-widget-slider .noUi-horizontal .noUi-origin > .noUi-tooltip {\n -webkit-transform: translate(50%, 0);\n transform: translate(50%, 0);\n left: auto;\n bottom: 10px;\n}\n.widget-slider .noUi-vertical .noUi-origin > .noUi-tooltip,\n.jupyter-widget-slider .noUi-vertical .noUi-origin > .noUi-tooltip {\n -webkit-transform: translate(0, -18px);\n transform: translate(0, -18px);\n top: auto;\n right: 28px;\n}\n.widget-slider .noUi-connect,\n.jupyter-widget-slider .noUi-connect {\n background: #2196f3;\n}\n.widget-slider .noUi-horizontal,\n.jupyter-widget-slider .noUi-horizontal {\n height: var(--jp-widgets-slider-track-thickness);\n}\n.widget-slider .noUi-vertical,\n.jupyter-widget-slider .noUi-vertical {\n width: var(--jp-widgets-slider-track-thickness);\n height: 100%;\n}\n.widget-slider .noUi-horizontal .noUi-handle,\n.jupyter-widget-slider .noUi-horizontal .noUi-handle {\n width: var(--jp-widgets-slider-handle-size);\n height: var(--jp-widgets-slider-handle-size);\n border-radius: 50%;\n top: calc((var(--jp-widgets-slider-track-thickness) - var(--jp-widgets-slider-handle-size)) / 2);\n right: calc(var(--jp-widgets-slider-handle-size) / -2);\n}\n.widget-slider .noUi-vertical .noUi-handle,\n.jupyter-widget-slider .noUi-vertical .noUi-handle {\n height: var(--jp-widgets-slider-handle-size);\n width: var(--jp-widgets-slider-handle-size);\n border-radius: 50%;\n right: calc((var(--jp-widgets-slider-handle-size) - var(--jp-widgets-slider-track-thickness)) / -2);\n top: calc(var(--jp-widgets-slider-handle-size) / -2);\n}\n.widget-slider .noUi-handle:after,\n.jupyter-widget-slider .noUi-handle:after {\n content: none;\n}\n.widget-slider .noUi-handle:before,\n.jupyter-widget-slider .noUi-handle:before {\n content: none;\n}\n.widget-slider .noUi-target,\n.jupyter-widget-slider .noUi-target {\n background: #fafafa;\n border-radius: 4px;\n border: 1px;\n /* box-shadow: inset 0 1px 1px #F0F0F0, 0 3px 6px -5px #BBB; */\n}\n.widget-slider .ui-slider,\n.jupyter-widget-slider .ui-slider {\n border: var(--jp-widgets-slider-border-width) solid var(--jp-layout-color3);\n background: var(--jp-layout-color3);\n box-sizing: border-box;\n position: relative;\n border-radius: 0px;\n}\n.widget-slider .noUi-handle,\n.jupyter-widget-slider .noUi-handle {\n width: var(--jp-widgets-slider-handle-size);\n border: 1px solid #d9d9d9;\n border-radius: 3px;\n background: #fff;\n cursor: default;\n box-shadow: none;\n outline: none;\n}\n.widget-slider .noUi-target:not([disabled]) .noUi-handle:hover,\n.jupyter-widget-slider .noUi-target:not([disabled]) .noUi-handle:hover,\n.widget-slider .noUi-target:not([disabled]) .noUi-handle:focus,\n.jupyter-widget-slider .noUi-target:not([disabled]) .noUi-handle:focus {\n background-color: var(--jp-widgets-slider-active-handle-color);\n border: var(--jp-widgets-slider-border-width) solid var(--jp-widgets-slider-active-handle-color);\n}\n.widget-slider [disabled].noUi-target,\n.jupyter-widget-slider [disabled].noUi-target {\n opacity: 0.35;\n}\n.widget-slider .noUi-connects,\n.jupyter-widget-slider .noUi-connects {\n overflow: visible;\n z-index: 0;\n background: var(--jp-layout-color3);\n}\n.widget-slider .noUi-vertical .noUi-connect,\n.jupyter-widget-slider .noUi-vertical .noUi-connect {\n width: calc(100% + 2px);\n right: -1px;\n}\n.widget-slider .noUi-horizontal .noUi-connect,\n.jupyter-widget-slider .noUi-horizontal .noUi-connect {\n height: calc(100% + 2px);\n top: -1px;\n}\n',""]);const d=a},7654:(e,n,t)=>{t.d(n,{A:()=>w});var i=t(4786),r=t.n(i),o=t(9451),a=t.n(o),d=t(1451),s=t(9013),l=t(7298),g=t.n(l),p=new URL(t(2426),t.b),c=a()(r());c.i(d.A),c.i(s.A);var u=g()(p);c.push([e.id,`/* Copyright (c) Jupyter Development Team.\n * Distributed under the terms of the Modified BSD License.\n */\n\n/*\n * We assume that the CSS variables in\n * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css\n * have been defined.\n */\n\n:root {\n --jp-widgets-color: var(--jp-content-font-color1);\n --jp-widgets-label-color: var(--jp-widgets-color);\n --jp-widgets-readout-color: var(--jp-widgets-color);\n --jp-widgets-font-size: var(--jp-ui-font-size1);\n --jp-widgets-margin: 2px;\n --jp-widgets-inline-height: 28px;\n --jp-widgets-inline-width: 300px;\n --jp-widgets-inline-width-short: calc(\n var(--jp-widgets-inline-width) / 2 - var(--jp-widgets-margin)\n );\n --jp-widgets-inline-width-tiny: calc(\n var(--jp-widgets-inline-width-short) / 2 - var(--jp-widgets-margin)\n );\n --jp-widgets-inline-margin: 4px; /* margin between inline elements */\n --jp-widgets-inline-label-width: 80px;\n --jp-widgets-border-width: var(--jp-border-width);\n --jp-widgets-vertical-height: 200px;\n --jp-widgets-horizontal-tab-height: 24px;\n --jp-widgets-horizontal-tab-width: 144px;\n --jp-widgets-horizontal-tab-top-border: 2px;\n --jp-widgets-progress-thickness: 20px;\n --jp-widgets-container-padding: 15px;\n --jp-widgets-input-padding: 4px;\n --jp-widgets-radio-item-height-adjustment: 8px;\n --jp-widgets-radio-item-height: calc(\n var(--jp-widgets-inline-height) -\n var(--jp-widgets-radio-item-height-adjustment)\n );\n --jp-widgets-slider-track-thickness: 4px;\n --jp-widgets-slider-border-width: var(--jp-widgets-border-width);\n --jp-widgets-slider-handle-size: 16px;\n --jp-widgets-slider-handle-border-color: var(--jp-border-color1);\n --jp-widgets-slider-handle-background-color: var(--jp-layout-color1);\n --jp-widgets-slider-active-handle-color: var(--jp-brand-color1);\n --jp-widgets-menu-item-height: 24px;\n --jp-widgets-dropdown-arrow: url(${u});\n --jp-widgets-input-color: var(--jp-ui-font-color1);\n --jp-widgets-input-background-color: var(--jp-layout-color1);\n --jp-widgets-input-border-color: var(--jp-border-color1);\n --jp-widgets-input-focus-border-color: var(--jp-brand-color2);\n --jp-widgets-input-border-width: var(--jp-widgets-border-width);\n --jp-widgets-disabled-opacity: 0.6;\n\n /* From Material Design Lite */\n --md-shadow-key-umbra-opacity: 0.2;\n --md-shadow-key-penumbra-opacity: 0.14;\n --md-shadow-ambient-shadow-opacity: 0.12;\n}\n\n.jupyter-widgets {\n margin: var(--jp-widgets-margin);\n box-sizing: border-box;\n color: var(--jp-widgets-color);\n overflow: visible;\n}\n\n.jp-Output-result > .jupyter-widgets {\n margin-left: 0;\n margin-right: 0;\n}\n\n/* vbox and hbox */\n\n/* */\n.widget-inline-hbox, /* */\n .jupyter-widget-inline-hbox {\n /* Horizontal widgets */\n box-sizing: border-box;\n display: flex;\n flex-direction: row;\n align-items: baseline;\n}\n\n/* */\n.widget-inline-vbox, /* */\n .jupyter-widget-inline-vbox {\n /* Vertical Widgets */\n box-sizing: border-box;\n display: flex;\n flex-direction: column;\n align-items: center;\n}\n\n/* */\n.widget-box, /* */\n.jupyter-widget-box {\n box-sizing: border-box;\n display: flex;\n margin: 0;\n overflow: auto;\n}\n\n/* */\n.widget-gridbox, /* */\n.jupyter-widget-gridbox {\n box-sizing: border-box;\n display: grid;\n margin: 0;\n overflow: auto;\n}\n\n/* */\n.widget-hbox, /* */\n.jupyter-widget-hbox {\n flex-direction: row;\n}\n\n/* */\n.widget-vbox, /* */\n.jupyter-widget-vbox {\n flex-direction: column;\n}\n\n/* General Tags Styling */\n\n.jupyter-widget-tagsinput {\n display: flex;\n flex-direction: row;\n flex-wrap: wrap;\n align-items: center;\n overflow: auto;\n\n cursor: text;\n}\n\n.jupyter-widget-tag {\n padding-left: 10px;\n padding-right: 10px;\n padding-top: 0px;\n padding-bottom: 0px;\n display: inline-block;\n white-space: nowrap;\n overflow: hidden;\n text-overflow: ellipsis;\n text-align: center;\n font-size: var(--jp-widgets-font-size);\n\n height: calc(var(--jp-widgets-inline-height) - 2px);\n border: 0px solid;\n line-height: calc(var(--jp-widgets-inline-height) - 2px);\n box-shadow: none;\n\n color: var(--jp-ui-font-color1);\n background-color: var(--jp-layout-color2);\n border-color: var(--jp-border-color2);\n border: none;\n user-select: none;\n\n cursor: grab;\n transition: margin-left 200ms;\n margin: 1px 1px 1px 1px;\n}\n\n.jupyter-widget-tag.mod-active {\n /* MD Lite 4dp shadow */\n box-shadow: 0 4px 5px 0 rgba(0, 0, 0, var(--md-shadow-key-penumbra-opacity)),\n 0 1px 10px 0 rgba(0, 0, 0, var(--md-shadow-ambient-shadow-opacity)),\n 0 2px 4px -1px rgba(0, 0, 0, var(--md-shadow-key-umbra-opacity));\n color: var(--jp-ui-font-color1);\n background-color: var(--jp-layout-color3);\n}\n\n.jupyter-widget-colortag {\n color: var(--jp-inverse-ui-font-color1);\n}\n\n.jupyter-widget-colortag.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n}\n\n.jupyter-widget-taginput {\n color: var(--jp-ui-font-color0);\n background-color: var(--jp-layout-color0);\n\n cursor: text;\n text-align: left;\n}\n\n.jupyter-widget-taginput:focus {\n outline: none;\n}\n\n.jupyter-widget-tag-close {\n margin-left: var(--jp-widgets-inline-margin);\n padding: 2px 0px 2px 2px;\n}\n\n.jupyter-widget-tag-close:hover {\n cursor: pointer;\n}\n\n/* Tag "Primary" Styling */\n\n.jupyter-widget-tag.mod-primary {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-brand-color1);\n}\n\n.jupyter-widget-tag.mod-primary.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-brand-color0);\n}\n\n/* Tag "Success" Styling */\n\n.jupyter-widget-tag.mod-success {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-success-color1);\n}\n\n.jupyter-widget-tag.mod-success.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-success-color0);\n}\n\n/* Tag "Info" Styling */\n\n.jupyter-widget-tag.mod-info {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-info-color1);\n}\n\n.jupyter-widget-tag.mod-info.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-info-color0);\n}\n\n/* Tag "Warning" Styling */\n\n.jupyter-widget-tag.mod-warning {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-warn-color1);\n}\n\n.jupyter-widget-tag.mod-warning.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-warn-color0);\n}\n\n/* Tag "Danger" Styling */\n\n.jupyter-widget-tag.mod-danger {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-error-color1);\n}\n\n.jupyter-widget-tag.mod-danger.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-error-color0);\n}\n\n/* General Button Styling */\n\n.jupyter-button {\n padding-left: 10px;\n padding-right: 10px;\n padding-top: 0px;\n padding-bottom: 0px;\n display: inline-block;\n white-space: nowrap;\n overflow: hidden;\n text-overflow: ellipsis;\n text-align: center;\n font-size: var(--jp-widgets-font-size);\n cursor: pointer;\n\n height: var(--jp-widgets-inline-height);\n border: 0px solid;\n line-height: var(--jp-widgets-inline-height);\n box-shadow: none;\n\n color: var(--jp-ui-font-color1);\n background-color: var(--jp-layout-color2);\n border-color: var(--jp-border-color2);\n border: none;\n user-select: none;\n}\n\n.jupyter-button i.fa {\n margin-right: var(--jp-widgets-inline-margin);\n pointer-events: none;\n}\n\n.jupyter-button:empty:before {\n content: '\\200b'; /* zero-width space */\n}\n\n.jupyter-widgets.jupyter-button:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n.jupyter-button i.fa.center {\n margin-right: 0;\n}\n\n.jupyter-button:hover:enabled,\n.jupyter-button:focus:enabled {\n /* MD Lite 2dp shadow */\n box-shadow: 0 2px 2px 0 rgba(0, 0, 0, var(--md-shadow-key-penumbra-opacity)),\n 0 3px 1px -2px rgba(0, 0, 0, var(--md-shadow-key-umbra-opacity)),\n 0 1px 5px 0 rgba(0, 0, 0, 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*/\n\n.jupyter-button.mod-success {\n color: var(--jp-ui-inverse-font-color1);\n background-color: var(--jp-success-color1);\n}\n\n.jupyter-button.mod-success.mod-active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-success-color0);\n}\n\n.jupyter-button.mod-success:active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-success-color0);\n}\n\n/* Button "Info" Styling */\n\n.jupyter-button.mod-info {\n color: var(--jp-ui-inverse-font-color1);\n background-color: var(--jp-info-color1);\n}\n\n.jupyter-button.mod-info.mod-active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-info-color0);\n}\n\n.jupyter-button.mod-info:active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-info-color0);\n}\n\n/* Button "Warning" Styling */\n\n.jupyter-button.mod-warning {\n color: var(--jp-ui-inverse-font-color1);\n background-color: var(--jp-warn-color1);\n}\n\n.jupyter-button.mod-warning.mod-active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-warn-color0);\n}\n\n.jupyter-button.mod-warning:active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-warn-color0);\n}\n\n/* Button "Danger" Styling */\n\n.jupyter-button.mod-danger {\n color: var(--jp-ui-inverse-font-color1);\n background-color: var(--jp-error-color1);\n}\n\n.jupyter-button.mod-danger.mod-active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-error-color0);\n}\n\n.jupyter-button.mod-danger:active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-error-color0);\n}\n\n/* Widget Button, Widget Toggle Button, Widget Upload */\n\n/* */\n.widget-button, /* */\n/* */ .widget-toggle-button, /* */\n/* */ .widget-upload, /* */\n.jupyter-widget-button,\n.jupyter-widget-toggle-button,\n.jupyter-widget-upload {\n width: var(--jp-widgets-inline-width-short);\n}\n\n/* Widget Label Styling */\n\n/* Override Bootstrap label css */\n.jupyter-widgets label {\n margin-bottom: initial;\n}\n\n/* */\n.widget-label-basic, /* */\n.jupyter-widget-label-basic {\n /* Basic Label */\n color: var(--jp-widgets-label-color);\n font-size: var(--jp-widgets-font-size);\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-label, /* */\n.jupyter-widget-label {\n /* Label */\n color: var(--jp-widgets-label-color);\n font-size: var(--jp-widgets-font-size);\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-inline-hbox .widget-label, /* */\n.jupyter-widget-inline-hbox .jupyter-widget-label {\n /* Horizontal Widget Label */\n color: var(--jp-widgets-label-color);\n text-align: right;\n margin-right: calc(var(--jp-widgets-inline-margin) * 2);\n width: var(--jp-widgets-inline-label-width);\n flex-shrink: 0;\n}\n\n/* */\n.widget-inline-vbox .widget-label, /* 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0.14), 0 1px 5px 0 rgba(0, 0, 0, 0.12);\n\n box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.2), 0 3px 1px -2px rgba(0, 0, 0, 0.14),\n 0 1px 5px 0 rgba(0, 0, 0, 0.12);\n}\n\n/* */\n.widget-inline-hbox .widget-readout, /* */\n.jupyter-widget-inline-hbox .jupyter-widget-readout {\n /* Horizontal Readout */\n text-align: center;\n max-width: var(--jp-widgets-inline-width-short);\n min-width: var(--jp-widgets-inline-width-tiny);\n margin-left: var(--jp-widgets-inline-margin);\n}\n\n/* */\n.widget-inline-vbox .widget-readout, /* */\n.jupyter-widget-inline-vbox .jupyter-widget-readout {\n /* Vertical Readout */\n margin-top: var(--jp-widgets-inline-margin);\n /* as wide as the widget */\n width: inherit;\n}\n\n/* Widget Checkbox Styling */\n\n/* */\n.widget-checkbox, /* */\n.jupyter-widget-checkbox {\n width: var(--jp-widgets-inline-width);\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-checkbox input[type='checkbox'], /* */\n.jupyter-widget-checkbox input[type='checkbox'] {\n margin: 0px calc(var(--jp-widgets-inline-margin) * 2) 0px 0px;\n line-height: var(--jp-widgets-inline-height);\n font-size: large;\n flex-grow: 1;\n flex-shrink: 0;\n align-self: center;\n}\n\n/* Widget Valid Styling */\n\n/* */\n.widget-valid, /* */\n.jupyter-widget-valid {\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n width: var(--jp-widgets-inline-width-short);\n font-size: var(--jp-widgets-font-size);\n}\n\n/* */\n.widget-valid i, /* */\n.jupyter-widget-valid i {\n line-height: var(--jp-widgets-inline-height);\n margin-right: var(--jp-widgets-inline-margin);\n margin-left: var(--jp-widgets-inline-margin);\n}\n\n/* */\n.widget-valid.mod-valid i, /* */\n.jupyter-widget-valid.mod-valid i {\n color: green;\n}\n\n/* */\n.widget-valid.mod-invalid i, /* */\n.jupyter-widget-valid.mod-invalid i {\n color: red;\n}\n\n/* */\n.widget-valid.mod-valid .widget-valid-readout, /* */\n.jupyter-widget-valid.mod-valid .jupyter-widget-valid-readout {\n display: none;\n}\n\n/* Widget Text and TextArea Styling */\n\n/* */\n.widget-textarea, /* */\n/* */ .widget-text, /* */\n.jupyter-widget-textarea,\n.jupyter-widget-text {\n width: var(--jp-widgets-inline-width);\n}\n\n/* */\n.widget-text input[type='text'], /* */\n/* */ .widget-text input[type='number'], /* */\n/* */ .widget-text input[type='password'], /* */\n.jupyter-widget-text input[type='text'],\n.jupyter-widget-text input[type='number'],\n.jupyter-widget-text input[type='password'] {\n height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-text input[type='text']:disabled, /* */\n/* */ .widget-text input[type='number']:disabled, /* */\n/* */ .widget-text input[type='password']:disabled, /* */\n/* */ .widget-textarea textarea:disabled, /* */\n.jupyter-widget-text input[type='text']:disabled,\n.jupyter-widget-text input[type='number']:disabled,\n.jupyter-widget-text input[type='password']:disabled,\n.jupyter-widget-textarea textarea:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* */\n.widget-text input[type='text'], /* */\n/* */ .widget-text input[type='number'], /* */\n/* */ .widget-text input[type='password'], /* */\n/* */ .widget-textarea textarea, /* */\n.jupyter-widget-text input[type='text'],\n.jupyter-widget-text input[type='number'],\n.jupyter-widget-text input[type='password'],\n.jupyter-widget-textarea textarea {\n box-sizing: border-box;\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n background-color: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n font-size: var(--jp-widgets-font-size);\n flex-grow: 1;\n min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n flex-shrink: 1;\n outline: none !important;\n}\n\n/* */\n.widget-text input[type='text'], /* */\n/* */ .widget-text input[type='password'], /* */\n/* */ .widget-textarea textarea, /* */\n.jupyter-widget-text input[type='text'],\n.jupyter-widget-text input[type='password'],\n.jupyter-widget-textarea textarea {\n padding: var(--jp-widgets-input-padding)\n calc(var(--jp-widgets-input-padding) * 2);\n}\n\n/* */\n.widget-text input[type='number'], /* */\n.jupyter-widget-text input[type='number'] {\n padding: var(--jp-widgets-input-padding) 0 var(--jp-widgets-input-padding)\n calc(var(--jp-widgets-input-padding) * 2);\n}\n\n/* */\n.widget-textarea textarea, /* */\n.jupyter-widget-textarea textarea {\n height: inherit;\n width: inherit;\n}\n\n/* */\n.widget-text input:focus, /* */\n/* */ .widget-textarea textarea:focus, /* */\n.jupyter-widget-text input:focus,\n.jupyter-widget-textarea textarea:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n/* Horizontal Slider */\n/* */\n.widget-hslider, /* */\n.jupyter-widget-hslider {\n width: var(--jp-widgets-inline-width);\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n\n /* Override the align-items baseline. This way, the description and readout\n still seem to align their baseline properly, and we don't have to have\n align-self: stretch in the .slider-container. */\n align-items: center;\n}\n\n/* */\n.widgets-slider .slider-container, /* */\n.jupyter-widgets-slider .slider-container {\n overflow: visible;\n}\n\n/* */\n.widget-hslider .slider-container, /* */\n.jupyter-widget-hslider .slider-container {\n margin-left: calc(\n var(--jp-widgets-slider-handle-size) / 2 - 2 *\n var(--jp-widgets-slider-border-width)\n );\n margin-right: calc(\n var(--jp-widgets-slider-handle-size) / 2 - 2 *\n var(--jp-widgets-slider-border-width)\n );\n flex: 1 1 var(--jp-widgets-inline-width-short);\n}\n\n/* Vertical Slider */\n\n/* */\n.widget-vbox .widget-label, /* */\n.jupyter-widget-vbox .jupyter-widget-label {\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-vslider, /* */\n.jupyter-widget-vslider {\n /* Vertical Slider */\n height: var(--jp-widgets-vertical-height);\n width: var(--jp-widgets-inline-width-tiny);\n}\n\n/* */\n.widget-vslider .slider-container, /* */\n.jupyter-widget-vslider .slider-container {\n flex: 1 1 var(--jp-widgets-inline-width-short);\n margin-left: auto;\n margin-right: auto;\n margin-bottom: calc(\n var(--jp-widgets-slider-handle-size) / 2 - 2 *\n var(--jp-widgets-slider-border-width)\n );\n margin-top: calc(\n var(--jp-widgets-slider-handle-size) / 2 - 2 *\n var(--jp-widgets-slider-border-width)\n );\n display: flex;\n flex-direction: column;\n}\n\n/* Widget Progress Styling */\n\n.progress-bar {\n -webkit-transition: none;\n -moz-transition: none;\n -ms-transition: none;\n -o-transition: none;\n transition: none;\n}\n\n.progress-bar {\n height: var(--jp-widgets-inline-height);\n}\n\n.progress-bar {\n background-color: var(--jp-brand-color1);\n}\n\n.progress-bar-success {\n background-color: var(--jp-success-color1);\n}\n\n.progress-bar-info {\n background-color: var(--jp-info-color1);\n}\n\n.progress-bar-warning {\n background-color: var(--jp-warn-color1);\n}\n\n.progress-bar-danger {\n background-color: var(--jp-error-color1);\n}\n\n.progress {\n background-color: var(--jp-layout-color2);\n border: none;\n box-shadow: none;\n}\n\n/* Horisontal Progress */\n\n/* */\n.widget-hprogress, /* */\n.jupyter-widget-hprogress {\n /* Progress Bar */\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n width: var(--jp-widgets-inline-width);\n align-items: center;\n}\n\n/* */\n.widget-hprogress .progress, /* */\n.jupyter-widget-hprogress .progress {\n flex-grow: 1;\n margin-top: var(--jp-widgets-input-padding);\n margin-bottom: var(--jp-widgets-input-padding);\n align-self: stretch;\n /* Override bootstrap style */\n height: initial;\n}\n\n/* Vertical Progress */\n\n/* */\n.widget-vprogress, /* */\n.jupyter-widget-vprogress {\n height: var(--jp-widgets-vertical-height);\n width: var(--jp-widgets-inline-width-tiny);\n}\n\n/* */\n.widget-vprogress .progress, /* */\n.jupyter-widget-vprogress .progress {\n flex-grow: 1;\n width: var(--jp-widgets-progress-thickness);\n margin-left: auto;\n margin-right: auto;\n margin-bottom: 0;\n}\n\n/* Select Widget Styling */\n\n/* */\n.widget-dropdown, /* */\n.jupyter-widget-dropdown {\n height: var(--jp-widgets-inline-height);\n width: var(--jp-widgets-inline-width);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-dropdown > select, /* */\n.jupyter-widget-dropdown > select {\n padding-right: 20px;\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n border-radius: 0;\n height: inherit;\n flex: 1 1 var(--jp-widgets-inline-width-short);\n min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n box-sizing: border-box;\n outline: none !important;\n box-shadow: none;\n background-color: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n font-size: var(--jp-widgets-font-size);\n vertical-align: top;\n padding-left: calc(var(--jp-widgets-input-padding) * 2);\n appearance: none;\n -webkit-appearance: none;\n -moz-appearance: none;\n background-repeat: no-repeat;\n background-size: 20px;\n background-position: right center;\n background-image: var(--jp-widgets-dropdown-arrow);\n}\n/* */\n.widget-dropdown > select:focus, /* */\n.jupyter-widget-dropdown > select:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n/* */\n.widget-dropdown > select:disabled, /* */\n.jupyter-widget-dropdown > select:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* To disable the dotted border in Firefox around select controls.\n See http://stackoverflow.com/a/18853002 */\n/* */\n.widget-dropdown > select:-moz-focusring, /* */\n.jupyter-widget-dropdown > select:-moz-focusring {\n color: transparent;\n text-shadow: 0 0 0 #000;\n}\n\n/* Select and SelectMultiple */\n\n/* */\n.widget-select, /* */\n.jupyter-widget-select {\n width: var(--jp-widgets-inline-width);\n line-height: var(--jp-widgets-inline-height);\n\n /* Because Firefox defines the baseline of a select as the bottom of the\n control, we align the entire control to the top and add padding to the\n select to get an approximate first line baseline alignment. */\n align-items: flex-start;\n}\n\n/* */\n.widget-select > select, /* */\n.jupyter-widget-select > select {\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n background-color: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n font-size: var(--jp-widgets-font-size);\n flex: 1 1 var(--jp-widgets-inline-width-short);\n outline: none !important;\n overflow: auto;\n height: inherit;\n\n /* Because Firefox defines the baseline of a select as the bottom of the\n control, we align the entire control to the top and add padding to the\n select to get an approximate first line baseline alignment. */\n padding-top: 5px;\n}\n\n/* */\n.widget-select > select:focus, /* */\n.jupyter-widget-select > select:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n.wiget-select > select > option,\n.jupyter-wiget-select > select > option {\n padding-left: var(--jp-widgets-input-padding);\n line-height: var(--jp-widgets-inline-height);\n /* line-height doesn't work on some browsers for select options */\n padding-top: calc(\n var(--jp-widgets-inline-height) - var(--jp-widgets-font-size) / 2\n );\n padding-bottom: calc(\n var(--jp-widgets-inline-height) - var(--jp-widgets-font-size) / 2\n );\n}\n\n/* Toggle Buttons Styling */\n\n/* */\n.widget-toggle-buttons, /* */\n.jupyter-widget-toggle-buttons {\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-toggle-buttons .widget-toggle-button, /* */\n.jupyter-widget-toggle-buttons .jupyter-widget-toggle-button {\n margin-left: var(--jp-widgets-margin);\n margin-right: var(--jp-widgets-margin);\n}\n\n/* */\n.widget-toggle-buttons .jupyter-button:disabled, /* */\n.jupyter-widget-toggle-buttons .jupyter-button:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Radio Buttons Styling */\n\n/* */\n.widget-radio, /* */\n.jupyter-widget-radio {\n width: var(--jp-widgets-inline-width);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-radio-box, /* */\n.jupyter-widget-radio-box {\n display: flex;\n flex-direction: column;\n align-items: stretch;\n box-sizing: border-box;\n flex-grow: 1;\n margin-bottom: var(--jp-widgets-radio-item-height-adjustment);\n}\n\n/* */\n.widget-radio-box-vertical, /* */\n.jupyter-widget-radio-box-vertical {\n flex-direction: column;\n}\n\n/* */\n.widget-radio-box-horizontal, /* */\n.jupyter-widget-radio-box-horizontal {\n flex-direction: row;\n}\n\n/* */\n.widget-radio-box label, /* */\n.jupyter-widget-radio-box label {\n height: var(--jp-widgets-radio-item-height);\n line-height: var(--jp-widgets-radio-item-height);\n font-size: var(--jp-widgets-font-size);\n}\n\n.widget-radio-box-horizontal label,\n.jupyter-widget-radio-box-horizontal label {\n margin: 0 calc(var(--jp-widgets-input-padding) * 2) 0 0;\n}\n\n/* */\n.widget-radio-box input, /* */\n.jupyter-widget-radio-box input {\n height: var(--jp-widgets-radio-item-height);\n line-height: var(--jp-widgets-radio-item-height);\n margin: 0 calc(var(--jp-widgets-input-padding) * 2) 0 1px;\n float: left;\n}\n\n/* Color Picker Styling */\n\n/* */\n.widget-colorpicker, /* */\n.jupyter-widget-colorpicker {\n width: var(--jp-widgets-inline-width);\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-colorpicker > .widget-colorpicker-input, /* */\n.jupyter-widget-colorpicker > .jupyter-widget-colorpicker-input {\n flex-grow: 1;\n flex-shrink: 1;\n min-width: var(--jp-widgets-inline-width-tiny);\n}\n\n/* */\n.widget-colorpicker input[type='color'], /* */\n.jupyter-widget-colorpicker input[type='color'] {\n width: var(--jp-widgets-inline-height);\n height: var(--jp-widgets-inline-height);\n padding: 0 2px; /* make the color square actually square on Chrome on OS X */\n background: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n border-left: none;\n flex-grow: 0;\n flex-shrink: 0;\n box-sizing: border-box;\n align-self: stretch;\n outline: none !important;\n}\n\n/* */\n.widget-colorpicker.concise input[type='color'], /* */\n.jupyter-widget-colorpicker.concise input[type='color'] {\n border-left: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n}\n\n/* */\n.widget-colorpicker input[type='color']:focus, /* */\n/* */ .widget-colorpicker input[type='text']:focus, /* */\n.jupyter-widget-colorpicker input[type='color']:focus,\n.jupyter-widget-colorpicker input[type='text']:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n/* */\n.widget-colorpicker input[type='text'], /* */\n.jupyter-widget-colorpicker input[type='text'] {\n flex-grow: 1;\n outline: none !important;\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n background: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n font-size: var(--jp-widgets-font-size);\n padding: var(--jp-widgets-input-padding)\n calc(var(--jp-widgets-input-padding) * 2);\n min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n flex-shrink: 1;\n box-sizing: border-box;\n}\n\n/* */\n.widget-colorpicker input[type='text']:disabled, /* */\n.jupyter-widget-colorpicker input[type='text']:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Date Picker Styling */\n\n/* */\n.widget-datepicker, /* */\n.jupyter-widget-datepicker {\n width: var(--jp-widgets-inline-width);\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* */\n.widget-datepicker input[type='date'], /* */\n.jupyter-widget-datepicker input[type='date'] {\n flex-grow: 1;\n flex-shrink: 1;\n min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n outline: none !important;\n height: var(--jp-widgets-inline-height);\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n background-color: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n font-size: var(--jp-widgets-font-size);\n padding: var(--jp-widgets-input-padding)\n calc(var(--jp-widgets-input-padding) * 2);\n box-sizing: border-box;\n}\n\n/* */\n.widget-datepicker input[type='date']:focus, /* */\n.jupyter-widget-datepicker input[type='date']:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n/* */\n.widget-datepicker input[type='date']:invalid, /* */\n.jupyter-widget-datepicker input[type='date']:invalid {\n border-color: var(--jp-warn-color1);\n}\n\n/* */\n.widget-datepicker input[type='date']:disabled, /* */\n.jupyter-widget-datepicker input[type='date']:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Play Widget */\n\n/* */\n.widget-play, /* */\n.jupyter-widget-play {\n width: var(--jp-widgets-inline-width-short);\n display: flex;\n align-items: stretch;\n}\n\n/* */\n.widget-play .jupyter-button, /* */\n.jupyter-widget-play .jupyter-button {\n flex-grow: 1;\n height: auto;\n}\n\n/* */\n.widget-play .jupyter-button:disabled, /* */\n.jupyter-widget-play .jupyter-button:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Tab Widget */\n\n/* */\n.jupyter-widgets.widget-tab, /* */\n.jupyter-widgets.jupyter-widget-tab {\n display: flex;\n flex-direction: column;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar {\n /* Necessary so that a tab can be shifted down to overlay the border of the box below. */\n overflow-x: visible;\n overflow-y: visible;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar > .p-TabBar-content, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar > .lm-TabBar-content {\n /* Make sure that the tab grows from bottom up */\n align-items: flex-end;\n min-width: 0;\n min-height: 0;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .widget-tab-contents, /* */\n.jupyter-widgets.jupyter-widget-tab > .widget-tab-contents {\n width: 100%;\n box-sizing: border-box;\n margin: 0;\n background: var(--jp-layout-color1);\n color: var(--jp-ui-font-color1);\n border: var(--jp-border-width) solid var(--jp-border-color1);\n padding: var(--jp-widgets-container-padding);\n flex-grow: 1;\n overflow: auto;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar {\n font: var(--jp-widgets-font-size) Helvetica, Arial, sans-serif;\n min-height: calc(\n var(--jp-widgets-horizontal-tab-height) + var(--jp-border-width)\n );\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab {\n flex: 0 1 var(--jp-widgets-horizontal-tab-width);\n min-width: 35px;\n min-height: calc(\n var(--jp-widgets-horizontal-tab-height) + var(--jp-border-width)\n );\n line-height: var(--jp-widgets-horizontal-tab-height);\n margin-left: calc(-1 * var(--jp-border-width));\n padding: 0px 10px;\n background: var(--jp-layout-color2);\n color: var(--jp-ui-font-color2);\n border: var(--jp-border-width) solid var(--jp-border-color1);\n border-bottom: none;\n position: relative;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab.lm-mod-current {\n color: var(--jp-ui-font-color0);\n /* We want the background to match the tab content background */\n background: var(--jp-layout-color1);\n min-height: calc(\n var(--jp-widgets-horizontal-tab-height) + 2 * var(--jp-border-width)\n );\n transform: translateY(var(--jp-border-width));\n overflow: visible;\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab.lm-mod-current:before {\n position: absolute;\n top: calc(-1 * var(--jp-border-width));\n left: calc(-1 * var(--jp-border-width));\n content: '';\n height: var(--jp-widgets-horizontal-tab-top-border);\n width: calc(100% + 2 * var(--jp-border-width));\n background: var(--jp-brand-color1);\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child, /* */\n/* */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab:first-child, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab:first-child {\n margin-left: 0;\n}\n\n/* */\n.jupyter-widgets.widget-tab\n > .p-TabBar\n .p-TabBar-tab:hover:not(.p-mod-current),\n/* */\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .p-TabBar\n .p-TabBar-tab:hover:not(.p-mod-current),\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar\n .lm-TabBar-tab:hover:not(.lm-mod-current) {\n background: var(--jp-layout-color1);\n color: var(--jp-ui-font-color1);\n}\n\n/* */\n.jupyter-widgets.widget-tab\n > .p-TabBar\n .p-mod-closable\n > .p-TabBar-tabCloseIcon,\n/* */\n/* */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar\n.p-mod-closable\n> .p-TabBar-tabCloseIcon,\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar\n .lm-mod-closable\n > .lm-TabBar-tabCloseIcon {\n margin-left: 4px;\n}\n\n/* This font-awesome strategy may not work across FA4 and FA5, but we don't\nactually support closable tabs, so it really doesn't matter */\n/* */\n.jupyter-widgets.widget-tab\n > .p-TabBar\n .p-mod-closable\n > .p-TabBar-tabCloseIcon:before,\n/* */\n/* */\n.jupyter-widgets.jupyter-widget-widget-tab\n> .p-TabBar\n.p-mod-closable\n> .p-TabBar-tabCloseIcon:before,\n/* */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar\n .lm-mod-closable\n > .lm-TabBar-tabCloseIcon:before {\n font-family: FontAwesome;\n content: '\\f00d'; /* close */\n}\n\n/* */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon, /* */\n/* */ .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel, /* */\n/* */ .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon, /* */\n/* */ .jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabIcon, /* */\n/* */ .jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabLabel, /* */\n/* */ .jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabCloseIcon, /* */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabIcon,\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabLabel,\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabCloseIcon {\n line-height: var(--jp-widgets-horizontal-tab-height);\n}\n\n/* Accordion Widget */\n\n.jupyter-widget-Collapse {\n display: flex;\n flex-direction: column;\n align-items: stretch;\n}\n\n.jupyter-widget-Collapse-header {\n padding: var(--jp-widgets-input-padding);\n cursor: pointer;\n color: var(--jp-ui-font-color2);\n background-color: var(--jp-layout-color2);\n border: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n padding: calc(var(--jp-widgets-container-padding) * 2 / 3)\n var(--jp-widgets-container-padding);\n font-weight: bold;\n}\n\n.jupyter-widget-Collapse-header:hover {\n background-color: var(--jp-layout-color1);\n color: var(--jp-ui-font-color1);\n}\n\n.jupyter-widget-Collapse-open > .jupyter-widget-Collapse-header {\n background-color: var(--jp-layout-color1);\n color: var(--jp-ui-font-color0);\n cursor: default;\n border-bottom: none;\n}\n\n.jupyter-widget-Collapse-contents {\n padding: var(--jp-widgets-container-padding);\n background-color: var(--jp-layout-color1);\n color: var(--jp-ui-font-color1);\n border-left: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n border-right: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n border-bottom: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n overflow: auto;\n}\n\n.jupyter-widget-Accordion {\n display: flex;\n flex-direction: column;\n align-items: stretch;\n}\n\n.jupyter-widget-Accordion .jupyter-widget-Collapse {\n margin-bottom: 0;\n}\n\n.jupyter-widget-Accordion .jupyter-widget-Collapse + .jupyter-widget-Collapse {\n margin-top: 4px;\n}\n\n/* HTML widget */\n\n/* */\n.widget-html, /* */\n/* */ .widget-htmlmath, /* */\n.jupyter-widget-html,\n.jupyter-widget-htmlmath {\n font-size: var(--jp-widgets-font-size);\n}\n\n/* */\n.widget-html > .widget-html-content, /* */\n/* */.widget-htmlmath > .widget-html-content, /* */\n.jupyter-widget-html > .jupyter-widget-html-content,\n.jupyter-widget-htmlmath > .jupyter-widget-html-content {\n /* Fill out the area in the HTML widget */\n align-self: stretch;\n flex-grow: 1;\n flex-shrink: 1;\n /* Makes sure the baseline is still aligned with other elements */\n line-height: var(--jp-widgets-inline-height);\n /* Make it possible to have absolutely-positioned elements in the html */\n position: relative;\n}\n\n/* Image widget */\n\n/* */\n.widget-image, /* */\n.jupyter-widget-image {\n max-width: 100%;\n height: auto;\n}\n`,""]);const w=c},9451:e=>{e.exports=function(e){var n=[];return n.toString=function(){return this.map((function(n){var t="",i=void 0!==n[5];return n[4]&&(t+="@supports 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".concat(t.layer):""," {")),i+=t.css,r&&(i+="}"),t.media&&(i+="}"),t.supports&&(i+="}");var o=t.sourceMap;o&&"undefined"!=typeof btoa&&(i+="\n/*# sourceMappingURL=data:application/json;base64,".concat(btoa(unescape(encodeURIComponent(JSON.stringify(o))))," */")),n.styleTagTransform(i,e,n.options)}(n,e,t)},remove:function(){!function(e){if(null===e.parentNode)return!1;e.parentNode.removeChild(e)}(n)}}}},9252:e=>{e.exports=function(e,n){if(n.styleSheet)n.styleSheet.cssText=e;else{for(;n.firstChild;)n.removeChild(n.firstChild);n.appendChild(document.createTextNode(e))}}},8701:(e,n,t)=>{t.d(n,{A:()=>i});const i="2.0.0"},1446:(e,n,t)=>{t.r(n),t.d(n,{KernelWidgetManager:()=>T,LabWidgetManager:()=>v,WidgetManager:()=>f,WidgetRenderer:()=>w,default:()=>ce,output:()=>i,registerWidgetManager:()=>te});var i={};t.r(i),t.d(i,{OUTPUT_WIDGET_VERSION:()=>P,OutputModel:()=>U,OutputView:()=>k});var r=t(8612),o=t(1378),a=t(4970),d=t(9895),s=t(1113),l=t(632),g=t(4053),p=t(9012),c=t(7262),u=t(5256);class w extends u.Panel{constructor(e,n){super(),this._manager=new c.PromiseDelegate,this._rerenderMimeModel=null,this.mimeType=e.mimeType,n&&(this.manager=n)}set manager(e){e.restored.connect(this._rerender,this),this._manager.resolve(e)}async renderModel(e){const n=e.data[this.mimeType];this.node.textContent="Loading widget...";const t=await this._manager.promise;if(""===n.model_id)return this.hide(),Promise.resolve();let i,r;try{i=await t.get_model(n.model_id)}catch(n){return t.restoredStatus?(this.node.textContent="Error displaying widget: model not found",this.addClass("jupyter-widgets"),void console.error(n)):void(this._rerenderMimeModel=e)}this._rerenderMimeModel=null;try{const e=await t.create_view(i);r=e.luminoWidget||e.pWidget}catch(e){return this.node.textContent="Error displaying widget",this.addClass("jupyter-widgets"),void console.error(e)}this.node.textContent="",this.addWidget(r),r.disposed.connect((()=>{this.hide(),n.model_id=""}))}dispose(){this.isDisposed||(this._manager=null,super.dispose())}_rerender(){this._rerenderMimeModel&&(this.node.textContent="",this.removeClass("jupyter-widgets"),this.renderModel(this._rerenderMimeModel))}}var h=t(7401),E=t(8875),b=t(4602),j=t(4585);class y{constructor(){this._cache=Object.create(null)}set(e,n,t){if(e in this._cache||(this._cache[e]=Object.create(null)),n in this._cache[e])throw`Version ${n} of key ${e} already registered.`;this._cache[e][n]=t}get(e,n){if(e in this._cache){const t=this._cache[e],i=(0,j.maxSatisfying)(Object.keys(t),n);if(null!==i)return t[i]}}getAllVersions(e){if(e in this._cache)return this._cache[e]}}const m="application/vnd.jupyter.widget-view+json",D="application/vnd.jupyter.widget-state+json";class v extends E.ManagerBase{constructor(e){super(),this._handleCommOpen=async(e,n)=>{const t=new h.shims.services.Comm(e);await this.handle_comm_open(t,n)},this._restored=new b.Signal(this),this._restoredStatus=!1,this._kernelRestoreInProgress=!1,this._isDisposed=!1,this._registry=new y,this._modelsSync=new Map,this._onUnhandledIOPubMessage=new b.Signal(this),this._rendermime=e}callbacks(e){return{iopub:{output:e=>{this._onUnhandledIOPubMessage.emit(e)}}}}_handleKernelChanged({oldValue:e,newValue:n}){e&&e.removeCommTarget(this.comm_target_name,this._handleCommOpen),n&&n.registerCommTarget(this.comm_target_name,this._handleCommOpen)}disconnect(){super.disconnect(),this._restoredStatus=!1}async _loadFromKernel(){var e;if(!this.kernel)throw new Error("Kernel not set");if(!1!==(null===(e=this.kernel)||void 0===e?void 0:e.handleComms))return super._loadFromKernel()}async _create_comm(e,n,t,i,r){const o=this.kernel;if(!o)throw new Error("No current kernel");const a=o.createComm(e,n);return(t||i)&&a.open(t,i,r),new h.shims.services.Comm(a)}async _get_comm_info(){const e=this.kernel;if(!e)throw new Error("No current kernel");const n=await e.requestCommInfo({target_name:this.comm_target_name});return"ok"===n.content.status?n.content.comms:{}}get isDisposed(){return this._isDisposed}dispose(){this.isDisposed||(this._isDisposed=!0,this._commRegistration&&this._commRegistration.dispose())}async resolveUrl(e){return e}async loadClass(e,n,t){"@jupyter-widgets/base"!==n&&"@jupyter-widgets/controls"!==n||!(0,j.valid)(t)||(t=`^${t}`);const i=this._registry.getAllVersions(n);if(!i)throw new Error(`No version of module ${n} is registered`);const r=this._registry.get(n,t);if(!r){const e=Object.keys(i);throw new Error(`Module ${n}, version ${t} is not registered, however, ${e.join(",")} ${e.length>1?"are":"is"}`)}let o;o="function"==typeof r?await r():await r;const a=o[e];if(!a)throw new Error(`Class ${e} not found in module ${n}`);return a}get rendermime(){return this._rendermime}get restored(){return this._restored}get restoredStatus(){return this._restoredStatus}get onUnhandledIOPubMessage(){return this._onUnhandledIOPubMessage}register(e){this._registry.set(e.name,e.version,e.exports)}register_model(e,n){super.register_model(e,n),n.then((n=>{this._modelsSync.set(e,n),n.once("comm:close",(()=>{this._modelsSync.delete(e)}))}))}async clear_state(){await super.clear_state(),this._modelsSync=new Map}get_state_sync(e={}){const n=[];for(const e of this._modelsSync.values())e.comm_live&&n.push(e);return(0,E.serialize_state)(n,e)}}class T extends v{constructor(e,n){super(n),this._kernel=e,e.statusChanged.connect(((e,n)=>{this._handleKernelStatusChange(n)})),e.connectionStatusChanged.connect(((e,n)=>{this._handleKernelConnectionStatusChange(n)})),this._handleKernelChanged({name:"kernel",oldValue:null,newValue:e}),this.restoreWidgets()}_handleKernelConnectionStatusChange(e){"connected"===e&&(this._kernelRestoreInProgress||this.restoreWidgets())}_handleKernelStatusChange(e){"restarting"===e&&this.disconnect()}async restoreWidgets(){try{this._kernelRestoreInProgress=!0,await this._loadFromKernel(),this._restoredStatus=!0,this._restored.emit()}catch(e){}this._kernelRestoreInProgress=!1}dispose(){this.isDisposed||(this._kernel=null,super.dispose())}get kernel(){return this._kernel}}class f extends v{constructor(e,n,t){var i,r;super(n),this._context=e,e.sessionContext.kernelChanged.connect(((e,n)=>{this._handleKernelChanged(n)})),e.sessionContext.statusChanged.connect(((e,n)=>{this._handleKernelStatusChange(n)})),e.sessionContext.connectionStatusChanged.connect(((e,n)=>{this._handleKernelConnectionStatusChange(n)})),(null===(i=e.sessionContext.session)||void 0===i?void 0:i.kernel)&&this._handleKernelChanged({name:"kernel",oldValue:null,newValue:null===(r=e.sessionContext.session)||void 0===r?void 0:r.kernel}),this.restoreWidgets(this._context.model),this._settings=t,e.saveState.connect(((e,n)=>{"started"===n&&t.saveState&&this._saveState()}))}_saveState(){const e=this.get_state_sync({drop_defaults:!0});this._context.model.setMetadata?this._context.model.setMetadata("widgets",{"application/vnd.jupyter.widget-state+json":e}):this._context.model.metadata.set("widgets",{"application/vnd.jupyter.widget-state+json":e})}_handleKernelConnectionStatusChange(e){"connected"===e&&(this._kernelRestoreInProgress||this.restoreWidgets(this._context.model,{loadKernel:!0,loadNotebook:!1}))}_handleKernelStatusChange(e){"restarting"===e&&this.disconnect()}async restoreWidgets(e,{loadKernel:n,loadNotebook:t}={loadKernel:!0,loadNotebook:!0}){try{if(await this.context.sessionContext.ready,n)try{this._kernelRestoreInProgress=!0,await this._loadFromKernel()}finally{this._kernelRestoreInProgress=!1}t&&await this._loadFromNotebook(e),this._restoredStatus=!0,this._restored.emit()}catch(e){}}async _loadFromNotebook(e){const n=e.getMetadata?e.getMetadata("widgets"):e.metadata.get("widgets");if(n&&n[D]){let e=n[D];e=this.filterExistingModelState(e),await this.set_state(e)}}dispose(){this.isDisposed||(this._context=null,super.dispose())}async resolveUrl(e){const n=await this.context.urlResolver.resolveUrl(e);return this.context.urlResolver.getDownloadUrl(n)}get context(){return this._context}get kernel(){var e,n,t;return null!==(t=null===(n=null===(e=this._context.sessionContext)||void 0===e?void 0:e.session)||void 0===n?void 0:n.kernel)&&void 0!==t?t:null}register_model(e,n){super.register_model(e,n),this.setDirty()}async clear_state(){await super.clear_state(),this.setDirty()}setDirty(){this._settings.saveState&&(this._context.model.dirty=!0)}}var x=t(1489),C=t(7860),R=t(8596),A=t.n(R);const P=x.OUTPUT_WIDGET_VERSION;class U extends x.OutputModel{defaults(){return Object.assign(Object.assign({},super.defaults()),{msg_id:"",outputs:[]})}initialize(e,n){super.initialize(e,n),this._outputs=new C.OutputAreaModel({trusted:!0}),this._msgHook=e=>(this.add(e),!1),this.widget_manager instanceof f&&this.widget_manager.context.sessionContext.kernelChanged.connect(((e,n)=>{this._handleKernelChanged(n)})),this.listenTo(this,"change:msg_id",this.reset_msg_id),this.listenTo(this,"change:outputs",this.setOutputs),this.setOutputs()}_handleKernelChanged({oldValue:e}){const n=this.get("msg_id");n&&e&&(e.removeMessageHook(n,this._msgHook),this.set("msg_id",null))}reset_msg_id(){const e=this.widget_manager.kernel,n=this.get("msg_id"),t=this.previous("msg_id");t&&e&&e.removeMessageHook(t,this._msgHook),n&&e&&e.registerMessageHook(n,this._msgHook)}add(e){const n=e.header.msg_type;switch(n){case"execute_result":case"display_data":case"stream":case"error":{const t=e.content;t.output_type=n,this._outputs.add(t);break}case"clear_output":this.clear_output(e.content.wait)}this.set("outputs",this._outputs.toJSON(),{newMessage:!0}),this.save_changes()}clear_output(e=!1){this._outputs.clear(e)}get outputs(){return this._outputs}setOutputs(e,n,t){t&&t.newMessage||(this.clear_output(),this._outputs.fromJSON(JSON.parse(JSON.stringify(this.get("outputs")))))}}class k extends x.OutputView{_createElement(e){return this.luminoWidget=new h.JupyterLuminoPanelWidget({view:this}),this.luminoWidget.node}_setElement(e){if(this.el||e!==this.luminoWidget.node)throw new Error("Cannot reset the DOM element.");this.el=this.luminoWidget.node,this.$el=A()(this.luminoWidget.node)}render(){super.render(),this._outputView=new C.OutputArea({rendermime:this.model.widget_manager.rendermime,contentFactory:C.OutputArea.defaultContentFactory,model:this.model.outputs}),this.luminoWidget.insertWidget(0,this._outputView),this.luminoWidget.addClass("jupyter-widgets"),this.luminoWidget.addClass("widget-output"),this.update()}remove(){return this._outputView.dispose(),super.remove()}}var I=t(8701),S=t(3699),B=t.n(S),O=t(376),_=t.n(O),z=t(1412),N=t.n(z),M=t(9443),L=t.n(M),W=t(4999),H=t.n(W),F=t(9252),V=t.n(F),G=t(1556),Y={};Y.styleTagTransform=V(),Y.setAttributes=L(),Y.insert=N().bind(null,"head"),Y.domAPI=_(),Y.insertStyleElement=H(),B()(G.A,Y),G.A&&G.A.locals&&G.A.locals;var K=t(7654),J={};J.styleTagTransform=V(),J.setAttributes=L(),J.insert=N().bind(null,"head"),J.domAPI=_(),J.insertStyleElement=H(),B()(K.A,J),K.A&&K.A.locals&&K.A.locals;var Z=t(7144),$=t(5828);const X=new class{constructor(){this._widgetRegistered=new b.Signal(this),this._registry=[]}get widgets(){return[...this._registry]}push(e){this._registry.push(e),this._widgetRegistered.emit(e)}get widgetRegistered(){return this._widgetRegistered}},q={saveState:!1};function*Q(...e){for(const n of e)yield*n}async function ee(e){return await e.ready,e.session.kernel.id}async function ne(e,n,t,i,r){const o=await ee(n);let a,d=pe.widgetManagerProperty.get(o);d||(d=r(),X.widgets.forEach((e=>d.register(e))),X.widgetRegistered.connect(((e,n)=>{d.register(n)})),pe.widgetManagerProperty.set(o,d),a=o,e.disposed.connect((e=>{pe.widgetManagerProperty.get(a)&&pe.widgetManagerProperty.delete(a)})),n.kernelChanged.connect(((e,n)=>{const{newValue:t}=n;if(t){const e=t.id,n=pe.widgetManagerProperty.get(a);n&&(pe.widgetManagerProperty.delete(a),pe.widgetManagerProperty.set(e,n)),a=e}})));for(const e of i)e.manager=d;return t.removeMimeType(m),t.addFactory({safe:!1,mimeTypes:[m],createRenderer:e=>new w(e,d)},-10),new p.DisposableDelegate((()=>{t&&t.removeMimeType(m),d.dispose()}))}function te(e,n,t){let i;const r=ee(e.sessionContext).then((r=>{const o=pe.widgetManagerProperty.get(r);o?i=o:(i=new f(e,n,q),X.widgets.forEach((e=>i.register(e))),X.widgetRegistered.connect(((e,n)=>{i.register(n)})),pe.widgetManagerProperty.set(r,i));for(const e of 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r="warning";(Z.KernelMessage.isErrorMsg(t)||Z.KernelMessage.isStreamMsg(t)&&"stderr"===t.content.name)&&(r="error");const o=Object.assign(Object.assign({},t.content),{output_type:t.header.msg_type});i.rendermime=e.content.rendermime,i.log({type:"output",data:o,level:r})}))};if(null!==r&&r.load(oe.id).then((e=>{e.changed.connect(ae),ae(e)})).catch((e=>{console.error(e.message)})),n.addFactory({safe:!1,mimeTypes:[m],createRenderer:e=>new w(e)},-10),null!==t){const n=n=>Q(function*(e){for(const n of e.widgets)if("code"===n.model.type)for(const e of n.outputArea.widgets)for(const n of Array.from(e.children()))n instanceof w&&(yield n)}(n.content),function*(e,n){const t=(0,g.filter)(e.shell.widgets(),(e=>e.id.startsWith("LinkedOutputView-")&&e.path===n));for(const e of Array.from(t))for(const n of Array.from(e.children()))for(const e of Array.from(n.children()))e instanceof w&&(yield e)}(e,n.context.path));t.forEach((async e=>{await 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i=s(7401);function a(e,t){void 0!==t&&(e.textContent=t),void 0!==window.MathJax&&MathJax.Hub.Queue(["Typeset",MathJax.Hub,e])}function l(e){const t=document.createElement("div");return t.textContent=e,t.innerHTML}function d(e,t){return function(s){throw t&&console.error(new Error(e)),s}}var n=s(8701);class o extends i.StyleModel{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"DescriptionStyleModel",_model_module:"@jupyter-widgets/controls",_model_module_version:n.A})}}o.styleProperties={description_width:{selector:".widget-label",attribute:"width",default:null}};class r extends i.DOMWidgetModel{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"DescriptionModel",_view_name:"DescriptionView",_view_module:"@jupyter-widgets/controls",_model_module:"@jupyter-widgets/controls",_view_module_version:n.A,_model_module_version:n.A,description:"",description_allow_html:!1})}}class h extends i.DOMWidgetView{render(){this.label=document.createElement("label"),this.el.appendChild(this.label),this.label.className="widget-label",this.label.style.display="none",this.listenTo(this.model,"change:description",this.updateDescription),this.listenTo(this.model,"change:description_allow_html",this.updateDescription),this.listenTo(this.model,"change:tabbable",this.updateTabindex),this.updateDescription(),this.updateTabindex(),this.updateTooltip()}typeset(e,t){this.displayed.then((()=>{var s;const i=null===(s=this.model.widget_manager._rendermime)||void 0===s?void 0:s.latexTypesetter;if(!i)return a(e,t);void 0!==t&&(e.textContent=t),i.typeset(e)}))}updateDescription(){const e=this.model.get("description");0===e.length?this.label.style.display="none":(this.model.get("description_allow_html")?this.label.innerHTML=this.model.widget_manager.inline_sanitize(e):this.label.textContent=e,this.typeset(this.label),this.label.style.display="")}updateTooltip(){this.label&&(this.label.title=this.model.get("tooltip"))}}class u extends r{}class c extends h{}class p extends i.WidgetModel{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"CoreWidgetModel",_view_module:"@jupyter-widgets/controls",_model_module:"@jupyter-widgets/controls",_view_module_version:n.A,_model_module_version:n.A})}}class g extends i.DOMWidgetModel{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"CoreDOMWidgetModel",_view_module:"@jupyter-widgets/controls",_model_module:"@jupyter-widgets/controls",_view_module_version:n.A,_model_module_version:n.A})}}class m extends r{defaults(){return 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0,_model_name:"DirectionalLinkModel"})}initialize(e,t){super.initialize(e,t),this.on("change",this.updateBindings,this),this.updateBindings()}updateValue(e,t,s,i){if(!this._updating){this._updating=!0;try{s&&(s.set(i,e.get(t)),s.save_changes())}finally{this._updating=!1}}}updateBindings(){this.cleanup(),[this.sourceModel,this.sourceAttr]=this.get("source")||[null,null],[this.targetModel,this.targetAttr]=this.get("target")||[null,null],this.sourceModel&&(this.listenTo(this.sourceModel,"change:"+this.sourceAttr,(()=>{this.updateValue(this.sourceModel,this.sourceAttr,this.targetModel,this.targetAttr)})),this.updateValue(this.sourceModel,this.sourceAttr,this.targetModel,this.targetAttr),this.listenToOnce(this.sourceModel,"destroy",this.cleanup)),this.targetModel&&this.listenToOnce(this.targetModel,"destroy",this.cleanup)}cleanup(){this.sourceModel&&(this.stopListening(this.sourceModel,"change:"+this.sourceAttr,void 0),this.stopListening(this.sourceModel,"destroy",void 0)),this.targetModel&&this.stopListening(this.targetModel,"destroy",void 0)}}_.serializers=Object.assign(Object.assign({},p.serializers),{target:{deserialize:i.unpack_models},source:{deserialize:i.unpack_models}});class b extends _{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"LinkModel"})}updateBindings(){super.updateBindings(),this.targetModel&&this.listenTo(this.targetModel,"change:"+this.targetAttr,(()=>{this.updateValue(this.targetModel,this.targetAttr,this.sourceModel,this.sourceAttr)}))}cleanup(){super.cleanup(),this.targetModel&&this.stopListening(this.targetModel,"change:"+this.targetAttr,void 0)}}class v extends o{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"CheckboxStyleModel"})}}v.styleProperties=Object.assign(Object.assign({},o.styleProperties),{background:{selector:"",attribute:"background",default:null}});class x extends o{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"ToggleButtonStyleModel"})}}x.styleProperties=Object.assign(Object.assign({},o.styleProperties),{font_family:{selector:"",attribute:"font-family",default:""},font_size:{selector:"",attribute:"font-size",default:""},font_style:{selector:"",attribute:"font-style",default:""},font_variant:{selector:"",attribute:"font-variant",default:""},font_weight:{selector:"",attribute:"font-weight",default:""},text_color:{selector:"",attribute:"color",default:""},text_decoration:{selector:"",attribute:"text-decoration",default:""}});class f extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{value:!1,disabled:!1,_model_name:"BoolModel"})}}class w extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{indent:!0,style:null,_view_name:"CheckboxView",_model_name:"CheckboxModel"})}}class y extends h{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-inline-hbox"),this.el.classList.add("widget-checkbox"),this.label.innerHTML="​",this.checkboxLabel=document.createElement("label"),this.checkboxLabel.classList.add("widget-label-basic"),this.el.appendChild(this.checkboxLabel),this.checkbox=document.createElement("input"),this.checkbox.setAttribute("type","checkbox"),this.checkboxLabel.appendChild(this.checkbox),this.descriptionSpan=document.createElement("span"),this.checkboxLabel.appendChild(this.descriptionSpan),this.listenTo(this.model,"change:indent",this.updateIndent),this.listenTo(this.model,"change:tabbable",this.updateTabindex),this.update(),this.updateDescription(),this.updateIndent(),this.updateTabindex(),this.updateTooltip()}updateDescription(){if(null==this.checkboxLabel)return;const e=this.model.get("description");this.model.get("description_allow_html")?this.descriptionSpan.innerHTML=this.model.widget_manager.inline_sanitize(e):this.descriptionSpan.textContent=e,this.typeset(this.descriptionSpan),this.descriptionSpan.title=e,this.checkbox.title=e}updateIndent(){const e=this.model.get("indent");this.label.style.display=e?"":"none"}updateTabindex(){if(!this.checkbox)return;const e=this.model.get("tabbable");!0===e?this.checkbox.setAttribute("tabIndex","0"):!1===e?this.checkbox.setAttribute("tabIndex","-1"):null===e&&this.checkbox.removeAttribute("tabIndex")}updateTooltip(){if(!this.checkbox)return;const e=this.model.get("tooltip");e?0===this.model.get("description").length&&this.checkbox.setAttribute("title",e):this.checkbox.removeAttribute("title")}events(){return{'click input[type="checkbox"]':"_handle_click"}}_handle_click(){const e=this.model.get("value");this.model.set("value",!e,{updated_view:this}),this.touch()}update(e){return this.checkbox.checked=this.model.get("value"),void 0!==e&&e.updated_view==this||(this.checkbox.disabled=this.model.get("disabled")),super.update()}handle_message(e){"focus"==e.do?this.checkbox.focus():"blur"==e.do&&this.checkbox.blur()}}class C extends f{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"ToggleButtonView",_model_name:"ToggleButtonModel",tooltip:"",icon:"",button_style:"",style:null})}}class M extends i.DOMWidgetView{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("jupyter-button"),this.el.classList.add("widget-toggle-button"),this.listenTo(this.model,"change:button_style",this.update_button_style),this.listenTo(this.model,"change:tabbable",this.updateTabindex),this.set_button_style(),this.update()}update_button_style(){this.update_mapped_classes(M.class_map,"button_style")}set_button_style(){this.set_mapped_classes(M.class_map,"button_style")}update(e){if(this.model.get("value")?this.el.classList.add("mod-active"):this.el.classList.remove("mod-active"),void 0===e||e.updated_view!==this){this.el.disabled=this.model.get("disabled"),this.el.setAttribute("tabbable",this.model.get("tabbable")),this.el.setAttribute("title",this.model.get("tooltip"));const e=this.model.get("description"),t=this.model.get("icon");if(0===e.trim().length&&0===t.trim().length)this.el.innerHTML=" ";else{if(this.el.textContent="",t.trim().length){const e=document.createElement("i");this.el.appendChild(e),e.classList.add("fa"),e.classList.add("fa-"+t)}this.el.appendChild(document.createTextNode(e))}}return this.updateTabindex(),super.update()}events(){return{click:"_handle_click"}}_handle_click(e){e.preventDefault();const t=this.model.get("value");this.model.set("value",!t,{updated_view:this}),this.touch()}preinitialize(){this.tagName="button"}}M.class_map={primary:["mod-primary"],success:["mod-success"],info:["mod-info"],warning:["mod-warning"],danger:["mod-danger"]};class j extends f{defaults(){return Object.assign(Object.assign({},super.defaults()),{readout:"Invalid",_view_name:"ValidView",_model_name:"ValidModel"})}}class T extends h{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-valid"),this.el.classList.add("widget-inline-hbox"),this.icon=document.createElement("i"),this.icon.classList.add("fa","fa-fw"),this.el.appendChild(this.icon),this.readout=document.createElement("span"),this.readout.classList.add("widget-valid-readout"),this.readout.classList.add("widget-readout"),this.el.appendChild(this.readout),this.update()}update(){this.el.classList.remove("mod-valid"),this.el.classList.remove("mod-invalid"),this.icon.classList.remove("fa-check"),this.icon.classList.remove("fa-times"),this.readout.textContent=this.model.get("readout"),this.model.get("value")?(this.el.classList.add("mod-valid"),this.icon.classList.add("fa-check")):(this.el.classList.add("mod-invalid"),this.icon.classList.add("fa-times"))}}class S extends i.StyleModel{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"ButtonStyleModel",_model_module:"@jupyter-widgets/controls",_model_module_version:n.A})}}S.styleProperties={button_color:{selector:"",attribute:"background-color",default:null},font_family:{selector:"",attribute:"font-family",default:""},font_size:{selector:"",attribute:"font-size",default:""},font_style:{selector:"",attribute:"font-style",default:""},font_variant:{selector:"",attribute:"font-variant",default:""},font_weight:{selector:"",attribute:"font-weight",default:""},text_color:{selector:"",attribute:"color",default:""},text_decoration:{selector:"",attribute:"text-decoration",default:""}};class O extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{description:"",tooltip:"",disabled:!1,icon:"",button_style:"",_view_name:"ButtonView",_model_name:"ButtonModel",style:null})}}class k extends i.DOMWidgetView{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("jupyter-button"),this.el.classList.add("widget-button"),this.listenTo(this.model,"change:button_style",this.update_button_style),this.listenTo(this.model,"change:tabbable",this.updateTabindex),this.set_button_style(),this.update()}update(){this.el.disabled=this.model.get("disabled"),this.updateTabindex();const e=this.model.get("tooltip"),t=this.model.get("description"),s=this.model.get("icon");if(this.el.setAttribute("title",null!=e?e:t),t.length||s.length){if(this.el.textContent="",s.length){const e=document.createElement("i");e.classList.add("fa"),e.classList.add(...s.split(/[\s]+/).filter(Boolean).map((e=>`fa-${e}`))),0===t.length&&e.classList.add("center"),this.el.appendChild(e)}this.el.appendChild(document.createTextNode(t))}return super.update()}update_button_style(){this.update_mapped_classes(k.class_map,"button_style")}set_button_style(){this.set_mapped_classes(k.class_map,"button_style")}events(){return{click:"_handle_click"}}_handle_click(e){e.preventDefault(),this.send({event:"click"})}preinitialize(){this.tagName="button"}}k.class_map={primary:["mod-primary"],success:["mod-success"],info:["mod-info"],warning:["mod-warning"],danger:["mod-danger"]};var L=s(4053),V=s(6230),I=s(5256),A=s(8596),W=s.n(A);class E extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"BoxView",_model_name:"BoxModel",children:[],box_style:""})}}E.serializers=Object.assign(Object.assign({},g.serializers),{children:{deserialize:i.unpack_models}});class B extends E{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"HBoxView",_model_name:"HBoxModel"})}}class z extends E{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"VBoxView",_model_name:"VBoxModel"})}}class D extends i.DOMWidgetView{_createElement(e){return this.luminoWidget=new i.JupyterLuminoPanelWidget({view:this}),this.luminoWidget.node}_setElement(e){if(this.el||e!==this.luminoWidget.node)throw new Error("Cannot reset the DOM element.");this.el=this.luminoWidget.node,this.$el=W()(this.luminoWidget.node)}initialize(e){super.initialize(e),this.children_views=new i.ViewList(this.add_child_model,null,this),this.listenTo(this.model,"change:children",this.update_children),this.listenTo(this.model,"change:box_style",this.update_box_style),this.luminoWidget.addClass("jupyter-widgets"),this.luminoWidget.addClass("widget-container"),this.luminoWidget.addClass("widget-box")}render(){super.render(),this.update_children(),this.set_box_style()}update_children(){var e;null===(e=this.children_views)||void 0===e||e.update(this.model.get("children")).then((e=>{e.forEach((e=>{V.MessageLoop.postMessage(e.luminoWidget,I.Widget.ResizeMessage.UnknownSize)}))}))}update_box_style(){this.update_mapped_classes(D.class_map,"box_style")}set_box_style(){this.set_mapped_classes(D.class_map,"box_style")}add_child_model(e){const t=new I.Widget;return this.luminoWidget.addWidget(t),this.create_child_view(e).then((e=>{const s=L.ArrayExt.firstIndexOf(this.luminoWidget.widgets,t);return this.luminoWidget.insertWidget(s,e.luminoWidget),t.dispose(),e})).catch((0,i.reject)("Could not add child view to box",!0))}remove(){this.children_views=null,super.remove()}}D.class_map={success:["alert","alert-success"],info:["alert","alert-info"],warning:["alert","alert-warning"],danger:["alert","alert-danger"]};class P extends D{initialize(e){super.initialize(e),this.luminoWidget.addClass("widget-hbox")}}class U extends D{initialize(e){super.initialize(e),this.luminoWidget.addClass("widget-vbox")}}class F extends D{initialize(e){super.initialize(e),this.luminoWidget.addClass("widget-gridbox"),this.luminoWidget.removeClass("widget-box")}}class R extends E{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"GridBoxView",_model_name:"GridBoxModel"})}}class $ extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"ImageModel",_view_name:"ImageView",format:"png",width:"",height:"",value:new DataView(new ArrayBuffer(0))})}}$.serializers=Object.assign(Object.assign({},g.serializers),{value:{serialize:e=>new DataView(e.buffer.slice(0))}});class N extends i.DOMWidgetView{render(){super.render(),this.luminoWidget.addClass("jupyter-widgets"),this.luminoWidget.addClass("widget-image"),this.update()}update(){let e;const t=this.model.get("format"),s=this.model.get("value");if("url"!==t){const t=new Blob([s],{type:`image/${this.model.get("format")}`});e=URL.createObjectURL(t)}else e=new TextDecoder("utf-8").decode(s.buffer);const i=this.el.src;this.el.src=e,i&&URL.revokeObjectURL(i);const a=this.model.get("width");void 0!==a&&a.length>0?this.el.setAttribute("width",a):this.el.removeAttribute("width");const l=this.model.get("height");return void 0!==l&&l.length>0?this.el.setAttribute("height",l):this.el.removeAttribute("height"),super.update()}remove(){this.el.src&&URL.revokeObjectURL(this.el.src),super.remove()}preinitialize(){this.tagName="img"}}class H extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"VideoModel",_view_name:"VideoView",format:"mp4",width:"",height:"",autoplay:!0,loop:!0,controls:!0,value:new DataView(new ArrayBuffer(0))})}}H.serializers=Object.assign(Object.assign({},g.serializers),{value:{serialize:e=>new DataView(e.buffer.slice(0))}});class K extends i.DOMWidgetView{render(){super.render(),this.luminoWidget.addClass("jupyter-widgets"),this.luminoWidget.addClass("widget-image"),this.update()}update(){let e;const t=this.model.get("format"),s=this.model.get("value");if("url"!==t){const t=new Blob([s],{type:`video/${this.model.get("format")}`});e=URL.createObjectURL(t)}else e=new TextDecoder("utf-8").decode(s.buffer);const i=this.el.src;this.el.src=e,i&&URL.revokeObjectURL(i);const a=this.model.get("width");void 0!==a&&a.length>0?this.el.setAttribute("width",a):this.el.removeAttribute("width");const l=this.model.get("height");return void 0!==l&&l.length>0?this.el.setAttribute("height",l):this.el.removeAttribute("height"),this.el.loop=this.model.get("loop"),this.el.autoplay=this.model.get("autoplay"),this.el.controls=this.model.get("controls"),super.update()}remove(){this.el.src&&URL.revokeObjectURL(this.el.src),super.remove()}preinitialize(){this.tagName="video"}}class q extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"AudioModel",_view_name:"AudioView",format:"mp3",autoplay:!0,loop:!0,controls:!0,value:new DataView(new ArrayBuffer(0))})}}q.serializers=Object.assign(Object.assign({},g.serializers),{value:{serialize:e=>new DataView(e.buffer.slice(0))}});class G extends i.DOMWidgetView{render(){super.render(),this.luminoWidget.addClass("jupyter-widgets"),this.update()}update(){let e;const t=this.model.get("format"),s=this.model.get("value");if("url"!==t){const t=new Blob([s],{type:`audio/${this.model.get("format")}`});e=URL.createObjectURL(t)}else e=new TextDecoder("utf-8").decode(s.buffer);const i=this.el.src;return this.el.src=e,i&&URL.revokeObjectURL(i),this.el.loop=this.model.get("loop"),this.el.autoplay=this.model.get("autoplay"),this.el.controls=this.model.get("controls"),super.update()}remove(){this.el.src&&URL.revokeObjectURL(this.el.src),super.remove()}preinitialize(){this.tagName="audio"}}const J={aliceblue:"#f0f8ff",antiquewhite:"#faebd7",aqua:"#00ffff",aquamarine:"#7fffd4",azure:"#f0ffff",beige:"#f5f5dc",bisque:"#ffe4c4",black:"#000000",blanchedalmond:"#ffebcd",blue:"#0000ff",blueviolet:"#8a2be2",brown:"#a52a2a",burlywood:"#deb887",cadetblue:"#5f9ea0",chartreuse:"#7fff00",chocolate:"#d2691e",coral:"#ff7f50",cornflowerblue:"#6495ed",cornsilk:"#fff8dc",crimson:"#dc143c",cyan:"#00ffff",darkblue:"#00008b",darkcyan:"#008b8b",darkgoldenrod:"#b8860b",darkgray:"#a9a9a9",darkgrey:"#a9a9a9",darkgreen:"#006400",darkkhaki:"#bdb76b",darkmagenta:"#8b008b",darkolivegreen:"#556b2f",darkorange:"#ff8c00",darkorchid:"#9932cc",darkred:"#8b0000",darksalmon:"#e9967a",darkseagreen:"#8fbc8f",darkslateblue:"#483d8b",darkslategray:"#2f4f4f",darkslategrey:"#2f4f4f",darkturquoise:"#00ced1",darkviolet:"#9400d3",deeppink:"#ff1493",deepskyblue:"#00bfff",dimgray:"#696969",dimgrey:"#696969",dodgerblue:"#1e90ff",firebrick:"#b22222",floralwhite:"#fffaf0",forestgreen:"#228b22",fuchsia:"#ff00ff",gainsboro:"#dcdcdc",ghostwhite:"#f8f8ff",gold:"#ffd700",goldenrod:"#daa520",gray:"#808080",grey:"#808080",green:"#008000",greenyellow:"#adff2f",honeydew:"#f0fff0",hotpink:"#ff69b4",indianred:"#cd5c5c",indigo:"#4b0082",ivory:"#fffff0",khaki:"#f0e68c",lavender:"#e6e6fa",lavenderblush:"#fff0f5",lawngreen:"#7cfc00",lemonchiffon:"#fffacd",lightblue:"#add8e6",lightcoral:"#f08080",lightcyan:"#e0ffff",lightgoldenrodyellow:"#fafad2",lightgreen:"#90ee90",lightgray:"#d3d3d3",lightgrey:"#d3d3d3",lightpink:"#ffb6c1",lightsalmon:"#ffa07a",lightseagreen:"#20b2aa",lightskyblue:"#87cefa",lightslategray:"#778899",lightslategrey:"#778899",lightsteelblue:"#b0c4de",lightyellow:"#ffffe0",lime:"#00ff00",limegreen:"#32cd32",linen:"#faf0e6",magenta:"#ff00ff",maroon:"#800000",mediumaquamarine:"#66cdaa",mediumblue:"#0000cd",mediumorchid:"#ba55d3",mediumpurple:"#9370db",mediumseagreen:"#3cb371",mediumslateblue:"#7b68ee",mediumspringgreen:"#00fa9a",mediumturquoise:"#48d1cc",mediumvioletred:"#c71585",midnightblue:"#191970",mintcream:"#f5fffa",mistyrose:"#ffe4e1",moccasin:"#ffe4b5",navajowhite:"#ffdead",navy:"#000080",oldlace:"#fdf5e6",olive:"#808000",olivedrab:"#6b8e23",orange:"#ffa500",orangered:"#ff4500",orchid:"#da70d6",palegoldenrod:"#eee8aa",palegreen:"#98fb98",paleturquoise:"#afeeee",palevioletred:"#db7093",papayawhip:"#ffefd5",peachpuff:"#ffdab9",peru:"#cd853f",pink:"#ffc0cb",plum:"#dda0dd",powderblue:"#b0e0e6",purple:"#800080",red:"#ff0000",rosybrown:"#bc8f8f",royalblue:"#4169e1",saddlebrown:"#8b4513",salmon:"#fa8072",sandybrown:"#f4a460",seagreen:"#2e8b57",seashell:"#fff5ee",sienna:"#a0522d",silver:"#c0c0c0",skyblue:"#87ceeb",slateblue:"#6a5acd",slategray:"#708090",slategrey:"#708090",snow:"#fffafa",springgreen:"#00ff7f",steelblue:"#4682b4",tan:"#d2b48c",teal:"#008080",thistle:"#d8bfd8",tomato:"#ff6347",turquoise:"#40e0d0",violet:"#ee82ee",wheat:"#f5deb3",white:"#ffffff",whitesmoke:"#f5f5f5",yellow:"#ffff00",yellowgreen:"#9acd32"};class Y extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{value:"black",concise:!1,_model_name:"ColorPickerModel",_view_name:"ColorPickerView"})}}class Q extends h{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-inline-hbox"),this.el.classList.add("widget-colorpicker"),this._color_container=document.createElement("div"),this._color_container.className="widget-inline-hbox widget-colorpicker-input",this.el.appendChild(this._color_container),this._textbox=document.createElement("input"),this._textbox.setAttribute("type","text"),this._textbox.id=this.label.htmlFor=(0,i.uuid)(),this._color_container.appendChild(this._textbox),this._textbox.value=this.model.get("value"),this._colorpicker=document.createElement("input"),this._colorpicker.setAttribute("type","color"),this._color_container.appendChild(this._colorpicker),this.listenTo(this.model,"change:value",this._update_value),this.listenTo(this.model,"change:concise",this._update_concise),this._update_concise(),this._update_value(),this.update()}update(e){if(void 0===e||e.updated_view!=this){const e=this.model.get("disabled");this._textbox.disabled=e,this._colorpicker.disabled=e}return super.update()}events(){return this._picker_change,this._text_change,{'change [type="color"]':"_picker_change",'change [type="text"]':"_text_change"}}_update_value(){const e=this.model.get("value");var t,s;this._colorpicker.value=J[(t=e).toLowerCase()]||(7===(s=t).length?s:"#"+s.charAt(1)+s.charAt(1)+s.charAt(2)+s.charAt(2)+s.charAt(3)+s.charAt(3)),this._textbox.value=e}_update_concise(){this.model.get("concise")?(this.el.classList.add("concise"),this._textbox.style.display="none"):(this.el.classList.remove("concise"),this._textbox.style.display="")}_picker_change(){this.model.set("value",this._colorpicker.value),this.touch()}_text_change(){const e=this._validate_color(this._textbox.value,this.model.get("value"));this.model.set("value",e),this.touch()}_validate_color(e,t){return e.match(/#[a-fA-F0-9]{3}(?:[a-fA-F0-9]{3})?$/)||J[e.toLowerCase()]?e:t}}function X(e){return null===e?null:{year:e.getUTCFullYear(),month:e.getUTCMonth(),date:e.getUTCDate()}}function Z(e){if(null===e)return null;{const t=new Date;return t.setUTCFullYear(e.year,e.month,e.date),t.setUTCHours(0,0,0,0),t}}class ee extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{value:null,_model_name:"DatePickerModel",_view_name:"DatePickerView"})}}ee.serializers=Object.assign(Object.assign({},m.serializers),{value:{serialize:X,deserialize:Z}});class te extends h{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-inline-hbox"),this.el.classList.add("widget-datepicker"),this._datepicker=document.createElement("input"),this._datepicker.setAttribute("type","date"),this._datepicker.id=this.label.htmlFor=(0,i.uuid)(),this.el.appendChild(this._datepicker),this.listenTo(this.model,"change:value",this._update_value),this._update_value(),this.update()}update(e){return void 0!==e&&e.updated_view===this||(this._datepicker.disabled=this.model.get("disabled")),super.update()}events(){return this._picker_change,this._picker_focusout,{'change [type="date"]':"_picker_change",'focusout [type="date"]':"_picker_focusout"}}_update_value(){const e=this.model.get("value");this._datepicker.valueAsDate=e}_picker_change(){this._datepicker.validity.badInput||(this.model.set("value",this._datepicker.valueAsDate),this.touch())}_picker_focusout(){this._datepicker.validity.badInput&&(this.model.set("value",null),this.touch())}}const se=/(\d\d):(\d\d)(:(\d\d)(.(\d{1,3})\d*)?)?/;function ie(e){if(null===e)return null;{const t=se.exec(e);return null===t?null:{hours:Math.min(23,parseInt(t[1],10)),minutes:Math.min(59,parseInt(t[2],10)),seconds:t[4]?Math.min(59,parseInt(t[4],10)):0,milliseconds:t[6]?parseInt(t[6],10):0}}}function ae(e){if(null===e)return null;{const t=[`${e.hours.toString().padStart(2,"0")}:${e.minutes.toString().padStart(2,"0")}`];return(e.seconds>0||e.milliseconds>0)&&(t.push(`:${e.seconds.toString().padStart(2,"0")}`),e.milliseconds>0&&t.push(`.${e.milliseconds.toString().padStart(3,"0")}`)),t.join("")}}const le={serialize:ie,deserialize:ae};class de extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:de.model_name,_view_name:de.view_name,value:null,disabled:!1,min:null,max:null,step:60})}}de.serializers=Object.assign(Object.assign({},m.serializers),{value:le,min:le,max:le}),de.model_name="TimeModel",de.view_name="TimeView";class ne extends h{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-inline-hbox"),this.el.classList.add("widget-timepicker"),this._timepicker=document.createElement("input"),this._timepicker.setAttribute("type","time"),this._timepicker.id=this.label.htmlFor=(0,i.uuid)(),this.el.appendChild(this._timepicker),this.listenTo(this.model,"change:value",this._update_value),this.listenTo(this.model,"change",this.update2),this._update_value(),this.update2()}update2(e,t){return void 0!==t&&t.updated_view===this||(this._timepicker.disabled=this.model.get("disabled"),this._timepicker.min=this.model.get("min"),this._timepicker.max=this.model.get("max"),this._timepicker.step=this.model.get("step")),super.update()}events(){return this._picker_change,this._picker_focusout,{'change [type="time"]':"_picker_change",'focusout [type="time"]':"_picker_focusout"}}_update_value(e,t,s){void 0!==s&&s.updated_view===this||(this._timepicker.value=this.model.get("value"))}_picker_change(){this._timepicker.validity.badInput||(this.model.set("value",this._timepicker.value,{updated_view:this}),this.touch())}_picker_focusout(){this._timepicker.validity.badInput&&(this.model.set("value",null,{updated_view:this}),this.touch())}}function oe(e){return null===e?null:{year:e.getUTCFullYear(),month:e.getUTCMonth(),date:e.getUTCDate(),hours:e.getUTCHours(),minutes:e.getUTCMinutes(),seconds:e.getUTCSeconds(),milliseconds:e.getUTCMilliseconds()}}function re(e){if(null===e)return null;{const t=new Date;return t.setUTCFullYear(e.year,e.month,e.date),t.setUTCHours(e.hours,e.minutes,e.seconds,e.milliseconds),t}}const he={serialize:oe,deserialize:re};class ue extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"DatetimeModel",_view_name:"DatetimeView",value:null,disabled:!1,min:null,max:null})}}ue.serializers=Object.assign(Object.assign({},m.serializers),{value:he,min:he,max:he});class ce extends h{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-inline-hbox"),this.el.classList.add("widget-datetimepicker");const e=document.createElement("input");e.type="datetime-local","text"===e.type?(this._datepicker=document.createElement("input"),this._datepicker.setAttribute("type","date"),this._datepicker.id=this.label.htmlFor=(0,i.uuid)(),this._timepicker=document.createElement("input"),this._timepicker.setAttribute("type","time"),this._timepicker.id=(0,i.uuid)(),this.el.appendChild(this._datepicker),this.el.appendChild(this._timepicker)):(this._datetimepicker=e,this._datetimepicker.id=this.label.htmlFor=(0,i.uuid)(),this.el.appendChild(this._datetimepicker)),this.listenTo(this.model,"change:value",this._update_value),this.listenTo(this.model,"change",this.update2),this._update_value(),this.update2()}update2(e,t){if(void 0===t||t.updated_view!==this){const e=this.model.get("min"),t=this.model.get("max");this._datetimepicker?(this._datetimepicker.disabled=this.model.get("disabled"),this._datetimepicker.min=pe.dt_as_dt_string(e),this._datetimepicker.max=pe.dt_as_dt_string(t)):(this._datepicker.disabled=this.model.get("disabled"),this._datepicker.min=pe.dt_as_date_string(e),this._datepicker.max=pe.dt_as_date_string(t),this._timepicker.disabled=this.model.get("disabled"))}}events(){return this._picker_change,this._picker_focusout,{'change [type="date"]':"_picker_change",'change [type="time"]':"_picker_change",'change [type="datetime-local"]':"_picker_change",'focusout [type="date"]':"_picker_focusout",'focusout [type="datetime-local"]':"_picker_focusout",'focusout [type="time"]':"_picker_focusout"}}_update_value(e,t,s){if(void 0===s||s.updated_view!==this){const e=this.model.get("value");this._datetimepicker?this._datetimepicker.value=pe.dt_as_dt_string(e):(this._datepicker.valueAsDate=e,this._timepicker.value=pe.dt_as_time_string(e))}}_picker_change(){if(this._datetimepicker){if(!this._datetimepicker.validity.badInput){const e=this._datetimepicker.value;let t=e?new Date(e):null;t&&isNaN(t.valueOf())&&(t=null),this.model.set("value",t,{updated_view:this}),this.touch()}}else if(!this._datepicker.validity.badInput&&!this._timepicker.validity.badInput){const e=this._datepicker.valueAsDate,t=ie(this._timepicker.value);null!==e&&null!==t&&e.setHours(t.hours,t.minutes,t.seconds,t.milliseconds),this.model.set("value",null!==t&&e,{updated_view:this}),this.touch()}}_picker_focusout(){[this._datetimepicker,this._datepicker,this._timepicker].some((e=>e&&e.validity.badInput))&&(this.model.set("value",null),this.touch())}}var pe;function ge(e){return null===e?null:{year:e.getFullYear(),month:e.getMonth(),date:e.getDate(),hours:e.getHours(),minutes:e.getMinutes(),seconds:e.getSeconds(),milliseconds:e.getMilliseconds()}}function me(e){if(null===e)return null;{const t=new Date;return t.setFullYear(e.year,e.month,e.date),t.setHours(e.hours,e.minutes,e.seconds,e.milliseconds),t}}!function(e){function t(e){if(null===e)return"";const t=[];return t.push(`${e.getFullYear().toString().padStart(4,"0")}`),t.push(`-${(e.getMonth()+1).toString().padStart(2,"0")}`),t.push(`-${e.getDate().toString().padStart(2,"0")}`),t.push(`T${e.getHours().toString().padStart(2,"0")}`),t.push(`:${e.getMinutes().toString().padStart(2,"0")}`),(e.getSeconds()>0||e.getMilliseconds()>0)&&(t.push(`:${e.getSeconds().toString().padStart(2,"0")}`),e.getMilliseconds()>0&&t.push(`.${e.getMilliseconds().toString().padStart(3,"0")}`)),t.join("")}e.dt_as_dt_string=t,e.dt_as_date_string=function(e){return e?t(e).split("T",2)[0]:""},e.dt_as_time_string=function(e){return e?t(e).split("T",2)[1]:""}}(pe||(pe={}));const _e={serialize:ge,deserialize:me};class be extends ue{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"NaiveDatetimeModel"})}}be.serializers=Object.assign(Object.assign({},m.serializers),{value:_e,min:_e,max:_e});var ve=s(7102),xe=s(5715),fe=s.n(xe);class we extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"IntModel",value:0})}}class ye extends we{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"BoundedIntModel",max:100,min:0})}}class Ce extends o{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"SliderStyleModel"})}}Ce.styleProperties=Object.assign(Object.assign({},o.styleProperties),{handle_color:{selector:".noUi-handle",attribute:"background-color",default:null}});class Me extends ye{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"IntSliderModel",_view_name:"IntSliderView",step:1,orientation:"horizontal",readout:!0,readout_format:"d",continuous_update:!0,style:null,disabled:!1})}initialize(e,t){super.initialize(e,t),this.on("change:readout_format",this.update_readout_format,this),this.update_readout_format()}update_readout_format(){this.readout_formatter=(0,ve.GP)(this.get("readout_format"))}}class je extends Me{}class Te extends h{constructor(){super(...arguments),this._parse_value=parseInt}render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-inline-hbox"),this.el.classList.add("widget-slider"),this.el.classList.add("widget-hslider"),this.$slider=document.createElement("div"),this.$slider.classList.add("slider"),this.slider_container=document.createElement("div"),this.slider_container.classList.add("slider-container"),this.slider_container.appendChild(this.$slider),this.el.appendChild(this.slider_container),this.readout=document.createElement("div"),this.el.appendChild(this.readout),this.readout.classList.add("widget-readout"),this.readout.contentEditable="true",this.readout.style.display="none",this.createSlider(),this.model.on("change:orientation",this.regenSlider,this),this.model.on("change:max",this.updateSliderOptions,this),this.model.on("change:min",this.updateSliderOptions,this),this.model.on("change:step",this.updateSliderOptions,this),this.model.on("change:value",this.updateSliderValue,this),this.update()}update(e){return void 0!==e&&e.updated_view===this||(this.model.get("disabled")?(this.readout.contentEditable="false",this.$slider.setAttribute("disabled",!0)):(this.readout.contentEditable="true",this.$slider.removeAttribute("disabled")),"vertical"===this.model.get("orientation")?(this.el.classList.remove("widget-hslider"),this.el.classList.add("widget-vslider"),this.el.classList.remove("widget-inline-hbox"),this.el.classList.add("widget-inline-vbox")):(this.el.classList.remove("widget-vslider"),this.el.classList.add("widget-hslider"),this.el.classList.remove("widget-inline-vbox"),this.el.classList.add("widget-inline-hbox")),this.model.get("readout")?(this.readout.style.display="",this.displayed.then((()=>{this.readout_overflow()?this.readout.classList.add("overflow"):this.readout.classList.remove("overflow")}))):this.readout.style.display="none"),super.update()}readout_overflow(){return this.readout.scrollWidth>this.readout.clientWidth}events(){return{"blur [contentEditable=true]":"handleTextChange","keydown [contentEditable=true]":"handleKeyDown"}}handleKeyDown(e){13===e.keyCode&&(e.preventDefault(),this.handleTextChange())}createSlider(){const e=this.model.get("orientation"),t=this.model.get("behavior");fe().create(this.$slider,{start:this.model.get("value"),connect:!0,behaviour:t,range:{min:this.model.get("min"),max:this.model.get("max")},step:this.model.get("step"),animate:!1,orientation:e,direction:"horizontal"===e?"ltr":"rtl",format:{from:e=>Number(e),to:e=>this._validate_slide_value(e)}}),this.$slider.noUiSlider.on("update",((e,t)=>{this.handleSliderUpdateEvent(e,t)})),this.$slider.noUiSlider.on("change",((e,t)=>{this.handleSliderChangeEvent(e,t)}))}regenSlider(e){this.$slider.noUiSlider.destroy(),this.createSlider()}_validate_slide_value(e){return Math.round(e)}}class Se extends Te{constructor(){super(...arguments),this._range_regex=/^\s*([+-]?\d+)\s*[-:–]\s*([+-]?\d+)/}update(e){super.update(e);const t=this.model.get("value");this.readout.textContent=this.valueToString(t),this.model.get("value")!==t&&(this.model.set("value",t,{updated_view:this}),this.touch())}valueToString(e){const t=this.model.readout_formatter;return e.map((function(e){return t(e)})).join(" – ")}stringToValue(e){if(null===e)return null;const t=this._range_regex.exec(e);return t?[this._parse_value(t[1]),this._parse_value(t[2])]:null}handleTextChange(){let e=this.stringToValue(this.readout.textContent);const t=this.model.get("min"),s=this.model.get("max");null===e||isNaN(e[0])||isNaN(e[1])||e[0]>e[1]?this.readout.textContent=this.valueToString(this.model.get("value")):(e=[Math.max(Math.min(e[0],s),t),Math.max(Math.min(e[1],s),t)],e[0]!==this.model.get("value")[0]||e[1]!==this.model.get("value")[1]?(this.readout.textContent=this.valueToString(e),this.model.set("value",e),this.touch()):this.readout.textContent=this.valueToString(this.model.get("value")))}handleSliderChangeEvent(e,t){const s=e.map(this._validate_slide_value);this.readout.textContent=this.valueToString(s),this.handleSliderChanged(e,t)}handleSliderUpdateEvent(e,t){const s=e.map(this._validate_slide_value);this.readout.textContent=this.valueToString(s),this.model.get("continuous_update")&&this.handleSliderChanged(e,t)}handleSliderChanged(e,t){const s=e.map(this._validate_slide_value);this.model.set("value",s,{updated_view:this}),this.touch()}updateSliderOptions(e){this.$slider.noUiSlider.updateOptions({start:this.model.get("value"),range:{min:this.model.get("min"),max:this.model.get("max")},step:this.model.get("step")})}updateSliderValue(e,t,s){if(s.updated_view===this)return;const i=this.$slider.noUiSlider.get(),a=this.model.get("value");i[0]===a[0]&&i[1]===a[1]||this.$slider.noUiSlider.set(a)}}class Oe extends Te{update(e){super.update(e);const t=this.model.get("min"),s=this.model.get("max");let i=this.model.get("value");i>s?i=s:i=1){const e=s.substr(1);s=s[0]+e.replace(/[+-]/g,"")}t.value!==s&&(e.preventDefault(),t.value=s)}handleChanging(e){const t=e.target.value.trim();""===t||["-","-.",".","+.","+"].indexOf(t)>=0||this.model.get("continuous_update")&&this.handleChanged(e)}handleChanged(e){const t=e.target;let s=this._parse_value(t.value);if(isNaN(s))t.value=this.model.get("value");else{let e=s;void 0!==this.model.get("max")&&(e=Math.min(this.model.get("max"),e)),void 0!==this.model.get("min")&&(e=Math.max(this.model.get("min"),e)),e!==s&&(t.value=e,s=e),s!==this.model.get("value")&&(this.model.set("value",s,{updated_view:this}),this.touch())}}}class Ie extends o{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"ProgressStyleModel"})}}Ie.styleProperties=Object.assign(Object.assign({},o.styleProperties),{bar_color:{selector:".progress-bar",attribute:"background-color",default:null}});class Ae extends ye{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"IntProgressModel",_view_name:"ProgressView",orientation:"horizontal",bar_style:"",style:null})}}class We extends h{initialize(e){super.initialize(e),this.listenTo(this.model,"change:bar_style",this.update_bar_style),this.luminoWidget.addClass("jupyter-widgets")}render(){super.render();const e="horizontal"===this.model.get("orientation")?"widget-hprogress":"widget-vprogress";this.el.classList.add(e),this.progress=document.createElement("div"),this.progress.classList.add("progress"),this.progress.style.position="relative",this.el.appendChild(this.progress),this.bar=document.createElement("div"),this.bar.classList.add("progress-bar"),this.bar.style.position="absolute",this.bar.style.bottom="0px",this.bar.style.left="0px",this.progress.appendChild(this.bar),this.update(),this.set_bar_style()}update(){const e=this.model.get("value"),t=this.model.get("max"),s=this.model.get("min"),i=100*(e-s)/(t-s);return"horizontal"===this.model.get("orientation")?(this.el.classList.remove("widget-inline-vbox"),this.el.classList.remove("widget-vprogress"),this.el.classList.add("widget-inline-hbox"),this.el.classList.add("widget-hprogress"),this.bar.style.width=i+"%",this.bar.style.height="100%"):(this.el.classList.remove("widget-inline-hbox"),this.el.classList.remove("widget-hprogress"),this.el.classList.add("widget-inline-vbox"),this.el.classList.add("widget-vprogress"),this.bar.style.width="100%",this.bar.style.height=i+"%"),super.update()}update_bar_style(){this.update_mapped_classes(We.class_map,"bar_style",this.bar)}set_bar_style(){this.set_mapped_classes(We.class_map,"bar_style",this.bar)}}We.class_map={success:["progress-bar-success"],info:["progress-bar-info"],warning:["progress-bar-warning"],danger:["progress-bar-danger"]};class Ee extends ye{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"PlayModel",_view_name:"PlayView",repeat:!1,playing:!1,show_repeat:!0,interval:100,step:1,disabled:!1})}initialize(e,t){super.initialize(e,t)}loop(){if(!this.get("playing"))return;const e=this.get("value")+this.get("step");e<=this.get("max")?(this.set("value",e),this.schedule_next()):this.get("repeat")?(this.set("value",this.get("min")),this.schedule_next()):this.pause(),this.save_changes()}schedule_next(){this._timerId=window.setTimeout(this.loop.bind(this),this.get("interval"))}stop(){this.pause(),this.set("value",this.get("min")),this.save_changes()}pause(){window.clearTimeout(this._timerId),this._timerId=void 0,this.set("playing",!1),this.save_changes()}animate(){void 0===this._timerId&&(this.get("value")===this.get("max")?(this.set("value",this.get("min")),this.schedule_next(),this.save_changes()):this.loop(),this.save_changes())}play(){this.set("playing",!this.get("playing")),this.save_changes()}repeat(){this.set("repeat",!this.get("repeat")),this.save_changes()}}class Be extends i.DOMWidgetView{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-inline-hbox"),this.el.classList.add("widget-play"),this.playPauseButton=document.createElement("button"),this.stopButton=document.createElement("button"),this.repeatButton=document.createElement("button"),this.playPauseButton.className="jupyter-button",this.stopButton.className="jupyter-button",this.repeatButton.className="jupyter-button",this.el.appendChild(this.playPauseButton),this.el.appendChild(this.stopButton),this.el.appendChild(this.repeatButton);const e=document.createElement("i");e.className="fa fa-play",this.playPauseButton.appendChild(e);const t=document.createElement("i");t.className="fa fa-stop",this.stopButton.appendChild(t);const s=document.createElement("i");s.className="fa fa-retweet",this.repeatButton.appendChild(s),this.playPauseButton.onclick=this.model.play.bind(this.model),this.stopButton.onclick=this.model.stop.bind(this.model),this.repeatButton.onclick=this.model.repeat.bind(this.model),this.listenTo(this.model,"change:playing",this.onPlayingChanged),this.listenTo(this.model,"change:repeat",this.updateRepeat),this.listenTo(this.model,"change:show_repeat",this.updateRepeat),this.updatePlaying(),this.updateRepeat(),this.update()}update(){const e=this.model.get("disabled");this.playPauseButton.disabled=e,this.stopButton.disabled=e,this.repeatButton.disabled=e,this.updatePlaying()}onPlayingChanged(){this.updatePlaying();const e=this.model.previous("playing"),t=this.model.get("playing");!e&&t?this.model.animate():this.model.pause()}updatePlaying(){const e=this.model.get("playing");this.playPauseButton.getElementsByTagName("i")[0].className=e?"fa fa-pause":"fa fa-play"}updateRepeat(){const e=this.model.get("repeat");this.repeatButton.style.display=this.model.get("show_repeat")?this.playPauseButton.style.display:"none",e?this.repeatButton.classList.add("mod-active"):this.repeatButton.classList.remove("mod-active")}}class ze extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"FloatModel",value:0})}}class De extends ze{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"BoundedFloatModel",max:100,min:0})}}class Pe extends De{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"FloatSliderModel",_view_name:"FloatSliderView",step:1,orientation:"horizontal",_range:!1,readout:!0,readout_format:".2f",slider_color:null,continuous_update:!0,disabled:!1})}initialize(e,t){super.initialize(e,t),this.on("change:readout_format",this.update_readout_format,this),this.update_readout_format()}update_readout_format(){this.readout_formatter=(0,ve.GP)(this.get("readout_format"))}}class Ue extends De{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"FloatLogSliderModel",_view_name:"FloatLogSliderView",step:.1,orientation:"horizontal",_range:!1,readout:!0,readout_format:".3g",slider_color:null,continuous_update:!0,disabled:!1,base:10,value:1,min:0,max:4})}initialize(e,t){super.initialize(e,t),this.on("change:readout_format",this.update_readout_format,this),this.update_readout_format()}update_readout_format(){this.readout_formatter=(0,ve.GP)(this.get("readout_format"))}}class Fe extends Pe{}class Re extends Oe{constructor(){super(...arguments),this._parse_value=parseFloat}_validate_slide_value(e){return e}}class $e extends Te{constructor(){super(...arguments),this._parse_value=parseFloat}update(e){super.update(e);const t=this.model.get("value");this.readout.textContent=this.valueToString(t)}logCalc(e){const t=this.model.get("min"),s=this.model.get("max"),i=this.model.get("base");let a=Math.log(e)/Math.log(i);return a>s?a=s:aNumber(e),to:e=>e}}),this.$slider.noUiSlider.on("update",((e,t)=>{this.handleSliderUpdateEvent(e,t)})),this.$slider.noUiSlider.on("change",((e,t)=>{this.handleSliderChangeEvent(e,t)}))}valueToString(e){return(0,this.model.readout_formatter)(e)}stringToValue(e){return null===e?NaN:this._parse_value(e)}handleTextChange(){let e=this.stringToValue(this.readout.textContent);const t=this.model.get("min"),s=this.model.get("max"),i=this.model.get("base");isNaN(e)?this.readout.textContent=this.valueToString(this.model.get("value")):(e=Math.max(Math.min(e,Math.pow(i,s)),Math.pow(i,t)),e!==this.model.get("value")?(this.readout.textContent=this.valueToString(e),this.model.set("value",e),this.touch()):this.readout.textContent=this.valueToString(this.model.get("value")))}handleSliderUpdateEvent(e,t){const s=this.model.get("base"),i=Math.pow(s,this._validate_slide_value(e[0]));this.readout.textContent=this.valueToString(i),this.model.get("continuous_update")&&this.handleSliderChanged(e,t)}handleSliderChangeEvent(e,t){const s=this.model.get("base"),i=Math.pow(s,this._validate_slide_value(e[0]));this.readout.textContent=this.valueToString(i),this.handleSliderChanged(e,t)}handleSliderChanged(e,t){if(this._updating_slider)return;const s=this.model.get("base"),i=Math.pow(s,this._validate_slide_value(e[0]));this.model.set("value",i,{updated_view:this}),this.touch()}updateSliderValue(e,t,s){if(s.updated_view===this)return;const i=this.logCalc(this.model.get("value"));this.$slider.noUiSlider.set(i)}updateSliderOptions(e){this.$slider.noUiSlider.updateOptions({start:this.logCalc(this.model.get("value")),range:{min:this.model.get("min"),max:this.model.get("max")},step:this.model.get("step")})}_validate_slide_value(e){return e}}class Ne extends Se{constructor(){super(...arguments),this._parse_value=parseFloat,this._range_regex=/^\s*([+-]?(?:\d*\.?\d+|\d+\.)(?:[eE][-:]?\d+)?)\s*[-:–]\s*([+-]?(?:\d*\.?\d+|\d+\.)(?:[eE][+-]?\d+)?)/}_validate_slide_value(e){return e}}class He extends ze{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"FloatTextModel",_view_name:"FloatTextView",disabled:!1,continuous_update:!1})}}class Ke extends De{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"BoundedFloatTextModel",_view_name:"FloatTextView",disabled:!1,continuous_update:!1,step:.1})}}class qe extends Ve{constructor(){super(...arguments),this._parse_value=parseFloat,this._default_step="any"}handleKeypress(e){e.stopPropagation()}handleKeyUp(e){}}class Ge extends De{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"FloatProgressModel",_view_name:"ProgressView",orientation:"horizontal",bar_style:"",style:null})}}class Je extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"ControllerButtonModel",_view_name:"ControllerButtonView",value:0,pressed:!1})}}class Ye extends i.DOMWidgetView{render(){this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-controller-button"),this.el.style.width="fit-content",this.support=document.createElement("div"),this.support.style.position="relative",this.support.style.margin="1px",this.support.style.width="16px",this.support.style.height="16px",this.support.style.border="1px solid black",this.support.style.background="lightgray",this.el.appendChild(this.support),this.bar=document.createElement("div"),this.bar.style.position="absolute",this.bar.style.width="100%",this.bar.style.bottom="0px",this.bar.style.background="gray",this.support.appendChild(this.bar),this.update(),this.label=document.createElement("div"),this.label.textContent=this.model.get("description"),this.label.style.textAlign="center",this.el.appendChild(this.label)}update(){this.bar.style.height=100*this.model.get("value")+"%"}}class Qe extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"ControllerAxisModel",_view_name:"ControllerAxisView",value:0})}}class Xe extends i.DOMWidgetView{render(){this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-controller-axis"),this.el.style.width="16px",this.el.style.padding="4px",this.support=document.createElement("div"),this.support.style.position="relative",this.support.style.margin="1px",this.support.style.width="4px",this.support.style.height="64px",this.support.style.border="1px solid black",this.support.style.background="lightgray",this.bullet=document.createElement("div"),this.bullet.style.position="absolute",this.bullet.style.margin="-3px",this.bullet.style.boxSizing="unset",this.bullet.style.width="10px",this.bullet.style.height="10px",this.bullet.style.background="gray",this.label=document.createElement("div"),this.label.textContent=this.model.get("description"),this.label.style.textAlign="center",this.support.appendChild(this.bullet),this.el.appendChild(this.support),this.el.appendChild(this.label),this.update()}update(){this.bullet.style.top=50*(this.model.get("value")+1)+"%"}}class Ze extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"ControllerModel",_view_name:"ControllerView",index:0,name:"",mapping:"",connected:!1,timestamp:0,buttons:[],axes:[]})}initialize(e,t){super.initialize(e,t),void 0===navigator.getGamepads?(this.readout="This browser does not support gamepads.",console.error(this.readout)):(this.readout="Connect gamepad and press any button.",this.get("connected")?this.update_loop():this.wait_loop())}wait_loop(){const e=this.get("index"),t=navigator.getGamepads()[e];t?this.setup(t).then((e=>{this.set(e),this.save_changes(),window.requestAnimationFrame(this.update_loop.bind(this))})):window.requestAnimationFrame(this.wait_loop.bind(this))}setup(e){return this.set({name:e.id,mapping:e.mapping,connected:e.connected,timestamp:e.timestamp}),i.resolvePromisesDict({buttons:Promise.all(e.buttons.map(((e,t)=>this._create_button_model(t)))),axes:Promise.all(e.axes.map(((e,t)=>this._create_axis_model(t))))})}update_loop(){const e=this.get("index"),t=this.get("name"),s=navigator.getGamepads()[e];s&&e===s.index&&t===s.id?(this.set({timestamp:s.timestamp,connected:s.connected}),this.save_changes(),this.get("buttons").forEach((function(e,t){e.set({value:s.buttons[t].value,pressed:s.buttons[t].pressed}),e.save_changes()})),this.get("axes").forEach((function(e,t){e.set("value",s.axes[t]),e.save_changes()})),window.requestAnimationFrame(this.update_loop.bind(this))):this.reset_gamepad()}reset_gamepad(){this.get("buttons").forEach((function(e){e.close()})),this.get("axes").forEach((function(e){e.close()})),this.set({name:"",mapping:"",connected:!1,timestamp:0,buttons:[],axes:[]}),this.save_changes(),window.requestAnimationFrame(this.wait_loop.bind(this))}_create_button_model(e){return this.widget_manager.new_widget({model_name:"ControllerButtonModel",model_module:"@jupyter-widgets/controls",model_module_version:this.get("_model_module_version"),view_name:"ControllerButtonView",view_module:"@jupyter-widgets/controls",view_module_version:this.get("_view_module_version")}).then((function(t){return t.set("description",e),t}))}_create_axis_model(e){return this.widget_manager.new_widget({model_name:"ControllerAxisModel",model_module:"@jupyter-widgets/controls",model_module_version:this.get("_model_module_version"),view_name:"ControllerAxisView",view_module:"@jupyter-widgets/controls",view_module_version:this.get("_view_module_version")}).then((function(t){return t.set("description",e),t}))}}Ze.serializers=Object.assign(Object.assign({},g.serializers),{buttons:{deserialize:i.unpack_models},axes:{deserialize:i.unpack_models}});class et extends i.DOMWidgetView{_createElement(e){return this.luminoWidget=new i.JupyterLuminoPanelWidget({view:this}),this.luminoWidget.node}_setElement(e){if(this.el||e!==this.luminoWidget.node)throw new Error("Cannot reset the DOM element.");this.el=this.luminoWidget.node,this.$el=W()(this.luminoWidget.node)}initialize(e){super.initialize(e),this.button_views=new i.ViewList(this.add_button,null,this),this.listenTo(this.model,"change:buttons",((e,t)=>{this.button_views.update(t)})),this.axis_views=new i.ViewList(this.add_axis,null,this),this.listenTo(this.model,"change:axes",((e,t)=>{this.axis_views.update(t)})),this.listenTo(this.model,"change:name",this.update_label)}render(){this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-controller"),this.label=document.createElement("div"),this.el.appendChild(this.label),this.axis_box=new I.Panel,this.axis_box.node.style.display="flex",this.luminoWidget.addWidget(this.axis_box),this.button_box=new I.Panel,this.button_box.node.style.display="flex",this.luminoWidget.addWidget(this.button_box),this.button_views.update(this.model.get("buttons")),this.axis_views.update(this.model.get("axes")),this.update_label()}update_label(){this.label.textContent=this.model.get("name")||this.model.readout}add_button(e){const t=new I.Widget;return this.button_box.addWidget(t),this.create_child_view(e).then((e=>{const s=L.ArrayExt.firstIndexOf(this.button_box.widgets,t);return this.button_box.insertWidget(s,e.luminoWidget),t.dispose(),e})).catch((0,i.reject)("Could not add child button view to controller",!0))}add_axis(e){const t=new I.Widget;return this.axis_box.addWidget(t),this.create_child_view(e).then((e=>{const s=L.ArrayExt.firstIndexOf(this.axis_box.widgets,t);return this.axis_box.insertWidget(s,e.luminoWidget),t.dispose(),e})).catch((0,i.reject)("Could not add child axis view to controller",!0))}remove(){super.remove(),this.button_views.remove(),this.axis_views.remove()}}class tt extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"SelectionModel",index:"",_options_labels:[],disabled:!1})}}class st extends h{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-inline-hbox")}update(){super.update(),this.listbox&&(this.listbox.disabled=this.model.get("disabled")),this.updateTabindex(),this.updateTooltip()}updateTabindex(){if(!this.listbox)return;const e=this.model.get("tabbable");!0===e?this.listbox.setAttribute("tabIndex","0"):!1===e?this.listbox.setAttribute("tabIndex","-1"):null===e&&this.listbox.removeAttribute("tabIndex")}updateTooltip(){if(!this.listbox)return;const e=this.model.get("tooltip");e?0===this.model.get("description").length&&this.listbox.setAttribute("title",e):this.listbox.removeAttribute("title")}}class it extends tt{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"DropdownModel",_view_name:"DropdownView",button_style:""})}}class at extends st{render(){super.render(),this.el.classList.add("widget-dropdown"),this.listbox=document.createElement("select"),this.listbox.id=this.label.htmlFor=(0,i.uuid)(),this.el.appendChild(this.listbox),this._updateOptions(),this.update()}update(e){(null==e?void 0:e.updated_view)!==this&&this.model.hasChanged("_options_labels")&&this._updateOptions();const t=this.model.get("index");return this.listbox.selectedIndex=null===t?-1:t,super.update()}_updateOptions(){this.listbox.textContent="";const e=this.model.get("_options_labels");for(let t=0;te.value));let i=t.length!==s.length;if(!i)for(let e=0,a=t.length;e{const s=document.createElement("label");s.textContent=e,this.container.appendChild(s);const i=document.createElement("input");i.setAttribute("type","radio"),i.value=t.toString(),i.setAttribute("data-value",encodeURIComponent(e)),s.appendChild(i)}))),t.forEach(((e,t)=>{const s='input[data-value="'+encodeURIComponent(e)+'"]',i=this.container.querySelectorAll(s);if(i.length>0){const e=i[0];e.checked=this.model.get("index")===t,e.disabled=this.model.get("disabled")}})),setTimeout(this.adjustPadding,0,this),super.update(e)}adjustPadding(e){const t=window.getComputedStyle(e.el),s=parseInt(t.marginTop,10)+parseInt(t.marginBottom,10),i=e.label.offsetHeight+s,a=window.getComputedStyle(e.container),l=parseInt(a.marginBottom,10),d=(e.el.offsetHeight+s-l)%i,n=0===d?0:i-d;e.container.style.marginBottom=n+"px"}events(){return{'click input[type="radio"]':"_handle_click"}}_handle_click(e){const t=e.target;this.model.set("index",parseInt(t.value,10),{updated_view:this}),this.touch()}handle_message(e){if("focus"==e.do)this.container.firstElementChild.focus();else if("blur"==e.do)for(let e=0;ee.value));let h=!1;for(let e=0,a=t.length;e{let i;i=0!==e.trim().length||s[t]&&0!==s[t].trim().length?l(e):" ";const o=document.createElement("i"),r=document.createElement("button");s[t]&&(o.className="fa fa-"+s[t]),r.setAttribute("type","button"),r.className="widget-toggle-button jupyter-button",a&&r.classList.add(a),r.innerHTML=i,r.setAttribute("data-value",encodeURIComponent(e)),r.setAttribute("value",t.toString()),r.appendChild(o),r.disabled=n,d[t]&&r.setAttribute("title",d[t]),this.update_style_traits(r),this.buttongroup.appendChild(r)}))),t.forEach(((e,t)=>{const s='[data-value="'+encodeURIComponent(e)+'"]',i=this.buttongroup.querySelector(s);this.model.get("index")===t?i.classList.add("mod-active"):i.classList.remove("mod-active")})),this.stylePromise.then((function(e){e&&e.style()})),super.update(e)}update_style_traits(e){for(const t in this._css_state)if(Object.prototype.hasOwnProperty.call(this._css_state,"name"))if("margin"===t)this.buttongroup.style[t]=this._css_state[t];else if("width"!==t)if(e)e.style[t]=this._css_state[t];else{const e=this.buttongroup.querySelectorAll("button");e.length&&(e[0].style[t]=this._css_state[t])}}update_button_style(){const e=this.buttongroup.querySelectorAll("button");for(let t=0;tNumber(e),to:e=>Math.round(e)}}),this.$slider.noUiSlider.on("update",((e,t)=>{this.handleSliderUpdateEvent(e,t)})),this.$slider.noUiSlider.on("change",((e,t)=>{this.handleSliderChangeEvent(e,t)}))}events(){return{slide:"handleSliderChange",slidestop:"handleSliderChanged"}}updateSelection(){const e=this.model.get("index");this.updateReadout(e)}updateReadout(e){const t=this.model.get("_options_labels")[e];this.readout.textContent=t}handleSliderUpdateEvent(e,t){const s=e[0];this.updateReadout(s),this.model.get("continuous_update")&&this.handleSliderChanged(e,t)}handleSliderChangeEvent(e,t){const s=e[0];this.updateReadout(s),this.handleSliderChanged(e,t)}handleSliderChanged(e,t){const s=e[0];this.updateReadout(s),this.model.set("index",s,{updated_view:this}),this.touch()}updateSliderOptions(e){const t=this.model.get("_options_labels").length-1;this.$slider.noUiSlider.updateOptions({start:this.model.get("index"),range:{min:0,max:t},step:1})}updateSliderValue(e,t,s){if(s.updated_view===this)return;const i=this.$slider.noUiSlider.get(),a=this.model.get("index");i!==a&&this.$slider.noUiSlider.set(a)}}class gt extends tt{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"MultipleSelectionModel"})}}class mt extends gt{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"SelectMultipleModel",_view_name:"SelectMultipleView",rows:null})}}class _t extends dt{initialize(e){super.initialize(e),this.listbox.multiple=!0}render(){super.render(),this.el.classList.add("widget-select-multiple")}updateSelection(){const e=this.model.get("index")||[],t=this.listbox.options;this.listbox.selectedIndex=-1,e.forEach((e=>{t[e].selected=!0}))}_handle_change(){const e=Array.prototype.map.call(this.listbox.selectedOptions||[],(function(e){return e.index}));this.model.set("index",e,{updated_view:this}),this.touch()}}class bt extends gt{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"SelectionSliderModel",_view_name:"SelectionSliderView",orientation:"horizontal",readout:!0,continuous_update:!0})}}class vt extends pt{render(){super.render()}updateSelection(e){e=e||this.model.get("index"),this.updateReadout(e)}updateReadout(e){const t=this.model.get("_options_labels"),s=t[e[0]],i=t[e[1]];this.readout.textContent=`${s}-${i}`}handleSliderUpdateEvent(e,t){const s=e.map(Math.trunc);this.updateReadout(s),this.model.get("continuous_update")&&this.handleSliderChanged(e,t)}handleSliderChanged(e,t){const s=e.map(Math.round);this.updateReadout(s),this.model.set("index",s.slice(),{updated_view:this}),this.touch()}updateSliderValue(e,t,s){if(s.updated_view===this)return;const i=this.$slider.noUiSlider.get().map(Math.round),a=this.model.get("index").map(Math.round);i[0]===a[0]&&i[1]===a[1]||this.$slider.noUiSlider.set(a)}}var xt=s(4602),ft=s(6209);class wt extends I.Panel{constructor(){super(...arguments),this._widgetRemoved=new xt.Signal(this)}get widgetRemoved(){return this._widgetRemoved}onChildRemoved(e){this._widgetRemoved.emit(e.child)}}class yt extends I.Widget{constructor(e={}){super(),this._currentChanged=new xt.Signal(this),this.addClass("jupyter-widget-TabPanel"),this.tabBar=new I.TabBar(e),this.tabBar.addClass("jupyter-widget-TabPanel-tabBar"),this.tabContents=new wt,this.tabContents.addClass("jupyter-widget-TabPanel-tabContents"),this.tabBar.tabMoved.connect(this._onTabMoved,this),this.tabBar.currentChanged.connect(this._onCurrentChanged,this),this.tabBar.tabCloseRequested.connect(this._onTabCloseRequested,this),this.tabBar.tabActivateRequested.connect(this._onTabActivateRequested,this),this.tabContents.widgetRemoved.connect(this._onWidgetRemoved,this);const t=new I.PanelLayout;t.addWidget(this.tabBar),t.addWidget(this.tabContents),this.layout=t}get currentChanged(){return this._currentChanged}get currentIndex(){const e=this.tabBar.currentIndex;return-1===e?null:e}set currentIndex(e){this.tabBar.currentIndex=null===e?-1:e}get currentWidget(){const e=this.tabBar.currentTitle;return e?e.owner:null}set currentWidget(e){this.tabBar.currentTitle=e?e.title:null}get tabsMovable(){return this.tabBar.tabsMovable}set tabsMovable(e){this.tabBar.tabsMovable=e}get widgets(){return this.tabContents.widgets}addWidget(e){this.insertWidget(this.widgets.length,e)}insertWidget(e,t){t!==this.currentWidget&&t.hide(),this.tabContents.insertWidget(e,t),this.tabBar.insertTab(e,t.title)}_onCurrentChanged(e,t){const{previousIndex:s,previousTitle:i,currentIndex:a,currentTitle:l}=t,d=i?i.owner:null,n=l?l.owner:null;d&&d.hide(),n&&n.show(),this._currentChanged.emit({previousIndex:s,previousWidget:d,currentIndex:a,currentWidget:n}),(ft.Platform.IS_EDGE||ft.Platform.IS_IE)&&V.MessageLoop.flush()}_onTabActivateRequested(e,t){t.title.owner.activate()}_onTabCloseRequested(e,t){t.title.owner.close()}_onTabMoved(e,t){this.tabContents.insertWidget(t.toIndex,t.title.owner)}_onWidgetRemoved(e,t){this.tabBar.removeTab(t.title)}}class Ct{constructor(e,t={}){this._array=null,this._value=null,this._previousValue=null,this._selectionChanged=new xt.Signal(this),this._array=e,this._insertBehavior=t.insertBehavior||"select-item-if-needed",this._removeBehavior=t.removeBehavior||"select-item-after"}get selectionChanged(){return this._selectionChanged}adjustSelectionForSet(e){const t=this.index,s=this.value;if(e!==t)return;this._updateSelectedValue();const i=this.value;this._previousValue=null,s!==i&&this._selectionChanged.emit({previousIndex:t,previousValue:s,currentIndex:t,currentValue:i})}get value(){return this._value}set value(e){null===e||null===this._array?this.index=null:this.index=L.ArrayExt.firstIndexOf(this._array,e)}get index(){return this._index}set index(e){let t;if(null!==e&&null!==this._array?(t=Math.floor(e),(t<0||t>=this._array.length)&&(t=null)):t=null,this._index===t)return;const s=this._index,i=this._value;this._index=t,this._updateSelectedValue(),this._previousValue=i,this._selectionChanged.emit({previousIndex:s,previousValue:i,currentIndex:t,currentValue:this._value})}get insertBehavior(){return this._insertBehavior}set insertBehavior(e){this._insertBehavior=e}get removeBehavior(){return this._removeBehavior}set removeBehavior(e){this._removeBehavior=e}adjustSelectionForInsert(e,t){const s=this._value,i=this._index,a=this._insertBehavior;if("select-item"===a||"select-item-if-needed"===a&&null===i)return this._index=e,this._value=t,this._previousValue=s,void this._selectionChanged.emit({previousIndex:i,previousValue:s,currentIndex:e,currentValue:t});null!==i&&i>=e&&this._index++}clearSelection(){const e=this._index,t=this._value;this._index=null,this._value=null,this._previousValue=null,null!==e&&this._selectionChanged.emit({previousIndex:e,previousValue:t,currentIndex:this._index,currentValue:this._value})}adjustSelectionForRemove(e,t){if(null===this._index)return;const s=this._index,i=this._removeBehavior;if(s===e){if(!this._array||0===this._array.length)return this._index=null,this._value=null,this._previousValue=null,void this._selectionChanged.emit({previousIndex:e,previousValue:t,currentIndex:this._index,currentValue:this._value});if("select-item-after"===i)return this._index=Math.min(e,this._array.length-1),this._updateSelectedValue(),this._previousValue=null,void this._selectionChanged.emit({previousIndex:e,previousValue:t,currentIndex:this._index,currentValue:this._value});if("select-item-before"===i)return this._index=Math.max(0,e-1),this._updateSelectedValue(),this._previousValue=null,void this._selectionChanged.emit({previousIndex:e,previousValue:t,currentIndex:this._index,currentValue:this._value});if("select-previous-item"===i)return this._previousValue?this.value=this._previousValue:(this._index=Math.min(e,this._array.length-1),this._updateSelectedValue()),this._previousValue=null,void this._selectionChanged.emit({previousIndex:e,previousValue:t,currentIndex:this._index,currentValue:this.value});this._index=null,this._value=null,this._previousValue=null,this._selectionChanged.emit({previousIndex:e,previousValue:t,currentIndex:this._index,currentValue:this._value})}else s>e&&this._index--}_updateSelectedValue(){const e=this._index;this._value=null!==e&&this._array?this._array[e]:null}}const Mt="jupyter-widget-Collapse-open";class jt extends I.Widget{constructor(e){super(e),this._collapseChanged=new xt.Signal(this),this.addClass("jupyter-widget-Collapse"),this._header=new I.Widget,this._header.addClass("jupyter-widget-Collapse-header"),this._header.node.addEventListener("click",this);const t=document.createElement("i");t.classList.add("fa","fa-fw","fa-caret-right"),this._header.node.appendChild(t),this._header.node.appendChild(document.createElement("span")),this._content=new I.Panel,this._content.addClass("jupyter-widget-Collapse-contents");const s=new I.PanelLayout;this.layout=s,s.addWidget(this._header),s.addWidget(this._content),e.widget&&(this.widget=e.widget),this.collapsed=!1}dispose(){this.isDisposed||(super.dispose(),this._header=null,this._widget=null,this._content=null)}get widget(){return this._widget}set widget(e){const t=this._widget;t&&(t.disposed.disconnect(this._onChildDisposed,this),t.title.changed.disconnect(this._onTitleChanged,this),t.parent=null),this._widget=e,e.disposed.connect(this._onChildDisposed,this),e.title.changed.connect(this._onTitleChanged,this),this._onTitleChanged(e.title),this._content.addWidget(e)}get collapsed(){return this._collapsed}set collapsed(e){e!==this._collapsed&&(e?this._collapse():this._uncollapse())}toggle(){this.collapsed=!this.collapsed}get collapseChanged(){return this._collapseChanged}_collapse(){this._collapsed=!0,this._content&&this._content.hide(),this.removeClass(Mt),this._header.node.children[0].classList.add("fa-caret-right"),this._header.node.children[0].classList.remove("fa-caret-down"),this._collapseChanged.emit(void 0)}_uncollapse(){this._collapsed=!1,this._content&&this._content.show(),this.addClass(Mt),this._header.node.children[0].classList.add("fa-caret-down"),this._header.node.children[0].classList.remove("fa-caret-right"),this._collapseChanged.emit(void 0)}handleEvent(e){"click"===e.type&&this._evtClick(e)}_evtClick(e){this.toggle()}_onTitleChanged(e){this._header.node.children[1].textContent=this._widget.title.label}_onChildDisposed(e){this.dispose()}}const Tt="jupyter-widget-Accordion-child-active";class St extends I.Panel{constructor(e){super(e),this._selection=new Ct(this.widgets),this._selection.selectionChanged.connect(this._onSelectionChanged,this),this.addClass("jupyter-widget-Accordion")}get collapseWidgets(){return this.layout.widgets}get selection(){return this._selection}indexOf(e){return L.ArrayExt.findFirstIndex(this.collapseWidgets,(t=>t.widget===e))}addWidget(e){const t=this._wrapWidget(e);return t.collapsed=!0,super.addWidget(t),this._selection.adjustSelectionForInsert(this.widgets.length-1,t),t}insertWidget(e,t){const s=this._wrapWidget(t);s.collapsed=!0,super.insertWidget(e,s),this._selection.adjustSelectionForInsert(e,s)}removeWidget(e){const t=this.indexOf(e);if(t>=0){const s=this.collapseWidgets[t];e.parent=null,s.dispose(),this._selection.adjustSelectionForRemove(t,null)}}_wrapWidget(e){const t=new jt({widget:e});return t.addClass("jupyter-widget-Accordion-child"),t.collapseChanged.connect(this._onCollapseChange,this),t}_onCollapseChange(e){e.collapsed?this._selection.value===e&&e.collapsed&&(this._selection.value=null):this._selection.value=e}_onSelectionChanged(e,t){const s=t.previousValue,i=t.currentValue;s&&(s.collapsed=!0,s.removeClass(Tt)),i&&(i.collapsed=!1,i.addClass(Tt))}}class Ot extends E{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"SelectionContainerModel",selected_index:null,titles:[]})}}class kt extends Ot{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"AccordionModel",_view_name:"AccordionView"})}}class Lt extends St{constructor(e){const t=e.view;delete e.view,super(e),this._view=t}processMessage(e){var t;super.processMessage(e),null===(t=this._view)||void 0===t||t.processLuminoMessage(e)}dispose(){this.isDisposed||(super.dispose(),this._view.remove(),this._view=null)}}class Vt extends i.DOMWidgetView{_createElement(e){return this.luminoWidget=new Lt({view:this}),this.luminoWidget.node}_setElement(e){if(this.el||e!==this.luminoWidget.node)throw new Error("Cannot reset the DOM element.");this.el=this.luminoWidget.node,this.$el=W()(this.luminoWidget.node)}initialize(e){super.initialize(e),this.children_views=new i.ViewList(this.add_child_view,this.remove_child_view,this),this.listenTo(this.model,"change:children",(()=>this.updateChildren())),this.listenTo(this.model,"change:selected_index",(()=>this.update_selected_index())),this.listenTo(this.model,"change:titles",(()=>this.update_titles()))}render(){var e;super.render();const t=this.luminoWidget;t.addClass("jupyter-widgets"),t.addClass("widget-accordion"),t.addClass("widget-container"),t.selection.selectionChanged.connect((e=>{this.updatingChildren||(this.model.set("selected_index",t.selection.index),this.touch())})),null===(e=this.children_views)||void 0===e||e.update(this.model.get("children")),this.update_titles(),this.update_selected_index()}updateChildren(){var e;this.updatingChildren=!0,this.luminoWidget.selection.index=null,null===(e=this.children_views)||void 0===e||e.update(this.model.get("children")),this.update_selected_index(),this.updatingChildren=!1}update_titles(){const e=this.luminoWidget.collapseWidgets,t=this.model.get("titles");for(let s=0;s{const t=e.luminoWidget;return t.title.label=a.title.label,s.collapseWidgets[s.indexOf(a)].widget=t,a.dispose(),e})).catch((0,i.reject)("Could not add child view to box",!0))}remove(){this.children_views=null,super.remove()}}class It extends Ot{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"TabModel",_view_name:"TabView"})}}class At extends yt{constructor(e){const t=e.view;delete e.view,super(e),this._view=t,V.MessageLoop.installMessageHook(this.tabContents,((e,t)=>(this._view.processLuminoMessage(t),!0)))}dispose(){this.isDisposed||(super.dispose(),this._view.remove(),this._view=null)}}class Wt extends i.DOMWidgetView{constructor(){super(...arguments),this.updatingTabs=!1}_createElement(e){return this.luminoWidget=new At({view:this}),this.luminoWidget.node}_setElement(e){if(this.el||e!==this.luminoWidget.node)throw new Error("Cannot reset the DOM element.");this.el=this.luminoWidget.node,this.$el=W()(this.luminoWidget.node)}initialize(e){super.initialize(e),this.childrenViews=new i.ViewList(this.addChildView,(e=>{e.remove()}),this),this.listenTo(this.model,"change:children",(()=>this.updateTabs())),this.listenTo(this.model,"change:titles",(()=>this.updateTitles()))}render(){super.render();const e=this.luminoWidget;e.addClass("jupyter-widgets"),e.addClass("widget-container"),e.addClass("jupyter-widget-tab"),e.addClass("widget-tab"),e.tabsMovable=!0,e.tabBar.insertBehavior="none",e.tabBar.currentChanged.connect(this._onTabChanged,this),e.tabBar.tabMoved.connect(this._onTabMoved,this),e.tabBar.addClass("widget-tab-bar"),e.tabContents.addClass("widget-tab-contents"),e.tabBar.tabsMovable=!1,this.updateTabs(),this.update()}updateTabs(){var e;this.updatingTabs=!0,this.luminoWidget.currentIndex=null,null===(e=this.childrenViews)||void 0===e||e.update(this.model.get("children")),this.luminoWidget.currentIndex=this.model.get("selected_index"),this.updatingTabs=!1}addChildView(e,t){const s=this.model.get("titles")[t]||"",a=this.luminoWidget,l=new I.Widget;return l.title.label=s,a.addWidget(l),this.create_child_view(e).then((e=>{const t=e.luminoWidget;t.title.label=l.title.label,t.title.closable=!1;const s=L.ArrayExt.firstIndexOf(a.widgets,l);return a.insertWidget(s+1,t),l.dispose(),e})).catch((0,i.reject)("Could not add child view to box",!0))}update(){return this.updateSelectedIndex(),super.update()}updateTitles(){const e=this.model.get("titles")||[];(0,L.each)(this.luminoWidget.widgets,((t,s)=>{t.title.label=e[s]||""}))}updateSelectedIndex(){this.luminoWidget.currentIndex=this.model.get("selected_index")}remove(){this.childrenViews=null,super.remove()}_onTabChanged(e,t){if(!this.updatingTabs){const e=t.currentIndex;this.model.set("selected_index",-1===e?null:e),this.touch()}}_onTabMoved(e,t){const s=this.model.get("children").slice();L.ArrayExt.move(s,t.fromIndex,t.toIndex),this.model.set("children",s),this.touch()}}class Et extends Ot{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"StackModel",_view_name:"StackView"})}}class Bt extends D{initialize(e){super.initialize(e),this.listenTo(this.model,"change:selected_index",this.update_children)}update_children(){var e;let t;t=null===this.model.get("selected_index")?[]:[this.model.get("children")[this.model.get("selected_index")]],null===(e=this.children_views)||void 0===e||e.update(t).then((e=>{e.forEach((e=>{V.MessageLoop.postMessage(e.luminoWidget,I.Widget.ResizeMessage.UnknownSize)}))}))}}var zt=s(8996);function Dt(e){for(;e.firstChild;)e.removeChild(e.firstChild)}class Pt{constructor(e,t,s){this.start=e,this.dx=t,this.max=s}isSelected(e){let t,s;return this.dx>=0?(t=this.start,s=this.start+this.dx):(t=this.start+this.dx,s=this.start),t<=e&&ethis.max&&(this.dx=this.max-this.start),this.start+this.dx<0&&(this.dx=-this.start)}}class Ut extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{value:[],placeholder:"​",allowed_tags:null,allow_duplicates:!0})}}class Ft extends i.DOMWidgetView{constructor(){super(...arguments),this.hoveredTag=null,this.hoveredTagIndex=null}render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("jupyter-widget-tagsinput"),this.taginputWrapper=document.createElement("div"),this.model.get("value").length?this.taginputWrapper.style.display="none":this.taginputWrapper.style.display="inline-block",this.datalistID=(0,i.uuid)(),this.taginput=document.createElement("input"),this.taginput.classList.add("jupyter-widget-tag"),this.taginput.classList.add("jupyter-widget-taginput"),this.taginput.setAttribute("list",this.datalistID),this.taginput.setAttribute("type","text"),this.autocompleteList=document.createElement("datalist"),this.autocompleteList.id=this.datalistID,this.updateAutocomplete(),this.model.on("change:allowed_tags",this.updateAutocomplete.bind(this)),this.updatePlaceholder(),this.model.on("change:placeholder",this.updatePlaceholder.bind(this)),this.taginputWrapper.classList.add("widget-text"),this.taginputWrapper.appendChild(this.taginput),this.taginputWrapper.appendChild(this.autocompleteList),this.el.onclick=this.focus.bind(this),this.el.ondrop=e=>{const t=null==this.hoveredTagIndex?this.tags.length:this.hoveredTagIndex;return this.ondrop(e,t)},this.el.ondragover=this.ondragover.bind(this),this.taginput.onchange=this.handleValueAdded.bind(this),this.taginput.oninput=this.resizeInput.bind(this),this.taginput.onkeydown=this.handleKeyEvent.bind(this),this.taginput.onblur=this.loseFocus.bind(this),this.resizeInput(),this.inputIndex=this.model.get("value").length,this.selection=null,this.preventLoosingFocus=!1,this.update()}update(){this.preventLoosingFocus=!0,Dt(this.el),this.tags=[];const e=this.model.get("value");this.inputIndex=e.length;for(const t in e){const s=parseInt(t),i=this.createTag(e[s],s,null!=this.selection&&this.selection.isSelected(s));i.draggable=!0,i.ondragstart=((e,t)=>s=>{this.ondragstart(s,e,t,this.model.model_id)})(s,e[s]),i.ondrop=(e=>t=>{this.ondrop(t,e)})(s),i.ondragover=this.ondragover.bind(this),i.ondragenter=(e=>t=>{this.ondragenter(t,e)})(s),i.ondragend=this.ondragend.bind(this),this.tags.push(i),this.el.appendChild(i)}return this.el.insertBefore(this.taginputWrapper,this.el.children[this.inputIndex]),this.model.get("value").length?this.taginputWrapper.style.display="none":this.taginputWrapper.style.display="inline-block",this.preventLoosingFocus=!1,super.update()}updateAutocomplete(){Dt(this.autocompleteList);const e=this.model.get("allowed_tags");for(const t of e){const e=document.createElement("option");e.value=t,this.autocompleteList.appendChild(e)}}updatePlaceholder(){this.taginput.placeholder=this.model.get("placeholder"),this.resizeInput()}updateTags(){const e=this.model.get("value");for(const t in this.tags){const s=parseInt(t);this.updateTag(this.tags[s],e[s],s,null!=this.selection&&this.selection.isSelected(s))}}handleValueAdded(e){const t=this.taginput.value.replace(/^\s+|\s+$/g,""),s=this.inputIndex;""!=t&&(this.inputIndex++,this.addTag(s,t)&&(this.taginput.value="",this.resizeInput(),this.focus()))}addTag(e,t){const s=this.model.get("value");let i;try{i=this.validateValue(t)}catch(e){return!1}const a=this.model.get("allowed_tags");if(a.length&&!a.includes(i))return!1;if(!this.model.get("allow_duplicates")&&s.includes(i))return!1;this.selection=null;const l=[...s];return l.splice(e,0,i),this.model.set("value",l),this.model.save_changes(),!0}resizeInput(){let e;e=0!=this.taginput.value.length?this.taginput.value:this.model.get("placeholder");const t=e.length+1;this.taginput.setAttribute("size",String(t))}handleKeyEvent(e){const t=this.model.get("value").length;if(this.taginput.value.length)return;const s=this.inputIndex;switch(e.key){case"ArrowLeft":e.ctrlKey&&e.shiftKey&&this.select(s,-s),!e.ctrlKey&&e.shiftKey&&this.select(s,-1),e.ctrlKey?this.inputIndex=0:this.inputIndex--;break;case"ArrowRight":e.ctrlKey&&e.shiftKey&&this.select(s,t-s),!e.ctrlKey&&e.shiftKey&&this.select(s,1),e.ctrlKey?this.inputIndex=t:this.inputIndex++;break;case"Backspace":this.selection?this.removeSelectedTags():this.removeTag(this.inputIndex-1);break;case"Delete":this.selection?this.removeSelectedTags():this.removeTag(this.inputIndex);break;default:return}var i,a;e.shiftKey||(this.selection=null),this.inputIndex=(i=this.inputIndex,a=t,Math.min(Math.max(i,0),a)),this.update(),this.focus()}ondragstart(e,t,s,i){null!=e.dataTransfer&&(e.dataTransfer.setData("index",String(t)),e.dataTransfer.setData("tagValue",String(s)),e.dataTransfer.setData("origin",i))}ondrop(e,t){if(null==e.dataTransfer)return;e.preventDefault(),e.stopPropagation();const s=e.dataTransfer.getData("tagValue"),i=parseInt(e.dataTransfer.getData("index")),a=e.dataTransfer.getData("origin")==this.model.model_id;if(!isNaN(i)){if(a){const e=[...this.model.get("value")];return i=0;t--)null!=this.selection&&this.selection.isSelected(t)&&(e.splice(t,1),t()=>{this.removeTag(e),this.loseFocus()})(t),i}getTagText(e){return e}updateTag(e,t,s,i){i?e.classList.add("mod-active"):e.classList.remove("mod-active")}}$t.class_map={primary:"mod-primary",success:"mod-success",info:"mod-info",warning:"mod-warning",danger:"mod-danger"};class Nt extends Ut{defaults(){return Object.assign(Object.assign({},super.defaults()),{value:[],_view_name:"ColorsInputView",_model_name:"ColorsInputModel"})}}class Ht extends Ft{createTag(e,t,s){const i=document.createElement("div"),a=e,l=zt.Ay(e).darker().toString();i.classList.add("jupyter-widget-tag"),i.classList.add("jupyter-widget-colortag"),s?(i.classList.add("mod-active"),i.style.backgroundColor=l):i.style.backgroundColor=a;const d=document.createElement("i");return d.classList.add("fa"),d.classList.add("fa-times"),d.classList.add("jupyter-widget-tag-close"),i.appendChild(d),d.onmousedown=(e=>()=>{this.removeTag(e),this.loseFocus()})(t),i}updateTag(e,t,s,i){const a=t,l=zt.Ay(t).darker().toString();i?(e.classList.add("mod-active"),e.style.backgroundColor=l):(e.classList.remove("mod-active"),e.style.backgroundColor=a)}validateValue(e){if(null==zt.Ay(e))throw e+" is not a valid Color";return e}}class Kt extends Rt{defaults(){return Object.assign(Object.assign({},super.defaults()),{min:null,max:null})}}class qt extends $t{render(){this.model.on("change:format",(()=>{this.formatter=ve.GP(this.model.get("format")),this.update()})),this.formatter=ve.GP(this.model.get("format")),super.render()}getTagText(e){return this.formatter(this.parseNumber(e))}validateValue(e){const t=this.parseNumber(e),s=this.model.get("min"),i=this.model.get("max");if(isNaN(t)||null!=s&&ti)throw e+" is not a valid number, it should be in the range ["+s+", "+i+"]";return t}}class Gt extends Kt{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"FloatsInputView",_model_name:"FloatsInputModel",format:".1f"})}}class Jt extends qt{parseNumber(e){return parseFloat(e)}}class Yt extends Kt{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"IntsInputView",_model_name:"IntsInputModel",format:"d"})}}class Qt extends qt{parseNumber(e){const t=parseInt(e);if(t!=parseFloat(e))throw e+" should be an integer";return t}}class Xt extends o{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"StringStyleModel",_model_module:"@jupyter-widgets/controls",_model_module_version:n.A})}}Xt.styleProperties=Object.assign(Object.assign({},o.styleProperties),{background:{selector:"",attribute:"background",default:null},font_size:{selector:"",attribute:"font-size",default:""},text_color:{selector:"",attribute:"color",default:""}});class Zt extends Xt{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"HTMLStyleModel",_model_module:"@jupyter-widgets/controls",_model_module_version:n.A})}}Zt.styleProperties=Object.assign({},Xt.styleProperties);class es extends Xt{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"HTMLMathStyleModel",_model_module:"@jupyter-widgets/controls",_model_module_version:n.A})}}es.styleProperties=Object.assign({},Xt.styleProperties);class ts extends Xt{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"LabelStyleModel",_model_module:"@jupyter-widgets/controls",_model_module_version:n.A})}}ts.styleProperties=Object.assign(Object.assign({},Xt.styleProperties),{font_family:{selector:"",attribute:"font-family",default:""},font_style:{selector:"",attribute:"font-style",default:""},font_variant:{selector:"",attribute:"font-variant",default:""},font_weight:{selector:"",attribute:"font-weight",default:""},text_decoration:{selector:"",attribute:"text-decoration",default:""}});class ss extends o{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"TextStyleModel",_model_module:"@jupyter-widgets/controls",_model_module_version:n.A})}}ss.styleProperties=Object.assign(Object.assign({},o.styleProperties),{background:{selector:".widget-input",attribute:"background",default:null},font_size:{selector:".widget-input",attribute:"font-size",default:""},text_color:{selector:".widget-input",attribute:"color",default:""}});class is extends m{defaults(){return Object.assign(Object.assign({},super.defaults()),{value:"",disabled:!1,placeholder:"​",_model_name:"StringModel"})}}class as extends h{render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-inline-hbox")}}class ls extends is{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"HTMLView",_model_name:"HTMLModel"})}}class ds extends as{render(){super.render(),this.el.classList.add("widget-html"),this.content=document.createElement("div"),this.content.classList.add("widget-html-content"),this.el.appendChild(this.content),this.update()}update(){return this.content.innerHTML=this.model.get("value"),super.update()}handle_message(e){"focus"===e.do?this.content.focus():"blur"===e.do&&this.content.blur()}}class ns extends is{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"HTMLMathView",_model_name:"HTMLMathModel"})}}class os extends as{render(){super.render(),this.el.classList.add("widget-htmlmath"),this.content=document.createElement("div"),this.content.classList.add("widget-htmlmath-content"),this.el.appendChild(this.content),this.update()}update(){return this.content.innerHTML=this.model.get("value"),this.typeset(this.content),super.update()}handle_message(e){"focus"===e.do?this.content.focus():"blur"===e.do&&this.content.blur()}}class rs extends is{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"LabelView",_model_name:"LabelModel"})}}class hs extends as{render(){super.render(),this.el.classList.add("widget-label"),this.update()}update(){return this.typeset(this.el,this.model.get("value")),super.update()}}class us extends is{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"TextareaView",_model_name:"TextareaModel",rows:null,continuous_update:!0})}}class cs extends as{render(){super.render(),this.el.classList.add("widget-textarea"),this.textbox=document.createElement("textarea"),this.textbox.setAttribute("rows","5"),this.textbox.id=this.label.htmlFor=(0,i.uuid)(),this.textbox.classList.add("widget-input"),this.el.appendChild(this.textbox),this.update(),this.listenTo(this.model,"change:placeholder",((e,t,s)=>{this.update_placeholder(t)})),this.update_placeholder(),this.updateTooltip()}update_placeholder(e){const t=e||this.model.get("placeholder");this.textbox.setAttribute("placeholder",t.toString())}update(e){if(void 0===e||e.updated_view!==this){this.textbox.value=this.model.get("value");let e=this.model.get("rows");null===e&&(e=""),this.textbox.setAttribute("rows",e),this.textbox.disabled=this.model.get("disabled")}return this.updateTabindex(),this.updateTooltip(),super.update()}updateTabindex(){if(!this.textbox)return;const e=this.model.get("tabbable");!0===e?this.textbox.setAttribute("tabIndex","0"):!1===e?this.textbox.setAttribute("tabIndex","-1"):null===e&&this.textbox.removeAttribute("tabIndex")}updateTooltip(){if(!this.textbox)return;const e=this.model.get("tooltip");e?0===this.model.get("description").length&&this.textbox.setAttribute("title",e):this.textbox.removeAttribute("title")}events(){return{"keydown textarea":"handleKeyDown","keypress textarea":"handleKeypress","input textarea":"handleChanging","change textarea":"handleChanged"}}handleKeyDown(e){e.stopPropagation()}handleKeypress(e){e.stopPropagation()}handleChanging(e){this.model.get("continuous_update")&&this.handleChanged(e)}handleChanged(e){const t=e.target;this.model.set("value",t.value,{updated_view:this}),this.touch()}handle_message(e){"focus"===e.do?this.textbox.focus():"blur"===e.do&&this.textbox.blur()}}class ps extends is{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"TextView",_model_name:"TextModel",continuous_update:!0})}}class gs extends as{constructor(){super(...arguments),this.inputType="text"}render(){super.render(),this.el.classList.add("widget-text"),this.textbox=document.createElement("input"),this.textbox.setAttribute("type",this.inputType),this.textbox.id=this.label.htmlFor=(0,i.uuid)(),this.textbox.classList.add("widget-input"),this.el.appendChild(this.textbox),this.update(),this.listenTo(this.model,"change:placeholder",((e,t,s)=>{this.update_placeholder(t)})),this.update_placeholder(),this.updateTabindex(),this.updateTooltip()}update_placeholder(e){this.textbox.setAttribute("placeholder",e||this.model.get("placeholder"))}updateTabindex(){if(!this.textbox)return;const e=this.model.get("tabbable");!0===e?this.textbox.setAttribute("tabIndex","0"):!1===e?this.textbox.setAttribute("tabIndex","-1"):null===e&&this.textbox.removeAttribute("tabIndex")}updateTooltip(){if(!this.textbox)return;const e=this.model.get("tooltip");e?0===this.model.get("description").length&&this.textbox.setAttribute("title",e):this.textbox.removeAttribute("title")}update(e){return void 0!==e&&e.updated_view===this||(this.textbox.value!==this.model.get("value")&&(this.textbox.value=this.model.get("value")),this.textbox.disabled=this.model.get("disabled")),super.update()}events(){return{"keydown input":"handleKeyDown","keypress input":"handleKeypress","input input":"handleChanging","change input":"handleChanged"}}handleKeyDown(e){e.stopPropagation()}handleKeypress(e){e.stopPropagation(),13===e.keyCode&&this.send({event:"submit"})}handleChanging(e){this.model.get("continuous_update")&&this.handleChanged(e)}handleChanged(e){const t=e.target;this.model.set("value",t.value,{updated_view:this}),this.touch()}handle_message(e){"focus"===e.do?this.textbox.focus():"blur"===e.do&&this.textbox.blur()}}class ms extends ps{defaults(){return Object.assign(Object.assign({},super.defaults()),{_view_name:"PasswordView",_model_name:"PasswordModel"})}}class _s extends gs{constructor(){super(...arguments),this.inputType="password"}}class bs extends ps{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"ComboboxModel",_view_name:"ComboboxView",options:[],ensure_options:!1})}}class vs extends gs{constructor(){super(...arguments),this.isInitialRender=!0}render(){this.datalist=document.createElement("datalist"),this.datalist.id=(0,i.uuid)(),super.render(),this.textbox.setAttribute("list",this.datalist.id),this.el.appendChild(this.datalist),this.updateTooltip()}update(e){if(super.update(e),!this.datalist)return;const t=this.isValid(this.model.get("value"));if(this.highlightValidState(t),void 0!==e&&e.updated_view||!this.model.hasChanged("options")&&!this.isInitialRender)return;this.isInitialRender=!1;const s=this.model.get("options"),i=document.createDocumentFragment();for(const e of s){const t=document.createElement("option");t.value=e,i.appendChild(t)}this.datalist.replaceChildren(...i.children)}isValid(e){return!0!==this.model.get("ensure_option")||-1!==this.model.get("options").indexOf(e)}handleChanging(e){const t=e.target,s=this.isValid(t.value);this.highlightValidState(s),s&&super.handleChanging(e)}handleChanged(e){const t=e.target,s=this.isValid(t.value);this.highlightValidState(s),s&&super.handleChanged(e)}handle_message(e){"focus"===e.do?this.textbox.focus():"blur"===e.do&&this.textbox.blur()}highlightValidState(e){this.textbox.classList.toggle("jpwidgets-invalidComboValue",!e)}}class xs extends g{defaults(){return Object.assign(Object.assign({},super.defaults()),{_model_name:"FileUploadModel",_view_name:"FileUploadView",accept:"",description:"Upload",disabled:!1,icon:"upload",button_style:"",multiple:!1,value:[],error:"",style:null})}}xs.serializers=Object.assign(Object.assign({},g.serializers),{value:{serialize:e=>e}});class fs extends i.DOMWidgetView{preinitialize(){this.tagName="button"}render(){super.render(),this.el.classList.add("jupyter-widgets"),this.el.classList.add("widget-upload"),this.el.classList.add("jupyter-button"),this.fileInput=document.createElement("input"),this.fileInput.type="file",this.fileInput.style.display="none",this.el.addEventListener("click",(()=>{this.fileInput.click()})),this.fileInput.addEventListener("click",(()=>{this.fileInput.value=""})),this.fileInput.addEventListener("change",(()=>{var e;const t=[];Array.from(null!==(e=this.fileInput.files)&&void 0!==e?e:[]).forEach((e=>{t.push(new Promise(((t,s)=>{const i=new FileReader;i.onload=()=>{const s=i.result;t({content:s,name:e.name,type:e.type,size:e.size,last_modified:e.lastModified})},i.onerror=()=>{s()},i.onabort=i.onerror,i.readAsArrayBuffer(e)})))})),Promise.all(t).then((e=>{this.model.set({value:e,error:""}),this.touch()})).catch((e=>{console.error("error in file upload: %o",e),this.model.set({error:e}),this.touch()}))})),this.listenTo(this.model,"change:button_style",this.update_button_style),this.set_button_style(),this.update()}update(){this.el.disabled=this.model.get("disabled"),this.el.setAttribute("title",this.model.get("tooltip"));const e=this.model.get("value"),t=`${this.model.get("description")} (${e.length})`,s=this.model.get("icon");if(t.length||s.length){if(this.el.textContent="",s.length){const e=document.createElement("i");e.classList.add("fa"),e.classList.add("fa-"+s),0===t.length&&e.classList.add("center"),this.el.appendChild(e)}this.el.appendChild(document.createTextNode(t))}return this.fileInput.accept=this.model.get("accept"),this.fileInput.multiple=this.model.get("multiple"),super.update()}update_button_style(){this.update_mapped_classes(fs.class_map,"button_style",this.el)}set_button_style(){this.set_mapped_classes(fs.class_map,"button_style",this.el)}}fs.class_map={primary:["mod-primary"],success:["mod-success"],info:["mod-info"],warning:["mod-warning"],danger:["mod-danger"]};const ws=s(5394).rE},5394:e=>{e.exports={rE:"5.0.12"}}}]); \ No newline at end of file diff --git a/.venv-sp/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/647.8458d9c331000024a14a.js b/.venv-sp/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/647.8458d9c331000024a14a.js new file mode 100644 index 0000000..c31c649 --- /dev/null +++ b/.venv-sp/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/647.8458d9c331000024a14a.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunk_jupyter_widgets_jupyterlab_manager=self.webpackChunk_jupyter_widgets_jupyterlab_manager||[]).push([[647],{909:(e,t,s)=>{s.r(t),s.d(t,{BROKEN_FILE_SVG_ICON:()=>p,DOMWidgetModel:()=>z,DOMWidgetView:()=>I,ErrorWidgetView:()=>F,IJupyterWidgetRegistry:()=>G,JUPYTER_WIDGETS_VERSION:()=>x,JupyterLuminoPanelWidget:()=>N,JupyterLuminoWidget:()=>A,JupyterPhosphorPanelWidget:()=>V,JupyterPhosphorWidget:()=>T,LayoutModel:()=>D,LayoutView:()=>U,PROTOCOL_VERSION:()=>S,StyleModel:()=>$,StyleView:()=>R,ViewList:()=>H,WidgetModel:()=>C,WidgetView:()=>W,assign:()=>a,createErrorWidgetModel:()=>q,createErrorWidgetView:()=>K,difference:()=>r,isEqual:()=>l,isObject:()=>m,isSerializable:()=>_,pack_models:()=>L,put_buffers:()=>u,reject:()=>d,remove_buffers:()=>g,resolvePromisesDict:()=>h,shims:()=>B,unpack_models:()=>M,uuid:()=>c});var i=s(7262),n=s(6343),o=s.n(n);function r(e,t){return e.filter((e=>-1===t.indexOf(e)))}function l(e,t){return o()(e,t)}const a=Object.assign||function(e,...t){for(let s=1;s{const s={};for(let i=0;i=0&&t.item(s)!==this;);return s>-1};class j extends v.View{_removeElement(){this.undelegateEvents(),this.el.parentNode&&this.el.parentNode.removeChild(this.el)}_setElement(e){this.el=e}_setAttributes(e){for(const t in e)t in this.el?this.el[t]=e[t]:this.el.setAttribute(t,e[t])}delegate(e,t,s){"string"!=typeof t&&(s=t,t=null),void 0===this._domEvents&&(this._domEvents=[]);const i=this.el,n=t?function(e){let n=e.target||e.srcElement;for(;n&&n!==i;n=n.parentNode)if(k.call(n,t))return e.delegateTarget=n,s.handleEvent?s.handleEvent(e):s(e)}:s;return this.el.addEventListener(e,n,!1),this._domEvents.push({eventName:e,handler:n,listener:s,selector:t}),n}undelegate(e,t,s){if("function"==typeof t&&(s=t,t=null),this.el&&this._domEvents){const i=this._domEvents.slice();let n=i.length;for(;n--;){const o=i[n];!(o.eventName!==e||s&&o.listener!==s||t&&o.selector!==t)&&(this.el.removeEventListener(o.eventName,o.handler,!1),this._domEvents.splice(n,1))}}return this}undelegateEvents(){if(this.el&&this._domEvents){const e=this._domEvents.length;for(let t=0;tthis.views[e].then((e=>e.remove()))));return delete this.views,Promise.all(e).then((()=>{}))}return Promise.resolve()}_handle_comm_closed(e){this.trigger("comm:close"),this.close(!0)}_handle_comm_msg(e){const t=e.content.data,s=t.method;switch(s){case"update":case"echo_update":return this.state_change=this.state_change.then((()=>{var i,n,o;const r=t.state,l=null!==(i=t.buffer_paths)&&void 0!==i?i:[],a=null!==(o=null===(n=e.buffers)||void 0===n?void 0:n.slice(0,l.length))&&void 0!==o?o:[];if(u(r,l,a),e.parent_header&&"echo_update"===s){const t=e.parent_header.msg_id;Object.keys(r).filter((e=>this._expectedEchoMsgIds.has(e))).forEach((e=>{this._expectedEchoMsgIds.get(e)!==t?delete 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e}send_sync_message(e,t={}){if(!this.comm)return"";try{const s=(t={shell:Object.assign({},t.shell),iopub:Object.assign({},t.iopub),input:t.input}).iopub.status;t.iopub.status=e=>{this._handle_status(e),s&&s(e)};const i=g(e),n=this.comm.send({method:"update",state:i.state,buffer_paths:i.buffer_paths},t,{},i.buffers);return this._pending_msgs++,n}catch(e){console.error("Could not send widget sync message",e)}return""}save_changes(e){if(this.comm_live){const t={patch:!0};e&&(t.callbacks=e),this.save(this._buffered_state_diff,t),this._changing&&a(this._buffered_state_diff_synced,this._buffered_state_diff),this._buffered_state_diff={}}}on_some_change(e,t,s){this.on("change",((...i)=>{e.some(this.hasChanged,this)&&t.apply(s,i)}),this)}toJSON(e){return`IPY_MODEL_${this.model_id}`}static _deserialize_state(e,t){const s=this.serializers;let i;if(s){i={};for(const n in e)s[n]&&s[n].deserialize?i[n]=s[n].deserialize(e[n],t):i[n]=e[n]}else i=e;return h(i)}}class z extends C{defaults(){return 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t,n;if(e.crossDomain||e.scriptAttrs)return{send:function(r,i){t=S("`.\n this.sequenceIndex = Number(c === CharCodes.Lt);\n }\n };\n Tokenizer.prototype.stateCDATASequence = function (c) {\n if (c === Sequences.Cdata[this.sequenceIndex]) {\n if (++this.sequenceIndex === Sequences.Cdata.length) {\n this.state = State.InCommentLike;\n this.currentSequence = Sequences.CdataEnd;\n this.sequenceIndex = 0;\n this.sectionStart = this.index + 1;\n }\n }\n else {\n this.sequenceIndex = 0;\n this.state = State.InDeclaration;\n this.stateInDeclaration(c); // Reconsume the character\n }\n };\n /**\n * When we wait for one specific character, we can speed things up\n * by skipping through the buffer until we find it.\n *\n * @returns Whether the character was found.\n */\n Tokenizer.prototype.fastForwardTo = function (c) {\n while (++this.index < this.buffer.length + this.offset) {\n if (this.buffer.charCodeAt(this.index - this.offset) === c) {\n return true;\n }\n }\n /*\n * We increment the index at the end of the `parse` loop,\n * so set it to `buffer.length - 1` here.\n *\n * TODO: Refactor `parse` to increment index before calling states.\n */\n this.index = this.buffer.length + this.offset - 1;\n return false;\n };\n /**\n * Comments and CDATA end with `-->` and `]]>`.\n *\n * Their common qualities are:\n * - Their end sequences have a distinct character they start with.\n * - That character is then repeated, so we have to check multiple repeats.\n * - All characters but the start character of the sequence can be skipped.\n */\n Tokenizer.prototype.stateInCommentLike = function (c) {\n if (c === this.currentSequence[this.sequenceIndex]) {\n if (++this.sequenceIndex === this.currentSequence.length) {\n if (this.currentSequence === Sequences.CdataEnd) {\n this.cbs.oncdata(this.sectionStart, this.index, 2);\n }\n else {\n this.cbs.oncomment(this.sectionStart, this.index, 2);\n }\n this.sequenceIndex = 0;\n this.sectionStart = this.index + 1;\n this.state = State.Text;\n }\n }\n else if (this.sequenceIndex === 0) {\n // Fast-forward to the first character of the sequence\n if (this.fastForwardTo(this.currentSequence[0])) {\n this.sequenceIndex = 1;\n }\n }\n else if (c !== this.currentSequence[this.sequenceIndex - 1]) {\n // Allow long sequences, eg. --->, ]]]>\n this.sequenceIndex = 0;\n }\n };\n /**\n * HTML only allows ASCII alpha characters (a-z and A-Z) at the beginning of a tag name.\n *\n * XML allows a lot more characters here (@see https://www.w3.org/TR/REC-xml/#NT-NameStartChar).\n * We allow anything that wouldn't end the tag.\n */\n Tokenizer.prototype.isTagStartChar = function (c) {\n return this.xmlMode ? !isEndOfTagSection(c) : isASCIIAlpha(c);\n };\n Tokenizer.prototype.startSpecial = function (sequence, offset) {\n this.isSpecial = true;\n this.currentSequence = sequence;\n this.sequenceIndex = offset;\n this.state = State.SpecialStartSequence;\n };\n Tokenizer.prototype.stateBeforeTagName = function (c) {\n if (c === CharCodes.ExclamationMark) {\n this.state = State.BeforeDeclaration;\n this.sectionStart = this.index + 1;\n }\n else if (c === CharCodes.Questionmark) {\n this.state = State.InProcessingInstruction;\n this.sectionStart = this.index + 1;\n }\n else if (this.isTagStartChar(c)) {\n var lower = c | 0x20;\n this.sectionStart = this.index;\n if (!this.xmlMode && lower === Sequences.TitleEnd[2]) {\n this.startSpecial(Sequences.TitleEnd, 3);\n }\n else {\n this.state =\n !this.xmlMode && lower === Sequences.ScriptEnd[2]\n ? State.BeforeSpecialS\n : State.InTagName;\n }\n }\n else if (c === CharCodes.Slash) {\n this.state = State.BeforeClosingTagName;\n }\n else {\n this.state = State.Text;\n this.stateText(c);\n }\n };\n Tokenizer.prototype.stateInTagName = function (c) {\n if (isEndOfTagSection(c)) {\n this.cbs.onopentagname(this.sectionStart, this.index);\n this.sectionStart = -1;\n this.state = State.BeforeAttributeName;\n this.stateBeforeAttributeName(c);\n }\n };\n Tokenizer.prototype.stateBeforeClosingTagName = function (c) {\n if (isWhitespace(c)) {\n // Ignore\n }\n else if (c === CharCodes.Gt) {\n this.state = State.Text;\n }\n else {\n this.state = this.isTagStartChar(c)\n ? State.InClosingTagName\n : State.InSpecialComment;\n this.sectionStart = this.index;\n }\n };\n Tokenizer.prototype.stateInClosingTagName = function (c) {\n if (c === CharCodes.Gt || isWhitespace(c)) {\n this.cbs.onclosetag(this.sectionStart, this.index);\n this.sectionStart = -1;\n this.state = State.AfterClosingTagName;\n this.stateAfterClosingTagName(c);\n }\n };\n Tokenizer.prototype.stateAfterClosingTagName = function (c) {\n // Skip everything until \">\"\n if (c === CharCodes.Gt || this.fastForwardTo(CharCodes.Gt)) {\n this.state = State.Text;\n this.baseState = State.Text;\n this.sectionStart = this.index + 1;\n }\n };\n Tokenizer.prototype.stateBeforeAttributeName = function (c) {\n if (c === CharCodes.Gt) {\n this.cbs.onopentagend(this.index);\n if (this.isSpecial) {\n this.state = State.InSpecialTag;\n this.sequenceIndex = 0;\n }\n else {\n this.state = State.Text;\n }\n this.baseState = this.state;\n this.sectionStart = this.index + 1;\n }\n else if (c === CharCodes.Slash) {\n this.state = State.InSelfClosingTag;\n }\n else if (!isWhitespace(c)) {\n this.state = State.InAttributeName;\n this.sectionStart = this.index;\n }\n };\n Tokenizer.prototype.stateInSelfClosingTag = function (c) {\n if (c === CharCodes.Gt) {\n this.cbs.onselfclosingtag(this.index);\n this.state = State.Text;\n this.baseState = State.Text;\n this.sectionStart = this.index + 1;\n this.isSpecial = false; // Reset special state, in case of self-closing special tags\n }\n else if (!isWhitespace(c)) {\n this.state = State.BeforeAttributeName;\n this.stateBeforeAttributeName(c);\n }\n };\n Tokenizer.prototype.stateInAttributeName = function (c) {\n if (c === CharCodes.Eq || isEndOfTagSection(c)) {\n this.cbs.onattribname(this.sectionStart, this.index);\n this.sectionStart = -1;\n this.state = State.AfterAttributeName;\n this.stateAfterAttributeName(c);\n }\n };\n Tokenizer.prototype.stateAfterAttributeName = function (c) {\n if (c === CharCodes.Eq) {\n this.state = State.BeforeAttributeValue;\n }\n else if (c === CharCodes.Slash || c === CharCodes.Gt) {\n this.cbs.onattribend(QuoteType.NoValue, this.index);\n this.state = State.BeforeAttributeName;\n this.stateBeforeAttributeName(c);\n }\n else if (!isWhitespace(c)) {\n this.cbs.onattribend(QuoteType.NoValue, this.index);\n this.state = State.InAttributeName;\n this.sectionStart = this.index;\n }\n };\n Tokenizer.prototype.stateBeforeAttributeValue = function (c) {\n if (c === CharCodes.DoubleQuote) {\n this.state = State.InAttributeValueDq;\n this.sectionStart = this.index + 1;\n }\n else if (c === CharCodes.SingleQuote) {\n this.state = State.InAttributeValueSq;\n this.sectionStart = this.index + 1;\n }\n else if (!isWhitespace(c)) {\n this.sectionStart = this.index;\n this.state = State.InAttributeValueNq;\n this.stateInAttributeValueNoQuotes(c); // Reconsume token\n }\n };\n Tokenizer.prototype.handleInAttributeValue = function (c, quote) {\n if (c === quote ||\n (!this.decodeEntities && this.fastForwardTo(quote))) {\n this.cbs.onattribdata(this.sectionStart, this.index);\n this.sectionStart = -1;\n this.cbs.onattribend(quote === CharCodes.DoubleQuote\n ? QuoteType.Double\n : QuoteType.Single, this.index);\n this.state = State.BeforeAttributeName;\n }\n else if (this.decodeEntities && c === CharCodes.Amp) {\n this.baseState = this.state;\n this.state = State.BeforeEntity;\n }\n };\n Tokenizer.prototype.stateInAttributeValueDoubleQuotes = function (c) {\n this.handleInAttributeValue(c, CharCodes.DoubleQuote);\n };\n Tokenizer.prototype.stateInAttributeValueSingleQuotes = function (c) {\n this.handleInAttributeValue(c, CharCodes.SingleQuote);\n };\n Tokenizer.prototype.stateInAttributeValueNoQuotes = function (c) {\n if (isWhitespace(c) || c === CharCodes.Gt) {\n this.cbs.onattribdata(this.sectionStart, this.index);\n this.sectionStart = -1;\n this.cbs.onattribend(QuoteType.Unquoted, this.index);\n this.state = State.BeforeAttributeName;\n this.stateBeforeAttributeName(c);\n }\n else if (this.decodeEntities && c === CharCodes.Amp) {\n this.baseState = this.state;\n this.state = State.BeforeEntity;\n }\n };\n Tokenizer.prototype.stateBeforeDeclaration = function (c) {\n if (c === CharCodes.OpeningSquareBracket) {\n this.state = State.CDATASequence;\n this.sequenceIndex = 0;\n }\n else {\n this.state =\n c === CharCodes.Dash\n ? State.BeforeComment\n : State.InDeclaration;\n }\n };\n Tokenizer.prototype.stateInDeclaration = function (c) {\n if (c === CharCodes.Gt || this.fastForwardTo(CharCodes.Gt)) {\n this.cbs.ondeclaration(this.sectionStart, this.index);\n this.state = State.Text;\n this.sectionStart = this.index + 1;\n }\n };\n Tokenizer.prototype.stateInProcessingInstruction = function (c) {\n if (c === CharCodes.Gt || this.fastForwardTo(CharCodes.Gt)) {\n this.cbs.onprocessinginstruction(this.sectionStart, this.index);\n this.state = State.Text;\n this.sectionStart = this.index + 1;\n }\n };\n Tokenizer.prototype.stateBeforeComment = function (c) {\n if (c === CharCodes.Dash) {\n this.state = State.InCommentLike;\n this.currentSequence = Sequences.CommentEnd;\n // Allow short comments (eg. )\n this.sequenceIndex = 2;\n this.sectionStart = this.index + 1;\n }\n else {\n this.state = State.InDeclaration;\n }\n };\n Tokenizer.prototype.stateInSpecialComment = function (c) {\n if (c === CharCodes.Gt || this.fastForwardTo(CharCodes.Gt)) {\n this.cbs.oncomment(this.sectionStart, this.index, 0);\n this.state = State.Text;\n this.sectionStart = this.index + 1;\n }\n };\n Tokenizer.prototype.stateBeforeSpecialS = function (c) {\n var lower = c | 0x20;\n if (lower === Sequences.ScriptEnd[3]) {\n this.startSpecial(Sequences.ScriptEnd, 4);\n }\n else if (lower === Sequences.StyleEnd[3]) {\n this.startSpecial(Sequences.StyleEnd, 4);\n }\n else {\n this.state = State.InTagName;\n this.stateInTagName(c); // Consume the token again\n }\n };\n Tokenizer.prototype.stateBeforeEntity = function (c) {\n // Start excess with 1 to include the '&'\n this.entityExcess = 1;\n this.entityResult = 0;\n if (c === CharCodes.Number) {\n this.state = State.BeforeNumericEntity;\n }\n else if (c === CharCodes.Amp) {\n // We have two `&` characters in a row. Stay in the current state.\n }\n else {\n this.trieIndex = 0;\n this.trieCurrent = this.entityTrie[0];\n this.state = State.InNamedEntity;\n this.stateInNamedEntity(c);\n }\n };\n Tokenizer.prototype.stateInNamedEntity = function (c) {\n this.entityExcess += 1;\n this.trieIndex = (0, decode_js_1.determineBranch)(this.entityTrie, this.trieCurrent, this.trieIndex + 1, c);\n if (this.trieIndex < 0) {\n this.emitNamedEntity();\n this.index--;\n return;\n }\n this.trieCurrent = this.entityTrie[this.trieIndex];\n var masked = this.trieCurrent & decode_js_1.BinTrieFlags.VALUE_LENGTH;\n // If the branch is a value, store it and continue\n if (masked) {\n // The mask is the number of bytes of the value, including the current byte.\n var valueLength = (masked >> 14) - 1;\n // If we have a legacy entity while parsing strictly, just skip the number of bytes\n if (!this.allowLegacyEntity() && c !== CharCodes.Semi) {\n this.trieIndex += valueLength;\n }\n else {\n // Add 1 as we have already incremented the excess\n var entityStart = this.index - this.entityExcess + 1;\n if (entityStart > this.sectionStart) {\n this.emitPartial(this.sectionStart, entityStart);\n }\n // If this is a surrogate pair, consume the next two bytes\n this.entityResult = this.trieIndex;\n this.trieIndex += valueLength;\n this.entityExcess = 0;\n this.sectionStart = this.index + 1;\n if (valueLength === 0) {\n this.emitNamedEntity();\n }\n }\n }\n };\n Tokenizer.prototype.emitNamedEntity = function () {\n this.state = this.baseState;\n if (this.entityResult === 0) {\n return;\n }\n var valueLength = (this.entityTrie[this.entityResult] & decode_js_1.BinTrieFlags.VALUE_LENGTH) >>\n 14;\n switch (valueLength) {\n case 1: {\n this.emitCodePoint(this.entityTrie[this.entityResult] &\n ~decode_js_1.BinTrieFlags.VALUE_LENGTH);\n break;\n }\n case 2: {\n this.emitCodePoint(this.entityTrie[this.entityResult + 1]);\n break;\n }\n case 3: {\n this.emitCodePoint(this.entityTrie[this.entityResult + 1]);\n this.emitCodePoint(this.entityTrie[this.entityResult + 2]);\n }\n }\n };\n Tokenizer.prototype.stateBeforeNumericEntity = function (c) {\n if ((c | 0x20) === CharCodes.LowerX) {\n this.entityExcess++;\n this.state = State.InHexEntity;\n }\n else {\n this.state = State.InNumericEntity;\n this.stateInNumericEntity(c);\n }\n };\n Tokenizer.prototype.emitNumericEntity = function (strict) {\n var entityStart = this.index - this.entityExcess - 1;\n var numberStart = entityStart + 2 + Number(this.state === State.InHexEntity);\n if (numberStart !== this.index) {\n // Emit leading data if any\n if (entityStart > this.sectionStart) {\n this.emitPartial(this.sectionStart, entityStart);\n }\n this.sectionStart = this.index + Number(strict);\n this.emitCodePoint((0, decode_js_1.replaceCodePoint)(this.entityResult));\n }\n this.state = this.baseState;\n };\n Tokenizer.prototype.stateInNumericEntity = function (c) {\n if (c === CharCodes.Semi) {\n this.emitNumericEntity(true);\n }\n else if (isNumber(c)) {\n this.entityResult = this.entityResult * 10 + (c - CharCodes.Zero);\n this.entityExcess++;\n }\n else {\n if (this.allowLegacyEntity()) {\n this.emitNumericEntity(false);\n }\n else {\n this.state = this.baseState;\n }\n this.index--;\n }\n };\n Tokenizer.prototype.stateInHexEntity = function (c) {\n if (c === CharCodes.Semi) {\n this.emitNumericEntity(true);\n }\n else if (isNumber(c)) {\n this.entityResult = this.entityResult * 16 + (c - CharCodes.Zero);\n this.entityExcess++;\n }\n else if (isHexDigit(c)) {\n this.entityResult =\n this.entityResult * 16 + ((c | 0x20) - CharCodes.LowerA + 10);\n this.entityExcess++;\n }\n else {\n if (this.allowLegacyEntity()) {\n this.emitNumericEntity(false);\n }\n else {\n this.state = this.baseState;\n }\n this.index--;\n }\n };\n Tokenizer.prototype.allowLegacyEntity = function () {\n return (!this.xmlMode &&\n (this.baseState === State.Text ||\n this.baseState === State.InSpecialTag));\n };\n /**\n * Remove data that has already been consumed from the buffer.\n */\n Tokenizer.prototype.cleanup = function () {\n // If we are inside of text or attributes, emit what we already have.\n if (this.running && this.sectionStart !== this.index) {\n if (this.state === State.Text ||\n (this.state === State.InSpecialTag && this.sequenceIndex === 0)) {\n this.cbs.ontext(this.sectionStart, this.index);\n this.sectionStart = this.index;\n }\n else if (this.state === State.InAttributeValueDq ||\n this.state === State.InAttributeValueSq ||\n this.state === State.InAttributeValueNq) {\n this.cbs.onattribdata(this.sectionStart, this.index);\n this.sectionStart = this.index;\n }\n }\n };\n Tokenizer.prototype.shouldContinue = function () {\n return this.index < this.buffer.length + this.offset && this.running;\n };\n /**\n * Iterates through the buffer, calling the function corresponding to the current state.\n *\n * States that are more likely to be hit are higher up, as a performance improvement.\n */\n Tokenizer.prototype.parse = function () {\n while (this.shouldContinue()) {\n var c = this.buffer.charCodeAt(this.index - this.offset);\n switch (this.state) {\n case State.Text: {\n this.stateText(c);\n break;\n }\n case State.SpecialStartSequence: {\n this.stateSpecialStartSequence(c);\n break;\n }\n case State.InSpecialTag: {\n this.stateInSpecialTag(c);\n break;\n }\n case State.CDATASequence: {\n this.stateCDATASequence(c);\n break;\n }\n case State.InAttributeValueDq: {\n this.stateInAttributeValueDoubleQuotes(c);\n break;\n }\n case State.InAttributeName: {\n this.stateInAttributeName(c);\n break;\n }\n case State.InCommentLike: {\n this.stateInCommentLike(c);\n break;\n }\n case State.InSpecialComment: {\n this.stateInSpecialComment(c);\n break;\n }\n case State.BeforeAttributeName: {\n this.stateBeforeAttributeName(c);\n break;\n }\n case State.InTagName: {\n this.stateInTagName(c);\n break;\n }\n case State.InClosingTagName: {\n this.stateInClosingTagName(c);\n break;\n }\n case State.BeforeTagName: {\n this.stateBeforeTagName(c);\n break;\n }\n case State.AfterAttributeName: {\n this.stateAfterAttributeName(c);\n break;\n }\n case State.InAttributeValueSq: {\n this.stateInAttributeValueSingleQuotes(c);\n break;\n }\n case State.BeforeAttributeValue: {\n this.stateBeforeAttributeValue(c);\n break;\n }\n case State.BeforeClosingTagName: {\n this.stateBeforeClosingTagName(c);\n break;\n }\n case State.AfterClosingTagName: {\n this.stateAfterClosingTagName(c);\n break;\n }\n case State.BeforeSpecialS: {\n this.stateBeforeSpecialS(c);\n break;\n }\n case State.InAttributeValueNq: {\n this.stateInAttributeValueNoQuotes(c);\n break;\n }\n case State.InSelfClosingTag: {\n this.stateInSelfClosingTag(c);\n break;\n }\n case State.InDeclaration: {\n this.stateInDeclaration(c);\n break;\n }\n case State.BeforeDeclaration: {\n this.stateBeforeDeclaration(c);\n break;\n }\n case State.BeforeComment: {\n this.stateBeforeComment(c);\n break;\n }\n case State.InProcessingInstruction: {\n this.stateInProcessingInstruction(c);\n break;\n }\n case State.InNamedEntity: {\n this.stateInNamedEntity(c);\n break;\n }\n case State.BeforeEntity: {\n this.stateBeforeEntity(c);\n break;\n }\n case State.InHexEntity: {\n this.stateInHexEntity(c);\n break;\n }\n case State.InNumericEntity: {\n this.stateInNumericEntity(c);\n break;\n }\n default: {\n // `this._state === State.BeforeNumericEntity`\n this.stateBeforeNumericEntity(c);\n }\n }\n this.index++;\n }\n this.cleanup();\n };\n Tokenizer.prototype.finish = function () {\n if (this.state === State.InNamedEntity) {\n this.emitNamedEntity();\n }\n // If there is remaining data, emit it in a reasonable way\n if (this.sectionStart < this.index) {\n this.handleTrailingData();\n }\n this.cbs.onend();\n };\n /** Handle any trailing data. */\n Tokenizer.prototype.handleTrailingData = function () {\n var endIndex = this.buffer.length + this.offset;\n if (this.state === State.InCommentLike) {\n if (this.currentSequence === Sequences.CdataEnd) {\n this.cbs.oncdata(this.sectionStart, endIndex, 0);\n }\n else {\n this.cbs.oncomment(this.sectionStart, endIndex, 0);\n }\n }\n else if (this.state === State.InNumericEntity &&\n this.allowLegacyEntity()) {\n this.emitNumericEntity(false);\n // All trailing data will have been consumed\n }\n else if (this.state === State.InHexEntity &&\n this.allowLegacyEntity()) {\n this.emitNumericEntity(false);\n // All trailing data will have been consumed\n }\n else if (this.state === State.InTagName ||\n this.state === State.BeforeAttributeName ||\n this.state === State.BeforeAttributeValue ||\n this.state === State.AfterAttributeName ||\n this.state === State.InAttributeName ||\n this.state === State.InAttributeValueSq ||\n this.state === State.InAttributeValueDq ||\n this.state === State.InAttributeValueNq ||\n this.state === State.InClosingTagName) {\n /*\n * If we are currently in an opening or closing tag, us not calling the\n * respective callback signals that the tag should be ignored.\n */\n }\n else {\n this.cbs.ontext(this.sectionStart, endIndex);\n }\n };\n Tokenizer.prototype.emitPartial = function (start, endIndex) {\n if (this.baseState !== State.Text &&\n this.baseState !== State.InSpecialTag) {\n this.cbs.onattribdata(start, endIndex);\n }\n else {\n this.cbs.ontext(start, endIndex);\n }\n };\n Tokenizer.prototype.emitCodePoint = function (cp) {\n if (this.baseState !== State.Text &&\n this.baseState !== State.InSpecialTag) {\n this.cbs.onattribentity(cp);\n }\n else {\n this.cbs.ontextentity(cp);\n }\n };\n return Tokenizer;\n}());\nexports.default = Tokenizer;\n//# sourceMappingURL=Tokenizer.js.map","\"use strict\";\nvar __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n var desc = Object.getOwnPropertyDescriptor(m, k);\n if (!desc || (\"get\" in desc ? !m.__esModule : desc.writable || desc.configurable)) {\n desc = { enumerable: true, get: function() { return m[k]; } };\n }\n Object.defineProperty(o, k2, desc);\n}) : (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n o[k2] = m[k];\n}));\nvar __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\n}) : function(o, v) {\n o[\"default\"] = v;\n});\nvar __importStar = (this && this.__importStar) || function (mod) {\n if (mod && mod.__esModule) return mod;\n var result = {};\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\n __setModuleDefault(result, mod);\n return result;\n};\nvar __importDefault = (this && this.__importDefault) || function (mod) {\n return (mod && mod.__esModule) ? mod : { \"default\": mod };\n};\nObject.defineProperty(exports, \"__esModule\", { value: true });\nexports.DomUtils = exports.parseFeed = exports.getFeed = exports.ElementType = exports.Tokenizer = exports.createDomStream = exports.parseDOM = exports.parseDocument = exports.DefaultHandler = exports.DomHandler = exports.Parser = void 0;\nvar Parser_js_1 = require(\"./Parser.js\");\nvar Parser_js_2 = require(\"./Parser.js\");\nObject.defineProperty(exports, \"Parser\", { enumerable: true, get: function () { return Parser_js_2.Parser; } });\nvar domhandler_1 = require(\"domhandler\");\nvar domhandler_2 = require(\"domhandler\");\nObject.defineProperty(exports, \"DomHandler\", { enumerable: true, get: function () { return domhandler_2.DomHandler; } });\n// Old name for DomHandler\nObject.defineProperty(exports, \"DefaultHandler\", { enumerable: true, get: function () { return domhandler_2.DomHandler; } });\n// Helper methods\n/**\n * Parses the data, returns the resulting document.\n *\n * @param data The data that should be parsed.\n * @param options Optional options for the parser and DOM builder.\n */\nfunction parseDocument(data, options) {\n var handler = new domhandler_1.DomHandler(undefined, options);\n new Parser_js_1.Parser(handler, options).end(data);\n return handler.root;\n}\nexports.parseDocument = parseDocument;\n/**\n * Parses data, returns an array of the root nodes.\n *\n * Note that the root nodes still have a `Document` node as their parent.\n * Use `parseDocument` to get the `Document` node instead.\n *\n * @param data The data that should be parsed.\n * @param options Optional options for the parser and DOM builder.\n * @deprecated Use `parseDocument` instead.\n */\nfunction parseDOM(data, options) {\n return parseDocument(data, options).children;\n}\nexports.parseDOM = parseDOM;\n/**\n * Creates a parser instance, with an attached DOM handler.\n *\n * @param callback A callback that will be called once parsing has been completed.\n * @param options Optional options for the parser and DOM builder.\n * @param elementCallback An optional callback that will be called every time a tag has been completed inside of the DOM.\n */\nfunction createDomStream(callback, options, elementCallback) {\n var handler = new domhandler_1.DomHandler(callback, options, elementCallback);\n return new Parser_js_1.Parser(handler, options);\n}\nexports.createDomStream = createDomStream;\nvar Tokenizer_js_1 = require(\"./Tokenizer.js\");\nObject.defineProperty(exports, \"Tokenizer\", { enumerable: true, get: function () { return __importDefault(Tokenizer_js_1).default; } });\n/*\n * All of the following exports exist for backwards-compatibility.\n * They should probably be removed eventually.\n */\nexports.ElementType = __importStar(require(\"domelementtype\"));\nvar domutils_1 = require(\"domutils\");\nvar domutils_2 = require(\"domutils\");\nObject.defineProperty(exports, \"getFeed\", { enumerable: true, get: function () { return domutils_2.getFeed; } });\nvar parseFeedDefaultOptions = { xmlMode: true };\n/**\n * Parse a feed.\n *\n * @param feed The feed that should be parsed, as a string.\n * @param options Optionally, options for parsing. When using this, you should set `xmlMode` to `true`.\n */\nfunction parseFeed(feed, options) {\n if (options === void 0) { options = parseFeedDefaultOptions; }\n return (0, domutils_1.getFeed)(parseDOM(feed, options));\n}\nexports.parseFeed = parseFeed;\nexports.DomUtils = __importStar(require(\"domutils\"));\n//# sourceMappingURL=index.js.map","'use strict';\n\nObject.defineProperty(exports, '__esModule', { value: true });\n\n/*!\n * is-plain-object \n *\n * Copyright (c) 2014-2017, Jon Schlinkert.\n * Released under the MIT License.\n */\n\nfunction isObject(o) {\n return Object.prototype.toString.call(o) === '[object Object]';\n}\n\nfunction isPlainObject(o) {\n var ctor,prot;\n\n if (isObject(o) === false) return false;\n\n // If has modified constructor\n ctor = o.constructor;\n if (ctor === undefined) return true;\n\n // If has modified prototype\n prot = ctor.prototype;\n if (isObject(prot) === false) return false;\n\n // If constructor does not have an Object-specific method\n if (prot.hasOwnProperty('isPrototypeOf') === false) {\n return false;\n }\n\n // Most likely a plain Object\n return true;\n}\n\nexports.isPlainObject = isPlainObject;\n","/*!\n * jQuery JavaScript Library v3.7.1\n * https://jquery.com/\n *\n * Copyright OpenJS Foundation and other contributors\n * Released under the MIT license\n * https://jquery.org/license\n *\n * Date: 2023-08-28T13:37Z\n */\n( function( global, factory ) {\n\n\t\"use strict\";\n\n\tif ( typeof module === \"object\" && typeof module.exports === \"object\" ) {\n\n\t\t// For CommonJS and CommonJS-like environments where a proper `window`\n\t\t// is present, execute the factory and get jQuery.\n\t\t// For environments that do not have a `window` with a `document`\n\t\t// (such as Node.js), expose a factory as module.exports.\n\t\t// This accentuates the need for the creation of a real `window`.\n\t\t// e.g. var jQuery = require(\"jquery\")(window);\n\t\t// See ticket trac-14549 for more info.\n\t\tmodule.exports = global.document ?\n\t\t\tfactory( global, true ) :\n\t\t\tfunction( w ) {\n\t\t\t\tif ( !w.document ) {\n\t\t\t\t\tthrow new Error( \"jQuery requires a window with a document\" );\n\t\t\t\t}\n\t\t\t\treturn factory( w );\n\t\t\t};\n\t} else {\n\t\tfactory( global );\n\t}\n\n// Pass this if window is not defined yet\n} )( typeof window !== \"undefined\" ? window : this, function( window, noGlobal ) {\n\n// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1\n// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode\n// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common\n// enough that all such attempts are guarded in a try block.\n\"use strict\";\n\nvar arr = [];\n\nvar getProto = Object.getPrototypeOf;\n\nvar slice = arr.slice;\n\nvar flat = arr.flat ? function( array ) {\n\treturn arr.flat.call( array );\n} : function( array ) {\n\treturn arr.concat.apply( [], array );\n};\n\n\nvar push = arr.push;\n\nvar indexOf = arr.indexOf;\n\nvar class2type = {};\n\nvar toString = class2type.toString;\n\nvar hasOwn = class2type.hasOwnProperty;\n\nvar fnToString = hasOwn.toString;\n\nvar ObjectFunctionString = fnToString.call( Object );\n\nvar support = {};\n\nvar isFunction = function isFunction( obj ) {\n\n\t\t// Support: Chrome <=57, Firefox <=52\n\t\t// In some browsers, typeof returns \"function\" for HTML elements\n\t\t// (i.e., `typeof document.createElement( \"object\" ) === \"function\"`).\n\t\t// We don't want to classify *any* DOM node as a function.\n\t\t// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5\n\t\t// Plus for old WebKit, typeof returns \"function\" for HTML collections\n\t\t// (e.g., `typeof document.getElementsByTagName(\"div\") === \"function\"`). (gh-4756)\n\t\treturn typeof obj === \"function\" && typeof obj.nodeType !== \"number\" &&\n\t\t\ttypeof obj.item !== \"function\";\n\t};\n\n\nvar isWindow = function isWindow( obj ) {\n\t\treturn obj != null && obj === obj.window;\n\t};\n\n\nvar document = window.document;\n\n\n\n\tvar preservedScriptAttributes = {\n\t\ttype: true,\n\t\tsrc: true,\n\t\tnonce: true,\n\t\tnoModule: true\n\t};\n\n\tfunction DOMEval( code, node, doc ) {\n\t\tdoc = doc || document;\n\n\t\tvar i, val,\n\t\t\tscript = doc.createElement( \"script\" );\n\n\t\tscript.text = code;\n\t\tif ( node ) {\n\t\t\tfor ( i in preservedScriptAttributes ) {\n\n\t\t\t\t// Support: Firefox 64+, Edge 18+\n\t\t\t\t// Some browsers don't support the \"nonce\" property on scripts.\n\t\t\t\t// On the other hand, just using `getAttribute` is not enough as\n\t\t\t\t// the `nonce` attribute is reset to an empty string whenever it\n\t\t\t\t// becomes browsing-context connected.\n\t\t\t\t// See https://github.com/whatwg/html/issues/2369\n\t\t\t\t// See https://html.spec.whatwg.org/#nonce-attributes\n\t\t\t\t// The `node.getAttribute` check was added for the sake of\n\t\t\t\t// `jQuery.globalEval` so that it can fake a nonce-containing node\n\t\t\t\t// via an object.\n\t\t\t\tval = node[ i ] || node.getAttribute && node.getAttribute( i );\n\t\t\t\tif ( val ) {\n\t\t\t\t\tscript.setAttribute( i, val );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tdoc.head.appendChild( script ).parentNode.removeChild( script );\n\t}\n\n\nfunction toType( obj ) {\n\tif ( obj == null ) {\n\t\treturn obj + \"\";\n\t}\n\n\t// Support: Android <=2.3 only (functionish RegExp)\n\treturn typeof obj === \"object\" || typeof obj === \"function\" ?\n\t\tclass2type[ toString.call( obj ) ] || \"object\" :\n\t\ttypeof obj;\n}\n/* global Symbol */\n// Defining this global in .eslintrc.json would create a danger of using the global\n// unguarded in another place, it seems safer to define global only for this module\n\n\n\nvar version = \"3.7.1\",\n\n\trhtmlSuffix = /HTML$/i,\n\n\t// Define a local copy of jQuery\n\tjQuery = function( selector, context ) {\n\n\t\t// The jQuery object is actually just the init constructor 'enhanced'\n\t\t// Need init if jQuery is called (just allow error to be thrown if not included)\n\t\treturn new jQuery.fn.init( selector, context );\n\t};\n\njQuery.fn = jQuery.prototype = {\n\n\t// The current version of jQuery being used\n\tjquery: version,\n\n\tconstructor: jQuery,\n\n\t// The default length of a jQuery object is 0\n\tlength: 0,\n\n\ttoArray: function() {\n\t\treturn slice.call( this );\n\t},\n\n\t// Get the Nth element in the matched element set OR\n\t// Get the whole matched element set as a clean array\n\tget: function( num ) {\n\n\t\t// Return all the elements in a clean array\n\t\tif ( num == null ) {\n\t\t\treturn slice.call( this );\n\t\t}\n\n\t\t// Return just the one element from the set\n\t\treturn num < 0 ? this[ num + this.length ] : this[ num ];\n\t},\n\n\t// Take an array of elements and push it onto the stack\n\t// (returning the new matched element set)\n\tpushStack: function( elems ) {\n\n\t\t// Build a new jQuery matched element set\n\t\tvar ret = jQuery.merge( this.constructor(), elems );\n\n\t\t// Add the old object onto the stack (as a reference)\n\t\tret.prevObject = this;\n\n\t\t// Return the newly-formed element set\n\t\treturn ret;\n\t},\n\n\t// Execute a callback for every element in the matched set.\n\teach: function( callback ) {\n\t\treturn jQuery.each( this, callback );\n\t},\n\n\tmap: function( callback ) {\n\t\treturn this.pushStack( jQuery.map( this, function( elem, i ) {\n\t\t\treturn callback.call( elem, i, elem );\n\t\t} ) );\n\t},\n\n\tslice: function() {\n\t\treturn this.pushStack( slice.apply( this, arguments ) );\n\t},\n\n\tfirst: function() {\n\t\treturn this.eq( 0 );\n\t},\n\n\tlast: function() {\n\t\treturn this.eq( -1 );\n\t},\n\n\teven: function() {\n\t\treturn this.pushStack( jQuery.grep( this, function( _elem, i ) {\n\t\t\treturn ( i + 1 ) % 2;\n\t\t} ) );\n\t},\n\n\todd: function() {\n\t\treturn this.pushStack( jQuery.grep( this, function( _elem, i ) {\n\t\t\treturn i % 2;\n\t\t} ) );\n\t},\n\n\teq: function( i ) {\n\t\tvar len = this.length,\n\t\t\tj = +i + ( i < 0 ? len : 0 );\n\t\treturn this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );\n\t},\n\n\tend: function() {\n\t\treturn this.prevObject || this.constructor();\n\t},\n\n\t// For internal use only.\n\t// Behaves like an Array's method, not like a jQuery method.\n\tpush: push,\n\tsort: arr.sort,\n\tsplice: arr.splice\n};\n\njQuery.extend = jQuery.fn.extend = function() {\n\tvar options, name, src, copy, copyIsArray, clone,\n\t\ttarget = arguments[ 0 ] || {},\n\t\ti = 1,\n\t\tlength = arguments.length,\n\t\tdeep = false;\n\n\t// Handle a deep copy situation\n\tif ( typeof target === \"boolean\" ) {\n\t\tdeep = target;\n\n\t\t// Skip the boolean and the target\n\t\ttarget = arguments[ i ] || {};\n\t\ti++;\n\t}\n\n\t// Handle case when target is a string or something (possible in deep copy)\n\tif ( typeof target !== \"object\" && !isFunction( target ) ) {\n\t\ttarget = {};\n\t}\n\n\t// Extend jQuery itself if only one argument is passed\n\tif ( i === length ) {\n\t\ttarget = this;\n\t\ti--;\n\t}\n\n\tfor ( ; i < length; i++ ) {\n\n\t\t// Only deal with non-null/undefined values\n\t\tif ( ( options = arguments[ i ] ) != null ) {\n\n\t\t\t// Extend the base object\n\t\t\tfor ( name in options ) {\n\t\t\t\tcopy = options[ name ];\n\n\t\t\t\t// Prevent Object.prototype pollution\n\t\t\t\t// Prevent never-ending loop\n\t\t\t\tif ( name === \"__proto__\" || target === copy ) {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\n\t\t\t\t// Recurse if we're merging plain objects or arrays\n\t\t\t\tif ( deep && copy && ( jQuery.isPlainObject( copy ) ||\n\t\t\t\t\t( copyIsArray = Array.isArray( copy ) ) ) ) {\n\t\t\t\t\tsrc = target[ name ];\n\n\t\t\t\t\t// Ensure proper type for the source value\n\t\t\t\t\tif ( copyIsArray && !Array.isArray( src ) ) {\n\t\t\t\t\t\tclone = [];\n\t\t\t\t\t} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {\n\t\t\t\t\t\tclone = {};\n\t\t\t\t\t} else {\n\t\t\t\t\t\tclone = src;\n\t\t\t\t\t}\n\t\t\t\t\tcopyIsArray = false;\n\n\t\t\t\t\t// Never move original objects, clone them\n\t\t\t\t\ttarget[ name ] = jQuery.extend( deep, clone, copy );\n\n\t\t\t\t// Don't bring in undefined values\n\t\t\t\t} else if ( copy !== undefined ) {\n\t\t\t\t\ttarget[ name ] = copy;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Return the modified object\n\treturn target;\n};\n\njQuery.extend( {\n\n\t// Unique for each copy of jQuery on the page\n\texpando: \"jQuery\" + ( version + Math.random() ).replace( /\\D/g, \"\" ),\n\n\t// Assume jQuery is ready without the ready module\n\tisReady: true,\n\n\terror: function( msg ) {\n\t\tthrow new Error( msg );\n\t},\n\n\tnoop: function() {},\n\n\tisPlainObject: function( obj ) {\n\t\tvar proto, Ctor;\n\n\t\t// Detect obvious negatives\n\t\t// Use toString instead of jQuery.type to catch host objects\n\t\tif ( !obj || toString.call( obj ) !== \"[object Object]\" ) {\n\t\t\treturn false;\n\t\t}\n\n\t\tproto = getProto( obj );\n\n\t\t// Objects with no prototype (e.g., `Object.create( null )`) are plain\n\t\tif ( !proto ) {\n\t\t\treturn true;\n\t\t}\n\n\t\t// Objects with prototype are plain iff they were constructed by a global Object function\n\t\tCtor = hasOwn.call( proto, \"constructor\" ) && proto.constructor;\n\t\treturn typeof Ctor === \"function\" && fnToString.call( Ctor ) === ObjectFunctionString;\n\t},\n\n\tisEmptyObject: function( obj ) {\n\t\tvar name;\n\n\t\tfor ( name in obj ) {\n\t\t\treturn false;\n\t\t}\n\t\treturn true;\n\t},\n\n\t// Evaluates a script in a provided context; falls back to the global one\n\t// if not specified.\n\tglobalEval: function( code, options, doc ) {\n\t\tDOMEval( code, { nonce: options && options.nonce }, doc );\n\t},\n\n\teach: function( obj, callback ) {\n\t\tvar length, i = 0;\n\n\t\tif ( isArrayLike( obj ) ) {\n\t\t\tlength = obj.length;\n\t\t\tfor ( ; i < length; i++ ) {\n\t\t\t\tif ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tfor ( i in obj ) {\n\t\t\t\tif ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn obj;\n\t},\n\n\n\t// Retrieve the text value of an array of DOM nodes\n\ttext: function( elem ) {\n\t\tvar node,\n\t\t\tret = \"\",\n\t\t\ti = 0,\n\t\t\tnodeType = elem.nodeType;\n\n\t\tif ( !nodeType ) {\n\n\t\t\t// If no nodeType, this is expected to be an array\n\t\t\twhile ( ( node = elem[ i++ ] ) ) {\n\n\t\t\t\t// Do not traverse comment nodes\n\t\t\t\tret += jQuery.text( node );\n\t\t\t}\n\t\t}\n\t\tif ( nodeType === 1 || nodeType === 11 ) {\n\t\t\treturn elem.textContent;\n\t\t}\n\t\tif ( nodeType === 9 ) {\n\t\t\treturn elem.documentElement.textContent;\n\t\t}\n\t\tif ( nodeType === 3 || nodeType === 4 ) {\n\t\t\treturn elem.nodeValue;\n\t\t}\n\n\t\t// Do not include comment or processing instruction nodes\n\n\t\treturn ret;\n\t},\n\n\t// results is for internal usage only\n\tmakeArray: function( arr, results ) {\n\t\tvar ret = results || [];\n\n\t\tif ( arr != null ) {\n\t\t\tif ( isArrayLike( Object( arr ) ) ) {\n\t\t\t\tjQuery.merge( ret,\n\t\t\t\t\ttypeof arr === \"string\" ?\n\t\t\t\t\t\t[ arr ] : arr\n\t\t\t\t);\n\t\t\t} else {\n\t\t\t\tpush.call( ret, arr );\n\t\t\t}\n\t\t}\n\n\t\treturn ret;\n\t},\n\n\tinArray: function( elem, arr, i ) {\n\t\treturn arr == null ? -1 : indexOf.call( arr, elem, i );\n\t},\n\n\tisXMLDoc: function( elem ) {\n\t\tvar namespace = elem && elem.namespaceURI,\n\t\t\tdocElem = elem && ( elem.ownerDocument || elem ).documentElement;\n\n\t\t// Assume HTML when documentElement doesn't yet exist, such as inside\n\t\t// document fragments.\n\t\treturn !rhtmlSuffix.test( namespace || docElem && docElem.nodeName || \"HTML\" );\n\t},\n\n\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t// push.apply(_, arraylike) throws on ancient WebKit\n\tmerge: function( first, second ) {\n\t\tvar len = +second.length,\n\t\t\tj = 0,\n\t\t\ti = first.length;\n\n\t\tfor ( ; j < len; j++ ) {\n\t\t\tfirst[ i++ ] = second[ j ];\n\t\t}\n\n\t\tfirst.length = i;\n\n\t\treturn first;\n\t},\n\n\tgrep: function( elems, callback, invert ) {\n\t\tvar callbackInverse,\n\t\t\tmatches = [],\n\t\t\ti = 0,\n\t\t\tlength = elems.length,\n\t\t\tcallbackExpect = !invert;\n\n\t\t// Go through the array, only saving the items\n\t\t// that pass the validator function\n\t\tfor ( ; i < length; i++ ) {\n\t\t\tcallbackInverse = !callback( elems[ i ], i );\n\t\t\tif ( callbackInverse !== callbackExpect ) {\n\t\t\t\tmatches.push( elems[ i ] );\n\t\t\t}\n\t\t}\n\n\t\treturn matches;\n\t},\n\n\t// arg is for internal usage only\n\tmap: function( elems, callback, arg ) {\n\t\tvar length, value,\n\t\t\ti = 0,\n\t\t\tret = [];\n\n\t\t// Go through the array, translating each of the items to their new values\n\t\tif ( isArrayLike( elems ) ) {\n\t\t\tlength = elems.length;\n\t\t\tfor ( ; i < length; i++ ) {\n\t\t\t\tvalue = callback( elems[ i ], i, arg );\n\n\t\t\t\tif ( value != null ) {\n\t\t\t\t\tret.push( value );\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Go through every key on the object,\n\t\t} else {\n\t\t\tfor ( i in elems ) {\n\t\t\t\tvalue = callback( elems[ i ], i, arg );\n\n\t\t\t\tif ( value != null ) {\n\t\t\t\t\tret.push( value );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Flatten any nested arrays\n\t\treturn flat( ret );\n\t},\n\n\t// A global GUID counter for objects\n\tguid: 1,\n\n\t// jQuery.support is not used in Core but other projects attach their\n\t// properties to it so it needs to exist.\n\tsupport: support\n} );\n\nif ( typeof Symbol === \"function\" ) {\n\tjQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];\n}\n\n// Populate the class2type map\njQuery.each( \"Boolean Number String Function Array Date RegExp Object Error Symbol\".split( \" \" ),\n\tfunction( _i, name ) {\n\t\tclass2type[ \"[object \" + name + \"]\" ] = name.toLowerCase();\n\t} );\n\nfunction isArrayLike( obj ) {\n\n\t// Support: real iOS 8.2 only (not reproducible in simulator)\n\t// `in` check used to prevent JIT error (gh-2145)\n\t// hasOwn isn't used here due to false negatives\n\t// regarding Nodelist length in IE\n\tvar length = !!obj && \"length\" in obj && obj.length,\n\t\ttype = toType( obj );\n\n\tif ( isFunction( obj ) || isWindow( obj ) ) {\n\t\treturn false;\n\t}\n\n\treturn type === \"array\" || length === 0 ||\n\t\ttypeof length === \"number\" && length > 0 && ( length - 1 ) in obj;\n}\n\n\nfunction nodeName( elem, name ) {\n\n\treturn elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();\n\n}\nvar pop = arr.pop;\n\n\nvar sort = arr.sort;\n\n\nvar splice = arr.splice;\n\n\nvar whitespace = \"[\\\\x20\\\\t\\\\r\\\\n\\\\f]\";\n\n\nvar rtrimCSS = new RegExp(\n\t\"^\" + whitespace + \"+|((?:^|[^\\\\\\\\])(?:\\\\\\\\.)*)\" + whitespace + \"+$\",\n\t\"g\"\n);\n\n\n\n\n// Note: an element does not contain itself\njQuery.contains = function( a, b ) {\n\tvar bup = b && b.parentNode;\n\n\treturn a === bup || !!( bup && bup.nodeType === 1 && (\n\n\t\t// Support: IE 9 - 11+\n\t\t// IE doesn't have `contains` on SVG.\n\t\ta.contains ?\n\t\t\ta.contains( bup ) :\n\t\t\ta.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16\n\t) );\n};\n\n\n\n\n// CSS string/identifier serialization\n// https://drafts.csswg.org/cssom/#common-serializing-idioms\nvar rcssescape = /([\\0-\\x1f\\x7f]|^-?\\d)|^-$|[^\\x80-\\uFFFF\\w-]/g;\n\nfunction fcssescape( ch, asCodePoint ) {\n\tif ( asCodePoint ) {\n\n\t\t// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER\n\t\tif ( ch === \"\\0\" ) {\n\t\t\treturn \"\\uFFFD\";\n\t\t}\n\n\t\t// Control characters and (dependent upon position) numbers get escaped as code points\n\t\treturn ch.slice( 0, -1 ) + \"\\\\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + \" \";\n\t}\n\n\t// Other potentially-special ASCII characters get backslash-escaped\n\treturn \"\\\\\" + ch;\n}\n\njQuery.escapeSelector = function( sel ) {\n\treturn ( sel + \"\" ).replace( rcssescape, fcssescape );\n};\n\n\n\n\nvar preferredDoc = document,\n\tpushNative = push;\n\n( function() {\n\nvar i,\n\tExpr,\n\toutermostContext,\n\tsortInput,\n\thasDuplicate,\n\tpush = pushNative,\n\n\t// Local document vars\n\tdocument,\n\tdocumentElement,\n\tdocumentIsHTML,\n\trbuggyQSA,\n\tmatches,\n\n\t// Instance-specific data\n\texpando = jQuery.expando,\n\tdirruns = 0,\n\tdone = 0,\n\tclassCache = createCache(),\n\ttokenCache = createCache(),\n\tcompilerCache = createCache(),\n\tnonnativeSelectorCache = createCache(),\n\tsortOrder = function( a, b ) {\n\t\tif ( a === b ) {\n\t\t\thasDuplicate = true;\n\t\t}\n\t\treturn 0;\n\t},\n\n\tbooleans = \"checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|\" +\n\t\t\"loop|multiple|open|readonly|required|scoped\",\n\n\t// Regular expressions\n\n\t// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram\n\tidentifier = \"(?:\\\\\\\\[\\\\da-fA-F]{1,6}\" + whitespace +\n\t\t\"?|\\\\\\\\[^\\\\r\\\\n\\\\f]|[\\\\w-]|[^\\0-\\\\x7f])+\",\n\n\t// Attribute selectors: https://www.w3.org/TR/selectors/#attribute-selectors\n\tattributes = \"\\\\[\" + whitespace + \"*(\" + identifier + \")(?:\" + whitespace +\n\n\t\t// Operator (capture 2)\n\t\t\"*([*^$|!~]?=)\" + whitespace +\n\n\t\t// \"Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]\"\n\t\t\"*(?:'((?:\\\\\\\\.|[^\\\\\\\\'])*)'|\\\"((?:\\\\\\\\.|[^\\\\\\\\\\\"])*)\\\"|(\" + identifier + \"))|)\" +\n\t\twhitespace + \"*\\\\]\",\n\n\tpseudos = \":(\" + identifier + \")(?:\\\\((\" +\n\n\t\t// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:\n\t\t// 1. quoted (capture 3; capture 4 or capture 5)\n\t\t\"('((?:\\\\\\\\.|[^\\\\\\\\'])*)'|\\\"((?:\\\\\\\\.|[^\\\\\\\\\\\"])*)\\\")|\" +\n\n\t\t// 2. simple (capture 6)\n\t\t\"((?:\\\\\\\\.|[^\\\\\\\\()[\\\\]]|\" + attributes + \")*)|\" +\n\n\t\t// 3. anything else (capture 2)\n\t\t\".*\" +\n\t\t\")\\\\)|)\",\n\n\t// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter\n\trwhitespace = new RegExp( whitespace + \"+\", \"g\" ),\n\n\trcomma = new RegExp( \"^\" + whitespace + \"*,\" + whitespace + \"*\" ),\n\trleadingCombinator = new RegExp( \"^\" + whitespace + \"*([>+~]|\" + whitespace + \")\" +\n\t\twhitespace + \"*\" ),\n\trdescend = new RegExp( whitespace + \"|>\" ),\n\n\trpseudo = new RegExp( pseudos ),\n\tridentifier = new RegExp( \"^\" + identifier + \"$\" ),\n\n\tmatchExpr = {\n\t\tID: new RegExp( \"^#(\" + identifier + \")\" ),\n\t\tCLASS: new RegExp( \"^\\\\.(\" + identifier + \")\" ),\n\t\tTAG: new RegExp( \"^(\" + identifier + \"|[*])\" ),\n\t\tATTR: new RegExp( \"^\" + attributes ),\n\t\tPSEUDO: new RegExp( \"^\" + pseudos ),\n\t\tCHILD: new RegExp(\n\t\t\t\"^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\\\(\" +\n\t\t\t\twhitespace + \"*(even|odd|(([+-]|)(\\\\d*)n|)\" + whitespace + \"*(?:([+-]|)\" +\n\t\t\t\twhitespace + \"*(\\\\d+)|))\" + whitespace + \"*\\\\)|)\", \"i\" ),\n\t\tbool: new RegExp( \"^(?:\" + booleans + \")$\", \"i\" ),\n\n\t\t// For use in libraries implementing .is()\n\t\t// We use this for POS matching in `select`\n\t\tneedsContext: new RegExp( \"^\" + whitespace +\n\t\t\t\"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\\\(\" + whitespace +\n\t\t\t\"*((?:-\\\\d)?\\\\d*)\" + whitespace + \"*\\\\)|)(?=[^-]|$)\", \"i\" )\n\t},\n\n\trinputs = /^(?:input|select|textarea|button)$/i,\n\trheader = /^h\\d$/i,\n\n\t// Easily-parseable/retrievable ID or TAG or CLASS selectors\n\trquickExpr = /^(?:#([\\w-]+)|(\\w+)|\\.([\\w-]+))$/,\n\n\trsibling = /[+~]/,\n\n\t// CSS escapes\n\t// https://www.w3.org/TR/CSS21/syndata.html#escaped-characters\n\trunescape = new RegExp( \"\\\\\\\\[\\\\da-fA-F]{1,6}\" + whitespace +\n\t\t\"?|\\\\\\\\([^\\\\r\\\\n\\\\f])\", \"g\" ),\n\tfunescape = function( escape, nonHex ) {\n\t\tvar high = \"0x\" + escape.slice( 1 ) - 0x10000;\n\n\t\tif ( nonHex ) {\n\n\t\t\t// Strip the backslash prefix from a non-hex escape sequence\n\t\t\treturn nonHex;\n\t\t}\n\n\t\t// Replace a hexadecimal escape sequence with the encoded Unicode code point\n\t\t// Support: IE <=11+\n\t\t// For values outside the Basic Multilingual Plane (BMP), manually construct a\n\t\t// surrogate pair\n\t\treturn high < 0 ?\n\t\t\tString.fromCharCode( high + 0x10000 ) :\n\t\t\tString.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );\n\t},\n\n\t// Used for iframes; see `setDocument`.\n\t// Support: IE 9 - 11+, Edge 12 - 18+\n\t// Removing the function wrapper causes a \"Permission Denied\"\n\t// error in IE/Edge.\n\tunloadHandler = function() {\n\t\tsetDocument();\n\t},\n\n\tinDisabledFieldset = addCombinator(\n\t\tfunction( elem ) {\n\t\t\treturn elem.disabled === true && nodeName( elem, \"fieldset\" );\n\t\t},\n\t\t{ dir: \"parentNode\", next: \"legend\" }\n\t);\n\n// Support: IE <=9 only\n// Accessing document.activeElement can throw unexpectedly\n// https://bugs.jquery.com/ticket/13393\nfunction safeActiveElement() {\n\ttry {\n\t\treturn document.activeElement;\n\t} catch ( err ) { }\n}\n\n// Optimize for push.apply( _, NodeList )\ntry {\n\tpush.apply(\n\t\t( arr = slice.call( preferredDoc.childNodes ) ),\n\t\tpreferredDoc.childNodes\n\t);\n\n\t// Support: Android <=4.0\n\t// Detect silently failing push.apply\n\t// eslint-disable-next-line no-unused-expressions\n\tarr[ preferredDoc.childNodes.length ].nodeType;\n} catch ( e ) {\n\tpush = {\n\t\tapply: function( target, els ) {\n\t\t\tpushNative.apply( target, slice.call( els ) );\n\t\t},\n\t\tcall: function( target ) {\n\t\t\tpushNative.apply( target, slice.call( arguments, 1 ) );\n\t\t}\n\t};\n}\n\nfunction find( selector, context, results, seed ) {\n\tvar m, i, elem, nid, match, groups, newSelector,\n\t\tnewContext = context && context.ownerDocument,\n\n\t\t// nodeType defaults to 9, since context defaults to document\n\t\tnodeType = context ? context.nodeType : 9;\n\n\tresults = results || [];\n\n\t// Return early from calls with invalid selector or context\n\tif ( typeof selector !== \"string\" || !selector ||\n\t\tnodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {\n\n\t\treturn results;\n\t}\n\n\t// Try to shortcut find operations (as opposed to filters) in HTML documents\n\tif ( !seed ) {\n\t\tsetDocument( context );\n\t\tcontext = context || document;\n\n\t\tif ( documentIsHTML ) {\n\n\t\t\t// If the selector is sufficiently simple, try using a \"get*By*\" DOM method\n\t\t\t// (excepting DocumentFragment context, where the methods don't exist)\n\t\t\tif ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {\n\n\t\t\t\t// ID selector\n\t\t\t\tif ( ( m = match[ 1 ] ) ) {\n\n\t\t\t\t\t// Document context\n\t\t\t\t\tif ( nodeType === 9 ) {\n\t\t\t\t\t\tif ( ( elem = context.getElementById( m ) ) ) {\n\n\t\t\t\t\t\t\t// Support: IE 9 only\n\t\t\t\t\t\t\t// getElementById can match elements by name instead of ID\n\t\t\t\t\t\t\tif ( elem.id === m ) {\n\t\t\t\t\t\t\t\tpush.call( results, elem );\n\t\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t// Element context\n\t\t\t\t\t} else {\n\n\t\t\t\t\t\t// Support: IE 9 only\n\t\t\t\t\t\t// getElementById can match elements by name instead of ID\n\t\t\t\t\t\tif ( newContext && ( elem = newContext.getElementById( m ) ) &&\n\t\t\t\t\t\t\tfind.contains( context, elem ) &&\n\t\t\t\t\t\t\telem.id === m ) {\n\n\t\t\t\t\t\t\tpush.call( results, elem );\n\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t// Type selector\n\t\t\t\t} else if ( match[ 2 ] ) {\n\t\t\t\t\tpush.apply( results, context.getElementsByTagName( selector ) );\n\t\t\t\t\treturn results;\n\n\t\t\t\t// Class selector\n\t\t\t\t} else if ( ( m = match[ 3 ] ) && context.getElementsByClassName ) {\n\t\t\t\t\tpush.apply( results, context.getElementsByClassName( m ) );\n\t\t\t\t\treturn results;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Take advantage of querySelectorAll\n\t\t\tif ( !nonnativeSelectorCache[ selector + \" \" ] &&\n\t\t\t\t( !rbuggyQSA || !rbuggyQSA.test( selector ) ) ) {\n\n\t\t\t\tnewSelector = selector;\n\t\t\t\tnewContext = context;\n\n\t\t\t\t// qSA considers elements outside a scoping root when evaluating child or\n\t\t\t\t// descendant combinators, which is not what we want.\n\t\t\t\t// In such cases, we work around the behavior by prefixing every selector in the\n\t\t\t\t// list with an ID selector referencing the scope context.\n\t\t\t\t// The technique has to be used as well when a leading combinator is used\n\t\t\t\t// as such selectors are not recognized by querySelectorAll.\n\t\t\t\t// Thanks to Andrew Dupont for this technique.\n\t\t\t\tif ( nodeType === 1 &&\n\t\t\t\t\t( rdescend.test( selector ) || rleadingCombinator.test( selector ) ) ) {\n\n\t\t\t\t\t// Expand context for sibling selectors\n\t\t\t\t\tnewContext = rsibling.test( selector ) && testContext( context.parentNode ) ||\n\t\t\t\t\t\tcontext;\n\n\t\t\t\t\t// We can use :scope instead of the ID hack if the browser\n\t\t\t\t\t// supports it & if we're not changing the context.\n\t\t\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when\n\t\t\t\t\t// strict-comparing two documents; shallow comparisons work.\n\t\t\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\t\t\tif ( newContext != context || !support.scope ) {\n\n\t\t\t\t\t\t// Capture the context ID, setting it first if necessary\n\t\t\t\t\t\tif ( ( nid = context.getAttribute( \"id\" ) ) ) {\n\t\t\t\t\t\t\tnid = jQuery.escapeSelector( nid );\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tcontext.setAttribute( \"id\", ( nid = expando ) );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Prefix every selector in the list\n\t\t\t\t\tgroups = tokenize( selector );\n\t\t\t\t\ti = groups.length;\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tgroups[ i ] = ( nid ? \"#\" + nid : \":scope\" ) + \" \" +\n\t\t\t\t\t\t\ttoSelector( groups[ i ] );\n\t\t\t\t\t}\n\t\t\t\t\tnewSelector = groups.join( \",\" );\n\t\t\t\t}\n\n\t\t\t\ttry {\n\t\t\t\t\tpush.apply( results,\n\t\t\t\t\t\tnewContext.querySelectorAll( newSelector )\n\t\t\t\t\t);\n\t\t\t\t\treturn results;\n\t\t\t\t} catch ( qsaError ) {\n\t\t\t\t\tnonnativeSelectorCache( selector, true );\n\t\t\t\t} finally {\n\t\t\t\t\tif ( nid === expando ) {\n\t\t\t\t\t\tcontext.removeAttribute( \"id\" );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// All others\n\treturn select( selector.replace( rtrimCSS, \"$1\" ), context, results, seed );\n}\n\n/**\n * Create key-value caches of limited size\n * @returns {function(string, object)} Returns the Object data after storing it on itself with\n *\tproperty name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)\n *\tdeleting the oldest entry\n */\nfunction createCache() {\n\tvar keys = [];\n\n\tfunction cache( key, value ) {\n\n\t\t// Use (key + \" \") to avoid collision with native prototype properties\n\t\t// (see https://github.com/jquery/sizzle/issues/157)\n\t\tif ( keys.push( key + \" \" ) > Expr.cacheLength ) {\n\n\t\t\t// Only keep the most recent entries\n\t\t\tdelete cache[ keys.shift() ];\n\t\t}\n\t\treturn ( cache[ key + \" \" ] = value );\n\t}\n\treturn cache;\n}\n\n/**\n * Mark a function for special use by jQuery selector module\n * @param {Function} fn The function to mark\n */\nfunction markFunction( fn ) {\n\tfn[ expando ] = true;\n\treturn fn;\n}\n\n/**\n * Support testing using an element\n * @param {Function} fn Passed the created element and returns a boolean result\n */\nfunction assert( fn ) {\n\tvar el = document.createElement( \"fieldset\" );\n\n\ttry {\n\t\treturn !!fn( el );\n\t} catch ( e ) {\n\t\treturn false;\n\t} finally {\n\n\t\t// Remove from its parent by default\n\t\tif ( el.parentNode ) {\n\t\t\tel.parentNode.removeChild( el );\n\t\t}\n\n\t\t// release memory in IE\n\t\tel = null;\n\t}\n}\n\n/**\n * Returns a function to use in pseudos for input types\n * @param {String} type\n */\nfunction createInputPseudo( type ) {\n\treturn function( elem ) {\n\t\treturn nodeName( elem, \"input\" ) && elem.type === type;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for buttons\n * @param {String} type\n */\nfunction createButtonPseudo( type ) {\n\treturn function( elem ) {\n\t\treturn ( nodeName( elem, \"input\" ) || nodeName( elem, \"button\" ) ) &&\n\t\t\telem.type === type;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for :enabled/:disabled\n * @param {Boolean} disabled true for :disabled; false for :enabled\n */\nfunction createDisabledPseudo( disabled ) {\n\n\t// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable\n\treturn function( elem ) {\n\n\t\t// Only certain elements can match :enabled or :disabled\n\t\t// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled\n\t\t// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled\n\t\tif ( \"form\" in elem ) {\n\n\t\t\t// Check for inherited disabledness on relevant non-disabled elements:\n\t\t\t// * listed form-associated elements in a disabled fieldset\n\t\t\t// https://html.spec.whatwg.org/multipage/forms.html#category-listed\n\t\t\t// https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled\n\t\t\t// * option elements in a disabled optgroup\n\t\t\t// https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled\n\t\t\t// All such elements have a \"form\" property.\n\t\t\tif ( elem.parentNode && elem.disabled === false ) {\n\n\t\t\t\t// Option elements defer to a parent optgroup if present\n\t\t\t\tif ( \"label\" in elem ) {\n\t\t\t\t\tif ( \"label\" in elem.parentNode ) {\n\t\t\t\t\t\treturn elem.parentNode.disabled === disabled;\n\t\t\t\t\t} else {\n\t\t\t\t\t\treturn elem.disabled === disabled;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Support: IE 6 - 11+\n\t\t\t\t// Use the isDisabled shortcut property to check for disabled fieldset ancestors\n\t\t\t\treturn elem.isDisabled === disabled ||\n\n\t\t\t\t\t// Where there is no isDisabled, check manually\n\t\t\t\t\telem.isDisabled !== !disabled &&\n\t\t\t\t\t\tinDisabledFieldset( elem ) === disabled;\n\t\t\t}\n\n\t\t\treturn elem.disabled === disabled;\n\n\t\t// Try to winnow out elements that can't be disabled before trusting the disabled property.\n\t\t// Some victims get caught in our net (label, legend, menu, track), but it shouldn't\n\t\t// even exist on them, let alone have a boolean value.\n\t\t} else if ( \"label\" in elem ) {\n\t\t\treturn elem.disabled === disabled;\n\t\t}\n\n\t\t// Remaining elements are neither :enabled nor :disabled\n\t\treturn false;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for positionals\n * @param {Function} fn\n */\nfunction createPositionalPseudo( fn ) {\n\treturn markFunction( function( argument ) {\n\t\targument = +argument;\n\t\treturn markFunction( function( seed, matches ) {\n\t\t\tvar j,\n\t\t\t\tmatchIndexes = fn( [], seed.length, argument ),\n\t\t\t\ti = matchIndexes.length;\n\n\t\t\t// Match elements found at the specified indexes\n\t\t\twhile ( i-- ) {\n\t\t\t\tif ( seed[ ( j = matchIndexes[ i ] ) ] ) {\n\t\t\t\t\tseed[ j ] = !( matches[ j ] = seed[ j ] );\n\t\t\t\t}\n\t\t\t}\n\t\t} );\n\t} );\n}\n\n/**\n * Checks a node for validity as a jQuery selector context\n * @param {Element|Object=} context\n * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value\n */\nfunction testContext( context ) {\n\treturn context && typeof context.getElementsByTagName !== \"undefined\" && context;\n}\n\n/**\n * Sets document-related variables once based on the current document\n * @param {Element|Object} [node] An element or document object to use to set the document\n * @returns {Object} Returns the current document\n */\nfunction setDocument( node ) {\n\tvar subWindow,\n\t\tdoc = node ? node.ownerDocument || node : preferredDoc;\n\n\t// Return early if doc is invalid or already selected\n\t// Support: IE 11+, Edge 17 - 18+\n\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t// two documents; shallow comparisons work.\n\t// eslint-disable-next-line eqeqeq\n\tif ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {\n\t\treturn document;\n\t}\n\n\t// Update global variables\n\tdocument = doc;\n\tdocumentElement = document.documentElement;\n\tdocumentIsHTML = !jQuery.isXMLDoc( document );\n\n\t// Support: iOS 7 only, IE 9 - 11+\n\t// Older browsers didn't support unprefixed `matches`.\n\tmatches = documentElement.matches ||\n\t\tdocumentElement.webkitMatchesSelector ||\n\t\tdocumentElement.msMatchesSelector;\n\n\t// Support: IE 9 - 11+, Edge 12 - 18+\n\t// Accessing iframe documents after unload throws \"permission denied\" errors\n\t// (see trac-13936).\n\t// Limit the fix to IE & Edge Legacy; despite Edge 15+ implementing `matches`,\n\t// all IE 9+ and Edge Legacy versions implement `msMatchesSelector` as well.\n\tif ( documentElement.msMatchesSelector &&\n\n\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t// two documents; shallow comparisons work.\n\t\t// eslint-disable-next-line eqeqeq\n\t\tpreferredDoc != document &&\n\t\t( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {\n\n\t\t// Support: IE 9 - 11+, Edge 12 - 18+\n\t\tsubWindow.addEventListener( \"unload\", unloadHandler );\n\t}\n\n\t// Support: IE <10\n\t// Check if getElementById returns elements by name\n\t// The broken getElementById methods don't pick up programmatically-set names,\n\t// so use a roundabout getElementsByName test\n\tsupport.getById = assert( function( el ) {\n\t\tdocumentElement.appendChild( el ).id = jQuery.expando;\n\t\treturn !document.getElementsByName ||\n\t\t\t!document.getElementsByName( jQuery.expando ).length;\n\t} );\n\n\t// Support: IE 9 only\n\t// Check to see if it's possible to do matchesSelector\n\t// on a disconnected node.\n\tsupport.disconnectedMatch = assert( function( el ) {\n\t\treturn matches.call( el, \"*\" );\n\t} );\n\n\t// Support: IE 9 - 11+, Edge 12 - 18+\n\t// IE/Edge don't support the :scope pseudo-class.\n\tsupport.scope = assert( function() {\n\t\treturn document.querySelectorAll( \":scope\" );\n\t} );\n\n\t// Support: Chrome 105 - 111 only, Safari 15.4 - 16.3 only\n\t// Make sure the `:has()` argument is parsed unforgivingly.\n\t// We include `*` in the test to detect buggy implementations that are\n\t// _selectively_ forgiving (specifically when the list includes at least\n\t// one valid selector).\n\t// Note that we treat complete lack of support for `:has()` as if it were\n\t// spec-compliant support, which is fine because use of `:has()` in such\n\t// environments will fail in the qSA path and fall back to jQuery traversal\n\t// anyway.\n\tsupport.cssHas = assert( function() {\n\t\ttry {\n\t\t\tdocument.querySelector( \":has(*,:jqfake)\" );\n\t\t\treturn false;\n\t\t} catch ( e ) {\n\t\t\treturn true;\n\t\t}\n\t} );\n\n\t// ID filter and find\n\tif ( support.getById ) {\n\t\tExpr.filter.ID = function( id ) {\n\t\t\tvar attrId = id.replace( runescape, funescape );\n\t\t\treturn function( elem ) {\n\t\t\t\treturn elem.getAttribute( \"id\" ) === attrId;\n\t\t\t};\n\t\t};\n\t\tExpr.find.ID = function( id, context ) {\n\t\t\tif ( typeof context.getElementById !== \"undefined\" && documentIsHTML ) {\n\t\t\t\tvar elem = context.getElementById( id );\n\t\t\t\treturn elem ? [ elem ] : [];\n\t\t\t}\n\t\t};\n\t} else {\n\t\tExpr.filter.ID = function( id ) {\n\t\t\tvar attrId = id.replace( runescape, funescape );\n\t\t\treturn function( elem ) {\n\t\t\t\tvar node = typeof elem.getAttributeNode !== \"undefined\" &&\n\t\t\t\t\telem.getAttributeNode( \"id\" );\n\t\t\t\treturn node && node.value === attrId;\n\t\t\t};\n\t\t};\n\n\t\t// Support: IE 6 - 7 only\n\t\t// getElementById is not reliable as a find shortcut\n\t\tExpr.find.ID = function( id, context ) {\n\t\t\tif ( typeof context.getElementById !== \"undefined\" && documentIsHTML ) {\n\t\t\t\tvar node, i, elems,\n\t\t\t\t\telem = context.getElementById( id );\n\n\t\t\t\tif ( elem ) {\n\n\t\t\t\t\t// Verify the id attribute\n\t\t\t\t\tnode = elem.getAttributeNode( \"id\" );\n\t\t\t\t\tif ( node && node.value === id ) {\n\t\t\t\t\t\treturn [ elem ];\n\t\t\t\t\t}\n\n\t\t\t\t\t// Fall back on getElementsByName\n\t\t\t\t\telems = context.getElementsByName( id );\n\t\t\t\t\ti = 0;\n\t\t\t\t\twhile ( ( elem = elems[ i++ ] ) ) {\n\t\t\t\t\t\tnode = elem.getAttributeNode( \"id\" );\n\t\t\t\t\t\tif ( node && node.value === id ) {\n\t\t\t\t\t\t\treturn [ elem ];\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\treturn [];\n\t\t\t}\n\t\t};\n\t}\n\n\t// Tag\n\tExpr.find.TAG = function( tag, context ) {\n\t\tif ( typeof context.getElementsByTagName !== \"undefined\" ) {\n\t\t\treturn context.getElementsByTagName( tag );\n\n\t\t// DocumentFragment nodes don't have gEBTN\n\t\t} else {\n\t\t\treturn context.querySelectorAll( tag );\n\t\t}\n\t};\n\n\t// Class\n\tExpr.find.CLASS = function( className, context ) {\n\t\tif ( typeof context.getElementsByClassName !== \"undefined\" && documentIsHTML ) {\n\t\t\treturn context.getElementsByClassName( className );\n\t\t}\n\t};\n\n\t/* QSA/matchesSelector\n\t---------------------------------------------------------------------- */\n\n\t// QSA and matchesSelector support\n\n\trbuggyQSA = [];\n\n\t// Build QSA regex\n\t// Regex strategy adopted from Diego Perini\n\tassert( function( el ) {\n\n\t\tvar input;\n\n\t\tdocumentElement.appendChild( el ).innerHTML =\n\t\t\t\"\" +\n\t\t\t\"\";\n\n\t\t// Support: iOS <=7 - 8 only\n\t\t// Boolean attributes and \"value\" are not treated correctly in some XML documents\n\t\tif ( !el.querySelectorAll( \"[selected]\" ).length ) {\n\t\t\trbuggyQSA.push( \"\\\\[\" + whitespace + \"*(?:value|\" + booleans + \")\" );\n\t\t}\n\n\t\t// Support: iOS <=7 - 8 only\n\t\tif ( !el.querySelectorAll( \"[id~=\" + expando + \"-]\" ).length ) {\n\t\t\trbuggyQSA.push( \"~=\" );\n\t\t}\n\n\t\t// Support: iOS 8 only\n\t\t// https://bugs.webkit.org/show_bug.cgi?id=136851\n\t\t// In-page `selector#id sibling-combinator selector` fails\n\t\tif ( !el.querySelectorAll( \"a#\" + expando + \"+*\" ).length ) {\n\t\t\trbuggyQSA.push( \".#.+[+~]\" );\n\t\t}\n\n\t\t// Support: Chrome <=105+, Firefox <=104+, Safari <=15.4+\n\t\t// In some of the document kinds, these selectors wouldn't work natively.\n\t\t// This is probably OK but for backwards compatibility we want to maintain\n\t\t// handling them through jQuery traversal in jQuery 3.x.\n\t\tif ( !el.querySelectorAll( \":checked\" ).length ) {\n\t\t\trbuggyQSA.push( \":checked\" );\n\t\t}\n\n\t\t// Support: Windows 8 Native Apps\n\t\t// The type and name attributes are restricted during .innerHTML assignment\n\t\tinput = document.createElement( \"input\" );\n\t\tinput.setAttribute( \"type\", \"hidden\" );\n\t\tel.appendChild( input ).setAttribute( \"name\", \"D\" );\n\n\t\t// Support: IE 9 - 11+\n\t\t// IE's :disabled selector does not pick up the children of disabled fieldsets\n\t\t// Support: Chrome <=105+, Firefox <=104+, Safari <=15.4+\n\t\t// In some of the document kinds, these selectors wouldn't work natively.\n\t\t// This is probably OK but for backwards compatibility we want to maintain\n\t\t// handling them through jQuery traversal in jQuery 3.x.\n\t\tdocumentElement.appendChild( el ).disabled = true;\n\t\tif ( el.querySelectorAll( \":disabled\" ).length !== 2 ) {\n\t\t\trbuggyQSA.push( \":enabled\", \":disabled\" );\n\t\t}\n\n\t\t// Support: IE 11+, Edge 15 - 18+\n\t\t// IE 11/Edge don't find elements on a `[name='']` query in some cases.\n\t\t// Adding a temporary attribute to the document before the selection works\n\t\t// around the issue.\n\t\t// Interestingly, IE 10 & older don't seem to have the issue.\n\t\tinput = document.createElement( \"input\" );\n\t\tinput.setAttribute( \"name\", \"\" );\n\t\tel.appendChild( input );\n\t\tif ( !el.querySelectorAll( \"[name='']\" ).length ) {\n\t\t\trbuggyQSA.push( \"\\\\[\" + whitespace + \"*name\" + whitespace + \"*=\" +\n\t\t\t\twhitespace + \"*(?:''|\\\"\\\")\" );\n\t\t}\n\t} );\n\n\tif ( !support.cssHas ) {\n\n\t\t// Support: Chrome 105 - 110+, Safari 15.4 - 16.3+\n\t\t// Our regular `try-catch` mechanism fails to detect natively-unsupported\n\t\t// pseudo-classes inside `:has()` (such as `:has(:contains(\"Foo\"))`)\n\t\t// in browsers that parse the `:has()` argument as a forgiving selector list.\n\t\t// https://drafts.csswg.org/selectors/#relational now requires the argument\n\t\t// to be parsed unforgivingly, but browsers have not yet fully adjusted.\n\t\trbuggyQSA.push( \":has\" );\n\t}\n\n\trbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( \"|\" ) );\n\n\t/* Sorting\n\t---------------------------------------------------------------------- */\n\n\t// Document order sorting\n\tsortOrder = function( a, b ) {\n\n\t\t// Flag for duplicate removal\n\t\tif ( a === b ) {\n\t\t\thasDuplicate = true;\n\t\t\treturn 0;\n\t\t}\n\n\t\t// Sort on method existence if only one input has compareDocumentPosition\n\t\tvar compare = !a.compareDocumentPosition - !b.compareDocumentPosition;\n\t\tif ( compare ) {\n\t\t\treturn compare;\n\t\t}\n\n\t\t// Calculate position if both inputs belong to the same document\n\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t// two documents; shallow comparisons work.\n\t\t// eslint-disable-next-line eqeqeq\n\t\tcompare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?\n\t\t\ta.compareDocumentPosition( b ) :\n\n\t\t\t// Otherwise we know they are disconnected\n\t\t\t1;\n\n\t\t// Disconnected nodes\n\t\tif ( compare & 1 ||\n\t\t\t( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {\n\n\t\t\t// Choose the first element that is related to our preferred document\n\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t// two documents; shallow comparisons work.\n\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\tif ( a === document || a.ownerDocument == preferredDoc &&\n\t\t\t\tfind.contains( preferredDoc, a ) ) {\n\t\t\t\treturn -1;\n\t\t\t}\n\n\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t// two documents; shallow comparisons work.\n\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\tif ( b === document || b.ownerDocument == preferredDoc &&\n\t\t\t\tfind.contains( preferredDoc, b ) ) {\n\t\t\t\treturn 1;\n\t\t\t}\n\n\t\t\t// Maintain original order\n\t\t\treturn sortInput ?\n\t\t\t\t( indexOf.call( sortInput, a ) - indexOf.call( sortInput, b ) ) :\n\t\t\t\t0;\n\t\t}\n\n\t\treturn compare & 4 ? -1 : 1;\n\t};\n\n\treturn document;\n}\n\nfind.matches = function( expr, elements ) {\n\treturn find( expr, null, null, elements );\n};\n\nfind.matchesSelector = function( elem, expr ) {\n\tsetDocument( elem );\n\n\tif ( documentIsHTML &&\n\t\t!nonnativeSelectorCache[ expr + \" \" ] &&\n\t\t( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) {\n\n\t\ttry {\n\t\t\tvar ret = matches.call( elem, expr );\n\n\t\t\t// IE 9's matchesSelector returns false on disconnected nodes\n\t\t\tif ( ret || support.disconnectedMatch ||\n\n\t\t\t\t\t// As well, disconnected nodes are said to be in a document\n\t\t\t\t\t// fragment in IE 9\n\t\t\t\t\telem.document && elem.document.nodeType !== 11 ) {\n\t\t\t\treturn ret;\n\t\t\t}\n\t\t} catch ( e ) {\n\t\t\tnonnativeSelectorCache( expr, true );\n\t\t}\n\t}\n\n\treturn find( expr, document, null, [ elem ] ).length > 0;\n};\n\nfind.contains = function( context, elem ) {\n\n\t// Set document vars if needed\n\t// Support: IE 11+, Edge 17 - 18+\n\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t// two documents; shallow comparisons work.\n\t// eslint-disable-next-line eqeqeq\n\tif ( ( context.ownerDocument || context ) != document ) {\n\t\tsetDocument( context );\n\t}\n\treturn jQuery.contains( context, elem );\n};\n\n\nfind.attr = function( elem, name ) {\n\n\t// Set document vars if needed\n\t// Support: IE 11+, Edge 17 - 18+\n\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t// two documents; shallow comparisons work.\n\t// eslint-disable-next-line eqeqeq\n\tif ( ( elem.ownerDocument || elem ) != document ) {\n\t\tsetDocument( elem );\n\t}\n\n\tvar fn = Expr.attrHandle[ name.toLowerCase() ],\n\n\t\t// Don't get fooled by Object.prototype properties (see trac-13807)\n\t\tval = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?\n\t\t\tfn( elem, name, !documentIsHTML ) :\n\t\t\tundefined;\n\n\tif ( val !== undefined ) {\n\t\treturn val;\n\t}\n\n\treturn elem.getAttribute( name );\n};\n\nfind.error = function( msg ) {\n\tthrow new Error( \"Syntax error, unrecognized expression: \" + msg );\n};\n\n/**\n * Document sorting and removing duplicates\n * @param {ArrayLike} results\n */\njQuery.uniqueSort = function( results ) {\n\tvar elem,\n\t\tduplicates = [],\n\t\tj = 0,\n\t\ti = 0;\n\n\t// Unless we *know* we can detect duplicates, assume their presence\n\t//\n\t// Support: Android <=4.0+\n\t// Testing for detecting duplicates is unpredictable so instead assume we can't\n\t// depend on duplicate detection in all browsers without a stable sort.\n\thasDuplicate = !support.sortStable;\n\tsortInput = !support.sortStable && slice.call( results, 0 );\n\tsort.call( results, sortOrder );\n\n\tif ( hasDuplicate ) {\n\t\twhile ( ( elem = results[ i++ ] ) ) {\n\t\t\tif ( elem === results[ i ] ) {\n\t\t\t\tj = duplicates.push( i );\n\t\t\t}\n\t\t}\n\t\twhile ( j-- ) {\n\t\t\tsplice.call( results, duplicates[ j ], 1 );\n\t\t}\n\t}\n\n\t// Clear input after sorting to release objects\n\t// See https://github.com/jquery/sizzle/pull/225\n\tsortInput = null;\n\n\treturn results;\n};\n\njQuery.fn.uniqueSort = function() {\n\treturn this.pushStack( jQuery.uniqueSort( slice.apply( this ) ) );\n};\n\nExpr = jQuery.expr = {\n\n\t// Can be adjusted by the user\n\tcacheLength: 50,\n\n\tcreatePseudo: markFunction,\n\n\tmatch: matchExpr,\n\n\tattrHandle: {},\n\n\tfind: {},\n\n\trelative: {\n\t\t\">\": { dir: \"parentNode\", first: true },\n\t\t\" \": { dir: \"parentNode\" },\n\t\t\"+\": { dir: \"previousSibling\", first: true },\n\t\t\"~\": { dir: \"previousSibling\" }\n\t},\n\n\tpreFilter: {\n\t\tATTR: function( match ) {\n\t\t\tmatch[ 1 ] = match[ 1 ].replace( runescape, funescape );\n\n\t\t\t// Move the given value to match[3] whether quoted or unquoted\n\t\t\tmatch[ 3 ] = ( match[ 3 ] || match[ 4 ] || match[ 5 ] || \"\" )\n\t\t\t\t.replace( runescape, funescape );\n\n\t\t\tif ( match[ 2 ] === \"~=\" ) {\n\t\t\t\tmatch[ 3 ] = \" \" + match[ 3 ] + \" \";\n\t\t\t}\n\n\t\t\treturn match.slice( 0, 4 );\n\t\t},\n\n\t\tCHILD: function( match ) {\n\n\t\t\t/* matches from matchExpr[\"CHILD\"]\n\t\t\t\t1 type (only|nth|...)\n\t\t\t\t2 what (child|of-type)\n\t\t\t\t3 argument (even|odd|\\d*|\\d*n([+-]\\d+)?|...)\n\t\t\t\t4 xn-component of xn+y argument ([+-]?\\d*n|)\n\t\t\t\t5 sign of xn-component\n\t\t\t\t6 x of xn-component\n\t\t\t\t7 sign of y-component\n\t\t\t\t8 y of y-component\n\t\t\t*/\n\t\t\tmatch[ 1 ] = match[ 1 ].toLowerCase();\n\n\t\t\tif ( match[ 1 ].slice( 0, 3 ) === \"nth\" ) {\n\n\t\t\t\t// nth-* requires argument\n\t\t\t\tif ( !match[ 3 ] ) {\n\t\t\t\t\tfind.error( match[ 0 ] );\n\t\t\t\t}\n\n\t\t\t\t// numeric x and y parameters for Expr.filter.CHILD\n\t\t\t\t// remember that false/true cast respectively to 0/1\n\t\t\t\tmatch[ 4 ] = +( match[ 4 ] ?\n\t\t\t\t\tmatch[ 5 ] + ( match[ 6 ] || 1 ) :\n\t\t\t\t\t2 * ( match[ 3 ] === \"even\" || match[ 3 ] === \"odd\" )\n\t\t\t\t);\n\t\t\t\tmatch[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === \"odd\" );\n\n\t\t\t// other types prohibit arguments\n\t\t\t} else if ( match[ 3 ] ) {\n\t\t\t\tfind.error( match[ 0 ] );\n\t\t\t}\n\n\t\t\treturn match;\n\t\t},\n\n\t\tPSEUDO: function( match ) {\n\t\t\tvar excess,\n\t\t\t\tunquoted = !match[ 6 ] && match[ 2 ];\n\n\t\t\tif ( matchExpr.CHILD.test( match[ 0 ] ) ) {\n\t\t\t\treturn null;\n\t\t\t}\n\n\t\t\t// Accept quoted arguments as-is\n\t\t\tif ( match[ 3 ] ) {\n\t\t\t\tmatch[ 2 ] = match[ 4 ] || match[ 5 ] || \"\";\n\n\t\t\t// Strip excess characters from unquoted arguments\n\t\t\t} else if ( unquoted && rpseudo.test( unquoted ) &&\n\n\t\t\t\t// Get excess from tokenize (recursively)\n\t\t\t\t( excess = tokenize( unquoted, true ) ) &&\n\n\t\t\t\t// advance to the next closing parenthesis\n\t\t\t\t( excess = unquoted.indexOf( \")\", unquoted.length - excess ) - unquoted.length ) ) {\n\n\t\t\t\t// excess is a negative index\n\t\t\t\tmatch[ 0 ] = match[ 0 ].slice( 0, excess );\n\t\t\t\tmatch[ 2 ] = unquoted.slice( 0, excess );\n\t\t\t}\n\n\t\t\t// Return only captures needed by the pseudo filter method (type and argument)\n\t\t\treturn match.slice( 0, 3 );\n\t\t}\n\t},\n\n\tfilter: {\n\n\t\tTAG: function( nodeNameSelector ) {\n\t\t\tvar expectedNodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();\n\t\t\treturn nodeNameSelector === \"*\" ?\n\t\t\t\tfunction() {\n\t\t\t\t\treturn true;\n\t\t\t\t} :\n\t\t\t\tfunction( elem ) {\n\t\t\t\t\treturn nodeName( elem, expectedNodeName );\n\t\t\t\t};\n\t\t},\n\n\t\tCLASS: function( className ) {\n\t\t\tvar pattern = classCache[ className + \" \" ];\n\n\t\t\treturn pattern ||\n\t\t\t\t( pattern = new RegExp( \"(^|\" + whitespace + \")\" + className +\n\t\t\t\t\t\"(\" + whitespace + \"|$)\" ) ) &&\n\t\t\t\tclassCache( className, function( elem ) {\n\t\t\t\t\treturn pattern.test(\n\t\t\t\t\t\ttypeof elem.className === \"string\" && elem.className ||\n\t\t\t\t\t\t\ttypeof elem.getAttribute !== \"undefined\" &&\n\t\t\t\t\t\t\t\telem.getAttribute( \"class\" ) ||\n\t\t\t\t\t\t\t\"\"\n\t\t\t\t\t);\n\t\t\t\t} );\n\t\t},\n\n\t\tATTR: function( name, operator, check ) {\n\t\t\treturn function( elem ) {\n\t\t\t\tvar result = find.attr( elem, name );\n\n\t\t\t\tif ( result == null ) {\n\t\t\t\t\treturn operator === \"!=\";\n\t\t\t\t}\n\t\t\t\tif ( !operator ) {\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\n\t\t\t\tresult += \"\";\n\n\t\t\t\tif ( operator === \"=\" ) {\n\t\t\t\t\treturn result === check;\n\t\t\t\t}\n\t\t\t\tif ( operator === \"!=\" ) {\n\t\t\t\t\treturn result !== check;\n\t\t\t\t}\n\t\t\t\tif ( operator === \"^=\" ) {\n\t\t\t\t\treturn check && result.indexOf( check ) === 0;\n\t\t\t\t}\n\t\t\t\tif ( operator === \"*=\" ) {\n\t\t\t\t\treturn check && result.indexOf( check ) > -1;\n\t\t\t\t}\n\t\t\t\tif ( operator === \"$=\" ) {\n\t\t\t\t\treturn check && result.slice( -check.length ) === check;\n\t\t\t\t}\n\t\t\t\tif ( operator === \"~=\" ) {\n\t\t\t\t\treturn ( \" \" + result.replace( rwhitespace, \" \" ) + \" \" )\n\t\t\t\t\t\t.indexOf( check ) > -1;\n\t\t\t\t}\n\t\t\t\tif ( operator === \"|=\" ) {\n\t\t\t\t\treturn result === check || result.slice( 0, check.length + 1 ) === check + \"-\";\n\t\t\t\t}\n\n\t\t\t\treturn false;\n\t\t\t};\n\t\t},\n\n\t\tCHILD: function( type, what, _argument, first, last ) {\n\t\t\tvar simple = type.slice( 0, 3 ) !== \"nth\",\n\t\t\t\tforward = type.slice( -4 ) !== \"last\",\n\t\t\t\tofType = what === \"of-type\";\n\n\t\t\treturn first === 1 && last === 0 ?\n\n\t\t\t\t// Shortcut for :nth-*(n)\n\t\t\t\tfunction( elem ) {\n\t\t\t\t\treturn !!elem.parentNode;\n\t\t\t\t} :\n\n\t\t\t\tfunction( elem, _context, xml ) {\n\t\t\t\t\tvar cache, outerCache, node, nodeIndex, start,\n\t\t\t\t\t\tdir = simple !== forward ? \"nextSibling\" : \"previousSibling\",\n\t\t\t\t\t\tparent = elem.parentNode,\n\t\t\t\t\t\tname = ofType && elem.nodeName.toLowerCase(),\n\t\t\t\t\t\tuseCache = !xml && !ofType,\n\t\t\t\t\t\tdiff = false;\n\n\t\t\t\t\tif ( parent ) {\n\n\t\t\t\t\t\t// :(first|last|only)-(child|of-type)\n\t\t\t\t\t\tif ( simple ) {\n\t\t\t\t\t\t\twhile ( dir ) {\n\t\t\t\t\t\t\t\tnode = elem;\n\t\t\t\t\t\t\t\twhile ( ( node = node[ dir ] ) ) {\n\t\t\t\t\t\t\t\t\tif ( ofType ?\n\t\t\t\t\t\t\t\t\t\tnodeName( node, name ) :\n\t\t\t\t\t\t\t\t\t\tnode.nodeType === 1 ) {\n\n\t\t\t\t\t\t\t\t\t\treturn false;\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t// Reverse direction for :only-* (if we haven't yet done so)\n\t\t\t\t\t\t\t\tstart = dir = type === \"only\" && !start && \"nextSibling\";\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tstart = [ forward ? parent.firstChild : parent.lastChild ];\n\n\t\t\t\t\t\t// non-xml :nth-child(...) stores cache data on `parent`\n\t\t\t\t\t\tif ( forward && useCache ) {\n\n\t\t\t\t\t\t\t// Seek `elem` from a previously-cached index\n\t\t\t\t\t\t\touterCache = parent[ expando ] || ( parent[ expando ] = {} );\n\t\t\t\t\t\t\tcache = outerCache[ type ] || [];\n\t\t\t\t\t\t\tnodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];\n\t\t\t\t\t\t\tdiff = nodeIndex && cache[ 2 ];\n\t\t\t\t\t\t\tnode = nodeIndex && parent.childNodes[ nodeIndex ];\n\n\t\t\t\t\t\t\twhile ( ( node = ++nodeIndex && node && node[ dir ] ||\n\n\t\t\t\t\t\t\t\t// Fallback to seeking `elem` from the start\n\t\t\t\t\t\t\t\t( diff = nodeIndex = 0 ) || start.pop() ) ) {\n\n\t\t\t\t\t\t\t\t// When found, cache indexes on `parent` and break\n\t\t\t\t\t\t\t\tif ( node.nodeType === 1 && ++diff && node === elem ) {\n\t\t\t\t\t\t\t\t\touterCache[ type ] = [ dirruns, nodeIndex, diff ];\n\t\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t// Use previously-cached element index if available\n\t\t\t\t\t\t\tif ( useCache ) {\n\t\t\t\t\t\t\t\touterCache = elem[ expando ] || ( elem[ expando ] = {} );\n\t\t\t\t\t\t\t\tcache = outerCache[ type ] || [];\n\t\t\t\t\t\t\t\tnodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];\n\t\t\t\t\t\t\t\tdiff = nodeIndex;\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t// xml :nth-child(...)\n\t\t\t\t\t\t\t// or :nth-last-child(...) or :nth(-last)?-of-type(...)\n\t\t\t\t\t\t\tif ( diff === false ) {\n\n\t\t\t\t\t\t\t\t// Use the same loop as above to seek `elem` from the start\n\t\t\t\t\t\t\t\twhile ( ( node = ++nodeIndex && node && node[ dir ] ||\n\t\t\t\t\t\t\t\t\t( diff = nodeIndex = 0 ) || start.pop() ) ) {\n\n\t\t\t\t\t\t\t\t\tif ( ( ofType ?\n\t\t\t\t\t\t\t\t\t\tnodeName( node, name ) :\n\t\t\t\t\t\t\t\t\t\tnode.nodeType === 1 ) &&\n\t\t\t\t\t\t\t\t\t\t++diff ) {\n\n\t\t\t\t\t\t\t\t\t\t// Cache the index of each encountered element\n\t\t\t\t\t\t\t\t\t\tif ( useCache ) {\n\t\t\t\t\t\t\t\t\t\t\touterCache = node[ expando ] ||\n\t\t\t\t\t\t\t\t\t\t\t\t( node[ expando ] = {} );\n\t\t\t\t\t\t\t\t\t\t\touterCache[ type ] = [ dirruns, diff ];\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\tif ( node === elem ) {\n\t\t\t\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// Incorporate the offset, then check against cycle size\n\t\t\t\t\t\tdiff -= last;\n\t\t\t\t\t\treturn diff === first || ( diff % first === 0 && diff / first >= 0 );\n\t\t\t\t\t}\n\t\t\t\t};\n\t\t},\n\n\t\tPSEUDO: function( pseudo, argument ) {\n\n\t\t\t// pseudo-class names are case-insensitive\n\t\t\t// https://www.w3.org/TR/selectors/#pseudo-classes\n\t\t\t// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters\n\t\t\t// Remember that setFilters inherits from pseudos\n\t\t\tvar args,\n\t\t\t\tfn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||\n\t\t\t\t\tfind.error( \"unsupported pseudo: \" + pseudo );\n\n\t\t\t// The user may use createPseudo to indicate that\n\t\t\t// arguments are needed to create the filter function\n\t\t\t// just as jQuery does\n\t\t\tif ( fn[ expando ] ) {\n\t\t\t\treturn fn( argument );\n\t\t\t}\n\n\t\t\t// But maintain support for old signatures\n\t\t\tif ( fn.length > 1 ) {\n\t\t\t\targs = [ pseudo, pseudo, \"\", argument ];\n\t\t\t\treturn Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?\n\t\t\t\t\tmarkFunction( function( seed, matches ) {\n\t\t\t\t\t\tvar idx,\n\t\t\t\t\t\t\tmatched = fn( seed, argument ),\n\t\t\t\t\t\t\ti = matched.length;\n\t\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\t\tidx = indexOf.call( seed, matched[ i ] );\n\t\t\t\t\t\t\tseed[ idx ] = !( matches[ idx ] = matched[ i ] );\n\t\t\t\t\t\t}\n\t\t\t\t\t} ) :\n\t\t\t\t\tfunction( elem ) {\n\t\t\t\t\t\treturn fn( elem, 0, args );\n\t\t\t\t\t};\n\t\t\t}\n\n\t\t\treturn fn;\n\t\t}\n\t},\n\n\tpseudos: {\n\n\t\t// Potentially complex pseudos\n\t\tnot: markFunction( function( selector ) {\n\n\t\t\t// Trim the selector passed to compile\n\t\t\t// to avoid treating leading and trailing\n\t\t\t// spaces as combinators\n\t\t\tvar input = [],\n\t\t\t\tresults = [],\n\t\t\t\tmatcher = compile( selector.replace( rtrimCSS, \"$1\" ) );\n\n\t\t\treturn matcher[ expando ] ?\n\t\t\t\tmarkFunction( function( seed, matches, _context, xml ) {\n\t\t\t\t\tvar elem,\n\t\t\t\t\t\tunmatched = matcher( seed, null, xml, [] ),\n\t\t\t\t\t\ti = seed.length;\n\n\t\t\t\t\t// Match elements unmatched by `matcher`\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tif ( ( elem = unmatched[ i ] ) ) {\n\t\t\t\t\t\t\tseed[ i ] = !( matches[ i ] = elem );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t} ) :\n\t\t\t\tfunction( elem, _context, xml ) {\n\t\t\t\t\tinput[ 0 ] = elem;\n\t\t\t\t\tmatcher( input, null, xml, results );\n\n\t\t\t\t\t// Don't keep the element\n\t\t\t\t\t// (see https://github.com/jquery/sizzle/issues/299)\n\t\t\t\t\tinput[ 0 ] = null;\n\t\t\t\t\treturn !results.pop();\n\t\t\t\t};\n\t\t} ),\n\n\t\thas: markFunction( function( selector ) {\n\t\t\treturn function( elem ) {\n\t\t\t\treturn find( selector, elem ).length > 0;\n\t\t\t};\n\t\t} ),\n\n\t\tcontains: markFunction( function( text ) {\n\t\t\ttext = text.replace( runescape, funescape );\n\t\t\treturn function( elem ) {\n\t\t\t\treturn ( elem.textContent || jQuery.text( elem ) ).indexOf( text ) > -1;\n\t\t\t};\n\t\t} ),\n\n\t\t// \"Whether an element is represented by a :lang() selector\n\t\t// is based solely on the element's language value\n\t\t// being equal to the identifier C,\n\t\t// or beginning with the identifier C immediately followed by \"-\".\n\t\t// The matching of C against the element's language value is performed case-insensitively.\n\t\t// The identifier C does not have to be a valid language name.\"\n\t\t// https://www.w3.org/TR/selectors/#lang-pseudo\n\t\tlang: markFunction( function( lang ) {\n\n\t\t\t// lang value must be a valid identifier\n\t\t\tif ( !ridentifier.test( lang || \"\" ) ) {\n\t\t\t\tfind.error( \"unsupported lang: \" + lang );\n\t\t\t}\n\t\t\tlang = lang.replace( runescape, funescape ).toLowerCase();\n\t\t\treturn function( elem ) {\n\t\t\t\tvar elemLang;\n\t\t\t\tdo {\n\t\t\t\t\tif ( ( elemLang = documentIsHTML ?\n\t\t\t\t\t\telem.lang :\n\t\t\t\t\t\telem.getAttribute( \"xml:lang\" ) || elem.getAttribute( \"lang\" ) ) ) {\n\n\t\t\t\t\t\telemLang = elemLang.toLowerCase();\n\t\t\t\t\t\treturn elemLang === lang || elemLang.indexOf( lang + \"-\" ) === 0;\n\t\t\t\t\t}\n\t\t\t\t} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );\n\t\t\t\treturn false;\n\t\t\t};\n\t\t} ),\n\n\t\t// Miscellaneous\n\t\ttarget: function( elem ) {\n\t\t\tvar hash = window.location && window.location.hash;\n\t\t\treturn hash && hash.slice( 1 ) === elem.id;\n\t\t},\n\n\t\troot: function( elem ) {\n\t\t\treturn elem === documentElement;\n\t\t},\n\n\t\tfocus: function( elem ) {\n\t\t\treturn elem === safeActiveElement() &&\n\t\t\t\tdocument.hasFocus() &&\n\t\t\t\t!!( elem.type || elem.href || ~elem.tabIndex );\n\t\t},\n\n\t\t// Boolean properties\n\t\tenabled: createDisabledPseudo( false ),\n\t\tdisabled: createDisabledPseudo( true ),\n\n\t\tchecked: function( elem ) {\n\n\t\t\t// In CSS3, :checked should return both checked and selected elements\n\t\t\t// https://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked\n\t\t\treturn ( nodeName( elem, \"input\" ) && !!elem.checked ) ||\n\t\t\t\t( nodeName( elem, \"option\" ) && !!elem.selected );\n\t\t},\n\n\t\tselected: function( elem ) {\n\n\t\t\t// Support: IE <=11+\n\t\t\t// Accessing the selectedIndex property\n\t\t\t// forces the browser to treat the default option as\n\t\t\t// selected when in an optgroup.\n\t\t\tif ( elem.parentNode ) {\n\t\t\t\t// eslint-disable-next-line no-unused-expressions\n\t\t\t\telem.parentNode.selectedIndex;\n\t\t\t}\n\n\t\t\treturn elem.selected === true;\n\t\t},\n\n\t\t// Contents\n\t\tempty: function( elem ) {\n\n\t\t\t// https://www.w3.org/TR/selectors/#empty-pseudo\n\t\t\t// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),\n\t\t\t// but not by others (comment: 8; processing instruction: 7; etc.)\n\t\t\t// nodeType < 6 works because attributes (2) do not appear as children\n\t\t\tfor ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {\n\t\t\t\tif ( elem.nodeType < 6 ) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn true;\n\t\t},\n\n\t\tparent: function( elem ) {\n\t\t\treturn !Expr.pseudos.empty( elem );\n\t\t},\n\n\t\t// Element/input types\n\t\theader: function( elem ) {\n\t\t\treturn rheader.test( elem.nodeName );\n\t\t},\n\n\t\tinput: function( elem ) {\n\t\t\treturn rinputs.test( elem.nodeName );\n\t\t},\n\n\t\tbutton: function( elem ) {\n\t\t\treturn nodeName( elem, \"input\" ) && elem.type === \"button\" ||\n\t\t\t\tnodeName( elem, \"button\" );\n\t\t},\n\n\t\ttext: function( elem ) {\n\t\t\tvar attr;\n\t\t\treturn nodeName( elem, \"input\" ) && elem.type === \"text\" &&\n\n\t\t\t\t// Support: IE <10 only\n\t\t\t\t// New HTML5 attribute values (e.g., \"search\") appear\n\t\t\t\t// with elem.type === \"text\"\n\t\t\t\t( ( attr = elem.getAttribute( \"type\" ) ) == null ||\n\t\t\t\t\tattr.toLowerCase() === \"text\" );\n\t\t},\n\n\t\t// Position-in-collection\n\t\tfirst: createPositionalPseudo( function() {\n\t\t\treturn [ 0 ];\n\t\t} ),\n\n\t\tlast: createPositionalPseudo( function( _matchIndexes, length ) {\n\t\t\treturn [ length - 1 ];\n\t\t} ),\n\n\t\teq: createPositionalPseudo( function( _matchIndexes, length, argument ) {\n\t\t\treturn [ argument < 0 ? argument + length : argument ];\n\t\t} ),\n\n\t\teven: createPositionalPseudo( function( matchIndexes, length ) {\n\t\t\tvar i = 0;\n\t\t\tfor ( ; i < length; i += 2 ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t} ),\n\n\t\todd: createPositionalPseudo( function( matchIndexes, length ) {\n\t\t\tvar i = 1;\n\t\t\tfor ( ; i < length; i += 2 ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t} ),\n\n\t\tlt: createPositionalPseudo( function( matchIndexes, length, argument ) {\n\t\t\tvar i;\n\n\t\t\tif ( argument < 0 ) {\n\t\t\t\ti = argument + length;\n\t\t\t} else if ( argument > length ) {\n\t\t\t\ti = length;\n\t\t\t} else {\n\t\t\t\ti = argument;\n\t\t\t}\n\n\t\t\tfor ( ; --i >= 0; ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t} ),\n\n\t\tgt: createPositionalPseudo( function( matchIndexes, length, argument ) {\n\t\t\tvar i = argument < 0 ? argument + length : argument;\n\t\t\tfor ( ; ++i < length; ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t} )\n\t}\n};\n\nExpr.pseudos.nth = Expr.pseudos.eq;\n\n// Add button/input type pseudos\nfor ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {\n\tExpr.pseudos[ i ] = createInputPseudo( i );\n}\nfor ( i in { submit: true, reset: true } ) {\n\tExpr.pseudos[ i ] = createButtonPseudo( i );\n}\n\n// Easy API for creating new setFilters\nfunction setFilters() {}\nsetFilters.prototype = Expr.filters = Expr.pseudos;\nExpr.setFilters = new setFilters();\n\nfunction tokenize( selector, parseOnly ) {\n\tvar matched, match, tokens, type,\n\t\tsoFar, groups, preFilters,\n\t\tcached = tokenCache[ selector + \" \" ];\n\n\tif ( cached ) {\n\t\treturn parseOnly ? 0 : cached.slice( 0 );\n\t}\n\n\tsoFar = selector;\n\tgroups = [];\n\tpreFilters = Expr.preFilter;\n\n\twhile ( soFar ) {\n\n\t\t// Comma and first run\n\t\tif ( !matched || ( match = rcomma.exec( soFar ) ) ) {\n\t\t\tif ( match ) {\n\n\t\t\t\t// Don't consume trailing commas as valid\n\t\t\t\tsoFar = soFar.slice( match[ 0 ].length ) || soFar;\n\t\t\t}\n\t\t\tgroups.push( ( tokens = [] ) );\n\t\t}\n\n\t\tmatched = false;\n\n\t\t// Combinators\n\t\tif ( ( match = rleadingCombinator.exec( soFar ) ) ) {\n\t\t\tmatched = match.shift();\n\t\t\ttokens.push( {\n\t\t\t\tvalue: matched,\n\n\t\t\t\t// Cast descendant combinators to space\n\t\t\t\ttype: match[ 0 ].replace( rtrimCSS, \" \" )\n\t\t\t} );\n\t\t\tsoFar = soFar.slice( matched.length );\n\t\t}\n\n\t\t// Filters\n\t\tfor ( type in Expr.filter ) {\n\t\t\tif ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||\n\t\t\t\t( match = preFilters[ type ]( match ) ) ) ) {\n\t\t\t\tmatched = match.shift();\n\t\t\t\ttokens.push( {\n\t\t\t\t\tvalue: matched,\n\t\t\t\t\ttype: type,\n\t\t\t\t\tmatches: match\n\t\t\t\t} );\n\t\t\t\tsoFar = soFar.slice( matched.length );\n\t\t\t}\n\t\t}\n\n\t\tif ( !matched ) {\n\t\t\tbreak;\n\t\t}\n\t}\n\n\t// Return the length of the invalid excess\n\t// if we're just parsing\n\t// Otherwise, throw an error or return tokens\n\tif ( parseOnly ) {\n\t\treturn soFar.length;\n\t}\n\n\treturn soFar ?\n\t\tfind.error( selector ) :\n\n\t\t// Cache the tokens\n\t\ttokenCache( selector, groups ).slice( 0 );\n}\n\nfunction toSelector( tokens ) {\n\tvar i = 0,\n\t\tlen = tokens.length,\n\t\tselector = \"\";\n\tfor ( ; i < len; i++ ) {\n\t\tselector += tokens[ i ].value;\n\t}\n\treturn selector;\n}\n\nfunction addCombinator( matcher, combinator, base ) {\n\tvar dir = combinator.dir,\n\t\tskip = combinator.next,\n\t\tkey = skip || dir,\n\t\tcheckNonElements = base && key === \"parentNode\",\n\t\tdoneName = done++;\n\n\treturn combinator.first ?\n\n\t\t// Check against closest ancestor/preceding element\n\t\tfunction( elem, context, xml ) {\n\t\t\twhile ( ( elem = elem[ dir ] ) ) {\n\t\t\t\tif ( elem.nodeType === 1 || checkNonElements ) {\n\t\t\t\t\treturn matcher( elem, context, xml );\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t} :\n\n\t\t// Check against all ancestor/preceding elements\n\t\tfunction( elem, context, xml ) {\n\t\t\tvar oldCache, outerCache,\n\t\t\t\tnewCache = [ dirruns, doneName ];\n\n\t\t\t// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching\n\t\t\tif ( xml ) {\n\t\t\t\twhile ( ( elem = elem[ dir ] ) ) {\n\t\t\t\t\tif ( elem.nodeType === 1 || checkNonElements ) {\n\t\t\t\t\t\tif ( matcher( elem, context, xml ) ) {\n\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\twhile ( ( elem = elem[ dir ] ) ) {\n\t\t\t\t\tif ( elem.nodeType === 1 || checkNonElements ) {\n\t\t\t\t\t\touterCache = elem[ expando ] || ( elem[ expando ] = {} );\n\n\t\t\t\t\t\tif ( skip && nodeName( elem, skip ) ) {\n\t\t\t\t\t\t\telem = elem[ dir ] || elem;\n\t\t\t\t\t\t} else if ( ( oldCache = outerCache[ key ] ) &&\n\t\t\t\t\t\t\toldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {\n\n\t\t\t\t\t\t\t// Assign to newCache so results back-propagate to previous elements\n\t\t\t\t\t\t\treturn ( newCache[ 2 ] = oldCache[ 2 ] );\n\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t// Reuse newcache so results back-propagate to previous elements\n\t\t\t\t\t\t\touterCache[ key ] = newCache;\n\n\t\t\t\t\t\t\t// A match means we're done; a fail means we have to keep checking\n\t\t\t\t\t\t\tif ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {\n\t\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t};\n}\n\nfunction elementMatcher( matchers ) {\n\treturn matchers.length > 1 ?\n\t\tfunction( elem, context, xml ) {\n\t\t\tvar i = matchers.length;\n\t\t\twhile ( i-- ) {\n\t\t\t\tif ( !matchers[ i ]( elem, context, xml ) ) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn true;\n\t\t} :\n\t\tmatchers[ 0 ];\n}\n\nfunction multipleContexts( selector, contexts, results ) {\n\tvar i = 0,\n\t\tlen = contexts.length;\n\tfor ( ; i < len; i++ ) {\n\t\tfind( selector, contexts[ i ], results );\n\t}\n\treturn results;\n}\n\nfunction condense( unmatched, map, filter, context, xml ) {\n\tvar elem,\n\t\tnewUnmatched = [],\n\t\ti = 0,\n\t\tlen = unmatched.length,\n\t\tmapped = map != null;\n\n\tfor ( ; i < len; i++ ) {\n\t\tif ( ( elem = unmatched[ i ] ) ) {\n\t\t\tif ( !filter || filter( elem, context, xml ) ) {\n\t\t\t\tnewUnmatched.push( elem );\n\t\t\t\tif ( mapped ) {\n\t\t\t\t\tmap.push( i );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn newUnmatched;\n}\n\nfunction setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {\n\tif ( postFilter && !postFilter[ expando ] ) {\n\t\tpostFilter = setMatcher( postFilter );\n\t}\n\tif ( postFinder && !postFinder[ expando ] ) {\n\t\tpostFinder = setMatcher( postFinder, postSelector );\n\t}\n\treturn markFunction( function( seed, results, context, xml ) {\n\t\tvar temp, i, elem, matcherOut,\n\t\t\tpreMap = [],\n\t\t\tpostMap = [],\n\t\t\tpreexisting = results.length,\n\n\t\t\t// Get initial elements from seed or context\n\t\t\telems = seed ||\n\t\t\t\tmultipleContexts( selector || \"*\",\n\t\t\t\t\tcontext.nodeType ? [ context ] : context, [] ),\n\n\t\t\t// Prefilter to get matcher input, preserving a map for seed-results synchronization\n\t\t\tmatcherIn = preFilter && ( seed || !selector ) ?\n\t\t\t\tcondense( elems, preMap, preFilter, context, xml ) :\n\t\t\t\telems;\n\n\t\tif ( matcher ) {\n\n\t\t\t// If we have a postFinder, or filtered seed, or non-seed postFilter\n\t\t\t// or preexisting results,\n\t\t\tmatcherOut = postFinder || ( seed ? preFilter : preexisting || postFilter ) ?\n\n\t\t\t\t// ...intermediate processing is necessary\n\t\t\t\t[] :\n\n\t\t\t\t// ...otherwise use results directly\n\t\t\t\tresults;\n\n\t\t\t// Find primary matches\n\t\t\tmatcher( matcherIn, matcherOut, context, xml );\n\t\t} else {\n\t\t\tmatcherOut = matcherIn;\n\t\t}\n\n\t\t// Apply postFilter\n\t\tif ( postFilter ) {\n\t\t\ttemp = condense( matcherOut, postMap );\n\t\t\tpostFilter( temp, [], context, xml );\n\n\t\t\t// Un-match failing elements by moving them back to matcherIn\n\t\t\ti = temp.length;\n\t\t\twhile ( i-- ) {\n\t\t\t\tif ( ( elem = temp[ i ] ) ) {\n\t\t\t\t\tmatcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif ( seed ) {\n\t\t\tif ( postFinder || preFilter ) {\n\t\t\t\tif ( postFinder ) {\n\n\t\t\t\t\t// Get the final matcherOut by condensing this intermediate into postFinder contexts\n\t\t\t\t\ttemp = [];\n\t\t\t\t\ti = matcherOut.length;\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tif ( ( elem = matcherOut[ i ] ) ) {\n\n\t\t\t\t\t\t\t// Restore matcherIn since elem is not yet a final match\n\t\t\t\t\t\t\ttemp.push( ( matcherIn[ i ] = elem ) );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tpostFinder( null, ( matcherOut = [] ), temp, xml );\n\t\t\t\t}\n\n\t\t\t\t// Move matched elements from seed to results to keep them synchronized\n\t\t\t\ti = matcherOut.length;\n\t\t\t\twhile ( i-- ) {\n\t\t\t\t\tif ( ( elem = matcherOut[ i ] ) &&\n\t\t\t\t\t\t( temp = postFinder ? indexOf.call( seed, elem ) : preMap[ i ] ) > -1 ) {\n\n\t\t\t\t\t\tseed[ temp ] = !( results[ temp ] = elem );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Add elements to results, through postFinder if defined\n\t\t} else {\n\t\t\tmatcherOut = condense(\n\t\t\t\tmatcherOut === results ?\n\t\t\t\t\tmatcherOut.splice( preexisting, matcherOut.length ) :\n\t\t\t\t\tmatcherOut\n\t\t\t);\n\t\t\tif ( postFinder ) {\n\t\t\t\tpostFinder( null, results, matcherOut, xml );\n\t\t\t} else {\n\t\t\t\tpush.apply( results, matcherOut );\n\t\t\t}\n\t\t}\n\t} );\n}\n\nfunction matcherFromTokens( tokens ) {\n\tvar checkContext, matcher, j,\n\t\tlen = tokens.length,\n\t\tleadingRelative = Expr.relative[ tokens[ 0 ].type ],\n\t\timplicitRelative = leadingRelative || Expr.relative[ \" \" ],\n\t\ti = leadingRelative ? 1 : 0,\n\n\t\t// The foundational matcher ensures that elements are reachable from top-level context(s)\n\t\tmatchContext = addCombinator( function( elem ) {\n\t\t\treturn elem === checkContext;\n\t\t}, implicitRelative, true ),\n\t\tmatchAnyContext = addCombinator( function( elem ) {\n\t\t\treturn indexOf.call( checkContext, elem ) > -1;\n\t\t}, implicitRelative, true ),\n\t\tmatchers = [ function( elem, context, xml ) {\n\n\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t// two documents; shallow comparisons work.\n\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\tvar ret = ( !leadingRelative && ( xml || context != outermostContext ) ) || (\n\t\t\t\t( checkContext = context ).nodeType ?\n\t\t\t\t\tmatchContext( elem, context, xml ) :\n\t\t\t\t\tmatchAnyContext( elem, context, xml ) );\n\n\t\t\t// Avoid hanging onto element\n\t\t\t// (see https://github.com/jquery/sizzle/issues/299)\n\t\t\tcheckContext = null;\n\t\t\treturn ret;\n\t\t} ];\n\n\tfor ( ; i < len; i++ ) {\n\t\tif ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {\n\t\t\tmatchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];\n\t\t} else {\n\t\t\tmatcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );\n\n\t\t\t// Return special upon seeing a positional matcher\n\t\t\tif ( matcher[ expando ] ) {\n\n\t\t\t\t// Find the next relative operator (if any) for proper handling\n\t\t\t\tj = ++i;\n\t\t\t\tfor ( ; j < len; j++ ) {\n\t\t\t\t\tif ( Expr.relative[ tokens[ j ].type ] ) {\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn setMatcher(\n\t\t\t\t\ti > 1 && elementMatcher( matchers ),\n\t\t\t\t\ti > 1 && toSelector(\n\n\t\t\t\t\t\t// If the preceding token was a descendant combinator, insert an implicit any-element `*`\n\t\t\t\t\t\ttokens.slice( 0, i - 1 )\n\t\t\t\t\t\t\t.concat( { value: tokens[ i - 2 ].type === \" \" ? \"*\" : \"\" } )\n\t\t\t\t\t).replace( rtrimCSS, \"$1\" ),\n\t\t\t\t\tmatcher,\n\t\t\t\t\ti < j && matcherFromTokens( tokens.slice( i, j ) ),\n\t\t\t\t\tj < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),\n\t\t\t\t\tj < len && toSelector( tokens )\n\t\t\t\t);\n\t\t\t}\n\t\t\tmatchers.push( matcher );\n\t\t}\n\t}\n\n\treturn elementMatcher( matchers );\n}\n\nfunction matcherFromGroupMatchers( elementMatchers, setMatchers ) {\n\tvar bySet = setMatchers.length > 0,\n\t\tbyElement = elementMatchers.length > 0,\n\t\tsuperMatcher = function( seed, context, xml, results, outermost ) {\n\t\t\tvar elem, j, matcher,\n\t\t\t\tmatchedCount = 0,\n\t\t\t\ti = \"0\",\n\t\t\t\tunmatched = seed && [],\n\t\t\t\tsetMatched = [],\n\t\t\t\tcontextBackup = outermostContext,\n\n\t\t\t\t// We must always have either seed elements or outermost context\n\t\t\t\telems = seed || byElement && Expr.find.TAG( \"*\", outermost ),\n\n\t\t\t\t// Use integer dirruns iff this is the outermost matcher\n\t\t\t\tdirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),\n\t\t\t\tlen = elems.length;\n\n\t\t\tif ( outermost ) {\n\n\t\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t\t// two documents; shallow comparisons work.\n\t\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\t\toutermostContext = context == document || context || outermost;\n\t\t\t}\n\n\t\t\t// Add elements passing elementMatchers directly to results\n\t\t\t// Support: iOS <=7 - 9 only\n\t\t\t// Tolerate NodeList properties (IE: \"length\"; Safari: ) matching\n\t\t\t// elements by id. (see trac-14142)\n\t\t\tfor ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {\n\t\t\t\tif ( byElement && elem ) {\n\t\t\t\t\tj = 0;\n\n\t\t\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t\t\t// two documents; shallow comparisons work.\n\t\t\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\t\t\tif ( !context && elem.ownerDocument != document ) {\n\t\t\t\t\t\tsetDocument( elem );\n\t\t\t\t\t\txml = !documentIsHTML;\n\t\t\t\t\t}\n\t\t\t\t\twhile ( ( matcher = elementMatchers[ j++ ] ) ) {\n\t\t\t\t\t\tif ( matcher( elem, context || document, xml ) ) {\n\t\t\t\t\t\t\tpush.call( results, elem );\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif ( outermost ) {\n\t\t\t\t\t\tdirruns = dirrunsUnique;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Track unmatched elements for set filters\n\t\t\t\tif ( bySet ) {\n\n\t\t\t\t\t// They will have gone through all possible matchers\n\t\t\t\t\tif ( ( elem = !matcher && elem ) ) {\n\t\t\t\t\t\tmatchedCount--;\n\t\t\t\t\t}\n\n\t\t\t\t\t// Lengthen the array for every element, matched or not\n\t\t\t\t\tif ( seed ) {\n\t\t\t\t\t\tunmatched.push( elem );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// `i` is now the count of elements visited above, and adding it to `matchedCount`\n\t\t\t// makes the latter nonnegative.\n\t\t\tmatchedCount += i;\n\n\t\t\t// Apply set filters to unmatched elements\n\t\t\t// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`\n\t\t\t// equals `i`), unless we didn't visit _any_ elements in the above loop because we have\n\t\t\t// no element matchers and no seed.\n\t\t\t// Incrementing an initially-string \"0\" `i` allows `i` to remain a string only in that\n\t\t\t// case, which will result in a \"00\" `matchedCount` that differs from `i` but is also\n\t\t\t// numerically zero.\n\t\t\tif ( bySet && i !== matchedCount ) {\n\t\t\t\tj = 0;\n\t\t\t\twhile ( ( matcher = setMatchers[ j++ ] ) ) {\n\t\t\t\t\tmatcher( unmatched, setMatched, context, xml );\n\t\t\t\t}\n\n\t\t\t\tif ( seed ) {\n\n\t\t\t\t\t// Reintegrate element matches to eliminate the need for sorting\n\t\t\t\t\tif ( matchedCount > 0 ) {\n\t\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\t\tif ( !( unmatched[ i ] || setMatched[ i ] ) ) {\n\t\t\t\t\t\t\t\tsetMatched[ i ] = pop.call( results );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Discard index placeholder values to get only actual matches\n\t\t\t\t\tsetMatched = condense( setMatched );\n\t\t\t\t}\n\n\t\t\t\t// Add matches to results\n\t\t\t\tpush.apply( results, setMatched );\n\n\t\t\t\t// Seedless set matches succeeding multiple successful matchers stipulate sorting\n\t\t\t\tif ( outermost && !seed && setMatched.length > 0 &&\n\t\t\t\t\t( matchedCount + setMatchers.length ) > 1 ) {\n\n\t\t\t\t\tjQuery.uniqueSort( results );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Override manipulation of globals by nested matchers\n\t\t\tif ( outermost ) {\n\t\t\t\tdirruns = dirrunsUnique;\n\t\t\t\toutermostContext = contextBackup;\n\t\t\t}\n\n\t\t\treturn unmatched;\n\t\t};\n\n\treturn bySet ?\n\t\tmarkFunction( superMatcher ) :\n\t\tsuperMatcher;\n}\n\nfunction compile( selector, match /* Internal Use Only */ ) {\n\tvar i,\n\t\tsetMatchers = [],\n\t\telementMatchers = [],\n\t\tcached = compilerCache[ selector + \" \" ];\n\n\tif ( !cached ) {\n\n\t\t// Generate a function of recursive functions that can be used to check each element\n\t\tif ( !match ) {\n\t\t\tmatch = tokenize( selector );\n\t\t}\n\t\ti = match.length;\n\t\twhile ( i-- ) {\n\t\t\tcached = matcherFromTokens( match[ i ] );\n\t\t\tif ( cached[ expando ] ) {\n\t\t\t\tsetMatchers.push( cached );\n\t\t\t} else {\n\t\t\t\telementMatchers.push( cached );\n\t\t\t}\n\t\t}\n\n\t\t// Cache the compiled function\n\t\tcached = compilerCache( selector,\n\t\t\tmatcherFromGroupMatchers( elementMatchers, setMatchers ) );\n\n\t\t// Save selector and tokenization\n\t\tcached.selector = selector;\n\t}\n\treturn cached;\n}\n\n/**\n * A low-level selection function that works with jQuery's compiled\n * selector functions\n * @param {String|Function} selector A selector or a pre-compiled\n * selector function built with jQuery selector compile\n * @param {Element} context\n * @param {Array} [results]\n * @param {Array} [seed] A set of elements to match against\n */\nfunction select( selector, context, results, seed ) {\n\tvar i, tokens, token, type, find,\n\t\tcompiled = typeof selector === \"function\" && selector,\n\t\tmatch = !seed && tokenize( ( selector = compiled.selector || selector ) );\n\n\tresults = results || [];\n\n\t// Try to minimize operations if there is only one selector in the list and no seed\n\t// (the latter of which guarantees us context)\n\tif ( match.length === 1 ) {\n\n\t\t// Reduce context if the leading compound selector is an ID\n\t\ttokens = match[ 0 ] = match[ 0 ].slice( 0 );\n\t\tif ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === \"ID\" &&\n\t\t\t\tcontext.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {\n\n\t\t\tcontext = ( Expr.find.ID(\n\t\t\t\ttoken.matches[ 0 ].replace( runescape, funescape ),\n\t\t\t\tcontext\n\t\t\t) || [] )[ 0 ];\n\t\t\tif ( !context ) {\n\t\t\t\treturn results;\n\n\t\t\t// Precompiled matchers will still verify ancestry, so step up a level\n\t\t\t} else if ( compiled ) {\n\t\t\t\tcontext = context.parentNode;\n\t\t\t}\n\n\t\t\tselector = selector.slice( tokens.shift().value.length );\n\t\t}\n\n\t\t// Fetch a seed set for right-to-left matching\n\t\ti = matchExpr.needsContext.test( selector ) ? 0 : tokens.length;\n\t\twhile ( i-- ) {\n\t\t\ttoken = tokens[ i ];\n\n\t\t\t// Abort if we hit a combinator\n\t\t\tif ( Expr.relative[ ( type = token.type ) ] ) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif ( ( find = Expr.find[ type ] ) ) {\n\n\t\t\t\t// Search, expanding context for leading sibling combinators\n\t\t\t\tif ( ( seed = find(\n\t\t\t\t\ttoken.matches[ 0 ].replace( runescape, funescape ),\n\t\t\t\t\trsibling.test( tokens[ 0 ].type ) &&\n\t\t\t\t\t\ttestContext( context.parentNode ) || context\n\t\t\t\t) ) ) {\n\n\t\t\t\t\t// If seed is empty or no tokens remain, we can return early\n\t\t\t\t\ttokens.splice( i, 1 );\n\t\t\t\t\tselector = seed.length && toSelector( tokens );\n\t\t\t\t\tif ( !selector ) {\n\t\t\t\t\t\tpush.apply( results, seed );\n\t\t\t\t\t\treturn results;\n\t\t\t\t\t}\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Compile and execute a filtering function if one is not provided\n\t// Provide `match` to avoid retokenization if we modified the selector above\n\t( compiled || compile( selector, match ) )(\n\t\tseed,\n\t\tcontext,\n\t\t!documentIsHTML,\n\t\tresults,\n\t\t!context || rsibling.test( selector ) && testContext( context.parentNode ) || context\n\t);\n\treturn results;\n}\n\n// One-time assignments\n\n// Support: Android <=4.0 - 4.1+\n// Sort stability\nsupport.sortStable = expando.split( \"\" ).sort( sortOrder ).join( \"\" ) === expando;\n\n// Initialize against the default document\nsetDocument();\n\n// Support: Android <=4.0 - 4.1+\n// Detached nodes confoundingly follow *each other*\nsupport.sortDetached = assert( function( el ) {\n\n\t// Should return 1, but returns 4 (following)\n\treturn el.compareDocumentPosition( document.createElement( \"fieldset\" ) ) & 1;\n} );\n\njQuery.find = find;\n\n// Deprecated\njQuery.expr[ \":\" ] = jQuery.expr.pseudos;\njQuery.unique = jQuery.uniqueSort;\n\n// These have always been private, but they used to be documented as part of\n// Sizzle so let's maintain them for now for backwards compatibility purposes.\nfind.compile = compile;\nfind.select = select;\nfind.setDocument = setDocument;\nfind.tokenize = tokenize;\n\nfind.escape = jQuery.escapeSelector;\nfind.getText = jQuery.text;\nfind.isXML = jQuery.isXMLDoc;\nfind.selectors = jQuery.expr;\nfind.support = jQuery.support;\nfind.uniqueSort = jQuery.uniqueSort;\n\n\t/* eslint-enable */\n\n} )();\n\n\nvar dir = function( elem, dir, until ) {\n\tvar matched = [],\n\t\ttruncate = until !== undefined;\n\n\twhile ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {\n\t\tif ( elem.nodeType === 1 ) {\n\t\t\tif ( truncate && jQuery( elem ).is( until ) ) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tmatched.push( elem );\n\t\t}\n\t}\n\treturn matched;\n};\n\n\nvar siblings = function( n, elem ) {\n\tvar matched = [];\n\n\tfor ( ; n; n = n.nextSibling ) {\n\t\tif ( n.nodeType === 1 && n !== elem ) {\n\t\t\tmatched.push( n );\n\t\t}\n\t}\n\n\treturn matched;\n};\n\n\nvar rneedsContext = jQuery.expr.match.needsContext;\n\nvar rsingleTag = ( /^<([a-z][^\\/\\0>:\\x20\\t\\r\\n\\f]*)[\\x20\\t\\r\\n\\f]*\\/?>(?:<\\/\\1>|)$/i );\n\n\n\n// Implement the identical functionality for filter and not\nfunction winnow( elements, qualifier, not ) {\n\tif ( isFunction( qualifier ) ) {\n\t\treturn jQuery.grep( elements, function( elem, i ) {\n\t\t\treturn !!qualifier.call( elem, i, elem ) !== not;\n\t\t} );\n\t}\n\n\t// Single element\n\tif ( qualifier.nodeType ) {\n\t\treturn jQuery.grep( elements, function( elem ) {\n\t\t\treturn ( elem === qualifier ) !== not;\n\t\t} );\n\t}\n\n\t// Arraylike of elements (jQuery, arguments, Array)\n\tif ( typeof qualifier !== \"string\" ) {\n\t\treturn jQuery.grep( elements, function( elem ) {\n\t\t\treturn ( indexOf.call( qualifier, elem ) > -1 ) !== not;\n\t\t} );\n\t}\n\n\t// Filtered directly for both simple and complex selectors\n\treturn jQuery.filter( qualifier, elements, not );\n}\n\njQuery.filter = function( expr, elems, not ) {\n\tvar elem = elems[ 0 ];\n\n\tif ( not ) {\n\t\texpr = \":not(\" + expr + \")\";\n\t}\n\n\tif ( elems.length === 1 && elem.nodeType === 1 ) {\n\t\treturn jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];\n\t}\n\n\treturn jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {\n\t\treturn elem.nodeType === 1;\n\t} ) );\n};\n\njQuery.fn.extend( {\n\tfind: function( selector ) {\n\t\tvar i, ret,\n\t\t\tlen = this.length,\n\t\t\tself = this;\n\n\t\tif ( typeof selector !== \"string\" ) {\n\t\t\treturn this.pushStack( jQuery( selector ).filter( function() {\n\t\t\t\tfor ( i = 0; i < len; i++ ) {\n\t\t\t\t\tif ( jQuery.contains( self[ i ], this ) ) {\n\t\t\t\t\t\treturn true;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} ) );\n\t\t}\n\n\t\tret = this.pushStack( [] );\n\n\t\tfor ( i = 0; i < len; i++ ) {\n\t\t\tjQuery.find( selector, self[ i ], ret );\n\t\t}\n\n\t\treturn len > 1 ? jQuery.uniqueSort( ret ) : ret;\n\t},\n\tfilter: function( selector ) {\n\t\treturn this.pushStack( winnow( this, selector || [], false ) );\n\t},\n\tnot: function( selector ) {\n\t\treturn this.pushStack( winnow( this, selector || [], true ) );\n\t},\n\tis: function( selector ) {\n\t\treturn !!winnow(\n\t\t\tthis,\n\n\t\t\t// If this is a positional/relative selector, check membership in the returned set\n\t\t\t// so $(\"p:first\").is(\"p:last\") won't return true for a doc with two \"p\".\n\t\t\ttypeof selector === \"string\" && rneedsContext.test( selector ) ?\n\t\t\t\tjQuery( selector ) :\n\t\t\t\tselector || [],\n\t\t\tfalse\n\t\t).length;\n\t}\n} );\n\n\n// Initialize a jQuery object\n\n\n// A central reference to the root jQuery(document)\nvar rootjQuery,\n\n\t// A simple way to check for HTML strings\n\t// Prioritize #id over to avoid XSS via location.hash (trac-9521)\n\t// Strict HTML recognition (trac-11290: must start with <)\n\t// Shortcut simple #id case for speed\n\trquickExpr = /^(?:\\s*(<[\\w\\W]+>)[^>]*|#([\\w-]+))$/,\n\n\tinit = jQuery.fn.init = function( selector, context, root ) {\n\t\tvar match, elem;\n\n\t\t// HANDLE: $(\"\"), $(null), $(undefined), $(false)\n\t\tif ( !selector ) {\n\t\t\treturn this;\n\t\t}\n\n\t\t// Method init() accepts an alternate rootjQuery\n\t\t// so migrate can support jQuery.sub (gh-2101)\n\t\troot = root || rootjQuery;\n\n\t\t// Handle HTML strings\n\t\tif ( typeof selector === \"string\" ) {\n\t\t\tif ( selector[ 0 ] === \"<\" &&\n\t\t\t\tselector[ selector.length - 1 ] === \">\" &&\n\t\t\t\tselector.length >= 3 ) {\n\n\t\t\t\t// Assume that strings that start and end with <> are HTML and skip the regex check\n\t\t\t\tmatch = [ null, selector, null ];\n\n\t\t\t} else {\n\t\t\t\tmatch = rquickExpr.exec( selector );\n\t\t\t}\n\n\t\t\t// Match html or make sure no context is specified for #id\n\t\t\tif ( match && ( match[ 1 ] || !context ) ) {\n\n\t\t\t\t// HANDLE: $(html) -> $(array)\n\t\t\t\tif ( match[ 1 ] ) {\n\t\t\t\t\tcontext = context instanceof jQuery ? context[ 0 ] : context;\n\n\t\t\t\t\t// Option to run scripts is true for back-compat\n\t\t\t\t\t// Intentionally let the error be thrown if parseHTML is not present\n\t\t\t\t\tjQuery.merge( this, jQuery.parseHTML(\n\t\t\t\t\t\tmatch[ 1 ],\n\t\t\t\t\t\tcontext && context.nodeType ? context.ownerDocument || context : document,\n\t\t\t\t\t\ttrue\n\t\t\t\t\t) );\n\n\t\t\t\t\t// HANDLE: $(html, props)\n\t\t\t\t\tif ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {\n\t\t\t\t\t\tfor ( match in context ) {\n\n\t\t\t\t\t\t\t// Properties of context are called as methods if possible\n\t\t\t\t\t\t\tif ( isFunction( this[ match ] ) ) {\n\t\t\t\t\t\t\t\tthis[ match ]( context[ match ] );\n\n\t\t\t\t\t\t\t// ...and otherwise set as attributes\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tthis.attr( match, context[ match ] );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\treturn this;\n\n\t\t\t\t// HANDLE: $(#id)\n\t\t\t\t} else {\n\t\t\t\t\telem = document.getElementById( match[ 2 ] );\n\n\t\t\t\t\tif ( elem ) {\n\n\t\t\t\t\t\t// Inject the element directly into the jQuery object\n\t\t\t\t\t\tthis[ 0 ] = elem;\n\t\t\t\t\t\tthis.length = 1;\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t}\n\n\t\t\t// HANDLE: $(expr, $(...))\n\t\t\t} else if ( !context || context.jquery ) {\n\t\t\t\treturn ( context || root ).find( selector );\n\n\t\t\t// HANDLE: $(expr, context)\n\t\t\t// (which is just equivalent to: $(context).find(expr)\n\t\t\t} else {\n\t\t\t\treturn this.constructor( context ).find( selector );\n\t\t\t}\n\n\t\t// HANDLE: $(DOMElement)\n\t\t} else if ( selector.nodeType ) {\n\t\t\tthis[ 0 ] = selector;\n\t\t\tthis.length = 1;\n\t\t\treturn this;\n\n\t\t// HANDLE: $(function)\n\t\t// Shortcut for document ready\n\t\t} else if ( isFunction( selector ) ) {\n\t\t\treturn root.ready !== undefined ?\n\t\t\t\troot.ready( selector ) :\n\n\t\t\t\t// Execute immediately if ready is not present\n\t\t\t\tselector( jQuery );\n\t\t}\n\n\t\treturn jQuery.makeArray( selector, this );\n\t};\n\n// Give the init function the jQuery prototype for later instantiation\ninit.prototype = jQuery.fn;\n\n// Initialize central reference\nrootjQuery = jQuery( document );\n\n\nvar rparentsprev = /^(?:parents|prev(?:Until|All))/,\n\n\t// Methods guaranteed to produce a unique set when starting from a unique set\n\tguaranteedUnique = {\n\t\tchildren: true,\n\t\tcontents: true,\n\t\tnext: true,\n\t\tprev: true\n\t};\n\njQuery.fn.extend( {\n\thas: function( target ) {\n\t\tvar targets = jQuery( target, this ),\n\t\t\tl = targets.length;\n\n\t\treturn this.filter( function() {\n\t\t\tvar i = 0;\n\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\tif ( jQuery.contains( this, targets[ i ] ) ) {\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\t\t\t}\n\t\t} );\n\t},\n\n\tclosest: function( selectors, context ) {\n\t\tvar cur,\n\t\t\ti = 0,\n\t\t\tl = this.length,\n\t\t\tmatched = [],\n\t\t\ttargets = typeof selectors !== \"string\" && jQuery( selectors );\n\n\t\t// Positional selectors never match, since there's no _selection_ context\n\t\tif ( !rneedsContext.test( selectors ) ) {\n\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\tfor ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {\n\n\t\t\t\t\t// Always skip document fragments\n\t\t\t\t\tif ( cur.nodeType < 11 && ( targets ?\n\t\t\t\t\t\ttargets.index( cur ) > -1 :\n\n\t\t\t\t\t\t// Don't pass non-elements to jQuery#find\n\t\t\t\t\t\tcur.nodeType === 1 &&\n\t\t\t\t\t\t\tjQuery.find.matchesSelector( cur, selectors ) ) ) {\n\n\t\t\t\t\t\tmatched.push( cur );\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );\n\t},\n\n\t// Determine the position of an element within the set\n\tindex: function( elem ) {\n\n\t\t// No argument, return index in parent\n\t\tif ( !elem ) {\n\t\t\treturn ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;\n\t\t}\n\n\t\t// Index in selector\n\t\tif ( typeof elem === \"string\" ) {\n\t\t\treturn indexOf.call( jQuery( elem ), this[ 0 ] );\n\t\t}\n\n\t\t// Locate the position of the desired element\n\t\treturn indexOf.call( this,\n\n\t\t\t// If it receives a jQuery object, the first element is used\n\t\t\telem.jquery ? elem[ 0 ] : elem\n\t\t);\n\t},\n\n\tadd: function( selector, context ) {\n\t\treturn this.pushStack(\n\t\t\tjQuery.uniqueSort(\n\t\t\t\tjQuery.merge( this.get(), jQuery( selector, context ) )\n\t\t\t)\n\t\t);\n\t},\n\n\taddBack: function( selector ) {\n\t\treturn this.add( selector == null ?\n\t\t\tthis.prevObject : this.prevObject.filter( selector )\n\t\t);\n\t}\n} );\n\nfunction sibling( cur, dir ) {\n\twhile ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}\n\treturn cur;\n}\n\njQuery.each( {\n\tparent: function( elem ) {\n\t\tvar parent = elem.parentNode;\n\t\treturn parent && parent.nodeType !== 11 ? parent : null;\n\t},\n\tparents: function( elem ) {\n\t\treturn dir( elem, \"parentNode\" );\n\t},\n\tparentsUntil: function( elem, _i, until ) {\n\t\treturn dir( elem, \"parentNode\", until );\n\t},\n\tnext: function( elem ) {\n\t\treturn sibling( elem, \"nextSibling\" );\n\t},\n\tprev: function( elem ) {\n\t\treturn sibling( elem, \"previousSibling\" );\n\t},\n\tnextAll: function( elem ) {\n\t\treturn dir( elem, \"nextSibling\" );\n\t},\n\tprevAll: function( elem ) {\n\t\treturn dir( elem, \"previousSibling\" );\n\t},\n\tnextUntil: function( elem, _i, until ) {\n\t\treturn dir( elem, \"nextSibling\", until );\n\t},\n\tprevUntil: function( elem, _i, until ) {\n\t\treturn dir( elem, \"previousSibling\", until );\n\t},\n\tsiblings: function( elem ) {\n\t\treturn siblings( ( elem.parentNode || {} ).firstChild, elem );\n\t},\n\tchildren: function( elem ) {\n\t\treturn siblings( elem.firstChild );\n\t},\n\tcontents: function( elem ) {\n\t\tif ( elem.contentDocument != null &&\n\n\t\t\t// Support: IE 11+\n\t\t\t// elements with no `data` attribute has an object\n\t\t\t// `contentDocument` with a `null` prototype.\n\t\t\tgetProto( elem.contentDocument ) ) {\n\n\t\t\treturn elem.contentDocument;\n\t\t}\n\n\t\t// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only\n\t\t// Treat the template element as a regular one in browsers that\n\t\t// don't support it.\n\t\tif ( nodeName( elem, \"template\" ) ) {\n\t\t\telem = elem.content || elem;\n\t\t}\n\n\t\treturn jQuery.merge( [], elem.childNodes );\n\t}\n}, function( name, fn ) {\n\tjQuery.fn[ name ] = function( until, selector ) {\n\t\tvar matched = jQuery.map( this, fn, until );\n\n\t\tif ( name.slice( -5 ) !== \"Until\" ) {\n\t\t\tselector = until;\n\t\t}\n\n\t\tif ( selector && typeof selector === \"string\" ) {\n\t\t\tmatched = jQuery.filter( selector, matched );\n\t\t}\n\n\t\tif ( this.length > 1 ) {\n\n\t\t\t// Remove duplicates\n\t\t\tif ( !guaranteedUnique[ name ] ) {\n\t\t\t\tjQuery.uniqueSort( matched );\n\t\t\t}\n\n\t\t\t// Reverse order for parents* and prev-derivatives\n\t\t\tif ( rparentsprev.test( name ) ) {\n\t\t\t\tmatched.reverse();\n\t\t\t}\n\t\t}\n\n\t\treturn this.pushStack( matched );\n\t};\n} );\nvar rnothtmlwhite = ( /[^\\x20\\t\\r\\n\\f]+/g );\n\n\n\n// Convert String-formatted options into Object-formatted ones\nfunction createOptions( options ) {\n\tvar object = {};\n\tjQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {\n\t\tobject[ flag ] = true;\n\t} );\n\treturn object;\n}\n\n/*\n * Create a callback list using the following parameters:\n *\n *\toptions: an optional list of space-separated options that will change how\n *\t\t\tthe callback list behaves or a more traditional option object\n *\n * By default a callback list will act like an event callback list and can be\n * \"fired\" multiple times.\n *\n * Possible options:\n *\n *\tonce:\t\t\twill ensure the callback list can only be fired once (like a Deferred)\n *\n *\tmemory:\t\t\twill keep track of previous values and will call any callback added\n *\t\t\t\t\tafter the list has been fired right away with the latest \"memorized\"\n *\t\t\t\t\tvalues (like a Deferred)\n *\n *\tunique:\t\t\twill ensure a callback can only be added once (no duplicate in the list)\n *\n *\tstopOnFalse:\tinterrupt callings when a callback returns false\n *\n */\njQuery.Callbacks = function( options ) {\n\n\t// Convert options from String-formatted to Object-formatted if needed\n\t// (we check in cache first)\n\toptions = typeof options === \"string\" ?\n\t\tcreateOptions( options ) :\n\t\tjQuery.extend( {}, options );\n\n\tvar // Flag to know if list is currently firing\n\t\tfiring,\n\n\t\t// Last fire value for non-forgettable lists\n\t\tmemory,\n\n\t\t// Flag to know if list was already fired\n\t\tfired,\n\n\t\t// Flag to prevent firing\n\t\tlocked,\n\n\t\t// Actual callback list\n\t\tlist = [],\n\n\t\t// Queue of execution data for repeatable lists\n\t\tqueue = [],\n\n\t\t// Index of currently firing callback (modified by add/remove as needed)\n\t\tfiringIndex = -1,\n\n\t\t// Fire callbacks\n\t\tfire = function() {\n\n\t\t\t// Enforce single-firing\n\t\t\tlocked = locked || options.once;\n\n\t\t\t// Execute callbacks for all pending executions,\n\t\t\t// respecting firingIndex overrides and runtime changes\n\t\t\tfired = firing = true;\n\t\t\tfor ( ; queue.length; firingIndex = -1 ) {\n\t\t\t\tmemory = queue.shift();\n\t\t\t\twhile ( ++firingIndex < list.length ) {\n\n\t\t\t\t\t// Run callback and check for early termination\n\t\t\t\t\tif ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&\n\t\t\t\t\t\toptions.stopOnFalse ) {\n\n\t\t\t\t\t\t// Jump to end and forget the data so .add doesn't re-fire\n\t\t\t\t\t\tfiringIndex = list.length;\n\t\t\t\t\t\tmemory = false;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Forget the data if we're done with it\n\t\t\tif ( !options.memory ) {\n\t\t\t\tmemory = false;\n\t\t\t}\n\n\t\t\tfiring = false;\n\n\t\t\t// Clean up if we're done firing for good\n\t\t\tif ( locked ) {\n\n\t\t\t\t// Keep an empty list if we have data for future add calls\n\t\t\t\tif ( memory ) {\n\t\t\t\t\tlist = [];\n\n\t\t\t\t// Otherwise, this object is spent\n\t\t\t\t} else {\n\t\t\t\t\tlist = \"\";\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\n\t\t// Actual Callbacks object\n\t\tself = {\n\n\t\t\t// Add a callback or a collection of callbacks to the list\n\t\t\tadd: function() {\n\t\t\t\tif ( list ) {\n\n\t\t\t\t\t// If we have memory from a past run, we should fire after adding\n\t\t\t\t\tif ( memory && !firing ) {\n\t\t\t\t\t\tfiringIndex = list.length - 1;\n\t\t\t\t\t\tqueue.push( memory );\n\t\t\t\t\t}\n\n\t\t\t\t\t( function add( args ) {\n\t\t\t\t\t\tjQuery.each( args, function( _, arg ) {\n\t\t\t\t\t\t\tif ( isFunction( arg ) ) {\n\t\t\t\t\t\t\t\tif ( !options.unique || !self.has( arg ) ) {\n\t\t\t\t\t\t\t\t\tlist.push( arg );\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} else if ( arg && arg.length && toType( arg ) !== \"string\" ) {\n\n\t\t\t\t\t\t\t\t// Inspect recursively\n\t\t\t\t\t\t\t\tadd( arg );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} );\n\t\t\t\t\t} )( arguments );\n\n\t\t\t\t\tif ( memory && !firing ) {\n\t\t\t\t\t\tfire();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Remove a callback from the list\n\t\t\tremove: function() {\n\t\t\t\tjQuery.each( arguments, function( _, arg ) {\n\t\t\t\t\tvar index;\n\t\t\t\t\twhile ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {\n\t\t\t\t\t\tlist.splice( index, 1 );\n\n\t\t\t\t\t\t// Handle firing indexes\n\t\t\t\t\t\tif ( index <= firingIndex ) {\n\t\t\t\t\t\t\tfiringIndex--;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t} );\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Check if a given callback is in the list.\n\t\t\t// If no argument is given, return whether or not list has callbacks attached.\n\t\t\thas: function( fn ) {\n\t\t\t\treturn fn ?\n\t\t\t\t\tjQuery.inArray( fn, list ) > -1 :\n\t\t\t\t\tlist.length > 0;\n\t\t\t},\n\n\t\t\t// Remove all callbacks from the list\n\t\t\tempty: function() {\n\t\t\t\tif ( list ) {\n\t\t\t\t\tlist = [];\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Disable .fire and .add\n\t\t\t// Abort any current/pending executions\n\t\t\t// Clear all callbacks and values\n\t\t\tdisable: function() {\n\t\t\t\tlocked = queue = [];\n\t\t\t\tlist = memory = \"\";\n\t\t\t\treturn this;\n\t\t\t},\n\t\t\tdisabled: function() {\n\t\t\t\treturn !list;\n\t\t\t},\n\n\t\t\t// Disable .fire\n\t\t\t// Also disable .add unless we have memory (since it would have no effect)\n\t\t\t// Abort any pending executions\n\t\t\tlock: function() {\n\t\t\t\tlocked = queue = [];\n\t\t\t\tif ( !memory && !firing ) {\n\t\t\t\t\tlist = memory = \"\";\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\t\t\tlocked: function() {\n\t\t\t\treturn !!locked;\n\t\t\t},\n\n\t\t\t// Call all callbacks with the given context and arguments\n\t\t\tfireWith: function( context, args ) {\n\t\t\t\tif ( !locked ) {\n\t\t\t\t\targs = args || [];\n\t\t\t\t\targs = [ context, args.slice ? args.slice() : args ];\n\t\t\t\t\tqueue.push( args );\n\t\t\t\t\tif ( !firing ) {\n\t\t\t\t\t\tfire();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Call all the callbacks with the given arguments\n\t\t\tfire: function() {\n\t\t\t\tself.fireWith( this, arguments );\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// To know if the callbacks have already been called at least once\n\t\t\tfired: function() {\n\t\t\t\treturn !!fired;\n\t\t\t}\n\t\t};\n\n\treturn self;\n};\n\n\nfunction Identity( v ) {\n\treturn v;\n}\nfunction Thrower( ex ) {\n\tthrow ex;\n}\n\nfunction adoptValue( value, resolve, reject, noValue ) {\n\tvar method;\n\n\ttry {\n\n\t\t// Check for promise aspect first to privilege synchronous behavior\n\t\tif ( value && isFunction( ( method = value.promise ) ) ) {\n\t\t\tmethod.call( value ).done( resolve ).fail( reject );\n\n\t\t// Other thenables\n\t\t} else if ( value && isFunction( ( method = value.then ) ) ) {\n\t\t\tmethod.call( value, resolve, reject );\n\n\t\t// Other non-thenables\n\t\t} else {\n\n\t\t\t// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:\n\t\t\t// * false: [ value ].slice( 0 ) => resolve( value )\n\t\t\t// * true: [ value ].slice( 1 ) => resolve()\n\t\t\tresolve.apply( undefined, [ value ].slice( noValue ) );\n\t\t}\n\n\t// For Promises/A+, convert exceptions into rejections\n\t// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in\n\t// Deferred#then to conditionally suppress rejection.\n\t} catch ( value ) {\n\n\t\t// Support: Android 4.0 only\n\t\t// Strict mode functions invoked without .call/.apply get global-object context\n\t\treject.apply( undefined, [ value ] );\n\t}\n}\n\njQuery.extend( {\n\n\tDeferred: function( func ) {\n\t\tvar tuples = [\n\n\t\t\t\t// action, add listener, callbacks,\n\t\t\t\t// ... .then handlers, argument index, [final state]\n\t\t\t\t[ \"notify\", \"progress\", jQuery.Callbacks( \"memory\" ),\n\t\t\t\t\tjQuery.Callbacks( \"memory\" ), 2 ],\n\t\t\t\t[ \"resolve\", \"done\", jQuery.Callbacks( \"once memory\" ),\n\t\t\t\t\tjQuery.Callbacks( \"once memory\" ), 0, \"resolved\" ],\n\t\t\t\t[ \"reject\", \"fail\", jQuery.Callbacks( \"once memory\" ),\n\t\t\t\t\tjQuery.Callbacks( \"once memory\" ), 1, \"rejected\" ]\n\t\t\t],\n\t\t\tstate = \"pending\",\n\t\t\tpromise = {\n\t\t\t\tstate: function() {\n\t\t\t\t\treturn state;\n\t\t\t\t},\n\t\t\t\talways: function() {\n\t\t\t\t\tdeferred.done( arguments ).fail( arguments );\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\t\t\t\t\"catch\": function( fn ) {\n\t\t\t\t\treturn promise.then( null, fn );\n\t\t\t\t},\n\n\t\t\t\t// Keep pipe for back-compat\n\t\t\t\tpipe: function( /* fnDone, fnFail, fnProgress */ ) {\n\t\t\t\t\tvar fns = arguments;\n\n\t\t\t\t\treturn jQuery.Deferred( function( newDefer ) {\n\t\t\t\t\t\tjQuery.each( tuples, function( _i, tuple ) {\n\n\t\t\t\t\t\t\t// Map tuples (progress, done, fail) to arguments (done, fail, progress)\n\t\t\t\t\t\t\tvar fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];\n\n\t\t\t\t\t\t\t// deferred.progress(function() { bind to newDefer or newDefer.notify })\n\t\t\t\t\t\t\t// deferred.done(function() { bind to newDefer or newDefer.resolve })\n\t\t\t\t\t\t\t// deferred.fail(function() { bind to newDefer or newDefer.reject })\n\t\t\t\t\t\t\tdeferred[ tuple[ 1 ] ]( function() {\n\t\t\t\t\t\t\t\tvar returned = fn && fn.apply( this, arguments );\n\t\t\t\t\t\t\t\tif ( returned && isFunction( returned.promise ) ) {\n\t\t\t\t\t\t\t\t\treturned.promise()\n\t\t\t\t\t\t\t\t\t\t.progress( newDefer.notify )\n\t\t\t\t\t\t\t\t\t\t.done( newDefer.resolve )\n\t\t\t\t\t\t\t\t\t\t.fail( newDefer.reject );\n\t\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t\tnewDefer[ tuple[ 0 ] + \"With\" ](\n\t\t\t\t\t\t\t\t\t\tthis,\n\t\t\t\t\t\t\t\t\t\tfn ? [ returned ] : arguments\n\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t} );\n\t\t\t\t\t\tfns = null;\n\t\t\t\t\t} ).promise();\n\t\t\t\t},\n\t\t\t\tthen: function( onFulfilled, onRejected, onProgress ) {\n\t\t\t\t\tvar maxDepth = 0;\n\t\t\t\t\tfunction resolve( depth, deferred, handler, special ) {\n\t\t\t\t\t\treturn function() {\n\t\t\t\t\t\t\tvar that = this,\n\t\t\t\t\t\t\t\targs = arguments,\n\t\t\t\t\t\t\t\tmightThrow = function() {\n\t\t\t\t\t\t\t\t\tvar returned, then;\n\n\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.3.3.3\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-59\n\t\t\t\t\t\t\t\t\t// Ignore double-resolution attempts\n\t\t\t\t\t\t\t\t\tif ( depth < maxDepth ) {\n\t\t\t\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\treturned = handler.apply( that, args );\n\n\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.1\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-48\n\t\t\t\t\t\t\t\t\tif ( returned === deferred.promise() ) {\n\t\t\t\t\t\t\t\t\t\tthrow new TypeError( \"Thenable self-resolution\" );\n\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t// Support: Promises/A+ sections 2.3.3.1, 3.5\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-54\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-75\n\t\t\t\t\t\t\t\t\t// Retrieve `then` only once\n\t\t\t\t\t\t\t\t\tthen = returned &&\n\n\t\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.4\n\t\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-64\n\t\t\t\t\t\t\t\t\t\t// Only check objects and functions for thenability\n\t\t\t\t\t\t\t\t\t\t( typeof returned === \"object\" ||\n\t\t\t\t\t\t\t\t\t\t\ttypeof returned === \"function\" ) &&\n\t\t\t\t\t\t\t\t\t\treturned.then;\n\n\t\t\t\t\t\t\t\t\t// Handle a returned thenable\n\t\t\t\t\t\t\t\t\tif ( isFunction( then ) ) {\n\n\t\t\t\t\t\t\t\t\t\t// Special processors (notify) just wait for resolution\n\t\t\t\t\t\t\t\t\t\tif ( special ) {\n\t\t\t\t\t\t\t\t\t\t\tthen.call(\n\t\t\t\t\t\t\t\t\t\t\t\treturned,\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Identity, special ),\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Thrower, special )\n\t\t\t\t\t\t\t\t\t\t\t);\n\n\t\t\t\t\t\t\t\t\t\t// Normal processors (resolve) also hook into progress\n\t\t\t\t\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t\t\t\t\t// ...and disregard older resolution values\n\t\t\t\t\t\t\t\t\t\t\tmaxDepth++;\n\n\t\t\t\t\t\t\t\t\t\t\tthen.call(\n\t\t\t\t\t\t\t\t\t\t\t\treturned,\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Identity, special ),\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Thrower, special ),\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Identity,\n\t\t\t\t\t\t\t\t\t\t\t\t\tdeferred.notifyWith )\n\t\t\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t// Handle all other returned values\n\t\t\t\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t\t\t\t// Only substitute handlers pass on context\n\t\t\t\t\t\t\t\t\t\t// and multiple values (non-spec behavior)\n\t\t\t\t\t\t\t\t\t\tif ( handler !== Identity ) {\n\t\t\t\t\t\t\t\t\t\t\tthat = undefined;\n\t\t\t\t\t\t\t\t\t\t\targs = [ returned ];\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t// Process the value(s)\n\t\t\t\t\t\t\t\t\t\t// Default process is resolve\n\t\t\t\t\t\t\t\t\t\t( special || deferred.resolveWith )( that, args );\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t},\n\n\t\t\t\t\t\t\t\t// Only normal processors (resolve) catch and reject exceptions\n\t\t\t\t\t\t\t\tprocess = special ?\n\t\t\t\t\t\t\t\t\tmightThrow :\n\t\t\t\t\t\t\t\t\tfunction() {\n\t\t\t\t\t\t\t\t\t\ttry {\n\t\t\t\t\t\t\t\t\t\t\tmightThrow();\n\t\t\t\t\t\t\t\t\t\t} catch ( e ) {\n\n\t\t\t\t\t\t\t\t\t\t\tif ( jQuery.Deferred.exceptionHook ) {\n\t\t\t\t\t\t\t\t\t\t\t\tjQuery.Deferred.exceptionHook( e,\n\t\t\t\t\t\t\t\t\t\t\t\t\tprocess.error );\n\t\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.3.3.4.1\n\t\t\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-61\n\t\t\t\t\t\t\t\t\t\t\t// Ignore post-resolution exceptions\n\t\t\t\t\t\t\t\t\t\t\tif ( depth + 1 >= maxDepth ) {\n\n\t\t\t\t\t\t\t\t\t\t\t\t// Only substitute handlers pass on context\n\t\t\t\t\t\t\t\t\t\t\t\t// and multiple values (non-spec behavior)\n\t\t\t\t\t\t\t\t\t\t\t\tif ( handler !== Thrower ) {\n\t\t\t\t\t\t\t\t\t\t\t\t\tthat = undefined;\n\t\t\t\t\t\t\t\t\t\t\t\t\targs = [ e ];\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t\t\tdeferred.rejectWith( that, args );\n\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t};\n\n\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.3.3.1\n\t\t\t\t\t\t\t// https://promisesaplus.com/#point-57\n\t\t\t\t\t\t\t// Re-resolve promises immediately to dodge false rejection from\n\t\t\t\t\t\t\t// subsequent errors\n\t\t\t\t\t\t\tif ( depth ) {\n\t\t\t\t\t\t\t\tprocess();\n\t\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t\t// Call an optional hook to record the error, in case of exception\n\t\t\t\t\t\t\t\t// since it's otherwise lost when execution goes async\n\t\t\t\t\t\t\t\tif ( jQuery.Deferred.getErrorHook ) {\n\t\t\t\t\t\t\t\t\tprocess.error = jQuery.Deferred.getErrorHook();\n\n\t\t\t\t\t\t\t\t// The deprecated alias of the above. While the name suggests\n\t\t\t\t\t\t\t\t// returning the stack, not an error instance, jQuery just passes\n\t\t\t\t\t\t\t\t// it directly to `console.warn` so both will work; an instance\n\t\t\t\t\t\t\t\t// just better cooperates with source maps.\n\t\t\t\t\t\t\t\t} else if ( jQuery.Deferred.getStackHook ) {\n\t\t\t\t\t\t\t\t\tprocess.error = jQuery.Deferred.getStackHook();\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\twindow.setTimeout( process );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t};\n\t\t\t\t\t}\n\n\t\t\t\t\treturn jQuery.Deferred( function( newDefer ) {\n\n\t\t\t\t\t\t// progress_handlers.add( ... )\n\t\t\t\t\t\ttuples[ 0 ][ 3 ].add(\n\t\t\t\t\t\t\tresolve(\n\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\tnewDefer,\n\t\t\t\t\t\t\t\tisFunction( onProgress ) ?\n\t\t\t\t\t\t\t\t\tonProgress :\n\t\t\t\t\t\t\t\t\tIdentity,\n\t\t\t\t\t\t\t\tnewDefer.notifyWith\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t);\n\n\t\t\t\t\t\t// fulfilled_handlers.add( ... )\n\t\t\t\t\t\ttuples[ 1 ][ 3 ].add(\n\t\t\t\t\t\t\tresolve(\n\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\tnewDefer,\n\t\t\t\t\t\t\t\tisFunction( onFulfilled ) ?\n\t\t\t\t\t\t\t\t\tonFulfilled :\n\t\t\t\t\t\t\t\t\tIdentity\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t);\n\n\t\t\t\t\t\t// rejected_handlers.add( ... )\n\t\t\t\t\t\ttuples[ 2 ][ 3 ].add(\n\t\t\t\t\t\t\tresolve(\n\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\tnewDefer,\n\t\t\t\t\t\t\t\tisFunction( onRejected ) ?\n\t\t\t\t\t\t\t\t\tonRejected :\n\t\t\t\t\t\t\t\t\tThrower\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t);\n\t\t\t\t\t} ).promise();\n\t\t\t\t},\n\n\t\t\t\t// Get a promise for this deferred\n\t\t\t\t// If obj is provided, the promise aspect is added to the object\n\t\t\t\tpromise: function( obj ) {\n\t\t\t\t\treturn obj != null ? jQuery.extend( obj, promise ) : promise;\n\t\t\t\t}\n\t\t\t},\n\t\t\tdeferred = {};\n\n\t\t// Add list-specific methods\n\t\tjQuery.each( tuples, function( i, tuple ) {\n\t\t\tvar list = tuple[ 2 ],\n\t\t\t\tstateString = tuple[ 5 ];\n\n\t\t\t// promise.progress = list.add\n\t\t\t// promise.done = list.add\n\t\t\t// promise.fail = list.add\n\t\t\tpromise[ tuple[ 1 ] ] = list.add;\n\n\t\t\t// Handle state\n\t\t\tif ( stateString ) {\n\t\t\t\tlist.add(\n\t\t\t\t\tfunction() {\n\n\t\t\t\t\t\t// state = \"resolved\" (i.e., fulfilled)\n\t\t\t\t\t\t// state = \"rejected\"\n\t\t\t\t\t\tstate = stateString;\n\t\t\t\t\t},\n\n\t\t\t\t\t// rejected_callbacks.disable\n\t\t\t\t\t// fulfilled_callbacks.disable\n\t\t\t\t\ttuples[ 3 - i ][ 2 ].disable,\n\n\t\t\t\t\t// rejected_handlers.disable\n\t\t\t\t\t// fulfilled_handlers.disable\n\t\t\t\t\ttuples[ 3 - i ][ 3 ].disable,\n\n\t\t\t\t\t// progress_callbacks.lock\n\t\t\t\t\ttuples[ 0 ][ 2 ].lock,\n\n\t\t\t\t\t// progress_handlers.lock\n\t\t\t\t\ttuples[ 0 ][ 3 ].lock\n\t\t\t\t);\n\t\t\t}\n\n\t\t\t// progress_handlers.fire\n\t\t\t// fulfilled_handlers.fire\n\t\t\t// rejected_handlers.fire\n\t\t\tlist.add( tuple[ 3 ].fire );\n\n\t\t\t// deferred.notify = function() { deferred.notifyWith(...) }\n\t\t\t// deferred.resolve = function() { deferred.resolveWith(...) }\n\t\t\t// deferred.reject = function() { deferred.rejectWith(...) }\n\t\t\tdeferred[ tuple[ 0 ] ] = function() {\n\t\t\t\tdeferred[ tuple[ 0 ] + \"With\" ]( this === deferred ? undefined : this, arguments );\n\t\t\t\treturn this;\n\t\t\t};\n\n\t\t\t// deferred.notifyWith = list.fireWith\n\t\t\t// deferred.resolveWith = list.fireWith\n\t\t\t// deferred.rejectWith = list.fireWith\n\t\t\tdeferred[ tuple[ 0 ] + \"With\" ] = list.fireWith;\n\t\t} );\n\n\t\t// Make the deferred a promise\n\t\tpromise.promise( deferred );\n\n\t\t// Call given func if any\n\t\tif ( func ) {\n\t\t\tfunc.call( deferred, deferred );\n\t\t}\n\n\t\t// All done!\n\t\treturn deferred;\n\t},\n\n\t// Deferred helper\n\twhen: function( singleValue ) {\n\t\tvar\n\n\t\t\t// count of uncompleted subordinates\n\t\t\tremaining = arguments.length,\n\n\t\t\t// count of unprocessed arguments\n\t\t\ti = remaining,\n\n\t\t\t// subordinate fulfillment data\n\t\t\tresolveContexts = Array( i ),\n\t\t\tresolveValues = slice.call( arguments ),\n\n\t\t\t// the primary Deferred\n\t\t\tprimary = jQuery.Deferred(),\n\n\t\t\t// subordinate callback factory\n\t\t\tupdateFunc = function( i ) {\n\t\t\t\treturn function( value ) {\n\t\t\t\t\tresolveContexts[ i ] = this;\n\t\t\t\t\tresolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;\n\t\t\t\t\tif ( !( --remaining ) ) {\n\t\t\t\t\t\tprimary.resolveWith( resolveContexts, resolveValues );\n\t\t\t\t\t}\n\t\t\t\t};\n\t\t\t};\n\n\t\t// Single- and empty arguments are adopted like Promise.resolve\n\t\tif ( remaining <= 1 ) {\n\t\t\tadoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,\n\t\t\t\t!remaining );\n\n\t\t\t// Use .then() to unwrap secondary thenables (cf. gh-3000)\n\t\t\tif ( primary.state() === \"pending\" ||\n\t\t\t\tisFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {\n\n\t\t\t\treturn primary.then();\n\t\t\t}\n\t\t}\n\n\t\t// Multiple arguments are aggregated like Promise.all array elements\n\t\twhile ( i-- ) {\n\t\t\tadoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );\n\t\t}\n\n\t\treturn primary.promise();\n\t}\n} );\n\n\n// These usually indicate a programmer mistake during development,\n// warn about them ASAP rather than swallowing them by default.\nvar rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;\n\n// If `jQuery.Deferred.getErrorHook` is defined, `asyncError` is an error\n// captured before the async barrier to get the original error cause\n// which may otherwise be hidden.\njQuery.Deferred.exceptionHook = function( error, asyncError ) {\n\n\t// Support: IE 8 - 9 only\n\t// Console exists when dev tools are open, which can happen at any time\n\tif ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {\n\t\twindow.console.warn( \"jQuery.Deferred exception: \" + error.message,\n\t\t\terror.stack, asyncError );\n\t}\n};\n\n\n\n\njQuery.readyException = function( error ) {\n\twindow.setTimeout( function() {\n\t\tthrow error;\n\t} );\n};\n\n\n\n\n// The deferred used on DOM ready\nvar readyList = jQuery.Deferred();\n\njQuery.fn.ready = function( fn ) {\n\n\treadyList\n\t\t.then( fn )\n\n\t\t// Wrap jQuery.readyException in a function so that the lookup\n\t\t// happens at the time of error handling instead of callback\n\t\t// registration.\n\t\t.catch( function( error ) {\n\t\t\tjQuery.readyException( error );\n\t\t} );\n\n\treturn this;\n};\n\njQuery.extend( {\n\n\t// Is the DOM ready to be used? Set to true once it occurs.\n\tisReady: false,\n\n\t// A counter to track how many items to wait for before\n\t// the ready event fires. See trac-6781\n\treadyWait: 1,\n\n\t// Handle when the DOM is ready\n\tready: function( wait ) {\n\n\t\t// Abort if there are pending holds or we're already ready\n\t\tif ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Remember that the DOM is ready\n\t\tjQuery.isReady = true;\n\n\t\t// If a normal DOM Ready event fired, decrement, and wait if need be\n\t\tif ( wait !== true && --jQuery.readyWait > 0 ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// If there are functions bound, to execute\n\t\treadyList.resolveWith( document, [ jQuery ] );\n\t}\n} );\n\njQuery.ready.then = readyList.then;\n\n// The ready event handler and self cleanup method\nfunction completed() {\n\tdocument.removeEventListener( \"DOMContentLoaded\", completed );\n\twindow.removeEventListener( \"load\", completed );\n\tjQuery.ready();\n}\n\n// Catch cases where $(document).ready() is called\n// after the browser event has already occurred.\n// Support: IE <=9 - 10 only\n// Older IE sometimes signals \"interactive\" too soon\nif ( document.readyState === \"complete\" ||\n\t( document.readyState !== \"loading\" && !document.documentElement.doScroll ) ) {\n\n\t// Handle it asynchronously to allow scripts the opportunity to delay ready\n\twindow.setTimeout( jQuery.ready );\n\n} else {\n\n\t// Use the handy event callback\n\tdocument.addEventListener( \"DOMContentLoaded\", completed );\n\n\t// A fallback to window.onload, that will always work\n\twindow.addEventListener( \"load\", completed );\n}\n\n\n\n\n// Multifunctional method to get and set values of a collection\n// The value/s can optionally be executed if it's a function\nvar access = function( elems, fn, key, value, chainable, emptyGet, raw ) {\n\tvar i = 0,\n\t\tlen = elems.length,\n\t\tbulk = key == null;\n\n\t// Sets many values\n\tif ( toType( key ) === \"object\" ) {\n\t\tchainable = true;\n\t\tfor ( i in key ) {\n\t\t\taccess( elems, fn, i, key[ i ], true, emptyGet, raw );\n\t\t}\n\n\t// Sets one value\n\t} else if ( value !== undefined ) {\n\t\tchainable = true;\n\n\t\tif ( !isFunction( value ) ) {\n\t\t\traw = true;\n\t\t}\n\n\t\tif ( bulk ) {\n\n\t\t\t// Bulk operations run against the entire set\n\t\t\tif ( raw ) {\n\t\t\t\tfn.call( elems, value );\n\t\t\t\tfn = null;\n\n\t\t\t// ...except when executing function values\n\t\t\t} else {\n\t\t\t\tbulk = fn;\n\t\t\t\tfn = function( elem, _key, value ) {\n\t\t\t\t\treturn bulk.call( jQuery( elem ), value );\n\t\t\t\t};\n\t\t\t}\n\t\t}\n\n\t\tif ( fn ) {\n\t\t\tfor ( ; i < len; i++ ) {\n\t\t\t\tfn(\n\t\t\t\t\telems[ i ], key, raw ?\n\t\t\t\t\t\tvalue :\n\t\t\t\t\t\tvalue.call( elems[ i ], i, fn( elems[ i ], key ) )\n\t\t\t\t);\n\t\t\t}\n\t\t}\n\t}\n\n\tif ( chainable ) {\n\t\treturn elems;\n\t}\n\n\t// Gets\n\tif ( bulk ) {\n\t\treturn fn.call( elems );\n\t}\n\n\treturn len ? fn( elems[ 0 ], key ) : emptyGet;\n};\n\n\n// Matches dashed string for camelizing\nvar rmsPrefix = /^-ms-/,\n\trdashAlpha = /-([a-z])/g;\n\n// Used by camelCase as callback to replace()\nfunction fcamelCase( _all, letter ) {\n\treturn letter.toUpperCase();\n}\n\n// Convert dashed to camelCase; used by the css and data modules\n// Support: IE <=9 - 11, Edge 12 - 15\n// Microsoft forgot to hump their vendor prefix (trac-9572)\nfunction camelCase( string ) {\n\treturn string.replace( rmsPrefix, \"ms-\" ).replace( rdashAlpha, fcamelCase );\n}\nvar acceptData = function( owner ) {\n\n\t// Accepts only:\n\t// - Node\n\t// - Node.ELEMENT_NODE\n\t// - Node.DOCUMENT_NODE\n\t// - Object\n\t// - Any\n\treturn owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );\n};\n\n\n\n\nfunction Data() {\n\tthis.expando = jQuery.expando + Data.uid++;\n}\n\nData.uid = 1;\n\nData.prototype = {\n\n\tcache: function( owner ) {\n\n\t\t// Check if the owner object already has a cache\n\t\tvar value = owner[ this.expando ];\n\n\t\t// If not, create one\n\t\tif ( !value ) {\n\t\t\tvalue = {};\n\n\t\t\t// We can accept data for non-element nodes in modern browsers,\n\t\t\t// but we should not, see trac-8335.\n\t\t\t// Always return an empty object.\n\t\t\tif ( acceptData( owner ) ) {\n\n\t\t\t\t// If it is a node unlikely to be stringify-ed or looped over\n\t\t\t\t// use plain assignment\n\t\t\t\tif ( owner.nodeType ) {\n\t\t\t\t\towner[ this.expando ] = value;\n\n\t\t\t\t// Otherwise secure it in a non-enumerable property\n\t\t\t\t// configurable must be true to allow the property to be\n\t\t\t\t// deleted when data is removed\n\t\t\t\t} else {\n\t\t\t\t\tObject.defineProperty( owner, this.expando, {\n\t\t\t\t\t\tvalue: value,\n\t\t\t\t\t\tconfigurable: true\n\t\t\t\t\t} );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn value;\n\t},\n\tset: function( owner, data, value ) {\n\t\tvar prop,\n\t\t\tcache = this.cache( owner );\n\n\t\t// Handle: [ owner, key, value ] args\n\t\t// Always use camelCase key (gh-2257)\n\t\tif ( typeof data === \"string\" ) {\n\t\t\tcache[ camelCase( data ) ] = value;\n\n\t\t// Handle: [ owner, { properties } ] args\n\t\t} else {\n\n\t\t\t// Copy the properties one-by-one to the cache object\n\t\t\tfor ( prop in data ) {\n\t\t\t\tcache[ camelCase( prop ) ] = data[ prop ];\n\t\t\t}\n\t\t}\n\t\treturn cache;\n\t},\n\tget: function( owner, key ) {\n\t\treturn key === undefined ?\n\t\t\tthis.cache( owner ) :\n\n\t\t\t// Always use camelCase key (gh-2257)\n\t\t\towner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];\n\t},\n\taccess: function( owner, key, value ) {\n\n\t\t// In cases where either:\n\t\t//\n\t\t// 1. No key was specified\n\t\t// 2. A string key was specified, but no value provided\n\t\t//\n\t\t// Take the \"read\" path and allow the get method to determine\n\t\t// which value to return, respectively either:\n\t\t//\n\t\t// 1. The entire cache object\n\t\t// 2. The data stored at the key\n\t\t//\n\t\tif ( key === undefined ||\n\t\t\t\t( ( key && typeof key === \"string\" ) && value === undefined ) ) {\n\n\t\t\treturn this.get( owner, key );\n\t\t}\n\n\t\t// When the key is not a string, or both a key and value\n\t\t// are specified, set or extend (existing objects) with either:\n\t\t//\n\t\t// 1. An object of properties\n\t\t// 2. A key and value\n\t\t//\n\t\tthis.set( owner, key, value );\n\n\t\t// Since the \"set\" path can have two possible entry points\n\t\t// return the expected data based on which path was taken[*]\n\t\treturn value !== undefined ? value : key;\n\t},\n\tremove: function( owner, key ) {\n\t\tvar i,\n\t\t\tcache = owner[ this.expando ];\n\n\t\tif ( cache === undefined ) {\n\t\t\treturn;\n\t\t}\n\n\t\tif ( key !== undefined ) {\n\n\t\t\t// Support array or space separated string of keys\n\t\t\tif ( Array.isArray( key ) ) {\n\n\t\t\t\t// If key is an array of keys...\n\t\t\t\t// We always set camelCase keys, so remove that.\n\t\t\t\tkey = key.map( camelCase );\n\t\t\t} else {\n\t\t\t\tkey = camelCase( key );\n\n\t\t\t\t// If a key with the spaces exists, use it.\n\t\t\t\t// Otherwise, create an array by matching non-whitespace\n\t\t\t\tkey = key in cache ?\n\t\t\t\t\t[ key ] :\n\t\t\t\t\t( key.match( rnothtmlwhite ) || [] );\n\t\t\t}\n\n\t\t\ti = key.length;\n\n\t\t\twhile ( i-- ) {\n\t\t\t\tdelete cache[ key[ i ] ];\n\t\t\t}\n\t\t}\n\n\t\t// Remove the expando if there's no more data\n\t\tif ( key === undefined || jQuery.isEmptyObject( cache ) ) {\n\n\t\t\t// Support: Chrome <=35 - 45\n\t\t\t// Webkit & Blink performance suffers when deleting properties\n\t\t\t// from DOM nodes, so set to undefined instead\n\t\t\t// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)\n\t\t\tif ( owner.nodeType ) {\n\t\t\t\towner[ this.expando ] = undefined;\n\t\t\t} else {\n\t\t\t\tdelete owner[ this.expando ];\n\t\t\t}\n\t\t}\n\t},\n\thasData: function( owner ) {\n\t\tvar cache = owner[ this.expando ];\n\t\treturn cache !== undefined && !jQuery.isEmptyObject( cache );\n\t}\n};\nvar dataPriv = new Data();\n\nvar dataUser = new Data();\n\n\n\n//\tImplementation Summary\n//\n//\t1. Enforce API surface and semantic compatibility with 1.9.x branch\n//\t2. Improve the module's maintainability by reducing the storage\n//\t\tpaths to a single mechanism.\n//\t3. Use the same single mechanism to support \"private\" and \"user\" data.\n//\t4. _Never_ expose \"private\" data to user code (TODO: Drop _data, _removeData)\n//\t5. Avoid exposing implementation details on user objects (eg. expando properties)\n//\t6. Provide a clear path for implementation upgrade to WeakMap in 2014\n\nvar rbrace = /^(?:\\{[\\w\\W]*\\}|\\[[\\w\\W]*\\])$/,\n\trmultiDash = /[A-Z]/g;\n\nfunction getData( data ) {\n\tif ( data === \"true\" ) {\n\t\treturn true;\n\t}\n\n\tif ( data === \"false\" ) {\n\t\treturn false;\n\t}\n\n\tif ( data === \"null\" ) {\n\t\treturn null;\n\t}\n\n\t// Only convert to a number if it doesn't change the string\n\tif ( data === +data + \"\" ) {\n\t\treturn +data;\n\t}\n\n\tif ( rbrace.test( data ) ) {\n\t\treturn JSON.parse( data );\n\t}\n\n\treturn data;\n}\n\nfunction dataAttr( elem, key, data ) {\n\tvar name;\n\n\t// If nothing was found internally, try to fetch any\n\t// data from the HTML5 data-* attribute\n\tif ( data === undefined && elem.nodeType === 1 ) {\n\t\tname = \"data-\" + key.replace( rmultiDash, \"-$&\" ).toLowerCase();\n\t\tdata = elem.getAttribute( name );\n\n\t\tif ( typeof data === \"string\" ) {\n\t\t\ttry {\n\t\t\t\tdata = getData( data );\n\t\t\t} catch ( e ) {}\n\n\t\t\t// Make sure we set the data so it isn't changed later\n\t\t\tdataUser.set( elem, key, data );\n\t\t} else {\n\t\t\tdata = undefined;\n\t\t}\n\t}\n\treturn data;\n}\n\njQuery.extend( {\n\thasData: function( elem ) {\n\t\treturn dataUser.hasData( elem ) || dataPriv.hasData( elem );\n\t},\n\n\tdata: function( elem, name, data ) {\n\t\treturn dataUser.access( elem, name, data );\n\t},\n\n\tremoveData: function( elem, name ) {\n\t\tdataUser.remove( elem, name );\n\t},\n\n\t// TODO: Now that all calls to _data and _removeData have been replaced\n\t// with direct calls to dataPriv methods, these can be deprecated.\n\t_data: function( elem, name, data ) {\n\t\treturn dataPriv.access( elem, name, data );\n\t},\n\n\t_removeData: function( elem, name ) {\n\t\tdataPriv.remove( elem, name );\n\t}\n} );\n\njQuery.fn.extend( {\n\tdata: function( key, value ) {\n\t\tvar i, name, data,\n\t\t\telem = this[ 0 ],\n\t\t\tattrs = elem && elem.attributes;\n\n\t\t// Gets all values\n\t\tif ( key === undefined ) {\n\t\t\tif ( this.length ) {\n\t\t\t\tdata = dataUser.get( elem );\n\n\t\t\t\tif ( elem.nodeType === 1 && !dataPriv.get( elem, \"hasDataAttrs\" ) ) {\n\t\t\t\t\ti = attrs.length;\n\t\t\t\t\twhile ( i-- ) {\n\n\t\t\t\t\t\t// Support: IE 11 only\n\t\t\t\t\t\t// The attrs elements can be null (trac-14894)\n\t\t\t\t\t\tif ( attrs[ i ] ) {\n\t\t\t\t\t\t\tname = attrs[ i ].name;\n\t\t\t\t\t\t\tif ( name.indexOf( \"data-\" ) === 0 ) {\n\t\t\t\t\t\t\t\tname = camelCase( name.slice( 5 ) );\n\t\t\t\t\t\t\t\tdataAttr( elem, name, data[ name ] );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tdataPriv.set( elem, \"hasDataAttrs\", true );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\treturn data;\n\t\t}\n\n\t\t// Sets multiple values\n\t\tif ( typeof key === \"object\" ) {\n\t\t\treturn this.each( function() {\n\t\t\t\tdataUser.set( this, key );\n\t\t\t} );\n\t\t}\n\n\t\treturn access( this, function( value ) {\n\t\t\tvar data;\n\n\t\t\t// The calling jQuery object (element matches) is not empty\n\t\t\t// (and therefore has an element appears at this[ 0 ]) and the\n\t\t\t// `value` parameter was not undefined. An empty jQuery object\n\t\t\t// will result in `undefined` for elem = this[ 0 ] which will\n\t\t\t// throw an exception if an attempt to read a data cache is made.\n\t\t\tif ( elem && value === undefined ) {\n\n\t\t\t\t// Attempt to get data from the cache\n\t\t\t\t// The key will always be camelCased in Data\n\t\t\t\tdata = dataUser.get( elem, key );\n\t\t\t\tif ( data !== undefined ) {\n\t\t\t\t\treturn data;\n\t\t\t\t}\n\n\t\t\t\t// Attempt to \"discover\" the data in\n\t\t\t\t// HTML5 custom data-* attrs\n\t\t\t\tdata = dataAttr( elem, key );\n\t\t\t\tif ( data !== undefined ) {\n\t\t\t\t\treturn data;\n\t\t\t\t}\n\n\t\t\t\t// We tried really hard, but the data doesn't exist.\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// Set the data...\n\t\t\tthis.each( function() {\n\n\t\t\t\t// We always store the camelCased key\n\t\t\t\tdataUser.set( this, key, value );\n\t\t\t} );\n\t\t}, null, value, arguments.length > 1, null, true );\n\t},\n\n\tremoveData: function( key ) {\n\t\treturn this.each( function() {\n\t\t\tdataUser.remove( this, key );\n\t\t} );\n\t}\n} );\n\n\njQuery.extend( {\n\tqueue: function( elem, type, data ) {\n\t\tvar queue;\n\n\t\tif ( elem ) {\n\t\t\ttype = ( type || \"fx\" ) + \"queue\";\n\t\t\tqueue = dataPriv.get( elem, type );\n\n\t\t\t// Speed up dequeue by getting out quickly if this is just a lookup\n\t\t\tif ( data ) {\n\t\t\t\tif ( !queue || Array.isArray( data ) ) {\n\t\t\t\t\tqueue = dataPriv.access( elem, type, jQuery.makeArray( data ) );\n\t\t\t\t} else {\n\t\t\t\t\tqueue.push( data );\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn queue || [];\n\t\t}\n\t},\n\n\tdequeue: function( elem, type ) {\n\t\ttype = type || \"fx\";\n\n\t\tvar queue = jQuery.queue( elem, type ),\n\t\t\tstartLength = queue.length,\n\t\t\tfn = queue.shift(),\n\t\t\thooks = jQuery._queueHooks( elem, type ),\n\t\t\tnext = function() {\n\t\t\t\tjQuery.dequeue( elem, type );\n\t\t\t};\n\n\t\t// If the fx queue is dequeued, always remove the progress sentinel\n\t\tif ( fn === \"inprogress\" ) {\n\t\t\tfn = queue.shift();\n\t\t\tstartLength--;\n\t\t}\n\n\t\tif ( fn ) {\n\n\t\t\t// Add a progress sentinel to prevent the fx queue from being\n\t\t\t// automatically dequeued\n\t\t\tif ( type === \"fx\" ) {\n\t\t\t\tqueue.unshift( \"inprogress\" );\n\t\t\t}\n\n\t\t\t// Clear up the last queue stop function\n\t\t\tdelete hooks.stop;\n\t\t\tfn.call( elem, next, hooks );\n\t\t}\n\n\t\tif ( !startLength && hooks ) {\n\t\t\thooks.empty.fire();\n\t\t}\n\t},\n\n\t// Not public - generate a queueHooks object, or return the current one\n\t_queueHooks: function( elem, type ) {\n\t\tvar key = type + \"queueHooks\";\n\t\treturn dataPriv.get( elem, key ) || dataPriv.access( elem, key, {\n\t\t\tempty: jQuery.Callbacks( \"once memory\" ).add( function() {\n\t\t\t\tdataPriv.remove( elem, [ type + \"queue\", key ] );\n\t\t\t} )\n\t\t} );\n\t}\n} );\n\njQuery.fn.extend( {\n\tqueue: function( type, data ) {\n\t\tvar setter = 2;\n\n\t\tif ( typeof type !== \"string\" ) {\n\t\t\tdata = type;\n\t\t\ttype = \"fx\";\n\t\t\tsetter--;\n\t\t}\n\n\t\tif ( arguments.length < setter ) {\n\t\t\treturn jQuery.queue( this[ 0 ], type );\n\t\t}\n\n\t\treturn data === undefined ?\n\t\t\tthis :\n\t\t\tthis.each( function() {\n\t\t\t\tvar queue = jQuery.queue( this, type, data );\n\n\t\t\t\t// Ensure a hooks for this queue\n\t\t\t\tjQuery._queueHooks( this, type );\n\n\t\t\t\tif ( type === \"fx\" && queue[ 0 ] !== \"inprogress\" ) {\n\t\t\t\t\tjQuery.dequeue( this, type );\n\t\t\t\t}\n\t\t\t} );\n\t},\n\tdequeue: function( type ) {\n\t\treturn this.each( function() {\n\t\t\tjQuery.dequeue( this, type );\n\t\t} );\n\t},\n\tclearQueue: function( type ) {\n\t\treturn this.queue( type || \"fx\", [] );\n\t},\n\n\t// Get a promise resolved when queues of a certain type\n\t// are emptied (fx is the type by default)\n\tpromise: function( type, obj ) {\n\t\tvar tmp,\n\t\t\tcount = 1,\n\t\t\tdefer = jQuery.Deferred(),\n\t\t\telements = this,\n\t\t\ti = this.length,\n\t\t\tresolve = function() {\n\t\t\t\tif ( !( --count ) ) {\n\t\t\t\t\tdefer.resolveWith( elements, [ elements ] );\n\t\t\t\t}\n\t\t\t};\n\n\t\tif ( typeof type !== \"string\" ) {\n\t\t\tobj = type;\n\t\t\ttype = undefined;\n\t\t}\n\t\ttype = type || \"fx\";\n\n\t\twhile ( i-- ) {\n\t\t\ttmp = dataPriv.get( elements[ i ], type + \"queueHooks\" );\n\t\t\tif ( tmp && tmp.empty ) {\n\t\t\t\tcount++;\n\t\t\t\ttmp.empty.add( resolve );\n\t\t\t}\n\t\t}\n\t\tresolve();\n\t\treturn defer.promise( obj );\n\t}\n} );\nvar pnum = ( /[+-]?(?:\\d*\\.|)\\d+(?:[eE][+-]?\\d+|)/ ).source;\n\nvar rcssNum = new RegExp( \"^(?:([+-])=|)(\" + pnum + \")([a-z%]*)$\", \"i\" );\n\n\nvar cssExpand = [ \"Top\", \"Right\", \"Bottom\", \"Left\" ];\n\nvar documentElement = document.documentElement;\n\n\n\n\tvar isAttached = function( elem ) {\n\t\t\treturn jQuery.contains( elem.ownerDocument, elem );\n\t\t},\n\t\tcomposed = { composed: true };\n\n\t// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only\n\t// Check attachment across shadow DOM boundaries when possible (gh-3504)\n\t// Support: iOS 10.0-10.2 only\n\t// Early iOS 10 versions support `attachShadow` but not `getRootNode`,\n\t// leading to errors. We need to check for `getRootNode`.\n\tif ( documentElement.getRootNode ) {\n\t\tisAttached = function( elem ) {\n\t\t\treturn jQuery.contains( elem.ownerDocument, elem ) ||\n\t\t\t\telem.getRootNode( composed ) === elem.ownerDocument;\n\t\t};\n\t}\nvar isHiddenWithinTree = function( elem, el ) {\n\n\t\t// isHiddenWithinTree might be called from jQuery#filter function;\n\t\t// in that case, element will be second argument\n\t\telem = el || elem;\n\n\t\t// Inline style trumps all\n\t\treturn elem.style.display === \"none\" ||\n\t\t\telem.style.display === \"\" &&\n\n\t\t\t// Otherwise, check computed style\n\t\t\t// Support: Firefox <=43 - 45\n\t\t\t// Disconnected elements can have computed display: none, so first confirm that elem is\n\t\t\t// in the document.\n\t\t\tisAttached( elem ) &&\n\n\t\t\tjQuery.css( elem, \"display\" ) === \"none\";\n\t};\n\n\n\nfunction adjustCSS( elem, prop, valueParts, tween ) {\n\tvar adjusted, scale,\n\t\tmaxIterations = 20,\n\t\tcurrentValue = tween ?\n\t\t\tfunction() {\n\t\t\t\treturn tween.cur();\n\t\t\t} :\n\t\t\tfunction() {\n\t\t\t\treturn jQuery.css( elem, prop, \"\" );\n\t\t\t},\n\t\tinitial = currentValue(),\n\t\tunit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? \"\" : \"px\" ),\n\n\t\t// Starting value computation is required for potential unit mismatches\n\t\tinitialInUnit = elem.nodeType &&\n\t\t\t( jQuery.cssNumber[ prop ] || unit !== \"px\" && +initial ) &&\n\t\t\trcssNum.exec( jQuery.css( elem, prop ) );\n\n\tif ( initialInUnit && initialInUnit[ 3 ] !== unit ) {\n\n\t\t// Support: Firefox <=54\n\t\t// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)\n\t\tinitial = initial / 2;\n\n\t\t// Trust units reported by jQuery.css\n\t\tunit = unit || initialInUnit[ 3 ];\n\n\t\t// Iteratively approximate from a nonzero starting point\n\t\tinitialInUnit = +initial || 1;\n\n\t\twhile ( maxIterations-- ) {\n\n\t\t\t// Evaluate and update our best guess (doubling guesses that zero out).\n\t\t\t// Finish if the scale equals or crosses 1 (making the old*new product non-positive).\n\t\t\tjQuery.style( elem, prop, initialInUnit + unit );\n\t\t\tif ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {\n\t\t\t\tmaxIterations = 0;\n\t\t\t}\n\t\t\tinitialInUnit = initialInUnit / scale;\n\n\t\t}\n\n\t\tinitialInUnit = initialInUnit * 2;\n\t\tjQuery.style( elem, prop, initialInUnit + unit );\n\n\t\t// Make sure we update the tween properties later on\n\t\tvalueParts = valueParts || [];\n\t}\n\n\tif ( valueParts ) {\n\t\tinitialInUnit = +initialInUnit || +initial || 0;\n\n\t\t// Apply relative offset (+=/-=) if specified\n\t\tadjusted = valueParts[ 1 ] ?\n\t\t\tinitialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :\n\t\t\t+valueParts[ 2 ];\n\t\tif ( tween ) {\n\t\t\ttween.unit = unit;\n\t\t\ttween.start = initialInUnit;\n\t\t\ttween.end = adjusted;\n\t\t}\n\t}\n\treturn adjusted;\n}\n\n\nvar defaultDisplayMap = {};\n\nfunction getDefaultDisplay( elem ) {\n\tvar temp,\n\t\tdoc = elem.ownerDocument,\n\t\tnodeName = elem.nodeName,\n\t\tdisplay = defaultDisplayMap[ nodeName ];\n\n\tif ( display ) {\n\t\treturn display;\n\t}\n\n\ttemp = doc.body.appendChild( doc.createElement( nodeName ) );\n\tdisplay = jQuery.css( temp, \"display\" );\n\n\ttemp.parentNode.removeChild( temp );\n\n\tif ( display === \"none\" ) {\n\t\tdisplay = \"block\";\n\t}\n\tdefaultDisplayMap[ nodeName ] = display;\n\n\treturn display;\n}\n\nfunction showHide( elements, show ) {\n\tvar display, elem,\n\t\tvalues = [],\n\t\tindex = 0,\n\t\tlength = elements.length;\n\n\t// Determine new display value for elements that need to change\n\tfor ( ; index < length; index++ ) {\n\t\telem = elements[ index ];\n\t\tif ( !elem.style ) {\n\t\t\tcontinue;\n\t\t}\n\n\t\tdisplay = elem.style.display;\n\t\tif ( show ) {\n\n\t\t\t// Since we force visibility upon cascade-hidden elements, an immediate (and slow)\n\t\t\t// check is required in this first loop unless we have a nonempty display value (either\n\t\t\t// inline or about-to-be-restored)\n\t\t\tif ( display === \"none\" ) {\n\t\t\t\tvalues[ index ] = dataPriv.get( elem, \"display\" ) || null;\n\t\t\t\tif ( !values[ index ] ) {\n\t\t\t\t\telem.style.display = \"\";\n\t\t\t\t}\n\t\t\t}\n\t\t\tif ( elem.style.display === \"\" && isHiddenWithinTree( elem ) ) {\n\t\t\t\tvalues[ index ] = getDefaultDisplay( elem );\n\t\t\t}\n\t\t} else {\n\t\t\tif ( display !== \"none\" ) {\n\t\t\t\tvalues[ index ] = \"none\";\n\n\t\t\t\t// Remember what we're overwriting\n\t\t\t\tdataPriv.set( elem, \"display\", display );\n\t\t\t}\n\t\t}\n\t}\n\n\t// Set the display of the elements in a second loop to avoid constant reflow\n\tfor ( index = 0; index < length; index++ ) {\n\t\tif ( values[ index ] != null ) {\n\t\t\telements[ index ].style.display = values[ index ];\n\t\t}\n\t}\n\n\treturn elements;\n}\n\njQuery.fn.extend( {\n\tshow: function() {\n\t\treturn showHide( this, true );\n\t},\n\thide: function() {\n\t\treturn showHide( this );\n\t},\n\ttoggle: function( state ) {\n\t\tif ( typeof state === \"boolean\" ) {\n\t\t\treturn state ? this.show() : this.hide();\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tif ( isHiddenWithinTree( this ) ) {\n\t\t\t\tjQuery( this ).show();\n\t\t\t} else {\n\t\t\t\tjQuery( this ).hide();\n\t\t\t}\n\t\t} );\n\t}\n} );\nvar rcheckableType = ( /^(?:checkbox|radio)$/i );\n\nvar rtagName = ( /<([a-z][^\\/\\0>\\x20\\t\\r\\n\\f]*)/i );\n\nvar rscriptType = ( /^$|^module$|\\/(?:java|ecma)script/i );\n\n\n\n( function() {\n\tvar fragment = document.createDocumentFragment(),\n\t\tdiv = fragment.appendChild( document.createElement( \"div\" ) ),\n\t\tinput = document.createElement( \"input\" );\n\n\t// Support: Android 4.0 - 4.3 only\n\t// Check state lost if the name is set (trac-11217)\n\t// Support: Windows Web Apps (WWA)\n\t// `name` and `type` must use .setAttribute for WWA (trac-14901)\n\tinput.setAttribute( \"type\", \"radio\" );\n\tinput.setAttribute( \"checked\", \"checked\" );\n\tinput.setAttribute( \"name\", \"t\" );\n\n\tdiv.appendChild( input );\n\n\t// Support: Android <=4.1 only\n\t// Older WebKit doesn't clone checked state correctly in fragments\n\tsupport.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;\n\n\t// Support: IE <=11 only\n\t// Make sure textarea (and checkbox) defaultValue is properly cloned\n\tdiv.innerHTML = \"\";\n\tsupport.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;\n\n\t// Support: IE <=9 only\n\t// IE <=9 replaces \";\n\tsupport.option = !!div.lastChild;\n} )();\n\n\n// We have to close these tags to support XHTML (trac-13200)\nvar wrapMap = {\n\n\t// XHTML parsers do not magically insert elements in the\n\t// same way that tag soup parsers do. So we cannot shorten\n\t// this by omitting or other required elements.\n\tthead: [ 1, \"\", \"
    \" ],\n\tcol: [ 2, \"\", \"
    \" ],\n\ttr: [ 2, \"\", \"
    \" ],\n\ttd: [ 3, \"\", \"
    \" ],\n\n\t_default: [ 0, \"\", \"\" ]\n};\n\nwrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;\nwrapMap.th = wrapMap.td;\n\n// Support: IE <=9 only\nif ( !support.option ) {\n\twrapMap.optgroup = wrapMap.option = [ 1, \"\" ];\n}\n\n\nfunction getAll( context, tag ) {\n\n\t// Support: IE <=9 - 11 only\n\t// Use typeof to avoid zero-argument method invocation on host objects (trac-15151)\n\tvar ret;\n\n\tif ( typeof context.getElementsByTagName !== \"undefined\" ) {\n\t\tret = context.getElementsByTagName( tag || \"*\" );\n\n\t} else if ( typeof context.querySelectorAll !== \"undefined\" ) {\n\t\tret = context.querySelectorAll( tag || \"*\" );\n\n\t} else {\n\t\tret = [];\n\t}\n\n\tif ( tag === undefined || tag && nodeName( context, tag ) ) {\n\t\treturn jQuery.merge( [ context ], ret );\n\t}\n\n\treturn ret;\n}\n\n\n// Mark scripts as having already been evaluated\nfunction setGlobalEval( elems, refElements ) {\n\tvar i = 0,\n\t\tl = elems.length;\n\n\tfor ( ; i < l; i++ ) {\n\t\tdataPriv.set(\n\t\t\telems[ i ],\n\t\t\t\"globalEval\",\n\t\t\t!refElements || dataPriv.get( refElements[ i ], \"globalEval\" )\n\t\t);\n\t}\n}\n\n\nvar rhtml = /<|&#?\\w+;/;\n\nfunction buildFragment( elems, context, scripts, selection, ignored ) {\n\tvar elem, tmp, tag, wrap, attached, j,\n\t\tfragment = context.createDocumentFragment(),\n\t\tnodes = [],\n\t\ti = 0,\n\t\tl = elems.length;\n\n\tfor ( ; i < l; i++ ) {\n\t\telem = elems[ i ];\n\n\t\tif ( elem || elem === 0 ) {\n\n\t\t\t// Add nodes directly\n\t\t\tif ( toType( elem ) === \"object\" ) {\n\n\t\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t\t// push.apply(_, arraylike) throws on ancient WebKit\n\t\t\t\tjQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );\n\n\t\t\t// Convert non-html into a text node\n\t\t\t} else if ( !rhtml.test( elem ) ) {\n\t\t\t\tnodes.push( context.createTextNode( elem ) );\n\n\t\t\t// Convert html into DOM nodes\n\t\t\t} else {\n\t\t\t\ttmp = tmp || fragment.appendChild( context.createElement( \"div\" ) );\n\n\t\t\t\t// Deserialize a standard representation\n\t\t\t\ttag = ( rtagName.exec( elem ) || [ \"\", \"\" ] )[ 1 ].toLowerCase();\n\t\t\t\twrap = wrapMap[ tag ] || wrapMap._default;\n\t\t\t\ttmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];\n\n\t\t\t\t// Descend through wrappers to the right content\n\t\t\t\tj = wrap[ 0 ];\n\t\t\t\twhile ( j-- ) {\n\t\t\t\t\ttmp = tmp.lastChild;\n\t\t\t\t}\n\n\t\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t\t// push.apply(_, arraylike) throws on ancient WebKit\n\t\t\t\tjQuery.merge( nodes, tmp.childNodes );\n\n\t\t\t\t// Remember the top-level container\n\t\t\t\ttmp = fragment.firstChild;\n\n\t\t\t\t// Ensure the created nodes are orphaned (trac-12392)\n\t\t\t\ttmp.textContent = \"\";\n\t\t\t}\n\t\t}\n\t}\n\n\t// Remove wrapper from fragment\n\tfragment.textContent = \"\";\n\n\ti = 0;\n\twhile ( ( elem = nodes[ i++ ] ) ) {\n\n\t\t// Skip elements already in the context collection (trac-4087)\n\t\tif ( selection && jQuery.inArray( elem, selection ) > -1 ) {\n\t\t\tif ( ignored ) {\n\t\t\t\tignored.push( elem );\n\t\t\t}\n\t\t\tcontinue;\n\t\t}\n\n\t\tattached = isAttached( elem );\n\n\t\t// Append to fragment\n\t\ttmp = getAll( fragment.appendChild( elem ), \"script\" );\n\n\t\t// Preserve script evaluation history\n\t\tif ( attached ) {\n\t\t\tsetGlobalEval( tmp );\n\t\t}\n\n\t\t// Capture executables\n\t\tif ( scripts ) {\n\t\t\tj = 0;\n\t\t\twhile ( ( elem = tmp[ j++ ] ) ) {\n\t\t\t\tif ( rscriptType.test( elem.type || \"\" ) ) {\n\t\t\t\t\tscripts.push( elem );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn fragment;\n}\n\n\nvar rtypenamespace = /^([^.]*)(?:\\.(.+)|)/;\n\nfunction returnTrue() {\n\treturn true;\n}\n\nfunction returnFalse() {\n\treturn false;\n}\n\nfunction on( elem, types, selector, data, fn, one ) {\n\tvar origFn, type;\n\n\t// Types can be a map of types/handlers\n\tif ( typeof types === \"object\" ) {\n\n\t\t// ( types-Object, selector, data )\n\t\tif ( typeof selector !== \"string\" ) {\n\n\t\t\t// ( types-Object, data )\n\t\t\tdata = data || selector;\n\t\t\tselector = undefined;\n\t\t}\n\t\tfor ( type in types ) {\n\t\t\ton( elem, type, selector, data, types[ type ], one );\n\t\t}\n\t\treturn elem;\n\t}\n\n\tif ( data == null && fn == null ) {\n\n\t\t// ( types, fn )\n\t\tfn = selector;\n\t\tdata = selector = undefined;\n\t} else if ( fn == null ) {\n\t\tif ( typeof selector === \"string\" ) {\n\n\t\t\t// ( types, selector, fn )\n\t\t\tfn = data;\n\t\t\tdata = undefined;\n\t\t} else {\n\n\t\t\t// ( types, data, fn )\n\t\t\tfn = data;\n\t\t\tdata = selector;\n\t\t\tselector = undefined;\n\t\t}\n\t}\n\tif ( fn === false ) {\n\t\tfn = returnFalse;\n\t} else if ( !fn ) {\n\t\treturn elem;\n\t}\n\n\tif ( one === 1 ) {\n\t\torigFn = fn;\n\t\tfn = function( event ) {\n\n\t\t\t// Can use an empty set, since event contains the info\n\t\t\tjQuery().off( event );\n\t\t\treturn origFn.apply( this, arguments );\n\t\t};\n\n\t\t// Use same guid so caller can remove using origFn\n\t\tfn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );\n\t}\n\treturn elem.each( function() {\n\t\tjQuery.event.add( this, types, fn, data, selector );\n\t} );\n}\n\n/*\n * Helper functions for managing events -- not part of the public interface.\n * Props to Dean Edwards' addEvent library for many of the ideas.\n */\njQuery.event = {\n\n\tglobal: {},\n\n\tadd: function( elem, types, handler, data, selector ) {\n\n\t\tvar handleObjIn, eventHandle, tmp,\n\t\t\tevents, t, handleObj,\n\t\t\tspecial, handlers, type, namespaces, origType,\n\t\t\telemData = dataPriv.get( elem );\n\n\t\t// Only attach events to objects that accept data\n\t\tif ( !acceptData( elem ) ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Caller can pass in an object of custom data in lieu of the handler\n\t\tif ( handler.handler ) {\n\t\t\thandleObjIn = handler;\n\t\t\thandler = handleObjIn.handler;\n\t\t\tselector = handleObjIn.selector;\n\t\t}\n\n\t\t// Ensure that invalid selectors throw exceptions at attach time\n\t\t// Evaluate against documentElement in case elem is a non-element node (e.g., document)\n\t\tif ( selector ) {\n\t\t\tjQuery.find.matchesSelector( documentElement, selector );\n\t\t}\n\n\t\t// Make sure that the handler has a unique ID, used to find/remove it later\n\t\tif ( !handler.guid ) {\n\t\t\thandler.guid = jQuery.guid++;\n\t\t}\n\n\t\t// Init the element's event structure and main handler, if this is the first\n\t\tif ( !( events = elemData.events ) ) {\n\t\t\tevents = elemData.events = Object.create( null );\n\t\t}\n\t\tif ( !( eventHandle = elemData.handle ) ) {\n\t\t\teventHandle = elemData.handle = function( e ) {\n\n\t\t\t\t// Discard the second event of a jQuery.event.trigger() and\n\t\t\t\t// when an event is called after a page has unloaded\n\t\t\t\treturn typeof jQuery !== \"undefined\" && jQuery.event.triggered !== e.type ?\n\t\t\t\t\tjQuery.event.dispatch.apply( elem, arguments ) : undefined;\n\t\t\t};\n\t\t}\n\n\t\t// Handle multiple events separated by a space\n\t\ttypes = ( types || \"\" ).match( rnothtmlwhite ) || [ \"\" ];\n\t\tt = types.length;\n\t\twhile ( t-- ) {\n\t\t\ttmp = rtypenamespace.exec( types[ t ] ) || [];\n\t\t\ttype = origType = tmp[ 1 ];\n\t\t\tnamespaces = ( tmp[ 2 ] || \"\" ).split( \".\" ).sort();\n\n\t\t\t// There *must* be a type, no attaching namespace-only handlers\n\t\t\tif ( !type ) {\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\t// If event changes its type, use the special event handlers for the changed type\n\t\t\tspecial = jQuery.event.special[ type ] || {};\n\n\t\t\t// If selector defined, determine special event api type, otherwise given type\n\t\t\ttype = ( selector ? special.delegateType : special.bindType ) || type;\n\n\t\t\t// Update special based on newly reset type\n\t\t\tspecial = jQuery.event.special[ type ] || {};\n\n\t\t\t// handleObj is passed to all event handlers\n\t\t\thandleObj = jQuery.extend( {\n\t\t\t\ttype: type,\n\t\t\t\torigType: origType,\n\t\t\t\tdata: data,\n\t\t\t\thandler: handler,\n\t\t\t\tguid: handler.guid,\n\t\t\t\tselector: selector,\n\t\t\t\tneedsContext: selector && jQuery.expr.match.needsContext.test( selector ),\n\t\t\t\tnamespace: namespaces.join( \".\" )\n\t\t\t}, handleObjIn );\n\n\t\t\t// Init the event handler queue if we're the first\n\t\t\tif ( !( handlers = events[ type ] ) ) {\n\t\t\t\thandlers = events[ type ] = [];\n\t\t\t\thandlers.delegateCount = 0;\n\n\t\t\t\t// Only use addEventListener if the special events handler returns false\n\t\t\t\tif ( !special.setup ||\n\t\t\t\t\tspecial.setup.call( elem, data, namespaces, eventHandle ) === false ) {\n\n\t\t\t\t\tif ( elem.addEventListener ) {\n\t\t\t\t\t\telem.addEventListener( type, eventHandle );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif ( special.add ) {\n\t\t\t\tspecial.add.call( elem, handleObj );\n\n\t\t\t\tif ( !handleObj.handler.guid ) {\n\t\t\t\t\thandleObj.handler.guid = handler.guid;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Add to the element's handler list, delegates in front\n\t\t\tif ( selector ) {\n\t\t\t\thandlers.splice( handlers.delegateCount++, 0, handleObj );\n\t\t\t} else {\n\t\t\t\thandlers.push( handleObj );\n\t\t\t}\n\n\t\t\t// Keep track of which events have ever been used, for event optimization\n\t\t\tjQuery.event.global[ type ] = true;\n\t\t}\n\n\t},\n\n\t// Detach an event or set of events from an element\n\tremove: function( elem, types, handler, selector, mappedTypes ) {\n\n\t\tvar j, origCount, tmp,\n\t\t\tevents, t, handleObj,\n\t\t\tspecial, handlers, type, namespaces, origType,\n\t\t\telemData = dataPriv.hasData( elem ) && dataPriv.get( elem );\n\n\t\tif ( !elemData || !( events = elemData.events ) ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Once for each type.namespace in types; type may be omitted\n\t\ttypes = ( types || \"\" ).match( rnothtmlwhite ) || [ \"\" ];\n\t\tt = types.length;\n\t\twhile ( t-- ) {\n\t\t\ttmp = rtypenamespace.exec( types[ t ] ) || [];\n\t\t\ttype = origType = tmp[ 1 ];\n\t\t\tnamespaces = ( tmp[ 2 ] || \"\" ).split( \".\" ).sort();\n\n\t\t\t// Unbind all events (on this namespace, if provided) for the element\n\t\t\tif ( !type ) {\n\t\t\t\tfor ( type in events ) {\n\t\t\t\t\tjQuery.event.remove( elem, type + types[ t ], handler, selector, true );\n\t\t\t\t}\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tspecial = jQuery.event.special[ type ] || {};\n\t\t\ttype = ( selector ? special.delegateType : special.bindType ) || type;\n\t\t\thandlers = events[ type ] || [];\n\t\t\ttmp = tmp[ 2 ] &&\n\t\t\t\tnew RegExp( \"(^|\\\\.)\" + namespaces.join( \"\\\\.(?:.*\\\\.|)\" ) + \"(\\\\.|$)\" );\n\n\t\t\t// Remove matching events\n\t\t\torigCount = j = handlers.length;\n\t\t\twhile ( j-- ) {\n\t\t\t\thandleObj = handlers[ j ];\n\n\t\t\t\tif ( ( mappedTypes || origType === handleObj.origType ) &&\n\t\t\t\t\t( !handler || handler.guid === handleObj.guid ) &&\n\t\t\t\t\t( !tmp || tmp.test( handleObj.namespace ) ) &&\n\t\t\t\t\t( !selector || selector === handleObj.selector ||\n\t\t\t\t\t\tselector === \"**\" && handleObj.selector ) ) {\n\t\t\t\t\thandlers.splice( j, 1 );\n\n\t\t\t\t\tif ( handleObj.selector ) {\n\t\t\t\t\t\thandlers.delegateCount--;\n\t\t\t\t\t}\n\t\t\t\t\tif ( special.remove ) {\n\t\t\t\t\t\tspecial.remove.call( elem, handleObj );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Remove generic event handler if we removed something and no more handlers exist\n\t\t\t// (avoids potential for endless recursion during removal of special event handlers)\n\t\t\tif ( origCount && !handlers.length ) {\n\t\t\t\tif ( !special.teardown ||\n\t\t\t\t\tspecial.teardown.call( elem, namespaces, elemData.handle ) === false ) {\n\n\t\t\t\t\tjQuery.removeEvent( elem, type, elemData.handle );\n\t\t\t\t}\n\n\t\t\t\tdelete events[ type ];\n\t\t\t}\n\t\t}\n\n\t\t// Remove data and the expando if it's no longer used\n\t\tif ( jQuery.isEmptyObject( events ) ) {\n\t\t\tdataPriv.remove( elem, \"handle events\" );\n\t\t}\n\t},\n\n\tdispatch: function( nativeEvent ) {\n\n\t\tvar i, j, ret, matched, handleObj, handlerQueue,\n\t\t\targs = new Array( arguments.length ),\n\n\t\t\t// Make a writable jQuery.Event from the native event object\n\t\t\tevent = jQuery.event.fix( nativeEvent ),\n\n\t\t\thandlers = (\n\t\t\t\tdataPriv.get( this, \"events\" ) || Object.create( null )\n\t\t\t)[ event.type ] || [],\n\t\t\tspecial = jQuery.event.special[ event.type ] || {};\n\n\t\t// Use the fix-ed jQuery.Event rather than the (read-only) native event\n\t\targs[ 0 ] = event;\n\n\t\tfor ( i = 1; i < arguments.length; i++ ) {\n\t\t\targs[ i ] = arguments[ i ];\n\t\t}\n\n\t\tevent.delegateTarget = this;\n\n\t\t// Call the preDispatch hook for the mapped type, and let it bail if desired\n\t\tif ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Determine handlers\n\t\thandlerQueue = jQuery.event.handlers.call( this, event, handlers );\n\n\t\t// Run delegates first; they may want to stop propagation beneath us\n\t\ti = 0;\n\t\twhile ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {\n\t\t\tevent.currentTarget = matched.elem;\n\n\t\t\tj = 0;\n\t\t\twhile ( ( handleObj = matched.handlers[ j++ ] ) &&\n\t\t\t\t!event.isImmediatePropagationStopped() ) {\n\n\t\t\t\t// If the event is namespaced, then each handler is only invoked if it is\n\t\t\t\t// specially universal or its namespaces are a superset of the event's.\n\t\t\t\tif ( !event.rnamespace || handleObj.namespace === false ||\n\t\t\t\t\tevent.rnamespace.test( handleObj.namespace ) ) {\n\n\t\t\t\t\tevent.handleObj = handleObj;\n\t\t\t\t\tevent.data = handleObj.data;\n\n\t\t\t\t\tret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||\n\t\t\t\t\t\thandleObj.handler ).apply( matched.elem, args );\n\n\t\t\t\t\tif ( ret !== undefined ) {\n\t\t\t\t\t\tif ( ( event.result = ret ) === false ) {\n\t\t\t\t\t\t\tevent.preventDefault();\n\t\t\t\t\t\t\tevent.stopPropagation();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Call the postDispatch hook for the mapped type\n\t\tif ( special.postDispatch ) {\n\t\t\tspecial.postDispatch.call( this, event );\n\t\t}\n\n\t\treturn event.result;\n\t},\n\n\thandlers: function( event, handlers ) {\n\t\tvar i, handleObj, sel, matchedHandlers, matchedSelectors,\n\t\t\thandlerQueue = [],\n\t\t\tdelegateCount = handlers.delegateCount,\n\t\t\tcur = event.target;\n\n\t\t// Find delegate handlers\n\t\tif ( delegateCount &&\n\n\t\t\t// Support: IE <=9\n\t\t\t// Black-hole SVG instance trees (trac-13180)\n\t\t\tcur.nodeType &&\n\n\t\t\t// Support: Firefox <=42\n\t\t\t// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)\n\t\t\t// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click\n\t\t\t// Support: IE 11 only\n\t\t\t// ...but not arrow key \"clicks\" of radio inputs, which can have `button` -1 (gh-2343)\n\t\t\t!( event.type === \"click\" && event.button >= 1 ) ) {\n\n\t\t\tfor ( ; cur !== this; cur = cur.parentNode || this ) {\n\n\t\t\t\t// Don't check non-elements (trac-13208)\n\t\t\t\t// Don't process clicks on disabled elements (trac-6911, trac-8165, trac-11382, trac-11764)\n\t\t\t\tif ( cur.nodeType === 1 && !( event.type === \"click\" && cur.disabled === true ) ) {\n\t\t\t\t\tmatchedHandlers = [];\n\t\t\t\t\tmatchedSelectors = {};\n\t\t\t\t\tfor ( i = 0; i < delegateCount; i++ ) {\n\t\t\t\t\t\thandleObj = handlers[ i ];\n\n\t\t\t\t\t\t// Don't conflict with Object.prototype properties (trac-13203)\n\t\t\t\t\t\tsel = handleObj.selector + \" \";\n\n\t\t\t\t\t\tif ( matchedSelectors[ sel ] === undefined ) {\n\t\t\t\t\t\t\tmatchedSelectors[ sel ] = handleObj.needsContext ?\n\t\t\t\t\t\t\t\tjQuery( sel, this ).index( cur ) > -1 :\n\t\t\t\t\t\t\t\tjQuery.find( sel, this, null, [ cur ] ).length;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif ( matchedSelectors[ sel ] ) {\n\t\t\t\t\t\t\tmatchedHandlers.push( handleObj );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif ( matchedHandlers.length ) {\n\t\t\t\t\t\thandlerQueue.push( { elem: cur, handlers: matchedHandlers } );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Add the remaining (directly-bound) handlers\n\t\tcur = this;\n\t\tif ( delegateCount < handlers.length ) {\n\t\t\thandlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );\n\t\t}\n\n\t\treturn handlerQueue;\n\t},\n\n\taddProp: function( name, hook ) {\n\t\tObject.defineProperty( jQuery.Event.prototype, name, {\n\t\t\tenumerable: true,\n\t\t\tconfigurable: true,\n\n\t\t\tget: isFunction( hook ) ?\n\t\t\t\tfunction() {\n\t\t\t\t\tif ( this.originalEvent ) {\n\t\t\t\t\t\treturn hook( this.originalEvent );\n\t\t\t\t\t}\n\t\t\t\t} :\n\t\t\t\tfunction() {\n\t\t\t\t\tif ( this.originalEvent ) {\n\t\t\t\t\t\treturn this.originalEvent[ name ];\n\t\t\t\t\t}\n\t\t\t\t},\n\n\t\t\tset: function( value ) {\n\t\t\t\tObject.defineProperty( this, name, {\n\t\t\t\t\tenumerable: true,\n\t\t\t\t\tconfigurable: true,\n\t\t\t\t\twritable: true,\n\t\t\t\t\tvalue: value\n\t\t\t\t} );\n\t\t\t}\n\t\t} );\n\t},\n\n\tfix: function( originalEvent ) {\n\t\treturn originalEvent[ jQuery.expando ] ?\n\t\t\toriginalEvent :\n\t\t\tnew jQuery.Event( originalEvent );\n\t},\n\n\tspecial: {\n\t\tload: {\n\n\t\t\t// Prevent triggered image.load events from bubbling to window.load\n\t\t\tnoBubble: true\n\t\t},\n\t\tclick: {\n\n\t\t\t// Utilize native event to ensure correct state for checkable inputs\n\t\t\tsetup: function( data ) {\n\n\t\t\t\t// For mutual compressibility with _default, replace `this` access with a local var.\n\t\t\t\t// `|| data` is dead code meant only to preserve the variable through minification.\n\t\t\t\tvar el = this || data;\n\n\t\t\t\t// Claim the first handler\n\t\t\t\tif ( rcheckableType.test( el.type ) &&\n\t\t\t\t\tel.click && nodeName( el, \"input\" ) ) {\n\n\t\t\t\t\t// dataPriv.set( el, \"click\", ... )\n\t\t\t\t\tleverageNative( el, \"click\", true );\n\t\t\t\t}\n\n\t\t\t\t// Return false to allow normal processing in the caller\n\t\t\t\treturn false;\n\t\t\t},\n\t\t\ttrigger: function( data ) {\n\n\t\t\t\t// For mutual compressibility with _default, replace `this` access with a local var.\n\t\t\t\t// `|| data` is dead code meant only to preserve the variable through minification.\n\t\t\t\tvar el = this || data;\n\n\t\t\t\t// Force setup before triggering a click\n\t\t\t\tif ( rcheckableType.test( el.type ) &&\n\t\t\t\t\tel.click && nodeName( el, \"input\" ) ) {\n\n\t\t\t\t\tleverageNative( el, \"click\" );\n\t\t\t\t}\n\n\t\t\t\t// Return non-false to allow normal event-path propagation\n\t\t\t\treturn true;\n\t\t\t},\n\n\t\t\t// For cross-browser consistency, suppress native .click() on links\n\t\t\t// Also prevent it if we're currently inside a leveraged native-event stack\n\t\t\t_default: function( event ) {\n\t\t\t\tvar target = event.target;\n\t\t\t\treturn rcheckableType.test( target.type ) &&\n\t\t\t\t\ttarget.click && nodeName( target, \"input\" ) &&\n\t\t\t\t\tdataPriv.get( target, \"click\" ) ||\n\t\t\t\t\tnodeName( target, \"a\" );\n\t\t\t}\n\t\t},\n\n\t\tbeforeunload: {\n\t\t\tpostDispatch: function( event ) {\n\n\t\t\t\t// Support: Firefox 20+\n\t\t\t\t// Firefox doesn't alert if the returnValue field is not set.\n\t\t\t\tif ( event.result !== undefined && event.originalEvent ) {\n\t\t\t\t\tevent.originalEvent.returnValue = event.result;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n};\n\n// Ensure the presence of an event listener that handles manually-triggered\n// synthetic events by interrupting progress until reinvoked in response to\n// *native* events that it fires directly, ensuring that state changes have\n// already occurred before other listeners are invoked.\nfunction leverageNative( el, type, isSetup ) {\n\n\t// Missing `isSetup` indicates a trigger call, which must force setup through jQuery.event.add\n\tif ( !isSetup ) {\n\t\tif ( dataPriv.get( el, type ) === undefined ) {\n\t\t\tjQuery.event.add( el, type, returnTrue );\n\t\t}\n\t\treturn;\n\t}\n\n\t// Register the controller as a special universal handler for all event namespaces\n\tdataPriv.set( el, type, false );\n\tjQuery.event.add( el, type, {\n\t\tnamespace: false,\n\t\thandler: function( event ) {\n\t\t\tvar result,\n\t\t\t\tsaved = dataPriv.get( this, type );\n\n\t\t\tif ( ( event.isTrigger & 1 ) && this[ type ] ) {\n\n\t\t\t\t// Interrupt processing of the outer synthetic .trigger()ed event\n\t\t\t\tif ( !saved ) {\n\n\t\t\t\t\t// Store arguments for use when handling the inner native event\n\t\t\t\t\t// There will always be at least one argument (an event object), so this array\n\t\t\t\t\t// will not be confused with a leftover capture object.\n\t\t\t\t\tsaved = slice.call( arguments );\n\t\t\t\t\tdataPriv.set( this, type, saved );\n\n\t\t\t\t\t// Trigger the native event and capture its result\n\t\t\t\t\tthis[ type ]();\n\t\t\t\t\tresult = dataPriv.get( this, type );\n\t\t\t\t\tdataPriv.set( this, type, false );\n\n\t\t\t\t\tif ( saved !== result ) {\n\n\t\t\t\t\t\t// Cancel the outer synthetic event\n\t\t\t\t\t\tevent.stopImmediatePropagation();\n\t\t\t\t\t\tevent.preventDefault();\n\n\t\t\t\t\t\treturn result;\n\t\t\t\t\t}\n\n\t\t\t\t// If this is an inner synthetic event for an event with a bubbling surrogate\n\t\t\t\t// (focus or blur), assume that the surrogate already propagated from triggering\n\t\t\t\t// the native event and prevent that from happening again here.\n\t\t\t\t// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the\n\t\t\t\t// bubbling surrogate propagates *after* the non-bubbling base), but that seems\n\t\t\t\t// less bad than duplication.\n\t\t\t\t} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {\n\t\t\t\t\tevent.stopPropagation();\n\t\t\t\t}\n\n\t\t\t// If this is a native event triggered above, everything is now in order\n\t\t\t// Fire an inner synthetic event with the original arguments\n\t\t\t} else if ( saved ) {\n\n\t\t\t\t// ...and capture the result\n\t\t\t\tdataPriv.set( this, type, jQuery.event.trigger(\n\t\t\t\t\tsaved[ 0 ],\n\t\t\t\t\tsaved.slice( 1 ),\n\t\t\t\t\tthis\n\t\t\t\t) );\n\n\t\t\t\t// Abort handling of the native event by all jQuery handlers while allowing\n\t\t\t\t// native handlers on the same element to run. On target, this is achieved\n\t\t\t\t// by stopping immediate propagation just on the jQuery event. However,\n\t\t\t\t// the native event is re-wrapped by a jQuery one on each level of the\n\t\t\t\t// propagation so the only way to stop it for jQuery is to stop it for\n\t\t\t\t// everyone via native `stopPropagation()`. This is not a problem for\n\t\t\t\t// focus/blur which don't bubble, but it does also stop click on checkboxes\n\t\t\t\t// and radios. We accept this limitation.\n\t\t\t\tevent.stopPropagation();\n\t\t\t\tevent.isImmediatePropagationStopped = returnTrue;\n\t\t\t}\n\t\t}\n\t} );\n}\n\njQuery.removeEvent = function( elem, type, handle ) {\n\n\t// This \"if\" is needed for plain objects\n\tif ( elem.removeEventListener ) {\n\t\telem.removeEventListener( type, handle );\n\t}\n};\n\njQuery.Event = function( src, props ) {\n\n\t// Allow instantiation without the 'new' keyword\n\tif ( !( this instanceof jQuery.Event ) ) {\n\t\treturn new jQuery.Event( src, props );\n\t}\n\n\t// Event object\n\tif ( src && src.type ) {\n\t\tthis.originalEvent = src;\n\t\tthis.type = src.type;\n\n\t\t// Events bubbling up the document may have been marked as prevented\n\t\t// by a handler lower down the tree; reflect the correct value.\n\t\tthis.isDefaultPrevented = src.defaultPrevented ||\n\t\t\t\tsrc.defaultPrevented === undefined &&\n\n\t\t\t\t// Support: Android <=2.3 only\n\t\t\t\tsrc.returnValue === false ?\n\t\t\treturnTrue :\n\t\t\treturnFalse;\n\n\t\t// Create target properties\n\t\t// Support: Safari <=6 - 7 only\n\t\t// Target should not be a text node (trac-504, trac-13143)\n\t\tthis.target = ( src.target && src.target.nodeType === 3 ) ?\n\t\t\tsrc.target.parentNode :\n\t\t\tsrc.target;\n\n\t\tthis.currentTarget = src.currentTarget;\n\t\tthis.relatedTarget = src.relatedTarget;\n\n\t// Event type\n\t} else {\n\t\tthis.type = src;\n\t}\n\n\t// Put explicitly provided properties onto the event object\n\tif ( props ) {\n\t\tjQuery.extend( this, props );\n\t}\n\n\t// Create a timestamp if incoming event doesn't have one\n\tthis.timeStamp = src && src.timeStamp || Date.now();\n\n\t// Mark it as fixed\n\tthis[ jQuery.expando ] = true;\n};\n\n// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding\n// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html\njQuery.Event.prototype = {\n\tconstructor: jQuery.Event,\n\tisDefaultPrevented: returnFalse,\n\tisPropagationStopped: returnFalse,\n\tisImmediatePropagationStopped: returnFalse,\n\tisSimulated: false,\n\n\tpreventDefault: function() {\n\t\tvar e = this.originalEvent;\n\n\t\tthis.isDefaultPrevented = returnTrue;\n\n\t\tif ( e && !this.isSimulated ) {\n\t\t\te.preventDefault();\n\t\t}\n\t},\n\tstopPropagation: function() {\n\t\tvar e = this.originalEvent;\n\n\t\tthis.isPropagationStopped = returnTrue;\n\n\t\tif ( e && !this.isSimulated ) {\n\t\t\te.stopPropagation();\n\t\t}\n\t},\n\tstopImmediatePropagation: function() {\n\t\tvar e = this.originalEvent;\n\n\t\tthis.isImmediatePropagationStopped = returnTrue;\n\n\t\tif ( e && !this.isSimulated ) {\n\t\t\te.stopImmediatePropagation();\n\t\t}\n\n\t\tthis.stopPropagation();\n\t}\n};\n\n// Includes all common event props including KeyEvent and MouseEvent specific props\njQuery.each( {\n\taltKey: true,\n\tbubbles: true,\n\tcancelable: true,\n\tchangedTouches: true,\n\tctrlKey: true,\n\tdetail: true,\n\teventPhase: true,\n\tmetaKey: true,\n\tpageX: true,\n\tpageY: true,\n\tshiftKey: true,\n\tview: true,\n\t\"char\": true,\n\tcode: true,\n\tcharCode: true,\n\tkey: true,\n\tkeyCode: true,\n\tbutton: true,\n\tbuttons: true,\n\tclientX: true,\n\tclientY: true,\n\toffsetX: true,\n\toffsetY: true,\n\tpointerId: true,\n\tpointerType: true,\n\tscreenX: true,\n\tscreenY: true,\n\ttargetTouches: true,\n\ttoElement: true,\n\ttouches: true,\n\twhich: true\n}, jQuery.event.addProp );\n\njQuery.each( { focus: \"focusin\", blur: \"focusout\" }, function( type, delegateType ) {\n\n\tfunction focusMappedHandler( nativeEvent ) {\n\t\tif ( document.documentMode ) {\n\n\t\t\t// Support: IE 11+\n\t\t\t// Attach a single focusin/focusout handler on the document while someone wants\n\t\t\t// focus/blur. This is because the former are synchronous in IE while the latter\n\t\t\t// are async. In other browsers, all those handlers are invoked synchronously.\n\n\t\t\t// `handle` from private data would already wrap the event, but we need\n\t\t\t// to change the `type` here.\n\t\t\tvar handle = dataPriv.get( this, \"handle\" ),\n\t\t\t\tevent = jQuery.event.fix( nativeEvent );\n\t\t\tevent.type = nativeEvent.type === \"focusin\" ? \"focus\" : \"blur\";\n\t\t\tevent.isSimulated = true;\n\n\t\t\t// First, handle focusin/focusout\n\t\t\thandle( nativeEvent );\n\n\t\t\t// ...then, handle focus/blur\n\t\t\t//\n\t\t\t// focus/blur don't bubble while focusin/focusout do; simulate the former by only\n\t\t\t// invoking the handler at the lower level.\n\t\t\tif ( event.target === event.currentTarget ) {\n\n\t\t\t\t// The setup part calls `leverageNative`, which, in turn, calls\n\t\t\t\t// `jQuery.event.add`, so event handle will already have been set\n\t\t\t\t// by this point.\n\t\t\t\thandle( event );\n\t\t\t}\n\t\t} else {\n\n\t\t\t// For non-IE browsers, attach a single capturing handler on the document\n\t\t\t// while someone wants focusin/focusout.\n\t\t\tjQuery.event.simulate( delegateType, nativeEvent.target,\n\t\t\t\tjQuery.event.fix( nativeEvent ) );\n\t\t}\n\t}\n\n\tjQuery.event.special[ type ] = {\n\n\t\t// Utilize native event if possible so blur/focus sequence is correct\n\t\tsetup: function() {\n\n\t\t\tvar attaches;\n\n\t\t\t// Claim the first handler\n\t\t\t// dataPriv.set( this, \"focus\", ... )\n\t\t\t// dataPriv.set( this, \"blur\", ... )\n\t\t\tleverageNative( this, type, true );\n\n\t\t\tif ( document.documentMode ) {\n\n\t\t\t\t// Support: IE 9 - 11+\n\t\t\t\t// We use the same native handler for focusin & focus (and focusout & blur)\n\t\t\t\t// so we need to coordinate setup & teardown parts between those events.\n\t\t\t\t// Use `delegateType` as the key as `type` is already used by `leverageNative`.\n\t\t\t\tattaches = dataPriv.get( this, delegateType );\n\t\t\t\tif ( !attaches ) {\n\t\t\t\t\tthis.addEventListener( delegateType, focusMappedHandler );\n\t\t\t\t}\n\t\t\t\tdataPriv.set( this, delegateType, ( attaches || 0 ) + 1 );\n\t\t\t} else {\n\n\t\t\t\t// Return false to allow normal processing in the caller\n\t\t\t\treturn false;\n\t\t\t}\n\t\t},\n\t\ttrigger: function() {\n\n\t\t\t// Force setup before trigger\n\t\t\tleverageNative( this, type );\n\n\t\t\t// Return non-false to allow normal event-path propagation\n\t\t\treturn true;\n\t\t},\n\n\t\tteardown: function() {\n\t\t\tvar attaches;\n\n\t\t\tif ( document.documentMode ) {\n\t\t\t\tattaches = dataPriv.get( this, delegateType ) - 1;\n\t\t\t\tif ( !attaches ) {\n\t\t\t\t\tthis.removeEventListener( delegateType, focusMappedHandler );\n\t\t\t\t\tdataPriv.remove( this, delegateType );\n\t\t\t\t} else {\n\t\t\t\t\tdataPriv.set( this, delegateType, attaches );\n\t\t\t\t}\n\t\t\t} else {\n\n\t\t\t\t// Return false to indicate standard teardown should be applied\n\t\t\t\treturn false;\n\t\t\t}\n\t\t},\n\n\t\t// Suppress native focus or blur if we're currently inside\n\t\t// a leveraged native-event stack\n\t\t_default: function( event ) {\n\t\t\treturn dataPriv.get( event.target, type );\n\t\t},\n\n\t\tdelegateType: delegateType\n\t};\n\n\t// Support: Firefox <=44\n\t// Firefox doesn't have focus(in | out) events\n\t// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787\n\t//\n\t// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1\n\t// focus(in | out) events fire after focus & blur events,\n\t// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order\n\t// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857\n\t//\n\t// Support: IE 9 - 11+\n\t// To preserve relative focusin/focus & focusout/blur event order guaranteed on the 3.x branch,\n\t// attach a single handler for both events in IE.\n\tjQuery.event.special[ delegateType ] = {\n\t\tsetup: function() {\n\n\t\t\t// Handle: regular nodes (via `this.ownerDocument`), window\n\t\t\t// (via `this.document`) & document (via `this`).\n\t\t\tvar doc = this.ownerDocument || this.document || this,\n\t\t\t\tdataHolder = document.documentMode ? this : doc,\n\t\t\t\tattaches = dataPriv.get( dataHolder, delegateType );\n\n\t\t\t// Support: IE 9 - 11+\n\t\t\t// We use the same native handler for focusin & focus (and focusout & blur)\n\t\t\t// so we need to coordinate setup & teardown parts between those events.\n\t\t\t// Use `delegateType` as the key as `type` is already used by `leverageNative`.\n\t\t\tif ( !attaches ) {\n\t\t\t\tif ( document.documentMode ) {\n\t\t\t\t\tthis.addEventListener( delegateType, focusMappedHandler );\n\t\t\t\t} else {\n\t\t\t\t\tdoc.addEventListener( type, focusMappedHandler, true );\n\t\t\t\t}\n\t\t\t}\n\t\t\tdataPriv.set( dataHolder, delegateType, ( attaches || 0 ) + 1 );\n\t\t},\n\t\tteardown: function() {\n\t\t\tvar doc = this.ownerDocument || this.document || this,\n\t\t\t\tdataHolder = document.documentMode ? this : doc,\n\t\t\t\tattaches = dataPriv.get( dataHolder, delegateType ) - 1;\n\n\t\t\tif ( !attaches ) {\n\t\t\t\tif ( document.documentMode ) {\n\t\t\t\t\tthis.removeEventListener( delegateType, focusMappedHandler );\n\t\t\t\t} else {\n\t\t\t\t\tdoc.removeEventListener( type, focusMappedHandler, true );\n\t\t\t\t}\n\t\t\t\tdataPriv.remove( dataHolder, delegateType );\n\t\t\t} else {\n\t\t\t\tdataPriv.set( dataHolder, delegateType, attaches );\n\t\t\t}\n\t\t}\n\t};\n} );\n\n// Create mouseenter/leave events using mouseover/out and event-time checks\n// so that event delegation works in jQuery.\n// Do the same for pointerenter/pointerleave and pointerover/pointerout\n//\n// Support: Safari 7 only\n// Safari sends mouseenter too often; see:\n// https://bugs.chromium.org/p/chromium/issues/detail?id=470258\n// for the description of the bug (it existed in older Chrome versions as well).\njQuery.each( {\n\tmouseenter: \"mouseover\",\n\tmouseleave: \"mouseout\",\n\tpointerenter: \"pointerover\",\n\tpointerleave: \"pointerout\"\n}, function( orig, fix ) {\n\tjQuery.event.special[ orig ] = {\n\t\tdelegateType: fix,\n\t\tbindType: fix,\n\n\t\thandle: function( event ) {\n\t\t\tvar ret,\n\t\t\t\ttarget = this,\n\t\t\t\trelated = event.relatedTarget,\n\t\t\t\thandleObj = event.handleObj;\n\n\t\t\t// For mouseenter/leave call the handler if related is outside the target.\n\t\t\t// NB: No relatedTarget if the mouse left/entered the browser window\n\t\t\tif ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {\n\t\t\t\tevent.type = handleObj.origType;\n\t\t\t\tret = handleObj.handler.apply( this, arguments );\n\t\t\t\tevent.type = fix;\n\t\t\t}\n\t\t\treturn ret;\n\t\t}\n\t};\n} );\n\njQuery.fn.extend( {\n\n\ton: function( types, selector, data, fn ) {\n\t\treturn on( this, types, selector, data, fn );\n\t},\n\tone: function( types, selector, data, fn ) {\n\t\treturn on( this, types, selector, data, fn, 1 );\n\t},\n\toff: function( types, selector, fn ) {\n\t\tvar handleObj, type;\n\t\tif ( types && types.preventDefault && types.handleObj ) {\n\n\t\t\t// ( event ) dispatched jQuery.Event\n\t\t\thandleObj = types.handleObj;\n\t\t\tjQuery( types.delegateTarget ).off(\n\t\t\t\thandleObj.namespace ?\n\t\t\t\t\thandleObj.origType + \".\" + handleObj.namespace :\n\t\t\t\t\thandleObj.origType,\n\t\t\t\thandleObj.selector,\n\t\t\t\thandleObj.handler\n\t\t\t);\n\t\t\treturn this;\n\t\t}\n\t\tif ( typeof types === \"object\" ) {\n\n\t\t\t// ( types-object [, selector] )\n\t\t\tfor ( type in types ) {\n\t\t\t\tthis.off( type, selector, types[ type ] );\n\t\t\t}\n\t\t\treturn this;\n\t\t}\n\t\tif ( selector === false || typeof selector === \"function\" ) {\n\n\t\t\t// ( types [, fn] )\n\t\t\tfn = selector;\n\t\t\tselector = undefined;\n\t\t}\n\t\tif ( fn === false ) {\n\t\t\tfn = returnFalse;\n\t\t}\n\t\treturn this.each( function() {\n\t\t\tjQuery.event.remove( this, types, fn, selector );\n\t\t} );\n\t}\n} );\n\n\nvar\n\n\t// Support: IE <=10 - 11, Edge 12 - 13 only\n\t// In IE/Edge using regex groups here causes severe slowdowns.\n\t// See https://connect.microsoft.com/IE/feedback/details/1736512/\n\trnoInnerhtml = /\\s*$/g;\n\n// Prefer a tbody over its parent table for containing new rows\nfunction manipulationTarget( elem, content ) {\n\tif ( nodeName( elem, \"table\" ) &&\n\t\tnodeName( content.nodeType !== 11 ? content : content.firstChild, \"tr\" ) ) {\n\n\t\treturn jQuery( elem ).children( \"tbody\" )[ 0 ] || elem;\n\t}\n\n\treturn elem;\n}\n\n// Replace/restore the type attribute of script elements for safe DOM manipulation\nfunction disableScript( elem ) {\n\telem.type = ( elem.getAttribute( \"type\" ) !== null ) + \"/\" + elem.type;\n\treturn elem;\n}\nfunction restoreScript( elem ) {\n\tif ( ( elem.type || \"\" ).slice( 0, 5 ) === \"true/\" ) {\n\t\telem.type = elem.type.slice( 5 );\n\t} else {\n\t\telem.removeAttribute( \"type\" );\n\t}\n\n\treturn elem;\n}\n\nfunction cloneCopyEvent( src, dest ) {\n\tvar i, l, type, pdataOld, udataOld, udataCur, events;\n\n\tif ( dest.nodeType !== 1 ) {\n\t\treturn;\n\t}\n\n\t// 1. Copy private data: events, handlers, etc.\n\tif ( dataPriv.hasData( src ) ) {\n\t\tpdataOld = dataPriv.get( src );\n\t\tevents = pdataOld.events;\n\n\t\tif ( events ) {\n\t\t\tdataPriv.remove( dest, \"handle events\" );\n\n\t\t\tfor ( type in events ) {\n\t\t\t\tfor ( i = 0, l = events[ type ].length; i < l; i++ ) {\n\t\t\t\t\tjQuery.event.add( dest, type, events[ type ][ i ] );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// 2. Copy user data\n\tif ( dataUser.hasData( src ) ) {\n\t\tudataOld = dataUser.access( src );\n\t\tudataCur = jQuery.extend( {}, udataOld );\n\n\t\tdataUser.set( dest, udataCur );\n\t}\n}\n\n// Fix IE bugs, see support tests\nfunction fixInput( src, dest ) {\n\tvar nodeName = dest.nodeName.toLowerCase();\n\n\t// Fails to persist the checked state of a cloned checkbox or radio button.\n\tif ( nodeName === \"input\" && rcheckableType.test( src.type ) ) {\n\t\tdest.checked = src.checked;\n\n\t// Fails to return the selected option to the default selected state when cloning options\n\t} else if ( nodeName === \"input\" || nodeName === \"textarea\" ) {\n\t\tdest.defaultValue = src.defaultValue;\n\t}\n}\n\nfunction domManip( collection, args, callback, ignored ) {\n\n\t// Flatten any nested arrays\n\targs = flat( args );\n\n\tvar fragment, first, scripts, hasScripts, node, doc,\n\t\ti = 0,\n\t\tl = collection.length,\n\t\tiNoClone = l - 1,\n\t\tvalue = args[ 0 ],\n\t\tvalueIsFunction = isFunction( value );\n\n\t// We can't cloneNode fragments that contain checked, in WebKit\n\tif ( valueIsFunction ||\n\t\t\t( l > 1 && typeof value === \"string\" &&\n\t\t\t\t!support.checkClone && rchecked.test( value ) ) ) {\n\t\treturn collection.each( function( index ) {\n\t\t\tvar self = collection.eq( index );\n\t\t\tif ( valueIsFunction ) {\n\t\t\t\targs[ 0 ] = value.call( this, index, self.html() );\n\t\t\t}\n\t\t\tdomManip( self, args, callback, ignored );\n\t\t} );\n\t}\n\n\tif ( l ) {\n\t\tfragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );\n\t\tfirst = fragment.firstChild;\n\n\t\tif ( fragment.childNodes.length === 1 ) {\n\t\t\tfragment = first;\n\t\t}\n\n\t\t// Require either new content or an interest in ignored elements to invoke the callback\n\t\tif ( first || ignored ) {\n\t\t\tscripts = jQuery.map( getAll( fragment, \"script\" ), disableScript );\n\t\t\thasScripts = scripts.length;\n\n\t\t\t// Use the original fragment for the last item\n\t\t\t// instead of the first because it can end up\n\t\t\t// being emptied incorrectly in certain situations (trac-8070).\n\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\tnode = fragment;\n\n\t\t\t\tif ( i !== iNoClone ) {\n\t\t\t\t\tnode = jQuery.clone( node, true, true );\n\n\t\t\t\t\t// Keep references to cloned scripts for later restoration\n\t\t\t\t\tif ( hasScripts ) {\n\n\t\t\t\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t\t\t\t// push.apply(_, arraylike) throws on ancient WebKit\n\t\t\t\t\t\tjQuery.merge( scripts, getAll( node, \"script\" ) );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tcallback.call( collection[ i ], node, i );\n\t\t\t}\n\n\t\t\tif ( hasScripts ) {\n\t\t\t\tdoc = scripts[ scripts.length - 1 ].ownerDocument;\n\n\t\t\t\t// Re-enable scripts\n\t\t\t\tjQuery.map( scripts, restoreScript );\n\n\t\t\t\t// Evaluate executable scripts on first document insertion\n\t\t\t\tfor ( i = 0; i < hasScripts; i++ ) {\n\t\t\t\t\tnode = scripts[ i ];\n\t\t\t\t\tif ( rscriptType.test( node.type || \"\" ) &&\n\t\t\t\t\t\t!dataPriv.access( node, \"globalEval\" ) &&\n\t\t\t\t\t\tjQuery.contains( doc, node ) ) {\n\n\t\t\t\t\t\tif ( node.src && ( node.type || \"\" ).toLowerCase() !== \"module\" ) {\n\n\t\t\t\t\t\t\t// Optional AJAX dependency, but won't run scripts if not present\n\t\t\t\t\t\t\tif ( jQuery._evalUrl && !node.noModule ) {\n\t\t\t\t\t\t\t\tjQuery._evalUrl( node.src, {\n\t\t\t\t\t\t\t\t\tnonce: node.nonce || node.getAttribute( \"nonce\" )\n\t\t\t\t\t\t\t\t}, doc );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t// Unwrap a CDATA section containing script contents. This shouldn't be\n\t\t\t\t\t\t\t// needed as in XML documents they're already not visible when\n\t\t\t\t\t\t\t// inspecting element contents and in HTML documents they have no\n\t\t\t\t\t\t\t// meaning but we're preserving that logic for backwards compatibility.\n\t\t\t\t\t\t\t// This will be removed completely in 4.0. See gh-4904.\n\t\t\t\t\t\t\tDOMEval( node.textContent.replace( rcleanScript, \"\" ), node, doc );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn collection;\n}\n\nfunction remove( elem, selector, keepData ) {\n\tvar node,\n\t\tnodes = selector ? jQuery.filter( selector, elem ) : elem,\n\t\ti = 0;\n\n\tfor ( ; ( node = nodes[ i ] ) != null; i++ ) {\n\t\tif ( !keepData && node.nodeType === 1 ) {\n\t\t\tjQuery.cleanData( getAll( node ) );\n\t\t}\n\n\t\tif ( node.parentNode ) {\n\t\t\tif ( keepData && isAttached( node ) ) {\n\t\t\t\tsetGlobalEval( getAll( node, \"script\" ) );\n\t\t\t}\n\t\t\tnode.parentNode.removeChild( node );\n\t\t}\n\t}\n\n\treturn elem;\n}\n\njQuery.extend( {\n\thtmlPrefilter: function( html ) {\n\t\treturn html;\n\t},\n\n\tclone: function( elem, dataAndEvents, deepDataAndEvents ) {\n\t\tvar i, l, srcElements, destElements,\n\t\t\tclone = elem.cloneNode( true ),\n\t\t\tinPage = isAttached( elem );\n\n\t\t// Fix IE cloning issues\n\t\tif ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&\n\t\t\t\t!jQuery.isXMLDoc( elem ) ) {\n\n\t\t\t// We eschew jQuery#find here for performance reasons:\n\t\t\t// https://jsperf.com/getall-vs-sizzle/2\n\t\t\tdestElements = getAll( clone );\n\t\t\tsrcElements = getAll( elem );\n\n\t\t\tfor ( i = 0, l = srcElements.length; i < l; i++ ) {\n\t\t\t\tfixInput( srcElements[ i ], destElements[ i ] );\n\t\t\t}\n\t\t}\n\n\t\t// Copy the events from the original to the clone\n\t\tif ( dataAndEvents ) {\n\t\t\tif ( deepDataAndEvents ) {\n\t\t\t\tsrcElements = srcElements || getAll( elem );\n\t\t\t\tdestElements = destElements || getAll( clone );\n\n\t\t\t\tfor ( i = 0, l = srcElements.length; i < l; i++ ) {\n\t\t\t\t\tcloneCopyEvent( srcElements[ i ], destElements[ i ] );\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tcloneCopyEvent( elem, clone );\n\t\t\t}\n\t\t}\n\n\t\t// Preserve script evaluation history\n\t\tdestElements = getAll( clone, \"script\" );\n\t\tif ( destElements.length > 0 ) {\n\t\t\tsetGlobalEval( destElements, !inPage && getAll( elem, \"script\" ) );\n\t\t}\n\n\t\t// Return the cloned set\n\t\treturn clone;\n\t},\n\n\tcleanData: function( elems ) {\n\t\tvar data, elem, type,\n\t\t\tspecial = jQuery.event.special,\n\t\t\ti = 0;\n\n\t\tfor ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {\n\t\t\tif ( acceptData( elem ) ) {\n\t\t\t\tif ( ( data = elem[ dataPriv.expando ] ) ) {\n\t\t\t\t\tif ( data.events ) {\n\t\t\t\t\t\tfor ( type in data.events ) {\n\t\t\t\t\t\t\tif ( special[ type ] ) {\n\t\t\t\t\t\t\t\tjQuery.event.remove( elem, type );\n\n\t\t\t\t\t\t\t// This is a shortcut to avoid jQuery.event.remove's overhead\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tjQuery.removeEvent( elem, type, data.handle );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Support: Chrome <=35 - 45+\n\t\t\t\t\t// Assign undefined instead of using delete, see Data#remove\n\t\t\t\t\telem[ dataPriv.expando ] = undefined;\n\t\t\t\t}\n\t\t\t\tif ( elem[ dataUser.expando ] ) {\n\n\t\t\t\t\t// Support: Chrome <=35 - 45+\n\t\t\t\t\t// Assign undefined instead of using delete, see Data#remove\n\t\t\t\t\telem[ dataUser.expando ] = undefined;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n} );\n\njQuery.fn.extend( {\n\tdetach: function( selector ) {\n\t\treturn remove( this, selector, true );\n\t},\n\n\tremove: function( selector ) {\n\t\treturn remove( this, selector );\n\t},\n\n\ttext: function( value ) {\n\t\treturn access( this, function( value ) {\n\t\t\treturn value === undefined ?\n\t\t\t\tjQuery.text( this ) :\n\t\t\t\tthis.empty().each( function() {\n\t\t\t\t\tif ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {\n\t\t\t\t\t\tthis.textContent = value;\n\t\t\t\t\t}\n\t\t\t\t} );\n\t\t}, null, value, arguments.length );\n\t},\n\n\tappend: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {\n\t\t\t\tvar target = manipulationTarget( this, elem );\n\t\t\t\ttarget.appendChild( elem );\n\t\t\t}\n\t\t} );\n\t},\n\n\tprepend: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {\n\t\t\t\tvar target = manipulationTarget( this, elem );\n\t\t\t\ttarget.insertBefore( elem, target.firstChild );\n\t\t\t}\n\t\t} );\n\t},\n\n\tbefore: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.parentNode ) {\n\t\t\t\tthis.parentNode.insertBefore( elem, this );\n\t\t\t}\n\t\t} );\n\t},\n\n\tafter: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.parentNode ) {\n\t\t\t\tthis.parentNode.insertBefore( elem, this.nextSibling );\n\t\t\t}\n\t\t} );\n\t},\n\n\tempty: function() {\n\t\tvar elem,\n\t\t\ti = 0;\n\n\t\tfor ( ; ( elem = this[ i ] ) != null; i++ ) {\n\t\t\tif ( elem.nodeType === 1 ) {\n\n\t\t\t\t// Prevent memory leaks\n\t\t\t\tjQuery.cleanData( getAll( elem, false ) );\n\n\t\t\t\t// Remove any remaining nodes\n\t\t\t\telem.textContent = \"\";\n\t\t\t}\n\t\t}\n\n\t\treturn this;\n\t},\n\n\tclone: function( dataAndEvents, deepDataAndEvents ) {\n\t\tdataAndEvents = dataAndEvents == null ? false : dataAndEvents;\n\t\tdeepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;\n\n\t\treturn this.map( function() {\n\t\t\treturn jQuery.clone( this, dataAndEvents, deepDataAndEvents );\n\t\t} );\n\t},\n\n\thtml: function( value ) {\n\t\treturn access( this, function( value ) {\n\t\t\tvar elem = this[ 0 ] || {},\n\t\t\t\ti = 0,\n\t\t\t\tl = this.length;\n\n\t\t\tif ( value === undefined && elem.nodeType === 1 ) {\n\t\t\t\treturn elem.innerHTML;\n\t\t\t}\n\n\t\t\t// See if we can take a shortcut and just use innerHTML\n\t\t\tif ( typeof value === \"string\" && !rnoInnerhtml.test( value ) &&\n\t\t\t\t!wrapMap[ ( rtagName.exec( value ) || [ \"\", \"\" ] )[ 1 ].toLowerCase() ] ) {\n\n\t\t\t\tvalue = jQuery.htmlPrefilter( value );\n\n\t\t\t\ttry {\n\t\t\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\t\t\telem = this[ i ] || {};\n\n\t\t\t\t\t\t// Remove element nodes and prevent memory leaks\n\t\t\t\t\t\tif ( elem.nodeType === 1 ) {\n\t\t\t\t\t\t\tjQuery.cleanData( getAll( elem, false ) );\n\t\t\t\t\t\t\telem.innerHTML = value;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\telem = 0;\n\n\t\t\t\t// If using innerHTML throws an exception, use the fallback method\n\t\t\t\t} catch ( e ) {}\n\t\t\t}\n\n\t\t\tif ( elem ) {\n\t\t\t\tthis.empty().append( value );\n\t\t\t}\n\t\t}, null, value, arguments.length );\n\t},\n\n\treplaceWith: function() {\n\t\tvar ignored = [];\n\n\t\t// Make the changes, replacing each non-ignored context element with the new content\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tvar parent = this.parentNode;\n\n\t\t\tif ( jQuery.inArray( this, ignored ) < 0 ) {\n\t\t\t\tjQuery.cleanData( getAll( this ) );\n\t\t\t\tif ( parent ) {\n\t\t\t\t\tparent.replaceChild( elem, this );\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Force callback invocation\n\t\t}, ignored );\n\t}\n} );\n\njQuery.each( {\n\tappendTo: \"append\",\n\tprependTo: \"prepend\",\n\tinsertBefore: \"before\",\n\tinsertAfter: \"after\",\n\treplaceAll: \"replaceWith\"\n}, function( name, original ) {\n\tjQuery.fn[ name ] = function( selector ) {\n\t\tvar elems,\n\t\t\tret = [],\n\t\t\tinsert = jQuery( selector ),\n\t\t\tlast = insert.length - 1,\n\t\t\ti = 0;\n\n\t\tfor ( ; i <= last; i++ ) {\n\t\t\telems = i === last ? this : this.clone( true );\n\t\t\tjQuery( insert[ i ] )[ original ]( elems );\n\n\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t// .get() because push.apply(_, arraylike) throws on ancient WebKit\n\t\t\tpush.apply( ret, elems.get() );\n\t\t}\n\n\t\treturn this.pushStack( ret );\n\t};\n} );\nvar rnumnonpx = new RegExp( \"^(\" + pnum + \")(?!px)[a-z%]+$\", \"i\" );\n\nvar rcustomProp = /^--/;\n\n\nvar getStyles = function( elem ) {\n\n\t\t// Support: IE <=11 only, Firefox <=30 (trac-15098, trac-14150)\n\t\t// IE throws on elements created in popups\n\t\t// FF meanwhile throws on frame elements through \"defaultView.getComputedStyle\"\n\t\tvar view = elem.ownerDocument.defaultView;\n\n\t\tif ( !view || !view.opener ) {\n\t\t\tview = window;\n\t\t}\n\n\t\treturn view.getComputedStyle( elem );\n\t};\n\nvar swap = function( elem, options, callback ) {\n\tvar ret, name,\n\t\told = {};\n\n\t// Remember the old values, and insert the new ones\n\tfor ( name in options ) {\n\t\told[ name ] = elem.style[ name ];\n\t\telem.style[ name ] = options[ name ];\n\t}\n\n\tret = callback.call( elem );\n\n\t// Revert the old values\n\tfor ( name in options ) {\n\t\telem.style[ name ] = old[ name ];\n\t}\n\n\treturn ret;\n};\n\n\nvar rboxStyle = new RegExp( cssExpand.join( \"|\" ), \"i\" );\n\n\n\n( function() {\n\n\t// Executing both pixelPosition & boxSizingReliable tests require only one layout\n\t// so they're executed at the same time to save the second computation.\n\tfunction computeStyleTests() {\n\n\t\t// This is a singleton, we need to execute it only once\n\t\tif ( !div ) {\n\t\t\treturn;\n\t\t}\n\n\t\tcontainer.style.cssText = \"position:absolute;left:-11111px;width:60px;\" +\n\t\t\t\"margin-top:1px;padding:0;border:0\";\n\t\tdiv.style.cssText =\n\t\t\t\"position:relative;display:block;box-sizing:border-box;overflow:scroll;\" +\n\t\t\t\"margin:auto;border:1px;padding:1px;\" +\n\t\t\t\"width:60%;top:1%\";\n\t\tdocumentElement.appendChild( container ).appendChild( div );\n\n\t\tvar divStyle = window.getComputedStyle( div );\n\t\tpixelPositionVal = divStyle.top !== \"1%\";\n\n\t\t// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44\n\t\treliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;\n\n\t\t// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3\n\t\t// Some styles come back with percentage values, even though they shouldn't\n\t\tdiv.style.right = \"60%\";\n\t\tpixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;\n\n\t\t// Support: IE 9 - 11 only\n\t\t// Detect misreporting of content dimensions for box-sizing:border-box elements\n\t\tboxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;\n\n\t\t// Support: IE 9 only\n\t\t// Detect overflow:scroll screwiness (gh-3699)\n\t\t// Support: Chrome <=64\n\t\t// Don't get tricked when zoom affects offsetWidth (gh-4029)\n\t\tdiv.style.position = \"absolute\";\n\t\tscrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;\n\n\t\tdocumentElement.removeChild( container );\n\n\t\t// Nullify the div so it wouldn't be stored in the memory and\n\t\t// it will also be a sign that checks already performed\n\t\tdiv = null;\n\t}\n\n\tfunction roundPixelMeasures( measure ) {\n\t\treturn Math.round( parseFloat( measure ) );\n\t}\n\n\tvar pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,\n\t\treliableTrDimensionsVal, reliableMarginLeftVal,\n\t\tcontainer = document.createElement( \"div\" ),\n\t\tdiv = document.createElement( \"div\" );\n\n\t// Finish early in limited (non-browser) environments\n\tif ( !div.style ) {\n\t\treturn;\n\t}\n\n\t// Support: IE <=9 - 11 only\n\t// Style of cloned element affects source element cloned (trac-8908)\n\tdiv.style.backgroundClip = \"content-box\";\n\tdiv.cloneNode( true ).style.backgroundClip = \"\";\n\tsupport.clearCloneStyle = div.style.backgroundClip === \"content-box\";\n\n\tjQuery.extend( support, {\n\t\tboxSizingReliable: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn boxSizingReliableVal;\n\t\t},\n\t\tpixelBoxStyles: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn pixelBoxStylesVal;\n\t\t},\n\t\tpixelPosition: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn pixelPositionVal;\n\t\t},\n\t\treliableMarginLeft: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn reliableMarginLeftVal;\n\t\t},\n\t\tscrollboxSize: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn scrollboxSizeVal;\n\t\t},\n\n\t\t// Support: IE 9 - 11+, Edge 15 - 18+\n\t\t// IE/Edge misreport `getComputedStyle` of table rows with width/height\n\t\t// set in CSS while `offset*` properties report correct values.\n\t\t// Behavior in IE 9 is more subtle than in newer versions & it passes\n\t\t// some versions of this test; make sure not to make it pass there!\n\t\t//\n\t\t// Support: Firefox 70+\n\t\t// Only Firefox includes border widths\n\t\t// in computed dimensions. (gh-4529)\n\t\treliableTrDimensions: function() {\n\t\t\tvar table, tr, trChild, trStyle;\n\t\t\tif ( reliableTrDimensionsVal == null ) {\n\t\t\t\ttable = document.createElement( \"table\" );\n\t\t\t\ttr = document.createElement( \"tr\" );\n\t\t\t\ttrChild = document.createElement( \"div\" );\n\n\t\t\t\ttable.style.cssText = \"position:absolute;left:-11111px;border-collapse:separate\";\n\t\t\t\ttr.style.cssText = \"box-sizing:content-box;border:1px solid\";\n\n\t\t\t\t// Support: Chrome 86+\n\t\t\t\t// Height set through cssText does not get applied.\n\t\t\t\t// Computed height then comes back as 0.\n\t\t\t\ttr.style.height = \"1px\";\n\t\t\t\ttrChild.style.height = \"9px\";\n\n\t\t\t\t// Support: Android 8 Chrome 86+\n\t\t\t\t// In our bodyBackground.html iframe,\n\t\t\t\t// display for all div elements is set to \"inline\",\n\t\t\t\t// which causes a problem only in Android 8 Chrome 86.\n\t\t\t\t// Ensuring the div is `display: block`\n\t\t\t\t// gets around this issue.\n\t\t\t\ttrChild.style.display = \"block\";\n\n\t\t\t\tdocumentElement\n\t\t\t\t\t.appendChild( table )\n\t\t\t\t\t.appendChild( tr )\n\t\t\t\t\t.appendChild( trChild );\n\n\t\t\t\ttrStyle = window.getComputedStyle( tr );\n\t\t\t\treliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +\n\t\t\t\t\tparseInt( trStyle.borderTopWidth, 10 ) +\n\t\t\t\t\tparseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;\n\n\t\t\t\tdocumentElement.removeChild( table );\n\t\t\t}\n\t\t\treturn reliableTrDimensionsVal;\n\t\t}\n\t} );\n} )();\n\n\nfunction curCSS( elem, name, computed ) {\n\tvar width, minWidth, maxWidth, ret,\n\t\tisCustomProp = rcustomProp.test( name ),\n\n\t\t// Support: Firefox 51+\n\t\t// Retrieving style before computed somehow\n\t\t// fixes an issue with getting wrong values\n\t\t// on detached elements\n\t\tstyle = elem.style;\n\n\tcomputed = computed || getStyles( elem );\n\n\t// getPropertyValue is needed for:\n\t// .css('filter') (IE 9 only, trac-12537)\n\t// .css('--customProperty) (gh-3144)\n\tif ( computed ) {\n\n\t\t// Support: IE <=9 - 11+\n\t\t// IE only supports `\"float\"` in `getPropertyValue`; in computed styles\n\t\t// it's only available as `\"cssFloat\"`. We no longer modify properties\n\t\t// sent to `.css()` apart from camelCasing, so we need to check both.\n\t\t// Normally, this would create difference in behavior: if\n\t\t// `getPropertyValue` returns an empty string, the value returned\n\t\t// by `.css()` would be `undefined`. This is usually the case for\n\t\t// disconnected elements. However, in IE even disconnected elements\n\t\t// with no styles return `\"none\"` for `getPropertyValue( \"float\" )`\n\t\tret = computed.getPropertyValue( name ) || computed[ name ];\n\n\t\tif ( isCustomProp && ret ) {\n\n\t\t\t// Support: Firefox 105+, Chrome <=105+\n\t\t\t// Spec requires trimming whitespace for custom properties (gh-4926).\n\t\t\t// Firefox only trims leading whitespace. Chrome just collapses\n\t\t\t// both leading & trailing whitespace to a single space.\n\t\t\t//\n\t\t\t// Fall back to `undefined` if empty string returned.\n\t\t\t// This collapses a missing definition with property defined\n\t\t\t// and set to an empty string but there's no standard API\n\t\t\t// allowing us to differentiate them without a performance penalty\n\t\t\t// and returning `undefined` aligns with older jQuery.\n\t\t\t//\n\t\t\t// rtrimCSS treats U+000D CARRIAGE RETURN and U+000C FORM FEED\n\t\t\t// as whitespace while CSS does not, but this is not a problem\n\t\t\t// because CSS preprocessing replaces them with U+000A LINE FEED\n\t\t\t// (which *is* CSS whitespace)\n\t\t\t// https://www.w3.org/TR/css-syntax-3/#input-preprocessing\n\t\t\tret = ret.replace( rtrimCSS, \"$1\" ) || undefined;\n\t\t}\n\n\t\tif ( ret === \"\" && !isAttached( elem ) ) {\n\t\t\tret = jQuery.style( elem, name );\n\t\t}\n\n\t\t// A tribute to the \"awesome hack by Dean Edwards\"\n\t\t// Android Browser returns percentage for some values,\n\t\t// but width seems to be reliably pixels.\n\t\t// This is against the CSSOM draft spec:\n\t\t// https://drafts.csswg.org/cssom/#resolved-values\n\t\tif ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {\n\n\t\t\t// Remember the original values\n\t\t\twidth = style.width;\n\t\t\tminWidth = style.minWidth;\n\t\t\tmaxWidth = style.maxWidth;\n\n\t\t\t// Put in the new values to get a computed value out\n\t\t\tstyle.minWidth = style.maxWidth = style.width = ret;\n\t\t\tret = computed.width;\n\n\t\t\t// Revert the changed values\n\t\t\tstyle.width = width;\n\t\t\tstyle.minWidth = minWidth;\n\t\t\tstyle.maxWidth = maxWidth;\n\t\t}\n\t}\n\n\treturn ret !== undefined ?\n\n\t\t// Support: IE <=9 - 11 only\n\t\t// IE returns zIndex value as an integer.\n\t\tret + \"\" :\n\t\tret;\n}\n\n\nfunction addGetHookIf( conditionFn, hookFn ) {\n\n\t// Define the hook, we'll check on the first run if it's really needed.\n\treturn {\n\t\tget: function() {\n\t\t\tif ( conditionFn() ) {\n\n\t\t\t\t// Hook not needed (or it's not possible to use it due\n\t\t\t\t// to missing dependency), remove it.\n\t\t\t\tdelete this.get;\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// Hook needed; redefine it so that the support test is not executed again.\n\t\t\treturn ( this.get = hookFn ).apply( this, arguments );\n\t\t}\n\t};\n}\n\n\nvar cssPrefixes = [ \"Webkit\", \"Moz\", \"ms\" ],\n\temptyStyle = document.createElement( \"div\" ).style,\n\tvendorProps = {};\n\n// Return a vendor-prefixed property or undefined\nfunction vendorPropName( name ) {\n\n\t// Check for vendor prefixed names\n\tvar capName = name[ 0 ].toUpperCase() + name.slice( 1 ),\n\t\ti = cssPrefixes.length;\n\n\twhile ( i-- ) {\n\t\tname = cssPrefixes[ i ] + capName;\n\t\tif ( name in emptyStyle ) {\n\t\t\treturn name;\n\t\t}\n\t}\n}\n\n// Return a potentially-mapped jQuery.cssProps or vendor prefixed property\nfunction finalPropName( name ) {\n\tvar final = jQuery.cssProps[ name ] || vendorProps[ name ];\n\n\tif ( final ) {\n\t\treturn final;\n\t}\n\tif ( name in emptyStyle ) {\n\t\treturn name;\n\t}\n\treturn vendorProps[ name ] = vendorPropName( name ) || name;\n}\n\n\nvar\n\n\t// Swappable if display is none or starts with table\n\t// except \"table\", \"table-cell\", or \"table-caption\"\n\t// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display\n\trdisplayswap = /^(none|table(?!-c[ea]).+)/,\n\tcssShow = { position: \"absolute\", visibility: \"hidden\", display: \"block\" },\n\tcssNormalTransform = {\n\t\tletterSpacing: \"0\",\n\t\tfontWeight: \"400\"\n\t};\n\nfunction setPositiveNumber( _elem, value, subtract ) {\n\n\t// Any relative (+/-) values have already been\n\t// normalized at this point\n\tvar matches = rcssNum.exec( value );\n\treturn matches ?\n\n\t\t// Guard against undefined \"subtract\", e.g., when used as in cssHooks\n\t\tMath.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || \"px\" ) :\n\t\tvalue;\n}\n\nfunction boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {\n\tvar i = dimension === \"width\" ? 1 : 0,\n\t\textra = 0,\n\t\tdelta = 0,\n\t\tmarginDelta = 0;\n\n\t// Adjustment may not be necessary\n\tif ( box === ( isBorderBox ? \"border\" : \"content\" ) ) {\n\t\treturn 0;\n\t}\n\n\tfor ( ; i < 4; i += 2 ) {\n\n\t\t// Both box models exclude margin\n\t\t// Count margin delta separately to only add it after scroll gutter adjustment.\n\t\t// This is needed to make negative margins work with `outerHeight( true )` (gh-3982).\n\t\tif ( box === \"margin\" ) {\n\t\t\tmarginDelta += jQuery.css( elem, box + cssExpand[ i ], true, styles );\n\t\t}\n\n\t\t// If we get here with a content-box, we're seeking \"padding\" or \"border\" or \"margin\"\n\t\tif ( !isBorderBox ) {\n\n\t\t\t// Add padding\n\t\t\tdelta += jQuery.css( elem, \"padding\" + cssExpand[ i ], true, styles );\n\n\t\t\t// For \"border\" or \"margin\", add border\n\t\t\tif ( box !== \"padding\" ) {\n\t\t\t\tdelta += jQuery.css( elem, \"border\" + cssExpand[ i ] + \"Width\", true, styles );\n\n\t\t\t// But still keep track of it otherwise\n\t\t\t} else {\n\t\t\t\textra += jQuery.css( elem, \"border\" + cssExpand[ i ] + \"Width\", true, styles );\n\t\t\t}\n\n\t\t// If we get here with a border-box (content + padding + border), we're seeking \"content\" or\n\t\t// \"padding\" or \"margin\"\n\t\t} else {\n\n\t\t\t// For \"content\", subtract padding\n\t\t\tif ( box === \"content\" ) {\n\t\t\t\tdelta -= jQuery.css( elem, \"padding\" + cssExpand[ i ], true, styles );\n\t\t\t}\n\n\t\t\t// For \"content\" or \"padding\", subtract border\n\t\t\tif ( box !== \"margin\" ) {\n\t\t\t\tdelta -= jQuery.css( elem, \"border\" + cssExpand[ i ] + \"Width\", true, styles );\n\t\t\t}\n\t\t}\n\t}\n\n\t// Account for positive content-box scroll gutter when requested by providing computedVal\n\tif ( !isBorderBox && computedVal >= 0 ) {\n\n\t\t// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border\n\t\t// Assuming integer scroll gutter, subtract the rest and round down\n\t\tdelta += Math.max( 0, Math.ceil(\n\t\t\telem[ \"offset\" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -\n\t\t\tcomputedVal -\n\t\t\tdelta -\n\t\t\textra -\n\t\t\t0.5\n\n\t\t// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter\n\t\t// Use an explicit zero to avoid NaN (gh-3964)\n\t\t) ) || 0;\n\t}\n\n\treturn delta + marginDelta;\n}\n\nfunction getWidthOrHeight( elem, dimension, extra ) {\n\n\t// Start with computed style\n\tvar styles = getStyles( elem ),\n\n\t\t// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).\n\t\t// Fake content-box until we know it's needed to know the true value.\n\t\tboxSizingNeeded = !support.boxSizingReliable() || extra,\n\t\tisBorderBox = boxSizingNeeded &&\n\t\t\tjQuery.css( elem, \"boxSizing\", false, styles ) === \"border-box\",\n\t\tvalueIsBorderBox = isBorderBox,\n\n\t\tval = curCSS( elem, dimension, styles ),\n\t\toffsetProp = \"offset\" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );\n\n\t// Support: Firefox <=54\n\t// Return a confounding non-pixel value or feign ignorance, as appropriate.\n\tif ( rnumnonpx.test( val ) ) {\n\t\tif ( !extra ) {\n\t\t\treturn val;\n\t\t}\n\t\tval = \"auto\";\n\t}\n\n\n\t// Support: IE 9 - 11 only\n\t// Use offsetWidth/offsetHeight for when box sizing is unreliable.\n\t// In those cases, the computed value can be trusted to be border-box.\n\tif ( ( !support.boxSizingReliable() && isBorderBox ||\n\n\t\t// Support: IE 10 - 11+, Edge 15 - 18+\n\t\t// IE/Edge misreport `getComputedStyle` of table rows with width/height\n\t\t// set in CSS while `offset*` properties report correct values.\n\t\t// Interestingly, in some cases IE 9 doesn't suffer from this issue.\n\t\t!support.reliableTrDimensions() && nodeName( elem, \"tr\" ) ||\n\n\t\t// Fall back to offsetWidth/offsetHeight when value is \"auto\"\n\t\t// This happens for inline elements with no explicit setting (gh-3571)\n\t\tval === \"auto\" ||\n\n\t\t// Support: Android <=4.1 - 4.3 only\n\t\t// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)\n\t\t!parseFloat( val ) && jQuery.css( elem, \"display\", false, styles ) === \"inline\" ) &&\n\n\t\t// Make sure the element is visible & connected\n\t\telem.getClientRects().length ) {\n\n\t\tisBorderBox = jQuery.css( elem, \"boxSizing\", false, styles ) === \"border-box\";\n\n\t\t// Where available, offsetWidth/offsetHeight approximate border box dimensions.\n\t\t// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the\n\t\t// retrieved value as a content box dimension.\n\t\tvalueIsBorderBox = offsetProp in elem;\n\t\tif ( valueIsBorderBox ) {\n\t\t\tval = elem[ offsetProp ];\n\t\t}\n\t}\n\n\t// Normalize \"\" and auto\n\tval = parseFloat( val ) || 0;\n\n\t// Adjust for the element's box model\n\treturn ( val +\n\t\tboxModelAdjustment(\n\t\t\telem,\n\t\t\tdimension,\n\t\t\textra || ( isBorderBox ? \"border\" : \"content\" ),\n\t\t\tvalueIsBorderBox,\n\t\t\tstyles,\n\n\t\t\t// Provide the current computed size to request scroll gutter calculation (gh-3589)\n\t\t\tval\n\t\t)\n\t) + \"px\";\n}\n\njQuery.extend( {\n\n\t// Add in style property hooks for overriding the default\n\t// behavior of getting and setting a style property\n\tcssHooks: {\n\t\topacity: {\n\t\t\tget: function( elem, computed ) {\n\t\t\t\tif ( computed ) {\n\n\t\t\t\t\t// We should always get a number back from opacity\n\t\t\t\t\tvar ret = curCSS( elem, \"opacity\" );\n\t\t\t\t\treturn ret === \"\" ? \"1\" : ret;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\t// Don't automatically add \"px\" to these possibly-unitless properties\n\tcssNumber: {\n\t\tanimationIterationCount: true,\n\t\taspectRatio: true,\n\t\tborderImageSlice: true,\n\t\tcolumnCount: true,\n\t\tflexGrow: true,\n\t\tflexShrink: true,\n\t\tfontWeight: true,\n\t\tgridArea: true,\n\t\tgridColumn: true,\n\t\tgridColumnEnd: true,\n\t\tgridColumnStart: true,\n\t\tgridRow: true,\n\t\tgridRowEnd: true,\n\t\tgridRowStart: true,\n\t\tlineHeight: true,\n\t\topacity: true,\n\t\torder: true,\n\t\torphans: true,\n\t\tscale: true,\n\t\twidows: true,\n\t\tzIndex: true,\n\t\tzoom: true,\n\n\t\t// SVG-related\n\t\tfillOpacity: true,\n\t\tfloodOpacity: true,\n\t\tstopOpacity: true,\n\t\tstrokeMiterlimit: true,\n\t\tstrokeOpacity: true\n\t},\n\n\t// Add in properties whose names you wish to fix before\n\t// setting or getting the value\n\tcssProps: {},\n\n\t// Get and set the style property on a DOM Node\n\tstyle: function( elem, name, value, extra ) {\n\n\t\t// Don't set styles on text and comment nodes\n\t\tif ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Make sure that we're working with the right name\n\t\tvar ret, type, hooks,\n\t\t\torigName = camelCase( name ),\n\t\t\tisCustomProp = rcustomProp.test( name ),\n\t\t\tstyle = elem.style;\n\n\t\t// Make sure that we're working with the right name. We don't\n\t\t// want to query the value if it is a CSS custom property\n\t\t// since they are user-defined.\n\t\tif ( !isCustomProp ) {\n\t\t\tname = finalPropName( origName );\n\t\t}\n\n\t\t// Gets hook for the prefixed version, then unprefixed version\n\t\thooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];\n\n\t\t// Check if we're setting a value\n\t\tif ( value !== undefined ) {\n\t\t\ttype = typeof value;\n\n\t\t\t// Convert \"+=\" or \"-=\" to relative numbers (trac-7345)\n\t\t\tif ( type === \"string\" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {\n\t\t\t\tvalue = adjustCSS( elem, name, ret );\n\n\t\t\t\t// Fixes bug trac-9237\n\t\t\t\ttype = \"number\";\n\t\t\t}\n\n\t\t\t// Make sure that null and NaN values aren't set (trac-7116)\n\t\t\tif ( value == null || value !== value ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// If a number was passed in, add the unit (except for certain CSS properties)\n\t\t\t// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append\n\t\t\t// \"px\" to a few hardcoded values.\n\t\t\tif ( type === \"number\" && !isCustomProp ) {\n\t\t\t\tvalue += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? \"\" : \"px\" );\n\t\t\t}\n\n\t\t\t// background-* props affect original clone's values\n\t\t\tif ( !support.clearCloneStyle && value === \"\" && name.indexOf( \"background\" ) === 0 ) {\n\t\t\t\tstyle[ name ] = \"inherit\";\n\t\t\t}\n\n\t\t\t// If a hook was provided, use that value, otherwise just set the specified value\n\t\t\tif ( !hooks || !( \"set\" in hooks ) ||\n\t\t\t\t( value = hooks.set( elem, value, extra ) ) !== undefined ) {\n\n\t\t\t\tif ( isCustomProp ) {\n\t\t\t\t\tstyle.setProperty( name, value );\n\t\t\t\t} else {\n\t\t\t\t\tstyle[ name ] = value;\n\t\t\t\t}\n\t\t\t}\n\n\t\t} else {\n\n\t\t\t// If a hook was provided get the non-computed value from there\n\t\t\tif ( hooks && \"get\" in hooks &&\n\t\t\t\t( ret = hooks.get( elem, false, extra ) ) !== undefined ) {\n\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\t// Otherwise just get the value from the style object\n\t\t\treturn style[ name ];\n\t\t}\n\t},\n\n\tcss: function( elem, name, extra, styles ) {\n\t\tvar val, num, hooks,\n\t\t\torigName = camelCase( name ),\n\t\t\tisCustomProp = rcustomProp.test( name );\n\n\t\t// Make sure that we're working with the right name. We don't\n\t\t// want to modify the value if it is a CSS custom property\n\t\t// since they are user-defined.\n\t\tif ( !isCustomProp ) {\n\t\t\tname = finalPropName( origName );\n\t\t}\n\n\t\t// Try prefixed name followed by the unprefixed name\n\t\thooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];\n\n\t\t// If a hook was provided get the computed value from there\n\t\tif ( hooks && \"get\" in hooks ) {\n\t\t\tval = hooks.get( elem, true, extra );\n\t\t}\n\n\t\t// Otherwise, if a way to get the computed value exists, use that\n\t\tif ( val === undefined ) {\n\t\t\tval = curCSS( elem, name, styles );\n\t\t}\n\n\t\t// Convert \"normal\" to computed value\n\t\tif ( val === \"normal\" && name in cssNormalTransform ) {\n\t\t\tval = cssNormalTransform[ name ];\n\t\t}\n\n\t\t// Make numeric if forced or a qualifier was provided and val looks numeric\n\t\tif ( extra === \"\" || extra ) {\n\t\t\tnum = parseFloat( val );\n\t\t\treturn extra === true || isFinite( num ) ? num || 0 : val;\n\t\t}\n\n\t\treturn val;\n\t}\n} );\n\njQuery.each( [ \"height\", \"width\" ], function( _i, dimension ) {\n\tjQuery.cssHooks[ dimension ] = {\n\t\tget: function( elem, computed, extra ) {\n\t\t\tif ( computed ) {\n\n\t\t\t\t// Certain elements can have dimension info if we invisibly show them\n\t\t\t\t// but it must have a current display style that would benefit\n\t\t\t\treturn rdisplayswap.test( jQuery.css( elem, \"display\" ) ) &&\n\n\t\t\t\t\t// Support: Safari 8+\n\t\t\t\t\t// Table columns in Safari have non-zero offsetWidth & zero\n\t\t\t\t\t// getBoundingClientRect().width unless display is changed.\n\t\t\t\t\t// Support: IE <=11 only\n\t\t\t\t\t// Running getBoundingClientRect on a disconnected node\n\t\t\t\t\t// in IE throws an error.\n\t\t\t\t\t( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?\n\t\t\t\t\tswap( elem, cssShow, function() {\n\t\t\t\t\t\treturn getWidthOrHeight( elem, dimension, extra );\n\t\t\t\t\t} ) :\n\t\t\t\t\tgetWidthOrHeight( elem, dimension, extra );\n\t\t\t}\n\t\t},\n\n\t\tset: function( elem, value, extra ) {\n\t\t\tvar matches,\n\t\t\t\tstyles = getStyles( elem ),\n\n\t\t\t\t// Only read styles.position if the test has a chance to fail\n\t\t\t\t// to avoid forcing a reflow.\n\t\t\t\tscrollboxSizeBuggy = !support.scrollboxSize() &&\n\t\t\t\t\tstyles.position === \"absolute\",\n\n\t\t\t\t// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)\n\t\t\t\tboxSizingNeeded = scrollboxSizeBuggy || extra,\n\t\t\t\tisBorderBox = boxSizingNeeded &&\n\t\t\t\t\tjQuery.css( elem, \"boxSizing\", false, styles ) === \"border-box\",\n\t\t\t\tsubtract = extra ?\n\t\t\t\t\tboxModelAdjustment(\n\t\t\t\t\t\telem,\n\t\t\t\t\t\tdimension,\n\t\t\t\t\t\textra,\n\t\t\t\t\t\tisBorderBox,\n\t\t\t\t\t\tstyles\n\t\t\t\t\t) :\n\t\t\t\t\t0;\n\n\t\t\t// Account for unreliable border-box dimensions by comparing offset* to computed and\n\t\t\t// faking a content-box to get border and padding (gh-3699)\n\t\t\tif ( isBorderBox && scrollboxSizeBuggy ) {\n\t\t\t\tsubtract -= Math.ceil(\n\t\t\t\t\telem[ \"offset\" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -\n\t\t\t\t\tparseFloat( styles[ dimension ] ) -\n\t\t\t\t\tboxModelAdjustment( elem, dimension, \"border\", false, styles ) -\n\t\t\t\t\t0.5\n\t\t\t\t);\n\t\t\t}\n\n\t\t\t// Convert to pixels if value adjustment is needed\n\t\t\tif ( subtract && ( matches = rcssNum.exec( value ) ) &&\n\t\t\t\t( matches[ 3 ] || \"px\" ) !== \"px\" ) {\n\n\t\t\t\telem.style[ dimension ] = value;\n\t\t\t\tvalue = jQuery.css( elem, dimension );\n\t\t\t}\n\n\t\t\treturn setPositiveNumber( elem, value, subtract );\n\t\t}\n\t};\n} );\n\njQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,\n\tfunction( elem, computed ) {\n\t\tif ( computed ) {\n\t\t\treturn ( parseFloat( curCSS( elem, \"marginLeft\" ) ) ||\n\t\t\t\telem.getBoundingClientRect().left -\n\t\t\t\t\tswap( elem, { marginLeft: 0 }, function() {\n\t\t\t\t\t\treturn elem.getBoundingClientRect().left;\n\t\t\t\t\t} )\n\t\t\t) + \"px\";\n\t\t}\n\t}\n);\n\n// These hooks are used by animate to expand properties\njQuery.each( {\n\tmargin: \"\",\n\tpadding: \"\",\n\tborder: \"Width\"\n}, function( prefix, suffix ) {\n\tjQuery.cssHooks[ prefix + suffix ] = {\n\t\texpand: function( value ) {\n\t\t\tvar i = 0,\n\t\t\t\texpanded = {},\n\n\t\t\t\t// Assumes a single number if not a string\n\t\t\t\tparts = typeof value === \"string\" ? value.split( \" \" ) : [ value ];\n\n\t\t\tfor ( ; i < 4; i++ ) {\n\t\t\t\texpanded[ prefix + cssExpand[ i ] + suffix ] =\n\t\t\t\t\tparts[ i ] || parts[ i - 2 ] || parts[ 0 ];\n\t\t\t}\n\n\t\t\treturn expanded;\n\t\t}\n\t};\n\n\tif ( prefix !== \"margin\" ) {\n\t\tjQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;\n\t}\n} );\n\njQuery.fn.extend( {\n\tcss: function( name, value ) {\n\t\treturn access( this, function( elem, name, value ) {\n\t\t\tvar styles, len,\n\t\t\t\tmap = {},\n\t\t\t\ti = 0;\n\n\t\t\tif ( Array.isArray( name ) ) {\n\t\t\t\tstyles = getStyles( elem );\n\t\t\t\tlen = name.length;\n\n\t\t\t\tfor ( ; i < len; i++ ) {\n\t\t\t\t\tmap[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );\n\t\t\t\t}\n\n\t\t\t\treturn map;\n\t\t\t}\n\n\t\t\treturn value !== undefined ?\n\t\t\t\tjQuery.style( elem, name, value ) :\n\t\t\t\tjQuery.css( elem, name );\n\t\t}, name, value, arguments.length > 1 );\n\t}\n} );\n\n\nfunction Tween( elem, options, prop, end, easing ) {\n\treturn new Tween.prototype.init( elem, options, prop, end, easing );\n}\njQuery.Tween = Tween;\n\nTween.prototype = {\n\tconstructor: Tween,\n\tinit: function( elem, options, prop, end, easing, unit ) {\n\t\tthis.elem = elem;\n\t\tthis.prop = prop;\n\t\tthis.easing = easing || jQuery.easing._default;\n\t\tthis.options = options;\n\t\tthis.start = this.now = this.cur();\n\t\tthis.end = end;\n\t\tthis.unit = unit || ( jQuery.cssNumber[ prop ] ? \"\" : \"px\" );\n\t},\n\tcur: function() {\n\t\tvar hooks = Tween.propHooks[ this.prop ];\n\n\t\treturn hooks && hooks.get ?\n\t\t\thooks.get( this ) :\n\t\t\tTween.propHooks._default.get( this );\n\t},\n\trun: function( percent ) {\n\t\tvar eased,\n\t\t\thooks = Tween.propHooks[ this.prop ];\n\n\t\tif ( this.options.duration ) {\n\t\t\tthis.pos = eased = jQuery.easing[ this.easing ](\n\t\t\t\tpercent, this.options.duration * percent, 0, 1, this.options.duration\n\t\t\t);\n\t\t} else {\n\t\t\tthis.pos = eased = percent;\n\t\t}\n\t\tthis.now = ( this.end - this.start ) * eased + this.start;\n\n\t\tif ( this.options.step ) {\n\t\t\tthis.options.step.call( this.elem, this.now, this );\n\t\t}\n\n\t\tif ( hooks && hooks.set ) {\n\t\t\thooks.set( this );\n\t\t} else {\n\t\t\tTween.propHooks._default.set( this );\n\t\t}\n\t\treturn this;\n\t}\n};\n\nTween.prototype.init.prototype = Tween.prototype;\n\nTween.propHooks = {\n\t_default: {\n\t\tget: function( tween ) {\n\t\t\tvar result;\n\n\t\t\t// Use a property on the element directly when it is not a DOM element,\n\t\t\t// or when there is no matching style property that exists.\n\t\t\tif ( tween.elem.nodeType !== 1 ||\n\t\t\t\ttween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {\n\t\t\t\treturn tween.elem[ tween.prop ];\n\t\t\t}\n\n\t\t\t// Passing an empty string as a 3rd parameter to .css will automatically\n\t\t\t// attempt a parseFloat and fallback to a string if the parse fails.\n\t\t\t// Simple values such as \"10px\" are parsed to Float;\n\t\t\t// complex values such as \"rotate(1rad)\" are returned as-is.\n\t\t\tresult = jQuery.css( tween.elem, tween.prop, \"\" );\n\n\t\t\t// Empty strings, null, undefined and \"auto\" are converted to 0.\n\t\t\treturn !result || result === \"auto\" ? 0 : result;\n\t\t},\n\t\tset: function( tween ) {\n\n\t\t\t// Use step hook for back compat.\n\t\t\t// Use cssHook if its there.\n\t\t\t// Use .style if available and use plain properties where available.\n\t\t\tif ( jQuery.fx.step[ tween.prop ] ) {\n\t\t\t\tjQuery.fx.step[ tween.prop ]( tween );\n\t\t\t} else if ( tween.elem.nodeType === 1 && (\n\t\t\t\tjQuery.cssHooks[ tween.prop ] ||\n\t\t\t\t\ttween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {\n\t\t\t\tjQuery.style( tween.elem, tween.prop, tween.now + tween.unit );\n\t\t\t} else {\n\t\t\t\ttween.elem[ tween.prop ] = tween.now;\n\t\t\t}\n\t\t}\n\t}\n};\n\n// Support: IE <=9 only\n// Panic based approach to setting things on disconnected nodes\nTween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {\n\tset: function( tween ) {\n\t\tif ( tween.elem.nodeType && tween.elem.parentNode ) {\n\t\t\ttween.elem[ tween.prop ] = tween.now;\n\t\t}\n\t}\n};\n\njQuery.easing = {\n\tlinear: function( p ) {\n\t\treturn p;\n\t},\n\tswing: function( p ) {\n\t\treturn 0.5 - Math.cos( p * Math.PI ) / 2;\n\t},\n\t_default: \"swing\"\n};\n\njQuery.fx = Tween.prototype.init;\n\n// Back compat <1.8 extension point\njQuery.fx.step = {};\n\n\n\n\nvar\n\tfxNow, inProgress,\n\trfxtypes = /^(?:toggle|show|hide)$/,\n\trrun = /queueHooks$/;\n\nfunction schedule() {\n\tif ( inProgress ) {\n\t\tif ( document.hidden === false && window.requestAnimationFrame ) {\n\t\t\twindow.requestAnimationFrame( schedule );\n\t\t} else {\n\t\t\twindow.setTimeout( schedule, jQuery.fx.interval );\n\t\t}\n\n\t\tjQuery.fx.tick();\n\t}\n}\n\n// Animations created synchronously will run synchronously\nfunction createFxNow() {\n\twindow.setTimeout( function() {\n\t\tfxNow = undefined;\n\t} );\n\treturn ( fxNow = Date.now() );\n}\n\n// Generate parameters to create a standard animation\nfunction genFx( type, includeWidth ) {\n\tvar which,\n\t\ti = 0,\n\t\tattrs = { height: type };\n\n\t// If we include width, step value is 1 to do all cssExpand values,\n\t// otherwise step value is 2 to skip over Left and Right\n\tincludeWidth = includeWidth ? 1 : 0;\n\tfor ( ; i < 4; i += 2 - includeWidth ) {\n\t\twhich = cssExpand[ i ];\n\t\tattrs[ \"margin\" + which ] = attrs[ \"padding\" + which ] = type;\n\t}\n\n\tif ( includeWidth ) {\n\t\tattrs.opacity = attrs.width = type;\n\t}\n\n\treturn attrs;\n}\n\nfunction createTween( value, prop, animation ) {\n\tvar tween,\n\t\tcollection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ \"*\" ] ),\n\t\tindex = 0,\n\t\tlength = collection.length;\n\tfor ( ; index < length; index++ ) {\n\t\tif ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {\n\n\t\t\t// We're done with this property\n\t\t\treturn tween;\n\t\t}\n\t}\n}\n\nfunction defaultPrefilter( elem, props, opts ) {\n\tvar prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,\n\t\tisBox = \"width\" in props || \"height\" in props,\n\t\tanim = this,\n\t\torig = {},\n\t\tstyle = elem.style,\n\t\thidden = elem.nodeType && isHiddenWithinTree( elem ),\n\t\tdataShow = dataPriv.get( elem, \"fxshow\" );\n\n\t// Queue-skipping animations hijack the fx hooks\n\tif ( !opts.queue ) {\n\t\thooks = jQuery._queueHooks( elem, \"fx\" );\n\t\tif ( hooks.unqueued == null ) {\n\t\t\thooks.unqueued = 0;\n\t\t\toldfire = hooks.empty.fire;\n\t\t\thooks.empty.fire = function() {\n\t\t\t\tif ( !hooks.unqueued ) {\n\t\t\t\t\toldfire();\n\t\t\t\t}\n\t\t\t};\n\t\t}\n\t\thooks.unqueued++;\n\n\t\tanim.always( function() {\n\n\t\t\t// Ensure the complete handler is called before this completes\n\t\t\tanim.always( function() {\n\t\t\t\thooks.unqueued--;\n\t\t\t\tif ( !jQuery.queue( elem, \"fx\" ).length ) {\n\t\t\t\t\thooks.empty.fire();\n\t\t\t\t}\n\t\t\t} );\n\t\t} );\n\t}\n\n\t// Detect show/hide animations\n\tfor ( prop in props ) {\n\t\tvalue = props[ prop ];\n\t\tif ( rfxtypes.test( value ) ) {\n\t\t\tdelete props[ prop ];\n\t\t\ttoggle = toggle || value === \"toggle\";\n\t\t\tif ( value === ( hidden ? \"hide\" : \"show\" ) ) {\n\n\t\t\t\t// Pretend to be hidden if this is a \"show\" and\n\t\t\t\t// there is still data from a stopped show/hide\n\t\t\t\tif ( value === \"show\" && dataShow && dataShow[ prop ] !== undefined ) {\n\t\t\t\t\thidden = true;\n\n\t\t\t\t// Ignore all other no-op show/hide data\n\t\t\t\t} else {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t}\n\t\t\torig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );\n\t\t}\n\t}\n\n\t// Bail out if this is a no-op like .hide().hide()\n\tpropTween = !jQuery.isEmptyObject( props );\n\tif ( !propTween && jQuery.isEmptyObject( orig ) ) {\n\t\treturn;\n\t}\n\n\t// Restrict \"overflow\" and \"display\" styles during box animations\n\tif ( isBox && elem.nodeType === 1 ) {\n\n\t\t// Support: IE <=9 - 11, Edge 12 - 15\n\t\t// Record all 3 overflow attributes because IE does not infer the shorthand\n\t\t// from identically-valued overflowX and overflowY and Edge just mirrors\n\t\t// the overflowX value there.\n\t\topts.overflow = [ style.overflow, style.overflowX, style.overflowY ];\n\n\t\t// Identify a display type, preferring old show/hide data over the CSS cascade\n\t\trestoreDisplay = dataShow && dataShow.display;\n\t\tif ( restoreDisplay == null ) {\n\t\t\trestoreDisplay = dataPriv.get( elem, \"display\" );\n\t\t}\n\t\tdisplay = jQuery.css( elem, \"display\" );\n\t\tif ( display === \"none\" ) {\n\t\t\tif ( restoreDisplay ) {\n\t\t\t\tdisplay = restoreDisplay;\n\t\t\t} else {\n\n\t\t\t\t// Get nonempty value(s) by temporarily forcing visibility\n\t\t\t\tshowHide( [ elem ], true );\n\t\t\t\trestoreDisplay = elem.style.display || restoreDisplay;\n\t\t\t\tdisplay = jQuery.css( elem, \"display\" );\n\t\t\t\tshowHide( [ elem ] );\n\t\t\t}\n\t\t}\n\n\t\t// Animate inline elements as inline-block\n\t\tif ( display === \"inline\" || display === \"inline-block\" && restoreDisplay != null ) {\n\t\t\tif ( jQuery.css( elem, \"float\" ) === \"none\" ) {\n\n\t\t\t\t// Restore the original display value at the end of pure show/hide animations\n\t\t\t\tif ( !propTween ) {\n\t\t\t\t\tanim.done( function() {\n\t\t\t\t\t\tstyle.display = restoreDisplay;\n\t\t\t\t\t} );\n\t\t\t\t\tif ( restoreDisplay == null ) {\n\t\t\t\t\t\tdisplay = style.display;\n\t\t\t\t\t\trestoreDisplay = display === \"none\" ? \"\" : display;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tstyle.display = \"inline-block\";\n\t\t\t}\n\t\t}\n\t}\n\n\tif ( opts.overflow ) {\n\t\tstyle.overflow = \"hidden\";\n\t\tanim.always( function() {\n\t\t\tstyle.overflow = opts.overflow[ 0 ];\n\t\t\tstyle.overflowX = opts.overflow[ 1 ];\n\t\t\tstyle.overflowY = opts.overflow[ 2 ];\n\t\t} );\n\t}\n\n\t// Implement show/hide animations\n\tpropTween = false;\n\tfor ( prop in orig ) {\n\n\t\t// General show/hide setup for this element animation\n\t\tif ( !propTween ) {\n\t\t\tif ( dataShow ) {\n\t\t\t\tif ( \"hidden\" in dataShow ) {\n\t\t\t\t\thidden = dataShow.hidden;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tdataShow = dataPriv.access( elem, \"fxshow\", { display: restoreDisplay } );\n\t\t\t}\n\n\t\t\t// Store hidden/visible for toggle so `.stop().toggle()` \"reverses\"\n\t\t\tif ( toggle ) {\n\t\t\t\tdataShow.hidden = !hidden;\n\t\t\t}\n\n\t\t\t// Show elements before animating them\n\t\t\tif ( hidden ) {\n\t\t\t\tshowHide( [ elem ], true );\n\t\t\t}\n\n\t\t\t/* eslint-disable no-loop-func */\n\n\t\t\tanim.done( function() {\n\n\t\t\t\t/* eslint-enable no-loop-func */\n\n\t\t\t\t// The final step of a \"hide\" animation is actually hiding the element\n\t\t\t\tif ( !hidden ) {\n\t\t\t\t\tshowHide( [ elem ] );\n\t\t\t\t}\n\t\t\t\tdataPriv.remove( elem, \"fxshow\" );\n\t\t\t\tfor ( prop in orig ) {\n\t\t\t\t\tjQuery.style( elem, prop, orig[ prop ] );\n\t\t\t\t}\n\t\t\t} );\n\t\t}\n\n\t\t// Per-property setup\n\t\tpropTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );\n\t\tif ( !( prop in dataShow ) ) {\n\t\t\tdataShow[ prop ] = propTween.start;\n\t\t\tif ( hidden ) {\n\t\t\t\tpropTween.end = propTween.start;\n\t\t\t\tpropTween.start = 0;\n\t\t\t}\n\t\t}\n\t}\n}\n\nfunction propFilter( props, specialEasing ) {\n\tvar index, name, easing, value, hooks;\n\n\t// camelCase, specialEasing and expand cssHook pass\n\tfor ( index in props ) {\n\t\tname = camelCase( index );\n\t\teasing = specialEasing[ name ];\n\t\tvalue = props[ index ];\n\t\tif ( Array.isArray( value ) ) {\n\t\t\teasing = value[ 1 ];\n\t\t\tvalue = props[ index ] = value[ 0 ];\n\t\t}\n\n\t\tif ( index !== name ) {\n\t\t\tprops[ name ] = value;\n\t\t\tdelete props[ index ];\n\t\t}\n\n\t\thooks = jQuery.cssHooks[ name ];\n\t\tif ( hooks && \"expand\" in hooks ) {\n\t\t\tvalue = hooks.expand( value );\n\t\t\tdelete props[ name ];\n\n\t\t\t// Not quite $.extend, this won't overwrite existing keys.\n\t\t\t// Reusing 'index' because we have the correct \"name\"\n\t\t\tfor ( index in value ) {\n\t\t\t\tif ( !( index in props ) ) {\n\t\t\t\t\tprops[ index ] = value[ index ];\n\t\t\t\t\tspecialEasing[ index ] = easing;\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tspecialEasing[ name ] = easing;\n\t\t}\n\t}\n}\n\nfunction Animation( elem, properties, options ) {\n\tvar result,\n\t\tstopped,\n\t\tindex = 0,\n\t\tlength = Animation.prefilters.length,\n\t\tdeferred = jQuery.Deferred().always( function() {\n\n\t\t\t// Don't match elem in the :animated selector\n\t\t\tdelete tick.elem;\n\t\t} ),\n\t\ttick = function() {\n\t\t\tif ( stopped ) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t\tvar currentTime = fxNow || createFxNow(),\n\t\t\t\tremaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),\n\n\t\t\t\t// Support: Android 2.3 only\n\t\t\t\t// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (trac-12497)\n\t\t\t\ttemp = remaining / animation.duration || 0,\n\t\t\t\tpercent = 1 - temp,\n\t\t\t\tindex = 0,\n\t\t\t\tlength = animation.tweens.length;\n\n\t\t\tfor ( ; index < length; index++ ) {\n\t\t\t\tanimation.tweens[ index ].run( percent );\n\t\t\t}\n\n\t\t\tdeferred.notifyWith( elem, [ animation, percent, remaining ] );\n\n\t\t\t// If there's more to do, yield\n\t\t\tif ( percent < 1 && length ) {\n\t\t\t\treturn remaining;\n\t\t\t}\n\n\t\t\t// If this was an empty animation, synthesize a final progress notification\n\t\t\tif ( !length ) {\n\t\t\t\tdeferred.notifyWith( elem, [ animation, 1, 0 ] );\n\t\t\t}\n\n\t\t\t// Resolve the animation and report its conclusion\n\t\t\tdeferred.resolveWith( elem, [ animation ] );\n\t\t\treturn false;\n\t\t},\n\t\tanimation = deferred.promise( {\n\t\t\telem: elem,\n\t\t\tprops: jQuery.extend( {}, properties ),\n\t\t\topts: jQuery.extend( true, {\n\t\t\t\tspecialEasing: {},\n\t\t\t\teasing: jQuery.easing._default\n\t\t\t}, options ),\n\t\t\toriginalProperties: properties,\n\t\t\toriginalOptions: options,\n\t\t\tstartTime: fxNow || createFxNow(),\n\t\t\tduration: options.duration,\n\t\t\ttweens: [],\n\t\t\tcreateTween: function( prop, end ) {\n\t\t\t\tvar tween = jQuery.Tween( elem, animation.opts, prop, end,\n\t\t\t\t\tanimation.opts.specialEasing[ prop ] || animation.opts.easing );\n\t\t\t\tanimation.tweens.push( tween );\n\t\t\t\treturn tween;\n\t\t\t},\n\t\t\tstop: function( gotoEnd ) {\n\t\t\t\tvar index = 0,\n\n\t\t\t\t\t// If we are going to the end, we want to run all the tweens\n\t\t\t\t\t// otherwise we skip this part\n\t\t\t\t\tlength = gotoEnd ? animation.tweens.length : 0;\n\t\t\t\tif ( stopped ) {\n\t\t\t\t\treturn this;\n\t\t\t\t}\n\t\t\t\tstopped = true;\n\t\t\t\tfor ( ; index < length; index++ ) {\n\t\t\t\t\tanimation.tweens[ index ].run( 1 );\n\t\t\t\t}\n\n\t\t\t\t// Resolve when we played the last frame; otherwise, reject\n\t\t\t\tif ( gotoEnd ) {\n\t\t\t\t\tdeferred.notifyWith( elem, [ animation, 1, 0 ] );\n\t\t\t\t\tdeferred.resolveWith( elem, [ animation, gotoEnd ] );\n\t\t\t\t} else {\n\t\t\t\t\tdeferred.rejectWith( elem, [ animation, gotoEnd ] );\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t}\n\t\t} ),\n\t\tprops = animation.props;\n\n\tpropFilter( props, animation.opts.specialEasing );\n\n\tfor ( ; index < length; index++ ) {\n\t\tresult = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );\n\t\tif ( result ) {\n\t\t\tif ( isFunction( result.stop ) ) {\n\t\t\t\tjQuery._queueHooks( animation.elem, animation.opts.queue ).stop =\n\t\t\t\t\tresult.stop.bind( result );\n\t\t\t}\n\t\t\treturn result;\n\t\t}\n\t}\n\n\tjQuery.map( props, createTween, animation );\n\n\tif ( isFunction( animation.opts.start ) ) {\n\t\tanimation.opts.start.call( elem, animation );\n\t}\n\n\t// Attach callbacks from options\n\tanimation\n\t\t.progress( animation.opts.progress )\n\t\t.done( animation.opts.done, animation.opts.complete )\n\t\t.fail( animation.opts.fail )\n\t\t.always( animation.opts.always );\n\n\tjQuery.fx.timer(\n\t\tjQuery.extend( tick, {\n\t\t\telem: elem,\n\t\t\tanim: animation,\n\t\t\tqueue: animation.opts.queue\n\t\t} )\n\t);\n\n\treturn animation;\n}\n\njQuery.Animation = jQuery.extend( Animation, {\n\n\ttweeners: {\n\t\t\"*\": [ function( prop, value ) {\n\t\t\tvar tween = this.createTween( prop, value );\n\t\t\tadjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );\n\t\t\treturn tween;\n\t\t} ]\n\t},\n\n\ttweener: function( props, callback ) {\n\t\tif ( isFunction( props ) ) {\n\t\t\tcallback = props;\n\t\t\tprops = [ \"*\" ];\n\t\t} else {\n\t\t\tprops = props.match( rnothtmlwhite );\n\t\t}\n\n\t\tvar prop,\n\t\t\tindex = 0,\n\t\t\tlength = props.length;\n\n\t\tfor ( ; index < length; index++ ) {\n\t\t\tprop = props[ index ];\n\t\t\tAnimation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];\n\t\t\tAnimation.tweeners[ prop ].unshift( callback );\n\t\t}\n\t},\n\n\tprefilters: [ defaultPrefilter ],\n\n\tprefilter: function( callback, prepend ) {\n\t\tif ( prepend ) {\n\t\t\tAnimation.prefilters.unshift( callback );\n\t\t} else {\n\t\t\tAnimation.prefilters.push( callback );\n\t\t}\n\t}\n} );\n\njQuery.speed = function( speed, easing, fn ) {\n\tvar opt = speed && typeof speed === \"object\" ? jQuery.extend( {}, speed ) : {\n\t\tcomplete: fn || !fn && easing ||\n\t\t\tisFunction( speed ) && speed,\n\t\tduration: speed,\n\t\teasing: fn && easing || easing && !isFunction( easing ) && easing\n\t};\n\n\t// Go to the end state if fx are off\n\tif ( jQuery.fx.off ) {\n\t\topt.duration = 0;\n\n\t} else {\n\t\tif ( typeof opt.duration !== \"number\" ) {\n\t\t\tif ( opt.duration in jQuery.fx.speeds ) {\n\t\t\t\topt.duration = jQuery.fx.speeds[ opt.duration ];\n\n\t\t\t} else {\n\t\t\t\topt.duration = jQuery.fx.speeds._default;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Normalize opt.queue - true/undefined/null -> \"fx\"\n\tif ( opt.queue == null || opt.queue === true ) {\n\t\topt.queue = \"fx\";\n\t}\n\n\t// Queueing\n\topt.old = opt.complete;\n\n\topt.complete = function() {\n\t\tif ( isFunction( opt.old ) ) {\n\t\t\topt.old.call( this );\n\t\t}\n\n\t\tif ( opt.queue ) {\n\t\t\tjQuery.dequeue( this, opt.queue );\n\t\t}\n\t};\n\n\treturn opt;\n};\n\njQuery.fn.extend( {\n\tfadeTo: function( speed, to, easing, callback ) {\n\n\t\t// Show any hidden elements after setting opacity to 0\n\t\treturn this.filter( isHiddenWithinTree ).css( \"opacity\", 0 ).show()\n\n\t\t\t// Animate to the value specified\n\t\t\t.end().animate( { opacity: to }, speed, easing, callback );\n\t},\n\tanimate: function( prop, speed, easing, callback ) {\n\t\tvar empty = jQuery.isEmptyObject( prop ),\n\t\t\toptall = jQuery.speed( speed, easing, callback ),\n\t\t\tdoAnimation = function() {\n\n\t\t\t\t// Operate on a copy of prop so per-property easing won't be lost\n\t\t\t\tvar anim = Animation( this, jQuery.extend( {}, prop ), optall );\n\n\t\t\t\t// Empty animations, or finishing resolves immediately\n\t\t\t\tif ( empty || dataPriv.get( this, \"finish\" ) ) {\n\t\t\t\t\tanim.stop( true );\n\t\t\t\t}\n\t\t\t};\n\n\t\tdoAnimation.finish = doAnimation;\n\n\t\treturn empty || optall.queue === false ?\n\t\t\tthis.each( doAnimation ) :\n\t\t\tthis.queue( optall.queue, doAnimation );\n\t},\n\tstop: function( type, clearQueue, gotoEnd ) {\n\t\tvar stopQueue = function( hooks ) {\n\t\t\tvar stop = hooks.stop;\n\t\t\tdelete hooks.stop;\n\t\t\tstop( gotoEnd );\n\t\t};\n\n\t\tif ( typeof type !== \"string\" ) {\n\t\t\tgotoEnd = clearQueue;\n\t\t\tclearQueue = type;\n\t\t\ttype = undefined;\n\t\t}\n\t\tif ( clearQueue ) {\n\t\t\tthis.queue( type || \"fx\", [] );\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tvar dequeue = true,\n\t\t\t\tindex = type != null && type + \"queueHooks\",\n\t\t\t\ttimers = jQuery.timers,\n\t\t\t\tdata = dataPriv.get( this );\n\n\t\t\tif ( index ) {\n\t\t\t\tif ( data[ index ] && data[ index ].stop ) {\n\t\t\t\t\tstopQueue( data[ index ] );\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tfor ( index in data ) {\n\t\t\t\t\tif ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {\n\t\t\t\t\t\tstopQueue( data[ index ] );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tfor ( index = timers.length; index--; ) {\n\t\t\t\tif ( timers[ index ].elem === this &&\n\t\t\t\t\t( type == null || timers[ index ].queue === type ) ) {\n\n\t\t\t\t\ttimers[ index ].anim.stop( gotoEnd );\n\t\t\t\t\tdequeue = false;\n\t\t\t\t\ttimers.splice( index, 1 );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Start the next in the queue if the last step wasn't forced.\n\t\t\t// Timers currently will call their complete callbacks, which\n\t\t\t// will dequeue but only if they were gotoEnd.\n\t\t\tif ( dequeue || !gotoEnd ) {\n\t\t\t\tjQuery.dequeue( this, type );\n\t\t\t}\n\t\t} );\n\t},\n\tfinish: function( type ) {\n\t\tif ( type !== false ) {\n\t\t\ttype = type || \"fx\";\n\t\t}\n\t\treturn this.each( function() {\n\t\t\tvar index,\n\t\t\t\tdata = dataPriv.get( this ),\n\t\t\t\tqueue = data[ type + \"queue\" ],\n\t\t\t\thooks = data[ type + \"queueHooks\" ],\n\t\t\t\ttimers = jQuery.timers,\n\t\t\t\tlength = queue ? queue.length : 0;\n\n\t\t\t// Enable finishing flag on private data\n\t\t\tdata.finish = true;\n\n\t\t\t// Empty the queue first\n\t\t\tjQuery.queue( this, type, [] );\n\n\t\t\tif ( hooks && hooks.stop ) {\n\t\t\t\thooks.stop.call( this, true );\n\t\t\t}\n\n\t\t\t// Look for any active animations, and finish them\n\t\t\tfor ( index = timers.length; index--; ) {\n\t\t\t\tif ( timers[ index ].elem === this && timers[ index ].queue === type ) {\n\t\t\t\t\ttimers[ index ].anim.stop( true );\n\t\t\t\t\ttimers.splice( index, 1 );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Look for any animations in the old queue and finish them\n\t\t\tfor ( index = 0; index < length; index++ ) {\n\t\t\t\tif ( queue[ index ] && queue[ index ].finish ) {\n\t\t\t\t\tqueue[ index ].finish.call( this );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Turn off finishing flag\n\t\t\tdelete data.finish;\n\t\t} );\n\t}\n} );\n\njQuery.each( [ \"toggle\", \"show\", \"hide\" ], function( _i, name ) {\n\tvar cssFn = jQuery.fn[ name ];\n\tjQuery.fn[ name ] = function( speed, easing, callback ) {\n\t\treturn speed == null || typeof speed === \"boolean\" ?\n\t\t\tcssFn.apply( this, arguments ) :\n\t\t\tthis.animate( genFx( name, true ), speed, easing, callback );\n\t};\n} );\n\n// Generate shortcuts for custom animations\njQuery.each( {\n\tslideDown: genFx( \"show\" ),\n\tslideUp: genFx( \"hide\" ),\n\tslideToggle: genFx( \"toggle\" ),\n\tfadeIn: { opacity: \"show\" },\n\tfadeOut: { opacity: \"hide\" },\n\tfadeToggle: { opacity: \"toggle\" }\n}, function( name, props ) {\n\tjQuery.fn[ name ] = function( speed, easing, callback ) {\n\t\treturn this.animate( props, speed, easing, callback );\n\t};\n} );\n\njQuery.timers = [];\njQuery.fx.tick = function() {\n\tvar timer,\n\t\ti = 0,\n\t\ttimers = jQuery.timers;\n\n\tfxNow = Date.now();\n\n\tfor ( ; i < timers.length; i++ ) {\n\t\ttimer = timers[ i ];\n\n\t\t// Run the timer and safely remove it when done (allowing for external removal)\n\t\tif ( !timer() && timers[ i ] === timer ) {\n\t\t\ttimers.splice( i--, 1 );\n\t\t}\n\t}\n\n\tif ( !timers.length ) {\n\t\tjQuery.fx.stop();\n\t}\n\tfxNow = undefined;\n};\n\njQuery.fx.timer = function( timer ) {\n\tjQuery.timers.push( timer );\n\tjQuery.fx.start();\n};\n\njQuery.fx.interval = 13;\njQuery.fx.start = function() {\n\tif ( inProgress ) {\n\t\treturn;\n\t}\n\n\tinProgress = true;\n\tschedule();\n};\n\njQuery.fx.stop = function() {\n\tinProgress = null;\n};\n\njQuery.fx.speeds = {\n\tslow: 600,\n\tfast: 200,\n\n\t// Default speed\n\t_default: 400\n};\n\n\n// Based off of the plugin by Clint Helfers, with permission.\njQuery.fn.delay = function( time, type ) {\n\ttime = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;\n\ttype = type || \"fx\";\n\n\treturn this.queue( type, function( next, hooks ) {\n\t\tvar timeout = window.setTimeout( next, time );\n\t\thooks.stop = function() {\n\t\t\twindow.clearTimeout( timeout );\n\t\t};\n\t} );\n};\n\n\n( function() {\n\tvar input = document.createElement( \"input\" ),\n\t\tselect = document.createElement( \"select\" ),\n\t\topt = select.appendChild( document.createElement( \"option\" ) );\n\n\tinput.type = \"checkbox\";\n\n\t// Support: Android <=4.3 only\n\t// Default value for a checkbox should be \"on\"\n\tsupport.checkOn = input.value !== \"\";\n\n\t// Support: IE <=11 only\n\t// Must access selectedIndex to make default options select\n\tsupport.optSelected = opt.selected;\n\n\t// Support: IE <=11 only\n\t// An input loses its value after becoming a radio\n\tinput = document.createElement( \"input\" );\n\tinput.value = \"t\";\n\tinput.type = \"radio\";\n\tsupport.radioValue = input.value === \"t\";\n} )();\n\n\nvar boolHook,\n\tattrHandle = jQuery.expr.attrHandle;\n\njQuery.fn.extend( {\n\tattr: function( name, value ) {\n\t\treturn access( this, jQuery.attr, name, value, arguments.length > 1 );\n\t},\n\n\tremoveAttr: function( name ) {\n\t\treturn this.each( function() {\n\t\t\tjQuery.removeAttr( this, name );\n\t\t} );\n\t}\n} );\n\njQuery.extend( {\n\tattr: function( elem, name, value ) {\n\t\tvar ret, hooks,\n\t\t\tnType = elem.nodeType;\n\n\t\t// Don't get/set attributes on text, comment and attribute nodes\n\t\tif ( nType === 3 || nType === 8 || nType === 2 ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Fallback to prop when attributes are not supported\n\t\tif ( typeof elem.getAttribute === \"undefined\" ) {\n\t\t\treturn jQuery.prop( elem, name, value );\n\t\t}\n\n\t\t// Attribute hooks are determined by the lowercase version\n\t\t// Grab necessary hook if one is defined\n\t\tif ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {\n\t\t\thooks = jQuery.attrHooks[ name.toLowerCase() ] ||\n\t\t\t\t( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );\n\t\t}\n\n\t\tif ( value !== undefined ) {\n\t\t\tif ( value === null ) {\n\t\t\t\tjQuery.removeAttr( elem, name );\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif ( hooks && \"set\" in hooks &&\n\t\t\t\t( ret = hooks.set( elem, value, name ) ) !== undefined ) {\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\telem.setAttribute( name, value + \"\" );\n\t\t\treturn value;\n\t\t}\n\n\t\tif ( hooks && \"get\" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {\n\t\t\treturn ret;\n\t\t}\n\n\t\tret = jQuery.find.attr( elem, name );\n\n\t\t// Non-existent attributes return null, we normalize to undefined\n\t\treturn ret == null ? undefined : ret;\n\t},\n\n\tattrHooks: {\n\t\ttype: {\n\t\t\tset: function( elem, value ) {\n\t\t\t\tif ( !support.radioValue && value === \"radio\" &&\n\t\t\t\t\tnodeName( elem, \"input\" ) ) {\n\t\t\t\t\tvar val = elem.value;\n\t\t\t\t\telem.setAttribute( \"type\", value );\n\t\t\t\t\tif ( val ) {\n\t\t\t\t\t\telem.value = val;\n\t\t\t\t\t}\n\t\t\t\t\treturn value;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\tremoveAttr: function( elem, value ) {\n\t\tvar name,\n\t\t\ti = 0,\n\n\t\t\t// Attribute names can contain non-HTML whitespace characters\n\t\t\t// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2\n\t\t\tattrNames = value && value.match( rnothtmlwhite );\n\n\t\tif ( attrNames && elem.nodeType === 1 ) {\n\t\t\twhile ( ( name = attrNames[ i++ ] ) ) {\n\t\t\t\telem.removeAttribute( name );\n\t\t\t}\n\t\t}\n\t}\n} );\n\n// Hooks for boolean attributes\nboolHook = {\n\tset: function( elem, value, name ) {\n\t\tif ( value === false ) {\n\n\t\t\t// Remove boolean attributes when set to false\n\t\t\tjQuery.removeAttr( elem, name );\n\t\t} else {\n\t\t\telem.setAttribute( name, name );\n\t\t}\n\t\treturn name;\n\t}\n};\n\njQuery.each( jQuery.expr.match.bool.source.match( /\\w+/g ), function( _i, name ) {\n\tvar getter = attrHandle[ name ] || jQuery.find.attr;\n\n\tattrHandle[ name ] = function( elem, name, isXML ) {\n\t\tvar ret, handle,\n\t\t\tlowercaseName = name.toLowerCase();\n\n\t\tif ( !isXML ) {\n\n\t\t\t// Avoid an infinite loop by temporarily removing this function from the getter\n\t\t\thandle = attrHandle[ lowercaseName ];\n\t\t\tattrHandle[ lowercaseName ] = ret;\n\t\t\tret = getter( elem, name, isXML ) != null ?\n\t\t\t\tlowercaseName :\n\t\t\t\tnull;\n\t\t\tattrHandle[ lowercaseName ] = handle;\n\t\t}\n\t\treturn ret;\n\t};\n} );\n\n\n\n\nvar rfocusable = /^(?:input|select|textarea|button)$/i,\n\trclickable = /^(?:a|area)$/i;\n\njQuery.fn.extend( {\n\tprop: function( name, value ) {\n\t\treturn access( this, jQuery.prop, name, value, arguments.length > 1 );\n\t},\n\n\tremoveProp: function( name ) {\n\t\treturn this.each( function() {\n\t\t\tdelete this[ jQuery.propFix[ name ] || name ];\n\t\t} );\n\t}\n} );\n\njQuery.extend( {\n\tprop: function( elem, name, value ) {\n\t\tvar ret, hooks,\n\t\t\tnType = elem.nodeType;\n\n\t\t// Don't get/set properties on text, comment and attribute nodes\n\t\tif ( nType === 3 || nType === 8 || nType === 2 ) {\n\t\t\treturn;\n\t\t}\n\n\t\tif ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {\n\n\t\t\t// Fix name and attach hooks\n\t\t\tname = jQuery.propFix[ name ] || name;\n\t\t\thooks = jQuery.propHooks[ name ];\n\t\t}\n\n\t\tif ( value !== undefined ) {\n\t\t\tif ( hooks && \"set\" in hooks &&\n\t\t\t\t( ret = hooks.set( elem, value, name ) ) !== undefined ) {\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\treturn ( elem[ name ] = value );\n\t\t}\n\n\t\tif ( hooks && \"get\" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {\n\t\t\treturn ret;\n\t\t}\n\n\t\treturn elem[ name ];\n\t},\n\n\tpropHooks: {\n\t\ttabIndex: {\n\t\t\tget: function( elem ) {\n\n\t\t\t\t// Support: IE <=9 - 11 only\n\t\t\t\t// elem.tabIndex doesn't always return the\n\t\t\t\t// correct value when it hasn't been explicitly set\n\t\t\t\t// Use proper attribute retrieval (trac-12072)\n\t\t\t\tvar tabindex = jQuery.find.attr( elem, \"tabindex\" );\n\n\t\t\t\tif ( tabindex ) {\n\t\t\t\t\treturn parseInt( tabindex, 10 );\n\t\t\t\t}\n\n\t\t\t\tif (\n\t\t\t\t\trfocusable.test( elem.nodeName ) ||\n\t\t\t\t\trclickable.test( elem.nodeName ) &&\n\t\t\t\t\telem.href\n\t\t\t\t) {\n\t\t\t\t\treturn 0;\n\t\t\t\t}\n\n\t\t\t\treturn -1;\n\t\t\t}\n\t\t}\n\t},\n\n\tpropFix: {\n\t\t\"for\": \"htmlFor\",\n\t\t\"class\": \"className\"\n\t}\n} );\n\n// Support: IE <=11 only\n// Accessing the selectedIndex property\n// forces the browser to respect setting selected\n// on the option\n// The getter ensures a default option is selected\n// when in an optgroup\n// eslint rule \"no-unused-expressions\" is disabled for this code\n// since it considers such accessions noop\nif ( !support.optSelected ) {\n\tjQuery.propHooks.selected = {\n\t\tget: function( elem ) {\n\n\t\t\t/* eslint no-unused-expressions: \"off\" */\n\n\t\t\tvar parent = elem.parentNode;\n\t\t\tif ( parent && parent.parentNode ) {\n\t\t\t\tparent.parentNode.selectedIndex;\n\t\t\t}\n\t\t\treturn null;\n\t\t},\n\t\tset: function( elem ) {\n\n\t\t\t/* eslint no-unused-expressions: \"off\" */\n\n\t\t\tvar parent = elem.parentNode;\n\t\t\tif ( parent ) {\n\t\t\t\tparent.selectedIndex;\n\n\t\t\t\tif ( parent.parentNode ) {\n\t\t\t\t\tparent.parentNode.selectedIndex;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t};\n}\n\njQuery.each( [\n\t\"tabIndex\",\n\t\"readOnly\",\n\t\"maxLength\",\n\t\"cellSpacing\",\n\t\"cellPadding\",\n\t\"rowSpan\",\n\t\"colSpan\",\n\t\"useMap\",\n\t\"frameBorder\",\n\t\"contentEditable\"\n], function() {\n\tjQuery.propFix[ this.toLowerCase() ] = this;\n} );\n\n\n\n\n\t// Strip and collapse whitespace according to HTML spec\n\t// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace\n\tfunction stripAndCollapse( value ) {\n\t\tvar tokens = value.match( rnothtmlwhite ) || [];\n\t\treturn tokens.join( \" \" );\n\t}\n\n\nfunction getClass( elem ) {\n\treturn elem.getAttribute && elem.getAttribute( \"class\" ) || \"\";\n}\n\nfunction classesToArray( value ) {\n\tif ( Array.isArray( value ) ) {\n\t\treturn value;\n\t}\n\tif ( typeof value === \"string\" ) {\n\t\treturn value.match( rnothtmlwhite ) || [];\n\t}\n\treturn [];\n}\n\njQuery.fn.extend( {\n\taddClass: function( value ) {\n\t\tvar classNames, cur, curValue, className, i, finalValue;\n\n\t\tif ( isFunction( value ) ) {\n\t\t\treturn this.each( function( j ) {\n\t\t\t\tjQuery( this ).addClass( value.call( this, j, getClass( this ) ) );\n\t\t\t} );\n\t\t}\n\n\t\tclassNames = classesToArray( value );\n\n\t\tif ( classNames.length ) {\n\t\t\treturn this.each( function() {\n\t\t\t\tcurValue = getClass( this );\n\t\t\t\tcur = this.nodeType === 1 && ( \" \" + stripAndCollapse( curValue ) + \" \" );\n\n\t\t\t\tif ( cur ) {\n\t\t\t\t\tfor ( i = 0; i < classNames.length; i++ ) {\n\t\t\t\t\t\tclassName = classNames[ i ];\n\t\t\t\t\t\tif ( cur.indexOf( \" \" + className + \" \" ) < 0 ) {\n\t\t\t\t\t\t\tcur += className + \" \";\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Only assign if different to avoid unneeded rendering.\n\t\t\t\t\tfinalValue = stripAndCollapse( cur );\n\t\t\t\t\tif ( curValue !== finalValue ) {\n\t\t\t\t\t\tthis.setAttribute( \"class\", finalValue );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} );\n\t\t}\n\n\t\treturn this;\n\t},\n\n\tremoveClass: function( value ) {\n\t\tvar classNames, cur, curValue, className, i, finalValue;\n\n\t\tif ( isFunction( value ) ) {\n\t\t\treturn this.each( function( j ) {\n\t\t\t\tjQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );\n\t\t\t} );\n\t\t}\n\n\t\tif ( !arguments.length ) {\n\t\t\treturn this.attr( \"class\", \"\" );\n\t\t}\n\n\t\tclassNames = classesToArray( value );\n\n\t\tif ( classNames.length ) {\n\t\t\treturn this.each( function() {\n\t\t\t\tcurValue = getClass( this );\n\n\t\t\t\t// This expression is here for better compressibility (see addClass)\n\t\t\t\tcur = this.nodeType === 1 && ( \" \" + stripAndCollapse( curValue ) + \" \" );\n\n\t\t\t\tif ( cur ) {\n\t\t\t\t\tfor ( i = 0; i < classNames.length; i++ ) {\n\t\t\t\t\t\tclassName = classNames[ i ];\n\n\t\t\t\t\t\t// Remove *all* instances\n\t\t\t\t\t\twhile ( cur.indexOf( \" \" + className + \" \" ) > -1 ) {\n\t\t\t\t\t\t\tcur = cur.replace( \" \" + className + \" \", \" \" );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Only assign if different to avoid unneeded rendering.\n\t\t\t\t\tfinalValue = stripAndCollapse( cur );\n\t\t\t\t\tif ( curValue !== finalValue ) {\n\t\t\t\t\t\tthis.setAttribute( \"class\", finalValue );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} );\n\t\t}\n\n\t\treturn this;\n\t},\n\n\ttoggleClass: function( value, stateVal ) {\n\t\tvar classNames, className, i, self,\n\t\t\ttype = typeof value,\n\t\t\tisValidValue = type === \"string\" || Array.isArray( value );\n\n\t\tif ( isFunction( value ) ) {\n\t\t\treturn this.each( function( i ) {\n\t\t\t\tjQuery( this ).toggleClass(\n\t\t\t\t\tvalue.call( this, i, getClass( this ), stateVal ),\n\t\t\t\t\tstateVal\n\t\t\t\t);\n\t\t\t} );\n\t\t}\n\n\t\tif ( typeof stateVal === \"boolean\" && isValidValue ) {\n\t\t\treturn stateVal ? this.addClass( value ) : this.removeClass( value );\n\t\t}\n\n\t\tclassNames = classesToArray( value );\n\n\t\treturn this.each( function() {\n\t\t\tif ( isValidValue ) {\n\n\t\t\t\t// Toggle individual class names\n\t\t\t\tself = jQuery( this );\n\n\t\t\t\tfor ( i = 0; i < classNames.length; i++ ) {\n\t\t\t\t\tclassName = classNames[ i ];\n\n\t\t\t\t\t// Check each className given, space separated list\n\t\t\t\t\tif ( self.hasClass( className ) ) {\n\t\t\t\t\t\tself.removeClass( className );\n\t\t\t\t\t} else {\n\t\t\t\t\t\tself.addClass( className );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t// Toggle whole class name\n\t\t\t} else if ( value === undefined || type === \"boolean\" ) {\n\t\t\t\tclassName = getClass( this );\n\t\t\t\tif ( className ) {\n\n\t\t\t\t\t// Store className if set\n\t\t\t\t\tdataPriv.set( this, \"__className__\", className );\n\t\t\t\t}\n\n\t\t\t\t// If the element has a class name or if we're passed `false`,\n\t\t\t\t// then remove the whole classname (if there was one, the above saved it).\n\t\t\t\t// Otherwise bring back whatever was previously saved (if anything),\n\t\t\t\t// falling back to the empty string if nothing was stored.\n\t\t\t\tif ( this.setAttribute ) {\n\t\t\t\t\tthis.setAttribute( \"class\",\n\t\t\t\t\t\tclassName || value === false ?\n\t\t\t\t\t\t\t\"\" :\n\t\t\t\t\t\t\tdataPriv.get( this, \"__className__\" ) || \"\"\n\t\t\t\t\t);\n\t\t\t\t}\n\t\t\t}\n\t\t} );\n\t},\n\n\thasClass: function( selector ) {\n\t\tvar className, elem,\n\t\t\ti = 0;\n\n\t\tclassName = \" \" + selector + \" \";\n\t\twhile ( ( elem = this[ i++ ] ) ) {\n\t\t\tif ( elem.nodeType === 1 &&\n\t\t\t\t( \" \" + stripAndCollapse( getClass( elem ) ) + \" \" ).indexOf( className ) > -1 ) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\n\t\treturn false;\n\t}\n} );\n\n\n\n\nvar rreturn = /\\r/g;\n\njQuery.fn.extend( {\n\tval: function( value ) {\n\t\tvar hooks, ret, valueIsFunction,\n\t\t\telem = this[ 0 ];\n\n\t\tif ( !arguments.length ) {\n\t\t\tif ( elem ) {\n\t\t\t\thooks = jQuery.valHooks[ elem.type ] ||\n\t\t\t\t\tjQuery.valHooks[ elem.nodeName.toLowerCase() ];\n\n\t\t\t\tif ( hooks &&\n\t\t\t\t\t\"get\" in hooks &&\n\t\t\t\t\t( ret = hooks.get( elem, \"value\" ) ) !== undefined\n\t\t\t\t) {\n\t\t\t\t\treturn ret;\n\t\t\t\t}\n\n\t\t\t\tret = elem.value;\n\n\t\t\t\t// Handle most common string cases\n\t\t\t\tif ( typeof ret === \"string\" ) {\n\t\t\t\t\treturn ret.replace( rreturn, \"\" );\n\t\t\t\t}\n\n\t\t\t\t// Handle cases where value is null/undef or number\n\t\t\t\treturn ret == null ? \"\" : ret;\n\t\t\t}\n\n\t\t\treturn;\n\t\t}\n\n\t\tvalueIsFunction = isFunction( value );\n\n\t\treturn this.each( function( i ) {\n\t\t\tvar val;\n\n\t\t\tif ( this.nodeType !== 1 ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif ( valueIsFunction ) {\n\t\t\t\tval = value.call( this, i, jQuery( this ).val() );\n\t\t\t} else {\n\t\t\t\tval = value;\n\t\t\t}\n\n\t\t\t// Treat null/undefined as \"\"; convert numbers to string\n\t\t\tif ( val == null ) {\n\t\t\t\tval = \"\";\n\n\t\t\t} else if ( typeof val === \"number\" ) {\n\t\t\t\tval += \"\";\n\n\t\t\t} else if ( Array.isArray( val ) ) {\n\t\t\t\tval = jQuery.map( val, function( value ) {\n\t\t\t\t\treturn value == null ? \"\" : value + \"\";\n\t\t\t\t} );\n\t\t\t}\n\n\t\t\thooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];\n\n\t\t\t// If set returns undefined, fall back to normal setting\n\t\t\tif ( !hooks || !( \"set\" in hooks ) || hooks.set( this, val, \"value\" ) === undefined ) {\n\t\t\t\tthis.value = val;\n\t\t\t}\n\t\t} );\n\t}\n} );\n\njQuery.extend( {\n\tvalHooks: {\n\t\toption: {\n\t\t\tget: function( elem ) {\n\n\t\t\t\tvar val = jQuery.find.attr( elem, \"value\" );\n\t\t\t\treturn val != null ?\n\t\t\t\t\tval :\n\n\t\t\t\t\t// Support: IE <=10 - 11 only\n\t\t\t\t\t// option.text throws exceptions (trac-14686, trac-14858)\n\t\t\t\t\t// Strip and collapse whitespace\n\t\t\t\t\t// https://html.spec.whatwg.org/#strip-and-collapse-whitespace\n\t\t\t\t\tstripAndCollapse( jQuery.text( elem ) );\n\t\t\t}\n\t\t},\n\t\tselect: {\n\t\t\tget: function( elem ) {\n\t\t\t\tvar value, option, i,\n\t\t\t\t\toptions = elem.options,\n\t\t\t\t\tindex = elem.selectedIndex,\n\t\t\t\t\tone = elem.type === \"select-one\",\n\t\t\t\t\tvalues = one ? null : [],\n\t\t\t\t\tmax = one ? index + 1 : options.length;\n\n\t\t\t\tif ( index < 0 ) {\n\t\t\t\t\ti = max;\n\n\t\t\t\t} else {\n\t\t\t\t\ti = one ? index : 0;\n\t\t\t\t}\n\n\t\t\t\t// Loop through all the selected options\n\t\t\t\tfor ( ; i < max; i++ ) {\n\t\t\t\t\toption = options[ i ];\n\n\t\t\t\t\t// Support: IE <=9 only\n\t\t\t\t\t// IE8-9 doesn't update selected after form reset (trac-2551)\n\t\t\t\t\tif ( ( option.selected || i === index ) &&\n\n\t\t\t\t\t\t\t// Don't return options that are disabled or in a disabled optgroup\n\t\t\t\t\t\t\t!option.disabled &&\n\t\t\t\t\t\t\t( !option.parentNode.disabled ||\n\t\t\t\t\t\t\t\t!nodeName( option.parentNode, \"optgroup\" ) ) ) {\n\n\t\t\t\t\t\t// Get the specific value for the option\n\t\t\t\t\t\tvalue = jQuery( option ).val();\n\n\t\t\t\t\t\t// We don't need an array for one selects\n\t\t\t\t\t\tif ( one ) {\n\t\t\t\t\t\t\treturn value;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// Multi-Selects return an array\n\t\t\t\t\t\tvalues.push( value );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\treturn values;\n\t\t\t},\n\n\t\t\tset: function( elem, value ) {\n\t\t\t\tvar optionSet, option,\n\t\t\t\t\toptions = elem.options,\n\t\t\t\t\tvalues = jQuery.makeArray( value ),\n\t\t\t\t\ti = options.length;\n\n\t\t\t\twhile ( i-- ) {\n\t\t\t\t\toption = options[ i ];\n\n\t\t\t\t\t/* eslint-disable no-cond-assign */\n\n\t\t\t\t\tif ( option.selected =\n\t\t\t\t\t\tjQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1\n\t\t\t\t\t) {\n\t\t\t\t\t\toptionSet = true;\n\t\t\t\t\t}\n\n\t\t\t\t\t/* eslint-enable no-cond-assign */\n\t\t\t\t}\n\n\t\t\t\t// Force browsers to behave consistently when non-matching value is set\n\t\t\t\tif ( !optionSet ) {\n\t\t\t\t\telem.selectedIndex = -1;\n\t\t\t\t}\n\t\t\t\treturn values;\n\t\t\t}\n\t\t}\n\t}\n} );\n\n// Radios and checkboxes getter/setter\njQuery.each( [ \"radio\", \"checkbox\" ], function() {\n\tjQuery.valHooks[ this ] = {\n\t\tset: function( elem, value ) {\n\t\t\tif ( Array.isArray( value ) ) {\n\t\t\t\treturn ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );\n\t\t\t}\n\t\t}\n\t};\n\tif ( !support.checkOn ) {\n\t\tjQuery.valHooks[ this ].get = function( elem ) {\n\t\t\treturn elem.getAttribute( \"value\" ) === null ? \"on\" : elem.value;\n\t\t};\n\t}\n} );\n\n\n\n\n// Return jQuery for attributes-only inclusion\nvar location = window.location;\n\nvar nonce = { guid: Date.now() };\n\nvar rquery = ( /\\?/ );\n\n\n\n// Cross-browser xml parsing\njQuery.parseXML = function( data ) {\n\tvar xml, parserErrorElem;\n\tif ( !data || typeof data !== \"string\" ) {\n\t\treturn null;\n\t}\n\n\t// Support: IE 9 - 11 only\n\t// IE throws on parseFromString with invalid input.\n\ttry {\n\t\txml = ( new window.DOMParser() ).parseFromString( data, \"text/xml\" );\n\t} catch ( e ) {}\n\n\tparserErrorElem = xml && xml.getElementsByTagName( \"parsererror\" )[ 0 ];\n\tif ( !xml || parserErrorElem ) {\n\t\tjQuery.error( \"Invalid XML: \" + (\n\t\t\tparserErrorElem ?\n\t\t\t\tjQuery.map( parserErrorElem.childNodes, function( el ) {\n\t\t\t\t\treturn el.textContent;\n\t\t\t\t} ).join( \"\\n\" ) :\n\t\t\t\tdata\n\t\t) );\n\t}\n\treturn xml;\n};\n\n\nvar rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,\n\tstopPropagationCallback = function( e ) {\n\t\te.stopPropagation();\n\t};\n\njQuery.extend( jQuery.event, {\n\n\ttrigger: function( event, data, elem, onlyHandlers ) {\n\n\t\tvar i, cur, tmp, bubbleType, ontype, handle, special, lastElement,\n\t\t\teventPath = [ elem || document ],\n\t\t\ttype = hasOwn.call( event, \"type\" ) ? event.type : event,\n\t\t\tnamespaces = hasOwn.call( event, \"namespace\" ) ? event.namespace.split( \".\" ) : [];\n\n\t\tcur = lastElement = tmp = elem = elem || document;\n\n\t\t// Don't do events on text and comment nodes\n\t\tif ( elem.nodeType === 3 || elem.nodeType === 8 ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// focus/blur morphs to focusin/out; ensure we're not firing them right now\n\t\tif ( rfocusMorph.test( type + jQuery.event.triggered ) ) {\n\t\t\treturn;\n\t\t}\n\n\t\tif ( type.indexOf( \".\" ) > -1 ) {\n\n\t\t\t// Namespaced trigger; create a regexp to match event type in handle()\n\t\t\tnamespaces = type.split( \".\" );\n\t\t\ttype = namespaces.shift();\n\t\t\tnamespaces.sort();\n\t\t}\n\t\tontype = type.indexOf( \":\" ) < 0 && \"on\" + type;\n\n\t\t// Caller can pass in a jQuery.Event object, Object, or just an event type string\n\t\tevent = event[ jQuery.expando ] ?\n\t\t\tevent :\n\t\t\tnew jQuery.Event( type, typeof event === \"object\" && event );\n\n\t\t// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)\n\t\tevent.isTrigger = onlyHandlers ? 2 : 3;\n\t\tevent.namespace = namespaces.join( \".\" );\n\t\tevent.rnamespace = event.namespace ?\n\t\t\tnew RegExp( \"(^|\\\\.)\" + namespaces.join( \"\\\\.(?:.*\\\\.|)\" ) + \"(\\\\.|$)\" ) :\n\t\t\tnull;\n\n\t\t// Clean up the event in case it is being reused\n\t\tevent.result = undefined;\n\t\tif ( !event.target ) {\n\t\t\tevent.target = elem;\n\t\t}\n\n\t\t// Clone any incoming data and prepend the event, creating the handler arg list\n\t\tdata = data == null ?\n\t\t\t[ event ] :\n\t\t\tjQuery.makeArray( data, [ event ] );\n\n\t\t// Allow special events to draw outside the lines\n\t\tspecial = jQuery.event.special[ type ] || {};\n\t\tif ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Determine event propagation path in advance, per W3C events spec (trac-9951)\n\t\t// Bubble up to document, then to window; watch for a global ownerDocument var (trac-9724)\n\t\tif ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {\n\n\t\t\tbubbleType = special.delegateType || type;\n\t\t\tif ( !rfocusMorph.test( bubbleType + type ) ) {\n\t\t\t\tcur = cur.parentNode;\n\t\t\t}\n\t\t\tfor ( ; cur; cur = cur.parentNode ) {\n\t\t\t\teventPath.push( cur );\n\t\t\t\ttmp = cur;\n\t\t\t}\n\n\t\t\t// Only add window if we got to document (e.g., not plain obj or detached DOM)\n\t\t\tif ( tmp === ( elem.ownerDocument || document ) ) {\n\t\t\t\teventPath.push( tmp.defaultView || tmp.parentWindow || window );\n\t\t\t}\n\t\t}\n\n\t\t// Fire handlers on the event path\n\t\ti = 0;\n\t\twhile ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {\n\t\t\tlastElement = cur;\n\t\t\tevent.type = i > 1 ?\n\t\t\t\tbubbleType :\n\t\t\t\tspecial.bindType || type;\n\n\t\t\t// jQuery handler\n\t\t\thandle = ( dataPriv.get( cur, \"events\" ) || Object.create( null ) )[ event.type ] &&\n\t\t\t\tdataPriv.get( cur, \"handle\" );\n\t\t\tif ( handle ) {\n\t\t\t\thandle.apply( cur, data );\n\t\t\t}\n\n\t\t\t// Native handler\n\t\t\thandle = ontype && cur[ ontype ];\n\t\t\tif ( handle && handle.apply && acceptData( cur ) ) {\n\t\t\t\tevent.result = handle.apply( cur, data );\n\t\t\t\tif ( event.result === false ) {\n\t\t\t\t\tevent.preventDefault();\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tevent.type = type;\n\n\t\t// If nobody prevented the default action, do it now\n\t\tif ( !onlyHandlers && !event.isDefaultPrevented() ) {\n\n\t\t\tif ( ( !special._default ||\n\t\t\t\tspecial._default.apply( eventPath.pop(), data ) === false ) &&\n\t\t\t\tacceptData( elem ) ) {\n\n\t\t\t\t// Call a native DOM method on the target with the same name as the event.\n\t\t\t\t// Don't do default actions on window, that's where global variables be (trac-6170)\n\t\t\t\tif ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {\n\n\t\t\t\t\t// Don't re-trigger an onFOO event when we call its FOO() method\n\t\t\t\t\ttmp = elem[ ontype ];\n\n\t\t\t\t\tif ( tmp ) {\n\t\t\t\t\t\telem[ ontype ] = null;\n\t\t\t\t\t}\n\n\t\t\t\t\t// Prevent re-triggering of the same event, since we already bubbled it above\n\t\t\t\t\tjQuery.event.triggered = type;\n\n\t\t\t\t\tif ( event.isPropagationStopped() ) {\n\t\t\t\t\t\tlastElement.addEventListener( type, stopPropagationCallback );\n\t\t\t\t\t}\n\n\t\t\t\t\telem[ type ]();\n\n\t\t\t\t\tif ( event.isPropagationStopped() ) {\n\t\t\t\t\t\tlastElement.removeEventListener( type, stopPropagationCallback );\n\t\t\t\t\t}\n\n\t\t\t\t\tjQuery.event.triggered = undefined;\n\n\t\t\t\t\tif ( tmp ) {\n\t\t\t\t\t\telem[ ontype ] = tmp;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn event.result;\n\t},\n\n\t// Piggyback on a donor event to simulate a different one\n\t// Used only for `focus(in | out)` events\n\tsimulate: function( type, elem, event ) {\n\t\tvar e = jQuery.extend(\n\t\t\tnew jQuery.Event(),\n\t\t\tevent,\n\t\t\t{\n\t\t\t\ttype: type,\n\t\t\t\tisSimulated: true\n\t\t\t}\n\t\t);\n\n\t\tjQuery.event.trigger( e, null, elem );\n\t}\n\n} );\n\njQuery.fn.extend( {\n\n\ttrigger: function( type, data ) {\n\t\treturn this.each( function() {\n\t\t\tjQuery.event.trigger( type, data, this );\n\t\t} );\n\t},\n\ttriggerHandler: function( type, data ) {\n\t\tvar elem = this[ 0 ];\n\t\tif ( elem ) {\n\t\t\treturn jQuery.event.trigger( type, data, elem, true );\n\t\t}\n\t}\n} );\n\n\nvar\n\trbracket = /\\[\\]$/,\n\trCRLF = /\\r?\\n/g,\n\trsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,\n\trsubmittable = /^(?:input|select|textarea|keygen)/i;\n\nfunction buildParams( prefix, obj, traditional, add ) {\n\tvar name;\n\n\tif ( Array.isArray( obj ) ) {\n\n\t\t// Serialize array item.\n\t\tjQuery.each( obj, function( i, v ) {\n\t\t\tif ( traditional || rbracket.test( prefix ) ) {\n\n\t\t\t\t// Treat each array item as a scalar.\n\t\t\t\tadd( prefix, v );\n\n\t\t\t} else {\n\n\t\t\t\t// Item is non-scalar (array or object), encode its numeric index.\n\t\t\t\tbuildParams(\n\t\t\t\t\tprefix + \"[\" + ( typeof v === \"object\" && v != null ? i : \"\" ) + \"]\",\n\t\t\t\t\tv,\n\t\t\t\t\ttraditional,\n\t\t\t\t\tadd\n\t\t\t\t);\n\t\t\t}\n\t\t} );\n\n\t} else if ( !traditional && toType( obj ) === \"object\" ) {\n\n\t\t// Serialize object item.\n\t\tfor ( name in obj ) {\n\t\t\tbuildParams( prefix + \"[\" + name + \"]\", obj[ name ], traditional, add );\n\t\t}\n\n\t} else {\n\n\t\t// Serialize scalar item.\n\t\tadd( prefix, obj );\n\t}\n}\n\n// Serialize an array of form elements or a set of\n// key/values into a query string\njQuery.param = function( a, traditional ) {\n\tvar prefix,\n\t\ts = [],\n\t\tadd = function( key, valueOrFunction ) {\n\n\t\t\t// If value is a function, invoke it and use its return value\n\t\t\tvar value = isFunction( valueOrFunction ) ?\n\t\t\t\tvalueOrFunction() :\n\t\t\t\tvalueOrFunction;\n\n\t\t\ts[ s.length ] = encodeURIComponent( key ) + \"=\" +\n\t\t\t\tencodeURIComponent( value == null ? \"\" : value );\n\t\t};\n\n\tif ( a == null ) {\n\t\treturn \"\";\n\t}\n\n\t// If an array was passed in, assume that it is an array of form elements.\n\tif ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {\n\n\t\t// Serialize the form elements\n\t\tjQuery.each( a, function() {\n\t\t\tadd( this.name, this.value );\n\t\t} );\n\n\t} else {\n\n\t\t// If traditional, encode the \"old\" way (the way 1.3.2 or older\n\t\t// did it), otherwise encode params recursively.\n\t\tfor ( prefix in a ) {\n\t\t\tbuildParams( prefix, a[ prefix ], traditional, add );\n\t\t}\n\t}\n\n\t// Return the resulting serialization\n\treturn s.join( \"&\" );\n};\n\njQuery.fn.extend( {\n\tserialize: function() {\n\t\treturn jQuery.param( this.serializeArray() );\n\t},\n\tserializeArray: function() {\n\t\treturn this.map( function() {\n\n\t\t\t// Can add propHook for \"elements\" to filter or add form elements\n\t\t\tvar elements = jQuery.prop( this, \"elements\" );\n\t\t\treturn elements ? jQuery.makeArray( elements ) : this;\n\t\t} ).filter( function() {\n\t\t\tvar type = this.type;\n\n\t\t\t// Use .is( \":disabled\" ) so that fieldset[disabled] works\n\t\t\treturn this.name && !jQuery( this ).is( \":disabled\" ) &&\n\t\t\t\trsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&\n\t\t\t\t( this.checked || !rcheckableType.test( type ) );\n\t\t} ).map( function( _i, elem ) {\n\t\t\tvar val = jQuery( this ).val();\n\n\t\t\tif ( val == null ) {\n\t\t\t\treturn null;\n\t\t\t}\n\n\t\t\tif ( Array.isArray( val ) ) {\n\t\t\t\treturn jQuery.map( val, function( val ) {\n\t\t\t\t\treturn { name: elem.name, value: val.replace( rCRLF, \"\\r\\n\" ) };\n\t\t\t\t} );\n\t\t\t}\n\n\t\t\treturn { name: elem.name, value: val.replace( rCRLF, \"\\r\\n\" ) };\n\t\t} ).get();\n\t}\n} );\n\n\nvar\n\tr20 = /%20/g,\n\trhash = /#.*$/,\n\trantiCache = /([?&])_=[^&]*/,\n\trheaders = /^(.*?):[ \\t]*([^\\r\\n]*)$/mg,\n\n\t// trac-7653, trac-8125, trac-8152: local protocol detection\n\trlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,\n\trnoContent = /^(?:GET|HEAD)$/,\n\trprotocol = /^\\/\\//,\n\n\t/* Prefilters\n\t * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)\n\t * 2) These are called:\n\t * - BEFORE asking for a transport\n\t * - AFTER param serialization (s.data is a string if s.processData is true)\n\t * 3) key is the dataType\n\t * 4) the catchall symbol \"*\" can be used\n\t * 5) execution will start with transport dataType and THEN continue down to \"*\" if needed\n\t */\n\tprefilters = {},\n\n\t/* Transports bindings\n\t * 1) key is the dataType\n\t * 2) the catchall symbol \"*\" can be used\n\t * 3) selection will start with transport dataType and THEN go to \"*\" if needed\n\t */\n\ttransports = {},\n\n\t// Avoid comment-prolog char sequence (trac-10098); must appease lint and evade compression\n\tallTypes = \"*/\".concat( \"*\" ),\n\n\t// Anchor tag for parsing the document origin\n\toriginAnchor = document.createElement( \"a\" );\n\noriginAnchor.href = location.href;\n\n// Base \"constructor\" for jQuery.ajaxPrefilter and jQuery.ajaxTransport\nfunction addToPrefiltersOrTransports( structure ) {\n\n\t// dataTypeExpression is optional and defaults to \"*\"\n\treturn function( dataTypeExpression, func ) {\n\n\t\tif ( typeof dataTypeExpression !== \"string\" ) {\n\t\t\tfunc = dataTypeExpression;\n\t\t\tdataTypeExpression = \"*\";\n\t\t}\n\n\t\tvar dataType,\n\t\t\ti = 0,\n\t\t\tdataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];\n\n\t\tif ( isFunction( func ) ) {\n\n\t\t\t// For each dataType in the dataTypeExpression\n\t\t\twhile ( ( dataType = dataTypes[ i++ ] ) ) {\n\n\t\t\t\t// Prepend if requested\n\t\t\t\tif ( dataType[ 0 ] === \"+\" ) {\n\t\t\t\t\tdataType = dataType.slice( 1 ) || \"*\";\n\t\t\t\t\t( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );\n\n\t\t\t\t// Otherwise append\n\t\t\t\t} else {\n\t\t\t\t\t( structure[ dataType ] = structure[ dataType ] || [] ).push( func );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t};\n}\n\n// Base inspection function for prefilters and transports\nfunction inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {\n\n\tvar inspected = {},\n\t\tseekingTransport = ( structure === transports );\n\n\tfunction inspect( dataType ) {\n\t\tvar selected;\n\t\tinspected[ dataType ] = true;\n\t\tjQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {\n\t\t\tvar dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );\n\t\t\tif ( typeof dataTypeOrTransport === \"string\" &&\n\t\t\t\t!seekingTransport && !inspected[ dataTypeOrTransport ] ) {\n\n\t\t\t\toptions.dataTypes.unshift( dataTypeOrTransport );\n\t\t\t\tinspect( dataTypeOrTransport );\n\t\t\t\treturn false;\n\t\t\t} else if ( seekingTransport ) {\n\t\t\t\treturn !( selected = dataTypeOrTransport );\n\t\t\t}\n\t\t} );\n\t\treturn selected;\n\t}\n\n\treturn inspect( options.dataTypes[ 0 ] ) || !inspected[ \"*\" ] && inspect( \"*\" );\n}\n\n// A special extend for ajax options\n// that takes \"flat\" options (not to be deep extended)\n// Fixes trac-9887\nfunction ajaxExtend( target, src ) {\n\tvar key, deep,\n\t\tflatOptions = jQuery.ajaxSettings.flatOptions || {};\n\n\tfor ( key in src ) {\n\t\tif ( src[ key ] !== undefined ) {\n\t\t\t( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];\n\t\t}\n\t}\n\tif ( deep ) {\n\t\tjQuery.extend( true, target, deep );\n\t}\n\n\treturn target;\n}\n\n/* Handles responses to an ajax request:\n * - finds the right dataType (mediates between content-type and expected dataType)\n * - returns the corresponding response\n */\nfunction ajaxHandleResponses( s, jqXHR, responses ) {\n\n\tvar ct, type, finalDataType, firstDataType,\n\t\tcontents = s.contents,\n\t\tdataTypes = s.dataTypes;\n\n\t// Remove auto dataType and get content-type in the process\n\twhile ( dataTypes[ 0 ] === \"*\" ) {\n\t\tdataTypes.shift();\n\t\tif ( ct === undefined ) {\n\t\t\tct = s.mimeType || jqXHR.getResponseHeader( \"Content-Type\" );\n\t\t}\n\t}\n\n\t// Check if we're dealing with a known content-type\n\tif ( ct ) {\n\t\tfor ( type in contents ) {\n\t\t\tif ( contents[ type ] && contents[ type ].test( ct ) ) {\n\t\t\t\tdataTypes.unshift( type );\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Check to see if we have a response for the expected dataType\n\tif ( dataTypes[ 0 ] in responses ) {\n\t\tfinalDataType = dataTypes[ 0 ];\n\t} else {\n\n\t\t// Try convertible dataTypes\n\t\tfor ( type in responses ) {\n\t\t\tif ( !dataTypes[ 0 ] || s.converters[ type + \" \" + dataTypes[ 0 ] ] ) {\n\t\t\t\tfinalDataType = type;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif ( !firstDataType ) {\n\t\t\t\tfirstDataType = type;\n\t\t\t}\n\t\t}\n\n\t\t// Or just use first one\n\t\tfinalDataType = finalDataType || firstDataType;\n\t}\n\n\t// If we found a dataType\n\t// We add the dataType to the list if needed\n\t// and return the corresponding response\n\tif ( finalDataType ) {\n\t\tif ( finalDataType !== dataTypes[ 0 ] ) {\n\t\t\tdataTypes.unshift( finalDataType );\n\t\t}\n\t\treturn responses[ finalDataType ];\n\t}\n}\n\n/* Chain conversions given the request and the original response\n * Also sets the responseXXX fields on the jqXHR instance\n */\nfunction ajaxConvert( s, response, jqXHR, isSuccess ) {\n\tvar conv2, current, conv, tmp, prev,\n\t\tconverters = {},\n\n\t\t// Work with a copy of dataTypes in case we need to modify it for conversion\n\t\tdataTypes = s.dataTypes.slice();\n\n\t// Create converters map with lowercased keys\n\tif ( dataTypes[ 1 ] ) {\n\t\tfor ( conv in s.converters ) {\n\t\t\tconverters[ conv.toLowerCase() ] = s.converters[ conv ];\n\t\t}\n\t}\n\n\tcurrent = dataTypes.shift();\n\n\t// Convert to each sequential dataType\n\twhile ( current ) {\n\n\t\tif ( s.responseFields[ current ] ) {\n\t\t\tjqXHR[ s.responseFields[ current ] ] = response;\n\t\t}\n\n\t\t// Apply the dataFilter if provided\n\t\tif ( !prev && isSuccess && s.dataFilter ) {\n\t\t\tresponse = s.dataFilter( response, s.dataType );\n\t\t}\n\n\t\tprev = current;\n\t\tcurrent = dataTypes.shift();\n\n\t\tif ( current ) {\n\n\t\t\t// There's only work to do if current dataType is non-auto\n\t\t\tif ( current === \"*\" ) {\n\n\t\t\t\tcurrent = prev;\n\n\t\t\t// Convert response if prev dataType is non-auto and differs from current\n\t\t\t} else if ( prev !== \"*\" && prev !== current ) {\n\n\t\t\t\t// Seek a direct converter\n\t\t\t\tconv = converters[ prev + \" \" + current ] || converters[ \"* \" + current ];\n\n\t\t\t\t// If none found, seek a pair\n\t\t\t\tif ( !conv ) {\n\t\t\t\t\tfor ( conv2 in converters ) {\n\n\t\t\t\t\t\t// If conv2 outputs current\n\t\t\t\t\t\ttmp = conv2.split( \" \" );\n\t\t\t\t\t\tif ( tmp[ 1 ] === current ) {\n\n\t\t\t\t\t\t\t// If prev can be converted to accepted input\n\t\t\t\t\t\t\tconv = converters[ prev + \" \" + tmp[ 0 ] ] ||\n\t\t\t\t\t\t\t\tconverters[ \"* \" + tmp[ 0 ] ];\n\t\t\t\t\t\t\tif ( conv ) {\n\n\t\t\t\t\t\t\t\t// Condense equivalence converters\n\t\t\t\t\t\t\t\tif ( conv === true ) {\n\t\t\t\t\t\t\t\t\tconv = converters[ conv2 ];\n\n\t\t\t\t\t\t\t\t// Otherwise, insert the intermediate dataType\n\t\t\t\t\t\t\t\t} else if ( converters[ conv2 ] !== true ) {\n\t\t\t\t\t\t\t\t\tcurrent = tmp[ 0 ];\n\t\t\t\t\t\t\t\t\tdataTypes.unshift( tmp[ 1 ] );\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Apply converter (if not an equivalence)\n\t\t\t\tif ( conv !== true ) {\n\n\t\t\t\t\t// Unless errors are allowed to bubble, catch and return them\n\t\t\t\t\tif ( conv && s.throws ) {\n\t\t\t\t\t\tresponse = conv( response );\n\t\t\t\t\t} else {\n\t\t\t\t\t\ttry {\n\t\t\t\t\t\t\tresponse = conv( response );\n\t\t\t\t\t\t} catch ( e ) {\n\t\t\t\t\t\t\treturn {\n\t\t\t\t\t\t\t\tstate: \"parsererror\",\n\t\t\t\t\t\t\t\terror: conv ? e : \"No conversion from \" + prev + \" to \" + current\n\t\t\t\t\t\t\t};\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn { state: \"success\", data: response };\n}\n\njQuery.extend( {\n\n\t// Counter for holding the number of active queries\n\tactive: 0,\n\n\t// Last-Modified header cache for next request\n\tlastModified: {},\n\tetag: {},\n\n\tajaxSettings: {\n\t\turl: location.href,\n\t\ttype: \"GET\",\n\t\tisLocal: rlocalProtocol.test( location.protocol ),\n\t\tglobal: true,\n\t\tprocessData: true,\n\t\tasync: true,\n\t\tcontentType: \"application/x-www-form-urlencoded; charset=UTF-8\",\n\n\t\t/*\n\t\ttimeout: 0,\n\t\tdata: null,\n\t\tdataType: null,\n\t\tusername: null,\n\t\tpassword: null,\n\t\tcache: null,\n\t\tthrows: false,\n\t\ttraditional: false,\n\t\theaders: {},\n\t\t*/\n\n\t\taccepts: {\n\t\t\t\"*\": allTypes,\n\t\t\ttext: \"text/plain\",\n\t\t\thtml: \"text/html\",\n\t\t\txml: \"application/xml, text/xml\",\n\t\t\tjson: \"application/json, text/javascript\"\n\t\t},\n\n\t\tcontents: {\n\t\t\txml: /\\bxml\\b/,\n\t\t\thtml: /\\bhtml/,\n\t\t\tjson: /\\bjson\\b/\n\t\t},\n\n\t\tresponseFields: {\n\t\t\txml: \"responseXML\",\n\t\t\ttext: \"responseText\",\n\t\t\tjson: \"responseJSON\"\n\t\t},\n\n\t\t// Data converters\n\t\t// Keys separate source (or catchall \"*\") and destination types with a single space\n\t\tconverters: {\n\n\t\t\t// Convert anything to text\n\t\t\t\"* text\": String,\n\n\t\t\t// Text to html (true = no transformation)\n\t\t\t\"text html\": true,\n\n\t\t\t// Evaluate text as a json expression\n\t\t\t\"text json\": JSON.parse,\n\n\t\t\t// Parse text as xml\n\t\t\t\"text xml\": jQuery.parseXML\n\t\t},\n\n\t\t// For options that shouldn't be deep extended:\n\t\t// you can add your own custom options here if\n\t\t// and when you create one that shouldn't be\n\t\t// deep extended (see ajaxExtend)\n\t\tflatOptions: {\n\t\t\turl: true,\n\t\t\tcontext: true\n\t\t}\n\t},\n\n\t// Creates a full fledged settings object into target\n\t// with both ajaxSettings and settings fields.\n\t// If target is omitted, writes into ajaxSettings.\n\tajaxSetup: function( target, settings ) {\n\t\treturn settings ?\n\n\t\t\t// Building a settings object\n\t\t\tajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :\n\n\t\t\t// Extending ajaxSettings\n\t\t\tajaxExtend( jQuery.ajaxSettings, target );\n\t},\n\n\tajaxPrefilter: addToPrefiltersOrTransports( prefilters ),\n\tajaxTransport: addToPrefiltersOrTransports( transports ),\n\n\t// Main method\n\tajax: function( url, options ) {\n\n\t\t// If url is an object, simulate pre-1.5 signature\n\t\tif ( typeof url === \"object\" ) {\n\t\t\toptions = url;\n\t\t\turl = undefined;\n\t\t}\n\n\t\t// Force options to be an object\n\t\toptions = options || {};\n\n\t\tvar transport,\n\n\t\t\t// URL without anti-cache param\n\t\t\tcacheURL,\n\n\t\t\t// Response headers\n\t\t\tresponseHeadersString,\n\t\t\tresponseHeaders,\n\n\t\t\t// timeout handle\n\t\t\ttimeoutTimer,\n\n\t\t\t// Url cleanup var\n\t\t\turlAnchor,\n\n\t\t\t// Request state (becomes false upon send and true upon completion)\n\t\t\tcompleted,\n\n\t\t\t// To know if global events are to be dispatched\n\t\t\tfireGlobals,\n\n\t\t\t// Loop variable\n\t\t\ti,\n\n\t\t\t// uncached part of the url\n\t\t\tuncached,\n\n\t\t\t// Create the final options object\n\t\t\ts = jQuery.ajaxSetup( {}, options ),\n\n\t\t\t// Callbacks context\n\t\t\tcallbackContext = s.context || s,\n\n\t\t\t// Context for global events is callbackContext if it is a DOM node or jQuery collection\n\t\t\tglobalEventContext = s.context &&\n\t\t\t\t( callbackContext.nodeType || callbackContext.jquery ) ?\n\t\t\t\tjQuery( callbackContext ) :\n\t\t\t\tjQuery.event,\n\n\t\t\t// Deferreds\n\t\t\tdeferred = jQuery.Deferred(),\n\t\t\tcompleteDeferred = jQuery.Callbacks( \"once memory\" ),\n\n\t\t\t// Status-dependent callbacks\n\t\t\tstatusCode = s.statusCode || {},\n\n\t\t\t// Headers (they are sent all at once)\n\t\t\trequestHeaders = {},\n\t\t\trequestHeadersNames = {},\n\n\t\t\t// Default abort message\n\t\t\tstrAbort = \"canceled\",\n\n\t\t\t// Fake xhr\n\t\t\tjqXHR = {\n\t\t\t\treadyState: 0,\n\n\t\t\t\t// Builds headers hashtable if needed\n\t\t\t\tgetResponseHeader: function( key ) {\n\t\t\t\t\tvar match;\n\t\t\t\t\tif ( completed ) {\n\t\t\t\t\t\tif ( !responseHeaders ) {\n\t\t\t\t\t\t\tresponseHeaders = {};\n\t\t\t\t\t\t\twhile ( ( match = rheaders.exec( responseHeadersString ) ) ) {\n\t\t\t\t\t\t\t\tresponseHeaders[ match[ 1 ].toLowerCase() + \" \" ] =\n\t\t\t\t\t\t\t\t\t( responseHeaders[ match[ 1 ].toLowerCase() + \" \" ] || [] )\n\t\t\t\t\t\t\t\t\t\t.concat( match[ 2 ] );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t\tmatch = responseHeaders[ key.toLowerCase() + \" \" ];\n\t\t\t\t\t}\n\t\t\t\t\treturn match == null ? null : match.join( \", \" );\n\t\t\t\t},\n\n\t\t\t\t// Raw string\n\t\t\t\tgetAllResponseHeaders: function() {\n\t\t\t\t\treturn completed ? responseHeadersString : null;\n\t\t\t\t},\n\n\t\t\t\t// Caches the header\n\t\t\t\tsetRequestHeader: function( name, value ) {\n\t\t\t\t\tif ( completed == null ) {\n\t\t\t\t\t\tname = requestHeadersNames[ name.toLowerCase() ] =\n\t\t\t\t\t\t\trequestHeadersNames[ name.toLowerCase() ] || name;\n\t\t\t\t\t\trequestHeaders[ name ] = value;\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\n\t\t\t\t// Overrides response content-type header\n\t\t\t\toverrideMimeType: function( type ) {\n\t\t\t\t\tif ( completed == null ) {\n\t\t\t\t\t\ts.mimeType = type;\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\n\t\t\t\t// Status-dependent callbacks\n\t\t\t\tstatusCode: function( map ) {\n\t\t\t\t\tvar code;\n\t\t\t\t\tif ( map ) {\n\t\t\t\t\t\tif ( completed ) {\n\n\t\t\t\t\t\t\t// Execute the appropriate callbacks\n\t\t\t\t\t\t\tjqXHR.always( map[ jqXHR.status ] );\n\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t// Lazy-add the new callbacks in a way that preserves old ones\n\t\t\t\t\t\t\tfor ( code in map ) {\n\t\t\t\t\t\t\t\tstatusCode[ code ] = [ statusCode[ code ], map[ code ] ];\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\n\t\t\t\t// Cancel the request\n\t\t\t\tabort: function( statusText ) {\n\t\t\t\t\tvar finalText = statusText || strAbort;\n\t\t\t\t\tif ( transport ) {\n\t\t\t\t\t\ttransport.abort( finalText );\n\t\t\t\t\t}\n\t\t\t\t\tdone( 0, finalText );\n\t\t\t\t\treturn this;\n\t\t\t\t}\n\t\t\t};\n\n\t\t// Attach deferreds\n\t\tdeferred.promise( jqXHR );\n\n\t\t// Add protocol if not provided (prefilters might expect it)\n\t\t// Handle falsy url in the settings object (trac-10093: consistency with old signature)\n\t\t// We also use the url parameter if available\n\t\ts.url = ( ( url || s.url || location.href ) + \"\" )\n\t\t\t.replace( rprotocol, location.protocol + \"//\" );\n\n\t\t// Alias method option to type as per ticket trac-12004\n\t\ts.type = options.method || options.type || s.method || s.type;\n\n\t\t// Extract dataTypes list\n\t\ts.dataTypes = ( s.dataType || \"*\" ).toLowerCase().match( rnothtmlwhite ) || [ \"\" ];\n\n\t\t// A cross-domain request is in order when the origin doesn't match the current origin.\n\t\tif ( s.crossDomain == null ) {\n\t\t\turlAnchor = document.createElement( \"a\" );\n\n\t\t\t// Support: IE <=8 - 11, Edge 12 - 15\n\t\t\t// IE throws exception on accessing the href property if url is malformed,\n\t\t\t// e.g. http://example.com:80x/\n\t\t\ttry {\n\t\t\t\turlAnchor.href = s.url;\n\n\t\t\t\t// Support: IE <=8 - 11 only\n\t\t\t\t// Anchor's host property isn't correctly set when s.url is relative\n\t\t\t\turlAnchor.href = urlAnchor.href;\n\t\t\t\ts.crossDomain = originAnchor.protocol + \"//\" + originAnchor.host !==\n\t\t\t\t\turlAnchor.protocol + \"//\" + urlAnchor.host;\n\t\t\t} catch ( e ) {\n\n\t\t\t\t// If there is an error parsing the URL, assume it is crossDomain,\n\t\t\t\t// it can be rejected by the transport if it is invalid\n\t\t\t\ts.crossDomain = true;\n\t\t\t}\n\t\t}\n\n\t\t// Convert data if not already a string\n\t\tif ( s.data && s.processData && typeof s.data !== \"string\" ) {\n\t\t\ts.data = jQuery.param( s.data, s.traditional );\n\t\t}\n\n\t\t// Apply prefilters\n\t\tinspectPrefiltersOrTransports( prefilters, s, options, jqXHR );\n\n\t\t// If request was aborted inside a prefilter, stop there\n\t\tif ( completed ) {\n\t\t\treturn jqXHR;\n\t\t}\n\n\t\t// We can fire global events as of now if asked to\n\t\t// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (trac-15118)\n\t\tfireGlobals = jQuery.event && s.global;\n\n\t\t// Watch for a new set of requests\n\t\tif ( fireGlobals && jQuery.active++ === 0 ) {\n\t\t\tjQuery.event.trigger( \"ajaxStart\" );\n\t\t}\n\n\t\t// Uppercase the type\n\t\ts.type = s.type.toUpperCase();\n\n\t\t// Determine if request has content\n\t\ts.hasContent = !rnoContent.test( s.type );\n\n\t\t// Save the URL in case we're toying with the If-Modified-Since\n\t\t// and/or If-None-Match header later on\n\t\t// Remove hash to simplify url manipulation\n\t\tcacheURL = s.url.replace( rhash, \"\" );\n\n\t\t// More options handling for requests with no content\n\t\tif ( !s.hasContent ) {\n\n\t\t\t// Remember the hash so we can put it back\n\t\t\tuncached = s.url.slice( cacheURL.length );\n\n\t\t\t// If data is available and should be processed, append data to url\n\t\t\tif ( s.data && ( s.processData || typeof s.data === \"string\" ) ) {\n\t\t\t\tcacheURL += ( rquery.test( cacheURL ) ? \"&\" : \"?\" ) + s.data;\n\n\t\t\t\t// trac-9682: remove data so that it's not used in an eventual retry\n\t\t\t\tdelete s.data;\n\t\t\t}\n\n\t\t\t// Add or update anti-cache param if needed\n\t\t\tif ( s.cache === false ) {\n\t\t\t\tcacheURL = cacheURL.replace( rantiCache, \"$1\" );\n\t\t\t\tuncached = ( rquery.test( cacheURL ) ? \"&\" : \"?\" ) + \"_=\" + ( nonce.guid++ ) +\n\t\t\t\t\tuncached;\n\t\t\t}\n\n\t\t\t// Put hash and anti-cache on the URL that will be requested (gh-1732)\n\t\t\ts.url = cacheURL + uncached;\n\n\t\t// Change '%20' to '+' if this is encoded form body content (gh-2658)\n\t\t} else if ( s.data && s.processData &&\n\t\t\t( s.contentType || \"\" ).indexOf( \"application/x-www-form-urlencoded\" ) === 0 ) {\n\t\t\ts.data = s.data.replace( r20, \"+\" );\n\t\t}\n\n\t\t// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.\n\t\tif ( s.ifModified ) {\n\t\t\tif ( jQuery.lastModified[ cacheURL ] ) {\n\t\t\t\tjqXHR.setRequestHeader( \"If-Modified-Since\", jQuery.lastModified[ cacheURL ] );\n\t\t\t}\n\t\t\tif ( jQuery.etag[ cacheURL ] ) {\n\t\t\t\tjqXHR.setRequestHeader( \"If-None-Match\", jQuery.etag[ cacheURL ] );\n\t\t\t}\n\t\t}\n\n\t\t// Set the correct header, if data is being sent\n\t\tif ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {\n\t\t\tjqXHR.setRequestHeader( \"Content-Type\", s.contentType );\n\t\t}\n\n\t\t// Set the Accepts header for the server, depending on the dataType\n\t\tjqXHR.setRequestHeader(\n\t\t\t\"Accept\",\n\t\t\ts.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?\n\t\t\t\ts.accepts[ s.dataTypes[ 0 ] ] +\n\t\t\t\t\t( s.dataTypes[ 0 ] !== \"*\" ? \", \" + allTypes + \"; q=0.01\" : \"\" ) :\n\t\t\t\ts.accepts[ \"*\" ]\n\t\t);\n\n\t\t// Check for headers option\n\t\tfor ( i in s.headers ) {\n\t\t\tjqXHR.setRequestHeader( i, s.headers[ i ] );\n\t\t}\n\n\t\t// Allow custom headers/mimetypes and early abort\n\t\tif ( s.beforeSend &&\n\t\t\t( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {\n\n\t\t\t// Abort if not done already and return\n\t\t\treturn jqXHR.abort();\n\t\t}\n\n\t\t// Aborting is no longer a cancellation\n\t\tstrAbort = \"abort\";\n\n\t\t// Install callbacks on deferreds\n\t\tcompleteDeferred.add( s.complete );\n\t\tjqXHR.done( s.success );\n\t\tjqXHR.fail( s.error );\n\n\t\t// Get transport\n\t\ttransport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );\n\n\t\t// If no transport, we auto-abort\n\t\tif ( !transport ) {\n\t\t\tdone( -1, \"No Transport\" );\n\t\t} else {\n\t\t\tjqXHR.readyState = 1;\n\n\t\t\t// Send global event\n\t\t\tif ( fireGlobals ) {\n\t\t\t\tglobalEventContext.trigger( \"ajaxSend\", [ jqXHR, s ] );\n\t\t\t}\n\n\t\t\t// If request was aborted inside ajaxSend, stop there\n\t\t\tif ( completed ) {\n\t\t\t\treturn jqXHR;\n\t\t\t}\n\n\t\t\t// Timeout\n\t\t\tif ( s.async && s.timeout > 0 ) {\n\t\t\t\ttimeoutTimer = window.setTimeout( function() {\n\t\t\t\t\tjqXHR.abort( \"timeout\" );\n\t\t\t\t}, s.timeout );\n\t\t\t}\n\n\t\t\ttry {\n\t\t\t\tcompleted = false;\n\t\t\t\ttransport.send( requestHeaders, done );\n\t\t\t} catch ( e ) {\n\n\t\t\t\t// Rethrow post-completion exceptions\n\t\t\t\tif ( completed ) {\n\t\t\t\t\tthrow e;\n\t\t\t\t}\n\n\t\t\t\t// Propagate others as results\n\t\t\t\tdone( -1, e );\n\t\t\t}\n\t\t}\n\n\t\t// Callback for when everything is done\n\t\tfunction done( status, nativeStatusText, responses, headers ) {\n\t\t\tvar isSuccess, success, error, response, modified,\n\t\t\t\tstatusText = nativeStatusText;\n\n\t\t\t// Ignore repeat invocations\n\t\t\tif ( completed ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tcompleted = true;\n\n\t\t\t// Clear timeout if it exists\n\t\t\tif ( timeoutTimer ) {\n\t\t\t\twindow.clearTimeout( timeoutTimer );\n\t\t\t}\n\n\t\t\t// Dereference transport for early garbage collection\n\t\t\t// (no matter how long the jqXHR object will be used)\n\t\t\ttransport = undefined;\n\n\t\t\t// Cache response headers\n\t\t\tresponseHeadersString = headers || \"\";\n\n\t\t\t// Set readyState\n\t\t\tjqXHR.readyState = status > 0 ? 4 : 0;\n\n\t\t\t// Determine if successful\n\t\t\tisSuccess = status >= 200 && status < 300 || status === 304;\n\n\t\t\t// Get response data\n\t\t\tif ( responses ) {\n\t\t\t\tresponse = ajaxHandleResponses( s, jqXHR, responses );\n\t\t\t}\n\n\t\t\t// Use a noop converter for missing script but not if jsonp\n\t\t\tif ( !isSuccess &&\n\t\t\t\tjQuery.inArray( \"script\", s.dataTypes ) > -1 &&\n\t\t\t\tjQuery.inArray( \"json\", s.dataTypes ) < 0 ) {\n\t\t\t\ts.converters[ \"text script\" ] = function() {};\n\t\t\t}\n\n\t\t\t// Convert no matter what (that way responseXXX fields are always set)\n\t\t\tresponse = ajaxConvert( s, response, jqXHR, isSuccess );\n\n\t\t\t// If successful, handle type chaining\n\t\t\tif ( isSuccess ) {\n\n\t\t\t\t// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.\n\t\t\t\tif ( s.ifModified ) {\n\t\t\t\t\tmodified = jqXHR.getResponseHeader( \"Last-Modified\" );\n\t\t\t\t\tif ( modified ) {\n\t\t\t\t\t\tjQuery.lastModified[ cacheURL ] = modified;\n\t\t\t\t\t}\n\t\t\t\t\tmodified = jqXHR.getResponseHeader( \"etag\" );\n\t\t\t\t\tif ( modified ) {\n\t\t\t\t\t\tjQuery.etag[ cacheURL ] = modified;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// if no content\n\t\t\t\tif ( status === 204 || s.type === \"HEAD\" ) {\n\t\t\t\t\tstatusText = \"nocontent\";\n\n\t\t\t\t// if not modified\n\t\t\t\t} else if ( status === 304 ) {\n\t\t\t\t\tstatusText = \"notmodified\";\n\n\t\t\t\t// If we have data, let's convert it\n\t\t\t\t} else {\n\t\t\t\t\tstatusText = response.state;\n\t\t\t\t\tsuccess = response.data;\n\t\t\t\t\terror = response.error;\n\t\t\t\t\tisSuccess = !error;\n\t\t\t\t}\n\t\t\t} else {\n\n\t\t\t\t// Extract error from statusText and normalize for non-aborts\n\t\t\t\terror = statusText;\n\t\t\t\tif ( status || !statusText ) {\n\t\t\t\t\tstatusText = \"error\";\n\t\t\t\t\tif ( status < 0 ) {\n\t\t\t\t\t\tstatus = 0;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Set data for the fake xhr object\n\t\t\tjqXHR.status = status;\n\t\t\tjqXHR.statusText = ( nativeStatusText || statusText ) + \"\";\n\n\t\t\t// Success/Error\n\t\t\tif ( isSuccess ) {\n\t\t\t\tdeferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );\n\t\t\t} else {\n\t\t\t\tdeferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );\n\t\t\t}\n\n\t\t\t// Status-dependent callbacks\n\t\t\tjqXHR.statusCode( statusCode );\n\t\t\tstatusCode = undefined;\n\n\t\t\tif ( fireGlobals ) {\n\t\t\t\tglobalEventContext.trigger( isSuccess ? \"ajaxSuccess\" : \"ajaxError\",\n\t\t\t\t\t[ jqXHR, s, isSuccess ? success : error ] );\n\t\t\t}\n\n\t\t\t// Complete\n\t\t\tcompleteDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );\n\n\t\t\tif ( fireGlobals ) {\n\t\t\t\tglobalEventContext.trigger( \"ajaxComplete\", [ jqXHR, s ] );\n\n\t\t\t\t// Handle the global AJAX counter\n\t\t\t\tif ( !( --jQuery.active ) ) {\n\t\t\t\t\tjQuery.event.trigger( \"ajaxStop\" );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn jqXHR;\n\t},\n\n\tgetJSON: function( url, data, callback ) {\n\t\treturn jQuery.get( url, data, callback, \"json\" );\n\t},\n\n\tgetScript: function( url, callback ) {\n\t\treturn jQuery.get( url, undefined, callback, \"script\" );\n\t}\n} );\n\njQuery.each( [ \"get\", \"post\" ], function( _i, method ) {\n\tjQuery[ method ] = function( url, data, callback, type ) {\n\n\t\t// Shift arguments if data argument was omitted\n\t\tif ( isFunction( data ) ) {\n\t\t\ttype = type || callback;\n\t\t\tcallback = data;\n\t\t\tdata = undefined;\n\t\t}\n\n\t\t// The url can be an options object (which then must have .url)\n\t\treturn jQuery.ajax( jQuery.extend( {\n\t\t\turl: url,\n\t\t\ttype: method,\n\t\t\tdataType: type,\n\t\t\tdata: data,\n\t\t\tsuccess: callback\n\t\t}, jQuery.isPlainObject( url ) && url ) );\n\t};\n} );\n\njQuery.ajaxPrefilter( function( s ) {\n\tvar i;\n\tfor ( i in s.headers ) {\n\t\tif ( i.toLowerCase() === \"content-type\" ) {\n\t\t\ts.contentType = s.headers[ i ] || \"\";\n\t\t}\n\t}\n} );\n\n\njQuery._evalUrl = function( url, options, doc ) {\n\treturn jQuery.ajax( {\n\t\turl: url,\n\n\t\t// Make this explicit, since user can override this through ajaxSetup (trac-11264)\n\t\ttype: \"GET\",\n\t\tdataType: \"script\",\n\t\tcache: true,\n\t\tasync: false,\n\t\tglobal: false,\n\n\t\t// Only evaluate the response if it is successful (gh-4126)\n\t\t// dataFilter is not invoked for failure responses, so using it instead\n\t\t// of the default converter is kludgy but it works.\n\t\tconverters: {\n\t\t\t\"text script\": function() {}\n\t\t},\n\t\tdataFilter: function( response ) {\n\t\t\tjQuery.globalEval( response, options, doc );\n\t\t}\n\t} );\n};\n\n\njQuery.fn.extend( {\n\twrapAll: function( html ) {\n\t\tvar wrap;\n\n\t\tif ( this[ 0 ] ) {\n\t\t\tif ( isFunction( html ) ) {\n\t\t\t\thtml = html.call( this[ 0 ] );\n\t\t\t}\n\n\t\t\t// The elements to wrap the target around\n\t\t\twrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );\n\n\t\t\tif ( this[ 0 ].parentNode ) {\n\t\t\t\twrap.insertBefore( this[ 0 ] );\n\t\t\t}\n\n\t\t\twrap.map( function() {\n\t\t\t\tvar elem = this;\n\n\t\t\t\twhile ( elem.firstElementChild ) {\n\t\t\t\t\telem = elem.firstElementChild;\n\t\t\t\t}\n\n\t\t\t\treturn elem;\n\t\t\t} ).append( this );\n\t\t}\n\n\t\treturn this;\n\t},\n\n\twrapInner: function( html ) {\n\t\tif ( isFunction( html ) ) {\n\t\t\treturn this.each( function( i ) {\n\t\t\t\tjQuery( this ).wrapInner( html.call( this, i ) );\n\t\t\t} );\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tvar self = jQuery( this ),\n\t\t\t\tcontents = self.contents();\n\n\t\t\tif ( contents.length ) {\n\t\t\t\tcontents.wrapAll( html );\n\n\t\t\t} else {\n\t\t\t\tself.append( html );\n\t\t\t}\n\t\t} );\n\t},\n\n\twrap: function( html ) {\n\t\tvar htmlIsFunction = isFunction( html );\n\n\t\treturn this.each( function( i ) {\n\t\t\tjQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );\n\t\t} );\n\t},\n\n\tunwrap: function( selector ) {\n\t\tthis.parent( selector ).not( \"body\" ).each( function() {\n\t\t\tjQuery( this ).replaceWith( this.childNodes );\n\t\t} );\n\t\treturn this;\n\t}\n} );\n\n\njQuery.expr.pseudos.hidden = function( elem ) {\n\treturn !jQuery.expr.pseudos.visible( elem );\n};\njQuery.expr.pseudos.visible = function( elem ) {\n\treturn !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );\n};\n\n\n\n\njQuery.ajaxSettings.xhr = function() {\n\ttry {\n\t\treturn new window.XMLHttpRequest();\n\t} catch ( e ) {}\n};\n\nvar xhrSuccessStatus = {\n\n\t\t// File protocol always yields status code 0, assume 200\n\t\t0: 200,\n\n\t\t// Support: IE <=9 only\n\t\t// trac-1450: sometimes IE returns 1223 when it should be 204\n\t\t1223: 204\n\t},\n\txhrSupported = jQuery.ajaxSettings.xhr();\n\nsupport.cors = !!xhrSupported && ( \"withCredentials\" in xhrSupported );\nsupport.ajax = xhrSupported = !!xhrSupported;\n\njQuery.ajaxTransport( function( options ) {\n\tvar callback, errorCallback;\n\n\t// Cross domain only allowed if supported through XMLHttpRequest\n\tif ( support.cors || xhrSupported && !options.crossDomain ) {\n\t\treturn {\n\t\t\tsend: function( headers, complete ) {\n\t\t\t\tvar i,\n\t\t\t\t\txhr = options.xhr();\n\n\t\t\t\txhr.open(\n\t\t\t\t\toptions.type,\n\t\t\t\t\toptions.url,\n\t\t\t\t\toptions.async,\n\t\t\t\t\toptions.username,\n\t\t\t\t\toptions.password\n\t\t\t\t);\n\n\t\t\t\t// Apply custom fields if provided\n\t\t\t\tif ( options.xhrFields ) {\n\t\t\t\t\tfor ( i in options.xhrFields ) {\n\t\t\t\t\t\txhr[ i ] = options.xhrFields[ i ];\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Override mime type if needed\n\t\t\t\tif ( options.mimeType && xhr.overrideMimeType ) {\n\t\t\t\t\txhr.overrideMimeType( options.mimeType );\n\t\t\t\t}\n\n\t\t\t\t// X-Requested-With header\n\t\t\t\t// For cross-domain requests, seeing as conditions for a preflight are\n\t\t\t\t// akin to a jigsaw puzzle, we simply never set it to be sure.\n\t\t\t\t// (it can always be set on a per-request basis or even using ajaxSetup)\n\t\t\t\t// For same-domain requests, won't change header if already provided.\n\t\t\t\tif ( !options.crossDomain && !headers[ \"X-Requested-With\" ] ) {\n\t\t\t\t\theaders[ \"X-Requested-With\" ] = \"XMLHttpRequest\";\n\t\t\t\t}\n\n\t\t\t\t// Set headers\n\t\t\t\tfor ( i in headers ) {\n\t\t\t\t\txhr.setRequestHeader( i, headers[ i ] );\n\t\t\t\t}\n\n\t\t\t\t// Callback\n\t\t\t\tcallback = function( type ) {\n\t\t\t\t\treturn function() {\n\t\t\t\t\t\tif ( callback ) {\n\t\t\t\t\t\t\tcallback = errorCallback = xhr.onload =\n\t\t\t\t\t\t\t\txhr.onerror = xhr.onabort = xhr.ontimeout =\n\t\t\t\t\t\t\t\t\txhr.onreadystatechange = null;\n\n\t\t\t\t\t\t\tif ( type === \"abort\" ) {\n\t\t\t\t\t\t\t\txhr.abort();\n\t\t\t\t\t\t\t} else if ( type === \"error\" ) {\n\n\t\t\t\t\t\t\t\t// Support: IE <=9 only\n\t\t\t\t\t\t\t\t// On a manual native abort, IE9 throws\n\t\t\t\t\t\t\t\t// errors on any property access that is not readyState\n\t\t\t\t\t\t\t\tif ( typeof xhr.status !== \"number\" ) {\n\t\t\t\t\t\t\t\t\tcomplete( 0, \"error\" );\n\t\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t\tcomplete(\n\n\t\t\t\t\t\t\t\t\t\t// File: protocol always yields status 0; see trac-8605, trac-14207\n\t\t\t\t\t\t\t\t\t\txhr.status,\n\t\t\t\t\t\t\t\t\t\txhr.statusText\n\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tcomplete(\n\t\t\t\t\t\t\t\t\txhrSuccessStatus[ xhr.status ] || xhr.status,\n\t\t\t\t\t\t\t\t\txhr.statusText,\n\n\t\t\t\t\t\t\t\t\t// Support: IE <=9 only\n\t\t\t\t\t\t\t\t\t// IE9 has no XHR2 but throws on binary (trac-11426)\n\t\t\t\t\t\t\t\t\t// For XHR2 non-text, let the caller handle it (gh-2498)\n\t\t\t\t\t\t\t\t\t( xhr.responseType || \"text\" ) !== \"text\" ||\n\t\t\t\t\t\t\t\t\ttypeof xhr.responseText !== \"string\" ?\n\t\t\t\t\t\t\t\t\t\t{ binary: xhr.response } :\n\t\t\t\t\t\t\t\t\t\t{ text: xhr.responseText },\n\t\t\t\t\t\t\t\t\txhr.getAllResponseHeaders()\n\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t};\n\t\t\t\t};\n\n\t\t\t\t// Listen to events\n\t\t\t\txhr.onload = callback();\n\t\t\t\terrorCallback = xhr.onerror = xhr.ontimeout = callback( \"error\" );\n\n\t\t\t\t// Support: IE 9 only\n\t\t\t\t// Use onreadystatechange to replace onabort\n\t\t\t\t// to handle uncaught aborts\n\t\t\t\tif ( xhr.onabort !== undefined ) {\n\t\t\t\t\txhr.onabort = errorCallback;\n\t\t\t\t} else {\n\t\t\t\t\txhr.onreadystatechange = function() {\n\n\t\t\t\t\t\t// Check readyState before timeout as it changes\n\t\t\t\t\t\tif ( xhr.readyState === 4 ) {\n\n\t\t\t\t\t\t\t// Allow onerror to be called first,\n\t\t\t\t\t\t\t// but that will not handle a native abort\n\t\t\t\t\t\t\t// Also, save errorCallback to a variable\n\t\t\t\t\t\t\t// as xhr.onerror cannot be accessed\n\t\t\t\t\t\t\twindow.setTimeout( function() {\n\t\t\t\t\t\t\t\tif ( callback ) {\n\t\t\t\t\t\t\t\t\terrorCallback();\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t}\n\t\t\t\t\t};\n\t\t\t\t}\n\n\t\t\t\t// Create the abort callback\n\t\t\t\tcallback = callback( \"abort\" );\n\n\t\t\t\ttry {\n\n\t\t\t\t\t// Do send the request (this may raise an exception)\n\t\t\t\t\txhr.send( options.hasContent && options.data || null );\n\t\t\t\t} catch ( e ) {\n\n\t\t\t\t\t// trac-14683: Only rethrow if this hasn't been notified as an error yet\n\t\t\t\t\tif ( callback ) {\n\t\t\t\t\t\tthrow e;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t},\n\n\t\t\tabort: function() {\n\t\t\t\tif ( callback ) {\n\t\t\t\t\tcallback();\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t}\n} );\n\n\n\n\n// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)\njQuery.ajaxPrefilter( function( s ) {\n\tif ( s.crossDomain ) {\n\t\ts.contents.script = false;\n\t}\n} );\n\n// Install script dataType\njQuery.ajaxSetup( {\n\taccepts: {\n\t\tscript: \"text/javascript, application/javascript, \" +\n\t\t\t\"application/ecmascript, application/x-ecmascript\"\n\t},\n\tcontents: {\n\t\tscript: /\\b(?:java|ecma)script\\b/\n\t},\n\tconverters: {\n\t\t\"text script\": function( text ) {\n\t\t\tjQuery.globalEval( text );\n\t\t\treturn text;\n\t\t}\n\t}\n} );\n\n// Handle cache's special case and crossDomain\njQuery.ajaxPrefilter( \"script\", function( s ) {\n\tif ( s.cache === undefined ) {\n\t\ts.cache = false;\n\t}\n\tif ( s.crossDomain ) {\n\t\ts.type = \"GET\";\n\t}\n} );\n\n// Bind script tag hack transport\njQuery.ajaxTransport( \"script\", function( s ) {\n\n\t// This transport only deals with cross domain or forced-by-attrs requests\n\tif ( s.crossDomain || s.scriptAttrs ) {\n\t\tvar script, callback;\n\t\treturn {\n\t\t\tsend: function( _, complete ) {\n\t\t\t\tscript = jQuery( \"',\n '',\n '',\n '',\n '',\n '',\n ].join('\\n');\n var views = [];\n var cells = Jupyter.notebook.get_cells();\n Jupyter.notebook.get_cells().forEach(function (cell) {\n if (cell.output_area) {\n cell.output_area.outputs.forEach(function (output) {\n if (\n output.data &&\n output.data[VIEW_MIME_TYPE] &&\n state.state[output.data[VIEW_MIME_TYPE].model_id]\n ) {\n views.push(\n '\\n'\n );\n }\n });\n }\n });\n value += views.join('\\n');\n value += '\\n\\n\\n\\n';\n var content = document.createElement('textarea');\n content.setAttribute('readonly', 'true');\n content.style.width = '100%';\n content.style.minHeight = '250px';\n content.value = value;\n\n var mod = dialog.modal({\n show: true,\n title: 'Embed widgets',\n body: content,\n keyboard_manager: Jupyter.notebook.keyboard_manager,\n notebook: Jupyter.notebook,\n buttons: {\n 'Copy to Clipboard': {\n class: 'btn-primary',\n click: function (event) {\n content.select();\n return document.execCommand('copy');\n },\n },\n },\n });\n });\n }\n );\n });\n};\n\nvar action = {\n help: 'Embed interactive widgets',\n icon: 'fa-sliders',\n help_index: 'zz',\n handler: embed_widgets,\n};\n\nvar action_name = 'embed-interactive-widgets';\nvar prefix = 'widgets';\nrequirejs(['base/js/namespace'], function (Jupyter) {\n Jupyter.notebook.keyboard_manager.actions.register(\n action,\n action_name,\n prefix\n );\n});\n\nmodule.exports = {\n action: action,\n};\n","// Copyright (c) Jupyter Development Team.\n// Distributed under the terms of the Modified BSD License.\n'use strict';\n\nwindow['requirejs'].config({\n map: {\n '*': {\n '@jupyter-widgets/controls': 'nbextensions/jupyter-js-widgets/extension',\n '@jupyter-widgets/base': 'nbextensions/jupyter-js-widgets/extension',\n '@jupyter-widgets/output': 'nbextensions/jupyter-js-widgets/extension',\n },\n },\n});\n\nvar MIME_TYPE = 'application/vnd.jupyter.widget-view+json';\nvar CLASS_NAME = 'jupyter-widgets-view';\n\nvar mngr = require('./manager');\nrequire('./save_state');\nrequire('./embed_widgets');\nvar LuminoWidget = require('@lumino/widgets');\nvar LuminoMessaging = require('@lumino/messaging');\n\nvar NOTEBOOK_VERSION_INFO = Jupyter.version.split('.');\nvar NOTEBOOK_MAJOR = parseInt(NOTEBOOK_VERSION_INFO[0]);\nvar NOTEBOOK_MINOR = parseInt(NOTEBOOK_VERSION_INFO[1]);\nvar NOTEBOOK_PATCH = parseInt(NOTEBOOK_VERSION_INFO[2]);\n\nvar RENDER_SHOULD_THROW =\n NOTEBOOK_MAJOR > 6 ||\n (NOTEBOOK_MAJOR == 6 && NOTEBOOK_MINOR > 4) ||\n (NOTEBOOK_MAJOR == 6 && NOTEBOOK_MINOR == 4 && NOTEBOOK_PATCH > 4);\n\n/**\n * Create a widget manager for a kernel instance.\n */\nvar handle_kernel = function (Jupyter, kernel) {\n if (kernel.comm_manager && kernel.widget_manager === undefined) {\n // Clear any old widget manager\n if (Jupyter.WidgetManager) {\n Jupyter.WidgetManager._managers[0].clear_state();\n }\n\n // Create a new widget manager instance. Use the global\n // Jupyter.notebook handle.\n var manager = new mngr.WidgetManager(kernel.comm_manager, Jupyter.notebook);\n\n // For backwards compatibility and interactive use.\n Jupyter.WidgetManager = mngr.WidgetManager;\n\n // Store a handle to the manager so we know not to\n // another for this kernel. This also is a convenience\n // for the user.\n kernel.widget_manager = manager;\n }\n};\n\nfunction register_events(Jupyter, events, outputarea) {\n // If a kernel already exists, create a widget manager.\n if (Jupyter.notebook && Jupyter.notebook.kernel) {\n handle_kernel(Jupyter, Jupyter.notebook.kernel);\n }\n // When the kernel is created, create a widget manager.\n events.on(\n 'kernel_created.Kernel kernel_created.Session',\n function (event, data) {\n handle_kernel(Jupyter, data.kernel);\n }\n );\n\n // When a kernel dies, disconnect the widgets.\n events.on(\n 'kernel_killed.Session kernel_killed.Kernel kernel_restarting.Kernel',\n function (event, data) {\n var kernel = data.kernel;\n if (kernel && kernel.widget_manager) {\n kernel.widget_manager.disconnect();\n }\n }\n );\n\n /**\n * The views on this page. We keep this list so that we can call the view.remove()\n * method when a view is removed from the page.\n */\n var views = {};\n\n window.addEventListener('resize', () => {\n Object.keys(views).forEach((viewKey) => {\n LuminoMessaging.MessageLoop.postMessage(\n views[viewKey].luminoWidget,\n LuminoWidget.Widget.ResizeMessage.UnknownSize\n );\n });\n });\n\n var removeView = function (event, data) {\n var output = data.cell ? data.cell.output_area : data.output_area;\n var viewids = output ? output._jupyterWidgetViews : void 0;\n if (viewids) {\n viewids.forEach(function (id) {\n // this may be called after the widget is pulled off the page\n // so we temporarily put it back on the page as a kludge\n // so that Lumino can trigger the appropriate detach signals\n var view = views[id];\n view.el.style.display = 'none';\n document.body.appendChild(view.el);\n view.remove();\n delete views[id];\n });\n output._jupyterWidgetViews = [];\n }\n };\n\n // Deleting a cell does *not* clear the outputs first.\n events.on('delete.Cell', removeView);\n // add an event to the notebook element for *any* outputs that are cleared.\n Jupyter.notebook.container.on('clearing', '.output', removeView);\n\n // For before https://github.com/jupyter/notebook/pull/2411 is merged and\n // released. This does not handle the case where an empty cell is executed\n // to clear input.\n events.on('execute.CodeCell', removeView);\n events.on('clear_output.CodeCell', removeView);\n\n /**\n * Render data to the output area.\n */\n function render(output, data, node) {\n // data is a model id\n var manager =\n Jupyter.notebook &&\n Jupyter.notebook.kernel &&\n Jupyter.notebook.kernel.widget_manager;\n if (!manager) {\n var msg = 'Error rendering Jupyter widget: missing widget manager';\n if (RENDER_SHOULD_THROW) {\n throw new Error(msg);\n }\n node.textContent = msg;\n return;\n }\n\n // Missing model id means the view was removed. Hide this element.\n if (data.model_id === '') {\n if (RENDER_SHOULD_THROW) {\n throw new Error('Jupyter Widgets model not found');\n }\n node.style.display = 'none';\n return;\n }\n\n if (manager.has_model(data.model_id)) {\n manager\n .get_model(data.model_id)\n .then(function (model) {\n return manager.create_view(model, { output: output });\n })\n .then(function (view) {\n var id = view.cid;\n output._jupyterWidgetViews = output._jupyterWidgetViews || [];\n output._jupyterWidgetViews.push(id);\n views[id] = view;\n LuminoWidget.Widget.attach(view.luminoWidget, node);\n\n // Make the node completely disappear if the view is removed.\n view.once('remove', () => {\n // Since we have a mutable reference to the data, delete the\n // model id to indicate the view is removed.\n data.model_id = '';\n node.style.display = 'none';\n });\n });\n } else {\n var msg =\n 'A Jupyter widget could not be displayed because the widget state could not be found. This could happen if the kernel storing the widget is no longer available, or if the widget state was not saved in the notebook. You may be able to create the widget by running the appropriate cells.';\n if (RENDER_SHOULD_THROW) {\n throw new Error(msg);\n }\n node.textContent = msg;\n return;\n }\n }\n\n // `this` is the output area we are appending to\n var append_mime = function (json, md, element) {\n var toinsert = this.create_output_subarea(md, CLASS_NAME, MIME_TYPE);\n this.keyboard_manager.register_events(toinsert);\n render(this, json, toinsert[0]);\n element.append(toinsert);\n return toinsert;\n };\n\n // Register mime type with the output area\n outputarea.OutputArea.prototype.register_mime_type(MIME_TYPE, append_mime, {\n // An output widget could contain arbitrary user javascript\n safe: false,\n // Index of renderer in `output_area.display_order`\n index: 0,\n });\n}\n\nfunction load_ipython_extension() {\n return new Promise(function (resolve) {\n requirejs(\n ['base/js/namespace', 'base/js/events', 'notebook/js/outputarea'],\n function (Jupyter, events, outputarea) {\n require('@lumino/widgets/style/index.css');\n require('@jupyter-widgets/base/css/index.css');\n require('@jupyter-widgets/controls/css/widgets.css');\n register_events(Jupyter, events, outputarea);\n resolve();\n }\n );\n });\n}\n\nmodule.exports = {\n load_ipython_extension: load_ipython_extension,\n ...require('@jupyter-widgets/controls'),\n ...require('@jupyter-widgets/base'),\n ...require('./widget_output'),\n};\n","// Copyright (c) Jupyter Development Team.\n// Distributed under the terms of the Modified BSD License.\n'use strict';\n\nvar base = require('@jupyter-widgets/base');\nvar ManagerBase = require('@jupyter-widgets/base-manager').ManagerBase;\nvar widgets = require('@jupyter-widgets/controls');\nvar outputWidgets = require('./widget_output');\nvar saveState = require('./save_state');\nvar embedWidgets = require('./embed_widgets');\n\nvar MIME_TYPE = 'application/vnd.jupyter.widget-view+json';\n\nfunction polyfill_new_comm_buffers(\n manager,\n target_name,\n data,\n callbacks,\n metadata,\n comm_id,\n buffers\n) {\n /**\n * This polyfills services/kernel/comm/CommManager.new_comm to\n * accept the buffers argument.\n *\n * argument comm_id is optional\n */\n return new Promise(function (resolve) {\n requirejs(['services/kernels/comm'], function (comm) {\n var comm = new comm.Comm(target_name, comm_id);\n manager.register_comm(comm);\n // inline Comm.open(), but with buffers\n var content = {\n comm_id: comm_id,\n target_name: target_name,\n data: data || {},\n };\n comm.kernel.send_shell_message(\n 'comm_open',\n content,\n callbacks,\n metadata,\n buffers\n );\n resolve(comm);\n });\n });\n}\n\nfunction new_comm(\n manager,\n target_name,\n data,\n callbacks,\n metadata,\n comm_id,\n buffers\n) {\n // Checks whether new_comm needs a polyfill, and calls the correct version\n // Polyfill needed for notebook <5.1, in which the new_comm method does not support a buffers argument.\n // See https://github.com/jupyter-widgets/ipywidgets/pull/1817\n var need_polyfill = manager.new_comm.length < 6;\n if (need_polyfill) {\n return polyfill_new_comm_buffers.apply(null, arguments);\n }\n return manager.new_comm.apply(\n manager,\n Array.prototype.slice.call(arguments, 1)\n );\n}\n\n//--------------------------------------------------------------------\n// WidgetManager class\n//--------------------------------------------------------------------\n\nexport class WidgetManager extends ManagerBase {\n constructor(comm_manager, notebook) {\n super();\n // Managers are stored in *reverse* order, so that _managers[0] is the most recent.\n WidgetManager._managers.unshift(this);\n\n // Attach a comm manager\n this.notebook = notebook;\n this.keyboard_manager = notebook.keyboard_manager;\n this.comm_manager = comm_manager;\n\n var widget_md = notebook.metadata.widgets;\n\n // Steps that needs to be done:\n // 1. Register comm target\n // 2. Get any widget state from the kernel and open comms with existing state\n // 3. Check saved state for widgets, and restore any that would not overwrite\n // any live widgets.\n\n // Register with the comm manager. (1)\n this.comm_manager.register_target(\n this.comm_target_name,\n this.handle_comm_open.bind(this)\n );\n\n var that = this;\n\n this._loadFromKernel()\n .then(function () {\n // Now that we have mirrored any widgets from the kernel...\n // Restore any widgets from saved state that are not live (3)\n if (\n widget_md &&\n widget_md['application/vnd.jupyter.widget-state+json']\n ) {\n var state =\n notebook.metadata.widgets[\n 'application/vnd.jupyter.widget-state+json'\n ];\n state = that.filterExistingModelState(state);\n return that.set_state(state);\n }\n })\n .then(function () {\n // Rerender cells that have widget data\n that.notebook.get_cells().forEach(function (cell) {\n var rerender =\n cell.output_area &&\n cell.output_area.outputs.find(function (output) {\n return output.data && output.data[MIME_TYPE];\n });\n if (rerender) {\n that.notebook.render_cell_output(cell);\n }\n });\n });\n\n // Create the actions and menu\n this._init_actions();\n this._init_menu();\n }\n\n loadClass(className, moduleName, moduleVersion) {\n const failure = () => {\n throw new Error(\n 'Class ' + className + ' not found in module ' + moduleName\n );\n };\n if (moduleName === '@jupyter-widgets/controls') {\n return widgets[className]\n ? Promise.resolve(widgets[className])\n : failure();\n } else if (moduleName === '@jupyter-widgets/base') {\n return base[className] ? Promise.resolve(base[className]) : failure();\n } else if (moduleName == '@jupyter-widgets/output') {\n return outputWidgets[className]\n ? Promise.resolve(outputWidgets[className])\n : failure();\n } else {\n return new Promise(function (resolve, reject) {\n window.require([moduleName], resolve, reject);\n }).then(function (mod) {\n if (mod[className]) {\n return mod[className];\n } else {\n return failure();\n }\n });\n }\n }\n\n /**\n * Registers manager level actions with the notebook actions list\n */\n _init_actions() {\n var notifier = Jupyter.notification_area.widget('widgets');\n this.saveWidgetsAction = {\n handler: function () {\n this.get_state({\n drop_defaults: true,\n }).then(function (state) {\n Jupyter.notebook.metadata.widgets = {\n 'application/vnd.jupyter.widget-state+json': state,\n };\n Jupyter.menubar.actions\n .get('jupyter-notebook:save-notebook')\n .handler({\n notebook: Jupyter.notebook,\n });\n });\n }.bind(this),\n icon: 'fa-truck',\n help: 'Save the notebook with the widget state information for static rendering',\n };\n Jupyter.menubar.actions.register(\n this.saveWidgetsAction,\n 'save-with-widgets',\n 'widgets'\n );\n\n this.clearWidgetsAction = {\n handler: function () {\n delete Jupyter.notebook.metadata.widgets;\n Jupyter.menubar.actions.get('jupyter-notebook:save-notebook').handler({\n notebook: Jupyter.notebook,\n });\n },\n help: 'Clear the widget state information from the notebook',\n };\n Jupyter.menubar.actions.register(\n this.saveWidgetsAction,\n 'save-clear-widgets',\n 'widgets'\n );\n }\n\n /**\n * Initialize the widget menu\n */\n _init_menu() {\n // Add a widgets menubar item, before help.\n var widgetsMenu = document.createElement('li');\n widgetsMenu.classList.add('dropdown');\n var helpMenu = document.querySelector('#help_menu').parentElement;\n helpMenu.parentElement.insertBefore(widgetsMenu, helpMenu);\n\n var widgetsMenuLink = document.createElement('a');\n widgetsMenuLink.setAttribute('href', '#');\n widgetsMenuLink.setAttribute('data-toggle', 'dropdown');\n widgetsMenuLink.classList.add('dropdown-toggle');\n widgetsMenuLink.innerText = 'Widgets';\n widgetsMenu.appendChild(widgetsMenuLink);\n\n var widgetsSubmenu = document.createElement('ul');\n widgetsSubmenu.setAttribute('id', 'widget-submenu');\n widgetsSubmenu.classList.add('dropdown-menu');\n widgetsMenu.appendChild(widgetsSubmenu);\n\n var divider = document.createElement('ul');\n divider.classList.add('divider');\n\n widgetsSubmenu.appendChild(\n this._createMenuItem('Save Notebook Widget State', this.saveWidgetsAction)\n );\n widgetsSubmenu.appendChild(\n this._createMenuItem(\n 'Clear Notebook Widget State',\n this.clearWidgetsAction\n )\n );\n widgetsSubmenu.appendChild(divider);\n widgetsSubmenu.appendChild(\n this._createMenuItem('Download Widget State', saveState.action)\n );\n widgetsSubmenu.appendChild(\n this._createMenuItem('Embed Widgets', embedWidgets.action)\n );\n }\n\n /**\n * Creates a menu item for an action.\n * @param {string} title - display string for the menu item\n * @param {Action} action\n * @return {HTMLElement} menu item\n */\n _createMenuItem(title, action) {\n var item = document.createElement('li');\n item.setAttribute('title', action.help);\n\n var itemLink = document.createElement('a');\n itemLink.setAttribute('href', '#');\n itemLink.innerText = title;\n item.appendChild(itemLink);\n\n item.onclick = action.handler;\n return item;\n }\n\n _create_comm(comm_target_name, comm_id, data, metadata, buffers) {\n var that = this;\n return this._get_connected_kernel().then(function (kernel) {\n if (data || metadata) {\n return new_comm(\n kernel.comm_manager,\n comm_target_name,\n data,\n that.callbacks(),\n metadata,\n comm_id,\n buffers\n );\n } else {\n // Construct a comm that already is open on the kernel side. We\n // don't want to send an open message, which would supersede the\n // kernel comm object, so we instead do by hand the necessary parts\n // of the new_comm call above.\n return new Promise(function (resolve) {\n requirejs(['services/kernels/comm'], function (comm) {\n var new_comm = new comm.Comm(comm_target_name, comm_id);\n kernel.comm_manager.register_comm(new_comm);\n resolve(new_comm);\n });\n });\n }\n });\n }\n\n _get_comm_info() {\n /**\n * Gets a promise for the valid widget models.\n */\n var that = this;\n return this._get_connected_kernel().then(function (kernel) {\n return new Promise(function (resolve, reject) {\n kernel.comm_info('jupyter.widget', function (msg) {\n resolve(msg['content']['comms']);\n });\n });\n });\n }\n\n _get_connected_kernel() {\n /**\n * Gets a promise for a connected kernel\n */\n var that = this;\n return new Promise(function (resolve, reject) {\n if (\n that.comm_manager &&\n that.comm_manager.kernel &&\n that.comm_manager.kernel.is_connected()\n ) {\n resolve(that.comm_manager.kernel);\n } else {\n that.notebook.events.on(\n 'kernel_connected.Kernel',\n function (event, data) {\n resolve(data.kernel);\n }\n );\n }\n });\n }\n\n setViewOptions(options) {\n var options = options || {};\n if (!options.output && options.parent) {\n // use the parent output if we don't have one\n options.output = options.parent.options.output;\n }\n if (options.output) {\n options.iopub_callbacks = {\n output: options.output.handle_output.bind(options.output),\n clear_output: options.output.handle_clear_output.bind(options.output),\n };\n }\n return options;\n }\n\n /**\n * Callback handlers for a specific view\n */\n callbacks(view) {\n var callbacks = ManagerBase.prototype.callbacks.call(this, view);\n if (view && view.options.iopub_callbacks) {\n callbacks.iopub = view.options.iopub_callbacks;\n }\n return callbacks;\n }\n}\n\n/**\n * List of widget managers in *reverse* order\n * (_managers[0] is the most recent)\n */\nWidgetManager._managers = [];\n","// Copyright (c) Jupyter Development Team.\n// Distributed under the terms of the Modified BSD License.\n\n'use strict';\n\nvar save_state = function () {\n return new Promise(function (resolve, reject) {\n requirejs(['base/js/namespace'], function (Jupyter) {\n var wm = Jupyter.WidgetManager._managers[0];\n if (!wm) {\n reject('No widget manager');\n }\n return wm\n .get_state({\n drop_defaults: true,\n })\n .then(function (state) {\n var data =\n 'text/json;charset=utf-8,' +\n encodeURIComponent(JSON.stringify(state, null, ' '));\n var a = document.createElement('a');\n a.download = 'widget_state.json';\n a.href = 'data:' + data;\n a.click();\n resolve();\n });\n });\n });\n};\n\nvar action = {\n help: 'Download the widget state as a JSON file',\n icon: 'fa-sliders',\n help_index: 'zz',\n handler: save_state,\n};\n\nvar action_name = 'save-widget-state';\nvar prefix = 'widgets';\nrequirejs(['base/js/namespace'], function (Jupyter) {\n Jupyter.notebook.keyboard_manager.actions.register(\n action,\n action_name,\n prefix\n );\n});\n\nmodule.exports = { action: action };\n","// Copyright (c) Jupyter Development Team.\n// Distributed under the terms of the Modified BSD License.\n'use strict';\n\n// This widget is strongly coupled to the notebook because of the outputarea\n// dependency.\nvar outputBase = require('@jupyter-widgets/output');\nrequire('./widget_output.css');\n\nvar outputArea = new Promise(function (resolve, reject) {\n requirejs(['notebook/js/outputarea'], resolve, reject);\n});\n\nexport class OutputModel extends outputBase.OutputModel {\n defaults() {\n return {\n ...super.defaults(),\n msg_id: '',\n outputs: [],\n };\n }\n\n initialize(attributes, options) {\n super.initialize(attributes, options);\n this.listenTo(this, 'change:msg_id', this.reset_msg_id);\n\n if (this.comm && this.comm.kernel) {\n this.kernel = this.comm.kernel;\n this.kernel.set_callbacks_for_msg(this.model_id, this.callbacks(), false);\n }\n\n var that = this;\n // Create an output area to handle the data model part\n outputArea.then(function (outputArea) {\n that.output_area = new outputArea.OutputArea({\n selector: document.createElement('div'),\n config: { data: { OutputArea: {} } },\n prompt_area: false,\n events: that.widget_manager.notebook.events,\n keyboard_manager: that.widget_manager.keyboard_manager,\n });\n that.listenTo(\n that,\n 'new_message',\n function (msg) {\n that.output_area.handle_output(msg);\n that.set('outputs', that.output_area.toJSON(), { newMessage: true });\n that.save_changes();\n },\n that\n );\n that.listenTo(that, 'clear_output', function (msg) {\n that.output_area.handle_clear_output(msg);\n that.set('outputs', that.output_area.toJSON(), { newMessage: true });\n that.save_changes();\n });\n that.listenTo(that, 'change:outputs', that.setOutputs);\n that.setOutputs();\n });\n }\n\n // make callbacks\n callbacks() {\n // Merge our callbacks with the base class callbacks.\n var cb = super.callbacks();\n var iopub = cb.iopub || {};\n var iopubCallbacks = {\n ...iopub,\n output: function (msg) {\n this.trigger('new_message', msg);\n if (iopub.output) {\n iopub.output.apply(this, arguments);\n }\n }.bind(this),\n clear_output: function (msg) {\n this.trigger('clear_output', msg);\n if (iopub.clear_output) {\n iopub.clear_output.apply(this, arguments);\n }\n }.bind(this),\n };\n return { ...cb, iopub: iopubCallbacks };\n }\n\n reset_msg_id() {\n var kernel = this.kernel;\n // Pop previous message id\n var prev_msg_id = this.previous('msg_id');\n if (prev_msg_id && kernel) {\n var previous_callback = kernel.output_callback_overrides_pop(prev_msg_id);\n if (previous_callback !== this.model_id) {\n console.error(\n 'Popped wrong message (' +\n previous_callback +\n ' instead of ' +\n this.model_id +\n ') - likely the stack was not maintained in kernel.'\n );\n }\n }\n var msg_id = this.get('msg_id');\n if (msg_id && kernel) {\n kernel.output_callback_overrides_push(msg_id, this.model_id);\n }\n }\n\n setOutputs(model, value, options) {\n if (!(options && options.newMessage)) {\n // fromJSON does not clear the existing output\n this.output_area.clear_output();\n // fromJSON does not copy the message, so we make a deep copy\n this.output_area.fromJSON(\n JSON.parse(JSON.stringify(this.get('outputs')))\n );\n }\n }\n}\n\nexport class OutputView extends outputBase.OutputView {\n render() {\n var that = this;\n this.el.classList.add('jupyter-widgets-output-area');\n outputArea.then(function (outputArea) {\n that.output_area = new outputArea.OutputArea({\n selector: that.el,\n // use default values for the output area config\n config: { data: { OutputArea: {} } },\n prompt_area: false,\n events: that.model.widget_manager.notebook.events,\n keyboard_manager: that.model.widget_manager.keyboard_manager,\n });\n that.listenTo(\n that.model,\n 'new_message',\n function (msg) {\n that.output_area.handle_output(msg);\n },\n that\n );\n that.listenTo(that.model, 'clear_output', function (msg) {\n that.output_area.handle_clear_output(msg);\n // fake the event on the output area element. This can be\n // deleted when we can rely on\n // https://github.com/jupyter/notebook/pull/2411 being\n // available.\n that.output_area.element.trigger('clearing', { output_area: this });\n });\n // Render initial contents from the current model\n that.listenTo(that.model, 'change:outputs', that.setOutputs);\n that.setOutputs();\n });\n super.render();\n }\n\n setOutputs(model, value, options) {\n if (!(options && options.newMessage)) {\n // fromJSON does not clear the existing output\n this.output_area.clear_output();\n // fromJSON does not copy the message, so we make a deep copy\n this.output_area.fromJSON(\n JSON.parse(JSON.stringify(this.model.get('outputs')))\n );\n }\n }\n}\n","module.exports = __WEBPACK_EXTERNAL_MODULE__1308__;","let urlAlphabet =\n 'useandom-26T198340PX75pxJACKVERYMINDBUSHWOLF_GQZbfghjklqvwyzrict'\nlet customAlphabet = (alphabet, defaultSize = 21) => {\n return (size = defaultSize) => {\n let id = ''\n let i = size\n while (i--) {\n id += alphabet[(Math.random() * alphabet.length) | 0]\n }\n return id\n }\n}\nlet nanoid = (size = 21) => {\n let id = ''\n let i = size\n while (i--) {\n id += urlAlphabet[(Math.random() * 64) | 0]\n }\n return id\n}\nmodule.exports = { nanoid, customAlphabet }\n","// Current version.\nexport var VERSION = '1.13.7';\n\n// Establish the root object, `window` (`self`) in the browser, `global`\n// on the server, or `this` in some virtual machines. We use `self`\n// instead of `window` for `WebWorker` support.\nexport var root = (typeof self == 'object' && self.self === self && self) ||\n (typeof global == 'object' && global.global === global && global) ||\n Function('return this')() ||\n {};\n\n// Save bytes in the minified (but not gzipped) version:\nexport var ArrayProto = Array.prototype, ObjProto = Object.prototype;\nexport var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;\n\n// Create quick reference variables for speed access to core prototypes.\nexport var push = ArrayProto.push,\n slice = ArrayProto.slice,\n toString = ObjProto.toString,\n hasOwnProperty = ObjProto.hasOwnProperty;\n\n// Modern feature detection.\nexport var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',\n supportsDataView = typeof DataView !== 'undefined';\n\n// All **ECMAScript 5+** native function implementations that we hope to use\n// are declared here.\nexport var nativeIsArray = Array.isArray,\n nativeKeys = Object.keys,\n nativeCreate = Object.create,\n nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;\n\n// Create references to these builtin functions because we override them.\nexport var _isNaN = isNaN,\n _isFinite = isFinite;\n\n// Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.\nexport var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');\nexport var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',\n 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];\n\n// The largest integer that can be represented exactly.\nexport var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;\n","// Some functions take a variable number of arguments, or a few expected\n// arguments at the beginning and then a variable number of values to operate\n// on. This helper accumulates all remaining arguments past the function’s\n// argument length (or an explicit `startIndex`), into an array that becomes\n// the last argument. Similar to ES6’s \"rest parameter\".\nexport default function restArguments(func, startIndex) {\n startIndex = startIndex == null ? func.length - 1 : +startIndex;\n return function() {\n var length = Math.max(arguments.length - startIndex, 0),\n rest = Array(length),\n index = 0;\n for (; index < length; index++) {\n rest[index] = arguments[index + startIndex];\n }\n switch (startIndex) {\n case 0: return func.call(this, rest);\n case 1: return func.call(this, arguments[0], rest);\n case 2: return func.call(this, arguments[0], arguments[1], rest);\n }\n var args = Array(startIndex + 1);\n for (index = 0; index < startIndex; index++) {\n args[index] = arguments[index];\n }\n args[startIndex] = rest;\n return func.apply(this, args);\n };\n}\n","// Is a given variable an object?\nexport default function isObject(obj) {\n var type = typeof obj;\n return type === 'function' || (type === 'object' && !!obj);\n}\n","// Is a given value equal to null?\nexport default function isNull(obj) {\n return obj === null;\n}\n","// Is a given variable undefined?\nexport default function isUndefined(obj) {\n return obj === void 0;\n}\n","import { toString } from './_setup.js';\n\n// Is a given value a boolean?\nexport default function isBoolean(obj) {\n return obj === true || obj === false || toString.call(obj) === '[object Boolean]';\n}\n","// Is a given value a DOM element?\nexport default function isElement(obj) {\n return !!(obj && obj.nodeType === 1);\n}\n","import { toString } from './_setup.js';\n\n// Internal function for creating a `toString`-based type tester.\nexport default function tagTester(name) {\n var tag = '[object ' + name + ']';\n return function(obj) {\n return toString.call(obj) === tag;\n };\n}\n","import tagTester from './_tagTester.js';\n\nexport default tagTester('String');\n","import tagTester from './_tagTester.js';\n\nexport default tagTester('Number');\n","import tagTester from './_tagTester.js';\n\nexport default tagTester('Date');\n","import tagTester from './_tagTester.js';\n\nexport default tagTester('RegExp');\n","import tagTester from './_tagTester.js';\n\nexport default tagTester('Error');\n","import tagTester from './_tagTester.js';\n\nexport default tagTester('Symbol');\n","import tagTester from './_tagTester.js';\n\nexport default tagTester('ArrayBuffer');\n","import tagTester from './_tagTester.js';\nimport { root } from './_setup.js';\n\nvar isFunction = tagTester('Function');\n\n// Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old\n// v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).\nvar nodelist = root.document && root.document.childNodes;\nif (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {\n isFunction = function(obj) {\n return typeof obj == 'function' || false;\n };\n}\n\nexport default isFunction;\n","import tagTester from './_tagTester.js';\n\nexport default tagTester('Object');\n","import { supportsDataView } from './_setup.js';\nimport hasObjectTag from './_hasObjectTag.js';\n\n// In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.\n// In IE 11, the most common among them, this problem also applies to\n// `Map`, `WeakMap` and `Set`.\n// Also, there are cases where an application can override the native\n// `DataView` object, in cases like that we can't use the constructor\n// safely and should just rely on alternate `DataView` checks\nexport var hasDataViewBug = (\n supportsDataView && (!/\\[native code\\]/.test(String(DataView)) || hasObjectTag(new DataView(new ArrayBuffer(8))))\n ),\n isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));\n","import tagTester from './_tagTester.js';\nimport isFunction from './isFunction.js';\nimport isArrayBuffer from './isArrayBuffer.js';\nimport { hasDataViewBug } from './_stringTagBug.js';\n\nvar isDataView = tagTester('DataView');\n\n// In IE 10 - Edge 13, we need a different heuristic\n// to determine whether an object is a `DataView`.\n// Also, in cases where the native `DataView` is\n// overridden we can't rely on the tag itself.\nfunction alternateIsDataView(obj) {\n return obj != null && isFunction(obj.getInt8) && isArrayBuffer(obj.buffer);\n}\n\nexport default (hasDataViewBug ? alternateIsDataView : isDataView);\n","import { nativeIsArray } from './_setup.js';\nimport tagTester from './_tagTester.js';\n\n// Is a given value an array?\n// Delegates to ECMA5's native `Array.isArray`.\nexport default nativeIsArray || tagTester('Array');\n","import { hasOwnProperty } from './_setup.js';\n\n// Internal function to check whether `key` is an own property name of `obj`.\nexport default function has(obj, key) {\n return obj != null && hasOwnProperty.call(obj, key);\n}\n","import tagTester from './_tagTester.js';\nimport has from './_has.js';\n\nvar isArguments = tagTester('Arguments');\n\n// Define a fallback version of the method in browsers (ahem, IE < 9), where\n// there isn't any inspectable \"Arguments\" type.\n(function() {\n if (!isArguments(arguments)) {\n isArguments = function(obj) {\n return has(obj, 'callee');\n };\n }\n}());\n\nexport default isArguments;\n","import { _isFinite } from './_setup.js';\nimport isSymbol from './isSymbol.js';\n\n// Is a given object a finite number?\nexport default function isFinite(obj) {\n return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));\n}\n","import { _isNaN } from './_setup.js';\nimport isNumber from './isNumber.js';\n\n// Is the given value `NaN`?\nexport default function isNaN(obj) {\n return isNumber(obj) && _isNaN(obj);\n}\n","// Predicate-generating function. Often useful outside of Underscore.\nexport default function constant(value) {\n return function() {\n return value;\n };\n}\n","import { MAX_ARRAY_INDEX } from './_setup.js';\n\n// Common internal logic for `isArrayLike` and `isBufferLike`.\nexport default function createSizePropertyCheck(getSizeProperty) {\n return function(collection) {\n var sizeProperty = getSizeProperty(collection);\n return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;\n }\n}\n","// Internal helper to generate a function to obtain property `key` from `obj`.\nexport default function shallowProperty(key) {\n return function(obj) {\n return obj == null ? void 0 : obj[key];\n };\n}\n","import shallowProperty from './_shallowProperty.js';\n\n// Internal helper to obtain the `byteLength` property of an object.\nexport default shallowProperty('byteLength');\n","import createSizePropertyCheck from './_createSizePropertyCheck.js';\nimport getByteLength from './_getByteLength.js';\n\n// Internal helper to determine whether we should spend extensive checks against\n// `ArrayBuffer` et al.\nexport default createSizePropertyCheck(getByteLength);\n","import { supportsArrayBuffer, nativeIsView, toString } from './_setup.js';\nimport isDataView from './isDataView.js';\nimport constant from './constant.js';\nimport isBufferLike from './_isBufferLike.js';\n\n// Is a given value a typed array?\nvar typedArrayPattern = /\\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\\]/;\nfunction isTypedArray(obj) {\n // `ArrayBuffer.isView` is the most future-proof, so use it when available.\n // Otherwise, fall back on the above regular expression.\n return nativeIsView ? (nativeIsView(obj) && !isDataView(obj)) :\n isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));\n}\n\nexport default supportsArrayBuffer ? isTypedArray : constant(false);\n","import shallowProperty from './_shallowProperty.js';\n\n// Internal helper to obtain the `length` property of an object.\nexport default shallowProperty('length');\n","import { nonEnumerableProps, ObjProto } from './_setup.js';\nimport isFunction from './isFunction.js';\nimport has from './_has.js';\n\n// Internal helper to create a simple lookup structure.\n// `collectNonEnumProps` used to depend on `_.contains`, but this led to\n// circular imports. `emulatedSet` is a one-off solution that only works for\n// arrays of strings.\nfunction emulatedSet(keys) {\n var hash = {};\n for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;\n return {\n contains: function(key) { return hash[key] === true; },\n push: function(key) {\n hash[key] = true;\n return keys.push(key);\n }\n };\n}\n\n// Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't\n// be iterated by `for key in ...` and thus missed. Extends `keys` in place if\n// needed.\nexport default function collectNonEnumProps(obj, keys) {\n keys = emulatedSet(keys);\n var nonEnumIdx = nonEnumerableProps.length;\n var constructor = obj.constructor;\n var proto = (isFunction(constructor) && constructor.prototype) || ObjProto;\n\n // Constructor is a special case.\n var prop = 'constructor';\n if (has(obj, prop) && !keys.contains(prop)) keys.push(prop);\n\n while (nonEnumIdx--) {\n prop = nonEnumerableProps[nonEnumIdx];\n if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {\n keys.push(prop);\n }\n }\n}\n","import isObject from './isObject.js';\nimport { nativeKeys, hasEnumBug } from './_setup.js';\nimport has from './_has.js';\nimport collectNonEnumProps from './_collectNonEnumProps.js';\n\n// Retrieve the names of an object's own properties.\n// Delegates to **ECMAScript 5**'s native `Object.keys`.\nexport default function keys(obj) {\n if (!isObject(obj)) return [];\n if (nativeKeys) return nativeKeys(obj);\n var keys = [];\n for (var key in obj) if (has(obj, key)) keys.push(key);\n // Ahem, IE < 9.\n if (hasEnumBug) collectNonEnumProps(obj, keys);\n return keys;\n}\n","import getLength from './_getLength.js';\nimport isArray from './isArray.js';\nimport isString from './isString.js';\nimport isArguments from './isArguments.js';\nimport keys from './keys.js';\n\n// Is a given array, string, or object empty?\n// An \"empty\" object has no enumerable own-properties.\nexport default function isEmpty(obj) {\n if (obj == null) return true;\n // Skip the more expensive `toString`-based type checks if `obj` has no\n // `.length`.\n var length = getLength(obj);\n if (typeof length == 'number' && (\n isArray(obj) || isString(obj) || isArguments(obj)\n )) return length === 0;\n return getLength(keys(obj)) === 0;\n}\n","import keys from './keys.js';\n\n// Returns whether an object has a given set of `key:value` pairs.\nexport default function isMatch(object, attrs) {\n var _keys = keys(attrs), length = _keys.length;\n if (object == null) return !length;\n var obj = Object(object);\n for (var i = 0; i < length; i++) {\n var key = _keys[i];\n if (attrs[key] !== obj[key] || !(key in obj)) return false;\n }\n return true;\n}\n","import { VERSION } from './_setup.js';\n\n// If Underscore is called as a function, it returns a wrapped object that can\n// be used OO-style. This wrapper holds altered versions of all functions added\n// through `_.mixin`. Wrapped objects may be chained.\nexport default function _(obj) {\n if (obj instanceof _) return obj;\n if (!(this instanceof _)) return new _(obj);\n this._wrapped = obj;\n}\n\n_.VERSION = VERSION;\n\n// Extracts the result from a wrapped and chained object.\n_.prototype.value = function() {\n return this._wrapped;\n};\n\n// Provide unwrapping proxies for some methods used in engine operations\n// such as arithmetic and JSON stringification.\n_.prototype.valueOf = _.prototype.toJSON = _.prototype.value;\n\n_.prototype.toString = function() {\n return String(this._wrapped);\n};\n","import getByteLength from './_getByteLength.js';\n\n// Internal function to wrap or shallow-copy an ArrayBuffer,\n// typed array or DataView to a new view, reusing the buffer.\nexport default function toBufferView(bufferSource) {\n return new Uint8Array(\n bufferSource.buffer || bufferSource,\n bufferSource.byteOffset || 0,\n getByteLength(bufferSource)\n );\n}\n","import _ from './underscore.js';\nimport { toString, SymbolProto } from './_setup.js';\nimport getByteLength from './_getByteLength.js';\nimport isTypedArray from './isTypedArray.js';\nimport isFunction from './isFunction.js';\nimport { hasDataViewBug } from './_stringTagBug.js';\nimport isDataView from './isDataView.js';\nimport keys from './keys.js';\nimport has from './_has.js';\nimport toBufferView from './_toBufferView.js';\n\n// We use this string twice, so give it a name for minification.\nvar tagDataView = '[object DataView]';\n\n// Internal recursive comparison function for `_.isEqual`.\nfunction eq(a, b, aStack, bStack) {\n // Identical objects are equal. `0 === -0`, but they aren't identical.\n // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).\n if (a === b) return a !== 0 || 1 / a === 1 / b;\n // `null` or `undefined` only equal to itself (strict comparison).\n if (a == null || b == null) return false;\n // `NaN`s are equivalent, but non-reflexive.\n if (a !== a) return b !== b;\n // Exhaust primitive checks\n var type = typeof a;\n if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;\n return deepEq(a, b, aStack, bStack);\n}\n\n// Internal recursive comparison function for `_.isEqual`.\nfunction deepEq(a, b, aStack, bStack) {\n // Unwrap any wrapped objects.\n if (a instanceof _) a = a._wrapped;\n if (b instanceof _) b = b._wrapped;\n // Compare `[[Class]]` names.\n var className = toString.call(a);\n if (className !== toString.call(b)) return false;\n // Work around a bug in IE 10 - Edge 13.\n if (hasDataViewBug && className == '[object Object]' && isDataView(a)) {\n if (!isDataView(b)) return false;\n className = tagDataView;\n }\n switch (className) {\n // These types are compared by value.\n case '[object RegExp]':\n // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')\n case '[object String]':\n // Primitives and their corresponding object wrappers are equivalent; thus, `\"5\"` is\n // equivalent to `new String(\"5\")`.\n return '' + a === '' + b;\n case '[object Number]':\n // `NaN`s are equivalent, but non-reflexive.\n // Object(NaN) is equivalent to NaN.\n if (+a !== +a) return +b !== +b;\n // An `egal` comparison is performed for other numeric values.\n return +a === 0 ? 1 / +a === 1 / b : +a === +b;\n case '[object Date]':\n case '[object Boolean]':\n // Coerce dates and booleans to numeric primitive values. Dates are compared by their\n // millisecond representations. Note that invalid dates with millisecond representations\n // of `NaN` are not equivalent.\n return +a === +b;\n case '[object Symbol]':\n return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);\n case '[object ArrayBuffer]':\n case tagDataView:\n // Coerce to typed array so we can fall through.\n return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);\n }\n\n var areArrays = className === '[object Array]';\n if (!areArrays && isTypedArray(a)) {\n var byteLength = getByteLength(a);\n if (byteLength !== getByteLength(b)) return false;\n if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;\n areArrays = true;\n }\n if (!areArrays) {\n if (typeof a != 'object' || typeof b != 'object') return false;\n\n // Objects with different constructors are not equivalent, but `Object`s or `Array`s\n // from different frames are.\n var aCtor = a.constructor, bCtor = b.constructor;\n if (aCtor !== bCtor && !(isFunction(aCtor) && aCtor instanceof aCtor &&\n isFunction(bCtor) && bCtor instanceof bCtor)\n && ('constructor' in a && 'constructor' in b)) {\n return false;\n }\n }\n // Assume equality for cyclic structures. The algorithm for detecting cyclic\n // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.\n\n // Initializing stack of traversed objects.\n // It's done here since we only need them for objects and arrays comparison.\n aStack = aStack || [];\n bStack = bStack || [];\n var length = aStack.length;\n while (length--) {\n // Linear search. Performance is inversely proportional to the number of\n // unique nested structures.\n if (aStack[length] === a) return bStack[length] === b;\n }\n\n // Add the first object to the stack of traversed objects.\n aStack.push(a);\n bStack.push(b);\n\n // Recursively compare objects and arrays.\n if (areArrays) {\n // Compare array lengths to determine if a deep comparison is necessary.\n length = a.length;\n if (length !== b.length) return false;\n // Deep compare the contents, ignoring non-numeric properties.\n while (length--) {\n if (!eq(a[length], b[length], aStack, bStack)) return false;\n }\n } else {\n // Deep compare objects.\n var _keys = keys(a), key;\n length = _keys.length;\n // Ensure that both objects contain the same number of properties before comparing deep equality.\n if (keys(b).length !== length) return false;\n while (length--) {\n // Deep compare each member\n key = _keys[length];\n if (!(has(b, key) && eq(a[key], b[key], aStack, bStack))) return false;\n }\n }\n // Remove the first object from the stack of traversed objects.\n aStack.pop();\n bStack.pop();\n return true;\n}\n\n// Perform a deep comparison to check if two objects are equal.\nexport default function isEqual(a, b) {\n return eq(a, b);\n}\n","import isObject from './isObject.js';\nimport { hasEnumBug } from './_setup.js';\nimport collectNonEnumProps from './_collectNonEnumProps.js';\n\n// Retrieve all the enumerable property names of an object.\nexport default function allKeys(obj) {\n if (!isObject(obj)) return [];\n var keys = [];\n for (var key in obj) keys.push(key);\n // Ahem, IE < 9.\n if (hasEnumBug) collectNonEnumProps(obj, keys);\n return keys;\n}\n","import getLength from './_getLength.js';\nimport isFunction from './isFunction.js';\nimport allKeys from './allKeys.js';\n\n// Since the regular `Object.prototype.toString` type tests don't work for\n// some types in IE 11, we use a fingerprinting heuristic instead, based\n// on the methods. It's not great, but it's the best we got.\n// The fingerprint method lists are defined below.\nexport function ie11fingerprint(methods) {\n var length = getLength(methods);\n return function(obj) {\n if (obj == null) return false;\n // `Map`, `WeakMap` and `Set` have no enumerable keys.\n var keys = allKeys(obj);\n if (getLength(keys)) return false;\n for (var i = 0; i < length; i++) {\n if (!isFunction(obj[methods[i]])) return false;\n }\n // If we are testing against `WeakMap`, we need to ensure that\n // `obj` doesn't have a `forEach` method in order to distinguish\n // it from a regular `Map`.\n return methods !== weakMapMethods || !isFunction(obj[forEachName]);\n };\n}\n\n// In the interest of compact minification, we write\n// each string in the fingerprints only once.\nvar forEachName = 'forEach',\n hasName = 'has',\n commonInit = ['clear', 'delete'],\n mapTail = ['get', hasName, 'set'];\n\n// `Map`, `WeakMap` and `Set` each have slightly different\n// combinations of the above sublists.\nexport var mapMethods = commonInit.concat(forEachName, mapTail),\n weakMapMethods = commonInit.concat(mapTail),\n setMethods = ['add'].concat(commonInit, forEachName, hasName);\n","import tagTester from './_tagTester.js';\nimport { isIE11 } from './_stringTagBug.js';\nimport { ie11fingerprint, mapMethods } from './_methodFingerprint.js';\n\nexport default isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');\n","import tagTester from './_tagTester.js';\nimport { isIE11 } from './_stringTagBug.js';\nimport { ie11fingerprint, weakMapMethods } from './_methodFingerprint.js';\n\nexport default isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');\n","import tagTester from './_tagTester.js';\nimport { isIE11 } from './_stringTagBug.js';\nimport { ie11fingerprint, setMethods } from './_methodFingerprint.js';\n\nexport default isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');\n","import tagTester from './_tagTester.js';\n\nexport default tagTester('WeakSet');\n","import keys from './keys.js';\n\n// Retrieve the values of an object's properties.\nexport default function values(obj) {\n var _keys = keys(obj);\n var length = _keys.length;\n var values = Array(length);\n for (var i = 0; i < length; i++) {\n values[i] = obj[_keys[i]];\n }\n return values;\n}\n","import keys from './keys.js';\n\n// Convert an object into a list of `[key, value]` pairs.\n// The opposite of `_.object` with one argument.\nexport default function pairs(obj) {\n var _keys = keys(obj);\n var length = _keys.length;\n var pairs = Array(length);\n for (var i = 0; i < length; i++) {\n pairs[i] = [_keys[i], obj[_keys[i]]];\n }\n return pairs;\n}\n","import keys from './keys.js';\n\n// Invert the keys and values of an object. The values must be serializable.\nexport default function invert(obj) {\n var result = {};\n var _keys = keys(obj);\n for (var i = 0, length = _keys.length; i < length; i++) {\n result[obj[_keys[i]]] = _keys[i];\n }\n return result;\n}\n","import isFunction from './isFunction.js';\n\n// Return a sorted list of the function names available on the object.\nexport default function functions(obj) {\n var names = [];\n for (var key in obj) {\n if (isFunction(obj[key])) names.push(key);\n }\n return names.sort();\n}\n","// An internal function for creating assigner functions.\nexport default function createAssigner(keysFunc, defaults) {\n return function(obj) {\n var length = arguments.length;\n if (defaults) obj = Object(obj);\n if (length < 2 || obj == null) return obj;\n for (var index = 1; index < length; index++) {\n var source = arguments[index],\n keys = keysFunc(source),\n l = keys.length;\n for (var i = 0; i < l; i++) {\n var key = keys[i];\n if (!defaults || obj[key] === void 0) obj[key] = source[key];\n }\n }\n return obj;\n };\n}\n","import createAssigner from './_createAssigner.js';\nimport allKeys from './allKeys.js';\n\n// Extend a given object with all the properties in passed-in object(s).\nexport default createAssigner(allKeys);\n","import createAssigner from './_createAssigner.js';\nimport keys from './keys.js';\n\n// Assigns a given object with all the own properties in the passed-in\n// object(s).\n// (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)\nexport default createAssigner(keys);\n","import createAssigner from './_createAssigner.js';\nimport allKeys from './allKeys.js';\n\n// Fill in a given object with default properties.\nexport default createAssigner(allKeys, true);\n","import isObject from './isObject.js';\nimport { nativeCreate } from './_setup.js';\n\n// Create a naked function reference for surrogate-prototype-swapping.\nfunction ctor() {\n return function(){};\n}\n\n// An internal function for creating a new object that inherits from another.\nexport default function baseCreate(prototype) {\n if (!isObject(prototype)) return {};\n if (nativeCreate) return nativeCreate(prototype);\n var Ctor = ctor();\n Ctor.prototype = prototype;\n var result = new Ctor;\n Ctor.prototype = null;\n return result;\n}\n","import baseCreate from './_baseCreate.js';\nimport extendOwn from './extendOwn.js';\n\n// Creates an object that inherits from the given prototype object.\n// If additional properties are provided then they will be added to the\n// created object.\nexport default function create(prototype, props) {\n var result = baseCreate(prototype);\n if (props) extendOwn(result, props);\n return result;\n}\n","import isObject from './isObject.js';\nimport isArray from './isArray.js';\nimport extend from './extend.js';\n\n// Create a (shallow-cloned) duplicate of an object.\nexport default function clone(obj) {\n if (!isObject(obj)) return obj;\n return isArray(obj) ? obj.slice() : extend({}, obj);\n}\n","// Invokes `interceptor` with the `obj` and then returns `obj`.\n// The primary purpose of this method is to \"tap into\" a method chain, in\n// order to perform operations on intermediate results within the chain.\nexport default function tap(obj, interceptor) {\n interceptor(obj);\n return obj;\n}\n","import _ from './underscore.js';\nimport isArray from './isArray.js';\n\n// Normalize a (deep) property `path` to array.\n// Like `_.iteratee`, this function can be customized.\nexport default function toPath(path) {\n return isArray(path) ? path : [path];\n}\n_.toPath = toPath;\n","import _ from './underscore.js';\nimport './toPath.js';\n\n// Internal wrapper for `_.toPath` to enable minification.\n// Similar to `cb` for `_.iteratee`.\nexport default function toPath(path) {\n return _.toPath(path);\n}\n","// Internal function to obtain a nested property in `obj` along `path`.\nexport default function deepGet(obj, path) {\n var length = path.length;\n for (var i = 0; i < length; i++) {\n if (obj == null) return void 0;\n obj = obj[path[i]];\n }\n return length ? obj : void 0;\n}\n","import toPath from './_toPath.js';\nimport deepGet from './_deepGet.js';\nimport isUndefined from './isUndefined.js';\n\n// Get the value of the (deep) property on `path` from `object`.\n// If any property in `path` does not exist or if the value is\n// `undefined`, return `defaultValue` instead.\n// The `path` is normalized through `_.toPath`.\nexport default function get(object, path, defaultValue) {\n var value = deepGet(object, toPath(path));\n return isUndefined(value) ? defaultValue : value;\n}\n","import _has from './_has.js';\nimport toPath from './_toPath.js';\n\n// Shortcut function for checking if an object has a given property directly on\n// itself (in other words, not on a prototype). Unlike the internal `has`\n// function, this public version can also traverse nested properties.\nexport default function has(obj, path) {\n path = toPath(path);\n var length = path.length;\n for (var i = 0; i < length; i++) {\n var key = path[i];\n if (!_has(obj, key)) return false;\n obj = obj[key];\n }\n return !!length;\n}\n","// Keep the identity function around for default iteratees.\nexport default function identity(value) {\n return value;\n}\n","import extendOwn from './extendOwn.js';\nimport isMatch from './isMatch.js';\n\n// Returns a predicate for checking whether an object has a given set of\n// `key:value` pairs.\nexport default function matcher(attrs) {\n attrs = extendOwn({}, attrs);\n return function(obj) {\n return isMatch(obj, attrs);\n };\n}\n","import deepGet from './_deepGet.js';\nimport toPath from './_toPath.js';\n\n// Creates a function that, when passed an object, will traverse that object’s\n// properties down the given `path`, specified as an array of keys or indices.\nexport default function property(path) {\n path = toPath(path);\n return function(obj) {\n return deepGet(obj, path);\n };\n}\n","// Internal function that returns an efficient (for current engines) version\n// of the passed-in callback, to be repeatedly applied in other Underscore\n// functions.\nexport default function optimizeCb(func, context, argCount) {\n if (context === void 0) return func;\n switch (argCount == null ? 3 : argCount) {\n case 1: return function(value) {\n return func.call(context, value);\n };\n // The 2-argument case is omitted because we’re not using it.\n case 3: return function(value, index, collection) {\n return func.call(context, value, index, collection);\n };\n case 4: return function(accumulator, value, index, collection) {\n return func.call(context, accumulator, value, index, collection);\n };\n }\n return function() {\n return func.apply(context, arguments);\n };\n}\n","import identity from './identity.js';\nimport isFunction from './isFunction.js';\nimport isObject from './isObject.js';\nimport isArray from './isArray.js';\nimport matcher from './matcher.js';\nimport property from './property.js';\nimport optimizeCb from './_optimizeCb.js';\n\n// An internal function to generate callbacks that can be applied to each\n// element in a collection, returning the desired result — either `_.identity`,\n// an arbitrary callback, a property matcher, or a property accessor.\nexport default function baseIteratee(value, context, argCount) {\n if (value == null) return identity;\n if (isFunction(value)) return optimizeCb(value, context, argCount);\n if (isObject(value) && !isArray(value)) return matcher(value);\n return property(value);\n}\n","import _ from './underscore.js';\nimport baseIteratee from './_baseIteratee.js';\n\n// External wrapper for our callback generator. Users may customize\n// `_.iteratee` if they want additional predicate/iteratee shorthand styles.\n// This abstraction hides the internal-only `argCount` argument.\nexport default function iteratee(value, context) {\n return baseIteratee(value, context, Infinity);\n}\n_.iteratee = iteratee;\n","import _ from './underscore.js';\nimport baseIteratee from './_baseIteratee.js';\nimport iteratee from './iteratee.js';\n\n// The function we call internally to generate a callback. It invokes\n// `_.iteratee` if overridden, otherwise `baseIteratee`.\nexport default function cb(value, context, argCount) {\n if (_.iteratee !== iteratee) return _.iteratee(value, context);\n return baseIteratee(value, context, argCount);\n}\n","import cb from './_cb.js';\nimport keys from './keys.js';\n\n// Returns the results of applying the `iteratee` to each element of `obj`.\n// In contrast to `_.map` it returns an object.\nexport default function mapObject(obj, iteratee, context) {\n iteratee = cb(iteratee, context);\n var _keys = keys(obj),\n length = _keys.length,\n results = {};\n for (var index = 0; index < length; index++) {\n var currentKey = _keys[index];\n results[currentKey] = iteratee(obj[currentKey], currentKey, obj);\n }\n return results;\n}\n","// Predicate-generating function. Often useful outside of Underscore.\nexport default function noop(){}\n","import noop from './noop.js';\nimport get from './get.js';\n\n// Generates a function for a given object that returns a given property.\nexport default function propertyOf(obj) {\n if (obj == null) return noop;\n return function(path) {\n return get(obj, path);\n };\n}\n","import optimizeCb from './_optimizeCb.js';\n\n// Run a function **n** times.\nexport default function times(n, iteratee, context) {\n var accum = Array(Math.max(0, n));\n iteratee = optimizeCb(iteratee, context, 1);\n for (var i = 0; i < n; i++) accum[i] = iteratee(i);\n return accum;\n}\n","// Return a random integer between `min` and `max` (inclusive).\nexport default function random(min, max) {\n if (max == null) {\n max = min;\n min = 0;\n }\n return min + Math.floor(Math.random() * (max - min + 1));\n}\n","// A (possibly faster) way to get the current timestamp as an integer.\nexport default Date.now || function() {\n return new Date().getTime();\n};\n","import keys from './keys.js';\n\n// Internal helper to generate functions for escaping and unescaping strings\n// to/from HTML interpolation.\nexport default function createEscaper(map) {\n var escaper = function(match) {\n return map[match];\n };\n // Regexes for identifying a key that needs to be escaped.\n var source = '(?:' + keys(map).join('|') + ')';\n var testRegexp = RegExp(source);\n var replaceRegexp = RegExp(source, 'g');\n return function(string) {\n string = string == null ? '' : '' + string;\n return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;\n };\n}\n","// Internal list of HTML entities for escaping.\nexport default {\n '&': '&',\n '<': '<',\n '>': '>',\n '\"': '"',\n \"'\": ''',\n '`': '`'\n};\n","import createEscaper from './_createEscaper.js';\nimport escapeMap from './_escapeMap.js';\n\n// Function for escaping strings to HTML interpolation.\nexport default createEscaper(escapeMap);\n","import createEscaper from './_createEscaper.js';\nimport unescapeMap from './_unescapeMap.js';\n\n// Function for unescaping strings from HTML interpolation.\nexport default createEscaper(unescapeMap);\n","import invert from './invert.js';\nimport escapeMap from './_escapeMap.js';\n\n// Internal list of HTML entities for unescaping.\nexport default invert(escapeMap);\n","import _ from './underscore.js';\n\n// By default, Underscore uses ERB-style template delimiters. Change the\n// following template settings to use alternative delimiters.\nexport default _.templateSettings = {\n evaluate: /<%([\\s\\S]+?)%>/g,\n interpolate: /<%=([\\s\\S]+?)%>/g,\n escape: /<%-([\\s\\S]+?)%>/g\n};\n","import defaults from './defaults.js';\nimport _ from './underscore.js';\nimport './templateSettings.js';\n\n// When customizing `_.templateSettings`, if you don't want to define an\n// interpolation, evaluation or escaping regex, we need one that is\n// guaranteed not to match.\nvar noMatch = /(.)^/;\n\n// Certain characters need to be escaped so that they can be put into a\n// string literal.\nvar escapes = {\n \"'\": \"'\",\n '\\\\': '\\\\',\n '\\r': 'r',\n '\\n': 'n',\n '\\u2028': 'u2028',\n '\\u2029': 'u2029'\n};\n\nvar escapeRegExp = /\\\\|'|\\r|\\n|\\u2028|\\u2029/g;\n\nfunction escapeChar(match) {\n return '\\\\' + escapes[match];\n}\n\n// In order to prevent third-party code injection through\n// `_.templateSettings.variable`, we test it against the following regular\n// expression. It is intentionally a bit more liberal than just matching valid\n// identifiers, but still prevents possible loopholes through defaults or\n// destructuring assignment.\nvar bareIdentifier = /^\\s*(\\w|\\$)+\\s*$/;\n\n// JavaScript micro-templating, similar to John Resig's implementation.\n// Underscore templating handles arbitrary delimiters, preserves whitespace,\n// and correctly escapes quotes within interpolated code.\n// NB: `oldSettings` only exists for backwards compatibility.\nexport default function template(text, settings, oldSettings) {\n if (!settings && oldSettings) settings = oldSettings;\n settings = defaults({}, settings, _.templateSettings);\n\n // Combine delimiters into one regular expression via alternation.\n var matcher = RegExp([\n (settings.escape || noMatch).source,\n (settings.interpolate || noMatch).source,\n (settings.evaluate || noMatch).source\n ].join('|') + '|$', 'g');\n\n // Compile the template source, escaping string literals appropriately.\n var index = 0;\n var source = \"__p+='\";\n text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {\n source += text.slice(index, offset).replace(escapeRegExp, escapeChar);\n index = offset + match.length;\n\n if (escape) {\n source += \"'+\\n((__t=(\" + escape + \"))==null?'':_.escape(__t))+\\n'\";\n } else if (interpolate) {\n source += \"'+\\n((__t=(\" + interpolate + \"))==null?'':__t)+\\n'\";\n } else if (evaluate) {\n source += \"';\\n\" + evaluate + \"\\n__p+='\";\n }\n\n // Adobe VMs need the match returned to produce the correct offset.\n return match;\n });\n source += \"';\\n\";\n\n var argument = settings.variable;\n if (argument) {\n // Insure against third-party code injection. (CVE-2021-23358)\n if (!bareIdentifier.test(argument)) throw new Error(\n 'variable is not a bare identifier: ' + argument\n );\n } else {\n // If a variable is not specified, place data values in local scope.\n source = 'with(obj||{}){\\n' + source + '}\\n';\n argument = 'obj';\n }\n\n source = \"var __t,__p='',__j=Array.prototype.join,\" +\n \"print=function(){__p+=__j.call(arguments,'');};\\n\" +\n source + 'return __p;\\n';\n\n var render;\n try {\n render = new Function(argument, '_', source);\n } catch (e) {\n e.source = source;\n throw e;\n }\n\n var template = function(data) {\n return render.call(this, data, _);\n };\n\n // Provide the compiled source as a convenience for precompilation.\n template.source = 'function(' + argument + '){\\n' + source + '}';\n\n return template;\n}\n","import isFunction from './isFunction.js';\nimport toPath from './_toPath.js';\n\n// Traverses the children of `obj` along `path`. If a child is a function, it\n// is invoked with its parent as context. Returns the value of the final\n// child, or `fallback` if any child is undefined.\nexport default function result(obj, path, fallback) {\n path = toPath(path);\n var length = path.length;\n if (!length) {\n return isFunction(fallback) ? fallback.call(obj) : fallback;\n }\n for (var i = 0; i < length; i++) {\n var prop = obj == null ? void 0 : obj[path[i]];\n if (prop === void 0) {\n prop = fallback;\n i = length; // Ensure we don't continue iterating.\n }\n obj = isFunction(prop) ? prop.call(obj) : prop;\n }\n return obj;\n}\n","// Generate a unique integer id (unique within the entire client session).\n// Useful for temporary DOM ids.\nvar idCounter = 0;\nexport default function uniqueId(prefix) {\n var id = ++idCounter + '';\n return prefix ? prefix + id : id;\n}\n","import _ from './underscore.js';\n\n// Start chaining a wrapped Underscore object.\nexport default function chain(obj) {\n var instance = _(obj);\n instance._chain = true;\n return instance;\n}\n","import baseCreate from './_baseCreate.js';\nimport isObject from './isObject.js';\n\n// Internal function to execute `sourceFunc` bound to `context` with optional\n// `args`. Determines whether to execute a function as a constructor or as a\n// normal function.\nexport default function executeBound(sourceFunc, boundFunc, context, callingContext, args) {\n if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);\n var self = baseCreate(sourceFunc.prototype);\n var result = sourceFunc.apply(self, args);\n if (isObject(result)) return result;\n return self;\n}\n","import restArguments from './restArguments.js';\nimport executeBound from './_executeBound.js';\nimport _ from './underscore.js';\n\n// Partially apply a function by creating a version that has had some of its\n// arguments pre-filled, without changing its dynamic `this` context. `_` acts\n// as a placeholder by default, allowing any combination of arguments to be\n// pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.\nvar partial = restArguments(function(func, boundArgs) {\n var placeholder = partial.placeholder;\n var bound = function() {\n var position = 0, length = boundArgs.length;\n var args = Array(length);\n for (var i = 0; i < length; i++) {\n args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];\n }\n while (position < arguments.length) args.push(arguments[position++]);\n return executeBound(func, bound, this, this, args);\n };\n return bound;\n});\n\npartial.placeholder = _;\nexport default partial;\n","import restArguments from './restArguments.js';\nimport isFunction from './isFunction.js';\nimport executeBound from './_executeBound.js';\n\n// Create a function bound to a given object (assigning `this`, and arguments,\n// optionally).\nexport default restArguments(function(func, context, args) {\n if (!isFunction(func)) throw new TypeError('Bind must be called on a function');\n var bound = restArguments(function(callArgs) {\n return executeBound(func, bound, context, this, args.concat(callArgs));\n });\n return bound;\n});\n","import createSizePropertyCheck from './_createSizePropertyCheck.js';\nimport getLength from './_getLength.js';\n\n// Internal helper for collection methods to determine whether a collection\n// should be iterated as an array or as an object.\n// Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength\n// Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094\nexport default createSizePropertyCheck(getLength);\n","import getLength from './_getLength.js';\nimport isArrayLike from './_isArrayLike.js';\nimport isArray from './isArray.js';\nimport isArguments from './isArguments.js';\n\n// Internal implementation of a recursive `flatten` function.\nexport default function flatten(input, depth, strict, output) {\n output = output || [];\n if (!depth && depth !== 0) {\n depth = Infinity;\n } else if (depth <= 0) {\n return output.concat(input);\n }\n var idx = output.length;\n for (var i = 0, length = getLength(input); i < length; i++) {\n var value = input[i];\n if (isArrayLike(value) && (isArray(value) || isArguments(value))) {\n // Flatten current level of array or arguments object.\n if (depth > 1) {\n flatten(value, depth - 1, strict, output);\n idx = output.length;\n } else {\n var j = 0, len = value.length;\n while (j < len) output[idx++] = value[j++];\n }\n } else if (!strict) {\n output[idx++] = value;\n }\n }\n return output;\n}\n","import restArguments from './restArguments.js';\nimport flatten from './_flatten.js';\nimport bind from './bind.js';\n\n// Bind a number of an object's methods to that object. Remaining arguments\n// are the method names to be bound. Useful for ensuring that all callbacks\n// defined on an object belong to it.\nexport default restArguments(function(obj, keys) {\n keys = flatten(keys, false, false);\n var index = keys.length;\n if (index < 1) throw new Error('bindAll must be passed function names');\n while (index--) {\n var key = keys[index];\n obj[key] = bind(obj[key], obj);\n }\n return obj;\n});\n","import has from './_has.js';\n\n// Memoize an expensive function by storing its results.\nexport default function memoize(func, hasher) {\n var memoize = function(key) {\n var cache = memoize.cache;\n var address = '' + (hasher ? hasher.apply(this, arguments) : key);\n if (!has(cache, address)) cache[address] = func.apply(this, arguments);\n return cache[address];\n };\n memoize.cache = {};\n return memoize;\n}\n","import restArguments from './restArguments.js';\n\n// Delays a function for the given number of milliseconds, and then calls\n// it with the arguments supplied.\nexport default restArguments(function(func, wait, args) {\n return setTimeout(function() {\n return func.apply(null, args);\n }, wait);\n});\n","import partial from './partial.js';\nimport delay from './delay.js';\nimport _ from './underscore.js';\n\n// Defers a function, scheduling it to run after the current call stack has\n// cleared.\nexport default partial(delay, _, 1);\n","import now from './now.js';\n\n// Returns a function, that, when invoked, will only be triggered at most once\n// during a given window of time. Normally, the throttled function will run\n// as much as it can, without ever going more than once per `wait` duration;\n// but if you'd like to disable the execution on the leading edge, pass\n// `{leading: false}`. To disable execution on the trailing edge, ditto.\nexport default function throttle(func, wait, options) {\n var timeout, context, args, result;\n var previous = 0;\n if (!options) options = {};\n\n var later = function() {\n previous = options.leading === false ? 0 : now();\n timeout = null;\n result = func.apply(context, args);\n if (!timeout) context = args = null;\n };\n\n var throttled = function() {\n var _now = now();\n if (!previous && options.leading === false) previous = _now;\n var remaining = wait - (_now - previous);\n context = this;\n args = arguments;\n if (remaining <= 0 || remaining > wait) {\n if (timeout) {\n clearTimeout(timeout);\n timeout = null;\n }\n previous = _now;\n result = func.apply(context, args);\n if (!timeout) context = args = null;\n } else if (!timeout && options.trailing !== false) {\n timeout = setTimeout(later, remaining);\n }\n return result;\n };\n\n throttled.cancel = function() {\n clearTimeout(timeout);\n previous = 0;\n timeout = context = args = null;\n };\n\n return throttled;\n}\n","import restArguments from './restArguments.js';\nimport now from './now.js';\n\n// When a sequence of calls of the returned function ends, the argument\n// function is triggered. The end of a sequence is defined by the `wait`\n// parameter. If `immediate` is passed, the argument function will be\n// triggered at the beginning of the sequence instead of at the end.\nexport default function debounce(func, wait, immediate) {\n var timeout, previous, args, result, context;\n\n var later = function() {\n var passed = now() - previous;\n if (wait > passed) {\n timeout = setTimeout(later, wait - passed);\n } else {\n timeout = null;\n if (!immediate) result = func.apply(context, args);\n // This check is needed because `func` can recursively invoke `debounced`.\n if (!timeout) args = context = null;\n }\n };\n\n var debounced = restArguments(function(_args) {\n context = this;\n args = _args;\n previous = now();\n if (!timeout) {\n timeout = setTimeout(later, wait);\n if (immediate) result = func.apply(context, args);\n }\n return result;\n });\n\n debounced.cancel = function() {\n clearTimeout(timeout);\n timeout = args = context = null;\n };\n\n return debounced;\n}\n","import partial from './partial.js';\n\n// Returns the first function passed as an argument to the second,\n// allowing you to adjust arguments, run code before and after, and\n// conditionally execute the original function.\nexport default function wrap(func, wrapper) {\n return partial(wrapper, func);\n}\n","// Returns a negated version of the passed-in predicate.\nexport default function negate(predicate) {\n return function() {\n return !predicate.apply(this, arguments);\n };\n}\n","// Returns a function that is the composition of a list of functions, each\n// consuming the return value of the function that follows.\nexport default function compose() {\n var args = arguments;\n var start = args.length - 1;\n return function() {\n var i = start;\n var result = args[start].apply(this, arguments);\n while (i--) result = args[i].call(this, result);\n return result;\n };\n}\n","// Returns a function that will only be executed on and after the Nth call.\nexport default function after(times, func) {\n return function() {\n if (--times < 1) {\n return func.apply(this, arguments);\n }\n };\n}\n","// Returns a function that will only be executed up to (but not including) the\n// Nth call.\nexport default function before(times, func) {\n var memo;\n return function() {\n if (--times > 0) {\n memo = func.apply(this, arguments);\n }\n if (times <= 1) func = null;\n return memo;\n };\n}\n","import partial from './partial.js';\nimport before from './before.js';\n\n// Returns a function that will be executed at most one time, no matter how\n// often you call it. Useful for lazy initialization.\nexport default partial(before, 2);\n","import cb from './_cb.js';\nimport keys from './keys.js';\n\n// Returns the first key on an object that passes a truth test.\nexport default function findKey(obj, predicate, context) {\n predicate = cb(predicate, context);\n var _keys = keys(obj), key;\n for (var i = 0, length = _keys.length; i < length; i++) {\n key = _keys[i];\n if (predicate(obj[key], key, obj)) return key;\n }\n}\n","import cb from './_cb.js';\nimport getLength from './_getLength.js';\n\n// Internal function to generate `_.findIndex` and `_.findLastIndex`.\nexport default function createPredicateIndexFinder(dir) {\n return function(array, predicate, context) {\n predicate = cb(predicate, context);\n var length = getLength(array);\n var index = dir > 0 ? 0 : length - 1;\n for (; index >= 0 && index < length; index += dir) {\n if (predicate(array[index], index, array)) return index;\n }\n return -1;\n };\n}\n","import createPredicateIndexFinder from './_createPredicateIndexFinder.js';\n\n// Returns the first index on an array-like that passes a truth test.\nexport default createPredicateIndexFinder(1);\n","import createPredicateIndexFinder from './_createPredicateIndexFinder.js';\n\n// Returns the last index on an array-like that passes a truth test.\nexport default createPredicateIndexFinder(-1);\n","import cb from './_cb.js';\nimport getLength from './_getLength.js';\n\n// Use a comparator function to figure out the smallest index at which\n// an object should be inserted so as to maintain order. Uses binary search.\nexport default function sortedIndex(array, obj, iteratee, context) {\n iteratee = cb(iteratee, context, 1);\n var value = iteratee(obj);\n var low = 0, high = getLength(array);\n while (low < high) {\n var mid = Math.floor((low + high) / 2);\n if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;\n }\n return low;\n}\n","import getLength from './_getLength.js';\nimport { slice } from './_setup.js';\nimport isNaN from './isNaN.js';\n\n// Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.\nexport default function createIndexFinder(dir, predicateFind, sortedIndex) {\n return function(array, item, idx) {\n var i = 0, length = getLength(array);\n if (typeof idx == 'number') {\n if (dir > 0) {\n i = idx >= 0 ? idx : Math.max(idx + length, i);\n } else {\n length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;\n }\n } else if (sortedIndex && idx && length) {\n idx = sortedIndex(array, item);\n return array[idx] === item ? idx : -1;\n }\n if (item !== item) {\n idx = predicateFind(slice.call(array, i, length), isNaN);\n return idx >= 0 ? idx + i : -1;\n }\n for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {\n if (array[idx] === item) return idx;\n }\n return -1;\n };\n}\n","import sortedIndex from './sortedIndex.js';\nimport findIndex from './findIndex.js';\nimport createIndexFinder from './_createIndexFinder.js';\n\n// Return the position of the first occurrence of an item in an array,\n// or -1 if the item is not included in the array.\n// If the array is large and already in sort order, pass `true`\n// for **isSorted** to use binary search.\nexport default createIndexFinder(1, findIndex, sortedIndex);\n","import findLastIndex from './findLastIndex.js';\nimport createIndexFinder from './_createIndexFinder.js';\n\n// Return the position of the last occurrence of an item in an array,\n// or -1 if the item is not included in the array.\nexport default createIndexFinder(-1, findLastIndex);\n","import isArrayLike from './_isArrayLike.js';\nimport findIndex from './findIndex.js';\nimport findKey from './findKey.js';\n\n// Return the first value which passes a truth test.\nexport default function find(obj, predicate, context) {\n var keyFinder = isArrayLike(obj) ? findIndex : findKey;\n var key = keyFinder(obj, predicate, context);\n if (key !== void 0 && key !== -1) return obj[key];\n}\n","import find from './find.js';\nimport matcher from './matcher.js';\n\n// Convenience version of a common use case of `_.find`: getting the first\n// object containing specific `key:value` pairs.\nexport default function findWhere(obj, attrs) {\n return find(obj, matcher(attrs));\n}\n","import optimizeCb from './_optimizeCb.js';\nimport isArrayLike from './_isArrayLike.js';\nimport keys from './keys.js';\n\n// The cornerstone for collection functions, an `each`\n// implementation, aka `forEach`.\n// Handles raw objects in addition to array-likes. Treats all\n// sparse array-likes as if they were dense.\nexport default function each(obj, iteratee, context) {\n iteratee = optimizeCb(iteratee, context);\n var i, length;\n if (isArrayLike(obj)) {\n for (i = 0, length = obj.length; i < length; i++) {\n iteratee(obj[i], i, obj);\n }\n } else {\n var _keys = keys(obj);\n for (i = 0, length = _keys.length; i < length; i++) {\n iteratee(obj[_keys[i]], _keys[i], obj);\n }\n }\n return obj;\n}\n","import cb from './_cb.js';\nimport isArrayLike from './_isArrayLike.js';\nimport keys from './keys.js';\n\n// Return the results of applying the iteratee to each element.\nexport default function map(obj, iteratee, context) {\n iteratee = cb(iteratee, context);\n var _keys = !isArrayLike(obj) && keys(obj),\n length = (_keys || obj).length,\n results = Array(length);\n for (var index = 0; index < length; index++) {\n var currentKey = _keys ? _keys[index] : index;\n results[index] = iteratee(obj[currentKey], currentKey, obj);\n }\n return results;\n}\n","import isArrayLike from './_isArrayLike.js';\nimport keys from './keys.js';\nimport optimizeCb from './_optimizeCb.js';\n\n// Internal helper to create a reducing function, iterating left or right.\nexport default function createReduce(dir) {\n // Wrap code that reassigns argument variables in a separate function than\n // the one that accesses `arguments.length` to avoid a perf hit. (#1991)\n var reducer = function(obj, iteratee, memo, initial) {\n var _keys = !isArrayLike(obj) && keys(obj),\n length = (_keys || obj).length,\n index = dir > 0 ? 0 : length - 1;\n if (!initial) {\n memo = obj[_keys ? _keys[index] : index];\n index += dir;\n }\n for (; index >= 0 && index < length; index += dir) {\n var currentKey = _keys ? _keys[index] : index;\n memo = iteratee(memo, obj[currentKey], currentKey, obj);\n }\n return memo;\n };\n\n return function(obj, iteratee, memo, context) {\n var initial = arguments.length >= 3;\n return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);\n };\n}\n","import createReduce from './_createReduce.js';\n\n// **Reduce** builds up a single result from a list of values, aka `inject`,\n// or `foldl`.\nexport default createReduce(1);\n","import createReduce from './_createReduce.js';\n\n// The right-associative version of reduce, also known as `foldr`.\nexport default createReduce(-1);\n","import cb from './_cb.js';\nimport each from './each.js';\n\n// Return all the elements that pass a truth test.\nexport default function filter(obj, predicate, context) {\n var results = [];\n predicate = cb(predicate, context);\n each(obj, function(value, index, list) {\n if (predicate(value, index, list)) results.push(value);\n });\n return results;\n}\n","import filter from './filter.js';\nimport negate from './negate.js';\nimport cb from './_cb.js';\n\n// Return all the elements for which a truth test fails.\nexport default function reject(obj, predicate, context) {\n return filter(obj, negate(cb(predicate)), context);\n}\n","import cb from './_cb.js';\nimport isArrayLike from './_isArrayLike.js';\nimport keys from './keys.js';\n\n// Determine whether all of the elements pass a truth test.\nexport default function every(obj, predicate, context) {\n predicate = cb(predicate, context);\n var _keys = !isArrayLike(obj) && keys(obj),\n length = (_keys || obj).length;\n for (var index = 0; index < length; index++) {\n var currentKey = _keys ? _keys[index] : index;\n if (!predicate(obj[currentKey], currentKey, obj)) return false;\n }\n return true;\n}\n","import cb from './_cb.js';\nimport isArrayLike from './_isArrayLike.js';\nimport keys from './keys.js';\n\n// Determine if at least one element in the object passes a truth test.\nexport default function some(obj, predicate, context) {\n predicate = cb(predicate, context);\n var _keys = !isArrayLike(obj) && keys(obj),\n length = (_keys || obj).length;\n for (var index = 0; index < length; index++) {\n var currentKey = _keys ? _keys[index] : index;\n if (predicate(obj[currentKey], currentKey, obj)) return true;\n }\n return false;\n}\n","import isArrayLike from './_isArrayLike.js';\nimport values from './values.js';\nimport indexOf from './indexOf.js';\n\n// Determine if the array or object contains a given item (using `===`).\nexport default function contains(obj, item, fromIndex, guard) {\n if (!isArrayLike(obj)) obj = values(obj);\n if (typeof fromIndex != 'number' || guard) fromIndex = 0;\n return indexOf(obj, item, fromIndex) >= 0;\n}\n","import restArguments from './restArguments.js';\nimport isFunction from './isFunction.js';\nimport map from './map.js';\nimport deepGet from './_deepGet.js';\nimport toPath from './_toPath.js';\n\n// Invoke a method (with arguments) on every item in a collection.\nexport default restArguments(function(obj, path, args) {\n var contextPath, func;\n if (isFunction(path)) {\n func = path;\n } else {\n path = toPath(path);\n contextPath = path.slice(0, -1);\n path = path[path.length - 1];\n }\n return map(obj, function(context) {\n var method = func;\n if (!method) {\n if (contextPath && contextPath.length) {\n context = deepGet(context, contextPath);\n }\n if (context == null) return void 0;\n method = context[path];\n }\n return method == null ? method : method.apply(context, args);\n });\n});\n","import map from './map.js';\nimport property from './property.js';\n\n// Convenience version of a common use case of `_.map`: fetching a property.\nexport default function pluck(obj, key) {\n return map(obj, property(key));\n}\n","import filter from './filter.js';\nimport matcher from './matcher.js';\n\n// Convenience version of a common use case of `_.filter`: selecting only\n// objects containing specific `key:value` pairs.\nexport default function where(obj, attrs) {\n return filter(obj, matcher(attrs));\n}\n","import isArrayLike from './_isArrayLike.js';\nimport values from './values.js';\nimport cb from './_cb.js';\nimport each from './each.js';\n\n// Return the maximum element (or element-based computation).\nexport default function max(obj, iteratee, context) {\n var result = -Infinity, lastComputed = -Infinity,\n value, computed;\n if (iteratee == null || (typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null)) {\n obj = isArrayLike(obj) ? obj : values(obj);\n for (var i = 0, length = obj.length; i < length; i++) {\n value = obj[i];\n if (value != null && value > result) {\n result = value;\n }\n }\n } else {\n iteratee = cb(iteratee, context);\n each(obj, function(v, index, list) {\n computed = iteratee(v, index, list);\n if (computed > lastComputed || (computed === -Infinity && result === -Infinity)) {\n result = v;\n lastComputed = computed;\n }\n });\n }\n return result;\n}\n","import isArrayLike from './_isArrayLike.js';\nimport values from './values.js';\nimport cb from './_cb.js';\nimport each from './each.js';\n\n// Return the minimum element (or element-based computation).\nexport default function min(obj, iteratee, context) {\n var result = Infinity, lastComputed = Infinity,\n value, computed;\n if (iteratee == null || (typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null)) {\n obj = isArrayLike(obj) ? obj : values(obj);\n for (var i = 0, length = obj.length; i < length; i++) {\n value = obj[i];\n if (value != null && value < result) {\n result = value;\n }\n }\n } else {\n iteratee = cb(iteratee, context);\n each(obj, function(v, index, list) {\n computed = iteratee(v, index, list);\n if (computed < lastComputed || (computed === Infinity && result === Infinity)) {\n result = v;\n lastComputed = computed;\n }\n });\n }\n return result;\n}\n","import isArray from './isArray.js';\nimport { slice } from './_setup.js';\nimport isString from './isString.js';\nimport isArrayLike from './_isArrayLike.js';\nimport map from './map.js';\nimport identity from './identity.js';\nimport values from './values.js';\n\n// Safely create a real, live array from anything iterable.\nvar reStrSymbol = /[^\\ud800-\\udfff]|[\\ud800-\\udbff][\\udc00-\\udfff]|[\\ud800-\\udfff]/g;\nexport default function toArray(obj) {\n if (!obj) return [];\n if (isArray(obj)) return slice.call(obj);\n if (isString(obj)) {\n // Keep surrogate pair characters together.\n return obj.match(reStrSymbol);\n }\n if (isArrayLike(obj)) return map(obj, identity);\n return values(obj);\n}\n","import isArrayLike from './_isArrayLike.js';\nimport values from './values.js';\nimport getLength from './_getLength.js';\nimport random from './random.js';\nimport toArray from './toArray.js';\n\n// Sample **n** random values from a collection using the modern version of the\n// [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).\n// If **n** is not specified, returns a single random element.\n// The internal `guard` argument allows it to work with `_.map`.\nexport default function sample(obj, n, guard) {\n if (n == null || guard) {\n if (!isArrayLike(obj)) obj = values(obj);\n return obj[random(obj.length - 1)];\n }\n var sample = toArray(obj);\n var length = getLength(sample);\n n = Math.max(Math.min(n, length), 0);\n var last = length - 1;\n for (var index = 0; index < n; index++) {\n var rand = random(index, last);\n var temp = sample[index];\n sample[index] = sample[rand];\n sample[rand] = temp;\n }\n return sample.slice(0, n);\n}\n","import sample from './sample.js';\n\n// Shuffle a collection.\nexport default function shuffle(obj) {\n return sample(obj, Infinity);\n}\n","import cb from './_cb.js';\nimport pluck from './pluck.js';\nimport map from './map.js';\n\n// Sort the object's values by a criterion produced by an iteratee.\nexport default function sortBy(obj, iteratee, context) {\n var index = 0;\n iteratee = cb(iteratee, context);\n return pluck(map(obj, function(value, key, list) {\n return {\n value: value,\n index: index++,\n criteria: iteratee(value, key, list)\n };\n }).sort(function(left, right) {\n var a = left.criteria;\n var b = right.criteria;\n if (a !== b) {\n if (a > b || a === void 0) return 1;\n if (a < b || b === void 0) return -1;\n }\n return left.index - right.index;\n }), 'value');\n}\n","import cb from './_cb.js';\nimport each from './each.js';\n\n// An internal function used for aggregate \"group by\" operations.\nexport default function group(behavior, partition) {\n return function(obj, iteratee, context) {\n var result = partition ? [[], []] : {};\n iteratee = cb(iteratee, context);\n each(obj, function(value, index) {\n var key = iteratee(value, index, obj);\n behavior(result, value, key);\n });\n return result;\n };\n}\n","import group from './_group.js';\nimport has from './_has.js';\n\n// Groups the object's values by a criterion. Pass either a string attribute\n// to group by, or a function that returns the criterion.\nexport default group(function(result, value, key) {\n if (has(result, key)) result[key].push(value); else result[key] = [value];\n});\n","import group from './_group.js';\n\n// Indexes the object's values by a criterion, similar to `_.groupBy`, but for\n// when you know that your index values will be unique.\nexport default group(function(result, value, key) {\n result[key] = value;\n});\n","import group from './_group.js';\nimport has from './_has.js';\n\n// Counts instances of an object that group by a certain criterion. 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\ No newline at end of file diff --git a/.venv-sp/share/man/man1/ipython.1 b/.venv-sp/share/man/man1/ipython.1 new file mode 100644 index 0000000..0f4a191 --- /dev/null +++ b/.venv-sp/share/man/man1/ipython.1 @@ -0,0 +1,60 @@ +.\" Hey, EMACS: -*- nroff -*- +.\" First parameter, NAME, should be all caps +.\" Second parameter, SECTION, should be 1-8, maybe w/ subsection +.\" other parameters are allowed: see man(7), man(1) +.TH IPYTHON 1 "July 15, 2011" +.\" Please adjust this date whenever revising the manpage. +.\" +.\" Some roff macros, for reference: +.\" .nh disable hyphenation +.\" .hy enable hyphenation +.\" .ad l left justify +.\" .ad b justify to both left and right margins +.\" .nf disable filling +.\" .fi enable filling +.\" .br insert line break +.\" .sp insert n+1 empty lines +.\" for manpage-specific macros, see man(7) and groff_man(7) +.\" .SH section heading +.\" .SS secondary section heading +.\" +.\" +.\" To preview this page as plain text: nroff -man ipython.1 +.\" +.SH NAME +ipython \- Tools for Interactive Computing in Python. +.SH SYNOPSIS +.B ipython +.RI [ options ] " files" ... + +.B ipython subcommand +.RI [ options ] ... + +.SH DESCRIPTION +An interactive Python shell with automatic history (input and output), dynamic +object introspection, easier configuration, command completion, access to the +system shell, integration with numerical and scientific computing tools, +web notebook, Qt console, and more. + +For more information on how to use IPython, see 'ipython \-\-help', +or 'ipython \-\-help\-all' for all available command\(hyline options. + +.SH "ENVIRONMENT VARIABLES" +.sp +.PP +\fIIPYTHONDIR\fR +.RS 4 +This is the location where IPython stores all its configuration files. The default +is $HOME/.ipython if IPYTHONDIR is not defined. + +You can see the computed value of IPYTHONDIR with `ipython locate`. + +.SH FILES + +IPython uses various configuration files stored in profiles within IPYTHONDIR. +To generate the default configuration files and start configuring IPython, +do 'ipython profile create', and edit '*_config.py' files located in +IPYTHONDIR/profile_default. + +.SH AUTHORS +IPython is written by the IPython Development Team . diff --git a/.venv-sp/share/man/man1/ttx.1 b/.venv-sp/share/man/man1/ttx.1 new file mode 100644 index 0000000..bba23b5 --- /dev/null +++ b/.venv-sp/share/man/man1/ttx.1 @@ -0,0 +1,225 @@ +.Dd May 18, 2004 +.\" ttx is not specific to any OS, but contrary to what groff_mdoc(7) +.\" seems to imply, entirely omitting the .Os macro causes 'BSD' to +.\" be used, so I give a zero-width space as its argument. +.Os \& +.\" The "FontTools Manual" argument apparently has no effect in +.\" groff 1.18.1. I think it is a bug in the -mdoc groff package. +.Dt TTX 1 "FontTools Manual" +.Sh NAME +.Nm ttx +.Nd tool for manipulating TrueType and OpenType fonts +.Sh SYNOPSIS +.Nm +.Bk +.Op Ar option ... +.Ek +.Bk +.Ar file ... +.Ek +.Sh DESCRIPTION +.Nm +is a tool for manipulating TrueType and OpenType fonts. It can convert +TrueType and OpenType fonts to and from an +.Tn XML Ns -based format called +.Tn TTX . +.Tn TTX +files have a +.Ql .ttx +extension. +.Pp +For each +.Ar file +argument it is given, +.Nm +detects whether it is a +.Ql .ttf , +.Ql .otf +or +.Ql .ttx +file and acts accordingly: if it is a +.Ql .ttf +or +.Ql .otf +file, it generates a +.Ql .ttx +file; if it is a +.Ql .ttx +file, it generates a +.Ql .ttf +or +.Ql .otf +file. +.Pp +By default, every output file is created in the same directory as the +corresponding input file and with the same name except for the +extension, which is substituted appropriately. +.Nm +never overwrites existing files; if necessary, it appends a suffix to +the output file name before the extension, as in +.Pa Arial#1.ttf . +.Ss "General options" +.Bl -tag -width ".Fl t Ar table" +.It Fl h +Display usage information. +.It Fl d Ar dir +Write the output files to directory +.Ar dir +instead of writing every output file to the same directory as the +corresponding input file. +.It Fl o Ar file +Write the output to +.Ar file +instead of writing it to the same directory as the +corresponding input file. +.It Fl v +Be verbose. Write more messages to the standard output describing what +is being done. +.It Fl a +Allow virtual glyphs ID's on compile or decompile. +.El +.Ss "Dump options" +The following options control the process of dumping font files +(TrueType or OpenType) to +.Tn TTX +files. +.Bl -tag -width ".Fl t Ar table" +.It Fl l +List table information. Instead of dumping the font to a +.Tn TTX +file, display minimal information about each table. +.It Fl t Ar table +Dump table +.Ar table . +This option may be given multiple times to dump several tables at +once. When not specified, all tables are dumped. +.It Fl x Ar table +Exclude table +.Ar table +from the list of tables to dump. This option may be given multiple +times to exclude several tables from the dump. The +.Fl t +and +.Fl x +options are mutually exclusive. +.It Fl s +Split tables. Dump each table to a separate +.Tn TTX +file and write (under the name that would have been used for the output +file if the +.Fl s +option had not been given) one small +.Tn TTX +file containing references to the individual table dump files. This +file can be used as input to +.Nm +as long as the referenced files can be found in the same directory. +.It Fl i +.\" XXX: I suppose OpenType programs (exist and) are also affected. +Don't disassemble TrueType instructions. When this option is specified, +all TrueType programs (glyph programs, the font program and the +pre-program) are written to the +.Tn TTX +file as hexadecimal data instead of +assembly. This saves some time and results in smaller +.Tn TTX +files. +.It Fl y Ar n +When decompiling a TrueType Collection (TTC) file, +decompile font number +.Ar n , +starting from 0. +.El +.Ss "Compilation options" +The following options control the process of compiling +.Tn TTX +files into font files (TrueType or OpenType): +.Bl -tag -width ".Fl t Ar table" +.It Fl m Ar fontfile +Merge the input +.Tn TTX +file +.Ar file +with +.Ar fontfile . +No more than one +.Ar file +argument can be specified when this option is used. +.It Fl b +Don't recalculate glyph bounding boxes. Use the values in the +.Tn TTX +file as is. +.El +.Sh "THE TTX FILE FORMAT" +You can find some information about the +.Tn TTX +file format in +.Pa documentation.html . +In particular, you will find in that file the list of tables understood by +.Nm +and the relations between TrueType GlyphIDs and the glyph names used in +.Tn TTX +files. +.Sh EXAMPLES +In the following examples, all files are read from and written to the +current directory. Additionally, the name given for the output file +assumes in every case that it did not exist before +.Nm +was invoked. +.Pp +Dump the TrueType font contained in +.Pa FreeSans.ttf +to +.Pa FreeSans.ttx : +.Pp +.Dl ttx FreeSans.ttf +.Pp +Compile +.Pa MyFont.ttx +into a TrueType or OpenType font file: +.Pp +.Dl ttx MyFont.ttx +.Pp +List the tables in +.Pa FreeSans.ttf +along with some information: +.Pp +.Dl ttx -l FreeSans.ttf +.Pp +Dump the +.Sq cmap +table from +.Pa FreeSans.ttf +to +.Pa FreeSans.ttx : +.Pp +.Dl ttx -t cmap FreeSans.ttf +.Sh NOTES +On MS\-Windows and MacOS, +.Nm +is available as a graphical application to which files can be dropped. +.Sh SEE ALSO +.Pa documentation.html +.Pp +.Xr fontforge 1 , +.Xr ftinfo 1 , +.Xr gfontview 1 , +.Xr xmbdfed 1 , +.Xr Font::TTF 3pm +.Sh AUTHORS +.Nm +was written by +.An -nosplit +.An "Just van Rossum" Aq just@letterror.com . +.Pp +This manual page was written by +.An "Florent Rougon" Aq f.rougon@free.fr +for the Debian GNU/Linux system based on the existing FontTools +documentation. It may be freely used, modified and distributed without +restrictions. +.\" For Emacs: +.\" Local Variables: +.\" fill-column: 72 +.\" sentence-end: "[.?!][]\"')}]*\\($\\| $\\| \\| \\)[ \n]*" +.\" sentence-end-double-space: t +.\" End: \ No newline at end of file diff --git a/.venv-sp/share/xyzservices/providers.json b/.venv-sp/share/xyzservices/providers.json new file mode 100644 index 0000000..ac7992c --- /dev/null +++ b/.venv-sp/share/xyzservices/providers.json @@ -0,0 +1,17685 @@ +{ + "OpenStreetMap": { + "Mapnik": { + "url": "https://tile.openstreetmap.org/{z}/{x}/{y}.png", + "max_zoom": 19, + "html_attribution": "© OpenStreetMap contributors", + "attribution": "(C) OpenStreetMap contributors", + "name": "OpenStreetMap.Mapnik" + }, + "DE": { + "url": "https://tile.openstreetmap.de/{z}/{x}/{y}.png", + "max_zoom": 18, + "html_attribution": "© OpenStreetMap contributors", + "attribution": "(C) OpenStreetMap contributors", + "name": "OpenStreetMap.DE" + }, + "CH": { + "url": "https://tile.osm.ch/switzerland/{z}/{x}/{y}.png", + "max_zoom": 18, + "html_attribution": "© OpenStreetMap contributors", + "attribution": "(C) OpenStreetMap contributors", + "bounds": [ + [ + 45, + 5 + ], + [ + 48, + 11 + ] + ], + "name": "OpenStreetMap.CH" + }, + "France": { + "url": "https://{s}.tile.openstreetmap.fr/osmfr/{z}/{x}/{y}.png", + "max_zoom": 20, + "html_attribution": "© OpenStreetMap France | © OpenStreetMap contributors", + "attribution": "(C) OpenStreetMap France | (C) OpenStreetMap contributors", + "name": "OpenStreetMap.France" + }, + "HOT": { + "url": "https://{s}.tile.openstreetmap.fr/hot/{z}/{x}/{y}.png", + "max_zoom": 19, + "html_attribution": "© OpenStreetMap contributors, Tiles style by Humanitarian OpenStreetMap Team hosted by OpenStreetMap France", + "attribution": "(C) OpenStreetMap contributors, Tiles style by Humanitarian OpenStreetMap Team hosted by OpenStreetMap France", + "name": "OpenStreetMap.HOT" + }, + "BZH": { + "url": 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    Redirecting to the Jupyter Book...

    - - diff --git a/notebooks/assignments/assignment-3-zonal-stats.ipynb b/notebooks/assignments/assignment-3-zonal-stats.ipynb deleted file mode 100644 index e9b9a29..0000000 --- a/notebooks/assignments/assignment-3-zonal-stats.ipynb +++ /dev/null @@ -1,560 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9789b479", - "metadata": {}, - "source": [ - "# Generating Zonal Statistics with a Custom Script\n", - "This notebook demonstrates how to generate zonal statistics using a custom Python script, with a focus on analyzing night light intensity across administrative regions in Rwanda. You will learn how to set up your workspace, import and prepare geospatial data, clip global raster datasets to a country boundary, and visualize the results. The workflow includes using custom utilities for zonal statistics, merging results with administrative boundaries, and saving the outputs for further analysis. This hands-on approach provides practical experience with geospatial data processing, raster analysis, and the integration of Python geospatial libraries such as geopandas and rasterio.\n", - "\n", - "**Zonal statistics** are summary statistics (such as mean, sum, median, minimum, or maximum) calculated for values of a raster dataset within the boundaries of defined zones, such as polygons representing administrative regions or land cover types.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "7afd52ef", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('/Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/grades2.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "49c5a1c7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.46" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(df[df['q1_perc'] <= 60])/len(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "41c18dce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.34" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(df[df['q2_perc'] <= 60])/len(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "423b8b74", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.3" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(df[df['ass1_perc'] <= 60])/len(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "a91e50ed", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15, 4))\n", - "\n", - "for i, col in enumerate(['q1_perc', 'q2_perc', 'ass1_perc']):\n", - " plt.subplot(1, 3, i+1)\n", - " sns.histplot(df[col].dropna(), bins=15, edgecolor='black', cumulative=True, stat='density')\n", - " plt.title(f'Cumulative Histogram of {col}')\n", - " plt.xlabel(col)\n", - " plt.ylabel('Cumulative Density')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d207a461", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3addf77", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "37f25f48", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.46" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "23/50" - ] - }, - { - "cell_type": "markdown", - "id": "45454706", - "metadata": {}, - "source": [ - "## Setup and Imports" - ] - }, - { - "cell_type": "markdown", - "id": "49a7ce40", - "metadata": {}, - "source": [ - "### Required Packages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e510b1e", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import geopandas as gp\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import rasterio\n", - "from rasterio.mask import mask\n", - "from rasterio.warp import calculate_default_transform, reproject, Resampling\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "id": "5bfdcce3", - "metadata": {}, - "source": [ - "### Workspace Directory Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d5ac7d2c", - "metadata": {}, - "outputs": [], - "source": [ - "DIR_WORKSPACE = Path.cwd().parents[1]\n", - "DIR_DATA = DIR_WORKSPACE / \"data\"\n", - "DIR_SRC = DIR_WORKSPACE / \"src\"" - ] - }, - { - "cell_type": "markdown", - "id": "92ccdb69", - "metadata": {}, - "source": [ - "### Import Custom Python Script" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4377d5f2", - "metadata": {}, - "outputs": [], - "source": [ - "# Add src directory to path to import our custom script\n", - "sys.path.append(str(DIR_SRC))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "860fa277", - "metadata": {}, - "outputs": [], - "source": [ - "# We can now import our custom package\n", - "# There are cleaner ways to handle local imports but this works for now\n", - "from spatial_utils.zonal_stats import ZonalStatistics" - ] - }, - { - "cell_type": "markdown", - "id": "8fe7e43e", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "source": [ - "### Input Data Files \n", - "For the night lights file, please make sure you unzip it first. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db36f337", - "metadata": {}, - "outputs": [], - "source": [ - "# ==========================================\n", - "# FILE PATHS\n", - "# ===========================================\n", - "\n", - "# Unzipped night lights raster file with \"tif\" extension\n", - "FILE_NTL = DIR_DATA / \"ntl/VNL_npp_2024_global_vcmslcfg_v2_c202502261200.average_masked.dat.tif\"\n", - "\n", - "# Shapefile with cell level (admin region level 4) boundaries \n", - "FILE_SHP_ADM4 = DIR_DATA / \"geospatial/pop-demo-infra/adm4-pop-buildings.shp\"\n", - "\n", - "# Shapefile with national boundaries (admin region level 0)\n", - "FILE_SHP_ADM0 = DIR_DATA / \"geospatial/adm-bounds/geoBoundaries-RWA-ADM0-all/geoBoundaries-RWA-ADM0.shp\"" - ] - }, - { - "cell_type": "markdown", - "id": "e940ac74", - "metadata": {}, - "source": [ - "## Zonal Statistics Analysis" - ] - }, - { - "cell_type": "markdown", - "id": "b9607e4b", - "metadata": {}, - "source": [ - "### Clip Global Raster to Rwanda \n", - "To make the raster file smaller and more manageable, we will clip (cut) it to the boundary of Rwanda. This step extracts only the portion of the raster that falls within Rwanda’s borders, reducing file size and improving processing efficiency." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cade007", - "metadata": {}, - "outputs": [], - "source": [ - "def clip_raster_to_boundary(input_raster_path, boundary_gdf, output_path):\n", - " \"\"\"\n", - " Clip a global raster to a country boundary\n", - " \n", - " Parameters:\n", - " -----------\n", - " input_raster_path : str\n", - " Path to input raster file\n", - " boundary_gdf : geopandas.GeoDataFrame\n", - " Boundary for clipping\n", - " output_path : str\n", - " Path for output clipped raster\n", - " \n", - " Returns:\n", - " --------\n", - " str : Path to clipped raster\n", - " \"\"\"\n", - " print(f\"Clipping global raster to Rwanda boundary...\")\n", - " \n", - " with rasterio.open(input_raster_path) as src:\n", - " print(f\"Original raster info:\")\n", - " print(f\" - Shape: {src.width} x {src.height}\")\n", - " print(f\" - CRS: {src.crs}\")\n", - " print(f\" - Bounds: {src.bounds}\")\n", - " print(f\" - Resolution: {src.res}\")\n", - " \n", - " # Reproject boundary to match raster CRS if needed\n", - " if boundary_gdf.crs != src.crs:\n", - " print(f\"Reprojecting boundary from {boundary_gdf.crs} to {src.crs}\")\n", - " boundary_gdf = boundary_gdf.to_crs(src.crs)\n", - " \n", - " # Clip raster\n", - " clipped_data, clipped_transform = mask(\n", - " src, \n", - " boundary_gdf.geometry, \n", - " crop=True,\n", - " nodata=src.nodata\n", - " )\n", - " \n", - " # Update metadata\n", - " clipped_meta = src.meta.copy()\n", - " clipped_meta.update({\n", - " \"driver\": \"GTiff\",\n", - " \"height\": clipped_data.shape[1],\n", - " \"width\": clipped_data.shape[2],\n", - " \"transform\": clipped_transform\n", - " })\n", - " \n", - " # Create output directory if needed\n", - " os.makedirs(os.path.dirname(output_path), exist_ok=True)\n", - " \n", - " # Write clipped raster\n", - " with rasterio.open(output_path, \"w\", **clipped_meta) as dest:\n", - " dest.write(clipped_data)\n", - " \n", - " print(f\"✓ Clipped raster saved to: {output_path}\")\n", - " print(f\"Clipped raster info:\")\n", - " print(f\" - Shape: {clipped_data.shape[2]} x {clipped_data.shape[1]}\")\n", - " print(f\" - Data range: {np.nanmin(clipped_data)} to {np.nanmax(clipped_data)}\")\n", - " \n", - " return output_path" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a375006", - "metadata": {}, - "outputs": [], - "source": [ - "# =============================================\n", - "# CLIP THE GLOBAL RASTER TO RWANDA\n", - "# ===============================================\n", - "\n", - "# Specify output clipped raster file\n", - "clipped_raster_path = DIR_DATA / \"ntl/rwanda_2024_ntl.tif\"\n", - "\n", - "# Load level 0 boundaries in a GeoDataframe\n", - "gdf_adm0 = gp.read_file(FILE_SHP_ADM0)\n", - "\n", - "\n", - "# Call the clip raster function which utilises rasterio package\n", - "rwanda_raster_path = clip_raster_to_boundary(\n", - " FILE_NTL, \n", - " gdf_adm0, \n", - " clipped_raster_path\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "51b329c3", - "metadata": {}, - "source": [ - "### Visualize Clipped Raster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f7bc40b", - "metadata": {}, - "outputs": [], - "source": [ - "# =========================================\n", - "# LOAD AND VISUALIZE THE CLIPPED RASTER\n", - "# =========================================\n", - "\n", - "# Load cell boundaries\n", - "gdf_adm4 = gp.read_file(FILE_SHP_ADM4)\n", - "\n", - "with rasterio.open(rwanda_raster_path) as src:\n", - " rwanda_nightlight = src.read(1)\n", - " rwanda_extent = [src.bounds.left, src.bounds.right, src.bounds.bottom, src.bounds.top]\n", - "\n", - "# Create visualization\n", - "plt.figure(figsize=(15, 10))\n", - "\n", - "# Plot 1: Clipped raster\n", - "plt.subplot(2, 2, 1)\n", - "plt.imshow(rwanda_nightlight, extent=rwanda_extent, cmap='viridis', alpha=0.8)\n", - "plt.colorbar(label='Nightlight Intensity', shrink=0.7)\n", - "plt.title('Rwanda Nightlight Data (Clipped)')\n", - "plt.xlabel('Longitude')\n", - "plt.ylabel('Latitude')\n", - "\n", - "# Plot 2: Raster with district boundaries\n", - "plt.subplot(2, 2, 2)\n", - "plt.imshow(rwanda_nightlight, extent=rwanda_extent, cmap='viridis', alpha=0.8)\n", - "rwanda_districts_plot = gdf_adm4.to_crs(gdf_adm0.crs) if gdf_adm4.crs != gdf_adm0.crs else gdf_adm4\n", - "rwanda_districts_plot.boundary.plot(ax=plt.gca(), color='white', linewidth=0.5)\n", - "plt.colorbar(label='Nightlight Intensity', shrink=0.7)\n", - "plt.title('Rwanda Nightlight with District Boundaries')\n", - "plt.xlabel('Longitude')\n", - "plt.ylabel('Latitude')" - ] - }, - { - "cell_type": "markdown", - "id": "d7c0dad2", - "metadata": {}, - "source": [ - "### Generate Cell Level Zonal Statistics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b107a62b", - "metadata": {}, - "outputs": [], - "source": [ - "# ===================================================\n", - "# GENERATING ZONAL STATISTICS WITH A CUSTOM SCRIPT\n", - "# ===================================================\n", - "\n", - "# Create ZonalStatistics object using the clipped Rwanda data with\n", - "# the following args\n", - "# 1. Path of clipped night lights raster/image: rwanda_raster_path\n", - "# 2. Path of administrative boundaries shapefile: FILE_SHP_ADM4\n", - "# 3. Administrative level: 4\n", - "\n", - "admin_level = \"cell_id\"\n", - "\n", - "zs = ZonalStatistics(rwanda_raster_path, FILE_SHP_ADM4, admin_level)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df0c36e8", - "metadata": {}, - "outputs": [], - "source": [ - "# Load the data (this will print information about the datasets)\n", - "zs.load_data()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9e28e42", - "metadata": {}, - "outputs": [], - "source": [ - "# =======================================\n", - "# DEFINE WHICH STATISTICS TO CALCULATE\n", - "# =======================================\n", - "stats_to_calculate = ['mean', 'median', 'count', \"sum\"]\n", - "\n", - "print(f\"Calculating statistics: {stats_to_calculate}\")\n", - "print(\"This may take a few minutes depending on data size...\")\n", - "\n", - "# Calculate statistics\n", - "df_stats = zs.calculate_statistics(stats_to_calculate)\n", - "\n", - "# Display results\n", - "print(f\"\\nAnalysis complete! Results shape: {df_stats.shape}\")\n", - "display(df_stats.head(10))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f691f255", - "metadata": {}, - "outputs": [], - "source": [ - "# ==================================================\n", - "# SAVE RESULTS\n", - "# ==================================================\n", - "\n", - "# Ensure we grab region names from original shapefile\n", - "cols2keep = ['cell_id', 'province_n', 'district_n', 'sector_nam', 'cell_name']\n", - "df_stats2 = df_stats.merge(gdf_adm4[cols2keep], left_on=\"admin_name\", right_on='cell_id', how='left')\n", - "\n", - "# Save results with the following columns\n", - "cols2keep = ['province_n', 'district_n', 'sector_nam', 'cell_name', 'mean', 'median', 'count',\n", - " 'sum']\n", - "\n", - "df_stats2[cols2keep].to_csv(DIR_DATA / \"ntl/rw-adm4-ntl-stats.csv\", index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d8839dc", - "metadata": {}, - "outputs": [], - "source": [ - "## Report on Cells with Largest Night Light Values\n", - "largest_cells = df_stats2.nlargest(10, 'mean')\n", - "print(\"Cells with the largest night light values:\")\n", - "display(largest_cells)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "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": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/module-5/download-ntl.ipynb b/notebooks/module-5/download-ntl.ipynb index e69de29..87fe044 100644 --- a/notebooks/module-5/download-ntl.ipynb +++ b/notebooks/module-5/download-ntl.ipynb @@ -0,0 +1,1138 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "10f90f1b", + "metadata": {}, + "source": [ + "# Night Lights Data Download and Preprocessing\n", + "In this notebook, we first download nightlights using the ```blackmarblePY```-A Python package for interacting with NASA's Black Marble night lights imagery data repository. \n", + "For the downloads, we download annual composites-this combines daily nighlight images into a single image for the whole year utilzing alot of processing to deal with cloud cpve and noise. We also download momthly composites which are geenrated a simialr way as annual composites. The data covers the perios of 2012 to present. We do download 2012 to 2024.\n", + "\n", + "Next, we generate zonal statistics using spatial processing. Zonal statics computes summary statics for all pixels withing a zone. In our case, the zones are cells. For each zone, we calculate mean, median, sum, min and maximum. Also, we calculate number of pixels per cell.\n", + "\n", + "We then export these summary statistics intoCSV files which we will import into the database." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "fc847c86", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n", + "The watermark extension is already loaded. To reload it, use:\n", + " %reload_ext watermark\n", + "Last updated: Sat Sep 13 2025\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.12.7\n", + "IPython version : 9.5.0\n", + "\n", + "blackmarble: 2025.6.3\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "import glob\n", + "import re\n", + "from pathlib import Path\n", + "import datetime\n", + "import logging\n", + "from tqdm import tqdm\n", + "\n", + "import colorcet as cc\n", + "import contextily as cx\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from rasterstats import zonal_stats\n", + "from bokeh.plotting import figure, output_notebook, show\n", + "from bokeh.models import HoverTool, Title\n", + "\n", + "from pyproj.enums import WktVersion\n", + "\n", + "# Monkey-patch the default `to_wkt()` behavior to always return WKT1\n", + "import pyproj.crs.crs\n", + "\n", + "from blackmarble import BlackMarble\n", + "\n", + "from dotenv import load_dotenv\n", + "\n", + "logging.getLogger(\"blackmarblepy\").setLevel(logging.INFO)\n", + "load_dotenv()\n", + "\n", + "%load_ext autoreload\n", + "%load_ext watermark\n", + "%autoreload 2\n", + "%watermark -v -u -n -p blackmarble" + ] + }, + { + "cell_type": "markdown", + "id": "56933690", + "metadata": {}, + "source": [ + "# Setup Input Directories" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "5c02c608", + "metadata": {}, + "outputs": [], + "source": [ + "DIR_WORKSPACE = Path.cwd().parents[1]\n", + "DIR_DATA = DIR_WORKSPACE / \"data\"\n", + "DIR_NTL = DIR_DATA / \"ntl\"\n", + "DIR_POP = DIR_DATA / \"population-demography\"\n", + "DIR_RASTERS_ANNUAL = DIR_NTL / \"rasters/annual\"\n", + "DIR_RASTERS_MONTHLY = DIR_NTL / \"rasters/monthly\"\n", + "DIR_STATS_ANNUAL = DIR_NTL / \"stats/annual\"\n", + "DIR_STATS_MONTHLY = DIR_NTL / \"stats/monthly\"\n", + "DIR_DB_DATA = DIR_DATA / \"tmp-db-data\"\n", + "FILE_SHP_ADM4_POP = DIR_DATA / \"geospatial/pop-demo-infra/adm4-pop-buildings.shp\"\n", + "FILE_SHP_ADM4 = DIR_DATA / \"geospatial/adm-bounds/gadm41_RWA_4.json\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "af5bacc7", + "metadata": {}, + "outputs": [], + "source": [ + "# Load Rwanda Admin 0 Boundaries\n", + "gdf = geopandas.read_file(FILE_SHP_ADM0\n", + ")\n", + "# gdf.explore(tiles=\"CartoDB dark_matter\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcb67a25", + "metadata": {}, + "outputs": [], + "source": [ + "gdf.plot(figsize=(8, 8), edgecolor=\"k\", alpha=0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "6b77db2a", + "metadata": {}, + "source": [ + "# A Patch to Deal with CRS Issue in the Package" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98a34168", + "metadata": {}, + "outputs": [], + "source": [ + "_original_to_wkt = pyproj.crs.crs.CRS.to_wkt\n", + "\n", + "def patched_to_wkt(self, version=WktVersion.WKT1_GDAL, *args, **kwargs):\n", + " return _original_to_wkt(self, version=version, *args, **kwargs)\n", + "\n", + "pyproj.crs.crs.CRS.to_wkt = patched_to_wkt\n" + ] + }, + { + "cell_type": "markdown", + "id": "0db2a632", + "metadata": {}, + "source": [ + "# Download Annual Composites for 2012 to 2024" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "6f8a2b4b", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the BlackMarble interface.\n", + "bm = BlackMarble(output_directory=DIR_RASTERS_ANNUAL)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5dd4d37", + "metadata": {}, + "outputs": [], + "source": [ + "# Loop over years\n", + "for year in range(2012, 2025): # 2025 is exclusive, so it ends at 2024\n", + " print(f\"Downloading for {year}...\")\n", + " try:\n", + " bm.raster(\n", + " gdf=gdf,\n", + " product_id=\"VNP46A4\", # Annual composite\n", + " date_range=f\"{year}-01-01\",\n", + " )\n", + " except Exception as e:\n", + " print(f\"Failed to download for {year}: {e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0baad957", + "metadata": {}, + "source": [ + "# Download Monthly data for 2012 to 2024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6e3c09d", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the BlackMarble interface.\n", + "bm = BlackMarble(output_directory=DIR_RASTERS_MONTHLY, output_skip_if_exists=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1308cc74", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate list of year-month combinations\n", + "year_months = [(year, month) for year in range(2012, 2025) for month in range(1, 13)]\n", + "\n", + "# Iterate with progress bar\n", + "for year, month in tqdm(year_months, desc=\"Downloading monthly composites\"):\n", + " start_date = f\"{year}-{month:02d}-01\"\n", + "\n", + " try:\n", + " bm.raster(\n", + " gdf=gdf,\n", + " product_id=\"VNP46A3\", # Monthly Black Marble product\n", + " date_range=start_date,\n", + " )\n", + " except Exception as e:\n", + " print(f\"❌ Failed for {start_date}: {e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a9085beb", + "metadata": {}, + "source": [ + "# Generate Zonal Statistics " + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "139ba80d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 300 raster files...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 300/300 [05:05<00:00, 1.02s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✅ All zonal stats saved to /Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/ntl/stats/monthly/merged-zonal-stats-2012-2024.csv\n", + "✅ Individual CSVs saved in folder: /Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/ntl/stats/monthly\n" + ] + } + ], + "source": [ + "# === CONFIGURATION ===\n", + "RASTER_FOLDER = DIR_RASTERS_MONTHLY # Folder containing .tif files\n", + "SINGLE_CSV_FOLDER = DIR_STATS_MONTHLY\n", + "COMBINED_CSV_FILE = DIR_STATS_MONTHLY / \"merged-zonal-stats-2012-2024.csv\"\n", + "\n", + "\n", + "# Load polygons\n", + "gdf = gpd.read_file(FILE_SHP_ADM4_POP)\n", + "\n", + "# List all .tif files\n", + "tif_files = sorted(glob.glob(os.path.join(RASTER_FOLDER, \"*.tif\")))\n", + "\n", + "# Store all rows for combined CSV\n", + "all_stats = []\n", + "\n", + "print(f\"Processing {len(tif_files)} raster files...\")\n", + "\n", + "for tif_path in tqdm(tif_files):\n", + " filename = os.path.basename(tif_path)\n", + " \n", + " # Parse year and optionally month from filename using regex\n", + " year, month = None, None\n", + " match_ym = re.search(r\"(\\d{4})[_-]?(\\d{2})\", filename) # e.g., 2023_01\n", + " match_y = re.search(r\"(\\d{4})\", filename) # fallback if only year\n", + "\n", + " if match_ym:\n", + " year, month = int(match_ym.group(1)), int(match_ym.group(2))\n", + " elif match_y:\n", + " year = int(match_y.group(1))\n", + "\n", + " # Compute zonal statistics\n", + " stats = zonal_stats(\n", + " gdf,\n", + " tif_path,\n", + " stats=[\"mean\", \"median\", \"min\", \"max\", \"sum\", \"count\"],\n", + " nodata=None,\n", + " geojson_out=True\n", + " )\n", + "\n", + " # Extract polygon properties and add raster info\n", + " rows = []\n", + " for zone in stats:\n", + " row = zone[\"properties\"].copy()\n", + " # row.update(zone[\"stats\"])\n", + " row[\"raster\"] = filename\n", + " row[\"year\"] = year\n", + " if month:\n", + " row[\"month\"] = month\n", + " rows.append(row)\n", + " all_stats.append(row)\n", + "\n", + " # Save individual file\n", + " df_raster = pd.DataFrame(rows)\n", + " single_csv_path = os.path.join(SINGLE_CSV_FOLDER, f\"{filename.replace('.tif', '.csv')}\")\n", + " df_raster.to_csv(single_csv_path, index=False)\n", + "\n", + "df_all = pd.DataFrame(all_stats)\n", + "df_all.rename(columns={'province_n': 'province_name', 'district_n':'district_name', 'sector_nam': 'sector_name',\n", + " 'min': 'ntl_mean', 'max':'ntl_max', 'mean':'ntl_mean', 'count':'pixel_count','min':'ntl_min',\n", + " 'sum':'ntl_sum', 'median':'ntl_median', 'raster':'raster_filename'}, inplace=True)\n", + "\n", + "df_all = df_all[['cell_id', 'ntl_mean', 'ntl_median', 'ntl_min', 'ntl_max', 'ntl_sum', 'pixel_count', 'raster_filename', 'year', 'month']]\n", + "df_all.to_csv(COMBINED_CSV_FILE, index=False)\n", + "print(f\"\\n✅ All zonal stats saved to {COMBINED_CSV_FILE}\")\n", + "print(f\"✅ Individual CSVs saved in folder: {SINGLE_CSV_FOLDER}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "c0d056f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " cell_id ntl_mean ntl_median ntl_min ntl_max ntl_sum \\\n", + "0 RWA.1.1.1.1_1 0.0 0.0 0.0 0.0 0.0 \n", + "1 RWA.1.1.1.2_1 0.0 0.0 0.0 0.0 0.0 \n", + "2 RWA.1.1.1.3_1 0.0 0.0 0.0 0.0 0.0 \n", + "3 RWA.1.1.1.4_1 0.0 0.0 0.0 0.0 0.0 \n", + "4 RWA.1.1.2.1_1 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " pixel_count raster_filename year month \n", + "0 26 VNP46A3.A2012001.h20v09.001.2021124111708.tif 2012 NaN \n", + "1 27 VNP46A3.A2012001.h20v09.001.2021124111708.tif 2012 NaN \n", + "2 27 VNP46A3.A2012001.h20v09.001.2021124111708.tif 2012 NaN \n", + "3 43 VNP46A3.A2012001.h20v09.001.2021124111708.tif 2012 NaN \n", + "4 64 VNP46A3.A2012001.h20v09.001.2021124111708.tif 2012 NaN " + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_all.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32e2dfcf", + "metadata": {}, + "outputs": [], + "source": [ + "{'province_n': 'province_name', 'district_n':'district_name', 'sector_nam': 'sector_name',\n", + " 'min': 'ntl_mean', 'max':'ntl_max', 'mean':'ntl_mean', 'count':'pixel_count',\n", + " 'sum':'ntl_sum', 'median':'ntl_median', 'raster':'raster_filename'}" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "0f20ad15", + "metadata": {}, + "outputs": [], + "source": [ + "# Save combined file\n", + "df = pd.read_csv(DIR_DB_DATA / \"population.csv\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "1b4f3c1d", + "metadata": {}, + "outputs": [], + "source": [ + "df[['elderly_60', 'general_pop', 'children_under5',\n", + " 'youth_15_24', 'men_2020', 'women_2020', 'building_count']] = df[['elderly_60', 'general_pop', 'children_under5',\n", + " 'youth_15_24', 'men_2020', 'women_2020', 'building_count']].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "70958d6f", + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(DIR_DB_DATA / \"population.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "7bd03c85", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['cell_id', 'elderly_60', 'general_pop', 'children_under5',\n", + " 'youth_15_24', 'men_2020', 'women_2020', 'building_count'],\n", + " dtype='object')" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "beb29011", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4k/7vm8r4rj2g90lf39dt0pqwbr0000gn/T/ipykernel_27417/1666416216.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df.rename(columns={'min': 'ntl_mean', 'max':'ntl_max', 'mean':'ntl_mean', 'count':'pixel_count','min':'ntl_min',\n" + ] + } + ], + "source": [ + "df.rename(columns={'min': 'ntl_mean', 'max':'ntl_max', 'mean':'ntl_mean', 'count':'pixel_count','min':'ntl_min',\n", + " 'sum':'ntl_sum', 'median':'ntl_median', 'raster':'raster_filename'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "02902b6e", + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(DIR_DB_DATA / \"ntl-annual-2012-2024.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "1c3f84ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " cell_id elderly_60 general_pop children_under5 youth_15_24 \\\n", + "0 RWA.1.1.1.1_1 241 3855 495 758 \n", + "1 RWA.1.1.1.2_1 229 3669 470 720 \n", + "2 RWA.1.1.1.3_1 109 1756 225 345 \n", + "3 RWA.1.1.1.4_1 318 5174 666 1016 \n", + "4 RWA.1.1.2.1_1 335 6046 833 1264 \n", + "\n", + " men_2020 women_2020 building_count \n", + "0 1850 2004 1435 \n", + "1 1761 1907 884 \n", + "2 843 913 530 \n", + "3 2482 2691 1512 \n", + "4 2880 3166 2036 " + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c560ded5", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " VNP46A4_2024 = bm.raster(gdf, product_id=\"VNP46A4\",\n", + " date_range=(\"2024-01-01\", \"2024-12-31\"))\n", + "except KeyError as e:\n", + " if str(e) == \"'name'\":\n", + " raise RuntimeError(\n", + " \"No granules returned (empty manifest) or manifest schema changed. \"\n", + " \"Broaden date_range, check AOI/token, or upgrade blackmarble.\"\n", + " )\n", + " else:\n", + " raise\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77002aa3", + "metadata": {}, + "outputs": [], + "source": [ + "# Annual data: raster for 2024\n", + "VNP46A4_2024 = bm.raster(\n", + " gdf,\n", + " product_id=\"VNP46A4\",\n", + " date_range=(\"2024-01-01\", \"2024-12-31\"),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a09881b9", + "metadata": {}, + "outputs": [], + "source": [ + "gdf = geopandas.read_file(\n", + " \"https://geodata.ucdavis.edu/gadm/gadm4.1/json/gadm41_GHA_1.json.zip\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "8ddf9775", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 51912.000000\n", + "mean 3.579215\n", + "std 23.235693\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 894.000000\n", + "Name: ntl_sum, dtype: float64" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_an.ntl_sum.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "c9a76b45", + "metadata": {}, + "outputs": [], + "source": [ + "df_an = pd.read_csv(DIR_DB_DATA / \"ntl-annual-2012-2024.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "797f2178", + "metadata": {}, + "outputs": [], + "source": [ + "df_an['id'] = df_an.index + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "f9e9f894", + "metadata": {}, + "outputs": [], + "source": [ + "df_an[['id', 'cell_id', 'ntl_min', 'ntl_max', 'ntl_mean', 'pixel_count', 'ntl_sum',\n", + " 'ntl_median', 'raster_filename', 'year']].to_csv(DIR_DB_DATA / \"annual.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "766e5c55", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['cell_id', 'ntl_min', 'ntl_max', 'ntl_mean', 'pixel_count', 'ntl_sum',\n", + " 'ntl_median', 'raster_filename', 'year', 'id'],\n", + " dtype='object')" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_an.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "5dab55ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_an.ntl_sum.hist(bins=100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24ac01ca", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv-sp", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorials/langchain/rag-1.ipynb b/notebooks/tutorials/langchain/rag-1.ipynb deleted file mode 100644 index ce9f9fd..0000000 --- a/notebooks/tutorials/langchain/rag-1.ipynb +++ /dev/null @@ -1,1427 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5630b0ca", - "metadata": { - "id": "5630b0ca" - }, - "source": [ - "# Build a Retrieval Augmented Generation (RAG) App: Part 1\n", - "\n", - "One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. These are applications that can answer questions about specific source information. These applications use a technique known as Retrieval Augmented Generation, or [RAG](/docs/concepts/rag/).\n", - "\n", - "This is a multi-part tutorial:\n", - "\n", - "- [Part 1](/docs/tutorials/rag) (this guide) introduces RAG and walks through a minimal implementation.\n", - "- [Part 2](/docs/tutorials/qa_chat_history) extends the implementation to accommodate conversation-style interactions and multi-step retrieval processes.\n", - "\n", - "This tutorial will show how to build a simple Q&A application\n", - "over a text data source. Along the way we’ll go over a typical Q&A\n", - "architecture and highlight additional resources for more advanced Q&A techniques. We’ll also see\n", - "how LangSmith can help us trace and understand our application.\n", - "LangSmith will become increasingly helpful as our application grows in\n", - "complexity.\n", - "\n", - "If you're already familiar with basic retrieval, you might also be interested in\n", - "this [high-level overview of different retrieval techniques](/docs/concepts/retrieval).\n", - "\n", - "**Note**: Here we focus on Q&A for unstructured data. If you are interested for RAG over structured data, check out our tutorial on doing [question/answering over SQL data](/docs/tutorials/sql_qa).\n", - "\n", - "## Overview\n", - "A typical RAG application has two main components:\n", - "\n", - "**Indexing**: a pipeline for ingesting data from a source and indexing it. *This usually happens offline.*\n", - "\n", - "**Retrieval and generation**: the actual RAG chain, which takes the user query at run time and retrieves the relevant data from the index, then passes that to the model.\n", - "\n", - "Note: the indexing portion of this tutorial will largely follow the [semantic search tutorial](/docs/tutorials/retrievers).\n", - "\n", - "The most common full sequence from raw data to answer looks like:\n", - "\n", - "### Indexing\n", - "1. **Load**: First we need to load our data. This is done with [Document Loaders](/docs/concepts/document_loaders).\n", - "2. **Split**: [Text splitters](/docs/concepts/text_splitters) break large `Documents` into smaller chunks. This is useful both for indexing data and passing it into a model, as large chunks are harder to search over and won't fit in a model's finite context window.\n", - "3. **Store**: We need somewhere to store and index our splits, so that they can be searched over later. This is often done using a [VectorStore](/docs/concepts/vectorstores) and [Embeddings](/docs/concepts/embedding_models) model.\n", - "\n", - "![index_diagram](https://github.com/langchain-ai/langchain/blob/master/docs/static/img/rag_indexing.png?raw=1)\n", - "\n", - "### Retrieval and generation\n", - "4. **Retrieve**: Given a user input, relevant splits are retrieved from storage using a [Retriever](/docs/concepts/retrievers).\n", - "5. **Generate**: A [ChatModel](/docs/concepts/chat_models) / [LLM](/docs/concepts/text_llms) produces an answer using a prompt that includes both the question with the retrieved data\n", - "\n", - "![retrieval_diagram](https://github.com/langchain-ai/langchain/blob/master/docs/static/img/rag_retrieval_generation.png?raw=1)\n", - "\n", - "Once we've indexed our data, we will use [LangGraph](https://langchain-ai.github.io/langgraph/) as our orchestration framework to implement the retrieval and generation steps.\n", - "\n", - "## Setup\n", - "\n", - "### Jupyter Notebook\n", - "\n", - "This and other tutorials are perhaps most conveniently run in a [Jupyter notebooks](https://jupyter.org/). Going through guides in an interactive environment is a great way to better understand them. See [here](https://jupyter.org/install) for instructions on how to install.\n", - "\n", - "### Installation\n", - "\n", - "This tutorial requires these langchain dependencies:\n", - "\n", - "import Tabs from '@theme/Tabs';\n", - "import TabItem from '@theme/TabItem';\n", - "import CodeBlock from \"@theme/CodeBlock\";\n", - "\n", - "\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "9ff1b425", - "metadata": { - "id": "9ff1b425" - }, - "source": [ - " \n", - " \n", - " conda install langchain-text-splitters langchain-community langgraph -c conda-forge\n", - " \n", - "\n", - "\n", - "\n", - "For more details, see our [Installation guide](/docs/how_to/installation).\n", - "\n", - "### LangSmith\n", - "\n", - "Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls.\n", - "As these applications get more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent.\n", - "The best way to do this is with [LangSmith](https://smith.langchain.com).\n", - "\n", - "After you sign up at the link above, make sure to set your environment variables to start logging traces:\n", - "\n", - "```shell\n", - "export LANGSMITH_TRACING=\"true\"\n", - "export LANGSMITH_API_KEY=\"...\"\n", - "```\n", - "\n", - "Or, if in a notebook, you can set them with:\n", - "\n", - "```python\n", - "import getpass\n", - "import os\n", - "\n", - "os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n", - "os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass()\n", - "```\n", - "\n", - "## Components\n", - "\n", - "We will need to select three components from LangChain's suite of integrations.\n", - "\n", - "import ChatModelTabs from \"@theme/ChatModelTabs\";\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "929028d1", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "00ecad4c", - "metadata": {}, - "outputs": [], - "source": [ - "DIR_WORKSPACE = Path.cwd().parents[2]\n", - "DIR_DATA = DIR_WORKSPACE / \"data\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26ef9d35", - "metadata": { - "id": "26ef9d35" - }, - "outputs": [], - "source": [ - "# | output: false\n", - "# | echo: false\n", - "\n", - "from langchain_openai import ChatOpenAI\n", - "\n", - "llm = ChatOpenAI(model=\"gpt-4o-mini\")" - ] - }, - { - "cell_type": "markdown", - "id": "f1b78672-f21e-4827-843e-59514d18ca20", - "metadata": { - "id": "f1b78672-f21e-4827-843e-59514d18ca20" - }, - "source": [ - "import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a199c764-5dfd-45cf-a4d4-731f2c3d474f", - "metadata": { - "id": "a199c764-5dfd-45cf-a4d4-731f2c3d474f" - }, - "outputs": [], - "source": [ - "# | output: false\n", - "# | echo: false\n", - "\n", - "from langchain_openai import OpenAIEmbeddings\n", - "\n", - "embeddings = OpenAIEmbeddings()" - ] - }, - { - "cell_type": "markdown", - "id": "859ffca8-055e-4f5a-95fe-55906ed1d63f", - "metadata": { - "id": "859ffca8-055e-4f5a-95fe-55906ed1d63f" - }, - "source": [ - "import VectorStoreTabs from \"@theme/VectorStoreTabs\";\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f4db6b46-ea3f-4994-9d54-d7c84beb50cc", - "metadata": { - "id": "f4db6b46-ea3f-4994-9d54-d7c84beb50cc" - }, - "outputs": [], - "source": [ - "# | output: false\n", - "# | echo: false\n", - "\n", - "from langchain_core.vectorstores import InMemoryVectorStore\n", - "\n", - "vector_store = InMemoryVectorStore(embeddings)" - ] - }, - { - "cell_type": "markdown", - "id": "93b2d316-922c-4318-b72d-486fd6813b94", - "metadata": { - "id": "93b2d316-922c-4318-b72d-486fd6813b94" - }, - "source": [ - "## Preview\n", - "\n", - "In this guide we’ll build an app that answers questions about the website's content. The specific website we will use is the [LLM Powered Autonomous\n", - "Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post\n", - "by Lilian Weng, which allows us to ask questions about the contents of\n", - "the post.\n", - "\n", - "We can create a simple indexing pipeline and RAG chain to do this in ~50\n", - "lines of code.\n", - "\n", - "```python\n", - "import bs4\n", - "from langchain import hub\n", - "from langchain_community.document_loaders import WebBaseLoader\n", - "from langchain_core.documents import Document\n", - "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", - "from langgraph.graph import START, StateGraph\n", - "from typing_extensions import List, TypedDict\n", - "\n", - "# Load and chunk contents of the blog\n", - "loader = WebBaseLoader(\n", - " web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n", - " bs_kwargs=dict(\n", - " parse_only=bs4.SoupStrainer(\n", - " class_=(\"post-content\", \"post-title\", \"post-header\")\n", - " )\n", - " ),\n", - ")\n", - "docs = loader.load()\n", - "\n", - "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", - "all_splits = text_splitter.split_documents(docs)\n", - "\n", - "# Index chunks\n", - "_ = vector_store.add_documents(documents=all_splits)\n", - "\n", - "# Define prompt for question-answering\n", - "# N.B. for non-US LangSmith endpoints, you may need to specify\n", - "# api_url=\"https://api.smith.langchain.com\" in hub.pull.\n", - "prompt = hub.pull(\"rlm/rag-prompt\")\n", - "\n", - "\n", - "# Define state for application\n", - "class State(TypedDict):\n", - " question: str\n", - " context: List[Document]\n", - " answer: str\n", - "\n", - "\n", - "# Define application steps\n", - "def retrieve(state: State):\n", - " retrieved_docs = vector_store.similarity_search(state[\"question\"])\n", - " return {\"context\": retrieved_docs}\n", - "\n", - "\n", - "def generate(state: State):\n", - " docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n", - " messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n", - " response = llm.invoke(messages)\n", - " return {\"answer\": response.content}\n", - "\n", - "\n", - "# Compile application and test\n", - "graph_builder = StateGraph(State).add_sequence([retrieve, generate])\n", - "graph_builder.add_edge(START, \"retrieve\")\n", - "graph = graph_builder.compile()\n", - "```\n", - "\n", - "```python\n", - "response = graph.invoke({\"question\": \"What is Task Decomposition?\"})\n", - "print(response[\"answer\"])\n", - "```\n", - "\n", - "```\n", - "Task Decomposition is the process of breaking down a complicated task into smaller, manageable steps to facilitate easier execution and understanding. Techniques like Chain of Thought (CoT) and Tree of Thoughts (ToT) guide models to think step-by-step, allowing them to explore multiple reasoning possibilities. This method enhances performance on complex tasks and provides insight into the model's thinking process.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "9ff8204b-dabc-4790-80ea-50d4cf4fceb0", - "metadata": { - "id": "9ff8204b-dabc-4790-80ea-50d4cf4fceb0" - }, - "source": [ - "Check out the [LangSmith\n", - "trace](https://smith.langchain.com/public/65030797-7efa-4356-a7bd-b54b3dc70e17/r)." - ] - }, - { - "cell_type": "markdown", - "id": "efa9ea6a-f914-4f50-8e35-52e6c34b8001", - "metadata": { - "id": "efa9ea6a-f914-4f50-8e35-52e6c34b8001" - }, - "source": [ - "## Detailed walkthrough\n", - "\n", - "Let’s go through the above code step-by-step to really understand what’s\n", - "going on.\n", - "\n", - "## 1. Indexing {#indexing}\n", - "\n", - ":::note\n", - "\n", - "This section is an abbreviated version of the content in the [semantic search tutorial](/docs/tutorials/retrievers).\n", - "If you're comfortable with [document loaders](/docs/concepts/document_loaders), [embeddings](/docs/concepts/embedding_models), and [vector stores](/docs/concepts/vectorstores),\n", - "feel free to skip to the next section on [retrieval and generation](/docs/tutorials/rag/#orchestration).\n", - "\n", - ":::\n", - "\n", - "### Loading documents\n", - "\n", - "We need to first load the blog post contents. We can use\n", - "[DocumentLoaders](/docs/concepts/document_loaders)\n", - "for this, which are objects that load in data from a source and return a\n", - "list of\n", - "[Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html)\n", - "objects.\n", - "\n", - "In this case we’ll use the\n", - "[WebBaseLoader](/docs/integrations/document_loaders/web_base),\n", - "which uses `urllib` to load HTML from web URLs and `BeautifulSoup` to\n", - "parse it to text. We can customize the HTML -\\> text parsing by passing\n", - "in parameters into the `BeautifulSoup` parser via `bs_kwargs` (see\n", - "[BeautifulSoup\n", - "docs](https://beautiful-soup-4.readthedocs.io/en/latest/#beautifulsoup)).\n", - "In this case only HTML tags with class “post-content”, “post-title”, or\n", - "“post-header” are relevant, so we’ll remove all others." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7b0971b5-8579-4a89-bd2e-9029dda4c4f1", - "metadata": { - "id": "7b0971b5-8579-4a89-bd2e-9029dda4c4f1", - "outputId": "20a18843-4509-49c1-be4b-006ccd5e1ecd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total characters: 43047\n" - ] - } - ], - "source": [ - "import bs4\n", - "from langchain_community.document_loaders import WebBaseLoader\n", - "\n", - "# Only keep post title, headers, and content from the full HTML.\n", - "url_visit_rwanda = \"https://visitrwanda.com\"\n", - "url_demo = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\n", - "bs4_strainer = bs4.SoupStrainer(class_=(\"post-title\", \"post-header\", \"post-content\"))\n", - "loader = WebBaseLoader(\n", - " web_paths=(url_demo,),\n", - " bs_kwargs={\"parse_only\": bs4_strainer},\n", - ")\n", - "docs = loader.load()\n", - "\n", - "assert len(docs) == 1\n", - "print(f\"Total characters: {len(docs[0].page_content)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "cb9898fe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total characters: 31\n" - ] - } - ], - "source": [ - "from langchain_community.document_loaders import PyPDFLoader\n", - "\n", - "file_visit_rwanda = DIR_DATA / \"docs\" / \"Visit-Rwanda-Destination-Guide.pdf\"\n", - "pdf_loader = PyPDFLoader(file_visit_rwanda)\n", - "docs = pdf_loader.load()\n", - "\n", - "print(f\"Total characters: {len(docs[0].page_content)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "1a560025-fb86-4b7e-9586-da263bbad481", - "metadata": { - "id": "1a560025-fb86-4b7e-9586-da263bbad481", - "outputId": "d815c8a0-33a0-413d-d904-9088de58df60" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INTORE\n", - "RWANDA \n", - "TOURISM BROCHURE\n" - ] - } - ], - "source": [ - "print(docs[0].page_content[:500])" - ] - }, - { - "cell_type": "markdown", - "id": "e6f11795-e19f-4697-bc6e-6d477355a1cd", - "metadata": { - "id": "e6f11795-e19f-4697-bc6e-6d477355a1cd" - }, - "source": [ - "#### Go deeper\n", - "\n", - "`DocumentLoader`: Object that loads data from a source as list of `Documents`.\n", - "\n", - "- [Docs](/docs/how_to#document-loaders):\n", - " Detailed documentation on how to use `DocumentLoaders`.\n", - "- [Integrations](/docs/integrations/document_loaders/): 160+\n", - " integrations to choose from.\n", - "- [Interface](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.base.BaseLoader.html):\n", - " API reference for the base interface.\n", - "\n", - "### Splitting documents\n", - "\n", - "Our loaded document is over 42k characters which is too long to fit\n", - "into the context window of many models. Even for those models that could\n", - "fit the full post in their context window, models can struggle to find\n", - "information in very long inputs.\n", - "\n", - "To handle this we’ll split the `Document` into chunks for embedding and\n", - "vector storage. This should help us retrieve only the most relevant parts\n", - "of the blog post at run time.\n", - "\n", - "As in the [semantic search tutorial](/docs/tutorials/retrievers), we use a\n", - "[RecursiveCharacterTextSplitter](/docs/how_to/recursive_text_splitter),\n", - "which will recursively split the document using common separators like\n", - "new lines until each chunk is the appropriate size. This is the\n", - "recommended text splitter for generic text use cases." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "753e1484-e21b-4f62-9866-b3a5971f88a7", - "metadata": { - "id": "753e1484-e21b-4f62-9866-b3a5971f88a7", - "outputId": "9c81615b-1efa-4f36-9b0d-08257eb9bf44" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Split blog post into 73 sub-documents.\n" - ] - } - ], - "source": [ - "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", - "\n", - "text_splitter = RecursiveCharacterTextSplitter(\n", - " chunk_size=1000, # chunk size (characters)\n", - " chunk_overlap=200, # chunk overlap (characters)\n", - " add_start_index=True, # track index in original document\n", - ")\n", - "all_splits = text_splitter.split_documents(docs)\n", - "\n", - "print(f\"Split blog post into {len(all_splits)} sub-documents.\")" - ] - }, - { - "cell_type": "markdown", - "id": "f5193e01-6cf1-45b9-9ba5-38caf75162a6", - "metadata": { - "id": "f5193e01-6cf1-45b9-9ba5-38caf75162a6" - }, - "source": [ - "#### Go deeper\n", - "\n", - "`TextSplitter`: Object that splits a list of `Document`s into smaller\n", - "chunks. Subclass of `DocumentTransformer`s.\n", - "\n", - "- Learn more about splitting text using different methods by reading the [how-to docs](/docs/how_to#text-splitters)\n", - "- [Code (py or js)](/docs/integrations/document_loaders/source_code)\n", - "- [Scientific papers](/docs/integrations/document_loaders/grobid)\n", - "- [Interface](https://python.langchain.com/api_reference/text_splitters/base/langchain_text_splitters.base.TextSplitter.html): API reference for the base interface.\n", - "\n", - "`DocumentTransformer`: Object that performs a transformation on a list\n", - "of `Document` objects.\n", - "\n", - "- [Docs](/docs/how_to#text-splitters): Detailed documentation on how to use `DocumentTransformers`\n", - "- [Integrations](/docs/integrations/document_transformers/)\n", - "- [Interface](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.transformers.BaseDocumentTransformer.html): API reference for the base interface.\n", - "\n", - "### Storing documents\n", - "\n", - "Now we need to index our 66 text chunks so that we can search over them\n", - "at runtime. Following the [semantic search tutorial](/docs/tutorials/retrievers),\n", - "our approach is to [embed](/docs/concepts/embedding_models/) the contents of each document split and insert these embeddings\n", - "into a [vector store](/docs/concepts/vectorstores/). Given an input query, we can then use\n", - "vector search to retrieve relevant documents.\n", - "\n", - "We can embed and store all of our document splits in a single command\n", - "using the vector store and embeddings model selected at the [start of the tutorial](/docs/tutorials/rag/#components)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00d455e1-c681-4665-9470-58dbeca050d4", - "metadata": { - "id": "00d455e1-c681-4665-9470-58dbeca050d4", - "outputId": "1dc73d79-538f-4995-983e-79c4904473d4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['07c18af6-ad58-479a-bfb1-d508033f9c64', '9000bf8e-1993-446f-8d4d-f4e507ba4b8f', 'ba3b5d14-bed9-4f5f-88be-44c88aedc2e6']\n" - ] - } - ], - "source": [ - "document_ids = vector_store.add_documents(documents=all_splits)\n", - "\n", - "print(document_ids[:3])" - ] - }, - { - "cell_type": "markdown", - "id": "57666234-a5b3-4abc-b079-755241bb2b98", - "metadata": { - "id": "57666234-a5b3-4abc-b079-755241bb2b98" - }, - "source": [ - "#### Go deeper\n", - "\n", - "`Embeddings`: Wrapper around a text embedding model, used for converting\n", - "text to embeddings.\n", - "\n", - "- [Docs](/docs/how_to/embed_text): Detailed documentation on how to use embeddings.\n", - "- [Integrations](/docs/integrations/text_embedding/): 30+ integrations to choose from.\n", - "- [Interface](https://python.langchain.com/api_reference/core/embeddings/langchain_core.embeddings.Embeddings.html): API reference for the base interface.\n", - "\n", - "`VectorStore`: Wrapper around a vector database, used for storing and\n", - "querying embeddings.\n", - "\n", - "- [Docs](/docs/how_to/vectorstores): Detailed documentation on how to use vector stores.\n", - "- [Integrations](/docs/integrations/vectorstores/): 40+ integrations to choose from.\n", - "- [Interface](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html): API reference for the base interface.\n", - "\n", - "This completes the **Indexing** portion of the pipeline. At this point\n", - "we have a query-able vector store containing the chunked contents of our\n", - "blog post. Given a user question, we should ideally be able to return\n", - "the snippets of the blog post that answer the question.\n", - "\n", - "## 2. Retrieval and Generation {#orchestration}\n", - "\n", - "Now let’s write the actual application logic. We want to create a simple\n", - "application that takes a user question, searches for documents relevant\n", - "to that question, passes the retrieved documents and initial question to\n", - "a model, and returns an answer.\n", - "\n", - "For generation, we will use the chat model selected at the [start of the tutorial](/docs/tutorials/rag/#components).\n", - "\n", - "We’ll use a prompt for RAG that is checked into the LangChain prompt hub\n", - "([here](https://smith.langchain.com/hub/rlm/rag-prompt))." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46f378c5-858c-488f-8aef-8b59a6280791", - "metadata": { - "id": "46f378c5-858c-488f-8aef-8b59a6280791", - "outputId": "ab55c3ea-e97b-4396-de9e-f617bdb8460e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n", - "Question: (question goes here) \n", - "Context: (context goes here) \n", - "Answer:\n" - ] - } - ], - "source": [ - "from langchain import hub\n", - "\n", - "# N.B. for non-US LangSmith endpoints, you may need to specify\n", - "# api_url=\"https://api.smith.langchain.com\" in hub.pull.\n", - "prompt = hub.pull(\"rlm/rag-prompt\")\n", - "\n", - "example_messages = prompt.invoke(\n", - " {\"context\": \"(context goes here)\", \"question\": \"(question goes here)\"}\n", - ").to_messages()\n", - "\n", - "assert len(example_messages) == 1\n", - "print(example_messages[0].content)" - ] - }, - { - "cell_type": "markdown", - "id": "77dfe84d-cc19-4227-bee4-56b69508ab11", - "metadata": { - "id": "77dfe84d-cc19-4227-bee4-56b69508ab11" - }, - "source": [ - "We'll use [LangGraph](https://langchain-ai.github.io/langgraph/) to tie together the retrieval and generation steps into a single application. This will bring a number of benefits:\n", - "\n", - "- We can define our application logic once and automatically support multiple invocation modes, including streaming, async, and batched calls.\n", - "- We get streamlined deployments via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).\n", - "- LangSmith will automatically trace the steps of our application together.\n", - "- We can easily add key features to our application, including [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) and [human-in-the-loop approval](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/), with minimal code changes.\n", - "\n", - "To use LangGraph, we need to define three things:\n", - "\n", - "1. The state of our application;\n", - "2. The nodes of our application (i.e., application steps);\n", - "3. The \"control flow\" of our application (e.g., the ordering of the steps).\n", - "\n", - "#### State:\n", - "\n", - "The [state](https://langchain-ai.github.io/langgraph/concepts/low_level/#state) of our application controls what data is input to the application, transferred between steps, and output by the application. It is typically a `TypedDict`, but can also be a [Pydantic BaseModel](https://langchain-ai.github.io/langgraph/how-tos/state-model/).\n", - "\n", - "For a simple RAG application, we can just keep track of the input question, retrieved context, and generated answer:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bdc7c33-67f4-40c3-a0f5-9b846bc6e35c", - "metadata": { - "id": "3bdc7c33-67f4-40c3-a0f5-9b846bc6e35c" - }, - "outputs": [], - "source": [ - "from langchain_core.documents import Document\n", - "from typing_extensions import List, TypedDict\n", - "\n", - "\n", - "class State(TypedDict):\n", - " question: str\n", - " context: List[Document]\n", - " answer: str" - ] - }, - { - "cell_type": "markdown", - "id": "77868d9a-892f-4b2c-b706-850f96b4464f", - "metadata": { - "id": "77868d9a-892f-4b2c-b706-850f96b4464f" - }, - "source": [ - "#### Nodes (application steps)\n", - "\n", - "Let's start with a simple sequence of two steps: retrieval and generation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bdabbf44-cbee-46a4-98e4-794fdfc8bb3b", - "metadata": { - "id": "bdabbf44-cbee-46a4-98e4-794fdfc8bb3b" - }, - "outputs": [], - "source": [ - "def retrieve(state: State):\n", - " retrieved_docs = vector_store.similarity_search(state[\"question\"])\n", - " return {\"context\": retrieved_docs}\n", - "\n", - "\n", - "def generate(state: State):\n", - " docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n", - " messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n", - " response = llm.invoke(messages)\n", - " return {\"answer\": response.content}" - ] - }, - { - "cell_type": "markdown", - "id": "d1ac9dc3-d73d-48c3-be05-4b60e0b8bc17", - "metadata": { - "id": "d1ac9dc3-d73d-48c3-be05-4b60e0b8bc17" - }, - "source": [ - "Our retrieval step simply runs a similarity search using the input question, and the generation step formats the retrieved context and original question into a prompt for the chat model.\n", - "\n", - "#### Control flow\n", - "\n", - "Finally, we compile our application into a single `graph` object. In this case, we are just connecting the retrieval and generation steps into a single sequence." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "418ddefb-9a1d-42bf-9d23-e525268312a4", - "metadata": { - "id": "418ddefb-9a1d-42bf-9d23-e525268312a4" - }, - "outputs": [], - "source": [ - "from langgraph.graph import START, StateGraph\n", - "\n", - "graph_builder = StateGraph(State).add_sequence([retrieve, generate])\n", - "graph_builder.add_edge(START, \"retrieve\")\n", - "graph = graph_builder.compile()" - ] - }, - { - "cell_type": "markdown", - "id": "20b127f4-8411-4214-8cdd-a281771ab708", - "metadata": { - "id": "20b127f4-8411-4214-8cdd-a281771ab708" - }, - "source": [ - "LangGraph also comes with built-in utilities for visualizing the control flow of your application:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "feabc04f-b509-4452-8e2b-d7c7b7585a18", - "metadata": { - "id": "feabc04f-b509-4452-8e2b-d7c7b7585a18", - "outputId": "757445a8-1409-4597-903f-dd5e3aa2d3ae" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image, display\n", - "\n", - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "31f7dc4d-cac8-4be9-b44c-df097dc28c81", - "metadata": { - "id": "31f7dc4d-cac8-4be9-b44c-df097dc28c81" - }, - "source": [ - "
    \n", - "Do I need to use LangGraph?\n", - "\n", - "LangGraph is not required to build a RAG application. Indeed, we can implement the same application logic through invocations of the individual components:\n", - "\n", - "```python\n", - "question = \"...\"\n", - "\n", - "retrieved_docs = vector_store.similarity_search(question)\n", - "docs_content = \"\\n\\n\".join(doc.page_content for doc in retrieved_docs)\n", - "prompt = prompt.invoke({\"question\": question, \"context\": docs_content})\n", - "answer = llm.invoke(prompt)\n", - "```\n", - "\n", - "The benefits of LangGraph include:\n", - "\n", - "- Support for multiple invocation modes: this logic would need to be rewritten if we wanted to stream output tokens, or stream the results of individual steps;\n", - "- Automatic support for tracing via [LangSmith](https://docs.smith.langchain.com/) and deployments via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/);\n", - "- Support for persistence, human-in-the-loop, and other features.\n", - "\n", - "Many use-cases demand RAG in a conversational experience, such that a user can receive context-informed answers via a stateful conversation. As we will see in [Part 2](/docs/tutorials/qa_chat_history) of the tutorial, LangGraph's management and persistence of state simplifies these applications enormously.\n", - "\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "id": "eee9c057-5a08-46a3-8c7d-6a314d1e777d", - "metadata": { - "id": "eee9c057-5a08-46a3-8c7d-6a314d1e777d" - }, - "source": [ - "#### Usage\n", - "\n", - "Let's test our application! LangGraph supports multiple invocation modes, including sync, async, and streaming.\n", - "\n", - "Invoke:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "663b93ba-f0a7-44c4-a894-fe895bd5b009", - "metadata": { - "id": "663b93ba-f0a7-44c4-a894-fe895bd5b009", - "outputId": "e71e81fe-b9da-436f-a20d-f92d1ade0b55" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Context: [Document(id='a42dc78b-8f76-472a-9e25-180508af74f3', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 1585}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.'), Document(id='c0e45887-d0b0-483d-821a-bb5d8316d51d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 2192}, page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.'), Document(id='4cc7f318-35f5-440f-a4a4-145b5f0b918d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 29630}, page_content='Resources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.'), Document(id='f621ade4-9b0d-471f-a522-44eb5feeba0c', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 19373}, page_content=\"(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.\")]\n", - "\n", - "\n", - "Answer: Task decomposition is a technique used to break down complex tasks into smaller, manageable steps, allowing for more efficient problem-solving. This can be achieved through methods like chain of thought prompting or the tree of thoughts approach, which explores multiple reasoning possibilities at each step. It can be initiated through simple prompts, task-specific instructions, or human inputs.\n" - ] - } - ], - "source": [ - "result = graph.invoke({\"question\": \"What is Task Decomposition?\"})\n", - "\n", - "print(f\"Context: {result['context']}\\n\\n\")\n", - "print(f\"Answer: {result['answer']}\")" - ] - }, - { - "cell_type": "markdown", - "id": "4ef88f30-40ca-476b-808d-794cb72d401f", - "metadata": { - "id": "4ef88f30-40ca-476b-808d-794cb72d401f" - }, - "source": [ - "Stream steps:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e6314a96-aab8-4ecc-bbf9-094fa2aa0e50", - "metadata": { - "id": "e6314a96-aab8-4ecc-bbf9-094fa2aa0e50", - "outputId": "37fdb977-0e44-47e3-95d9-7e07ace33734" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'retrieve': {'context': [Document(id='a42dc78b-8f76-472a-9e25-180508af74f3', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 1585}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.'), Document(id='c0e45887-d0b0-483d-821a-bb5d8316d51d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 2192}, page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.'), Document(id='4cc7f318-35f5-440f-a4a4-145b5f0b918d', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 29630}, page_content='Resources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.'), Document(id='f621ade4-9b0d-471f-a522-44eb5feeba0c', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 19373}, page_content=\"(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.\")]}}\n", - "\n", - "----------------\n", - "\n", - "{'generate': {'answer': 'Task decomposition is the process of breaking down a complex task into smaller, more manageable steps. This technique, often enhanced by methods like Chain of Thought (CoT) or Tree of Thoughts, allows models to reason through tasks systematically and improves performance by clarifying the thought process. It can be achieved through simple prompts, task-specific instructions, or human inputs.'}}\n", - "\n", - "----------------\n", - "\n" - ] - } - ], - "source": [ - "for step in graph.stream(\n", - " {\"question\": \"What is Task Decomposition?\"}, stream_mode=\"updates\"\n", - "):\n", - " print(f\"{step}\\n\\n----------------\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "f860142d-d50b-4526-a03f-a59a763117fe", - "metadata": { - "id": "f860142d-d50b-4526-a03f-a59a763117fe" - }, - "source": [ - "Stream [tokens](/docs/concepts/tokens/):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28625cc3-0f77-4143-af51-ce0fd6682120", - "metadata": { - "id": "28625cc3-0f77-4143-af51-ce0fd6682120", - "outputId": "d3e0ea55-ceed-47b2-ba72-42b9315eac9b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "|Task| decomposition| is| the| process| of| breaking| down| complex| tasks| into| smaller|,| more| manageable| steps|.| It| can| be| achieved| through| techniques| like| Chain| of| Thought| (|Co|T|)| prompting|,| which| encourages| the| model| to| think| step| by| step|,| or| through| more| structured| methods| like| the| Tree| of| Thoughts|.| This| approach| not| only| simplifies| task| execution| but| also| provides| insights| into| the| model|'s| reasoning| process|.||" - ] - } - ], - "source": [ - "for message, metadata in graph.stream(\n", - " {\"question\": \"What is Task Decomposition?\"}, stream_mode=\"messages\"\n", - "):\n", - " print(message.content, end=\"|\")" - ] - }, - { - "cell_type": "markdown", - "id": "0fe09894-0cc5-4427-9a24-aef60d20705f", - "metadata": { - "id": "0fe09894-0cc5-4427-9a24-aef60d20705f" - }, - "source": [ - ":::tip\n", - "\n", - "For async invocations, use:\n", - "\n", - "```python\n", - "result = await graph.ainvoke(...)\n", - "```\n", - "\n", - "and\n", - "\n", - "```python\n", - "async for step in graph.astream(...):\n", - "```\n", - "\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "406534d4-66a3-4c27-b277-2bd2f5930cf5", - "metadata": { - "id": "406534d4-66a3-4c27-b277-2bd2f5930cf5" - }, - "source": [ - "#### Returning sources\n", - "\n", - "Note that by storing the retrieved context in the state of the graph, we recover sources for the model's generated answer in the `\"context\"` field of the state. See [this guide](/docs/how_to/qa_sources/) on returning sources for more detail.\n", - "\n", - "#### Go deeper\n", - "\n", - "[Chat models](/docs/concepts/chat_models) take in a sequence of messages and return a message.\n", - "\n", - "- [Docs](/docs/how_to#chat-models)\n", - "- [Integrations](/docs/integrations/chat/): 25+ integrations to choose from.\n", - "- [Interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html): API reference for the base interface.\n", - "\n", - "**Customizing the prompt**\n", - "\n", - "As shown above, we can load prompts (e.g., [this RAG\n", - "prompt](https://smith.langchain.com/hub/rlm/rag-prompt)) from the prompt\n", - "hub. The prompt can also be easily customized. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "956e7e78-c26c-4d2d-bf2e-4fc41ff40d37", - "metadata": { - "id": "956e7e78-c26c-4d2d-bf2e-4fc41ff40d37" - }, - "outputs": [], - "source": [ - "from langchain_core.prompts import PromptTemplate\n", - "\n", - "template = \"\"\"Use the following pieces of context to answer the question at the end.\n", - "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", - "Use three sentences maximum and keep the answer as concise as possible.\n", - "Always say \"thanks for asking!\" at the end of the answer.\n", - "\n", - "{context}\n", - "\n", - "Question: {question}\n", - "\n", - "Helpful Answer:\"\"\"\n", - "custom_rag_prompt = PromptTemplate.from_template(template)" - ] - }, - { - "cell_type": "markdown", - "id": "217cf819-da76-4595-8f75-33f931f1f92a", - "metadata": { - "id": "217cf819-da76-4595-8f75-33f931f1f92a" - }, - "source": [ - "## Query analysis\n", - "\n", - "So far, we are executing the retrieval using the raw input query. However, there are some advantages to allowing a model to generate the query for retrieval purposes. For example:\n", - "\n", - "- In addition to semantic search, we can build in structured filters (e.g., \"Find documents since the year 2020.\");\n", - "- The model can rewrite user queries, which may be multifaceted or include irrelevant language, into more effective search queries.\n", - "\n", - "[Query analysis](/docs/concepts/retrieval/#query-analysis) employs models to transform or construct optimized search queries from raw user input. We can easily incorporate a query analysis step into our application. For illustrative purposes, let's add some metadata to the documents in our vector store. We will add some (contrived) sections to the document which we can filter on later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df00956a-6565-4c05-b201-32854dd2a889", - "metadata": { - "id": "df00956a-6565-4c05-b201-32854dd2a889", - "outputId": "279d568e-7c8b-4e85-8bd8-7ea4bcff33b8" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/',\n", - " 'start_index': 8,\n", - " 'section': 'beginning'}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total_documents = len(all_splits)\n", - "third = total_documents // 3\n", - "\n", - "for i, document in enumerate(all_splits):\n", - " if i < third:\n", - " document.metadata[\"section\"] = \"beginning\"\n", - " elif i < 2 * third:\n", - " document.metadata[\"section\"] = \"middle\"\n", - " else:\n", - " document.metadata[\"section\"] = \"end\"\n", - "\n", - "\n", - "all_splits[0].metadata" - ] - }, - { - "cell_type": "markdown", - "id": "114878bd-a334-41ed-8013-ec4ce0a9112b", - "metadata": { - "id": "114878bd-a334-41ed-8013-ec4ce0a9112b" - }, - "source": [ - "We will need to update the documents in our vector store. We will use a simple [InMemoryVectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html) for this, as we will use some of its specific features (i.e., metadata filtering). Refer to the vector store [integration documentation](/docs/integrations/vectorstores/) for relevant features of your chosen vector store." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebb3cbd7-7c75-4cc0-a198-ff7c54a0c43a", - "metadata": { - "id": "ebb3cbd7-7c75-4cc0-a198-ff7c54a0c43a" - }, - "outputs": [], - "source": [ - "from langchain_core.vectorstores import InMemoryVectorStore\n", - "\n", - "vector_store = InMemoryVectorStore(embeddings)\n", - "_ = vector_store.add_documents(all_splits)" - ] - }, - { - "cell_type": "markdown", - "id": "c08aaccd-b3df-45e9-8646-d6ea20215e62", - "metadata": { - "id": "c08aaccd-b3df-45e9-8646-d6ea20215e62" - }, - "source": [ - "Let's next define a schema for our search query. We will use [structured output](/docs/concepts/structured_outputs/) for this purpose. Here we define a query as containing a string query and a document section (either \"beginning\", \"middle\", or \"end\"), but this can be defined however you like." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87f9c8c3-3e99-426d-aa65-fec4b9155c3f", - "metadata": { - "id": "87f9c8c3-3e99-426d-aa65-fec4b9155c3f" - }, - "outputs": [], - "source": [ - "from typing import Literal\n", - "\n", - "from typing_extensions import Annotated\n", - "\n", - "\n", - "class Search(TypedDict):\n", - " \"\"\"Search query.\"\"\"\n", - "\n", - " query: Annotated[str, ..., \"Search query to run.\"]\n", - " section: Annotated[\n", - " Literal[\"beginning\", \"middle\", \"end\"],\n", - " ...,\n", - " \"Section to query.\",\n", - " ]" - ] - }, - { - "cell_type": "markdown", - "id": "6399a870-cb06-4219-9b4f-cfa37cb8ab0f", - "metadata": { - "id": "6399a870-cb06-4219-9b4f-cfa37cb8ab0f" - }, - "source": [ - "Finally, we add a step to our LangGraph application to generate a query from the user's raw input:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e8fcdcb-a4ff-41c3-97c7-92d81ab29f38", - "metadata": { - "id": "7e8fcdcb-a4ff-41c3-97c7-92d81ab29f38" - }, - "outputs": [], - "source": [ - "class State(TypedDict):\n", - " question: str\n", - " # highlight-next-line\n", - " query: Search\n", - " context: List[Document]\n", - " answer: str\n", - "\n", - "\n", - "# highlight-next-line\n", - "def analyze_query(state: State):\n", - " # highlight-next-line\n", - " structured_llm = llm.with_structured_output(Search)\n", - " # highlight-next-line\n", - " query = structured_llm.invoke(state[\"question\"])\n", - " # highlight-next-line\n", - " return {\"query\": query}\n", - "\n", - "\n", - "def retrieve(state: State):\n", - " # highlight-start\n", - " query = state[\"query\"]\n", - " retrieved_docs = vector_store.similarity_search(\n", - " query[\"query\"],\n", - " filter=lambda doc: doc.metadata.get(\"section\") == query[\"section\"],\n", - " )\n", - " # highlight-end\n", - " return {\"context\": retrieved_docs}\n", - "\n", - "\n", - "def generate(state: State):\n", - " docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n", - " messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n", - " response = llm.invoke(messages)\n", - " return {\"answer\": response.content}\n", - "\n", - "\n", - "# highlight-start\n", - "graph_builder = StateGraph(State).add_sequence([analyze_query, retrieve, generate])\n", - "graph_builder.add_edge(START, \"analyze_query\")\n", - "# highlight-end\n", - "graph = graph_builder.compile()" - ] - }, - { - "cell_type": "markdown", - "id": "28a62d34", - "metadata": { - "id": "28a62d34" - }, - "source": [ - "
    \n", - "Full Code:\n", - "\n", - "```python\n", - "from typing import Literal\n", - "\n", - "import bs4\n", - "from langchain import hub\n", - "from langchain_community.document_loaders import WebBaseLoader\n", - "from langchain_core.documents import Document\n", - "from langchain_core.vectorstores import InMemoryVectorStore\n", - "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", - "from langgraph.graph import START, StateGraph\n", - "from typing_extensions import Annotated, List, TypedDict\n", - "\n", - "# Load and chunk contents of the blog\n", - "loader = WebBaseLoader(\n", - " web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n", - " bs_kwargs=dict(\n", - " parse_only=bs4.SoupStrainer(\n", - " class_=(\"post-content\", \"post-title\", \"post-header\")\n", - " )\n", - " ),\n", - ")\n", - "docs = loader.load()\n", - "\n", - "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", - "all_splits = text_splitter.split_documents(docs)\n", - "\n", - "\n", - "# Update metadata (illustration purposes)\n", - "total_documents = len(all_splits)\n", - "third = total_documents // 3\n", - "\n", - "for i, document in enumerate(all_splits):\n", - " if i < third:\n", - " document.metadata[\"section\"] = \"beginning\"\n", - " elif i < 2 * third:\n", - " document.metadata[\"section\"] = \"middle\"\n", - " else:\n", - " document.metadata[\"section\"] = \"end\"\n", - "\n", - "\n", - "# Index chunks\n", - "vector_store = InMemoryVectorStore(embeddings)\n", - "_ = vector_store.add_documents(all_splits)\n", - "\n", - "\n", - "# Define schema for search\n", - "class Search(TypedDict):\n", - " \"\"\"Search query.\"\"\"\n", - "\n", - " query: Annotated[str, ..., \"Search query to run.\"]\n", - " section: Annotated[\n", - " Literal[\"beginning\", \"middle\", \"end\"],\n", - " ...,\n", - " \"Section to query.\",\n", - " ]\n", - "\n", - "# Define prompt for question-answering\n", - "prompt = hub.pull(\"rlm/rag-prompt\")\n", - "\n", - "\n", - "# Define state for application\n", - "class State(TypedDict):\n", - " question: str\n", - " query: Search\n", - " context: List[Document]\n", - " answer: str\n", - "\n", - "\n", - "def analyze_query(state: State):\n", - " structured_llm = llm.with_structured_output(Search)\n", - " query = structured_llm.invoke(state[\"question\"])\n", - " return {\"query\": query}\n", - "\n", - "\n", - "def retrieve(state: State):\n", - " query = state[\"query\"]\n", - " retrieved_docs = vector_store.similarity_search(\n", - " query[\"query\"],\n", - " filter=lambda doc: doc.metadata.get(\"section\") == query[\"section\"],\n", - " )\n", - " return {\"context\": retrieved_docs}\n", - "\n", - "\n", - "def generate(state: State):\n", - " docs_content = \"\\n\\n\".join(doc.page_content for doc in state[\"context\"])\n", - " messages = prompt.invoke({\"question\": state[\"question\"], \"context\": docs_content})\n", - " response = llm.invoke(messages)\n", - " return {\"answer\": response.content}\n", - "\n", - "\n", - "graph_builder = StateGraph(State).add_sequence([analyze_query, retrieve, generate])\n", - "graph_builder.add_edge(START, \"analyze_query\")\n", - "graph = graph_builder.compile()\n", - "```\n", - "\n", - "
    " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a92d539-f85d-434b-b911-51a1cf9b81da", - "metadata": { - "id": "8a92d539-f85d-434b-b911-51a1cf9b81da", - "outputId": "77e24b9b-687b-40d0-cccf-f69f28ff33ff" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(Image(graph.get_graph().draw_mermaid_png()))" - ] - }, - { - "cell_type": "markdown", - "id": "653cf8dc-a201-43ea-9965-02fcfd2fc316", - "metadata": { - "id": "653cf8dc-a201-43ea-9965-02fcfd2fc316" - }, - "source": [ - "We can test our implementation by specifically asking for context from the end of the post. Note that the model includes different information in its answer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b420650-2d9e-4f5e-a8d8-ec36ae07423c", - "metadata": { - "id": "8b420650-2d9e-4f5e-a8d8-ec36ae07423c", - "outputId": "a1e42a3a-9126-4793-d23a-fd8a651a41dc" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'analyze_query': {'query': {'query': 'Task Decomposition', 'section': 'end'}}}\n", - "\n", - "----------------\n", - "\n", - "{'retrieve': {'context': [Document(id='d6cef137-e1e8-4ddc-91dc-b62bd33c6020', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 39221, 'section': 'end'}, page_content='Finite context length: The restricted context capacity limits the inclusion of historical information, detailed instructions, API call context, and responses. The design of the system has to work with this limited communication bandwidth, while mechanisms like self-reflection to learn from past mistakes would benefit a lot from long or infinite context windows. Although vector stores and retrieval can provide access to a larger knowledge pool, their representation power is not as powerful as full attention.\\n\\n\\nChallenges in long-term planning and task decomposition: Planning over a lengthy history and effectively exploring the solution space remain challenging. LLMs struggle to adjust plans when faced with unexpected errors, making them less robust compared to humans who learn from trial and error.'), Document(id='d1834ae1-eb6a-43d7-a023-08dfa5028799', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 39086, 'section': 'end'}, page_content='}\\n]\\nChallenges#\\nAfter going through key ideas and demos of building LLM-centered agents, I start to see a couple common limitations:'), Document(id='ca7f06e4-2c2e-4788-9a81-2418d82213d9', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 32942, 'section': 'end'}, page_content='}\\n]\\nThen after these clarification, the agent moved into the code writing mode with a different system message.\\nSystem message:'), Document(id='1fcc2736-30f4-4ef6-90f2-c64af92118cb', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'start_index': 35127, 'section': 'end'}, page_content='\"content\": \"You will get instructions for code to write.\\\\nYou will write a very long answer. Make sure that every detail of the architecture is, in the end, implemented as code.\\\\nMake sure that every detail of the architecture is, in the end, implemented as code.\\\\n\\\\nThink step by step and reason yourself to the right decisions to make sure we get it right.\\\\nYou will first lay out the names of the core classes, functions, methods that will be necessary, as well as a quick comment on their purpose.\\\\n\\\\nThen you will output the content of each file including ALL code.\\\\nEach file must strictly follow a markdown code block format, where the following tokens must be replaced such that\\\\nFILENAME is the lowercase file name including the file extension,\\\\nLANG is the markup code block language for the code\\'s language, and CODE is the code:\\\\n\\\\nFILENAME\\\\n```LANG\\\\nCODE\\\\n```\\\\n\\\\nYou will start with the \\\\\"entrypoint\\\\\" file, then go to the ones that are imported by that file, and so on.\\\\nPlease')]}}\n", - "\n", - "----------------\n", - "\n", - "{'generate': {'answer': 'The end of the post highlights that task decomposition faces challenges in long-term planning and adapting to unexpected errors. LLMs struggle with adjusting their plans, making them less robust compared to humans who learn from trial and error. This indicates a limitation in effectively exploring the solution space and handling complex tasks.'}}\n", - "\n", - "----------------\n", - "\n" - ] - } - ], - "source": [ - "for step in graph.stream(\n", - " {\"question\": \"What does the end of the post say about Task Decomposition?\"},\n", - " stream_mode=\"updates\",\n", - "):\n", - " print(f\"{step}\\n\\n----------------\\n\")" - ] - }, - { - "cell_type": "markdown", - "id": "5875a48a-c849-4da9-99e0-558b04884fb0", - "metadata": { - "id": "5875a48a-c849-4da9-99e0-558b04884fb0" - }, - "source": [ - "In both the streamed steps and the [LangSmith trace](https://smith.langchain.com/public/bdbaae61-130c-4338-8b59-9315dfee22a0/r), we can now observe the structured query that was fed into the retrieval step.\n", - "\n", - "Query Analysis is a rich problem with a wide range of approaches. Refer to the [how-to guides](/docs/how_to/#query-analysis) for more examples." - ] - }, - { - "cell_type": "markdown", - "id": "82e4d779", - "metadata": { - "id": "82e4d779" - }, - "source": [ - "## Next steps\n", - "\n", - "We've covered the steps to build a basic Q&A app over data:\n", - "\n", - "- Loading data with a [Document Loader](/docs/concepts/document_loaders)\n", - "- Chunking the indexed data with a [Text Splitter](/docs/concepts/text_splitters) to make it more easily usable by a model\n", - "- [Embedding the data](/docs/concepts/embedding_models) and storing the data in a [vectorstore](/docs/how_to/vectorstores)\n", - "- [Retrieving](/docs/concepts/retrievers) the previously stored chunks in response to incoming questions\n", - "- Generating an answer using the retrieved chunks as context.\n", - "\n", - "In [Part 2](/docs/tutorials/qa_chat_history) of the tutorial, we will extend the implementation here to accommodate conversation-style interactions and multi-step retrieval processes.\n", - "\n", - "Further reading:\n", - "\n", - "- [Return sources](/docs/how_to/qa_sources): Learn how to return source documents\n", - "- [Streaming](/docs/how_to/streaming): Learn how to stream outputs and intermediate steps\n", - "- [Add chat history](/docs/how_to/message_history): Learn how to add chat history to your app\n", - "- [Retrieval conceptual guide](/docs/concepts/retrieval): A high-level overview of specific retrieval techniques" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": ".venv-llms", - "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": "3.13.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sql/ntl_pop_db_script.sql b/sql/ntl_pop_db_script.sql new file mode 100644 index 0000000..e11e301 --- /dev/null +++ b/sql/ntl_pop_db_script.sql @@ -0,0 +1,78 @@ +-- Cell information +CREATE TABLE cells ( + cell_id TEXT PRIMARY KEY, + province_name TEXT, + district_name TEXT, + sector_name TEXT, + cell_name TEXT +); + +-- Population +CREATE TABLE pop ( + cell_id TEXT PRIMARY KEY, + elderly_60 REAL, + general_pop REAL, + children_under5 REAL, + youth_15_24 REAL, + men_2020 REAL, + women_2020 REAL, + building_count REAL +); + +-- Annual NTL stats +CREATE TABLE ntl_annual ( + id SERIAL PRIMARY KEY, + cell_id TEXT REFERENCES cells(cell_id), + ntl_min REAL, + ntl_max REAL, + ntl_mean REAL, + ntl_median REAL, + ntl_sum REAL, + pixel_count REAL, + raster_filename TEXT, + year INT +); + + +-- Monthly NTL stats +CREATE TABLE ntl_monthly ( + id SERIAL PRIMARY KEY, + cell_id TEXT REFERENCES cells(cell_id), + ntl_min REAL, + ntl_max REAL, + ntl_mean REAL, + ntl_median REAL, + ntl_sum REAL, + pixel_count REAL, + raster_filename TEXT, + year INT, + month INT, + date DATE +); + + +-- LOAD DATA FROM CSV FILES +COPY cells FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/cells.csv' DELIMITER ',' CSV HEADER; +COPY pop FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/population.csv' DELIMITER ',' CSV HEADER; +COPY ntl_annual FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/ntl-annual-2012-2024.csv' DELIMITER ',' CSV HEADER; +\copy ntl_monthly(cell_id, ntl_min, ntl_max, ntl_mean, ntl_median, ntl_sum, + pixel_count, raster_filename, year, month, date) +FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/merged-zonal-stats-2012-2024.csv' +CSV HEADER; + + +--BASIC QUERIES +-- Check average light in a given year +SELECT year, AVG(ntl_mean) +FROM ntl_annual +GROUP BY year +ORDER BY year; + +-- Get top 5 brightest cells in 2023 +SELECT cell_id, ntl_sum +FROM ntl_annual +WHERE year = 2023 +ORDER BY ntl_sum DESC +LIMIT 5; + + From e678b0c685784095f64968e39ac781777ae7e21d Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Sun, 14 Sep 2025 20:56:33 +0200 Subject: [PATCH 19/33] Added notebook for connecting to postgreSQL --- .../module-5/01-connecting-to-databases.ipynb | 316 ++++++++++++++ notebooks/tutorials/audio_files/bushiri.wav | Bin 2880096 -> 0 bytes notebooks/tutorials/audio_files/mcp.wav | Bin 377542 -> 0 bytes .../tutorials/langchain/requirements.txt | 0 notebooks/tutorials/usage_example.ipynb | 397 ------------------ ...{ntl_pop_db_script.sql => data_import.sql} | 0 sql/ntl_pop_db_schema.sql | 53 +++ sql/sql_basics.sql | 142 +++++++ 8 files changed, 511 insertions(+), 397 deletions(-) create mode 100644 notebooks/module-5/01-connecting-to-databases.ipynb delete mode 100644 notebooks/tutorials/audio_files/bushiri.wav delete mode 100644 notebooks/tutorials/audio_files/mcp.wav delete mode 100644 notebooks/tutorials/langchain/requirements.txt delete mode 100644 notebooks/tutorials/usage_example.ipynb rename sql/{ntl_pop_db_script.sql => data_import.sql} (100%) create mode 100644 sql/ntl_pop_db_schema.sql create mode 100644 sql/sql_basics.sql diff --git a/notebooks/module-5/01-connecting-to-databases.ipynb b/notebooks/module-5/01-connecting-to-databases.ipynb new file mode 100644 index 0000000..001fdae --- /dev/null +++ b/notebooks/module-5/01-connecting-to-databases.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a080151f", + "metadata": {}, + "source": [ + "# Connecting to PostgreSQL from Python\n", + "\n", + "In this notebook, we will learn how to connect a PostgreSQL database to Python. \n", + "This is an essential skill if you want to:\n", + "- Explore your data directly in **pandas** DataFrames \n", + "- Build **data pipelines (ETL/ELT)** to refresh your tables regularly \n", + "- Power **machine learning or LLM-based applications** that rely on structured data \n", + "- Prototype **dashboards and APIs** that serve insights to end-users \n", + "\n", + "We will go step by step:\n", + "1. Load database credentials securely from a `.env` file \n", + "2. Connect to PostgreSQL using **SQLAlchemy** and **psycopg2** \n", + "3. Run sanity checks to confirm the connection \n", + "4. Query our nightlights and population tables into pandas \n", + "5. Perform simple summaries, joins, and a time-series query " + ] + }, + { + "cell_type": "markdown", + "id": "88d556cd", + "metadata": {}, + "source": [ + "## Required Python Packages\n", + "Ensure that you installed the required packages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "491440a6", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from dotenv import load_dotenv\n", + "from sqlalchemy import create_engine, text\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "ef232a12", + "metadata": {}, + "source": [ + "## PostgreSQL Environment Variables\n", + "To connect, PostgreSQL needs a few basic pieces of information:\n", + "\n", + "- **PGHOST** → The host where PostgreSQL is running. \n", + " For local setups this is usually `localhost`. \n", + "- **PGPORT** → The port PostgreSQL listens on. \n", + " Default is `5432`. \n", + "- **PGDATABASE** → The database name you created for this lab, e.g. `ntl_pop`. \n", + "- **PGUSER** → Your PostgreSQL username. \n", + " This is usually the same as your system username when running locally. \n", + "\n", + "⚠️ **Note:** On a local setup, you usually don’t need a password if PostgreSQL is configured to trust local connections. \n", + "In that case, we will not use `PGPASSWORD`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15f789d9", + "metadata": {}, + "outputs": [], + "source": [ + "# 1) Load environment variables\n", + "# The .env file should be in the repo root with keys:\n", + "# PGHOST, PGPORT, PGDATABASE, PGUSER, PGPASSWORD\n", + "\n", + "# looks for .env in current directory by default\n", + "load_dotenv() \n", + "\n", + "PGHOST = os.getenv(\"PGHOST\")\n", + "PGPORT = os.getenv(\"PGPORT\", \"5432\")\n", + "PGDATABASE = os.getenv(\"PGDATABASE\")\n", + "PGUSER = os.getenv(\"PGUSER\")\n", + "PGPASSWORD = os.getenv(\"PGPASSWORD\")" + ] + }, + { + "cell_type": "markdown", + "id": "729bbf71", + "metadata": {}, + "source": [ + "## Create SQLAlchemy engine \n", + "At this stage, we are establishing the **connection between Python and PostgreSQL**. \n", + "The `SQLAlchemy` package plays the role of a **database toolkit and Object Relational Mapper (ORM)**. In our case, we are mainly using it as a **bridge**: it translates Python code into SQL statements that PostgreSQL can understand, and it manages the underlying connection details for us (user, host, port, database). By creating an **engine object**, we set up a reusable gateway that allows us to open sessions, run queries, and easily pull results into Python tools such as **pandas** for analysis. While `psycopg2` handles the low-level communication with PostgreSQL, `SQLAlchemy` provides a higher-level, more user-friendly interface. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be1eecf7", + "metadata": {}, + "outputs": [], + "source": [ + "# Create SQLAlchemy engine (no password needed for local connections)\n", + "engine = create_engine(\n", + " f\"postgresql+psycopg2://{PGUSER}@{PGHOST}:{PGPORT}/{PGDATABASE}\",\n", + " pool_pre_ping=True,\n", + ")\n", + "\n", + "# Test the connection\n", + "with engine.connect() as conn:\n", + " who = conn.execute(text(\"SELECT current_user, current_database();\")).fetchone()\n", + " print(\"Connected as:\", who)" + ] + }, + { + "cell_type": "markdown", + "id": "9ef873a3", + "metadata": {}, + "source": [ + "## Running SQL Commands\n", + "Once the connection is established through the SQLAlchemy engine, we can begin sending SQL commands from Python. \n", + "What happens in the background is: \n", + "\n", + "1. **You write a SQL query as a string in Python** (e.g., `\"SELECT * FROM ntl_annual LIMIT 5;\"`). \n", + "2. **SQLAlchemy passes this query to the underlying driver (`psycopg2`)**, which handles the low-level communication with PostgreSQL. \n", + "3. **PostgreSQL executes the query on the database** and returns the results (rows and columns). \n", + "4. **SQLAlchemy collects the results** and makes them available to Python. \n", + "5. If we use `pandas.read_sql`, the results are automatically converted into a **DataFrame** for analysis, filtering, and visualization. \n", + "\n", + "In short: Python → SQLAlchemy → psycopg2 → PostgreSQL → back to Python as DataFrame. \n", + "This allows us to stay in a familiar Python environment while still harnessing the power of SQL. \n", + "\n", + "In the same way, we can also **create new tables**, **update existing tables with additional data**, or even **delete records**—all from within Python. \n", + "For example: \n", + "- Use `CREATE TABLE` statements to define new tables. \n", + "- Use `INSERT` or `COPY` to add more rows of data. \n", + "- Use `UPDATE` to modify existing records. \n", + "- Use `DROP` to remove tables you no longer need. \n", + "\n", + "This makes Python a powerful interface for both **querying** and **managing** your database directly. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8f3cc4c", + "metadata": {}, + "outputs": [], + "source": [ + "def peek(sql: str) -> pd.DataFrame:\n", + " \"\"\"Run a SQL query and return the results as a pandas DataFrame.\"\"\"\n", + " return pd.read_sql(sql, engine)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9b26b3b", + "metadata": {}, + "outputs": [], + "source": [ + "# ========== 3. Sanity checks ==========\n", + "tables = [\"cells\", \"pop\", \"ntl_annual\", \"ntl_monthly\"]\n", + "\n", + "print(\"\\n--- Table counts ---\")\n", + "for t in tables:\n", + " cnt = peek(f\"SELECT COUNT(*) AS n FROM {t};\")\n", + " print(f\"{t}: {cnt.loc[0, 'n']} rows\")\n", + "\n", + "print(\"\\n--- First 5 rows from cells ---\")\n", + "print(peek(\"SELECT * FROM cells LIMIT 5\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "385c2f3b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# ========== 4. Annual summaries ==========\n", + "print(\"\\n--- Average ntl_mean per year ---\")\n", + "annual_avg = peek(\"\"\"\n", + "SELECT year, AVG(ntl_mean) AS avg_ntl_mean\n", + "FROM ntl_annual\n", + "GROUP BY year\n", + "ORDER BY year;\n", + "\"\"\")\n", + "print(annual_avg.head())\n", + "\n", + "print(\"\\n--- Top 5 brightest cells in 2023 ---\")\n", + "brightest = peek(\"\"\"\n", + "SELECT cell_id, ntl_mean\n", + "FROM ntl_annual\n", + "WHERE year = 2023\n", + "ORDER BY ntl_mean DESC\n", + "LIMIT 5;\n", + "\"\"\")\n", + "print(brightest)\n", + "\n", + "\n", + "# ========== 5. Join with population ==========\n", + "print(\"\\n--- Light per capita (2023) ---\")\n", + "per_capita = peek(\"\"\"\n", + "SELECT\n", + " c.cell_name,\n", + " c.district_name,\n", + " a.year,\n", + " a.ntl_sum / NULLIF(p.general_pop, 0) AS light_per_capita,\n", + " a.ntl_sum,\n", + " p.general_pop\n", + "FROM ntl_annual a\n", + "JOIN cells c ON a.cell_id = c.cell_id\n", + "JOIN pop p ON a.cell_id = p.cell_id\n", + "WHERE a.year = 2023\n", + "ORDER BY light_per_capita DESC NULLS LAST\n", + "LIMIT 10;\n", + "\"\"\")\n", + "print(per_capita)" + ] + }, + { + "cell_type": "markdown", + "id": "bf0dbfd5", + "metadata": {}, + "source": [ + "# Exercise: Working with Your Database\n", + "\n", + "Now that you’ve seen how to connect and query PostgreSQL from Python, it’s time to practice. \n", + "In this exercise, you will write and run your own SQL commands inside Python. \n", + "\n", + "### Tasks\n", + "\n", + "1. **Create a New Table** \n", + " - Write a SQL command in Python to create a small table called `demo_cells` with the following columns: \n", + " - `id` (integer, primary key) \n", + " - `cell_name` (text) \n", + " - `population` (integer) \n", + "\n", + "2. **Insert Data** \n", + " - Insert at least **two rows** of data into `demo_cells`. \n", + " - Hint: use `INSERT INTO demo_cells (...) VALUES (...);`. \n", + "\n", + "3. **Query Your Data** \n", + " - Select all rows from `demo_cells` and load them into a pandas DataFrame. \n", + " - Display the results. \n", + "\n", + "4. **Update Data** \n", + " - Update one of the rows (for example, change the population of a cell). \n", + " - Run a `SELECT` again to confirm the update worked. \n", + "\n", + "5. **Cleanup (Optional)** \n", + " - Drop the table when you are done: `DROP TABLE demo_cells;`. \n", + "\n", + "---\n", + "\n", + "💡 *Tip:* Remember that you can use the `engine.execute(text(\"SQL HERE\"))` pattern for commands like `CREATE`, `INSERT`, `UPDATE`, or `DROP`, and `pd.read_sql(\"SQL HERE\", engine)` when you want to return results into a DataFrame. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2edcf84", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "## Summary: Working with PostgreSQL in Python\n", + "\n", + "In this notebook, you learned how to connect Python to a PostgreSQL database, configure the connection using environment variables, and create a SQLAlchemy engine that acts as a bridge between Python and SQL. You executed key operations such as checking table counts, exploring data with a helper function, and running queries to analyze nightlight trends, identify the brightest cells, and compute light-per-capita statistics by joining nightlights with population data. In the same way, Python can interact with many other databases — whether they are hosted locally on your machine, running on a server in the cloud, or provided as enterprise solutions. This includes both proprietary systems such as Microsoft SQL Server and Oracle, as well as open-source databases like MySQL, MariaDB, and SQLite. Thanks to libraries like SQLAlchemy, the workflow you practiced here is portable: once you learn how to query and manage data in Python, you can apply the same approach across different database platforms.\n", + "\n", + "## Next Steps: Exploring Databases with Python\n", + "After learning how to connect Python to PostgreSQL and run queries, you can explore: \n", + "\n", + "- **Advanced SQL**: joins, window functions, subqueries, CTEs \n", + "- **Database management**: creating/updating tables, indexes, transactions \n", + "- **Python integration**: automate queries, build ETL pipelines, visualize results \n", + "- **Other databases**: SQLite, MySQL/MariaDB, SQL Server, Oracle \n", + "- **Scaling up**: cloud-hosted databases, connection pooling, performance tuning \n", + "- **Applications**: power ML workflows, LLM-based apps (RAG), dashboards, or APIs " + ] + }, + { + "cell_type": "markdown", + "id": "fef87e9a", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": 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diff --git a/notebooks/tutorials/usage_example.ipynb b/notebooks/tutorials/usage_example.ipynb deleted file mode 100644 index ba8ad17..0000000 --- a/notebooks/tutorials/usage_example.ipynb +++ /dev/null @@ -1,397 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "UyjAb5ynxdt6", - "metadata": { - "id": "UyjAb5ynxdt6" - }, - "source": [ - "# Usage Guide for Fine-Tuned Whisper ASR Model on Chichewa\n", - "\n", - "This notebook provides a step-by-step guide to using a fine-tuned Whisper model for Automatic Speech Recognition (ASR) in **Chichewa**. The model has been fine-tuned on a Chichewa dataset consisting of approximately **24 hours of speech data**, and it has undergone several iterations, resulting in multiple versions with differing levels of performance. This guide directs you to the **best-performing model** to ensure optimal transcription accuracy.\n", - "\n", - "### Key Highlights:\n", - "1. **Fine-Tuned on Chichewa**: The model has been specifically trained on 24 hours of Chichewa speech, making it well-suited for transcription tasks in this language. The fine-tuning process has significantly improved its performance for Chichewa ASR tasks.\n", - " \n", - "2. **No Tokenizer Required**: Unlike other Whisper models, this fine-tuned version does not use a tokenizer. Instead, the model works directly with the processor, simplifying the inference process.\n", - "\n", - "3. **Best Performance via Commit Hash**: To ensure you are using the best-performing model, you will load the model by passing the **`revision` parameter** along with the exact **commit hash** corresponding to the version that achieved the lowest Word Error Rate (WER). This ensures you are working with the most accurate version of the model.\n", - "\n", - "4. **Best Performance**: This notebook demonstrates how to load and use the model version with the best WER for Chichewa ASR tasks. Following this guide will allow you to achieve the highest transcription quality.\n", - "\n", - "### What You’ll Learn:\n", - "- How to load and use the fine-tuned Whisper model specifically for Chichewa ASR.\n", - "- How to process and transcribe audio files data using this model.\n", - "- How to ensure you are using the version of the model that delivers the best transcription performance for Chichewa by utilizing the `revision` parameter with the commit hash.\n", - "\n", - "By following this guide, you’ll be equipped to leverage this specialized ASR model to produce high-quality transcriptions of Chichewa speech with minimal setup and effort.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bf03a6b8-ad07-4116-b436-479042a84014", - "metadata": { - "id": "bf03a6b8-ad07-4116-b436-479042a84014" - }, - "outputs": [], - "source": [ - "import librosa\n", - "from datasets import Audio, load_dataset, DatasetDict, load_from_disk\n", - "\n", - "import torch\n", - "\n", - "from transformers import (WhisperFeatureExtractor, WhisperTokenizer,\n", - " WhisperProcessor, logging)\n", - "from transformers import WhisperForConditionalGeneration\n", - "from transformers import (pipeline, AutoModel, AutoTokenizer,\n", - "AutoProcessor, AutoModelForSpeechSeq2Seq)\n", - "\n", - "# Suppress Warnings\n", - "import warnings\n", - "# Suppress all warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "# Suppress Hugging Face logs except errors\n", - "logging.set_verbosity_error()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e72c38f8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using device: mps\n" - ] - } - ], - "source": [ - "# Check if MPS is available (for Apple Silicon)\n", - "device = torch.device(\"mps\") if torch.backends.mps.is_available() else torch.device(\"cpu\")\n", - "print(f\"Using device: {device}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5120216f", - "metadata": {}, - "outputs": [], - "source": [ - "# Audio Files to Test" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "63dd3fe6", - "metadata": {}, - "outputs": [], - "source": [ - "audio1 = \"./audio_files/bushiri.wav\"\n", - "audio2 = \"./audio_files/mcp.wav\"" - ] - }, - { - "cell_type": "markdown", - "id": "6c767de0", - "metadata": { - "id": "6c767de0" - }, - "source": [ - "## 1. Best-Performing Model and WER Information\n", - "\n", - "In this section, we provide the commit hash for the best-performing model and its corresponding Word Error Rate (WER) based on evaluation.\n", - "\n", - "### Best-Performing Model:\n", - "\n", - "The model with the best WER performance can be loaded using the following **commit hash**:\n", - "\n", - "```python\n", - "# Best-performing model hash\n", - "HUGGINGFACE_MODEL_ID = \"dmatekenya/whisper-large-chichewa\"\n", - "BEST_MODEL_COMMIT_HASH = \"bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a\" \n", - "```\n", - "\n", - "### Note/Warning\n", - "While the model endpoint remains the same (i.e., dmatekenya/whisper-large-chichewa), it is crucial to include the specific commit hash (COMMIT_HASH) provided above when loading the model to access the best-performing version. Please use this [full url](https://huggingface.co/dmatekenya/whisper-large-v3-chichewa/commit/bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a) to access this commit.\n", - "Without the commit hash, the latest version may be loaded, which could have a higher Word Error Rate (WER) than the version evaluated as best. To ensure you get the most accurate results, always include the commit hash.\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "id": "9a1ce662", - "metadata": { - "id": "9a1ce662" - }, - "source": [ - "## 2. Performing Speech-to-Text Inference on Audio Files\n", - "In this section, I will demonstrate how to transcribe an individual audio file directly using the fine-tuned Whisper model, instead of loading a dataset through the HuggingFace `datasets` package. This approach is particularly useful when deploying the model within an application." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f396b5dd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using device: mps\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 5.89it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transcript from model: \n", - " Hello a Malawi azanga konseko omwe muliko, ine ndine mwana wanu a Shepard Bushiri zikomo chifukwa chakuti aumene muliko uthenga kumene ukufikani. Ine uthenga ndi okuti ndikuyamba program yogawa chimanga kwa ma family kapena kwa anthu muno mu Malawi 1 million. Project imeneyi ipanga kuti chimanga chonse chimene chakhala chikugawidwa chikwana 17000 imene value yake ikhoza kukwanira.\n" - ] - } - ], - "source": [ - "import torch\n", - "import librosa\n", - "from transformers import WhisperProcessor, WhisperForConditionalGeneration\n", - "\n", - "# Check if MPS is available (for Apple Silicon)\n", - "device = torch.device(\"mps\") if torch.backends.mps.is_available() else torch.device(\"cpu\")\n", - "print(f\"Using device: {device}\")\n", - "\n", - "# HuggingFace endpoint for this finetuned model\n", - "finetuned_model_id = \"dmatekenya/whisper-large-v3-chichewa\"\n", - "best_model_commit_hash = \"bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a\"\n", - "\n", - "# Whisper base model endpoint\n", - "whisper_base_model_id = \"openai/whisper-large-v3\"\n", - "whisper_base_model_language = \"shona\"\n", - "\n", - "# Load processor\n", - "processor = WhisperProcessor.from_pretrained(\n", - " whisper_base_model_id,\n", - " language=whisper_base_model_language,\n", - " task=\"transcribe\"\n", - ")\n", - "\n", - "# Load model (keeping original precision for accuracy)\n", - "model = WhisperForConditionalGeneration.from_pretrained(\n", - " finetuned_model_id,\n", - " revision=best_model_commit_hash\n", - ")\n", - "model = model.to(device)\n", - "\n", - "# Set model to evaluation mode (important for inference speed)\n", - "model.eval()\n", - "\n", - "# Load audio\n", - "y, sr = librosa.load(audio1, sr=16000)\n", - "\n", - "# Prepare input features\n", - "input_features = processor(y, return_tensors=\"pt\", sampling_rate=sr).input_features\n", - "input_features = input_features.to(device)\n", - "\n", - "# Generate transcription with minimal but safe optimizations\n", - "with torch.no_grad(): # Critical: disable gradient computation for 2-4x speedup\n", - " generated_ids = model.generate(\n", - " inputs=input_features,\n", - " # Keep the model's default generation parameters for accuracy\n", - " # Only add safe optimizations:\n", - " use_cache=True, # Enable KV cache for faster generation\n", - " pad_token_id=processor.tokenizer.pad_token_id\n", - " )\n", - "\n", - "# Decode transcription\n", - "transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", - "print(f\"Transcript from model: \\n {transcription}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "05c12e97", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "\n", - "# Check if MPS is available (for Apple Silicon)\n", - "device = torch.device(\"mps\") if torch.backends.mps.is_available() else torch.device(\"cpu\")\n", - "print(f\"Using device: {device}\")\n", - "\n", - "# HuggingFace endpoint for this finetuned model\n", - "finetuned_model_id = \"dmatekenya/whisper-large-v3-chichewa\"\n", - "\n", - "# Use the best model commit hash from the earlier cell\n", - "best_model_commit_hash = \"bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a\"\n", - "\n", - "# Load whisper processor using base model\n", - "# Whisper base model endpoint\n", - "whisper_base_model_id = \"openai/whisper-large-v3\"\n", - "# Language-I used Shona when finetuning, so use it when loading base model\n", - "whisper_base_model_language = \"shona\"\n", - "processor = WhisperProcessor.from_pretrained(whisper_base_model_id,\n", - " language=whisper_base_model_language,\n", - " task=\"transcribe\")\n", - "# Load the finetuned model and move to device\n", - "model = WhisperForConditionalGeneration.from_pretrained(finetuned_model_id,\n", - " revision=best_model_commit_hash)\n", - "model = model.to(device)\n", - "\n", - "# Use one of the predefined audio files\n", - "y, sr = librosa.load(audio2, sr=16000)\n", - "\n", - "# Prepare input features for the model and move to device\n", - "input_features = processor(y, return_tensors=\"pt\", sampling_rate=sr).input_features\n", - "input_features = input_features.to(device)\n", - "\n", - "# Generate transcription\n", - "generated_ids = model.generate(inputs=input_features)\n", - "transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", - "\n", - "# Print the transcript\n", - "print(f\"Transcript from model: \\n {transcription}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0bb57bec", - "metadata": { - "colab": { - "referenced_widgets": [ - "99c5da0bca8e48569a1c505ce7435505" - ] - }, - "id": "0bb57bec", - "outputId": "ceae6d10-e5fa-4215-ae4e-9f0d1a902595" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Fetching 2 files: 0%| | 0/2 [03:51 \u001b[39m\u001b[32m19\u001b[39m model = \u001b[43mWhisperForConditionalGeneration\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinetuned_model_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 20\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbest_model_commit_hash\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 22\u001b[39m \u001b[38;5;66;03m# Use one of the predefined audio files\u001b[39;00m\n\u001b[32m 23\u001b[39m audio_file = audio1 \u001b[38;5;66;03m# Using the predefined audio1 path\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/modeling_utils.py:317\u001b[39m, in \u001b[36mrestore_default_torch_dtype.._wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 315\u001b[39m old_dtype = torch.get_default_dtype()\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 318\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 319\u001b[39m torch.set_default_dtype(old_dtype)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/modeling_utils.py:4923\u001b[39m, in \u001b[36mPreTrainedModel.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[39m\n\u001b[32m 4913\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 4914\u001b[39m gguf_file\n\u001b[32m 4915\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m device_map \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 4916\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ((\u001b[38;5;28misinstance\u001b[39m(device_map, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mdisk\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m device_map.values()) \u001b[38;5;129;01mor\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mdisk\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m device_map)\n\u001b[32m 4917\u001b[39m ):\n\u001b[32m 4918\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 4919\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mOne or more modules is configured to be mapped to disk. Disk offload is not supported for models \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 4920\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mloaded from GGUF files.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 4921\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m4923\u001b[39m checkpoint_files, sharded_metadata = \u001b[43m_get_resolved_checkpoint_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 4924\u001b[39m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4925\u001b[39m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4926\u001b[39m \u001b[43m \u001b[49m\u001b[43mvariant\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvariant\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4927\u001b[39m \u001b[43m \u001b[49m\u001b[43mgguf_file\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgguf_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4928\u001b[39m \u001b[43m \u001b[49m\u001b[43mfrom_tf\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfrom_tf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4929\u001b[39m \u001b[43m \u001b[49m\u001b[43mfrom_flax\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfrom_flax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4930\u001b[39m \u001b[43m \u001b[49m\u001b[43muse_safetensors\u001b[49m\u001b[43m=\u001b[49m\u001b[43muse_safetensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4931\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4932\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4933\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4934\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4935\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4936\u001b[39m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m=\u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4937\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4938\u001b[39m \u001b[43m \u001b[49m\u001b[43mcommit_hash\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4939\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_remote_code\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_auto_class\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 4940\u001b[39m \u001b[43m \u001b[49m\u001b[43mtransformers_explicit_filename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtransformers_explicit_filename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4941\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 4943\u001b[39m is_sharded = sharded_metadata \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 4944\u001b[39m is_quantized = hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/modeling_utils.py:1337\u001b[39m, in \u001b[36m_get_resolved_checkpoint_files\u001b[39m\u001b[34m(pretrained_model_name_or_path, subfolder, variant, gguf_file, from_tf, from_flax, use_safetensors, cache_dir, force_download, proxies, local_files_only, token, user_agent, revision, commit_hash, is_remote_code, transformers_explicit_filename)\u001b[39m\n\u001b[32m 1335\u001b[39m sharded_metadata = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1336\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_sharded:\n\u001b[32m-> \u001b[39m\u001b[32m1337\u001b[39m checkpoint_files, sharded_metadata = \u001b[43mget_checkpoint_shard_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1338\u001b[39m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1339\u001b[39m \u001b[43m \u001b[49m\u001b[43mresolved_archive_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1340\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1341\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1342\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1343\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1344\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1345\u001b[39m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m=\u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1346\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1347\u001b[39m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1348\u001b[39m \u001b[43m \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1349\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1350\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1351\u001b[39m checkpoint_files = [resolved_archive_file] \u001b[38;5;28;01mif\u001b[39;00m pretrained_model_name_or_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/utils/hub.py:1120\u001b[39m, in \u001b[36mget_checkpoint_shard_files\u001b[39m\u001b[34m(pretrained_model_name_or_path, index_filename, cache_dir, force_download, proxies, resume_download, local_files_only, token, user_agent, revision, subfolder, _commit_hash, **deprecated_kwargs)\u001b[39m\n\u001b[32m 1116\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m shard_filenames, sharded_metadata\n\u001b[32m 1118\u001b[39m \u001b[38;5;66;03m# At this stage pretrained_model_name_or_path is a model identifier on the Hub. Try to get everything from cache,\u001b[39;00m\n\u001b[32m 1119\u001b[39m \u001b[38;5;66;03m# or download the files\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1120\u001b[39m cached_filenames = \u001b[43mcached_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1121\u001b[39m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1122\u001b[39m \u001b[43m \u001b[49m\u001b[43mshard_filenames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1123\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1124\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1125\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1126\u001b[39m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1127\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1128\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1129\u001b[39m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m=\u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1130\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1131\u001b[39m \u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubfolder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1132\u001b[39m \u001b[43m \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1133\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1135\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m cached_filenames, sharded_metadata\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/transformers/utils/hub.py:494\u001b[39m, in \u001b[36mcached_files\u001b[39m\u001b[34m(path_or_repo_id, filenames, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[39m\n\u001b[32m 479\u001b[39m hf_hub_download(\n\u001b[32m 480\u001b[39m path_or_repo_id,\n\u001b[32m 481\u001b[39m filenames[\u001b[32m0\u001b[39m],\n\u001b[32m (...)\u001b[39m\u001b[32m 491\u001b[39m local_files_only=local_files_only,\n\u001b[32m 492\u001b[39m )\n\u001b[32m 493\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m494\u001b[39m \u001b[43msnapshot_download\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 495\u001b[39m \u001b[43m \u001b[49m\u001b[43mpath_or_repo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 496\u001b[39m \u001b[43m \u001b[49m\u001b[43mallow_patterns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfull_filenames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 497\u001b[39m \u001b[43m \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 498\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 499\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 500\u001b[39m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m=\u001b[49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 501\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 502\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 503\u001b[39m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 504\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 505\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 506\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 508\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 509\u001b[39m \u001b[38;5;66;03m# We cannot recover from them\u001b[39;00m\n\u001b[32m 510\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, RepositoryNotFoundError) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, GatedRepoError):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py:114\u001b[39m, in \u001b[36mvalidate_hf_hub_args.._inner_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 111\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[32m 112\u001b[39m kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.\u001b[34m__name__\u001b[39m, has_token=has_token, kwargs=kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m114\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/huggingface_hub/_snapshot_download.py:332\u001b[39m, in \u001b[36msnapshot_download\u001b[39m\u001b[34m(repo_id, repo_type, revision, cache_dir, local_dir, library_name, library_version, user_agent, proxies, etag_timeout, force_download, token, local_files_only, allow_patterns, ignore_patterns, max_workers, tqdm_class, headers, endpoint, local_dir_use_symlinks, resume_download)\u001b[39m\n\u001b[32m 330\u001b[39m _inner_hf_hub_download(file)\n\u001b[32m 331\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m332\u001b[39m \u001b[43mthread_map\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 333\u001b[39m \u001b[43m \u001b[49m\u001b[43m_inner_hf_hub_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 334\u001b[39m \u001b[43m \u001b[49m\u001b[43mfiltered_repo_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 335\u001b[39m \u001b[43m \u001b[49m\u001b[43mdesc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtqdm_desc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 336\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_workers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 337\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# User can use its own tqdm class or the default one from `huggingface_hub.utils`\u001b[39;49;00m\n\u001b[32m 338\u001b[39m \u001b[43m \u001b[49m\u001b[43mtqdm_class\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtqdm_class\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mhf_tqdm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 339\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 341\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m local_dir \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 342\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(os.path.realpath(local_dir))\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/tqdm/contrib/concurrent.py:69\u001b[39m, in \u001b[36mthread_map\u001b[39m\u001b[34m(fn, *iterables, **tqdm_kwargs)\u001b[39m\n\u001b[32m 55\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 56\u001b[39m \u001b[33;03mEquivalent of `list(map(fn, *iterables))`\u001b[39;00m\n\u001b[32m 57\u001b[39m \u001b[33;03mdriven by `concurrent.futures.ThreadPoolExecutor`.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 66\u001b[39m \u001b[33;03m [default: max(32, cpu_count() + 4)].\u001b[39;00m\n\u001b[32m 67\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 68\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mconcurrent\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfutures\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ThreadPoolExecutor\n\u001b[32m---> \u001b[39m\u001b[32m69\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_executor_map\u001b[49m\u001b[43m(\u001b[49m\u001b[43mThreadPoolExecutor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43miterables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mtqdm_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/tqdm/contrib/concurrent.py:51\u001b[39m, in \u001b[36m_executor_map\u001b[39m\u001b[34m(PoolExecutor, fn, *iterables, **tqdm_kwargs)\u001b[39m\n\u001b[32m 47\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m ensure_lock(tqdm_class, lock_name=lock_name) \u001b[38;5;28;01mas\u001b[39;00m lk:\n\u001b[32m 48\u001b[39m \u001b[38;5;66;03m# share lock in case workers are already using `tqdm`\u001b[39;00m\n\u001b[32m 49\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m PoolExecutor(max_workers=max_workers, initializer=tqdm_class.set_lock,\n\u001b[32m 50\u001b[39m initargs=(lk,)) \u001b[38;5;28;01mas\u001b[39;00m ex:\n\u001b[32m---> \u001b[39m\u001b[32m51\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtqdm_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mex\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43miterables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m=\u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/.venv-llms/lib/python3.13/site-packages/tqdm/std.py:1181\u001b[39m, in \u001b[36mtqdm.__iter__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1178\u001b[39m time = \u001b[38;5;28mself\u001b[39m._time\n\u001b[32m 1180\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1181\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 1182\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\n\u001b[32m 1183\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Update and possibly print the progressbar.\u001b[39;49;00m\n\u001b[32m 1184\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;49;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.13/concurrent/futures/_base.py:619\u001b[39m, in \u001b[36mExecutor.map..result_iterator\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 616\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m fs:\n\u001b[32m 617\u001b[39m \u001b[38;5;66;03m# Careful not to keep a reference to the popped future\u001b[39;00m\n\u001b[32m 618\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m619\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m \u001b[43m_result_or_cancel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 620\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 621\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m _result_or_cancel(fs.pop(), end_time - time.monotonic())\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.13/concurrent/futures/_base.py:317\u001b[39m, in \u001b[36m_result_or_cancel\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 315\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 316\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfut\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 318\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 319\u001b[39m fut.cancel()\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.13/concurrent/futures/_base.py:451\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 448\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state == FINISHED:\n\u001b[32m 449\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.__get_result()\n\u001b[32m--> \u001b[39m\u001b[32m451\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_condition\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 453\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[32m 454\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/anaconda3/lib/python3.13/threading.py:359\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 357\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[32m 358\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m359\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 360\u001b[39m gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 361\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " - ] - } - ], - "source": [ - "# HuggingFace endpoint for this finetuned model\n", - "finetuned_model_id = \"dmatekenya/whisper-large-v3-chichewa\"\n", - "\n", - "# Use the best model commit hash from the earlier cell\n", - "best_model_commit_hash = \"bff60fb08ba9f294e05bfcab4306f30b6a0cfc0a\"\n", - "\n", - "# Whisper base model endpoint\n", - "whisper_base_model_id = \"openai/whisper-large-v3\"\n", - "\n", - "# Language-I used Shona when finetuning, so use it when loading base model\n", - "whisper_base_model_language = \"shona\"\n", - "\n", - "# Load whisper processor using base model for\n", - "processor = WhisperProcessor.from_pretrained(whisper_base_model_id,\n", - " language=whisper_base_model_language,\n", - " task=\"transcribe\")\n", - "# Load the finetuned model and use revision parameter to ensure\n", - "# we load the best model\n", - "model = WhisperForConditionalGeneration.from_pretrained(finetuned_model_id,\n", - " revision=best_model_commit_hash)\n", - "\n", - "# Use one of the predefined audio files\n", - "audio_file = audio1 # Using the predefined audio1 path\n", - "y, sr = librosa.load(audio1, sr=16000)\n", - "\n", - "# Prepare input features for the model\n", - "input_features = processor(y, return_tensors=\"pt\", sampling_rate=sr).input_features\n", - "\n", - "# Generate transcription\n", - "generated_ids = model.generate(inputs=input_features)\n", - "transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n", - "\n", - "# Print the transcript\n", - "print(f\"Transcript from model: \\n {transcription}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5654815", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "machine_shape": "hm", - "provenance": [] - }, - "kernelspec": { - "display_name": ".venv-llms", - "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": "3.13.5" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": {} - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/sql/ntl_pop_db_script.sql b/sql/data_import.sql similarity index 100% rename from sql/ntl_pop_db_script.sql rename to sql/data_import.sql diff --git a/sql/ntl_pop_db_schema.sql b/sql/ntl_pop_db_schema.sql new file mode 100644 index 0000000..5a6d83b --- /dev/null +++ b/sql/ntl_pop_db_schema.sql @@ -0,0 +1,53 @@ +-- Cell information +CREATE TABLE cells ( + cell_id TEXT PRIMARY KEY, + province_name TEXT, + district_name TEXT, + sector_name TEXT, + cell_name TEXT +); + +-- Population +CREATE TABLE pop ( + cell_id TEXT PRIMARY KEY, + elderly_60 REAL, + general_pop REAL, + children_under5 REAL, + youth_15_24 REAL, + men_2020 REAL, + women_2020 REAL, + building_count REAL +); + +-- Annual NTL stats +CREATE TABLE ntl_annual ( + id SERIAL PRIMARY KEY, + cell_id TEXT REFERENCES cells(cell_id), + ntl_min REAL, + ntl_max REAL, + ntl_mean REAL, + ntl_median REAL, + ntl_sum REAL, + pixel_count REAL, + raster_filename TEXT, + year INT +); + + +-- Monthly NTL stats +CREATE TABLE ntl_monthly ( + id SERIAL PRIMARY KEY, + cell_id TEXT REFERENCES cells(cell_id), + ntl_min REAL, + ntl_max REAL, + ntl_mean REAL, + ntl_median REAL, + ntl_sum REAL, + pixel_count REAL, + raster_filename TEXT, + year INT, + month INT, + date DATE +); + + diff --git a/sql/sql_basics.sql b/sql/sql_basics.sql new file mode 100644 index 0000000..f1b4d0d --- /dev/null +++ b/sql/sql_basics.sql @@ -0,0 +1,142 @@ +-- ============================================================ +-- basic_sql.sql +-- Basic SQL exploration of ntl_pop database +-- ============================================================ + +-- 1. Quick sanity checks +\echo '--- Sanity checks: tables and counts ---' +\dt + +SELECT 'cells' AS table, COUNT(*) FROM cells +UNION ALL SELECT 'pop', COUNT(*) FROM pop +UNION ALL SELECT 'ntl_annual', COUNT(*) FROM ntl_annual +UNION ALL SELECT 'ntl_monthly', COUNT(*) FROM ntl_monthly; + +SELECT * FROM cells LIMIT 5; +SELECT * FROM pop LIMIT 5; +SELECT * FROM ntl_annual LIMIT 5; +SELECT * FROM ntl_monthly LIMIT 5; + +-- ============================================================ + +-- 2. Annual summaries +\echo '--- Annual summaries ---' +SELECT year, AVG(ntl_mean) AS avg_ntl_mean +FROM ntl_annual +GROUP BY year +ORDER BY year; + +SELECT cell_id, ntl_mean +FROM ntl_annual +WHERE year = 2023 +ORDER BY ntl_mean DESC +LIMIT 5; + +-- ============================================================ + +-- 3. Population filters +\echo '--- Population filters (elderly > 50% of general pop) ---' +SELECT c.cell_name, p.elderly_60, p.general_pop +FROM cells c +JOIN pop p USING (cell_id) +WHERE p.elderly_60 > 0.5 * NULLIF(p.general_pop, 0) +ORDER BY p.elderly_60 DESC; + +-- ============================================================ + +-- 4. Joins and multi-table queries +\echo '--- Annual NTL joined with cell attributes ---' +SELECT + c.cell_name, + c.province_name, + c.district_name, + a.year, + a.ntl_mean, + a.ntl_sum +FROM ntl_annual a +JOIN cells c ON a.cell_id = c.cell_id +WHERE a.year = 2023 +ORDER BY a.ntl_mean DESC; + +\echo '--- Annual NTL + population: per capita light ---' +SELECT + c.cell_name, + c.district_name, + a.year, + a.ntl_sum / NULLIF(p.general_pop, 0) AS light_per_capita, + a.ntl_sum, + p.general_pop +FROM ntl_annual a +JOIN cells c ON a.cell_id = c.cell_id +JOIN pop p ON a.cell_id = p.cell_id +WHERE a.year = 2023 +ORDER BY light_per_capita DESC NULLS LAST; + +\echo '--- District-level aggregate: avg ntl_mean + total ntl_sum ---' +SELECT + c.province_name, + c.district_name, + AVG(a.ntl_mean) AS avg_ntl_mean, + SUM(a.ntl_sum) AS total_ntl_sum +FROM ntl_annual a +JOIN cells c ON a.cell_id = c.cell_id +WHERE a.year = 2023 +GROUP BY c.province_name, c.district_name +ORDER BY total_ntl_sum DESC; + +-- ============================================================ + +-- 5. Monthly time-series queries +\echo '--- Monthly trend for a specific cell ---' +SELECT year, month, ntl_mean +FROM ntl_monthly +WHERE cell_id = 'RW-001-123' +ORDER BY year, month; + +\echo '--- Monthly trend by date ---' +SELECT date, ntl_mean +FROM ntl_monthly +WHERE cell_id = 'RW-001-123' +ORDER BY date; + +\echo '--- Average ntl_mean per month across all cells ---' +SELECT year, month, AVG(ntl_mean) AS avg_ntl_mean +FROM ntl_monthly +GROUP BY year, month +ORDER BY year, month; + +\echo '--- District-level monthly averages (2023) ---' +SELECT + m.year, + m.month, + c.district_name, + AVG(m.ntl_mean) AS avg_ntl_mean +FROM ntl_monthly m +JOIN cells c ON m.cell_id = c.cell_id +WHERE m.year = 2023 +GROUP BY m.year, m.month, c.district_name +ORDER BY m.year, m.month, c.district_name; + +-- ============================================================ + +-- 6. Integrity checks and indexes +\echo '--- Integrity checks ---' +SELECT p.cell_id +FROM pop p +LEFT JOIN cells c USING (cell_id) +WHERE c.cell_id IS NULL; + +\echo '--- Recommended indexes for performance ---' +CREATE INDEX IF NOT EXISTS idx_annual_cell_id_year + ON ntl_annual (cell_id, year); +CREATE INDEX IF NOT EXISTS idx_monthly_cell_id_date + ON ntl_monthly (cell_id, date); +CREATE INDEX IF NOT EXISTS idx_cells_cell_id + ON cells (cell_id); +CREATE INDEX IF NOT EXISTS idx_pop_cell_id + ON pop (cell_id); + +-- ============================================================ +-- END OF SCRIPT +-- ============================================================ + From 9e44e30987b887b82a0c0ed0f742f44ec78902b7 Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Mon, 15 Sep 2025 15:46:25 +0200 Subject: [PATCH 20/33] Updated import.sql --- sql/data_import.sql | 76 ++++----------------------------------------- 1 file changed, 6 insertions(+), 70 deletions(-) diff --git a/sql/data_import.sql b/sql/data_import.sql index e11e301..256601d 100644 --- a/sql/data_import.sql +++ b/sql/data_import.sql @@ -1,78 +1,14 @@ --- Cell information -CREATE TABLE cells ( - cell_id TEXT PRIMARY KEY, - province_name TEXT, - district_name TEXT, - sector_name TEXT, - cell_name TEXT -); - --- Population -CREATE TABLE pop ( - cell_id TEXT PRIMARY KEY, - elderly_60 REAL, - general_pop REAL, - children_under5 REAL, - youth_15_24 REAL, - men_2020 REAL, - women_2020 REAL, - building_count REAL -); - --- Annual NTL stats -CREATE TABLE ntl_annual ( - id SERIAL PRIMARY KEY, - cell_id TEXT REFERENCES cells(cell_id), - ntl_min REAL, - ntl_max REAL, - ntl_mean REAL, - ntl_median REAL, - ntl_sum REAL, - pixel_count REAL, - raster_filename TEXT, - year INT -); - - --- Monthly NTL stats -CREATE TABLE ntl_monthly ( - id SERIAL PRIMARY KEY, - cell_id TEXT REFERENCES cells(cell_id), - ntl_min REAL, - ntl_max REAL, - ntl_mean REAL, - ntl_median REAL, - ntl_sum REAL, - pixel_count REAL, - raster_filename TEXT, - year INT, - month INT, - date DATE -); - -- LOAD DATA FROM CSV FILES COPY cells FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/cells.csv' DELIMITER ',' CSV HEADER; -COPY pop FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/population.csv' DELIMITER ',' CSV HEADER; + +COPY pop(cell_id, elderly_60, children_under5, youth_15_24, general_pop, men_2020, women_2020, building_count) +FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/population.csv' +CSV HEADER; + COPY ntl_annual FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/ntl-annual-2012-2024.csv' DELIMITER ',' CSV HEADER; + \copy ntl_monthly(cell_id, ntl_min, ntl_max, ntl_mean, ntl_median, ntl_sum, pixel_count, raster_filename, year, month, date) FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/merged-zonal-stats-2012-2024.csv' CSV HEADER; - - ---BASIC QUERIES --- Check average light in a given year -SELECT year, AVG(ntl_mean) -FROM ntl_annual -GROUP BY year -ORDER BY year; - --- Get top 5 brightest cells in 2023 -SELECT cell_id, ntl_sum -FROM ntl_annual -WHERE year = 2023 -ORDER BY ntl_sum DESC -LIMIT 5; - - From ced157eafb6bf3f087936ab5cc237f604b72cbfb Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Mon, 15 Sep 2025 16:11:29 +0200 Subject: [PATCH 21/33] Updated import.sql --- sql/data_import.sql | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/sql/data_import.sql b/sql/data_import.sql index 256601d..b927a33 100644 --- a/sql/data_import.sql +++ b/sql/data_import.sql @@ -6,9 +6,12 @@ COPY pop(cell_id, elderly_60, children_under5, youth_15_24, general_pop, men_202 FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/population.csv' CSV HEADER; -COPY ntl_annual FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/ntl-annual-2012-2024.csv' DELIMITER ',' CSV HEADER; +COPY ntl_annual (cell_id, ntl_min, ntl_max, ntl_mean, ntl_median, ntl_sum, + pixel_count, raster_filename, year) +FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/ntl-annual-2012-2024.csv' +DELIMITER ',' CSV HEADER; -\copy ntl_monthly(cell_id, ntl_min, ntl_max, ntl_mean, ntl_median, ntl_sum, +\COPY ntl_monthly(cell_id, ntl_min, ntl_max, ntl_mean, ntl_median, ntl_sum, pixel_count, raster_filename, year, month, date) -FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/merged-zonal-stats-2012-2024.csv' +FROM '/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/ntl-monthly-2012-2024.csv' CSV HEADER; From 7c43d26e928c979b4b9b334dd9fdd30d4256d74d Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Mon, 15 Sep 2025 16:31:50 +0200 Subject: [PATCH 22/33] Updated schema script --- sql/ntl_pop_db_schema.sql | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sql/ntl_pop_db_schema.sql b/sql/ntl_pop_db_schema.sql index 5a6d83b..e83519c 100644 --- a/sql/ntl_pop_db_schema.sql +++ b/sql/ntl_pop_db_schema.sql @@ -9,7 +9,8 @@ CREATE TABLE cells ( -- Population CREATE TABLE pop ( - cell_id TEXT PRIMARY KEY, + id SERIAL PRIMARY KEY, + cell_id TEXT REFERENCES cells(cell_id), elderly_60 REAL, general_pop REAL, children_under5 REAL, From 1b5ce67b70107d7381dabb59570022c12d1b6dc4 Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Tue, 16 Sep 2025 12:11:50 +0200 Subject: [PATCH 23/33] Updated database connection notebook --- .../module-5/01-connecting-to-databases.ipynb | 32 ++- notebooks/module-5/02-accessing-apis.ipynb | 241 ++++++++++++++++++ notebooks/module-5/03-web-scraping.ipynb | 0 3 files changed, 271 insertions(+), 2 deletions(-) create mode 100644 notebooks/module-5/02-accessing-apis.ipynb create mode 100644 notebooks/module-5/03-web-scraping.ipynb diff --git a/notebooks/module-5/01-connecting-to-databases.ipynb b/notebooks/module-5/01-connecting-to-databases.ipynb index 001fdae..623046c 100644 --- a/notebooks/module-5/01-connecting-to-databases.ipynb +++ b/notebooks/module-5/01-connecting-to-databases.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "491440a6", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "15f789d9", "metadata": {}, "outputs": [], @@ -85,6 +85,34 @@ "PGPASSWORD = os.getenv(\"PGPASSWORD\")" ] }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ef54c927", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PostgreSQL Connection Settings:\n", + "Host: localhost\n", + "Port: 5432\n", + "Database: ntl_pop\n", + "User: dmatekenya\n", + "Password: [NOT SET]\n" + ] + } + ], + "source": [ + "print(\"PostgreSQL Connection Settings:\")\n", + "print(f\"Host: {PGHOST}\")\n", + "print(f\"Port: {PGPORT}\")\n", + "print(f\"Database: {PGDATABASE}\")\n", + "print(f\"User: {PGUSER}\")\n", + "print(f\"Password: {'[SET]' if PGPASSWORD else '[NOT SET]'}\")" + ] + }, { "cell_type": "markdown", "id": "729bbf71", diff --git a/notebooks/module-5/02-accessing-apis.ipynb b/notebooks/module-5/02-accessing-apis.ipynb new file mode 100644 index 0000000..e89c0ba --- /dev/null +++ b/notebooks/module-5/02-accessing-apis.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e880b93b", + "metadata": {}, + "source": [ + "# Accessing APIs\n", + "\n", + "## Introduction\n", + "\n", + "This notebook will guide you through the fundamentals of accessing and working with web APIs using Python. APIs (Application Programming Interfaces) are essential tools for modern data science, allowing you to access real-time data from countless sources including government databases, weather services, financial markets, and research institutions.\n", + "\n", + "We'll work with real-world APIs to gather data about Rwanda, demonstrating practical techniques you can apply to any data science project. You'll learn to handle different types of APIs, from completely free services to those requiring authentication, while following professional best practices.\n", + "\n", + "### Why This Matters\n", + "\n", + "APIs are everywhere in modern data science. Whether you're building economic analysis dashboards, environmental monitoring systems, or social research platforms, the ability to programmatically access external data sources is crucial. This workshop teaches you not just how to make API calls, but how to do so reliably, ethically, and professionally.\n", + "\n", + "## Learning Outcomes\n", + "\n", + "By the end of this workshop, you will be able to:\n", + "\n", + "1. **Make successful API requests** using Python's `requests` library, handling different authentication methods and response formats\n", + "2. **Process and analyze JSON data** returned by APIs, converting it into pandas DataFrames for further analysis\n", + "3. **Implement professional practices** including error handling, rate limiting, retry logic, and proper credential management\n", + "4. **Work with multiple API types** from free government APIs to commercial services requiring API keys\n", + "5. **Combine data from multiple sources** to create comprehensive datasets for analysis\n", + "\n", + "---\n" + ] + }, + { + "cell_type": "markdown", + "id": "4392b73d", + "metadata": {}, + "source": [ + "## World bank APIs\n", + "\n", + "The World Bank API provides free access to development data from around the world. It's an ideal starting point for learning API consumption because it requires no authentication and provides reliable, well-structured data.\n", + "\n", + "### API Details\n", + "\n", + "#### Authentication\n", + "- **No API key required**\n", + "- **No registration needed**\n", + "- **Completely free to use**\n", + "- No rate limiting restrictions (reasonable use expected)\n", + "\n", + "#### Base URL\n", + "```\n", + "https://api.worldbank.org/v2/\n", + "```\n", + "\n", + "#### Required Python Packages\n", + "No special packages required - uses standard HTTP requests.\n", + "\n", + "#### Request Structure\n", + "The World Bank API follows a RESTful structure with the following pattern:\n", + "```\n", + "https://api.worldbank.org/v2/{endpoint}?{parameters}\n", + "```\n", + "\n", + "#### Common Endpoints\n", + "- **Countries**: `/country` - List all countries\n", + "- **Indicators**: `/indicator` - List all available indicators \n", + "- **Data**: `/country/{country_code}/indicator/{indicator_code}` - Get specific data\n", + "\n", + "#### Required Parameters\n", + "- **format**: Must be set to `json` (API defaults to XML)\n", + " ```python\n", + " params = {'format': 'json'}\n", + " ```\n", + "\n", + "#### Optional Parameters\n", + "- **date**: Specify year range (e.g., `2015:2023` or `2020`)\n", + "- **per_page**: Number of results per page (default: 50, max: 32,500)\n", + "- **page**: Page number for pagination\n", + "- **source**: Filter by data source\n", + "\n", + "#### Example Request\n", + "```python\n", + "import requests\n", + "\n", + "url = \"https://api.worldbank.org/v2/country/rw/indicator/SP.POP.TOTL\"\n", + "params = {\n", + " 'format': 'json',\n", + " 'date': '2015:2023',\n", + " 'per_page': 100\n", + "}\n", + "\n", + "response = requests.get(url, params=params)\n", + "data = response.json()\n", + "```\n", + "\n", + "#### Response Structure\n", + "The API returns an array with two elements:\n", + "1. **Metadata** (index 0): Information about the request and pagination\n", + "2. **Data** (index 1): Array of actual data records\n", + "\n", + "Each data record contains:\n", + "- `indicator`: Indicator information\n", + "- `country`: Country information \n", + "- `countryiso3code`: 3-letter country code\n", + "- `date`: Year\n", + "- `value`: Data value\n", + "- `unit`: Unit of measurement\n", + "- `decimal`: Number of decimal places\n", + "\n", + "#### Popular Indicators for Rwanda\n", + "- `SP.POP.TOTL` - Population, total\n", + "- `NY.GDP.MKTP.CD` - GDP (current US$)\n", + "- `NY.GDP.PCAP.CD` - GDP per capita (current US$)\n", + "- `SI.POV.DDAY` - Poverty headcount ratio at $2.15 a day\n", + "- `SP.DYN.LE00.IN` - Life expectancy at birth\n", + "- `SE.ADT.LITR.ZS` - Literacy rate, adult total\n", + "- `IT.NET.USER.ZS` - Individuals using the Internet\n", + "\n", + "#### Country Codes\n", + "- Rwanda: `rw`\n", + "- Uganda: `ug` \n", + "- Tanzania: `tz`\n", + "- Kenya: `ke`\n", + "- Democratic Republic of Congo: `cd`\n", + "\n", + "### Error Handling\n", + "The API returns HTTP status codes:\n", + "- `200` - Success\n", + "- `400` - Bad request (invalid parameters)\n", + "- `404` - Not found (invalid country/indicator code)\n", + "\n", + "### Data Quality Notes\n", + "- Some indicators may have missing data for certain years\n", + "- Always check for `null` values in the response\n", + "- Historical data availability varies by indicator and country\n", + "- Most recent data may be 1-2 years behind current year\n", + "\n", + "### Complete Documentation\n", + "**Official API Documentation**: https://datahelpdesk.worldbank.org/knowledgebase/articles/889392\n", + "\n", + "**Additional Resources**:\n", + "- **Indicator Catalog**: https://datacatalog.worldbank.org/\n", + "- **Country Classifications**: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519\n", + "- **API Terms of Use**: https://www.worldbank.org/en/about/legal/terms-of-use-for-datasets\n", + "\n", + "### Best Practices\n", + "- Always include `format=json` parameter\n", + "- Use specific date ranges to limit response size\n", + "- Handle missing/null values in your data processing\n", + "- Cache responses for repeated analysis to reduce server load\n", + "- Be respectful with request frequency (1-2 requests per second maximum)" + ] + }, + { + "cell_type": "markdown", + "id": "43fbf72d", + "metadata": {}, + "source": [ + "## Explore the API" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8393ab4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d655d47", + "metadata": {}, + "outputs": [], + "source": [ + "def get_multiple_wb_indicators():\n", + " \"\"\"\n", + " Fetch multiple economic indicators for Rwanda\n", + " \"\"\"\n", + " base_url = \"https://api.worldbank.org/v2\"\n", + " \n", + " # Different indicators\n", + " indicators = {\n", + " 'GDP_GROWTH': 'NY.GDP.MKTP.KD.ZG', # GDP growth\n", + " 'POVERTY_RATE': 'SI.POV.DDAY', # Poverty headcount\n", + " 'LIFE_EXPECTANCY': 'SP.DYN.LE00.IN', # Life expectancy\n", + " 'INTERNET_USERS': 'IT.NET.USER.ZS' # Internet users %\n", + " }\n", + " \n", + " all_data = {}\n", + " \n", + " for name, indicator_code in indicators.items():\n", + " url = f\"{base_url}/country/rw/indicator/{indicator_code}\"\n", + " params = {\n", + " 'format': 'json',\n", + " 'date': '2015:2022',\n", + " 'per_page': 50\n", + " }\n", + " \n", + " print(f\"Fetching {name}...\")\n", + " response = requests.get(url, params=params)\n", + " \n", + " if response.status_code == 200:\n", + " data = response.json()\n", + " if len(data) > 1 and data[1]:\n", + " # Convert to DataFrame\n", + " df = pd.DataFrame(data[1])\n", + " df_clean = df[['date', 'value']].dropna()\n", + " df_clean.columns = ['year', name.lower()]\n", + " all_data[name] = df_clean\n", + " \n", + " # Be respectful - add small delay\n", + " time.sleep(0.5)\n", + " \n", + " return all_data\n", + "\n", + "# Get multiple indicators\n", + "indicators_data = get_multiple_wb_indicators()\n", + "\n", + "# Display results\n", + "for name, df in indicators_data.items():\n", + " print(f\"\\n{name}:\")\n", + " print(df.head())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/module-5/03-web-scraping.ipynb b/notebooks/module-5/03-web-scraping.ipynb new file mode 100644 index 0000000..e69de29 From bc660f339b83e19b542946f7f2946ea990080144 Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Tue, 16 Sep 2025 12:41:14 +0200 Subject: [PATCH 24/33] Updated Notebooks --- .../module-5/01-connecting-to-databases.ipynb | 52 ++++++++++++++++--- 1 file changed, 46 insertions(+), 6 deletions(-) diff --git a/notebooks/module-5/01-connecting-to-databases.ipynb b/notebooks/module-5/01-connecting-to-databases.ipynb index 623046c..9fc26ee 100644 --- a/notebooks/module-5/01-connecting-to-databases.ipynb +++ b/notebooks/module-5/01-connecting-to-databases.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "id": "491440a6", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "15f789d9", "metadata": {}, "outputs": [], @@ -87,7 +87,28 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, + "id": "90dd0055", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'5432'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PGPORT" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "id": "ef54c927", "metadata": {}, "outputs": [ @@ -125,14 +146,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "be1eecf7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connection psql string: postgresql+psycopg2://dmatekenya:@localhost:5432/ntl_pop\n" + ] + } + ], "source": [ "# Create SQLAlchemy engine (no password needed for local connections)\n", + "connection_string = f\"postgresql+psycopg2://{PGUSER}:{PGPASSWORD}@{PGHOST}:{PGPORT}/{PGDATABASE}\"\n", + "print(\"Connection psql string:\", connection_string)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c2ec747", + "metadata": {}, + "outputs": [], + "source": [ "engine = create_engine(\n", - " f\"postgresql+psycopg2://{PGUSER}@{PGHOST}:{PGPORT}/{PGDATABASE}\",\n", + " connection_string,\n", " pool_pre_ping=True,\n", ")\n", "\n", From 946299eff5e46db3169657e40fbdca3c89ae64d8 Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Wed, 17 Sep 2025 09:08:54 +0200 Subject: [PATCH 25/33] Added FastAPI Minimal Application --- hello_world.py | 45 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 hello_world.py diff --git a/hello_world.py b/hello_world.py new file mode 100644 index 0000000..3393635 --- /dev/null +++ b/hello_world.py @@ -0,0 +1,45 @@ +from fastapi import FastAPI, Query, Path +from typing import Optional +from datetime import datetime + +# Create FastAPI application +app = FastAPI( + title="Hello World API", + description="A simple API to learn FastAPI basics", + version="1.0.0" +) + +# Basic endpoint - no parameters +@app.get("/") +def read_root(): + """Welcome message - simplest possible endpoint""" + return {"message": "Hello World!"} + +# Endpoint with path parameter +@app.get("/hello/{name}") +def say_hello(name: str): + """Say hello to a specific person""" + return {"message": f"Hello {name}!"} + +# Endpoint with query parameters +@app.get("/greet") +def greet_person( + name: str = Query(..., description="Person's name"), + age: Optional[int] = Query(None, description="Person's age"), + city: Optional[str] = Query("Unknown", description="Person's city") +): + """Greet a person with optional details""" + greeting = f"Hello {name}!" + + if age: + greeting += f" You are {age} years old." + + greeting += f" You're from {city}." + + return { + "greeting": greeting, + "name": name, + "age": age, + "city": city, + "timestamp": datetime.now().isoformat() + } \ No newline at end of file From fdc24f05197226d100e25b53322cc8bea070b0ec Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Wed, 17 Sep 2025 11:21:55 +0200 Subject: [PATCH 26/33] Added APIs notebook --- notebooks/module-5/02-accessing-apis.ipynb | 241 --- notebooks/module-5/intro-to-APIs.ipynb | 1533 ++++++++++++++++++++ 2 files changed, 1533 insertions(+), 241 deletions(-) delete mode 100644 notebooks/module-5/02-accessing-apis.ipynb create mode 100644 notebooks/module-5/intro-to-APIs.ipynb diff --git a/notebooks/module-5/02-accessing-apis.ipynb b/notebooks/module-5/02-accessing-apis.ipynb deleted file mode 100644 index e89c0ba..0000000 --- a/notebooks/module-5/02-accessing-apis.ipynb +++ /dev/null @@ -1,241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e880b93b", - "metadata": {}, - "source": [ - "# Accessing APIs\n", - "\n", - "## Introduction\n", - "\n", - "This notebook will guide you through the fundamentals of accessing and working with web APIs using Python. APIs (Application Programming Interfaces) are essential tools for modern data science, allowing you to access real-time data from countless sources including government databases, weather services, financial markets, and research institutions.\n", - "\n", - "We'll work with real-world APIs to gather data about Rwanda, demonstrating practical techniques you can apply to any data science project. You'll learn to handle different types of APIs, from completely free services to those requiring authentication, while following professional best practices.\n", - "\n", - "### Why This Matters\n", - "\n", - "APIs are everywhere in modern data science. Whether you're building economic analysis dashboards, environmental monitoring systems, or social research platforms, the ability to programmatically access external data sources is crucial. This workshop teaches you not just how to make API calls, but how to do so reliably, ethically, and professionally.\n", - "\n", - "## Learning Outcomes\n", - "\n", - "By the end of this workshop, you will be able to:\n", - "\n", - "1. **Make successful API requests** using Python's `requests` library, handling different authentication methods and response formats\n", - "2. **Process and analyze JSON data** returned by APIs, converting it into pandas DataFrames for further analysis\n", - "3. **Implement professional practices** including error handling, rate limiting, retry logic, and proper credential management\n", - "4. **Work with multiple API types** from free government APIs to commercial services requiring API keys\n", - "5. **Combine data from multiple sources** to create comprehensive datasets for analysis\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "markdown", - "id": "4392b73d", - "metadata": {}, - "source": [ - "## World bank APIs\n", - "\n", - "The World Bank API provides free access to development data from around the world. It's an ideal starting point for learning API consumption because it requires no authentication and provides reliable, well-structured data.\n", - "\n", - "### API Details\n", - "\n", - "#### Authentication\n", - "- **No API key required**\n", - "- **No registration needed**\n", - "- **Completely free to use**\n", - "- No rate limiting restrictions (reasonable use expected)\n", - "\n", - "#### Base URL\n", - "```\n", - "https://api.worldbank.org/v2/\n", - "```\n", - "\n", - "#### Required Python Packages\n", - "No special packages required - uses standard HTTP requests.\n", - "\n", - "#### Request Structure\n", - "The World Bank API follows a RESTful structure with the following pattern:\n", - "```\n", - "https://api.worldbank.org/v2/{endpoint}?{parameters}\n", - "```\n", - "\n", - "#### Common Endpoints\n", - "- **Countries**: `/country` - List all countries\n", - "- **Indicators**: `/indicator` - List all available indicators \n", - "- **Data**: `/country/{country_code}/indicator/{indicator_code}` - Get specific data\n", - "\n", - "#### Required Parameters\n", - "- **format**: Must be set to `json` (API defaults to XML)\n", - " ```python\n", - " params = {'format': 'json'}\n", - " ```\n", - "\n", - "#### Optional Parameters\n", - "- **date**: Specify year range (e.g., `2015:2023` or `2020`)\n", - "- **per_page**: Number of results per page (default: 50, max: 32,500)\n", - "- **page**: Page number for pagination\n", - "- **source**: Filter by data source\n", - "\n", - "#### Example Request\n", - "```python\n", - "import requests\n", - "\n", - "url = \"https://api.worldbank.org/v2/country/rw/indicator/SP.POP.TOTL\"\n", - "params = {\n", - " 'format': 'json',\n", - " 'date': '2015:2023',\n", - " 'per_page': 100\n", - "}\n", - "\n", - "response = requests.get(url, params=params)\n", - "data = response.json()\n", - "```\n", - "\n", - "#### Response Structure\n", - "The API returns an array with two elements:\n", - "1. **Metadata** (index 0): Information about the request and pagination\n", - "2. **Data** (index 1): Array of actual data records\n", - "\n", - "Each data record contains:\n", - "- `indicator`: Indicator information\n", - "- `country`: Country information \n", - "- `countryiso3code`: 3-letter country code\n", - "- `date`: Year\n", - "- `value`: Data value\n", - "- `unit`: Unit of measurement\n", - "- `decimal`: Number of decimal places\n", - "\n", - "#### Popular Indicators for Rwanda\n", - "- `SP.POP.TOTL` - Population, total\n", - "- `NY.GDP.MKTP.CD` - GDP (current US$)\n", - "- `NY.GDP.PCAP.CD` - GDP per capita (current US$)\n", - "- `SI.POV.DDAY` - Poverty headcount ratio at $2.15 a day\n", - "- `SP.DYN.LE00.IN` - Life expectancy at birth\n", - "- `SE.ADT.LITR.ZS` - Literacy rate, adult total\n", - "- `IT.NET.USER.ZS` - Individuals using the Internet\n", - "\n", - "#### Country Codes\n", - "- Rwanda: `rw`\n", - "- Uganda: `ug` \n", - "- Tanzania: `tz`\n", - "- Kenya: `ke`\n", - "- Democratic Republic of Congo: `cd`\n", - "\n", - "### Error Handling\n", - "The API returns HTTP status codes:\n", - "- `200` - Success\n", - "- `400` - Bad request (invalid parameters)\n", - "- `404` - Not found (invalid country/indicator code)\n", - "\n", - "### Data Quality Notes\n", - "- Some indicators may have missing data for certain years\n", - "- Always check for `null` values in the response\n", - "- Historical data availability varies by indicator and country\n", - "- Most recent data may be 1-2 years behind current year\n", - "\n", - "### Complete Documentation\n", - "**Official API Documentation**: https://datahelpdesk.worldbank.org/knowledgebase/articles/889392\n", - "\n", - "**Additional Resources**:\n", - "- **Indicator Catalog**: https://datacatalog.worldbank.org/\n", - "- **Country Classifications**: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519\n", - "- **API Terms of Use**: https://www.worldbank.org/en/about/legal/terms-of-use-for-datasets\n", - "\n", - "### Best Practices\n", - "- Always include `format=json` parameter\n", - "- Use specific date ranges to limit response size\n", - "- Handle missing/null values in your data processing\n", - "- Cache responses for repeated analysis to reduce server load\n", - "- Be respectful with request frequency (1-2 requests per second maximum)" - ] - }, - { - "cell_type": "markdown", - "id": "43fbf72d", - "metadata": {}, - "source": [ - "## Explore the API" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8393ab4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d655d47", - "metadata": {}, - "outputs": [], - "source": [ - "def get_multiple_wb_indicators():\n", - " \"\"\"\n", - " Fetch multiple economic indicators for Rwanda\n", - " \"\"\"\n", - " base_url = \"https://api.worldbank.org/v2\"\n", - " \n", - " # Different indicators\n", - " indicators = {\n", - " 'GDP_GROWTH': 'NY.GDP.MKTP.KD.ZG', # GDP growth\n", - " 'POVERTY_RATE': 'SI.POV.DDAY', # Poverty headcount\n", - " 'LIFE_EXPECTANCY': 'SP.DYN.LE00.IN', # Life expectancy\n", - " 'INTERNET_USERS': 'IT.NET.USER.ZS' # Internet users %\n", - " }\n", - " \n", - " all_data = {}\n", - " \n", - " for name, indicator_code in indicators.items():\n", - " url = f\"{base_url}/country/rw/indicator/{indicator_code}\"\n", - " params = {\n", - " 'format': 'json',\n", - " 'date': '2015:2022',\n", - " 'per_page': 50\n", - " }\n", - " \n", - " print(f\"Fetching {name}...\")\n", - " response = requests.get(url, params=params)\n", - " \n", - " if response.status_code == 200:\n", - " data = response.json()\n", - " if len(data) > 1 and data[1]:\n", - " # Convert to DataFrame\n", - " df = pd.DataFrame(data[1])\n", - " df_clean = df[['date', 'value']].dropna()\n", - " df_clean.columns = ['year', name.lower()]\n", - " all_data[name] = df_clean\n", - " \n", - " # Be respectful - add small delay\n", - " time.sleep(0.5)\n", - " \n", - " return all_data\n", - "\n", - "# Get multiple indicators\n", - "indicators_data = get_multiple_wb_indicators()\n", - "\n", - "# Display results\n", - "for name, df in indicators_data.items():\n", - " print(f\"\\n{name}:\")\n", - " print(df.head())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/module-5/intro-to-APIs.ipynb b/notebooks/module-5/intro-to-APIs.ipynb new file mode 100644 index 0000000..a443846 --- /dev/null +++ b/notebooks/module-5/intro-to-APIs.ipynb @@ -0,0 +1,1533 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "WioWyjbZy1Y8" + }, + "source": [ + "# Introduction to Using APIs\n", + "\n", + "In this notebook, we will go through a practical example of how to use Python to access an API.\n", + "For quick demonstration of the concepts, we will use the [Open Notify API](http://open-notify.org), [openWeather](https://openweathermap.org) and [WBG] (http://datatopics.worldbank.org/world-development-indicators/themes/people.html) APIs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rnDTWGbVy1ZA" + }, + "source": [ + "# Python Setup\n", + "Lets make sure we import the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "executionInfo": { + "elapsed": 1308, + "status": "ok", + "timestamp": 1676355712059, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "90gISwGey1ZA" + }, + "outputs": [], + "source": [ + "import requests\n", + "from matplotlib.pyplot import figure\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import json\n", + "from IPython.display import Image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P1OgIEvYy1ZC" + }, + "source": [ + "# Accessing a Useless API Just to Learn\n", + "The [Open Notify API](http://open-notify.org) is an open source project to provide a simple programming interface for some of NASA’s data related\n", + "space and spacecrafts. Even though this is a toy example, the process of using APIs is similar for any \n", + "API you will need to access in the future. For this tutorial, the task is to find the latest position of the Internation Space Station(ISS)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jtGizhjMy1ZE" + }, + "source": [ + "## Understanding The API\n", + "As mentioned during the introductory lecture, its important to read the API documentation \n", + "and understand the available **endpoints**. Also, to determine the exact endpoint for the data we are interested in getting.\n", + "- **Base URL:** For this API, the base url is: ```http://api.open-notify.org```. So, our full url which we will pass to requests will be ```base-url + endpoint```\n", + "- **Available Endpoints/Data:** You can check [here](http://open-notify.org/Open-Notify-API/) for avaible endpoints\n", + "- **Target Endpoint:** For our task, the endpoint is ```iss-now.json```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TIhr_FqXy1ZE" + }, + "source": [ + "## Making the Request\n", + "Now, lets request for the data. We create a simple function which makes the requests \n", + "and prints the status code to see if we got a good result." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "executionInfo": { + "elapsed": 2, + "status": "ok", + "timestamp": 1676355720957, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "2W26MBZDy1ZF" + }, + "outputs": [], + "source": [ + "base_url = 'http://api.open-notify.org/'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "executionInfo": { + "elapsed": 4, + "status": "ok", + "timestamp": 1676355724874, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "JIdqHoHty1ZF" + }, + "outputs": [], + "source": [ + "# endpoint for current position of ISS\n", + "pos_now = 'iss-now.json'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "executionInfo": { + "elapsed": 454, + "status": "ok", + "timestamp": 1676355734562, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "-av0o7kVy1ZG", + "outputId": "b028759b-c0ae-4f79-e6a3-07e3813f5e88" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'http://api.open-notify.org/iss-now.json'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Full url\n", + "full_url = base_url + pos_now\n", + "full_url" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "executionInfo": { + "elapsed": 545, + "status": "ok", + "timestamp": 1676355741426, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "UGG5bl2ry1ZH" + }, + "outputs": [], + "source": [ + "# Make a request using requests package\n", + "response = requests.get(full_url)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 3, + "status": "ok", + "timestamp": 1676355744242, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "FiUgfAgey1ZI", + "outputId": "6a681363-ad39-4532-b54d-a508682efcf1" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "200" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response.status_code" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F3N7CM4Py1ZI" + }, + "source": [ + "## Use json() to retrieve the json object/data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 554, + "status": "ok", + "timestamp": 1676355751619, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "4iW9ywixy1ZJ", + "outputId": "88bbbba3-6dd1-4e71-f481-c1e8037d2937" + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'response' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Get the data\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m dict_obj_from_json \u001b[38;5;241m=\u001b[39m \u001b[43mresponse\u001b[49m\u001b[38;5;241m.\u001b[39mjson()\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mtype\u001b[39m(dict_obj_from_json)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(dict_obj_from_json),\u001b[38;5;28mtype\u001b[39m(dict_obj_from_json)\n", + "\u001b[0;31mNameError\u001b[0m: name 'response' is not defined" + ] + } + ], + "source": [ + "# Get the data\n", + "dict_obj_from_json = response.json()\n", + "type(dict_obj_from_json)\n", + "print(dict_obj_from_json),type(dict_obj_from_json)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 472, + "status": "ok", + "timestamp": 1676355792404, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "If79Qft-y1ZJ", + "outputId": "da8e8f38-baf9-483e-b8fd-a98d33768179" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'longitude': '-146.8740', 'latitude': '-46.0885'}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geo_info = dict_obj_from_json['iss_position']\n", + "geo_info" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 304, + "status": "ok", + "timestamp": 1676355802552, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "wDUomxYWy1ZK", + "outputId": "13398c09-ace8-4c24-ab4a-b421a7aa7633" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-146.8740\n", + "-46.0885\n" + ] + } + ], + "source": [ + "lon = geo_info['longitude']\n", + "print(lon)\n", + "lat = geo_info['latitude']\n", + "print(lat)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 452, + "status": "ok", + "timestamp": 1676355809727, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "IRGRizV5y1ZK", + "outputId": "c06f3727-2c96-4b22-9c6f-e59ddc452ccf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-46.0885 -- -146.8740\n" + ] + } + ], + "source": [ + "geo_info = dict_obj_from_json['iss_position']\n", + "lon = geo_info['longitude']\n", + "lat = geo_info['latitude']\n", + "print(lat,'--', lon)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 324, + "status": "ok", + "timestamp": 1676355852267, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "AUiikw8ay1ZL", + "outputId": "e97cd0fe-db3b-44b7-ab5e-f3b7450d8cd3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-48.8931 -- -137.5187\n" + ] + } + ], + "source": [ + "base_url = 'http://api.open-notify.org/'\n", + "#endpoint for current position of ISS\n", + "pos_now = 'iss-now.json'\n", + "# Full url\n", + "full_url = base_url + pos_now\n", + "\n", + "\n", + "# Make a request using requests package\n", + "r = requests.get(full_url)\n", + "\n", + "# Get the data\n", + "dict_obj_from_json = r.json()\n", + "\n", + "# Extract Lat/Lon\n", + "geo_info = dict_obj_from_json['iss_position']\n", + "lon = geo_info['longitude']\n", + "lat = geo_info['latitude']\n", + "print(lat,'--', lon)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "code_folding": [ + 0 + ], + "executionInfo": { + "elapsed": 397, + "status": "ok", + "timestamp": 1676355873977, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "7tld9iMgy1ZM" + }, + "outputs": [], + "source": [ + "def request_iss_position(endpoint_url=None):\n", + " # Make a get request \n", + " response = requests.get(endpoint_url)\n", + "\n", + " # Print the status code of the response.\n", + " status_code = response.status_code\n", + " if status_code == 200:\n", + " print('Hooray!!, it worked')\n", + " \n", + " # access the returned data\n", + " dict_obj_from_json = response.json()\n", + " print('Type: {}'.format(type(dict_obj_from_json)))\n", + " print('This is the nested dictionary returned: \\n {}'.format(dict_obj_from_json))\n", + " lat = dict_obj_from_json['iss_position']['latitude']\n", + " lon = dict_obj_from_json['iss_position']['longitude']\n", + " print('ISS is passing on Lat: {}, and Lon: {} right about now'.format(lat, lon))\n", + " else:\n", + " print('What happened, lets see the code we got: {}'.format(status_code))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 516, + "status": "ok", + "timestamp": 1675397180435, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": 480 + }, + "id": "5kTLElRIy1ZM", + "outputId": "009d6af7-a92f-4b24-b2d7-4fe6e117a8c4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hooray!!, it worked\n", + "Type: \n", + "This is the nested dictionary returned: \n", + " {'iss_position': {'latitude': '-35.7143', 'longitude': '-67.6236'}, 'message': 'success', 'timestamp': 1675397176}\n", + "ISS is passing on Lat: -35.7143, and Lon: -67.6236 right about now\n" + ] + } + ], + "source": [ + "base_url = 'http://api.open-notify.org/'\n", + "iss_pos = base_url + 'iss-now.json'\n", + "request_iss_position(endpoint_url=iss_pos)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 3, + "status": "ok", + "timestamp": 1675397182738, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": 480 + }, + "id": "eUTXnt5my1ZM", + "outputId": "bc23a02c-783c-4d34-c2b5-7c8e27661ee5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What happened, lets see the code we got: 404\n" + ] + } + ], + "source": [ + "# Now, try to enter a wrong endpoint and see what happens\n", + "iss_pos2 = base_url + 'iss-today.json'\n", + "request_iss_position(endpoint_url=iss_pos2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gOZbdoOyy1ZN" + }, + "source": [ + "# Accessing the Data\n", + "Now, lets change our function so that we print the Latitude and Longitude of where ISS is right now." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1208, + "status": "ok", + "timestamp": 1676355892406, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": -60 + }, + "id": "XnVOmqLey1ZN", + "outputId": "9ddc5b09-a010-470b-eab8-a47e7902959f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hooray!!, it worked\n", + "Type: \n", + "This is the nested dictionary returned: \n", + " {'timestamp': 1676355890, 'iss_position': {'longitude': '-134.0074', 'latitude': '-49.6644'}, 'message': 'success'}\n", + "ISS is passing on Lat: -49.6644, and Lon: -134.0074 right about now\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request_iss_position(endpoint_url='http://api.open-notify.org/iss-now.json')\n", + "request_iss_position" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O2Y7O61ky1ZN" + }, + "source": [ + "# EXERCISE-1:\n", + "Using the same API, check how many Astronouts are in space right now?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "executionInfo": { + "elapsed": 486, + "status": "error", + "timestamp": 1675397797196, + "user": { + "displayName": "Michael Nana Kameni", + "userId": "10368762142761029516" + }, + "user_tz": 480 + }, + "id": "I5IDdQP3y1ZO", + "outputId": "998a323a-a991-4848-d868-1714b950e26a" + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m# Extract the dict object from the JSON response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: expected str, bytes or os.PathLike object, not Response" + ] + } + ], + "source": [ + "# base-url is same as above\n", + "# end point for this data: please check documentation\n", + "num_astros = 'http://api.open-notify.org/astros.json'\n", + "\n", + "# full_url number of astronouts\n", + "num_astros_url =\"http://api.open-notify.org/astros.json?callback=CALLBACK\" \n", + " \n", + "# Make request\n", + "response = requests.get(num_astros_url)\n", + "\n", + "# Get data if response is 200\n", + "if response.status_code == 200:\n", + " # Extract the dict object from the JSON response\n", + " res = response.json().loads(response)\n", + " \n", + " # please print the dict object above so you can inspect it and see how to retrieve the required data\n", + " print(res)\n", + " \n", + " # Get the number of people in spacw\n", + " #YOUR CODE\n", + " \n", + " # print number of people\n", + " #YOUR CODE" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WArb3fRky1ZO" + }, + "source": [ + "# Accessing the Open Weather API\n", + "## About the API\n", + "You can read all about it [here](https://openweathermap.org). In summary, they provide weather data for cities across the world. When you read the API documentation there are several things you need to be aware of and check as follows:\n", + "1. How to get credentials to enable access to the API. We only need an API key in this case. Did you get yours?\n", + "2. How to make API calls. What type of parameters are required to make and API call to this particular API. In this case, you will note that you will need location.\n", + "3. What type of information is available\n", + "4. Whats the cost of accessing the data. If free, are there any limits.\n", + "\n", + "## Potential Use Cases\n", + "1. **A Weather app on a Smartphone.** Extract data from the API and use it on your app\n", + "\n", + "2. **Historical climate data analysisis for multiple cities.** With climate change being a big thing now, you may want to do analysis comparing weather information across multiple cities.\n", + "\n", + "The API has many versions and subscriptions plans but in our exercise, we will use their 2.5 version which is the one which doesnt require subscription." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"../docs/images/open-weather-api.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example Task: Get current weather data and forecasts for a given city or multiple cities\n", + "Given a city name, lets get current weather data and forecasts and then save that information as a CSV file. \n", + "We are only interested in the following variables:\n", + "- **Weather:** temp, min and max temperature, humidity\n", + "- **Other:** Date, with no time\n", + "\n", + "### Approach\n", + "We will achieve this by defining three main functions:\n", + "1. A small helper function to retrieve city ID from given city name just because using ID is easier than name\n", + "2. A function to make thr API call and retrieve the weather data\n", + "3. A function to take the returned data, put it into a pandas dataframe.\n", + "4. A function putting everything together " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# String concatenation in Python" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dunstan Matekenya\n" + ] + } + ], + "source": [ + "a = 'Dunstan'\n", + "b = 'Matekenya'\n", + "\n", + "full_name = \"{} {}\".format(a,b)\n", + "\n", + "print(full_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "code_folding": [ + 0 + ], + "id": "OCai_fwxy1ZO" + }, + "outputs": [], + "source": [ + "def get_weather(base_url, category='forecast', api_key=None, city_id=None, lat=None, lon=None):\n", + " \"\"\"\n", + " Use request to make a get request to the open weather API\n", + " :param api_key: This is taken from the API documentation\n", + " :param category: Wether to get current weather, forecast or other type. Check API docs for details.\n", + " :param city_id: ID for the city we need taken from the city.list.json provided by the API\n", + " :param api_key: The API key you got from the API \n", + " :param lon: Longitude in case we want to use latitude and longitude\n", + " :param lat: Latitude in case we want to use latitude and longitude\n", + " \n", + " :return: A JSON oobject with the data\n", + " \"\"\"\n", + " # Create full_url based on whether we are using lat/lon, city ID\n", + " if city_id:\n", + " full_url = \"{}{}?id={}&APPID={}\".format(base_url, category, city_id, api_key)\n", + " \n", + " # use requests to retrieve data from the API\n", + " response = requests.get(full_url)\n", + "\n", + " # retrieve JSON from the response object\n", + " if response.status_code == 200:\n", + " json_obj = response.json()\n", + " else:\n", + " print('Something went wrong, see error below')\n", + " print(response.status_code)\n", + "\n", + " # return the JSON object\n", + " return json_obj" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "def city_id_from_name(city_file, name):\n", + " \"\"\"\n", + " Retrieve city ID given city name\n", + " :param city_file: JSON file with list of city names and corresponding IDs provided by open weather API\n", + " :param name: Name of city which we want to retrieve ID for\n", + " :return: An integer representing city ID\n", + " \"\"\"\n", + " # Load JSON file\n", + " fopen = open(city_file, encoding=\"utf8\")\n", + " city_list = json.load(fopen)\n", + " \n", + " # Get city ID which matches with city name\n", + " for c in city_list:\n", + " if c['name'] == name:\n", + " return c['id'] " + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [], + "source": [ + "def weather_json_to_pandas_df(weather_json):\n", + " \"\"\"\n", + " Converts the JSON with weather data into a pandas Dataframe\n", + " :param weather_json:The JSON object with weather information\n", + " :return: A pandas Dataframe\n", + " \"\"\"\n", + " \n", + " # using method for accessing a dictionary\n", + " # put weather items in a list\n", + " weather_items = weather_json['list']\n", + " \n", + " # save into a dataframe\n", + " data = [] # will hold our data\n", + " \n", + " for i in weather_items:\n", + " # get forecast time and truncate to date only\n", + " date_str = i['dt_txt'][:10]\n", + " \n", + " # get temperature, rain and humidity\n", + " temp = i['main']['temp']\n", + " temp_min = i['main']['temp_min']\n", + " temp_max = i['main']['temp_max']\n", + " hum = i['main']['humidity']\n", + " \n", + " data_item = {'Date': date_str, 'tempF': temp,'tempF_min': temp_min, \n", + " 'tempF_max': temp_min, 'humidity': hum}\n", + " \n", + " # append to list of create earlier on\n", + " data.append(data_item)\n", + " \n", + "\n", + " # create dataframe\n", + " df = pd.DataFrame(data)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "def save_weather_data(city_name, csv_filename, city_list_file):\n", + " \"\"\"\n", + " Puts everything together and saves weather into CSV file\n", + " :param city_name: Target city name\n", + " :param csv_filename: Full path of CSV filen to save data\n", + " :param city_list_file: File containing city names and IDs\n", + " :return: Saves data into CSV\n", + " \"\"\"\n", + " # ==============================\n", + " # GET WEATHER DATA\n", + " # ==============================\n", + " # Retrieve city ID and get data\n", + " cityid = city_id_from_name(city_list_file, city_name)\n", + " print('City-ID for {} is {}'.format(city_name, cityid))\n", + " \n", + " weather_data = get_weather(base_url=BASE_URL, api_key=API_KEY,\n", + " city_id=cityid)\n", + " \n", + " # Put data into Pandas Dataframe\n", + " df = weather_json_to_pandas_df(weather_json=weather_data)\n", + " \n", + " # Add city name \n", + " df['City'] = city_name\n", + " print()\n", + " print('First few rows of the data')\n", + " print(df.head())\n", + " \n", + " # Save data into CSV\n", + " df.to_csv(city_name, index=False)\n", + " \n", + " print()\n", + " print('Successfully Saved CSV file')\n", + " print(\"============================\")\n", + " \n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "# ========================================================\n", + "# HARD CODE BASE INFORMAITON AND USE THEM AS GLOBAL VARS\n", + "# ========================================================\n", + "# Harcode base URL, we use the 2.5 version which seems to be the free version with no need to subscribe\n", + "BASE_URL = \"http://api.openweathermap.org/data/2.5/\"\n", + "# Hardcode API key although its not recommended for security purposes\n", + "API_KEY = 'cd689df7ce5a01db2aafde528e3d87c4'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "city_file = \"/Users/dunstanmatekenya/Downloads/" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "927967" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get ID\n", + "kigali_id = city_id_from_name(city_file=\"../DATASETS/raw/city.list.json\",\n", + " name=\"Kigali\")\n", + "kigali_id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get weather data function\n", + "json_object = get_weather(base_url=BASE_URL, category='forecast', \n", + " api_key=API_KEY, city_id=kigali_id)\n", + "json_object" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert JSON to pandas Dataframe\n", + "df = weather_json_to_pandas_df(json_object)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# put everything together\n", + "save_weather_data(city_name='Lagos', \n", + " csv_filename=\"../DATASETS/lagos.csv\",\n", + " city_list_file=\"../DATASETS/raw/city.list.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "City-ID for Lilongwe is 927967\n", + "http://api.openweathermap.org/data/2.5/forecast?id=927967&APPID=cd689df7ce5a01db2aafde528e3d87c4\n", + "\n", + "First few rows of the data\n", + " Date tempF tempF_min tempF_max humidity City\n", + "0 2024-01-10 296.22 296.14 296.14 82 Lilongwe\n", + "1 2024-01-10 296.97 296.97 296.97 78 Lilongwe\n", + "2 2024-01-10 293.56 293.56 293.56 94 Lilongwe\n", + "3 2024-01-10 293.15 293.15 293.15 96 Lilongwe\n", + "4 2024-01-11 292.70 292.70 292.70 95 Lilongwe\n", + "\n", + "Successfully Saved CSV file\n", + "============================\n" + ] + } + ], + "source": [ + "# =======================================\n", + "# GET WEATHER FORECAST DATA FOR LILONGWE\n", + "# =======================================\n", + "city = 'Lilongwe'\n", + "city_json = \"../DATASETS/raw/city.list.json\"\n", + "out_csv = \"../DATASETS/LL-weather.csv\"\n", + "df_weather = save_weather_data(city_name=city, csv_filename=out_csv, city_list_file=city_json)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eZknSpF3y1ZP" + }, + "source": [ + "## EXERCISE-1: Instead of using city ID, lets use latitude and longitude to get the same information.\n", + "\n", + "### Task description:\n", + "- Modify the ```get_weather()``` so that it takes in latitude and longitude and returns today's temperature in degress Celsius.\n", + "- To test your function, get current weather data for 'lon': 30.1289, 'lat': -1.9695 and print out the **temperature, place name and country**. The result will look like screenshot below.\n", + "\n", + "### Tips:\n", + "- Go to the API documentation to find out how to use lat and lon in the API call\n", + "- Also, use the API documentation to check how to change the units " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={API key}" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "http://api.openweathermap.org/data/2.5/weather?&lat=30&lon=60&appid=cd689df7ce5a01db2aafde528e3d87c4\n" + ] + } + ], + "source": [ + "base_url = BASE_URL\n", + "category = 'weather'\n", + "lat = '30'\n", + "lon = '60'\n", + "\n", + "full_url = \"{}{}?&lat={}&lon={}&appid={}\".format(base_url, \n", + " category,lat, lon,API_KEY)\n", + "print(full_url)" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "response = requests.get(full_url)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'coord': {'lon': 60, 'lat': 30},\n", + " 'weather': [{'id': 801,\n", + " 'main': 'Clouds',\n", + " 'description': 'few clouds',\n", + " 'icon': '02n'}],\n", + " 'base': 'stations',\n", + " 'main': {'temp': 287,\n", + " 'feels_like': 284.75,\n", + " 'temp_min': 287,\n", + " 'temp_max': 287,\n", + " 'pressure': 1015,\n", + " 'humidity': 12,\n", + " 'sea_level': 1015,\n", + " 'grnd_level': 852},\n", + " 'visibility': 10000,\n", + " 'wind': {'speed': 1.77, 'deg': 173, 'gust': 1.81},\n", + " 'clouds': {'all': 24},\n", + " 'dt': 1704894490,\n", + " 'sys': {'country': 'IR', 'sunrise': 1704855411, 'sunset': 1704892625},\n", + " 'timezone': 12600,\n", + " 'id': 1159301,\n", + " 'name': 'Zahedan',\n", + " 'cod': 200}" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = response.json()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(\"../docs/images/current-temp-exercise.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VI9VzkaYy1ZR" + }, + "source": [ + "# Pulling Data World Bank Development Indicators API\n", + "We're using Pandas Datareader: a third-party library that makes it easy to pull data from APIs into a Pandas dataframe.\n", + "\n", + "Use it to pull live data from Yahoo! Finance, World Bank and others.\n", + "\n", + "\n", + "| Function | Task Performed |\n", + "|----|---|\n", + "| wb.get_countries() | List available countries, with their country codes, income levels etc. |\n", + "| wb.search() | Query the available indicators using a search term. |\n", + "| wb.WorldBankReader() | A class for downloading data series. |" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BFZpLT2Ay1ZR" + }, + "source": [ + "## Installing Pandas Datareader\n", + "Use the terminal to install the package pandas_datareader like so: \n", + "```pip3 install pandas_datareader```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dVFiuN0ey1ZS" + }, + "outputs": [], + "source": [ + "# Lets import World Bank module (wb) from pandas_datareader\n", + "from pandas_datareader import wb" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W_0VJ43by1ZS" + }, + "source": [ + "## List of Indicators\n", + "See [here](http://datatopics.worldbank.org/world-development-indicators/themes/people.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KWWp4vXWy1ZT" + }, + "outputs": [], + "source": [ + "# Find the GNI per capita indicator, get its ID number\n", + "indicator_name = \"GNI per capita, Atlas\"\n", + "result = wb.search(string=indicator_name, field = 'name')\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KvuE9wJky1ZT" + }, + "outputs": [], + "source": [ + "# Make a note of the indicator ID\n", + "GNI_ID = 'NY.GNP.PCAP.CD'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tIDkoVPfy1ZT" + }, + "outputs": [], + "source": [ + "# Get a list of countries and clean it up\n", + "countries = wb.get_countries()\n", + "countries.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ck9hGqMOy1ZU" + }, + "outputs": [], + "source": [ + "#Select only those observations and columns we need: \n", + "countries2 = countries[countries.incomeLevel != 'Aggregates']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TlNVYVm7y1ZU" + }, + "outputs": [], + "source": [ + "countries3 = countries2[['name', 'iso3c','incomeLevel']]\n", + "countries3.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "abN-0b7Fy1ZV" + }, + "outputs": [], + "source": [ + "OECD_founders = ['Belgium','France','Germany','Italy','Luxembourg','Netherlands','United States',\n", + " 'Canada','Japan']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T1hdDmr7y1ZV" + }, + "outputs": [], + "source": [ + "countries4 = countries3[countries3.name.isin(OECD_founders)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7tRmyUjCy1ZV" + }, + "outputs": [], + "source": [ + "countries4.name.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IPJi5Iksy1ZW" + }, + "outputs": [], + "source": [ + "type(countries4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PrIqbmrCy1ZW" + }, + "outputs": [], + "source": [ + "countries_iso = list(countries4.iso3c.values)\n", + "countries_iso" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Fb3iVpQuy1ZW" + }, + "outputs": [], + "source": [ + "# Let's plot GNI per capita since 1960 for the OECD countries\n", + "\n", + "\n", + "# Create a reader object, pass it the data ID and country codes we want\n", + "\n", + "reader = wb.WorldBankReader(symbols=GNI_ID,\n", + " countries = countries_iso,\n", + " start=1960)\n", + "GNI_capita = reader.read()\n", + "GNI_capita.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0z6qRr8uy1ZW" + }, + "outputs": [], + "source": [ + "GNI_capita = GNI_capita.unstack(level=0)[GNI_ID]\n", + "GNI_capita.head()\n", + "\n", + "GNI_capita.plot(figsize=[8,6])\n", + "plt.title('GNI per capita for OECD founders since 1960')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EpF9r1B-y1ZX" + }, + "source": [ + "# EXERCISE-2: Using WDI API\n", + "You going to generate a table of population for each country in Africa. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9aP5PIBzy1ZX" + }, + "source": [ + "## Get indicator name and identify indicator id/code\n", + "Use the website [here](http://datatopics.worldbank.org/world-development-indicators/themes/people.html) \n", + "to get indicator name for total population." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "X5_jdnz0y1ZX" + }, + "outputs": [], + "source": [ + "indicator_id = YOUR CODE HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AooAjPxcy1ZY" + }, + "source": [ + "## Get ISO codes for African Countries\n", + "In the data folder, there is a CSV file ```country_codes_africa.csv```. We will use pandas to read the file and extract country names and use them to generate a list of ISO codes for African countries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "icfO1pf5y1ZY" + }, + "outputs": [], + "source": [ + "# Get ISO codes for African Countries\n", + "iso_codes_file = YOUR CODE HERE\n", + "\n", + "# Read CSV into dataframe\n", + "df_iso = YOUR CODE HERE\n", + "\n", + "# Check the dataframe using the head function\n", + "\n", + "# Put the ISO codes into a list\n", + "countries = wb.get_countries()\n", + "names = list(df_iso.NAME_0.values)\n", + "afr_iso = countries.iso3c[countries.name.isin(names)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6N575dDzy1ZY" + }, + "source": [ + "## Get the Population Data for 2010\n", + "Please use the documentation for the function ```wb.WorldBankReader``` \n", + "to understand how to use the parameters ```start``` and ```end``` to get data for 2010 only." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gNztNrqQy1ZZ" + }, + "outputs": [], + "source": [ + "reader = wb.WorldBankReader(symbols=,\n", + " countries = countries_iso,\n", + " start=1960)\n", + "pop = YOUR CODE HERE\n", + "pop = pop.reset_index()\n", + "\n", + "# display the first 10 rows of the dataframe\n", + "YOUR CODE HERE\n", + "\n", + "# Rename the SP.POP.TOTL column. Check pandas documentation \n", + "# to see how you can rename a column\n", + "YOUR CODE HERE" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NMPMBa2My1ZZ" + }, + "source": [ + "## Which Country Has The Largest Population in Africa? \n", + "Please use this method ```max(list)``` to find the country with the largest population.\n", + "**Hint:** first put the population into a list and then use the method above." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BKbsAkOqy1ZZ" + }, + "source": [ + "Please answer the same question above this time using a method \n", + "provided by pandas. Check pandas documentation on how to find a maximum value for a column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "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": "3.10.8" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 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zuYE1t4!B6+F(D^>aaqO~-g4&j`WqGd`-GWYZ?*(H6auHNmO*2MB87!P)7gg-xyNJ) ze!Nx3-=JOM*XcU^1q2;~N`dPx8Zhn7dI<6k-u6oPI7)1RKN$-1K#I=pY37>373sfG z;N{!IEfnXbh{>dJ9!(#GoJQxEjoydTJsk6UjfOli$H*7oy1@F>jc(0dQb*0`!f*(; zr*~k+b>fM=Sz8JP=zNYR8sc7`kWBJ7o=31aW;xZgH4F2F8moDeN~sC7ja8nN`V|v5 z3x^|KtyJ;c>WAcoY(gSvK4&+8x`P z;>_CGR`~&(NSAohQqegx(7$ndFbWiy9|OnirT(LTJWT%K wtAF14U(%GH^ZY*xxi^MlM^6u|GWt Date: Wed, 17 Sep 2025 14:12:44 +0200 Subject: [PATCH 28/33] Updated APIs Notebook --- notebooks/module-5/03-web-scraping.ipynb | 143 ++++ notebooks/module-5/intro-to-APIs.ipynb | 822 ++++------------------- 2 files changed, 256 insertions(+), 709 deletions(-) diff --git a/notebooks/module-5/03-web-scraping.ipynb b/notebooks/module-5/03-web-scraping.ipynb index e69de29..cb3054d 100644 --- a/notebooks/module-5/03-web-scraping.ipynb +++ b/notebooks/module-5/03-web-scraping.ipynb @@ -0,0 +1,143 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "422cffd5", + "metadata": {}, + "outputs": [], + "source": [ + "# %% [markdown]\n", + "# # Web scraping demo: Books to Scrape\n", + "# https://books.toscrape.com/\n", + "# - Scrapes title, price, rating, availability, and product URL\n", + "# - Follows pagination (Next →)\n", + "# - Optional: scrape a specific category\n", + "\n", + "# %%\n", + "import time, re\n", + "from urllib.parse import urljoin\n", + "import requests\n", + "from bs4 import BeautifulSoup\n", + "import pandas as pd\n", + "from requests.adapters import HTTPAdapter\n", + "from urllib3.util.retry import Retry\n", + "\n", + "BASE = \"https://books.toscrape.com/\"\n", + "\n", + "def make_session():\n", + " s = requests.Session()\n", + " s.headers.update({\n", + " \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 \"\n", + " \"(KHTML, like Gecko) Chrome Safari\"\n", + " })\n", + " retries = Retry(\n", + " total=5,\n", + " backoff_factor=0.4,\n", + " status_forcelist=[429, 500, 502, 503, 504],\n", + " allowed_methods=[\"GET\"]\n", + " )\n", + " s.mount(\"http://\", HTTPAdapter(max_retries=retries))\n", + " s.mount(\"https://\", HTTPAdapter(max_retries=retries))\n", + " return s\n", + "\n", + "def clean_price(txt):\n", + " # '£51.77' -> 51.77\n", + " if not txt: return None\n", + " m = re.search(r\"(\\d+(?:\\.\\d+)?)\", txt.replace(\",\", \"\"))\n", + " return float(m.group(1)) if m else None\n", + "\n", + "def get_soup(session, url, sleep=0.2, timeout=15):\n", + " resp = session.get(url, timeout=timeout)\n", + " resp.raise_for_status()\n", + " time.sleep(sleep) # be polite\n", + " return BeautifulSoup(resp.text, \"html.parser\")\n", + "\n", + "def parse_book_card(card, page_url):\n", + " # Each book is inside

      \n", + " title_el = card.select_one(\"h3 a\")\n", + " title = title_el.get(\"title\") if title_el else None\n", + " rel_link = title_el.get(\"href\") if title_el else None\n", + " product_url = urljoin(page_url, rel_link) if rel_link else None\n", + "\n", + " price = clean_price(card.select_one(\".price_color\").get_text(strip=True) if card.select_one(\".price_color\") else None)\n", + "\n", + " # Rating is in class, e.g.,

      \n", + " rating_el = card.select_one(\".star-rating\")\n", + " rating = None\n", + " if rating_el:\n", + " classes = rating_el.get(\"class\", [])\n", + " # classes like [\"star-rating\", \"Three\"]\n", + " for c in classes:\n", + " if c in {\"One\",\"Two\",\"Three\",\"Four\",\"Five\"}:\n", + " rating = c\n", + "\n", + " # availability appears on product page; on list page it may not be present\n", + " availability = card.select_one(\".availability\")\n", + " availability = availability.get_text(strip=True) if availability else None\n", + "\n", + " return {\n", + " \"title\": title,\n", + " \"price\": price,\n", + " \"rating\": rating,\n", + " \"availability\": availability,\n", + " \"product_url\": product_url,\n", + " \"page_url\": page_url,\n", + " }\n", + "\n", + "def iter_pages(start_url, session, limit_pages=None):\n", + " \"\"\"Yield soup & url for each page, following 'next' links.\"\"\"\n", + " url = start_url\n", + " pages = 0\n", + " while url:\n", + " soup = get_soup(session, url)\n", + " yield soup, url\n", + " pages += 1\n", + " if limit_pages and pages >= limit_pages:\n", + " break\n", + " next_link = soup.select_one(\"li.next a\")\n", + " url = urljoin(url, next_link.get(\"href\")) if next_link else None\n", + "\n", + "def scrape_books(category_url=None, limit_pages=None):\n", + " \"\"\"\n", + " Scrape all books from the main listing or a specific category.\n", + " - category_url example: 'https://books.toscrape.com/catalogue/category/books/travel_2/index.html'\n", + " \"\"\"\n", + " session = make_session()\n", + " start = category_url or urljoin(BASE, \"catalogue/page-1.html\") # main listing\n", + " rows = []\n", + "\n", + " for soup, page_url in iter_pages(start, session, limit_pages=limit_pages):\n", + " cards = soup.select(\"article.product_pod\")\n", + " for c in cards:\n", + " rows.append(parse_book_card(c, page_url))\n", + " print(f\"[info] {page_url} -> +{len(cards)} items (total {len(rows)})\")\n", + "\n", + " return pd.DataFrame(rows)\n", + "\n", + "# %% Run: scrape first 2 pages of all books (quick classroom run)\n", + "df_demo = scrape_books(limit_pages=2)\n", + "df_demo.head(), df_demo.shape\n", + "\n", + "# %% Optional: scrape full site (all pages). Comment back in if you want the full run.\n", + "# df_all = scrape_books(limit_pages=None)\n", + "# df_all.to_csv(\"books_all.csv\", index=False)\n", + "# print(\"Saved books_all.csv with\", len(df_all), \"rows\")\n", + "\n", + "# %% Optional: pick a category\n", + "# How to find a category: browse homepage → click a category → copy its URL\n", + "# Example (Travel):\n", + "# cat_url = \"https://books.toscrape.com/catalogue/category/books/travel_2/index.html\"\n", + "# df_travel = scrape_books(category_url=cat_url, limit_pages=None)\n", + "# df_travel.to_csv(\"books_travel.csv\", index=False)\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/module-5/intro-to-APIs.ipynb b/notebooks/module-5/intro-to-APIs.ipynb index a443846..1d5677c 100644 --- a/notebooks/module-5/intro-to-APIs.ipynb +++ b/notebooks/module-5/intro-to-APIs.ipynb @@ -6,10 +6,30 @@ "id": "WioWyjbZy1Y8" }, "source": [ - "# Introduction to Using APIs\n", "\n", - "In this notebook, we will go through a practical example of how to use Python to access an API.\n", - "For quick demonstration of the concepts, we will use the [Open Notify API](http://open-notify.org), [openWeather](https://openweathermap.org) and [WBG] (http://datatopics.worldbank.org/world-development-indicators/themes/people.html) APIs." + "# Accessing APIs\n", + "\n", + "## Introduction\n", + "\n", + "This notebook will guide you through the fundamentals of accessing and working with web APIs using Python. APIs (Application Programming Interfaces) are essential tools for modern data science, allowing you to access real-time data from countless sources including government databases, weather services, financial markets, and research institutions.\n", + "\n", + "We'll work with real-world APIs to gather data about Rwanda, demonstrating practical techniques you can apply to any data science project. You'll learn to handle different types of APIs, from completely free services to those requiring authentication, while following professional best practices.\n", + "\n", + "### Why This Matters\n", + "\n", + "APIs are everywhere in modern data science. Whether you're building economic analysis dashboards, environmental monitoring systems, or social research platforms, the ability to programmatically access external data sources is crucial. This workshop teaches you not just how to make API calls, but how to do so reliably, ethically, and professionally.\n", + "\n", + "## Learning Outcomes\n", + "\n", + "By the end of this workshop, you will be able to:\n", + "\n", + "1. **Make successful API requests** using Python's `requests` library, handling different authentication methods and response formats\n", + "2. **Process and analyze JSON data** returned by APIs, converting it into pandas DataFrames for further analysis\n", + "3. **Implement professional practices** including error handling, rate limiting, retry logic, and proper credential management\n", + "4. **Work with multiple API types** from free government APIs to commercial services requiring API keys\n", + "5. **Combine data from multiple sources** to create comprehensive datasets for analysis\n", + "\n", + "---" ] }, { @@ -24,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": { "executionInfo": { "elapsed": 1308, @@ -40,6 +60,8 @@ }, "outputs": [], "source": [ + "import os\n", + "from dotenv import load_dotenv\n", "import requests\n", "from matplotlib.pyplot import figure\n", "import matplotlib.pyplot as plt\n", @@ -87,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "executionInfo": { "elapsed": 2, @@ -108,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "executionInfo": { "elapsed": 4, @@ -130,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -149,30 +171,16 @@ "id": "-av0o7kVy1ZG", "outputId": "b028759b-c0ae-4f79-e6a3-07e3813f5e88" }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - }, - "text/plain": [ - "'http://api.open-notify.org/iss-now.json'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Full url\n", + "# base_url + endpoint = full url which we can use in requests.\n", "full_url = base_url + pos_now\n", "full_url" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "executionInfo": { "elapsed": 545, @@ -189,12 +197,13 @@ "outputs": [], "source": [ "# Make a request using requests package\n", - "response = requests.get(full_url)" + "response = requests.get(full_url)\n", + "print(response.status_code)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -212,18 +221,7 @@ "id": "FiUgfAgey1ZI", "outputId": "6a681363-ad39-4532-b54d-a508682efcf1" }, - "outputs": [ - { - "data": { - "text/plain": [ - "200" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "response.status_code" ] @@ -239,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -257,19 +255,7 @@ "id": "4iW9ywixy1ZJ", "outputId": "88bbbba3-6dd1-4e71-f481-c1e8037d2937" }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'response' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Get the data\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m dict_obj_from_json \u001b[38;5;241m=\u001b[39m \u001b[43mresponse\u001b[49m\u001b[38;5;241m.\u001b[39mjson()\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mtype\u001b[39m(dict_obj_from_json)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(dict_obj_from_json),\u001b[38;5;28mtype\u001b[39m(dict_obj_from_json)\n", - "\u001b[0;31mNameError\u001b[0m: name 'response' is not defined" - ] - } - ], + "outputs": [], "source": [ "# Get the data\n", "dict_obj_from_json = response.json()\n", @@ -279,7 +265,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dict_obj_from_json.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -297,18 +292,7 @@ "id": "If79Qft-y1ZJ", "outputId": "da8e8f38-baf9-483e-b8fd-a98d33768179" }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'longitude': '-146.8740', 'latitude': '-46.0885'}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "geo_info = dict_obj_from_json['iss_position']\n", "geo_info" @@ -316,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -334,16 +318,7 @@ "id": "wDUomxYWy1ZK", "outputId": "13398c09-ace8-4c24-ab4a-b421a7aa7633" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-146.8740\n", - "-46.0885\n" - ] - } - ], + "outputs": [], "source": [ "lon = geo_info['longitude']\n", "print(lon)\n", @@ -353,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -371,15 +346,7 @@ "id": "IRGRizV5y1ZK", "outputId": "c06f3727-2c96-4b22-9c6f-e59ddc452ccf" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-46.0885 -- -146.8740\n" - ] - } - ], + "outputs": [], "source": [ "geo_info = dict_obj_from_json['iss_position']\n", "lon = geo_info['longitude']\n", @@ -389,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -407,15 +374,7 @@ "id": "AUiikw8ay1ZL", "outputId": "e97cd0fe-db3b-44b7-ab5e-f3b7450d8cd3" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-48.8931 -- -137.5187\n" - ] - } - ], + "outputs": [], "source": [ "base_url = 'http://api.open-notify.org/'\n", "#endpoint for current position of ISS\n", @@ -439,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "code_folding": [ 0 @@ -498,19 +457,7 @@ "id": "5kTLElRIy1ZM", "outputId": "009d6af7-a92f-4b24-b2d7-4fe6e117a8c4" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hooray!!, it worked\n", - "Type: \n", - "This is the nested dictionary returned: \n", - " {'iss_position': {'latitude': '-35.7143', 'longitude': '-67.6236'}, 'message': 'success', 'timestamp': 1675397176}\n", - "ISS is passing on Lat: -35.7143, and Lon: -67.6236 right about now\n" - ] - } - ], + "outputs": [], "source": [ "base_url = 'http://api.open-notify.org/'\n", "iss_pos = base_url + 'iss-now.json'\n", @@ -537,15 +484,7 @@ "id": "eUTXnt5my1ZM", "outputId": "bc23a02c-783c-4d34-c2b5-7c8e27661ee5" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What happened, lets see the code we got: 404\n" - ] - } - ], + "outputs": [], "source": [ "# Now, try to enter a wrong endpoint and see what happens\n", "iss_pos2 = base_url + 'iss-today.json'\n", @@ -564,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -582,29 +521,7 @@ "id": "XnVOmqLey1ZN", "outputId": "9ddc5b09-a010-470b-eab8-a47e7902959f" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hooray!!, it worked\n", - "Type: \n", - "This is the nested dictionary returned: \n", - " {'timestamp': 1676355890, 'iss_position': {'longitude': '-134.0074', 'latitude': '-49.6644'}, 'message': 'success'}\n", - "ISS is passing on Lat: -49.6644, and Lon: -134.0074 right about now\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "request_iss_position(endpoint_url='http://api.open-notify.org/iss-now.json')\n", "request_iss_position" @@ -644,19 +561,7 @@ "id": "I5IDdQP3y1ZO", "outputId": "998a323a-a991-4848-d868-1714b950e26a" }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m# Extract the dict object from the JSON response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: expected str, bytes or os.PathLike object, not Response" - ] - } - ], + "outputs": [], "source": [ "# base-url is same as above\n", "# end point for this data: please check documentation\n", @@ -707,23 +612,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "Image(\"../docs/images/open-weather-api.png\")" + "Image(\"/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/docs/images/open-weather-api.png\")" ] }, { @@ -744,38 +637,9 @@ "4. A function putting everything together " ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# String concatenation in Python" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dunstan Matekenya\n" - ] - } - ], - "source": [ - "a = 'Dunstan'\n", - "b = 'Matekenya'\n", - "\n", - "full_name = \"{} {}\".format(a,b)\n", - "\n", - "print(full_name)" - ] - }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 4, "metadata": { "code_folding": [ 0 @@ -816,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -839,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -882,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -924,72 +788,66 @@ ] }, { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "# ========================================================\n", - "# HARD CODE BASE INFORMAITON AND USE THEM AS GLOBAL VARS\n", - "# ========================================================\n", - "# Harcode base URL, we use the 2.5 version which seems to be the free version with no need to subscribe\n", - "BASE_URL = \"http://api.openweathermap.org/data/2.5/\"\n", - "# Hardcode API key although its not recommended for security purposes\n", - "API_KEY = 'cd689df7ce5a01db2aafde528e3d87c4'" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "city_file = \"/Users/dunstanmatekenya/Downloads/" + "## Simple Request on Weather API Using City Name\n", + "- ```BASE_URL: https://api.openweathermap.org/data/```\n", + "-\n", + "### Parameters:\n", + "- version: 2.5 for free plans\n", + "- category: weather \n", + " - weather (current weather by city)\n", + " - forecast (5-day forecast)\n", + "- Location (city name, lat/lon, city id)\n", + "- API key" ] }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 17, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "927967" - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "https://api.openweathermap.org/data/2.5/weather?q=Kigali&APPID=15b6dcf6a90d01c149cd46da48646612\n", + "URL: https://api.openweathermap.org/data/2.5/weather?q=Kigali&APPID=15b6dcf6a90d01c149cd46da48646612\n", + "Status: 200\n", + "Body: {\"coord\":{\"lon\":30.0588,\"lat\":-1.95},\"weather\":[{\"id\":500,\"main\":\"Rain\",\"description\":\"light rain\",\"icon\":\"10d\"}],\"base\":\"stations\",\"main\":{\"temp\":299.11,\"feels_like\":299.11,\"temp_min\":299.11,\"temp_max\":299.86,\"pressure\":1011,\"humidity\":51,\"sea_level\":1011,\"grnd_level\":850},\"visibility\":10000,\"wind\":{\"speed\":4.63,\"deg\":160},\"rain\":{\"1h\":0.62},\"clouds\":{\"all\":40},\"dt\":1758110816,\"sys\":{\"type\":2,\"id\":47683,\"country\":\"RW\",\"sunrise\":1758081081,\"sunset\":1758124644},\"timezone\":7200,\"id\":202061,\"name\":\"Kigali\",\"cod\":200}\n" + ] } ], "source": [ - "# Get ID\n", - "kigali_id = city_id_from_name(city_file=\"../DATASETS/raw/city.list.json\",\n", - " name=\"Kigali\")\n", - "kigali_id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get weather data function\n", - "json_object = get_weather(base_url=BASE_URL, category='forecast', \n", - " api_key=API_KEY, city_id=kigali_id)\n", - "json_object" + "# ========================================================\n", + "# HARD CODE BASE INFORMAITON AND USE THEM AS GLOBAL VARS\n", + "# ========================================================\n", + "# Harcode base URL, we use the 2.5 version which seems to be the free version with no need to subscribe\n", + "BASE_URL = \"https://api.openweathermap.org/data/2.5/\"\n", + "\n", + "API_KEY = os.getenv(\"OPENWEATHER_API_KEY\")\n", + "\n", + "\n", + "\n", + "category = 'weather'\n", + "city_name = \"Kigali\"\n", + "full_url = \"{}{}?q={}&APPID={}\".format(BASE_URL, category, city_name, API_KEY)\n", + "print(full_url)\n", + "\n", + "\n", + "# Send request\n", + "r = requests.get(full_url, timeout=20)\n", + "print(\"URL:\", r.url)\n", + "print(\"Status:\", r.status_code)\n", + "print(\"Body:\", r.text) # if 401, body text is usually explicit\n" ] }, { - "cell_type": "code", - "execution_count": 164, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Convert JSON to pandas Dataframe\n", - "df = weather_json_to_pandas_df(json_object)" + "## Use Functions to Get 16 Days Forecast" ] }, { @@ -997,38 +855,6 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# put everything together\n", - "save_weather_data(city_name='Lagos', \n", - " csv_filename=\"../DATASETS/lagos.csv\",\n", - " city_list_file=\"../DATASETS/raw/city.list.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "City-ID for Lilongwe is 927967\n", - "http://api.openweathermap.org/data/2.5/forecast?id=927967&APPID=cd689df7ce5a01db2aafde528e3d87c4\n", - "\n", - "First few rows of the data\n", - " Date tempF tempF_min tempF_max humidity City\n", - "0 2024-01-10 296.22 296.14 296.14 82 Lilongwe\n", - "1 2024-01-10 296.97 296.97 296.97 78 Lilongwe\n", - "2 2024-01-10 293.56 293.56 293.56 94 Lilongwe\n", - "3 2024-01-10 293.15 293.15 293.15 96 Lilongwe\n", - "4 2024-01-11 292.70 292.70 292.70 95 Lilongwe\n", - "\n", - "Successfully Saved CSV file\n", - "============================\n" - ] - } - ], "source": [ "# =======================================\n", "# GET WEATHER FORECAST DATA FOR LILONGWE\n", @@ -1063,428 +889,6 @@ "# https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={API key}" ] }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "http://api.openweathermap.org/data/2.5/weather?&lat=30&lon=60&appid=cd689df7ce5a01db2aafde528e3d87c4\n" - ] - } - ], - "source": [ - "base_url = BASE_URL\n", - "category = 'weather'\n", - "lat = '30'\n", - "lon = '60'\n", - "\n", - "full_url = \"{}{}?&lat={}&lon={}&appid={}\".format(base_url, \n", - " category,lat, lon,API_KEY)\n", - "print(full_url)" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": {}, - "outputs": [], - "source": [ - "response = requests.get(full_url)" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'coord': {'lon': 60, 'lat': 30},\n", - " 'weather': [{'id': 801,\n", - " 'main': 'Clouds',\n", - " 'description': 'few clouds',\n", - " 'icon': '02n'}],\n", - " 'base': 'stations',\n", - " 'main': {'temp': 287,\n", - " 'feels_like': 284.75,\n", - " 'temp_min': 287,\n", - " 'temp_max': 287,\n", - " 'pressure': 1015,\n", - " 'humidity': 12,\n", - " 'sea_level': 1015,\n", - " 'grnd_level': 852},\n", - " 'visibility': 10000,\n", - " 'wind': {'speed': 1.77, 'deg': 173, 'gust': 1.81},\n", - " 'clouds': {'all': 24},\n", - " 'dt': 1704894490,\n", - " 'sys': {'country': 'IR', 'sunrise': 1704855411, 'sunset': 1704892625},\n", - " 'timezone': 12600,\n", - " 'id': 1159301,\n", - " 'name': 'Zahedan',\n", - " 'cod': 200}" - ] - }, - "execution_count": 170, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results = response.json()\n", - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 139, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"../docs/images/current-temp-exercise.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VI9VzkaYy1ZR" - }, - "source": [ - "# Pulling Data World Bank Development Indicators API\n", - "We're using Pandas Datareader: a third-party library that makes it easy to pull data from APIs into a Pandas dataframe.\n", - "\n", - "Use it to pull live data from Yahoo! Finance, World Bank and others.\n", - "\n", - "\n", - "| Function | Task Performed |\n", - "|----|---|\n", - "| wb.get_countries() | List available countries, with their country codes, income levels etc. |\n", - "| wb.search() | Query the available indicators using a search term. |\n", - "| wb.WorldBankReader() | A class for downloading data series. |" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BFZpLT2Ay1ZR" - }, - "source": [ - "## Installing Pandas Datareader\n", - "Use the terminal to install the package pandas_datareader like so: \n", - "```pip3 install pandas_datareader```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dVFiuN0ey1ZS" - }, - "outputs": [], - "source": [ - "# Lets import World Bank module (wb) from pandas_datareader\n", - "from pandas_datareader import wb" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W_0VJ43by1ZS" - }, - "source": [ - "## List of Indicators\n", - "See [here](http://datatopics.worldbank.org/world-development-indicators/themes/people.html)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "KWWp4vXWy1ZT" - }, - "outputs": [], - "source": [ - "# Find the GNI per capita indicator, get its ID number\n", - "indicator_name = \"GNI per capita, Atlas\"\n", - "result = wb.search(string=indicator_name, field = 'name')\n", - "result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "KvuE9wJky1ZT" - }, - "outputs": [], - "source": [ - "# Make a note of the indicator ID\n", - "GNI_ID = 'NY.GNP.PCAP.CD'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "tIDkoVPfy1ZT" - }, - "outputs": [], - "source": [ - "# Get a list of countries and clean it up\n", - "countries = wb.get_countries()\n", - "countries.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ck9hGqMOy1ZU" - }, - "outputs": [], - "source": [ - "#Select only those observations and columns we need: \n", - "countries2 = countries[countries.incomeLevel != 'Aggregates']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "TlNVYVm7y1ZU" - }, - "outputs": [], - "source": [ - "countries3 = countries2[['name', 'iso3c','incomeLevel']]\n", - "countries3.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "abN-0b7Fy1ZV" - }, - "outputs": [], - "source": [ - "OECD_founders = ['Belgium','France','Germany','Italy','Luxembourg','Netherlands','United States',\n", - " 'Canada','Japan']\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "T1hdDmr7y1ZV" - }, - "outputs": [], - "source": [ - "countries4 = countries3[countries3.name.isin(OECD_founders)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7tRmyUjCy1ZV" - }, - "outputs": [], - "source": [ - "countries4.name.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "IPJi5Iksy1ZW" - }, - "outputs": [], - "source": [ - "type(countries4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PrIqbmrCy1ZW" - }, - "outputs": [], - "source": [ - "countries_iso = list(countries4.iso3c.values)\n", - "countries_iso" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Fb3iVpQuy1ZW" - }, - "outputs": [], - "source": [ - "# Let's plot GNI per capita since 1960 for the OECD countries\n", - "\n", - "\n", - "# Create a reader object, pass it the data ID and country codes we want\n", - "\n", - "reader = wb.WorldBankReader(symbols=GNI_ID,\n", - " countries = countries_iso,\n", - " start=1960)\n", - "GNI_capita = reader.read()\n", - "GNI_capita.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0z6qRr8uy1ZW" - }, - "outputs": [], - "source": [ - "GNI_capita = GNI_capita.unstack(level=0)[GNI_ID]\n", - "GNI_capita.head()\n", - "\n", - "GNI_capita.plot(figsize=[8,6])\n", - "plt.title('GNI per capita for OECD founders since 1960')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EpF9r1B-y1ZX" - }, - "source": [ - "# EXERCISE-2: Using WDI API\n", - "You going to generate a table of population for each country in Africa. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9aP5PIBzy1ZX" - }, - "source": [ - "## Get indicator name and identify indicator id/code\n", - "Use the website [here](http://datatopics.worldbank.org/world-development-indicators/themes/people.html) \n", - "to get indicator name for total population." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "X5_jdnz0y1ZX" - }, - "outputs": [], - "source": [ - "indicator_id = YOUR CODE HERE" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AooAjPxcy1ZY" - }, - "source": [ - "## Get ISO codes for African Countries\n", - "In the data folder, there is a CSV file ```country_codes_africa.csv```. We will use pandas to read the file and extract country names and use them to generate a list of ISO codes for African countries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "icfO1pf5y1ZY" - }, - "outputs": [], - "source": [ - "# Get ISO codes for African Countries\n", - "iso_codes_file = YOUR CODE HERE\n", - "\n", - "# Read CSV into dataframe\n", - "df_iso = YOUR CODE HERE\n", - "\n", - "# Check the dataframe using the head function\n", - "\n", - "# Put the ISO codes into a list\n", - "countries = wb.get_countries()\n", - "names = list(df_iso.NAME_0.values)\n", - "afr_iso = countries.iso3c[countries.name.isin(names)]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6N575dDzy1ZY" - }, - "source": [ - "## Get the Population Data for 2010\n", - "Please use the documentation for the function ```wb.WorldBankReader``` \n", - "to understand how to use the parameters ```start``` and ```end``` to get data for 2010 only." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "gNztNrqQy1ZZ" - }, - "outputs": [], - "source": [ - "reader = wb.WorldBankReader(symbols=,\n", - " countries = countries_iso,\n", - " start=1960)\n", - "pop = YOUR CODE HERE\n", - "pop = pop.reset_index()\n", - "\n", - "# display the first 10 rows of the dataframe\n", - "YOUR CODE HERE\n", - "\n", - "# Rename the SP.POP.TOTL column. Check pandas documentation \n", - "# to see how you can rename a column\n", - "YOUR CODE HERE" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NMPMBa2My1ZZ" - }, - "source": [ - "## Which Country Has The Largest Population in Africa? \n", - "Please use this method ```max(list)``` to find the country with the largest population.\n", - "**Hint:** first put the population into a list and then use the method above." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BKbsAkOqy1ZZ" - }, - "source": [ - "Please answer the same question above this time using a method \n", - "provided by pandas. Check pandas documentation on how to find a maximum value for a column" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1498,7 +902,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1512,7 +916,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.12.7" }, "toc": { "base_numbering": 1, From 2803e71dde730507f6fc160ccb072bd5858441ed Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Wed, 17 Sep 2025 15:17:33 +0200 Subject: [PATCH 29/33] Upated APIs Notebook --- notebooks/module-5/Lilongwe | 41 +++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 notebooks/module-5/Lilongwe diff --git a/notebooks/module-5/Lilongwe b/notebooks/module-5/Lilongwe new file mode 100644 index 0000000..f20b9b0 --- /dev/null +++ b/notebooks/module-5/Lilongwe @@ -0,0 +1,41 @@ +Date,tempF,tempF_min,tempF_max,humidity,City +2025-09-17,303.76,302.75,302.75,28,Lilongwe +2025-09-17,300.53,298.67,298.67,29,Lilongwe +2025-09-17,296.7,296.7,296.7,38,Lilongwe +2025-09-18,295.66,295.66,295.66,40,Lilongwe +2025-09-18,294.35,294.35,294.35,42,Lilongwe +2025-09-18,298.68,298.68,298.68,35,Lilongwe +2025-09-18,303.05,303.05,303.05,26,Lilongwe +2025-09-18,304.92,304.92,304.92,19,Lilongwe +2025-09-18,302.98,302.98,302.98,25,Lilongwe +2025-09-18,299.11,299.11,299.11,37,Lilongwe +2025-09-18,297.49,297.49,297.49,41,Lilongwe +2025-09-19,296.22,296.22,296.22,44,Lilongwe +2025-09-19,295.55,295.55,295.55,51,Lilongwe +2025-09-19,298.45,298.45,298.45,44,Lilongwe +2025-09-19,302.87,302.87,302.87,33,Lilongwe +2025-09-19,304.59,304.59,304.59,25,Lilongwe +2025-09-19,303.54,303.54,303.54,25,Lilongwe +2025-09-19,300.0,300.0,300.0,33,Lilongwe +2025-09-19,298.37,298.37,298.37,42,Lilongwe +2025-09-20,296.72,296.72,296.72,51,Lilongwe +2025-09-20,294.82,294.82,294.82,64,Lilongwe +2025-09-20,297.3,297.3,297.3,53,Lilongwe +2025-09-20,302.43,302.43,302.43,34,Lilongwe +2025-09-20,304.02,304.02,304.02,27,Lilongwe +2025-09-20,302.95,302.95,302.95,25,Lilongwe +2025-09-20,299.69,299.69,299.69,29,Lilongwe +2025-09-20,297.17,297.17,297.17,36,Lilongwe +2025-09-21,295.09,295.09,295.09,43,Lilongwe +2025-09-21,294.46,294.46,294.46,48,Lilongwe +2025-09-21,297.5,297.5,297.5,44,Lilongwe +2025-09-21,302.24,302.24,302.24,30,Lilongwe +2025-09-21,304.35,304.35,304.35,22,Lilongwe +2025-09-21,302.92,302.92,302.92,24,Lilongwe +2025-09-21,299.48,299.48,299.48,31,Lilongwe +2025-09-21,297.42,297.42,297.42,34,Lilongwe +2025-09-22,295.52,295.52,295.52,41,Lilongwe +2025-09-22,294.48,294.48,294.48,46,Lilongwe +2025-09-22,297.88,297.88,297.88,42,Lilongwe +2025-09-22,302.89,302.89,302.89,29,Lilongwe +2025-09-22,304.98,304.98,304.98,22,Lilongwe From 268537d89c9283630789f18f170142923635d7ae Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Wed, 17 Sep 2025 15:19:03 +0200 Subject: [PATCH 30/33] Upated APIs Notebook --- notebooks/module-5/intro-to-APIs.ipynb | 665 +++++++++++++++++++++++-- 1 file changed, 636 insertions(+), 29 deletions(-) diff --git a/notebooks/module-5/intro-to-APIs.ipynb b/notebooks/module-5/intro-to-APIs.ipynb index 1d5677c..567debf 100644 --- a/notebooks/module-5/intro-to-APIs.ipynb +++ b/notebooks/module-5/intro-to-APIs.ipynb @@ -70,6 +70,26 @@ "from IPython.display import Image" ] }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_dotenv() # take environment variables from .env." + ] + }, { "cell_type": "markdown", "metadata": { @@ -610,15 +630,6 @@ "The API has many versions and subscriptions plans but in our exercise, we will use their 2.5 version which is the one which doesnt require subscription." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Image(\"/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/docs/images/open-weather-api.png\")" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -639,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "metadata": { "code_folding": [ 0 @@ -680,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -703,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -746,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -792,20 +803,19 @@ "metadata": {}, "source": [ "## Simple Request on Weather API Using City Name\n", - "- ```BASE_URL: https://api.openweathermap.org/data/```\n", - "-\n", + "- ```BASE_URL: https://api.openweathermap.org/data/2.5/```\n", "### Parameters:\n", "- version: 2.5 for free plans\n", "- category: weather \n", " - weather (current weather by city)\n", - " - forecast (5-day forecast)\n", + " - forecast (up to 16 days forecast)\n", "- Location (city name, lat/lon, city id)\n", "- API key" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -813,9 +823,8 @@ "output_type": "stream", "text": [ "https://api.openweathermap.org/data/2.5/weather?q=Kigali&APPID=15b6dcf6a90d01c149cd46da48646612\n", - "URL: https://api.openweathermap.org/data/2.5/weather?q=Kigali&APPID=15b6dcf6a90d01c149cd46da48646612\n", "Status: 200\n", - "Body: {\"coord\":{\"lon\":30.0588,\"lat\":-1.95},\"weather\":[{\"id\":500,\"main\":\"Rain\",\"description\":\"light rain\",\"icon\":\"10d\"}],\"base\":\"stations\",\"main\":{\"temp\":299.11,\"feels_like\":299.11,\"temp_min\":299.11,\"temp_max\":299.86,\"pressure\":1011,\"humidity\":51,\"sea_level\":1011,\"grnd_level\":850},\"visibility\":10000,\"wind\":{\"speed\":4.63,\"deg\":160},\"rain\":{\"1h\":0.62},\"clouds\":{\"all\":40},\"dt\":1758110816,\"sys\":{\"type\":2,\"id\":47683,\"country\":\"RW\",\"sunrise\":1758081081,\"sunset\":1758124644},\"timezone\":7200,\"id\":202061,\"name\":\"Kigali\",\"cod\":200}\n" + "Body: {\"coord\":{\"lon\":30.0588,\"lat\":-1.95},\"weather\":[{\"id\":500,\"main\":\"Rain\",\"description\":\"light rain\",\"icon\":\"10d\"}],\"base\":\"stations\",\"main\":{\"temp\":299.11,\"feels_like\":299.11,\"temp_min\":299.11,\"temp_max\":299.86,\"pressure\":1010,\"humidity\":47,\"sea_level\":1010,\"grnd_level\":849},\"visibility\":10000,\"wind\":{\"speed\":3.09,\"deg\":160},\"rain\":{\"1h\":0.2},\"clouds\":{\"all\":40},\"dt\":1758112628,\"sys\":{\"type\":2,\"id\":47683,\"country\":\"RW\",\"sunrise\":1758081081,\"sunset\":1758124644},\"timezone\":7200,\"id\":202061,\"name\":\"Kigali\",\"cod\":200}\n" ] } ], @@ -828,8 +837,7 @@ "\n", "API_KEY = os.getenv(\"OPENWEATHER_API_KEY\")\n", "\n", - "\n", - "\n", + "# Create full URL for current weather in Kigali\n", "category = 'weather'\n", "city_name = \"Kigali\"\n", "full_url = \"{}{}?q={}&APPID={}\".format(BASE_URL, category, city_name, API_KEY)\n", @@ -837,10 +845,138 @@ "\n", "\n", "# Send request\n", - "r = requests.get(full_url, timeout=20)\n", - "print(\"URL:\", r.url)\n", - "print(\"Status:\", r.status_code)\n", - "print(\"Body:\", r.text) # if 401, body text is usually explicit\n" + "response = requests.get(full_url)\n", + "print(\"Status:\", response.status_code)\n", + "print(\"Body:\", response.text) # if 401, body text is usually explicit\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'coord': {'lon': 30.0588, 'lat': -1.95}, 'weather': [{'id': 500, 'main': 'Rain', 'description': 'light rain', 'icon': '10d'}], 'base': 'stations', 'main': {'temp': 299.11, 'feels_like': 299.11, 'temp_min': 299.11, 'temp_max': 299.86, 'pressure': 1010, 'humidity': 47, 'sea_level': 1010, 'grnd_level': 849}, 'visibility': 10000, 'wind': {'speed': 3.09, 'deg': 160}, 'rain': {'1h': 0.2}, 'clouds': {'all': 40}, 'dt': 1758112628, 'sys': {'type': 2, 'id': 47683, 'country': 'RW', 'sunrise': 1758081081, 'sunset': 1758124644}, 'timezone': 7200, 'id': 202061, 'name': 'Kigali', 'cod': 200}\n" + ] + } + ], + "source": [ + "weather_today_kigali = response.json()\n", + "print(weather_today_kigali)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "299.11" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather_today_kigali['main']['temp'] # temperature in Kelvin\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperature in Kigali:\n", + "Kelvin: 299.11K\n", + "Celsius: 25.96°C\n" + ] + } + ], + "source": [ + "# Get temperature in Kelvin from the weather data\n", + "temp_kelvin = weather_today_kigali['main']['temp']\n", + "\n", + "# Convert to Celsius (K - 273.15)\n", + "temp_celsius = temp_kelvin - 273.15\n", + "\n", + "print(f\"Temperature in Kigali:\")\n", + "print(f\"Kelvin: {temp_kelvin:.2f}K\")\n", + "print(f\"Celsius: {temp_celsius:.2f}°C\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1758112628" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather_today_kigali['dt']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weather data timestamp: 2025-09-17 14:37:08\n" + ] + } + ], + "source": [ + "from datetime import datetime\n", + "\n", + "# Get timestamp from weather data\n", + "timestamp = weather_today_kigali['dt']\n", + "\n", + "# Convert to datetime object and format\n", + "readable_time = datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S')\n", + "print(f\"Weather data timestamp: {readable_time}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['coord', 'weather', 'base', 'main', 'visibility', 'wind', 'rain', 'clouds', 'dt', 'sys', 'timezone', 'id', 'name', 'cod'])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather_today_kigali.keys()" ] }, { @@ -854,17 +990,488 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "City-ID for Lilongwe is 927967\n", + "\n", + "First few rows of the data\n", + " Date tempF tempF_min tempF_max humidity City\n", + "0 2025-09-17 303.76 302.75 302.75 28 Lilongwe\n", + "1 2025-09-17 300.53 298.67 298.67 29 Lilongwe\n", + "2 2025-09-17 296.70 296.70 296.70 38 Lilongwe\n", + "3 2025-09-18 295.66 295.66 295.66 40 Lilongwe\n", + "4 2025-09-18 294.35 294.35 294.35 42 Lilongwe\n", + "\n", + "Successfully Saved CSV file\n", + "============================\n" + ] + } + ], "source": [ "# =======================================\n", "# GET WEATHER FORECAST DATA FOR LILONGWE\n", "# =======================================\n", - "city = 'Lilongwe'\n", - "city_json = \"../DATASETS/raw/city.list.json\"\n", - "out_csv = \"../DATASETS/LL-weather.csv\"\n", + "city = 'Kigali'\n", + "city_json = \"/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/city.list.json\"\n", + "out_csv = \"LL-weather.csv\"\n", "df_weather = save_weather_data(city_name=city, csv_filename=out_csv, city_list_file=city_json)" ] }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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      DatetempFtempF_mintempF_maxhumidityCity
      02025-09-17303.76302.75302.7528Lilongwe
      12025-09-17300.53298.67298.6729Lilongwe
      22025-09-17296.70296.70296.7038Lilongwe
      32025-09-18295.66295.66295.6640Lilongwe
      42025-09-18294.35294.35294.3542Lilongwe
      52025-09-18298.68298.68298.6835Lilongwe
      62025-09-18303.05303.05303.0526Lilongwe
      72025-09-18304.92304.92304.9219Lilongwe
      82025-09-18302.98302.98302.9825Lilongwe
      92025-09-18299.11299.11299.1137Lilongwe
      102025-09-18297.49297.49297.4941Lilongwe
      112025-09-19296.22296.22296.2244Lilongwe
      122025-09-19295.55295.55295.5551Lilongwe
      132025-09-19298.45298.45298.4544Lilongwe
      142025-09-19302.87302.87302.8733Lilongwe
      152025-09-19304.59304.59304.5925Lilongwe
      162025-09-19303.54303.54303.5425Lilongwe
      172025-09-19300.00300.00300.0033Lilongwe
      182025-09-19298.37298.37298.3742Lilongwe
      192025-09-20296.72296.72296.7251Lilongwe
      202025-09-20294.82294.82294.8264Lilongwe
      212025-09-20297.30297.30297.3053Lilongwe
      222025-09-20302.43302.43302.4334Lilongwe
      232025-09-20304.02304.02304.0227Lilongwe
      242025-09-20302.95302.95302.9525Lilongwe
      252025-09-20299.69299.69299.6929Lilongwe
      262025-09-20297.17297.17297.1736Lilongwe
      272025-09-21295.09295.09295.0943Lilongwe
      282025-09-21294.46294.46294.4648Lilongwe
      292025-09-21297.50297.50297.5044Lilongwe
      302025-09-21302.24302.24302.2430Lilongwe
      312025-09-21304.35304.35304.3522Lilongwe
      322025-09-21302.92302.92302.9224Lilongwe
      332025-09-21299.48299.48299.4831Lilongwe
      342025-09-21297.42297.42297.4234Lilongwe
      352025-09-22295.52295.52295.5241Lilongwe
      362025-09-22294.48294.48294.4846Lilongwe
      372025-09-22297.88297.88297.8842Lilongwe
      382025-09-22302.89302.89302.8929Lilongwe
      392025-09-22304.98304.98304.9822Lilongwe
      \n", + "
      " + ], + "text/plain": [ + " Date tempF tempF_min tempF_max humidity City\n", + "0 2025-09-17 303.76 302.75 302.75 28 Lilongwe\n", + "1 2025-09-17 300.53 298.67 298.67 29 Lilongwe\n", + "2 2025-09-17 296.70 296.70 296.70 38 Lilongwe\n", + "3 2025-09-18 295.66 295.66 295.66 40 Lilongwe\n", + "4 2025-09-18 294.35 294.35 294.35 42 Lilongwe\n", + "5 2025-09-18 298.68 298.68 298.68 35 Lilongwe\n", + "6 2025-09-18 303.05 303.05 303.05 26 Lilongwe\n", + "7 2025-09-18 304.92 304.92 304.92 19 Lilongwe\n", + "8 2025-09-18 302.98 302.98 302.98 25 Lilongwe\n", + "9 2025-09-18 299.11 299.11 299.11 37 Lilongwe\n", + "10 2025-09-18 297.49 297.49 297.49 41 Lilongwe\n", + "11 2025-09-19 296.22 296.22 296.22 44 Lilongwe\n", + "12 2025-09-19 295.55 295.55 295.55 51 Lilongwe\n", + "13 2025-09-19 298.45 298.45 298.45 44 Lilongwe\n", + "14 2025-09-19 302.87 302.87 302.87 33 Lilongwe\n", + "15 2025-09-19 304.59 304.59 304.59 25 Lilongwe\n", + "16 2025-09-19 303.54 303.54 303.54 25 Lilongwe\n", + "17 2025-09-19 300.00 300.00 300.00 33 Lilongwe\n", + "18 2025-09-19 298.37 298.37 298.37 42 Lilongwe\n", + "19 2025-09-20 296.72 296.72 296.72 51 Lilongwe\n", + "20 2025-09-20 294.82 294.82 294.82 64 Lilongwe\n", + "21 2025-09-20 297.30 297.30 297.30 53 Lilongwe\n", + "22 2025-09-20 302.43 302.43 302.43 34 Lilongwe\n", + "23 2025-09-20 304.02 304.02 304.02 27 Lilongwe\n", + "24 2025-09-20 302.95 302.95 302.95 25 Lilongwe\n", + "25 2025-09-20 299.69 299.69 299.69 29 Lilongwe\n", + "26 2025-09-20 297.17 297.17 297.17 36 Lilongwe\n", + "27 2025-09-21 295.09 295.09 295.09 43 Lilongwe\n", + "28 2025-09-21 294.46 294.46 294.46 48 Lilongwe\n", + "29 2025-09-21 297.50 297.50 297.50 44 Lilongwe\n", + "30 2025-09-21 302.24 302.24 302.24 30 Lilongwe\n", + "31 2025-09-21 304.35 304.35 304.35 22 Lilongwe\n", + "32 2025-09-21 302.92 302.92 302.92 24 Lilongwe\n", + "33 2025-09-21 299.48 299.48 299.48 31 Lilongwe\n", + "34 2025-09-21 297.42 297.42 297.42 34 Lilongwe\n", + "35 2025-09-22 295.52 295.52 295.52 41 Lilongwe\n", + "36 2025-09-22 294.48 294.48 294.48 46 Lilongwe\n", + "37 2025-09-22 297.88 297.88 297.88 42 Lilongwe\n", + "38 2025-09-22 302.89 302.89 302.89 29 Lilongwe\n", + "39 2025-09-22 304.98 304.98 304.98 22 Lilongwe" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_weather" + ] + }, { "cell_type": "markdown", "metadata": { From f692bafabc21ae6e700b0f2a484074decb3557d0 Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Thu, 18 Sep 2025 09:19:33 +0200 Subject: [PATCH 31/33] Added FastAPI apps --- hello_world.py => hello_kigali.py | 26 ++++++++++++++++++++++---- notebooks/module-5/first_api.py | 29 +++++++++++++++++++++++++++++ 2 files changed, 51 insertions(+), 4 deletions(-) rename hello_world.py => hello_kigali.py (53%) create mode 100644 notebooks/module-5/first_api.py diff --git a/hello_world.py b/hello_kigali.py similarity index 53% rename from hello_world.py rename to hello_kigali.py index 3393635..2ecdfe2 100644 --- a/hello_world.py +++ b/hello_kigali.py @@ -2,6 +2,8 @@ from typing import Optional from datetime import datetime + + # Create FastAPI application app = FastAPI( title="Hello World API", @@ -9,19 +11,35 @@ version="1.0.0" ) -# Basic endpoint - no parameters + +# ========================================================== +# BASIC ENDPOINT - NO PARAMETERS- WILL SHOW "HELLO WORLD!" +# ========================================================= @app.get("/") def read_root(): """Welcome message - simplest possible endpoint""" - return {"message": "Hello World!"} + return {"message": "Welcome to Kigali!"} -# Endpoint with path parameter + +# =================================================== +# ENDPOINT WITH PATH PARAMETER +# =================================================== +# This means the URL includes a variable part (here: {name}) +# Example: calling /hello/Alice will pass "Alice" as the `name` argument @app.get("/hello/{name}") def say_hello(name: str): """Say hello to a specific person""" return {"message": f"Hello {name}!"} -# Endpoint with query parameters + +# ====================================================== +# ENDPOINT WITH QUERY PARAMETERS +# ====================================================== +# Unlike path parameters, query parameters are passed after the "?" in the URL. +# Example: /greet?name=Alice&age=25&city=Kigali +# - "name" is required (Query(...)) +# - "age" is optional (defaults to None if not provided) +# - "city" is optional with a default value of "Unknown" @app.get("/greet") def greet_person( name: str = Query(..., description="Person's name"), diff --git a/notebooks/module-5/first_api.py b/notebooks/module-5/first_api.py new file mode 100644 index 0000000..f2e1bca --- /dev/null +++ b/notebooks/module-5/first_api.py @@ -0,0 +1,29 @@ +from fastapi import FastAPI + +app = FastAPI() + +# 1. Static endpoint +@app.get("/") +def read_root(): + return {"message": "Welcome to my first API!"} + +# 2. Path parameter endpoint +@app.get("/greet/{name}/{city}") +def greet_person(name: str, city: str): + return {"message": f"Hello {name} from {city}!"} + +# 3. Data-serving endpoint (Rwanda sample data) +@app.get("/rwanda") +def get_rwanda_data(): + sample_data = { + "capital": "Kigali", + "population_millions": 13.8, + "official_languages": ["Kinyarwanda", "English", "French"], + "currency": "Rwandan franc (RWF)", + "famous_for": [ + "Mountain gorillas", + "Land of a Thousand Hills", + "Clean and green capital city" + ] + } + return {"rwanda_info": sample_data} From 0b98a080bc0d703aa29e3202e9be14687ae91cac Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Thu, 18 Sep 2025 14:15:55 +0200 Subject: [PATCH 32/33] Updated first_api script --- first_api.py | 235 ++++++++++++++++++++++++++++++++ hello_kigali.py | 4 +- notebooks/module-5/first_api.py | 29 ---- 3 files changed, 237 insertions(+), 31 deletions(-) create mode 100644 first_api.py delete mode 100644 notebooks/module-5/first_api.py diff --git a/first_api.py b/first_api.py new file mode 100644 index 0000000..29ecd0e --- /dev/null +++ b/first_api.py @@ -0,0 +1,235 @@ +import os +from typing import Optional, List +import pandas as pd +import psycopg2 +from fastapi import FastAPI, Query, Path, HTTPException +from dotenv import load_dotenv + +load_dotenv() + +app = FastAPI() + +DATA_PATH = "/Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/tmp-db-data/cells.csv" + +# 1. Static endpoint +@app.get("/") +def read_root(): + return {"message": "Welcome to my first API!"} + +# 2. Path parameter endpoint +@app.get("/greet/{name}/{city}") +def greet_person(name: str, city: str): + return {"message": f"Hello {name} from {city}!"} + +# 3. Data-serving endpoint (Rwanda sample data) +@app.get("/rwanda") +def get_rwanda_data(): + sample_data = { + "capital": "Kigali", + "population_millions": 13.8, + "official_languages": ["Kinyarwanda", "English", "French"], + "currency": "Rwandan franc (RWF)", + "famous_for": [ + "Mountain gorillas", + "Land of a Thousand Hills", + "Clean and green capital city" + ] + } + return {"rwanda_info": sample_data} + +# Sample in-memory dataset +data = [ + { + "name": "Denise", + "age": 30, + "city": "Kigali", + "country": "Rwanda", + "marital_status": "Guess" + }, + { + "name": "Donald", + "age": 20, + "city": "Kigali", + "country": "Rwanda", + "marital_status": "Happily Married" + } +] + + +@app.get("/trainees") +def get_trainee_info( + name: str = Query(..., description="Person's name (required)"), + marital_status: Optional[bool] = Query(False, description="Include marital status"), + age: Optional[bool] = Query(False, description="Include age") +): + """ + Return trainee info: + - Always return name, city, and country + - If marital_status=true, include marital_status + - If age=true, include age + """ + # Find the person + person = next((p for p in data if p["name"].lower() == name.lower()), None) + if not person: + raise HTTPException(status_code=404, detail="Person not found") + + # Base response + response = { + "name": person["name"], + "city": person["city"], + "country": person["country"] + } + + # Add optional fields + if marital_status: + response["marital_status"] = person["marital_status"] + if age: + response["age"] = person["age"] + + return {"Trainee-Info": response} + + +# ========================================================== +# EXERCISE-1 - EDIT trainees endpoint +# ========================================================= +# add children parameter (bool) to include number of children +# if children=true, include number_of_children in response +# access the app and see the results for yourself! +# http://localhost:8000/trainees?name=Denise&marital_status=true&age=true&children=true +# http://localhost:8000/trainees?name=Donald&marital_status=true&age=true&children=true + +@app.get("/cells") +def list_cells(): + df = pd.read_csv(DATA_PATH) + return df.to_dict(orient="records") + + + +# ========================================================== +# RETURN CELLS BY PROVINCE - PATH PARAMETER +# ========================================================= +@app.get("/cells/province/{province_name}") +def get_cells_by_province_path(province_name: str): + """ + Filter cells by province using a PATH parameter + Example: + /cells/province/Kigali + """ + df = pd.read_csv(DATA_PATH) + filtered = df[df["province_name"].str.lower() == province_name.lower()] + return filtered.to_dict(orient="records") + + +# ========================================================== +# RETURN CELLS BY PROVINCE - QUERY PARAMETER +# ========================================================= +@app.get("/cells-by-province") +def get_cells_by_province_query( + province_name: Optional[str] = Query(None, description="Optional province filter") +): + """ + Filter cells by province using a QUERY parameter + Example: + /cells-by-province?province_name=Kigali + /cells-by-province (returns all provinces if not specified) + """ + df = pd.read_csv(DATA_PATH) + if province_name: + df = df[df["province_name"].str.lower() == province_name.lower()] + return df.to_dict(orient="records") + +# ACCESS THE APP AND SEE THE RESULTS FOR YOURSELF! +# http://localhost:8000/cells +# http://localhost:8000/cells/province/Kigali +# http://localhost:8000/cells-by-province?province_name=Kigali + + + +# ========================================================== +# EXERCISE-2 - RETURN POPULATION BY PROVINCE +# ========================================================= +# 1. Use Path parameter to get population by province +# 2. Use Query parameter to get population by province +# Hint: use similar logic as cells by province above + + +# ========================================================== +# RETURNING DATA WITH A DATA MODEL (PYDANTIC) +# ========================================================= +from pydantic import BaseModel +from typing import Optional, List + + +# ============================================================= +# DEFINE PYDANTIC MODEL FOR CELL WHICH MATCHES CSV STRUCTURE +# ============================================================= +class Cell(BaseModel): + cell_id: str + province_name: Optional[str] = None + district_name: Optional[str] = None + sector_name: Optional[str] = None + cell_name: Optional[str] = None + + +# ============================================================= +# DEFINE ENDPOINT TO RETURN CELLS USING THE DATA MODEL +# ============================================================= +@app.get("/cells-with-data-model", response_model=List[Cell]) +def list_cells(): + df = pd.read_csv(DATA_PATH) + return df.to_dict(orient="records") + + + + +# ========================================================== +# RETURNING DATA FROM POSTGRESQL DATABASE +# ========================================================= + +# ============================================================= +# Load environment variables from .env file +# ============================================================= +load_dotenv() + +# Get database connection details from environment variables +PGHOST = os.getenv("PGHOST") +PGPORT = os.getenv("PGPORT") +PGDATABASE = os.getenv("PGDATABASE") +PGUSER = os.getenv("PGUSER") +PGPASSWORD = os.getenv("PGPASSWORD") + + +# ============================================================= +# HELPER FUNCTION TO CONNECT AND QUERY +# ============================================================= +def run_query(sql: str, params: tuple = ()): + conn = psycopg2.connect( + host=PGHOST, + port=PGPORT, + dbname=PGDATABASE, + user=PGUSER, + password=PGPASSWORD, + ) + try: + df = pd.read_sql(sql, conn, params=params) + finally: + conn.close() + return df.to_dict(orient="records") + +# ============================================================= +# ENDPOINTS (NO DATA MODEL) +# ============================================================= +@app.get("/cells-db") +def list_cells(): + """Return all rows from cells table""" + sql = "SELECT * FROM cells" + return run_query(sql) + + + +# ============================================================ +# EXERCISE-3 - RETURN POPULATION BY PROVINCE FROM DATABASE +# ============================================================ +# 1. use Path parameter to get population by province from DB +# 2. name the endpoint /population/province/{province_name} + diff --git a/hello_kigali.py b/hello_kigali.py index 2ecdfe2..749bdae 100644 --- a/hello_kigali.py +++ b/hello_kigali.py @@ -18,7 +18,7 @@ @app.get("/") def read_root(): """Welcome message - simplest possible endpoint""" - return {"message": "Welcome to Kigali!"} + return {"message": "Welcome to Nyagatare"} # =================================================== @@ -44,7 +44,7 @@ def say_hello(name: str): def greet_person( name: str = Query(..., description="Person's name"), age: Optional[int] = Query(None, description="Person's age"), - city: Optional[str] = Query("Unknown", description="Person's city") + city: Optional[str] = Query("Kigali", description="Person's city") ): """Greet a person with optional details""" greeting = f"Hello {name}!" diff --git a/notebooks/module-5/first_api.py b/notebooks/module-5/first_api.py deleted file mode 100644 index f2e1bca..0000000 --- a/notebooks/module-5/first_api.py +++ /dev/null @@ -1,29 +0,0 @@ -from fastapi import FastAPI - -app = FastAPI() - -# 1. Static endpoint -@app.get("/") -def read_root(): - return {"message": "Welcome to my first API!"} - -# 2. Path parameter endpoint -@app.get("/greet/{name}/{city}") -def greet_person(name: str, city: str): - return {"message": f"Hello {name} from {city}!"} - -# 3. Data-serving endpoint (Rwanda sample data) -@app.get("/rwanda") -def get_rwanda_data(): - sample_data = { - "capital": "Kigali", - "population_millions": 13.8, - "official_languages": ["Kinyarwanda", "English", "French"], - "currency": "Rwandan franc (RWF)", - "famous_for": [ - "Mountain gorillas", - "Land of a Thousand Hills", - "Clean and green capital city" - ] - } - return {"rwanda_info": sample_data} From 8324db2e7dcd2f7e66ac894b028214f09887491e Mon Sep 17 00:00:00 2001 From: dmatekenya Date: Fri, 19 Sep 2025 13:06:04 +0200 Subject: [PATCH 33/33] Updated assignment-2 solutions --- first_api.py | 26 +- .../assignments/assignment-2-solutions.ipynb | 317 ++++++++++++++---- 2 files changed, 265 insertions(+), 78 deletions(-) diff --git a/first_api.py b/first_api.py index 29ecd0e..6a7e926 100644 --- a/first_api.py +++ b/first_api.py @@ -44,14 +44,16 @@ def get_rwanda_data(): "age": 30, "city": "Kigali", "country": "Rwanda", - "marital_status": "Guess" + "marital_status": "Guess", + "children": 2 }, { "name": "Donald", "age": 20, "city": "Kigali", "country": "Rwanda", - "marital_status": "Happily Married" + "marital_status": "Happily Married", + "children": 0 } ] @@ -60,7 +62,8 @@ def get_rwanda_data(): def get_trainee_info( name: str = Query(..., description="Person's name (required)"), marital_status: Optional[bool] = Query(False, description="Include marital status"), - age: Optional[bool] = Query(False, description="Include age") + age: Optional[bool] = Query(False, description="Include age"), + children: Optional[bool] = Query(False, description="Include number of children") ): """ Return trainee info: @@ -70,6 +73,7 @@ def get_trainee_info( """ # Find the person person = next((p for p in data if p["name"].lower() == name.lower()), None) + print(person) if not person: raise HTTPException(status_code=404, detail="Person not found") @@ -85,6 +89,9 @@ def get_trainee_info( response["marital_status"] = person["marital_status"] if age: response["age"] = person["age"] + + if children: + response["number_of_children"] = person["children"] return {"Trainee-Info": response} @@ -98,11 +105,18 @@ def get_trainee_info( # http://localhost:8000/trainees?name=Denise&marital_status=true&age=true&children=true # http://localhost:8000/trainees?name=Donald&marital_status=true&age=true&children=true + +# ========================================================== +# RETURNING DATA -ALL CELLS FROM CSV +# ========================================================== + @app.get("/cells") def list_cells(): df = pd.read_csv(DATA_PATH) - return df.to_dict(orient="records") + dict_from_pandas = df.to_dict(orient="records") + + return dict_from_pandas # ========================================================== @@ -116,9 +130,10 @@ def get_cells_by_province_path(province_name: str): /cells/province/Kigali """ df = pd.read_csv(DATA_PATH) + filtered = df[df["province_name"].str.lower() == province_name.lower()] - return filtered.to_dict(orient="records") + return filtered.to_dict(orient="records") # ========================================================== # RETURN CELLS BY PROVINCE - QUERY PARAMETER @@ -136,6 +151,7 @@ def get_cells_by_province_query( df = pd.read_csv(DATA_PATH) if province_name: df = df[df["province_name"].str.lower() == province_name.lower()] + return df.to_dict(orient="records") # ACCESS THE APP AND SEE THE RESULTS FOR YOURSELF! diff --git a/notebooks/assignments/assignment-2-solutions.ipynb b/notebooks/assignments/assignment-2-solutions.ipynb index faf9b5e..efc2070 100644 --- a/notebooks/assignments/assignment-2-solutions.ipynb +++ b/notebooks/assignments/assignment-2-solutions.ipynb @@ -101,7 +101,7 @@ "id": "e49528b3", "metadata": {}, "source": [ - "# Part 1: Processing Excel Files\n", + "# Part 1: Processing Excel Files [40 Points]\n", "The primary goal is to preprocess an [Excel file](https://docs.google.com/spreadsheets/d/1uvTQYS22VfXXo1Hwkm1frFx_bKkLQkcf/edit?usp=share_link&ouid=113302179168925233984&rtpof=true&sd=true) with multiple sheets into a unified CSV dataset that consolidates multiple indicators. Having all indicators in a single file at the same analytical unit (national, subnational) is more efficient than managing separate files and enables easier cross-indicator analysis.\n", "\n", "## Task 1: Generate National-Level Summaries\n", @@ -131,12 +131,6 @@ "This structure enables both single-indicator and multi-indicator analysis at the subnational level." ] }, - { - "cell_type": "markdown", - "id": "9568e764", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "id": "8fc2d99d", @@ -152,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "071f49a4", "metadata": {}, "outputs": [], @@ -171,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "45c11551", "metadata": {}, "outputs": [], @@ -242,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "397da317", "metadata": {}, "outputs": [ @@ -433,7 +427,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Saved national indicators to /Users/dmatekenya/Library/CloudStorage/GoogleDrive-dmatekenya@gmail.com/My Drive/TEACHING/AIMS-DSCBI/data/rwanda/national_indicators.csv\n" + "Saved national indicators to /Users/dmatekenya/My Drive (dmatekenya@gmail.com)/TEACHING/AIMS-DSCBI/data/rwanda/national_indicators.csv\n" ] } ], @@ -454,6 +448,21 @@ "print(f\"Saved national indicators to {output_path}\")" ] }, + { + "cell_type": "markdown", + "id": "f4cf1f37", + "metadata": {}, + "source": [ + "
      \n", + "

      📝 Marking Notes for Part-1-Task-1

      \n", + "
        \n", + "
      • Total Points: 15
        • \n", + "
      • You can decide how to assign points
        • \n", + "
      • Someone gets full points if they have code and they have displayed the dataframe above in their notebook
        • \n", + "
      \n", + "
      " + ] + }, { "cell_type": "markdown", "id": "14a63a14", @@ -487,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "d08b7742", "metadata": {}, "outputs": [], @@ -578,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "01e15268", "metadata": {}, "outputs": [ @@ -2047,6 +2056,152 @@ "df_adm2 = extract_adm2_indicators(excel_path=FILE_EXCEL)" ] }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c2ca1a2d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      indicator_namedistrictaggregated_valueindicator_yearsurvey_namesurvey_year_labelaggregation_methodsource_sheet
      0Antenatal care from a skilled providerButare89.6000001992RW1992DHS1992meandhs-mobile_subnational_rwa
      1Antenatal care from a skilled providerButare/Gitarama93.6000001992RW1992DHS1992meandhs-mobile_subnational_rwa
      2Antenatal care from a skilled providerByumba94.5000001992RW1992DHS1992meandhs-mobile_subnational_rwa
      3Antenatal care from a skilled providerByumba/Kibungo94.2666671992RW1992DHS1992meandhs-mobile_subnational_rwa
      4Antenatal care from a skilled providerCyangugu90.9333331992RW1992DHS1992meandhs-mobile_subnational_rwa
      \n", + "
      " + ], + "text/plain": [ + " indicator_name district aggregated_value \\\n", + "0 Antenatal care from a skilled provider Butare 89.600000 \n", + "1 Antenatal care from a skilled provider Butare/Gitarama 93.600000 \n", + "2 Antenatal care from a skilled provider Byumba 94.500000 \n", + "3 Antenatal care from a skilled provider Byumba/Kibungo 94.266667 \n", + "4 Antenatal care from a skilled provider Cyangugu 90.933333 \n", + "\n", + " indicator_year survey_name survey_year_label aggregation_method \\\n", + "0 1992 RW1992DHS 1992 mean \n", + "1 1992 RW1992DHS 1992 mean \n", + "2 1992 RW1992DHS 1992 mean \n", + "3 1992 RW1992DHS 1992 mean \n", + "4 1992 RW1992DHS 1992 mean \n", + "\n", + " source_sheet \n", + "0 dhs-mobile_subnational_rwa \n", + "1 dhs-mobile_subnational_rwa \n", + "2 dhs-mobile_subnational_rwa \n", + "3 dhs-mobile_subnational_rwa \n", + "4 dhs-mobile_subnational_rwa " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_adm2.head()" + ] + }, + { + "cell_type": "markdown", + "id": "30a89bd9", + "metadata": {}, + "source": [ + "
      \n", + "

      📝 Marking Notes for Part-1-Task-2

      \n", + "
        \n", + "
      • Total Points: 25
        • \n", + "
      • You can decide how to assign points
        • \n", + "
      • Someone gets full points if they have code and they have displayed the dataframe above in their notebook
        • \n", + "
      • Results at district level or sector level is okay, otherwise, national level is not accepatab
        • \n", + "
      • Total Points-Part-1: 40
        • \n", + "
      \n", + "
      " + ] + }, { "cell_type": "markdown", "id": "8169202f", @@ -2146,7 +2301,7 @@ "id": "784d2e72", "metadata": {}, "source": [ - "# Part-2: Demographic and Nightlights Data\n", + "# Part-2: Demographic and Nightlights Data [60 Points]\n", "\n", "## Part A: Varible Generation and Data Integration\n", "\n", @@ -2234,7 +2389,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "3adf8782", "metadata": {}, "outputs": [], @@ -2245,7 +2400,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "d9549fee", "metadata": { "vscode": { @@ -2257,14 +2412,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/04/tc5rvshj26363cf30dxyhz4c0000gp/T/ipykernel_31686/3386350694.py:18: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "/var/folders/4k/7vm8r4rj2g90lf39dt0pqwbr0000gn/T/ipykernel_44216/3386350694.py:18: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df_pop['dependency_ratio'].fillna(df_pop['dependency_ratio'].median(), inplace=True)\n", - "/var/folders/04/tc5rvshj26363cf30dxyhz4c0000gp/T/ipykernel_31686/3386350694.py:24: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "/var/folders/4k/7vm8r4rj2g90lf39dt0pqwbr0000gn/T/ipykernel_44216/3386350694.py:24: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", @@ -2308,7 +2463,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "5a9438b4", "metadata": {}, "outputs": [], @@ -2348,7 +2503,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "id": "5d961de5", "metadata": { "vscode": { @@ -2417,53 +2572,33 @@ }, { "cell_type": "markdown", - "id": "cf4a9fdc", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, + "id": "0629c34d", + "metadata": {}, "source": [ - "## Data Integration" + "
      \n", + "

      📝 Marking Notes for Part-2-Variable Generation

      \n", + "
        \n", + "
      • Total Points: 10
        • \n", + "
      • You can decide how to assign points
        • \n", + "
      \n", + "
      " ] }, { - "cell_type": "code", - "execution_count": 23, - "id": "823bf3e4", + "cell_type": "markdown", + "id": "cf4a9fdc", "metadata": { "vscode": { "languageId": "markdown" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['cell_id', 'province_name', 'district_name', 'sector_name', 'cell_name',\n", - " 'lit_pixel_count_2015', 'lit_pixel_count_2020', 'lit_pixel_count_2024',\n", - " 'mean_nightlight_2015', 'mean_nightlight_2020', 'mean_nightlight_2024',\n", - " 'pixel_count_2015', 'pixel_count_2020', 'pixel_count_2024',\n", - " 'total_nightlight_2015', 'total_nightlight_2020',\n", - " 'total_nightlight_2024', 'nightlight_change_2015_2024',\n", - " 'mean_nightlight_change_2015_2024', 'lit_pixel_percentage_2015',\n", - " 'lit_pixel_percentage_2020', 'lit_pixel_percentage_2024',\n", - " 'lit_pixel_change_2015_2024'],\n", - " dtype='object')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "df_ntl_pivot.columns" + "## Data Integration" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "id": "22603370", "metadata": {}, "outputs": [], @@ -2473,6 +2608,20 @@ "df = pd.merge(df_pop, df_ntl_pivot, on=\"cell_id\", how=\"left\")" ] }, + { + "cell_type": "markdown", + "id": "26fdb4b8", + "metadata": {}, + "source": [ + "
      \n", + "

      📝 Marking Notes for Part-2-Data Integration

      \n", + "
        \n", + "
      • Total Points: 5
        • \n", + "
      • You can decide how to assign points
        • \n", + "
      \n", + "
      " + ] + }, { "cell_type": "markdown", "id": "4bf67167", @@ -2483,13 +2632,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 19, "id": "8f4e219b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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wQWn5mZiYkDt37jTLkxtBmXkGjzIjR46kY8eOKtdXrlyZU6dOKSyLiIiQT/LQvXv3dLcfHR1NUFAQefPmBeDGjRvUq1ePt2/fqszzLWWurAEKYGpqmqn1X5fjt8SrqamJo6Oj0nXJ9VzVd+Vb+Pr60r59e5YsWYK+vj4zZsxQmi75O3Xx4kXKly+vNE3y5A9v3ryRL0ue1GPs2LGsXr2a2bNnAxAfHy+fECK9OqVKu3btOH78OBs2bFBoSCU/O6pVq1YKDfLQ0FDq1Kkjv8CjSkhIiNJGQ3YukHzr74aqfSYvf//+PeHh4eTKlUvltrP7/WvYsCG2trYcOXIECwsLatWqRYUKFahcubJ4wLHww4iGlCAI/zeSn2v0/PnzLOdNPsE0NTVV2RBIPllO7vn6WmZ6wlSlCQsLA+DatWsZbuPz588Zppk6dSq7du3CzMyM6dOnU7FiRczMzNDW1gaSZiw8d+7cd5vFKrn80uv9Sa/8VJWLmtq/P7ls8mcBcO7cuQzTJ38eCQkJNG/enLdv31KnTh2GDRuGs7MzBgYGqKur8+TJE/Lnz/9NZa7qKntync1ofXJvxveI19jYWOXnk9F35Vu0adOGyMhIevTowcyZM8mZMydjxoxJky75c3z16lWGvVdff6c6deqEl5cX69evZ/r06WhoaHDgwAE+fvyIh4dHtk7MmzRpQu/evXn27Bnnz5+nbNmyhIaGcuDAASDtQ3g9PT25cOECBQsWZPLkyZQuXRoTExO0tLQAsLS05M2bNyo/n+z0zH/r74aq73/q5Z8+fUq3IZXd75+enh5nzpxhzJgxbNu2jc2bN7N582Yg6ZEX06ZNo169ehluTxCyQkx/LgjC/43kqcyTh3tkhb6+PgAfP35UONlMLfnqdXLP1/eUvP/Hjx8jJd2/qvJVuXLlDLeX/MwaX19f2rVrh42NjfxkCDIeFpXd+D98+KAyzY8sv+8p+Vgg6Qp9Rp9H8tTtly9f5smTJ9jY2LBjxw4qVqyIsbGxfAjY9y7zb/Wt8QYFBamckj+5Hvyoz7p79+7yZ0aNHTsWb2/vNGmSP8dRo0Zl+Bl+PT28lZUV1apV48OHDxw6dAhIGdaXnd6o5HgaNmwIpPRCbdmyhZiYGAoVKkTx4sXlaePj49myZQsAu3fvpnHjxlhYWMgbUfHx8Wmmbv8evvV34+PHjxkuz6hOZPf7B0mNy1WrVhEcHMzFixeZOnUqHh4e3L9/n4YNG3Lp0qV09y0IWSUaUoIg/N+oU6cO+vr6fPjwQeVzRlRxdHRETU2NmJgYlffj3Lt3D4ACBQp8c6xfK1y4MAB37979LttLHhqj7DlZQUFBCkOZUlPVG5eR5DLx9/dX2RD9keX3PeXOnVt+n1FyzJmRXObFixdXOPlM9i33Rv0I3xpvXFwcT58+VbrO398f+LGftaenp/w5SJ6enixfvlxh/bd+p5Lvg/T19SUoKIh9+/ahpaUlf8BudiTft7Rlyxbi4uLkDaqve6M+fvxIZGQkRkZGFCxYMM127t69S0JCQrbjUCW7vxvJkj93Vcvz5s2bbm8UZP/7l5qGhgalSpVi2LBhXLlyhZYtW5KQkMCqVauytT1BUEU0pARB+L9hYGAgnyhiwIABSh8Ym9q5c+c4f/48kHQVNPnkIfXDTZN9/vyZFStWAFCzZs3vGHWSxo0bAzBv3jyVDZGs0NHRAVD6ANBZs2apPAlLzpeZ4YOplS9fHl1dXV69esXu3bvTrL969SoXLlxAJpNRvXr1LG37Z0j+PObMmZPpPOmVeVxcXLrbym65f4tviTfZokWL0iz7+PEjW7duBUgzecL3NnbsWAYPHgxAz5495fcwAdStWxctLS0OHDjA48ePs7ztRo0aYWhoyN69e1m4cCGxsbH8+eefGBkZZTveP/74AzMzM4KCgli6dClnz55FJpPRpk0bhXTJn014eLjSOjF9+vRsx5Ce7P5uJFu5cqV8kpLUkutJZutDdr5/6SldujRAuvcCCkJ2iIaUIAj/V8aNG0eZMmV4//49ZcqUYe3atURHRyukefToEX369KFy5coKQ9GGDRsGJP3RT31C9unTJ9q3b8/Hjx+xtbWlZcuW3z3uHj16YG9vz8mTJ2nTpg0BAQEK6yMiItiyZQuenp6Z2l7yzfWDBg2S378kSRJr1qxh5syZSmfWAuQTbZw+fTpLDbpcuXLRq1cvAPr27cuNGzfk654+fUqHDh0AaN68ucoJEX4lw4YNw8jIiNWrV+Pp6UloaKjC+uDgYFatWsXEiRPly0qXLo2Ghgbnzp1jzZo18uVhYWG0adNG6clpsuRy/3riix/pW+KFpKv+ixYtkjeaIKlc2rZtS3R0NB4eHgoz9gFs27YNW1tblZM/ZMeMGTPo2bMniYmJdOjQQd6Qt7CwYMCAAcTFxVGzZk38/PwU8kmSxOXLl+nVq5fSXmhtbW1at25NbGysfHKI7A7rS6aurk7r1q0BGDJkCJIkUalSJaytrRXSGRgY4OzsTHx8PAMHDiQ2NhZIuq9t2rRpbN68WT7M73vK7u9GsqCgILp06SKf8EeSJBYtWsSOHTtQV1fP9O9Xdr5/3t7ezJkzJ029ffnypfwiWLFixTK1f0HItB8yqbogCMJP9OnTJ6lJkyby587o6OhIRYoUkUqUKCF/ng4gWVpaSnfu3FHIm/wAT0CysrKSPDw8JD09PQmQDA0NpcuXL6fZn6oH1KaWvM30+Pv7S3Z2dhIgqampSYUKFZJKlSolFShQQFJXV5cAqVSpUgp5VD2H5erVq5K2trYESLly5ZKKFy8uWVhYSIDUrl07+XNfTp48qZDvyZMnkpaWlvyhqRUqVJAqVaqk8FwpVccbFRUlValSRX6shQsXltzc3OSxu7m5SYGBgQp5kp9JlN4DWjNTdl/71gfySpIknT17Vv7ssOQH4ZYqVUqyt7eXZDKZBEgtWrRQyDN48GB5vNbW1lLx4sUlHR0dSVNTU1q8eLHKYx0/frz8Iaru7u5SpUqVpEqVKkkBAQGSJKU8R6pDhw5KjyOj5wKpej5YduJN/Zn16tVL/n8PDw9JR0dHAiRjY2Pp/v37aeL4Hg/kVSYxMVFq27atBEja2trSkSNHJElKeuB18nK+PP+pZMmSkpubm5QzZ075cn9/f6XbvXbtmkLerDw7SpUbN24oPMPs6+fSJduzZ4+8nhkZGUkeHh7y+jh69GiV38PM/B5979+N5OXjx4+XtLS0pJw5c0oeHh7yvIA0ffr0TMchSVn//vXv31++L1tbW6lkyZKSk5OT/PenSJEiUmhoqMoyEYTsED1SgiD839HX12fbtm2cPn2aLl26YGVlxYsXL7h16xaSJFG3bl1WrlzJo0ePKFKkiELeKVOmsHfvXqpXr05ERAS3b9/GxMSEnj17cuvWLflzjX4EJycnbt26xdSpUylRogRv3rzh5s2bxMbGUqlSJWbOnMmmTZsyta3ixYtz+vRpqlevTmJiIg8ePCBPnjzMmzdPftO8Mg4ODuzdu5dKlSoREhLC2bNnOXXqVIbDJCFpWNDhw4eZO3cuHh4e/PPPPzx69IjChQszceJEzp8/j7GxcWaL46crV64c9+/fZ9SoURQuXJjnz59z+/Zt1NTUqFWrFosWLWLu3LkKeaZPn86cOXNwcnLi3bt3/PPPP/zxxx+cOXOGWrVqqdzX8OHDGTt2LI6Ojty/f59Tp05x6tSpNL2p31t24022cOFC5s6dS86cObl79y56enq0adOGa9eufZfnk2WWTCbD19eXxo0bExMTQ8OGDTl79iwaGhqsXbuW/fv3yyd6uHHjBgEBARQoUIC+ffvi5+en8l6uYsWK4erqCmT92VGqFC1aFBcXFwBy5MhB06ZNlaarX78+Bw8epGzZsnz+/JmHDx/i6OjIunXrGD9+/DfHoUx2fzeSVahQgTNnzlC+fHmePHlCSEgIpUuXZseOHQwZMiRLsWT1+9ezZ0/GjRtHxYoViYuL4+bNm4SEhFCiRAnmz5/P5cuXlT5iQRC+hUySvsNgfEEQBEEQfgsvXrzAzs4OGxubTDWw/8sSExOxsrLi7du33L17VzyPSBAEBaJHShAEQRAEQYmDBw/y9u1bSpQoIRpRgiCkIRpSgiAIgiAIX/n8+TNeXl4A9O7d+ydHIwjCr0g0pARBEARBEL7w9fWlUqVK2NracuXKFZydndNMTy4IggCiISUIgiAIgiD34sULTp8+TXR0NH/++Sf79+9HU1PzZ4clCMIvSEw2IQiCIAiCIAiCkEWiR0oQBEEQBEEQBCGLRENKEARBEARBEAQhi0RDShAEQRAEQRAEIYtEQ0oQBEEQBEEQBCGLRENKEARBEARBEAQhi0RDShAEQRAEQRAEIYtEQ0oQBEEQBEEQBCGLRENKEARBEARBEAQhi0RDShAEQRAEQRAEIYtEQ0oQBEEQBEEQBCGLRENKEARBEARBEAQhi0RDShAEQRAEQRAEIYtEQ0oQBEEQBEEQBCGLRENKEARBEARBEAQhi0RDShAEQRAEQRAEIYtEQ0oQBEEQBEEQBCGLRENKEARBEARBEAQhi0RDShAEQRAEQRAEIYtEQ0oQBEEQBEEQBCGLRENKEARBEARBEAQhi0RDShAEQRAEQRAEIYtEQ0oQBEEQBEEQBCGLRENKEARBEARBEAQhizR+dgCCIPx/6Smz/dkhZGiJ9IKgT1E/O4x0GefUJTb0w88OI0NaBnl4HvjpZ4eRITuTnMRf2/+zw0iXRvG6BP/i9RLAKKfuL1+WkFSeMeHBPzuMdGnnMuLiP792jAClbYxouOLizw4jQ7u6lv7l66ZG8bo/OwThOxI9UoIgCIIgCIIgCFkkGlKCIAiCIAiCIAhZJBpSgiAIgiAIgiAIWSQaUoIgCIIgCIIgCFkkGlKCIAiCIAiCIAhZJBpSgiAIgiAIgiAIWSQaUoIgCIIgCIIgCFkkGlKCIAiCIAiCIAhZJBpSgiAIgiAIgiAIWSQaUoIgCIIgCIIgCFkkGlKCIAiCIAiCIAhZJBpSgiAIgiAIgiAIWSQaUsJ3VblyZQYMGPCzw8iQn58fMpmM0NDQnx3KT+Hr64uBgcHPDkMQBEEQBOE/S+NnByAIwo9la2vLgAEDFBq4LVq0oE6dOj8vqAw4VihJjSHdsS7ugoFFXhY37M6t3Ud+2P4kSWLlsqXs2bmd8E+fcHYuwqBhI7B3cEg338njx1i+ZBFvXr8mn6UlPXr3pVKVqvL1K5YuYdXypQp5jIyN2Xf4mPx9VFQUi+fP4/Spk4SFhWFubkGzli3p1qmjQr5N23biu24jH4OCcLCzZdjAfhR3d1MZ25XrN5gxZwFPn7/A1MSYzu1a07xxQ/n6XfsOMHrClDT5rp4+hra2NgDx8fEsWuHDgUNHCQwOwsTYmAZ1a9OjcwfU1DJ3HU6SJNatWsbB3TuJ+PSJgs7O9PEchq296rJ98ewpa1cs4fHDB3x4F0CPfp40atFaIc2+ndvYt3MbHwICALC2s6dNp66UKFMuU3F9bePRc/jsO8nH0HAc85kxvH1DijvZK0179PJtNh87z4N/3hAbH49jPjN6N6lJeTcneZqdpy7z99JNafJe952GtpZmpmJKrpe7U9XLwZmsl8u+qpeVv6qXK5XUy/2p6qXfiePs2rGdB/7+hIWFsnr9JgoULJipuH/Fsty0dTu+69YTGBiEg70dQz0HUNy9qMr0V69dZ8aceTx99hxTExM6tW9D8yaNlaY9eOQow0aNoUqlisydOU1h3fsPH5gzfxFnL1wgJjoGG2trvEaPpHAhJ6XbUkaSJHatXYnfgd1ERoTj4ORMu76DsbRVXqYAfgd2c+7YQV6/eAaAbf6CNO3UEwcnZ3ma43t3cGLfDgLfJ32H8tnY06BNZ9xKlsl0bKm1LGZJjYJ50NPW4PHHCJaee86r0M8q01fNb0q/SmnrcjOfS8QlSAAsa+FOnpzaadIcuP+OZedfZDnG7103AcIjPzN3ywGOXblNeORnLE2NGNLmTyq6F85yfMJ/j2hICcJ/kCRJJCQkoKGRva+wjo4OOjo63zmq70dbT5fXt/w577OVnjuWZpzhG61b7cumDev4e6wXVtY2+K5czoA+Pdm4fRd6enpK89y5fYsxI4fTrWcvKlapyumTJ/h7+DCWrFyFcxEXeTo7ewfmLVoif6+mrtgAmTt7JtevXmXs+EmYW1hw6eIFZk2bgp2VJRU9XAE4dPQ407zn8fdQT9xdXdi6cw+9Bg5h96a1mJvlTRPb67dv6TNwKE0a1Geq12hu3L7DxOmzMTQwoHrVyvJ0+np67N26XiFvciMKYNXaDWzdsZtJY0biYG/HPf8HjJ44hZz6+rRt2SxTZbt1/Wp2btqA56ixWFpbs9F3JSMH9GHFxu3oqijbmJhozCwsqVD1D5bOm600jYlpHjr37IuFpRUAxw7uw2v4IBb4rE+3kabMwQs3mLpmF6M7N8G9gB1bjp+nx7Rl7JkxDAsTwzTprz54ShmXAvRvUYdcujrsPHWZPjNXsmlCfwrZWsrT6evkYN+s4Qp5M3viD0n1cuOGdYxOVS/79+nJpgzq5egv9bJSlaqc+lIvl35VL+0zqJefP3/Gxc2Nqn/8wZSJEzId869YloeOHGP67DmMGjYEdzdXtu7YSe/+nuzasgFzM7M06V+/eUvvAYNo0vBPpowfx41bt5k0bQaGhoZUr1pFIe3bgABmzZ1PMSWNsvDwcDp07UGJ4sVZNHc2RoZGvHr9mpw59TMVd7IDW9ZxaMdGug0ejVk+K/Zs8GXG8P5MXbUJHV3l9eDBreuUrlwdR2cXNDW1OLB1HTNHDGDS8vUYmeQBwMjElOZdepPXIqmczx49wNxxQxm/aHW6jTRlGrla8GcRM+adfsrbsGiaFc2HV+1C9N52k+i4RJX5ImPj6bP1lsKy5EYUwODdd1CTyeTvrQ11GF+nMOefB2cpPvgxdTM2Pp6uU5ZgnEsf7/4dMTPKTUBQKHo6aRt/wv8nMbRPyLbIyEjat2+Pvr4+5ubmzJo1S2F9bGwsQ4cOJV++fOjp6VGqVCn8/Pzk65OHl+3atYsCBQqQI0cOqlevzqtXrxS2s3fvXooXL06OHDmwt7fHy8uL+Ph4+XqZTMaKFSto1KgRurq65M+fnz179ihs48CBAxQoUAAdHR2qVKnCixcv0hzP+fPnqVixIjo6OlhZWdGvXz8iIyPl621tbZk8eTKdO3cmZ86cWFtbs2zZMoVtvH79mpYtW2JkZISenh4eHh5cunSJFy9eoKamxtWrVxXSz58/HxsbGyRJIj3JQxEPHz6Mh4cH2tranDlzhqdPn9KgQQPy5s2Lvr4+JUqU4NixlKvKlStX5p9//mHgwIHIZDJkX/4gKRvat3jxYhwcHNDS0qJgwYKsXbs23Zh+pHuH/NgzehY3dx7+4fuSJIktGzfQoVMXKlethoOjI6O9JhAdHc3RQwdV5tuycQMlSpWifacu2Nra0b5TFzxKlmTzBsWGiYaGOsYmJvKXoaGRwvq7t29Tp149inl4YG5hQcPGTXDMX4C7d+/K06zZuJnGf9alSYP62NvZMsyzH2Z587B5+07lse3YjZlZXoZ59sPezpYmDerTqH5dfNcrXtWXyWSYGBsrvFK7decuVSqWp2L5suSzMKdGtSqULVmSe/4PMl22O7dspGWHTpSvXBVbe0cG/e1FTEw0J48eUpmvYCFnuvXtT+U/aqKpqaU0TenyFSlZtjyW1jZYWtvQsUcfcujo8uDenUzFltrqA6doUrkUTauUxiFfXka0b4S5sQGbj51Tmn5E+0Z0qV8VFwdrbMxNGdCyLjZmJpy8fk8hnUwGpga5FF6ZJUkSmzduoKOSenkknXq5+Uu97PClXnZQUS/VM6iXtevWo0u3HpQoWTrTMcOvWZZrNmykUYP6NGn4Z9L3Z9BAzPLmYcu2HUrTb92xE3OzvAwbNDDp+9PwTxr9WY/V6zYopEtISGDE6HH07t4VSwuLNNtZtXodefPmZcLYv3FxdiafhTmlS5bAytIyTVpVJEni8M7N/NmqIx7lK2Np50C3IaOJjYnm4gnVvfQ9R3hR7c8m2DgUwMLals4DRpAoJXL/RsrfIPcyFXArWRYzS2vMLK1p2qknOXR0eOp/V+V2ValfxIytN99y8UUIL0M+M/fUU7Q11KjoYJLBAULo5ziFV2rh0fEK60pYGxIQFs3dgPAsx/gj6uZOv8uER0Qxz7MzxQraYWFqRHEne5xs8mU5PuG/STSkhGwbMmQIJ0+eZOfOnRw5cgQ/Pz+uXbsmX9+pUyfOnTvHpk2buH37Ns2aNaNWrVo8fvxYniYqKopJkyaxevVqzp07R3h4OC1btpSvP3z4MG3btqVfv37cv3+fpUuX4uvry6RJkxRi8fLyonnz5ty+fZs6derQpk0bgoOTrli9evWKxo0bU6dOHW7evEnXrl0ZPlzxyuadO3eoWbMmjRs35vbt22zevJmzZ8/St29fhXSzZs3Cw8ODGzdu0Lt3b3r16sWDB0knlREREVSqVIm3b9+yZ88ebt26xdChQ0lMTMTW1pY//vgDHx8fhe35+PjQsWNHeQMnI0OHDmXKlCn4+/vj6upKREQEderU4dixY9y4cYOaNWtSv359Xr58CcCOHTuwtLRk/PjxBAQEEPBlGNTXdu7cSf/+/Rk0aBB3796lR48edOrUiZMnT2Yqrv+yt2/eEBQUSMnSKcNZtLS0KFqsOHdu31KZ7+7t25QspTgEplTpMmnyvHr5kj9rVafJn3UZPWIYb16/VljvVrQoZ06f4uOHD0iSxLWrV3j18h/Kly8PQFxcHPcfPKJsqZIK+cqWLMHNO8pPeG7duUfZkiUUlpUrXZL7/g+IS3URIurzZ2o0aEq1eo3p4zkU/4ePFPK4u7ly6eo1XnypTw8fPeH6rdtUKJu5oT/v3r4hJCiIYqlOxrW0tHApWgz/O7cztY3MSEhIwO/YYWKiP1OoiGuW8sbGx3P/+WvKuhZQWF7WpSA3H73I1DYSExOJjI4ht56uwvKo6Fj+6DeBqn296D1jBf4vXqvYQlqq6qX7d6yX9WtVp7GKepkdv2JZxsXF4f/gYZrvT5lSpbh5W3mj+9adu5QpVUrxGEqX4v59f4Xvz5IVqzA0NKBxgz+VbsfvzBmcCzkxaPhIKtWoQ/M27dm2c3em4k728d1bwoKDKFI8JX5NLS0Kurrz+H7mLxrExESTEB+Pfk7lDdDEhAQunjxKTHQ0joVdlKZRJW9ObYx0tbj5JlS+LD5R4u67cJzy5Ew3bw5NdZa1cGdFK3dG1SiInbGuyrQaajIqOZpw/NGHLMUHP65unrx2F7f8Nkz02U7FnmNoMHQ6y3YdIyFRdS+c8P9FDO0TsiUiIoKVK1eyZs0aqlevDsDq1aux/HKl7enTp2zcuJHXr19j8eVK3eDBgzl06BA+Pj5MnjwZSPojt2DBAkp9+aO1evVqChUqxOXLlylZsiSTJk1i+PDhdOjQAQB7e3smTJjA0KFDGTt2rDyejh070qpVKwAmT57M/PnzuXz5MrVq1WLx4sXY29vj7e2NTCajYMGC3Llzh2nTUsayz5gxg9atW8vvI8qfPz/z5s2jUqVKLF68mBw5cgBQp04devfuDcCwYcPw9vbGz88PJycnNmzYwMePH7ly5QpGRklXdx0dHeX76Nq1Kz179mT27Nloa2tz69Ytbt68yY4dyq+KKjN+/Hh5eQMYGxvj5pZyn8zEiRPZuXMne/bsoW/fvhgZGaGurk7OnDkxUzKEJdnMmTPp2LGj/Ng8PT25ePEiM2fOpEqVKirz/T8IDgoEwMhY8Yq8kbEx71Q0PAGCggIx+qoHx8jYmOCgIPl75yJFGO01AWsbG4KDgvBduYIeXTqyfvM2cn/pERw4ZBhTJ46nQZ2aqKtroKYmY/jfY/Dw8CA29AMhoWEkJCRgbKQ49MTY2JCgi8qHtwQFBWFsrHjiaGxkSHxCAqGhoZiamGBnY8OE0SMo4OBARGQk6zdvpX233mxb54ONddJwuS7t2xAREcGfzduirqZGQmIi/Xp2o07NP9Ip0RQhwUllYWioWE6GRsa8f6e6bDPr+dMnDOzRidjYWHR0dBg9eQY2dlkbkhT6KZKExESMcyue8Bnnzklg2KdMbcN3vx+fY2KpVbqofJm9RR4m9WxJfitzIj/HsPbQadqOm8+OKYOxMTfNcJtB37leBn1VL8d4TcAqVb3s3qUjG1LVy+z4FcsyJDT0y/dHsRyNjQ0JDErv+/PV983ISOH7c+PWLXbu2cvW9WtU7vv1m7ds2b6Tdq1b0rVTB+7eu8+0WbPR0tLkz7qZu0c17Mt3KNdXPYa5DIwI+vAuU9sA2LpyEYYmphQupniB5dXzJ0zo35242Fhy6OjQb+xU8tnYZXq7AAY6SUMsv+5NCvsch6m+6iFur0M/M+/0U/4JjkJXU516RcyYWt+ZATvuEBAenSZ9KRtD9LQ0OP74Y5bigx9XN19/CObS/SfUK1eMxUO78c+7QCb6bic+MYHejWtmOU7hv0c0pIRsefr0KbGxsZQpk3Ll08jIiIJfbka+fv06kiRRoIDi1Z+YmBiMU/2R19DQwMPDQ/7eyckJAwMD/P39KVmyJNeuXePKlSsKPVAJCQlER0cTFRWFrm7SlSFX15Sr0Hp6euTMmZMPH5KuWvn7+1O6dGmFXp/UcQNcu3aNJ0+esH59yvAXSZJITEzk+fPnFCpUKM1+ZDIZZmZm8v3cvHkTd3d3eSPqaw0bNqRv377s3LmTli1bsmrVKqpUqYKtra3S9MqkLitIGl7p5eXFvn37ePv2LfHx8Xz+/FneI5VZ/v7+dO/eXWFZuXLlmDt3rso8MTExxMTEKCxLfX/Nr+pdTjXc3d3lwylnzpkHkKZXUJKkTPcUqspTplx5+f8dHPNTxNWNZg3rc2DfXlq1bQfA1k0buXfnDtNnz8HM3Jyb168za9oU7K0t8Sic0hAnTXxpl6UmI+3xpD5ONxdn3FxSbjx3d3OhefsubNi6nRGDBgBJ92btO3SUaePH4GBvx8NHj5nmPR9TUxMa1K2dZp8nDh9k3ozJ8vfjZ8xREXvWy1YZS2sbFvluIOLTJ876nWDWpHFMX7Asy40pUFJeSGmWKbP//HUW7TjCfM/OCidpbvltcctvK3/vXsCWpqNms/7IGUZ2SDtpwb6z1/Dq+nem6mV6n3vSsXwlnXqJY35cXN1o+lW9/BY/uyyVxqTk+5NeMSo7huTlkZGRjBjjxdiRIzBMp+GZmJiIcyEn+vfpBUChggV5+uw5W7bvVNmQOn/8ML5zUy7yeU6cqTQekDKqBnL7t6zjot9Rhs9YhJaW4m+0uaUNExavJioygitnTrJ8xgRGzFyUbmOqooMxvcqnfMcmHn6QHFIa6Y1af/QxgkcfI+Tv/d9/YnYjF+o652XFhX/SpP+jYB6uvw4lJCouzbrM+t51M1GSMMqlz7iuzVFXU8PZ3ooPIWH47D8pGlK/CdGQErIlo3t6EhMTUVdX59q1a6irqyus09dXvNFW2QlV8rLExES8vLxo3DjtH8vkXiIATU3Fm45lMhmJX7rWM4o1eT89evSgX79+adZZW1tnaj8ZTd6gpaVFu3bt8PHxoXHjxmzYsIE5c+ZkGFtqX99gPmTIEA4fPszMmTNxdHRER0eHpk2bEhsbm6XtQtYbElOmTMHLy0thWepewl+VSUQic3ftIjQyaTap2NikP8pBgUGYmKRc3Q4JDlbZKAYwNjZR6H1KzmOYTh4dHR0cHBx5/SqpoRsTHc2ShfOZMnM25cpXAMAxfwEeP3rIypUr8Zg1BUOD3KirqxP01dXz4OCQNL1UKbEZp7naHhwSioa6Orlz51aaR01NjSKFnfjnVcqQqVnzF9OlfRtq10jqgSrg6MDbd+9ZsXqd0oZU6fIVcXIuIn+fXA9DggMxNkm5VyI0JDjNPTnZoampKZ9sokChwjx6cJ9dWzfSf+ioTG/DIKce6mpqBIYp3nMRHBaBce70JwU4eOEGY5ZtZnb/DpRxKZBuWjU1NYrYW/HPu0Cl66sUd8a9YQd5vYz7hnoZ9FW9DM4gT3K9fPUqaxdgvvarlGVqhgYGqKurE5imTELS9FIlU/r9CQ5J+v4Y5Obp02e8fRtAv0FD5OuT/w64ly7Pnm2bsLK0xNTEBHt7xQaJna0tx06oHjLtXqY8Dk4ps73FxSXVg7CQIAyMU75D4aEh5DLI+Dt0YOt69m1czdBp87C2d0yzXkNTk7z5kr5DdgUK8fyRP0d2bqbTgOFp0ia7/DKERztThuZqfpnB00BXk5BUvVK5dTTT9FKlRwIef4zAPFfav6Wm+lq4WuRm2rFHaTNmwo+qm6YGOdFQV0c91SymDvnyEhj6idj4eLSyOSGU8N8h7pESssXR0RFNTU0uXrwoXxYSEsKjR0k/cu7u7iQkJPDhwwccHR0VXqmHmMXHxytMwPDw4UNCQ0NxckqaXrRYsWI8fPgwzTYcHR0zPf1y4cKFFeIE0rwvVqwY9+7dU7ofLS3lN7t/zdXVlZs3b8rvzVKma9euHDt2jEWLFhEXF6e0gZgVZ86coWPHjjRq1AgXFxfMzMzSTKShpaVFQkJCutspVKgQZ8+eVVh2/vx5eU+cMiNGjCAsLEzhNWLEiGwfy79FQwIbGxssrayxtLLGzt4eY2MTrlxKqRNxcXHcvH4NF1fV04sXcXVVyANw+dKFdPPExsby4sVzeaMiPj6e+Ph4hVmpANTU1OUXADQ1NSnsVIALl68opLlw+QpFXYqgjJuLc5r05y9dpnAhJzRV/GGXJIkHj55gmqrHODo6GjU1xdjU1dSQVIz/19XTw8LSSv6ysbPH0NiYG1cuydPExcVx5+Z1Crlk7V6mTJEkeQMks7Q0NChsZ8n5O4onaOfvPqJoAVuV+fafv86oJRuZ3qctlTIxzbEkSTz4563KSRL0dHJgY2ODlZU1VunUyxv/Qr3Mrl+lLFPT1NSkkFNBLlxS/D5cvHyZoq7K7wVycynCxcuXFY/h0mUKFy6EpoYGdrY2bN+4ji3rVstflStWoETxYmxZtxqzvEkzaRZ1c+HFP4qN039evlQ6U2AyHV098uazkr/y2diR28iYu9dT4o+Pi+Ph7Rvkz+BepgNb1rFnvQ+DJntjV0D1b7kCSSI+Lv3vUHRcIu/CY+SvV6GfCY6KpWi+lIs0Gmoyipjl4sGHzA2bS2ZnrEdIVNoLgdUK5CEsOo6rr0KytL1kP6puuhew4+X7QHlDGuBFwEdMDXKJRtRvQnzKQrbo6+vTpUsXhgwZgrGxMXnz5mXUqFHyxk2BAgVo06YN7du3Z9asWbi7uxMYGMiJEydwcXGRP8NIU1OTv/76i3nz5qGpqUnfvn0pXbo0JUsm3d8xZswY6tWrh5WVFc2aNUNNTY3bt29z584dJk6cmKlYe/bsyaxZs/D09KRHjx5cu3YNX19fhTTDhg2jdOnS9OnTh27duqGnp4e/vz9Hjx5l/vz5mdpPq1atmDx5Mg0bNmTKlCmYm5tz48YNLCws5EMJCxUqROnSpRk2bBidO3f+5inIHR0d2bFjB/Xr10cmkzF69GiFH3RImm3w9OnTtGzZEm1tbUyUnCwNGTKE5s2bU6xYMapVq8bevXvZsWOHwgyAX9PW1v5hQ/m09XQxdbSVvzexs8LSrTCRwaGEvHr7Xfclk8lo3qo1a3xWYmWd1Lha47MyaRbJWim9LuPH/I1pnjz06pvUa9m8ZSt6d+/KWl8fKlSuzBk/P65cusySlavkeebPmU35ChXJa2ZOSEgwvitXEBkZSe169QHQ09fHvVhxFsydg7Z2DszMzblx/RoHD+xjRKoJUdq3asGIcRNxdnLCzcWZrbv2EPD+g/y5UHMWLuHDx0Amj/s7KbbGDdi0dQfT58ynaYP63Lpzjx179jN9QkqP4eIVPrgWKYy1lRWRkZGs37yNh48eM2rIQHmaShXKssxnLeZ58+Jgb8eDR49Zs3EzDevXzXTZNmreik1rfLCwtCaflRWb1vigrZ2DKtVrydPNmDAGY5M8dO6VNLlLXFwcL58nPf8mPi6OwI8fefroITq6uvIeKJ8lCylRuiwmefPyOSqKU8cOc/vGNSbOmpep2FLrUKcSwxdtoIi9FW75bdl64gIBgSG0qFYWAO9N+/gQHM6U3knPstp//jojF29gePtGuOa34WNo0lXuHFqa5NRN+k4v2n4YV0cbbMxMifgczfrDZ3j4zxv+7pj5oWgtWrVmtc9KLK2TGlerv9TLGqnqpdeXetk7g3q5NFW9nPelXpp9qZc+X+plnS/1EiAsLIz3794R+DFp6PLLf14ASb01RjlTeun/C2XZvnUrRo71wrmwE24uLmzbuYuAd+9p1qQRAHMXLOL9x49M9kr6fjRr3IiNW7Yxw3suTRo24NadO+zcvZdpk8YDSb99+R0Vp9jP+WWkRerl7Vq1pH2X7iz38aXmH9W4c+8+23buZuxI1b09X5PJZNRs1IJ9G1eT18ISs3xW7N20Gi3tHJSuWkOebul0LwyNk6Yzh6ThfDtWL6PncC9M8poT+uVeqxw6OuTQSRoSv3XVYlxLlMHINC/RnyO55HcM/9s3GDzJO9PxJdt79x1N3fLxNiyagPBomrrlIyY+kdNPU3oN+1dyICgylnVXk2bmbeGej4cfIggIj0ZXU526zmbYGeuy7PxzxTIg6ZlTJx9/JDHjASYq/Yi62aJ6WdYfOcuUNbtoU7M8/7wLZPnuY7SpVSH7gQr/KaIhJWTbjBkzkm5E//NPcubMyaBBgwgLC5Ov9/HxYeLEiQwaNIg3b95gbGxMmTJlFB4Eq6ury7Bhw2jdujWvX7+mfPnyrFqV8ge/Zs2a7Nu3j/HjxzN9+nQ0NTVxcnKia9eumY7T2tqa7du3M3DgQBYtWkTJkiXl05gnc3V15dSpU4waNYoKFSogSRIODg60aNEi0/vR0tLiyJEjDBo0iDp16hAfH0/hwoVZuHChQrouXbpw/vx5hf1nl7e3N507d6Zs2bKYmJgwbNgwwsMVhy6MHz+eHj164ODgQExMjNKhjg0bNmTu3LnMmDGDfv36YWdnh4+PD5UrV/7mGLPDxsMVT7+UqbqbeY8G4ILvNlZ3Gvzd99e2Q0diYmKYOXUKnz6FU7hIEbwXLFYYSvn+3TuFXlAXt6J4TZrCssWLWL5kEfksrZgwZarCs3o+vH/P2FEjCA0NxcDQkCJFXFjusxpz85SpksdPnsrihfMZN3ok4eHhmJmZ06NXH1q1akVcWNJN1bWqVyM0LJwlq3z5GBiEo70di7ynY2GedGX7Y1AQAe/fy7dpaWHBQu/pzJgzn03bdpLHxIQRg/orPEMq/NMnvKbMIDAomJz6ejgVyI/P0gW4OKdcdR05aCALlq5g4ozZBIeEYGpiQtNGDejVpWOmy7ZZmw7ExMSwYNZUIj59wqlwESbPWaDwDKkP798hk6WUbVDgR/p0aiN/v33jWrZvXIuLezFmLEh65EBISBDTJ4whJCgQXT197BzzM3HWPIUZAjOrdhl3QiOiWLzjCB9Dw8lvac6Sod2wME0aOvUx9BMBQSlXwrcev0B8QiITfbYz0We7fHmDiiWY3DNp0pvwqM+MW7mVwNBwcurq4GSTj9Wj++LqaJPpuJTVyzkZ1EtXt6KMnzSFpYsXsexLvZz4Vb38qKRerviqXp49fYqJXikN79FfTv67dOvB0MGe/6myrFXjD0LDwli6YlXS98fBnoVzZmFhbp4UU2AQ796l+v7ks2DRnFlM957Lpq3bMTU1YfjggWmeIZWRIs6F8Z4xlbkLF7N0hQ/5LMwZ6jmAurWzdu9MneZtiY2JYc2CmUR9+oS9U2GGTJmj8Ayp4A/vUUv1HTqxdzvxcXEsmDBSYVsN23ahUfukv5/hIcEsm+5FaHAQOrr6WNk7MHiSt8IMgZm18/ZbtDXU6FHODn0tDR59jGDcIX+FZ0iZ6msr3DOlp6VB7/L2GOpqEhmbwPOgSEbtu8/jj5EK23bLl5s8ObU5/jDrk0yk9iPqprmxIcuH92Daul00Gj6TvIa5aVurIl3+rIrwe5BJmbmBRBB+AF9fXwYMGEBoaOjPDuVfNWnSJDZt2sSdO1l/3s1/QU+Z7c8OIUNLpBcEfYr62WGkyzinLrGhWZ/m99+mZZCH54FZG77zM9iZ5CT+2v6fHUa6NIrXJfgXr5cARjl1f/myhKTyjAnP+oNb/03auYy4+M+vHSNAaRsjGq64mHHCn2xX19K/fN3UKJ65Hn3hv0HcIyUI/5KIiAiuXLnC/PnzlU5qIQiCIAiCIPx3iIaUIPxL+vbtS/ny5alUqVKaYX09e/ZEX19f6atnz54/KWJBEARBEARBFXGPlPDTdOzYkY4dO/7sMP41vr6+aSa5SDZ+/HgGD1Z+70+uXBnPTCUIgiAIgiD8u0RDShB+AXny5CFPnjw/OwxBEARBEAQhk8TQPkEQBEEQBEEQhCwSDSlBEARBEARBEIQsEg0pQRAEQRAEQRCELBINKUEQBEEQBEEQhCwSDSlBEARBEARBEIQsEg0pQRAEQRAEQRCELBINKUEQBEEQBEEQhCwSDSlBEARBEARBEIQsEg0pQRAEQRAEQRCELBINKUEQBEEQBEEQhCwSDSlBEARBEARBEIQskkmSJP3sIARBEARBEARB+P93+vRpZsyYwbVr1wgICGDnzp00bNgw3TynTp3C09OTe/fuYWFhwdChQ+nZs6dCmu3btzN69GiePn2Kg4MDkyZNolGjRj/wSEDjh25dEITfTtCnqJ8dQoaMc+rSU2b7s8NI1xLpBVrunX92GBmKvbGK6AOLf3YYGcpRpxehEb923TTQ1+XMs6CfHUaGKtgb//JlCUnlGRvy7meHkS4tQzPi3j//2WFkSDOvHXEfX/7sMDKkaWrNp6jPPzuMdOXU1fnZIfx0kZGRuLm50alTJ5o0aZJh+ufPn1OnTh26devGunXrOHfuHL1798bU1FSe/8KFC7Ro0YIJEybQqFEjdu7cSfPmzTl79iylSpX6YcciGlKCIAiCIAiCIPwrateuTe3atTOdfsmSJVhbWzNnzhwAChUqxNWrV5k5c6a8ITVnzhyqV6/OiBEjABgxYgSnTp1izpw5bNy48bsfQzJxj5QgCIIgCIIgCL+kCxcuUKNGDYVlNWvW5OrVq8TFxaWb5vz58z80NtEjJQiCIAiCIAhCtsXExBATE6OwTFtbG21t7W/e9rt378ibN6/Csrx58xIfH09gYCDm5uYq07x792OH94qGlCAIgiAIgiD8hr7X/cJmYzvi5eWlsGzs2LGMGzfuu2xfJpMpvE+eKy/1cmVpvl72vYmGlCAIgiAIgiD8htS/UztjxIgReHp6Kiz7Hr1RAGZmZml6lj58+ICGhgbGxsbppvm6l+p7E/dICYIgCIIgCMJvSF0m+y4vbW1tcuXKpfD6Xg2pMmXKcPToUYVlR44cwcPDA01NzXTTlC1b9rvEoIrokRIEQRAEQRAE4V8RERHBkydP5O+fP3/OzZs3MTIywtramhEjRvDmzRvWrFkDQM+ePVmwYAGenp5069aNCxcusHLlSoXZ+Pr370/FihWZNm0aDRo0YPfu3Rw7doyzZ8/+0GMRPVKCIAiCIAiC8BtSl32fV1ZcvXoVd3d33N3dAfD09MTd3Z0xY8YAEBAQwMuXKc8ts7Oz48CBA/j5+VG0aFEmTJjAvHnzFJ5BVbZsWTZt2oSPjw+urq74+vqyefPmH/oMKRA9UoIgCIIgCILwW1L/wZMxKFO5cmX5ZBHK+Pr6pllWqVIlrl+/nu52mzZtStOmTb81vCwRPVKCIAiCIAiCIAhZJHqkBEEQBEEQBOE39L1m7ftdiYaUIAiCIAiCIPyGfsbQvv8nYmif8NPJZDJ27dr1s8P4JuPGjaNo0aLfvB1bW1vmzJkjf59R2bx48QKZTMbNmzcB8PPzQyaTERoa+s2xCIIgCIIgCKqJHilB+IVcuXIFPT29bOcvW7YsAQEB5M6d+ztGlX2SJLFy2VL27NxO+KdPODsXYdCwEdg7OKSb7+TxYyxfsog3r1+Tz9KSHr37UqlKVfn6FUuXsGr5UoU8RsbG7Dt8TP4+KiqKxfPncfrUScLCwjA3t6BZy5Y0btr8uxybY4WS1BjSHeviLhhY5GVxw+7c2n3ku2z7R2hYtRhdm1SmWCEbTAxzUqLFWG49evXD97v57C18T14jMDwSBzNjhjasRDGHfErTXn/2hrl7z/L8QwjRcXGYG+aiaRkX2lUupjT9wesPGb72IFWK2DOny59ZikuSJFYsW8quHdv59OkTzkWKMCQTdfPE8WMsXZxSN3v17kvlqlWVpvVdtZLFCxfQolVrPAcPUZpmyqSJ7NqxnQGDBtOre7dMxb1n/UpOH9xDVEQ4dgWdadNnEPls7FXmOX1wNxeOH+LNP88AsHEsSKOOPbEvWFieZve6Fexdv0ohXy5DI2Zv2JepmH61smzVuo3Cuk3bduK7fhMfg4JxsLNl2MC+FC/qpjK2K9dvMmPuQp4+f4GpiTGd27aieeMGCmnCP31i3pIVHPc7TfinCPKZmzG4fx8qli0NwObtu9i8YzdvA5IeEupgb0vPzh2o8GV9dm3auRefjdv4GByMo60Nw/7qSXG3IkrTfgwMYsai5dx/+Jh/Xr+lTZMGDO/X85v2rzSmHXvw2biVj0FBONraMqx/L4q7uahMf+XGLWbMX8qTFy/IY2xMpzbNadGwvnx9XHw8K9ZuZPfBo3wIDMTWygrPXl0pX7pEluKSJIllS5ewc/sOPn0Kx7lIEYaNGIGDg2O6+Y4fO8aSRYt4/foVlpZW9O7blyqp6qbPypWcPHGcFy9eoK2tjaubG3/1H4Ctra08jYd7UaXbHjJkCF27ds3ScfxIYmjftxE9UoKQAUmSiI+P/1f2ZWpqiq6ubrbza2lpYWZmhuwX6apft9qXTRvW4Tl0OCtXr8PI2JgBfXoSGRmpMs+d27cYM3I4terUZfXGzdSqU5e/hw/j3t07Cuns7B3Ye+io/LV20xaF9XNnz+TihfOMHT+JjVt30KJ1G7xnTOe038nvcmzaerq8vuXPpr5jvsv2fjQ9HW0u3HrMqPnb/rV9HrrxkOm7TtGtekk2D25DMXsLei/bRUBIuNL0OlqatKzgxqq+zdg5vD3dqpdkwcHzbDt/J03at8HhzN5zhmL2yhtlGVm72pcN69cxeNhwfNYk1c2/emdcN/8eMZzadeqybuNmatepy8jhw7h7J2189+/dY9fOHTjmz69ye6dOnuTe3TuYmppmOu5DW9dxdMcmWvf25O+5K8ltaMTskQOIjlId98PbNyhZ+Q8GT53PiNlLMTLNi/eoAYQEflRIZ2Fjx6z1e+Uvr0VrMxXTr16Wh46eYNqcBXTr2I6tq5dTvKgrvQYOI+Dde6Xbev02gD6ewyhe1JWtq5fTrUNbpsyex9ETp+Rp4uLi6N5vEG8D3jF78nj2bl7LuBFDyGtqIk+TN48pA/r0YJPvMjb5LqNU8WL0GzqKJ8+eqzyOjBw8foqp85fSrX1Ltq5YSDHXIvQc+jcB7z8oTR8bF4dh7tx0a9eKgo6qG9vf4uBxP6bOW0y39q3YumoxxdyK0HPwSALeKY/p9dsAeg/5m2JuRdi6ajFd27diypxFHPU7I08zf5kPW3fvZ+TAPuxeu5LmDevRf+Q4/B89UbpNVVb7+rJh3TqGDh/O6nXrMTY2oU/PXunWzdu3bjFy+DDq1K3Lxs1bqFO3LsOHDVWom9evX6NZixb4rFnDwsVLSEhIoG+vXnz+/Fme5tDRYwqvMePGIZPJqFmzZpaO4Uf7Xg/k/V2JhtRvrHLlyvTt25e+fftiYGCAsbExf//9t3xKytjYWIYOHUq+fPnQ09OjVKlS+Pn5KWxj+/btODs7o62tja2tLbNmzVJYb2try4QJE2jdujX6+vpYWFgwf/78dON68+YNLVq0wNDQEGNjYxo0aMCLFy8ydUwdO3akYcOGeHl5kSdPHnLlykWPHj2IjY2Vp5EkienTp2Nvb4+Ojg5ubm5s25Zycpk8PO7w4cN4eHigra3NmTNnlO0ujaVLl2JlZYWuri7NmjVTGGJXuXJlBgwYoJC+YcOGdOzYUf7+66F9X7t8+TLu7u7kyJEDDw8Pbty4obD+66F9vr6+GBgYcPjwYQoVKoS+vj61atUiICBAnic+Pp5+/frJ68CwYcPo0KEDDRs2zNQxqyJJEls2bqBDpy5UrloNB0dHRntNIDo6mqOHDqrMt2XjBkqUKkX7Tl2wtbWjfacueJQsyeYN6xXSaWioY2xiIn8ZGhoprL97+zZ16tWjmIcH5hYWNGzcBMf8BXjgf/+bjivZvUN+7Bk9i5s7D3+X7f1o6/dfYNKyvZy4+H2OPzPW+l2nUSlnGpcugn1eI4Y2qoyZgT5bzt1Wmr6QZR5qF3PC0dyYfEa5qedRiLIFbbj+7I1CuoTEREasO0SvWqWxNM6V5bgkSWLThg106tyFKl/q5tgvdfNwOnVz04YNlCxVio6du2BrZ0fHzl0oUbIkmzYq1s2oqCjG/D2SkX+PJlcu5fF9+PCBGdOnMn7iZDQ0Mjc4RJIkju3aQt2WHSherjL5bB3oPGg0sTHRXPI7qjJft2HjqFKvCdYOBTC3sqVD/+FIiYn437yqkE5dXYPcRsbyV04Dw0zF9KuX5ZqNW2hcvw5NGtTD3s6WYQP/wiyPKZt37Fa6vS07dmOWNw/DBv6FvZ0tTRrUo1H9Ovhu2CRPs3PvAcLCPzF3+iTc3VywMDejWFFXCuZP6emoXKEcFcuWxtbaCltrK/r16oaurg6372b/O7hmyw4a161J03q1cbC1Zni/npiZmrJpl/Kew3zmZozo34sGtf5AXy/7F+nSjWnTdhrXq0XT+nVwsLVheP/emOUxZdOuvUrTb9m1D7O8pgzv3xsHWxua1q9Do7o18d24VZ5m7+FjdGvXioplSmGVz5yWjepTrpQHvpsyfyFIkiQ2blhPpy5dqVqtGo6OjnhNmEB09GcOHVRdNzduWE+pUqXp1CWpbnbq0oWSJUuyYX1K3Zy/cBH1/2yAg4MjBQoWZOw4L969C8D/fspna2JiovA65eeHR4kSWFlZZfoYhF+faEj95lavXo2GhgaXLl1i3rx5eHt7s2LFCgA6derEuXPn2LRpE7dv36ZZs2bUqlWLx48fA3Dt2jWaN29Oy5YtuXPnDuPGjWP06NFp5v+fMWMGrq6uXL9+nREjRjBw4ECOHlX+Rz8qKooqVaqgr6/P6dOnOXv2rPzkP3VjKD3Hjx/H39+fkydPsnHjRnbu3ImXl5d8/d9//42Pjw+LFy/m3r17DBw4kLZt23Lq1CmF7QwdOpQpU6bg7++Pq6trhvt98uQJW7ZsYe/evRw6dIibN2/Sp0+fTMWcGZGRkdSrV4+CBQty7do1xo0bx+DBgzPMFxUVxcyZM1m7di2nT5/m5cuXCvmmTZvG+vXr8fHx4dy5c4SHh3+Xe9bevnlDUFAgJUuXkS/T0tKiaLHi3Ll9S2W+u7dvU7JUGYVlpUqXSZPn1cuX/FmrOk3+rMvoEcN48/q1wnq3okU5c/oUHz98QJIkrl29wquX/1CqTNlvPjYhY3HxCfi//kCZgjYKy8sUtOHWiwAVuRT5v/7ArRcBeDhaKixfevgShvo6NC6tfDhTRpLrZqmv6qZ78eLcuaW6bt65fVshD0DpMmXS5JkxdQrlylegZCnlQ7gSExMZN/pv2rbrkOHwt9QC370lLCQI52Il5cs0tbQo6FKUJ/fT9uSoEhsTTUJCPHo5FRsm79+8YlCbPxnesQlLp4zmY8AbFVtI8auXZVxcHPcfPqJsKcUhYWVLleDmnbtKt3nr7r006cuVKsF9/4fEfRmdcPLMOdyKODNphjeVajekUeuOLPddS0JCgtJtJiQkcPDocT5/jsbNxVlpmozExcVx/9FjypZQHOpatkQxbt31z9Y2v1VSTI8oW6L4VzEV59bde0rz3LrnnyZ9uZIe3HvwSF6+sXFxaGlrKaTR1tLixm3ln5kyb968ISgwkNJlFOtmseIe3L51U2W+27dvU6qMYn0rXaYMt9OpzxEREQDkUjGsPigoiLNnz9LgGy9Q/ghq3+n1uxL3SP3mrKys8Pb2RiaTUbBgQe7cuYO3tzdVq1Zl48aNvH79GgsLCwAGDx7MoUOH8PHxYfLkycyePZtq1aoxevRoAAoUKMD9+/eZMWOGQi9LuXLlGD58uDzNuXPn8Pb2pnr16mni2bRpE2pqaqxYsUI+PM3HxwcDAwP8/PyoUaNGhsekpaXFqlWr0NXVxdnZmfHjxzNkyBAmTJjA58+fmT17NidOnKDMlx9Xe3t7zp49y9KlS6lUqZJ8O+PHj1caoyrR0dGsXr0aS8ukk7758+dTt25dZs2ahZmZWaa3o8r69etJSEhQOLbXr1/Tq1evdPPFxcWxZMkSHL6cZPTt25fx48fL18+fP58RI0bQqFEjABYsWMCBAwe+Od7goEAAjIwVe4qMjI15F6D6RDooKBAjY+M0eYKDguTvnYsUYbTXBKxtbAgOCsJ35Qp6dOnI+s3byG1gAMDAIcOYOnE8DerURF1dAzU1GcP/HoNbUfdvPjYhYyGRn0lIlDDOqXgV3DinLoHhUenmrT5uBSERn0lITKRnrdIKDaYbz96y89I9tgxuk84W0hekqm4aZaJuGn1VN42MCUpVN48cPsTDBw/wWbtO5XbW+Pqgrq5Oi1atshR3WEgwkHTvUmq5DIwI+vAu09vZ7rMYA2NTCrt7yJfZF3Smy+DR5M1nTXhoMPs2+jJlUA/GL1mPfi7V91z+6mUZEhpGQkICxkaK8RkbGRIUFKwitmCMjQy/Sm9EfEICoaFhmJoY8/ptAJev3aBuzT9Y5D2Nl69eM2nGHOITEujVpaM836MnT2nbrQ+xsbHo6ugwZ9pEHOxsVR5PekLCwklISMTY8OvYDAkMVn4sP1pIWFhSTGnKy5DAoBCleQKDgjEu5ZEmferyLVfSgzWbtuPh5oJVPgsuXrvBybMXSEhMzHRsQYGBX7b91WdvbKQwKkNZPuOv/gYZGxvL6/rXJEli9qxZFHV3x9FR+b1X+/buQU9XlypVq2U6/n/L7zws73sQDanfXOnSpRXupylTpgyzZs3i6tWrSJJEgQIFFNLHxMTIf2D8/f1p0EDx5tty5coxZ84cEhISUFdXl28ztTJlyqgcvnbt2jWePHlCzpw5FZZHR0fz9OnTTB2Tm5ubwn1GZcqUISIiglevXvHhwweio6PTNJBiY2Nxd1c8wfbwUPyhz4i1tbW8EZW838TERB4+fPhdGlL+/v5Kjy0jurq68kYUgLm5OR8+JI1dDwsL4/3795QsmXKFW11dneLFi5OYwR+smJgYYmJi5O8PHjzIlClT5O9nzpkHkOZ+LUmSsnwP19d5ypQrL/+/g2N+iri60axhfQ7s20urtu0A2LppI/fu3GH67DmYmZtz8/p1Zk2bgomJCbX+UH5D+/+LVrVLs/Dv9vL39ft6c+7G458Sy9cftaRk2dd8/mrG55g4bv8TwNx957A2yU3tYk5ERscycv0hxraohqG+TqZj2H/tARNHucuHLc+e+6Vu8nVwmaibabKk5Hn/7h2zZ85g3sJFaGtrK83u73+fzZs2smb9hgz3dfHEYdbOny5/389r5pcYvvpOIWVcqF8c3LqOS35HGTJ9IZpaKTG6lEj9W+KAQ6EijOjcjPPHDlCjcUoj5eKJw/RrMuM/V5ZKwku3zJT9bpEqi5SYiJGhAWOHD0ZdXR1np4J8+BiI7/pNCg0pOxtrtq1ZwaeICI6ePM3f4yfjs3hethtTqWNIHdvPvi82q7/zqss3afnw/r0ZN92b+m26IJOBlYUFDevUYNcB1RP67DtynPEz58m3NWfe/GzF9iXCTOeZPnUKTx4/YoWPr8qt7dm9m1q166isyz+TmGzi24iGlKCSuro6165dkzeIkunr6wPKf1iSf8AyouoHKTExkeLFi7N+/fo067JyQ7aqfSY3Dvbv30++fIo3qX/9A/cts+cl7y/1v2pqamnKJy4uLtPby2zZfk1TUzNNXF9vKzuf45QpUxSGTMpkMgYOHEj7Lkkzj8XGJh1bUGAQJiYpn11IcDBGX10hTM3Y2ESh9yk5j2E6eXR0dHBwcOT1q5cAxERHs2ThfKbMnE258hUAcMxfgMePHrJh3dr/+4bU3lM3uXz3mfz9mw/Krwz/SIZ6OqirydL0PgV/ikrTS/U1S+OkHpD8FiYEfYpi8aFL1C7mxKugUN4Gh9NvxR552sQvdbXYoLnsHtEBKxODNNur7GyPR3tPwqOSbgSPS66bQUGYpPpdCQ7JRt1MleeBvz8hwcF0bJvSW5aQkMCN69fZtmUzZy5c4uaNG4QEB9Ogbh2FNPO8Z7N100a8VqTcJ1K0dHnsnFKGgcXHJQ1vDg8OwsAoZVKDT6Eh5DJQHXeyw9s2cGDzGgZNnouVXfqzlmnn0CGfrQPv3ygOmS1aujzNq5f75cty84b1+Pn5YWiQG3V19TS9T8EhIWl6UVJiMyJQSXoNdXX5jKgmJsZoqGso/H20t7UhMCiYuLg4+e+upqYm1lZJF9icCzlx9/4D1m3extjhGQ/L/pph7lyoq6sRGKz4fQ4OCU3TS/VvMcydOymmNOUVirGRgdI8JkrLN/RL+SYNNzUyNGDeFC9iYmIJDQ8nj4kx3otXkM9c9UXJKuXLUKx8VSI/RwMQ++X7Evh13QwOSb9umpik6X0KDg5O04MKMH3qVE6fOsWylavImzev0u3duH6df168YMrUaSr3Kfx3iYbUb+7ixYtp3ufPnx93d3cSEhL48OEDFSpUUJq3cOHCnD17VmHZ+fPnKVCggMIfF2X7cHJyUrrNYsWKsXnzZvlEEdlx69YtPn/+jI6Ojnx/+vr6WFpaYmhoiLa2Ni9fvlQYxvc9vHz5krdv38qHQl64cAE1NTV5r56pqanCcIKEhATu3r1LlSpVMrX9woULs3bt2jTH9i1y585N3rx5uXz5svxzTkhI4MaNGxk+F2vEiBF4enoqLNPW1iYiNukeAUmSMDY24cqlixT88nnHxcVx8/o1ev/VX+V2i7i6cuXSRVq2aStfdvnSBVxcVU9VHBsby4sXz3H70qsYHx9PfHw8al81ENXU1DPsaft/EBEVTURU9E+NQVNDnUKWebj46CXVXFNO2i8+eknlIpmfPUwC+X0TdnmM2Da0rcL6hQfOExkTx9BGlTAzyKlkC6CXQwtjGxtCI5Iadcl18/JXdfPGtWv06ae6brq4unLp0kVapaqbly5ewMUtqW56lCzJhs1bFfJM8BqLja0d7Tt0RF1dnTp16lKyZCmFNP379qZ2nbq0atmC1Hcl5dDVI4duygUdSZLIbWjMvRtXsHYsCEB8XBwP79ykaefeKuMGOLRtPfs3+jJgoje2BQqlmxYgLjaWdy9fUMBZ8XuXQ1cPGxvjX74s6/2ZNFpCU1OTwgULcOHyVapVrihPd+HyVapULI8ybkWcOXX2vMKy85euULhQQTS/TGbh7lqEA4ePk5iYiJpa0h0i/7x6jamJcZqLV4ok+UWmrNLU1KRwgfxcuHqDPyqWSzmWqzeoUv7bplTPrqSYCnDhynX+qJRSnheuXqdKeeX3o7o5F8LvvOLfrvNXruHsVEBevsm0tbXIa2pCXHw8R0+dpWbViqiip6uLgak1n7408iVJwtjEhEsXL8jPOeLi4rh+7Sp/9R+gcjuurq5cuniRNl9GNwBcunARV7eU74IkSUyfNhW/EydYunxFmguzqe3etZNChQpToGBBlWl+JjG079uIhtRv7tWrV3h6etKjRw+uX7/O/PnzmTVrFgUKFKBNmza0b9+eWbNm4e7uTmBgICdOnMDFxYU6deowaNAgSpQowYQJE2jRogUXLlxgwYIFLFq0SGEf586dY/r06TRs2JCjR4+ydetW9u/frzSeNm3aMGPGDBo0aMD48eOxtLTk5cuX7NixgyFDhigMnVMlNjaWLl268Pfff/PPP/8wduxY+vbti5qaGjlz5mTw4MEMHDiQxMREypcvT3h4OOfPn0dfX58OHTpkuyxz5MhBhw4dmDlzJuHh4fTr14/mzZvLh/VVrVoVT09P9u/fj4ODA97e3ll6cG7r1q0ZNWqU/NhevHjBzJkzsx1vsr/++ospU6bg6OiIk5MT8+fPJyQkJMOhD9ra2kqHKUTEJp1gyWQymrdqzRqflVhZW2NpZc0an5XkyJGD6rVqy9OPH/M3pnny0KtvPwCat2xF7+5dWevrQ4XKlTnj58eVS5dZsjLlGTfz58ymfIWK5DUzJyQkGN+VK4iMjKR2vaTnkOjp6+NerDgL5s5BWzsHZubm3Lh+jYMH9tFvoGLjL7u09XQxdbSVvzexs8LSrTCRwaGEvHr7XfbxPRnm0sPazAjzPAYAFLBNqpfvgsJ4H6R8OvJv1a5yMUatP0xhq7y42Zqz/fwdAkI+0axs0uQtc/ed5UNYJJPaJE0HvOnsLcwMcmKXN+nq+o1nb1lz8hqtKhQFQFtTg/zmJgr7yKmTVAe/Xp4emUxGy9at8V21Eisra6ysrfFdlVQ3a6aqm+PG/I2paR76/JVUN1u0akXPbl1Z4+tDxUqVOX3Kj8uXLrPsS93U09PD4at7JHR0dMidO7d8eW4DA/l9fMk0NDQwMjHB3t6eN88Ue2m+jvuPhs05sHkNeS2syJvPkv2b16ClnYNSlVOGK6+cOR4DY1OadEq6f/Lg1nXsXrOcbsPGYZLXnLDgpH1o6+iQQyepd3DL8vm4lSqPUZ68fAoNYd9GXz5HRVL2j9ppA/kPlKVNquf5tG/VnBFek3AuVBC3Is5s3b2PgPcfaN4o6dljcxYt48PHj0weOwqA5o0bsGnbTqbPWUDTBvW4dfceO/YeYPr4lEcdtGjckA1bdzB19jxaN2/Cy1evWe67jjbNm8jTzF28jPJlSmGWJw+RUVEcOnqCK9dvstg7ZbhmVrVv3pgRk2bgXDA/bs6F2Lb3IAEfPtCiQV0AvJeu4kNgEFNGpTxr68HjpGHxUZ+jCQkN48Hjp2hqauBga6N0H1mOqWUTRkyYhrNTAdyKFGLbngMEvP9Ai4b1kmJaspIPHwOZMnoYAM0b1mPjjj1Mn7+EJvVrc+uuPzv2HWLGuJHybd6+58/7wECcHB35EBjIolVrkBIT6dy6RabjkslktGrdBp+VK7G2tsHK2hqflSvIkUOHWrVT6uaYv/8mT5489O2XVDdbtmpN965d8PXxoXLlyvj5+XHp8iVWrvKR55k2ZTKHDh5klvccdPX0CPxyP5a+vj45cuSQp4uIiODY0aMM8ByUjZL9d4ihfd9GNKR+c+3bt+fz58+ULFkSdXV1/vrrL7p37w4kTfIwceJEBg0axJs3bzA2NqZMmTLUqZM0jKJYsWJs2bKFMWPGMGHCBMzNzRk/frzCRBMAgwYN4tq1a3h5eZEzZ05mzZql8jkKurq6nD59mmHDhtG4cWM+ffpEvnz5qFatWqZ7qKpVq0b+/PmpWLEiMTExtGzZknHjxsnXT5gwgTx58jBlyhSePXuGgYEBxYoVY+TIkao3mgmOjo40btyYOnXqEBwcTJ06dRQalZ07d+bWrVu0b98eDQ0NBg4cmOneKEj6gd67dy89e/bE3d2dwoULM23aNJo0aZJx5nQMGzaMd+/e0b59e9TV1enevTs1a9ZMM6QzO9p26EhMTAwzp07h06dwChcpgveCxQrDJt+/eye/ogvg4lYUr0lTWLZ4EcuXLCKfpRUTpkzFuUjKwx0/vH/P2FEjCA0NxcDQkCJFXFjusxpzcwt5mvGTp7J44XzGjR5JeHg4Zmbm9OjVh0ZNmn3zcQHYeLji6ZcyHXIz76RJVy74bmN1p6wP2/nR6lUqysrxXeTv109LOsmesGQ3E5Yqnwb6W9VyL0hYZDTLDl/kY3gUjubGLOzeAAujpO9yYHgk71I9UyoxUWLe/nO8CQ5DQ00NS+Pc9K9XjqZlMp41M6vafamb07/UTeciRZi3UEndlKXUTVe3okyYPIWlixaxdPEiLC2tmDR1KkVcVD949Hur1awtsbExrF84k8iIT9gXLIznJG+FnqugD++RpYrbb98O4uPjWDxplMK26rfpTIO2SQ8GDQn8wLJpY4kIDyVnbgPsnYow0ns5xnnNM4zpVy/LWtWrEhoWxpKVa5IeGGtvx6LZ07D4MkzsY2CQwjOPLC3MWTh7GjPmLGDT9l3kMTFmhGc/qldNGcVgljcPS+fOZMachTRp25k8pia0bdGEzu1ay9MEBYcwctxkPgYFkVNfj/wODiz2np5mRsCsqF2tEmHh4SxZvZ6PQSHkt7Nh8bQJWJglDSsLDApO80yppl1SZo+9//Ax+4+dxMIsD0e2rMl2HIoxVSYsLJwlvuv4GBRMfjtbFs+YlCqmIIWYLC3MWTRjItPnL2Hjjj1J5TugN9Urp4x+iYmNZf5yX16/DUBXR4cKpUsyZfQwcuXUz1JsHTp2JCYmmqlTJvMpPJwiRVxYsFixbr57F4CaWkprwq1oUSZNmcriRQtZsmghllZWTJk6TaFubtua1Fvao5vig3XHenlR/8+Ue8ePHD6EBNSqVStLcQv/HTIpuzdeCP95lStXpmjRouk+t+hb2draMmDAgDTPT/pROnbsSGho6HeZvvt3lZiYSKFChWjevDkTJkzIcv6gT+nPyPYrMM6pS0+Z7c8OI11LpBdouXf+2WFkKPbGKqIPLP7ZYWQoR51e8uFovyoDfV3OpNMj9auoYG/8y5clJJVnbEjmZzP8GbQMzYh7n/0H9P5bNPPaEffx5c8OI0OaqYb2/apy6mZ+opx/w9xc32fIYf/wh99lO/81okdKEH5z//zzD0eOHKFSpUrExMSwYMECnj9/TuvWrTPOLAiCIAjCf5YY2vdtfudnaAn/Qfr6+ipfZ86c+WH7dXZ2VrlfZTMM/peoqanh6+tLiRIlKFeuHHfu3OHYsWMUKpTxDemCIAiCIAi/K9Ej9Rvz8/P74ft48eLFd93ezZs3Va7Lly+fyhkGv9WBAwdUTlWuasrT/worKyvOnTv3s8MQBEEQBOFfJmbt+zaiISX8p6h6aviPZmPzfWY3EgRBEARB+FWIoX3fRjSkBEEQBEEQBOE3JBpS30bcIyUIgiAIgiAIgpBFokdKEARBEARBEH5D4h6pbyMaUoIgCIIgCILwGxJD+76NGNonCIIgCIIgCIKQRaJHShAEQRAEQRB+Q2Jo37cRDSlBEARBEARB+A2JoX3fRgztEwRBEARBEARByCLRIyUIgiAIgiAIvyExtO/biIaUIAiCIAiCIPyGxNC+byMaUoIgCIIgCILwGxI9Ut9G3CMlCIIgCIIgCIKQRTJJkqSfHYQgCIIgCIIgCP+ufRYu32U79d7e+S7b+a8RQ/sEQfiuYkM//OwQMqRlkAct984/O4x0xd5YRU+Z7c8OI0NLpBdsuPH6Z4eRodbulsSEB//sMNKlncuI+BuHfnYYGdJwr/XLlyUklefH8KifHUa6THPp8jo44meHkSFLI30CQiN/dhgZMjfQIyYi7GeHkS5t/dw/OwQFMnGT1DcRQ/sEQRAEQRAEQRCySPRICYIgCIIgCMJvSE30SH0T0SMlCIIgCIIgCL8hmbrad3llx6JFi7CzsyNHjhwUL16cM2fOqEzbsWNHZDJZmpezs7M8ja+vr9I00dHR2YovM0RDShAEQRAEQRCEf83mzZsZMGAAo0aN4saNG1SoUIHatWvz8uVLpennzp1LQECA/PXq1SuMjIxo1qyZQrpcuXIppAsICCBHjhw/7DjE0D5BEARBEARB+A39rMkmZs+eTZcuXejatSsAc+bM4fDhwyxevJgpU6akSZ87d25y506ZqGPXrl2EhITQqVMnhXQymQwzM7MfG3wqokdKEARBEARBEH5Dauqy7/KKiYkhPDxc4RUTE6N0n7GxsVy7do0aNWooLK9Rowbnz5/PVNwrV67kjz/+wMbGRmF5REQENjY2WFpaUq9ePW7cuJG9gskk0ZASBEEQBEEQhN+QTE3tu7ymTJki7zVKfinrWQIIDAwkISGBvHnzKizPmzcv7969yzDmgIAADh48KO/NSubk5ISvry979uxh48aN5MiRg3LlyvH48ePsF1AGxNA+QRAEQRAEQRCybcSIEXh6eios09bWTjePTKY4rFCSpDTLlPH19cXAwICGDRsqLC9dujSlS5eWvy9XrhzFihVj/vz5zJs3L8PtZodoSAmCIAiCIAjCb+h7TX+ura2dYcMpmYmJCerq6ml6nz58+JCml+prkiSxatUq2rVrh5aWVrpp1dTUKFGixA/tkRJD+wRBEARBEAThNyRTl32XV1ZoaWlRvHhxjh49qrD86NGjlC1bNt28p06d4smTJ3Tp0iXD/UiSxM2bNzE3N89SfFkheqQEQRAEQRAEQfjXeHp60q5dOzw8PChTpgzLli3j5cuX9OzZE0gaKvjmzRvWrFmjkG/lypWUKlWKIkWKpNmml5cXpUuXJn/+/ISHhzNv3jxu3rzJwoULf9hxiIaUIAiCIAiCIPyGsvsw3W/VokULgoKCGD9+PAEBARQpUoQDBw7IZ+ELCAhI80ypsLAwtm/fzty5c5VuMzQ0lO7du/Pu3Tty586Nu7s7p0+fpmTJkj/sOERD6jdWuXJlihYtypw5czKV/sGDB3Ts2JGbN2/i5OTEzZs3f2h8v6OsfibKvHjxAjs7O27cuEHRokW/W2yCIAiCIPx/+V73SGVH79696d27t9J1vr6+aZblzp2bqKgoldvz9vbG29v7e4WXKaIh9RvbsWMHmpqamU4/duxY9PT0ePjwIfr6+j8sro4dOxIaGsquXbt+2D5Sk8lk7Ny5M83sLz9DVj+TX92mbTvxXbeRj0FBONjZMmxgP4q7u6lMf+X6DWbMWcDT5y8wNTGmc7vWNG/cUL5+174DjJ6QdjrVq6ePyW9yjY+PZ9EKHw4cOkpgcBAmxsY0qFubHp07oKb2/a+8NaxajK5NKlOskA0mhjkp0WIstx69+u77yS7HCiWpMaQ71sVdMLDIy+KG3bm1+8i/tn9Jkji1bQ3XTuwnOuIT+RwLUadzP/JY2arM43/5DGd2bSD43RsSExIwMstHmbrNcKtYXWn6M7s2cGLTSkrVbkytDn0yjGnT1u34rltPYGAQDvZ2DPUcQHH3oirTX712nRlz5vH02XNMTUzo1L4NzZs0Vpr24JGjDBs1hiqVKjJ35jSlaVb4rGbeoiW0admcYYMGZhhvso1HzuCz9wQfQ8NxtDRjePvGFC/koDTt0cu32Hz0LA9evCE2Ph5HS3N6N61FebdC8jQ7/S7x95INafJeXzMTba3M/w79V8pTkiRWLV/Knp3b+fTpE4Wdi+A5dAT2DsrLMJnfiWOsWLKIN69fk8/Skm69+lKpSlWFNB8/fGDx/LlcvHCOmOgYrKytGT56LE6FCgOwctkSjh85zIf379DQ1KSgUyG69+5L5bKl0t23quNYs3IZ+3fv4FP4Jwo5F6Hf4GHY2qs+jhfPnuK7fAmPHvjz/l0AvfsPoknL1lned3ox+a5Yyr5dO/j0KSmmAUOGY5dOTACnThxn1dJFvH3zGot8lnTt1YcKlVPKNioykpVLF3H21ElCQkLIX6Agf3kOwamwc4YxbdqyDd+1a7/US3uGDh5IcXd3lemvXrvOjNlzePrsGaamJnRq347mTZvI12/bsYu9+/fz5OkzAAoXcqJfn964FEmJpVa9BrwNCEiz7RbNmjJq+NAMYxb+e8RkE78xIyMjcubMmen0T58+pXz58tjY2GBsbKw0TVxc3PcKL0P/5r4yIzY29pu3kdXP5Fd26OhxpnnPo1undmxds5LiRd3oNXAIAe/eK03/+u1b+gwcSvGibmxds5JuHdsxZdZcjp7wU0inr6fHyQO7FF6pZwpatXYDW3fsZuTgAezetA7Pvr3wXb+RDVu2/5Dj1NPR5sKtx4yav+2HbP9baevp8vqWP5v6jvkp+z+3ZxMXDmyjTqe/6DZ5EfoGhqydPJSYz6qvKuro5aRCwzZ0mTCfntOWU7RSTXYvmc6TW1fSpH3z9AHXj+8nr7V9puI5dOQY02fPoVunjmxZt5piRd3o3d+TABXPLnn95i29BwyiWFE3tqxbTddOHZg605ujJ06mSfs2IIBZc+dTLJ1GxN1799m2azcF8jtmKt5kB89fZ+rqnXRvVINtU4dQzMmBHlOX8DYwWGn6q/5PKePixOLhPdg6eTAlCzvSZ/py/J+/Vkinr5MDvyUTFF5ZaUT9l8pz/RpfNm9Yh+eQ4azwXYexsTED+/YkKjJS9fZv32LsyOHUrF0X3w2bqVm7LmNGDOPe3TvyNOHh4fTq2hENDQ1mzl3Aui3b6TvAU+G33MrahoFDhrF641YWLffB3MICz769CQ5W/vmlZ9O61WzbuJ6/Bg1j0ao1GBobM7R/73SPIzo6GnOLfHTt/RdGKv5+f4uNa1ezdcN6+g8exhKftRgZGTP4r17pxnTvzi28/h5Ojdp1WbFuEzVq12XcyOHcT1W2MyaP59rlS4wcN4FV6zfjUao0g/r24uOHD+nGc+jIUabPmk23zp3YsmEtxdyL0vuvAQQEqKqXb+jdbwDF3IuyZcNaunbqyNQZszh6/IQ8zdVr16hdsyYrly5mnc9KzM3M6NnnL96nimXDWl9OHD4gfy1btACAGn9Uy1Q5/gwyNdl3ef2uREPqN1a5cmUGDBgAgK2tLZMnT6Zz587kzJkTa2trli1bJk8rk8m4du0a48ePRyaTMW7cOF68eIFMJmPLli1UrlyZHDlysG7dOoKCgmjVqhWWlpbo6uri4uLCxo0bFfa9bds2XFxc0NHRwdjYmD/++IPIyEjGjRvH6tWr2b17NzKZDJlMhp+fn8p9jRs3Ls3wtTlz5mBra6uwbNWqVTg7O6OtrY25uTl9+/aVHzdAo0aNkMlk8vcdO3ZM00M1YMAAKleurFB+ffv2xdPTExMTE6pXT7pafv/+ferUqYO+vj558+alXbt2BAYGZvkzycznAnD58mXc3d3JkSMHHh4eSp/inV5Mfn5+aGlpcebMGXn6WbNmYWJiQoCSK2uZtWbjZhr/WZcmDepjb2fLMM9+mOXNw+btO5Wm37JjN2ZmeRnm2Q97O1uaNKhPo/p18V2/SSGdTCbDxNhY4ZXarTt3qVKxPBXLlyWfhTk1qlWhbMmS3PN/kO1jSc/6/ReYtGwvJy7e/yHb/1b3DvmxZ/Qsbu48/K/vW5IkLh3cQYWGrSlUsgJ5rOxo2HsYcTHR3Dl3XGU+W+eiFCpZHtN8NhiZWVC6ThPyWtvz8sFdhXSx0Z/ZMX8y9bt7kkMvcxcg1mzYSKMG9WnS8M+kejloIGZ587Bl2w6l6bfu2Im5WV6GDRqYVC8b/kmjP+uxep1iT05CQgIjRo+jd/euWFpYKN1WVFQUI8aMY9zI4eTK4gWT1fv9aFKlNE2rlsEhnxkjOjTG3NiQzUfPKU0/okNjuvxZDRcHG2zM8zCgVX1szE05eV2xDGUyGaYGuRReWfFfKU9Jkti6cQPtO3WhUtVq2Ds6MmrcBGKiozly+KDKfFs2bsCjZCnadeqCja0d7Tp1oXiJkmzZuF6eZv1qH/LkNWPkWC8KOxfB3MICj5KlyGdpJU9To1ZtSpQqTT5LS+wdHPhrwCAiIyN4+PBhunErO44dmzfQumNnKlSuip2DI8NGexEdHc3xI4dU5nMq7EyPvwZQtXpNNDXTnzI6qyRJYtumDbTt1IWKVaph7+DIiLHjiY6O5lg6ZbttU1LZtunYGRtbO9p07EyxEiXYtimpLsRER3Pq5Al69O2Pm3txLK2s6dStJ2YWFuzesTXdmNas20CjBn/SpFFD7O3sGDbYE7O8edmyTfkFta3bd2BuZsawwZ7Y29nRpFFDGjWoz+q16+Rppk6aQMvmTXEqWAA7O1vG/j2SREni0uWUCzxGhoaYmJjIX6fOnMXK0hKP4sWyUqT/KjV1te/y+l39vkcupDFr1iz5iXjv3r3p1asXDx4knXwGBATg7OzMoEGDCAgIYPDgwfJ8w4YNo1+/fvj7+1OzZk2io6MpXrw4+/bt4+7du3Tv3p127dpx6dIl+bZatWpF586d8ff3x8/Pj8aNGyNJEoMHD6Z58+bUqlWLgIAAAgICFKbC/HpfmbF48WL69OlD9+7duXPnDnv27MHRMenq5ZUrST+APj4+BAQEyN9n1urVq9HQ0ODcuXMsXbqUgIAAKlWqRNGiRbl69SqHDh3i/fv3NG/ePEvbTS29zyUyMpJ69epRsGBBrl27xrhx4xQ+GyDDmJIbb+3atSMsLIxbt24xatQoli9fnu0pQ+Pi4rj/4BFlSyne4Fm2ZAlu3rmrNM+tO/coW7KEwrJypUty3/8BcfHx8mVRnz9To0FTqtVrTB/Pofg/fKSQx93NlUtXr/Hiy02qDx894fqt21QoWyZbxyJkX+iHACJCg3Fw9ZAv09DUwraQG68f3cvUNiRJ4tmd6wQFvMamkIvCugOr5pLfvTT2LsUzta24uDj8HzxMUy/LlCrFzdt3lOa5decuZUopDr8qW7oU9+/7K9TLJStWYWhoQOMGf6rc/6TpM6lQriylS2XtxufY+HjuP39FWdeCinG4FuTmo+eZ2kZiYiKRn6PJraersDwqOoY/+o6jau8x9J62NE2PVXr+S+X59s0bgoICKVk65XdAS0uLosWKc/f2LZX57t65rZAHoFSZMgp5zp05hVOhwvw9fAj1alSlU5uW7NmpvCEJSeW2e+cO9PX1KViwoMp0ygS8fUNwUBAeJVMeOqqlpYWbe3Hu3VF9HD9SUkyBlCilGFNR9+Lcu3NbZb57d+4o5AEoWbqM/DgSEhJITEhAS1ux4aetrc2dWzdVbjepXj6gbGnFelamdClu3lYez63bdyhT+ut6WTqpXsbFK80THR1NfHw8uXMpv/gQFxfH/gMHadigfqYeMvuz/Izpz/+fiHukBLk6derIb/obNmwY3t7e+Pn54eTkhJmZGRoaGujr62NmZgYg79EYMGAAjRsrjm9PfTL/119/cejQIbZu3UqpUqUICAggPj6exo0by2dncXFJOUHS0dEhJiZGvp/UlO0rIxMnTmTQoEH0799fvqxEiaQTdlNTUwAMDAyU7i8jjo6OTJ8+Xf5+zJgxFCtWjMmTJ8uXrVq1CisrKx49ekSBAgWyvI/0Ppf169eTkJDAqlWr0NXVxdnZmdevX9OrVy95/sWLF2cY08SJEzl27Bjdu3fn3r17tGvXjkaNGmU51mQhoWEkJCRgbGSosNzY2JCgi8qHsgQFBWFsrHhCZGxkSHxCAqGhoZiamGBnY8OE0SMo4OBARGQk6zdvpX233mxb54ONddLV3y7t2xAREcGfzduirqZGQmIi/Xp2o07NP7J9PEL2RISGAKCfW7Ee6OU2JCxQ+RDPZNFREczu1YKE+DhkamrU7dxfoUF29/wJAp4/odukRZmOJyQ09Eu9NFJYbmxsSGBQevXyq3psZKRQL2/cusXOPXvZun6N0m1A0r0+/g8esnH1qkzHmyw0PJKExESMcyuesBnnzklg6KdMbcN3/0k+x8RSq0zKPSL2+fIyqVdr8ltbEBkVzdqDp2g7dg47pg3FxjxPhtv8L5VncFDS3yujr2I1NDLm/TvVPe/BQYEYGin2ehsaGRMcFCR///bNG3Zt30qL1m1p36kL9+/dZc6s6WhqaVK7bn15unNnTjNu1HCio6MxNjHBe8ESjIyMiAqOyNQxAIR82W/amIzSPY4fKTibMWVUtrp6eji7uLJm1QpsbO0xNDLi+JFD+N+7i6WVtcrtyuvlV6MVjI2NCEz1uaWWVC+/rsfGKfXS1CRNnjnzF5LH1FRlQ/7EST8+RUTQoH49lbEK/32iISXIubq6yv8vk8kwMzPjQwbjkAE8PDwU3ickJDB16lQ2b97MmzdviImJISYmBj09PQDc3NyoVq0aLi4u1KxZkxo1atC0aVMMDQ2VbT7dfWXkw4cPvH37lmrVfsz45K/juXbtGidPnlQ6GcfTp0+z1ZBK73Px9/fHzc0NXd2Uq8xlyihePc1MTFpaWqxbtw5XV1dsbGwyNWtg8ueamra2NgrXpb66CidJaZcpJOfr9NKXzSQtd3Nxxs0l5cZedzcXmrfvwoat2xkxaACQdG/WvkNHmTZ+DA72djx89Jhp3vMxNTWhQd3aGR5XelrVLs3Cv9vL39fv6825Gz/uien/NbfPHmPf8pQZk1oP+9J4/7oeIKVbDwC0c+jSc9oyYqM/8+zudQ6vXYxhHnNsnYsSFviBQ6sX0nbkdDQyeLK9Ml9fHZYyCCdNvUSSL4+MjGTEGC/GjhyBoYGB0vzv3r1n2ixvls6fq3A/X9bjVnyfwddJbv+5ayzadoj5g7tinDtlCJxbflvc8tvK37sXtKPpiJmsP3yGkR2bKNmSqrh+vfLcf/AwE6ZOl/+GTPeelxysYsKkHyXVwSrJgiQpHHNiYiJOhQrTo89fABQo6MSLZ0/ZtX2rQkOqmEcJfNZvIjQ0lL27djBm5FC2b9sGMtV14tjhA3hPS7kINnnmXBWHIaUp1x/l6KEDzJo6Sf5+6ux5ymNStvArSn/zU+UZOW4C0yd60bReTdTU1SlQ0IlqNWvx+EHGQ7WzWkaq//6kTbtq9RoOHj7CqmWLVdbBnbv3UK5sGfJ8uWD7q/qde5O+B9GQEuS+ni1OJpORmJiYYb7kBlKyWbNm4e3tzZw5c3BxcUFPT48BAwbIJ2NQV1fn6NGjnD9/niNHjjB//nxGjRrFpUuXsLOzy9K+1NTU5D92yVJPQqGjo5Nh/MpktF1V8SQmJlK/fn2mTUs7u1R2h8ml97l8HaMymY3p/PnzAAQHBxMcHJzm2L42ZcoUvLy8FJaNHTuWkQN6Y2iQG3V1dYK+uiodHBySppcqmbGxcZqr2MEhoWioq5M7d26ledTU1ChS2Il/XqUMR5o1fzFd2rehdo2kHqgCjg68ffeeFavXfXNDau+pm1y++0z+/s2HkG/a3v+bgsXLYumYMitc/JfvTERoMDkNU64OR4WFop/bIN1tydTUMDLLB4CZrSOBb15ydvdGbJ2LEvD8EZFhoSwb0VOeXkpM5J8Ht7l8eBd/rzuEmpp6mm0aGhigrq6e5qp0Ur00SpMeVNTL4JCkemmQm6dPn/H2bQD9Bg2Rr0/+frqXLs+ebZt4/OQpwcEhtGzfSZ4mISGBazdusmnrdq6eO4W6etp4kxnk0kNdTY3A0HDFOMI+KTSMlDl4/jpjlm5k9oBOlHFJfxiZmpoaRRys+SfgY7rpkv3K5Vm5YnmKly5LcMRnAGJjk+picFAQJiYpJ7YhIcEYGSuPFcDI2ESh9yk5j2Gq4zM2McHWXnGyExtbO/xOKN4HqKOjg6WVNZZW1hRxcaVl4z/Ztm0bdZu1Ubn/suUrUahwyoiNuLhY+XEYpzqO0JAQDFSU+fdWrkIlCjmnPAw1+W9jmpiCgzEyUj2xhZGxCcHBivcPh4YEK/Qa5rO0Yu6SFXz+/JmoyAiMTUzxGjUMc4t8Krcrr5eBSuqlis86qV5+nT74y98fA4XlvmvWsXKVL8sWL6BA/vxKt/c2IICLl6/gPUP5TJO/kt/5/qbvQTSkhO/uzJkzNGjQgLZt2wJJfwQfP35MoUIpJ1gymYxy5cpRrlw5xowZg42NDTt37sTT0xMtLS0SEhIytS9TU1PevXuXdKXpy2Wj1M+3ypkzJ7a2thw/fpwqVaoo3Yampmaa/ZmamnL3ruK9PDdv3sxwavJixYqxfft2bG1t0dD48V+vwoULs3btWj5//ixvNF68eDHLMT19+pSBAweyfPlytmzZQvv27Tl+/Hi604WPGDECT09PhWXa2trwOQxNTU0KOxXgwuUrVKtcUb7+wuUrVKlYXun23FycOXVG8cb585cuU7iQE5oq4pYkiQePnpDfIeUkJjo6GrWvZhBSV1NDysRFgYxEREUTERX9zdv5f6Wto4u2TkrvqCRJ6BsY8ezONcztkk44EuLjeOF/iz9ad8vStiVJkjfM7IoUo9eMFQrrdy+egYmFFeUatFTaiIKk73ohp4JcuHSFalUqy5dfvHyZKhUrKM3j5lKEU2fOKiw7f+kyhQsXQlNDAztbG7ZvXKewfsGSZURGRn6ZeCEvRoaGadKMGT8JO1sbOrVvm24jCkBLQ4PCdlacv/OQP0qmPD7g/J2HVPVwUZlv/7lrjF6ykRn92lOpWMbTRUuSxIMXrylgrXxyh6/9yuWpp6eHkbkRuuFR8mMzNjbhyqWLFCjoBCQ1AG5ev0bPv/qjShEXV65cukiL1m3lyy5fvEAR15TPwcWtKC//+Uch36uXLzEzS//imSRlPNurrp4euqkuakmShJGxMdeuXCJ/quO4deMa3Xr3S3db34vymEy4evmiQkw3b1yjRx/VMTm7uHD10iWatUop2yuXLuLskvYRGTo6Oujo6PApPJzLFy/Qs6/qzyypXjpx4dJlqlVN+bt/8dJlqlSqqDSPm6sLp05/VS8vXkqql5opf3981qxl+YpVLF44D+fChVXGsGvPXowMDalQvpzKNML/B9EMFb47R0dHeY+Tv78/PXr04F2qqXAvXbrE5MmTuXr1Ki9fvmTHjh18/PhR3tCytbXl9u3bPHz4kMDAwHSnOa9cuTIfP35k+vTpPH36lIULF3LwoOIsQePGjWPWrFnMmzePx48fc/36debPny9fn9zQevfuHSEhST0MVatW5erVq6xZs4bHjx8zduzYNA0rZfr06UNwcDCtWrXi8uXLPHv2jCNHjtC5c+dMNw6zonXr1qipqdGlSxfu37/PgQMHmDlzZpZiSkhIoF27dtSoUYNOnTrh4+PD3bt3mTVrVrr71tbWJleuXAqv1EMc2rdqwfbd+9i5Zz/Pnr9gmvc8At5/kD8Xas7CJYwcN1GevnnjBgS8e8/0OfN59vwFO/fsZ8ee/XRs01KeZvEKH85dvMSrN2958OgxYyZO5eGjxzRv3ECeplKFsizzWcvps+d58zaA436nWbNxM1UrK/8D+q0Mc+nhVsCKQg5JJ58FbM1wK2BFXuOszX72o2jr6WLpVhhLt6Q/+iZ2Vli6FcbQKnMny99CJpNRqnZjzuzagP/ls3x49Zxdi6ajqZ0Dl3Ipw213LpzKsY0pDaMzuzbw9PZVQt6/JfDNSy7s38rtM0dxrZCUR1tHlzxWdgovTe0c6OTMRR6r9Hu127duxY7de9i5Zy/Pnr9g+uw5BLx7T7MmSfcEzl2wiJFjU3pamzVuxNuAd8zwnvulXu5l5+69dGib9AwebW1t8js6KLxy6uujp6dHfkcHNDU15f9P/dLRyUHu3LnI75j+c3aSdahbme0nLrLj5EWevnnH1NU7CAgMocUfSSdq3hv3MmJhSuNi/7lrjFy0jiHtGuCa35aPoeF8DA3nU9RneZpF2w5y9pY/r94H4v/iNaOXbuThP29o/kfmT/7+K+Upk8lo1qo1a31WcurkCZ49ecIkrzFo58hBjZopPdUTxv7NkgXzUuJt2Yorly6ybrUP/7x4zrrVPly9fJnmrVJ6kVq0asu9O3dY47OS169ecuTQQfbs3E7jZi0A+Pz5M0sXzufundu8C3jLwwf+TJ3oxccP76lVq1amyzr5OBq3aM2G1as463eC50+fMH3CWHLkyEG1Ginbmuo1hhWLUv7OxcXF8eTRQ548ekh8fByBHz/w5NFD3rz69mfeyWQymrZszTrfVZzxO8Gzp0+YOj4ppj9Sle3kcaNZtjAlpiYtWnPl8kU2rPHlnxfP2bDGl2uXL9M01fOtLl88z6UL5wh4+4arly4yoHd3rG1sqV1f9SQkAO3btmbHrt3s3L2HZ8+fM33WbALevaNZ06R7rOfOX8jIMWPl6Zs1aczbgABmzPbm2fPn7Ny9h52799ChXUojb9XqNSxYtASvsaPJZ25OYGAggYGBaR4Qm5iYyO49+/izXt1/5YLqtxKTTXybX/8TFv5zRo8ezfPnz6lZsya6urp0796dhg0bEhYWBkCuXLk4ffo0c+bMITw8HBsbG2bNmkXt2kk/uN26dcPPzw8PDw8iIiI4efJkmunMkxUqVIhFixYxefJkJkyYQJMmTRg8eLDCFOEdOnQgOjoab29vBg8ejImJCU2bNpWvnzVrFp6enixfvpx8+fLx4sULatasyejRoxk6dCjR0dF07tyZ9u3bc+eO8pmokllYWHDu3DmGDRtGzZo1iYmJwcbGhlq1av2Qh8Hq6+uzd+9eevbsibu7O4ULF2batGk0aZJyf0NGMU2YMIEXL16wd+9eAMzMzFixYgXNmzenevXqaaaXz6xa1asRGhbOklW+fAwMwtHejkXe07EwT5rU42NQEAHvUyYcsLSwYKH3dGbMmc+mbTvJY2LCiEH9qV61sjxN+KdPeE2ZQWBQMDn19XAqkB+fpQtwcU65Mjhy0EAWLF3BxBmzCQ4JwdTEhKaNGtCrS8dsHUdG6lUqysrxXeTv109LmuhjwpLdTFi6+4fsMytsPFzx9EuZQr6Z92gALvhuY3WnwaqyfTfl/mxJfGwsB1bN5XPkJywdC9Fu5DSFnquwwA8K95zExURzYNU8woM+oqGljYmFFY36jKBIWeW9yllRq8YfhIaFsXTFqqR66WDPwjmzsPgyzPVjYBDvUj3rzDKfBYvmzGK691w2bd2OqakJwwcPpHrVb48lK2qXLUZoRCSLtx/mY2gY+a3MWTK8BxamSUOVPoaEExCYMtR067HzxCckMnHVNiauSnnGWYOKJZncO6kREB75mXHLNxMYGk5OXR2cbC1ZPbYfro42mY7rv1Sebdp3JCYmhtnTpvDpUziFnYvgPX+xQu/K+3fvUJOl/Fa7uBVl3KQpLF+8iBVLFpHP0orxk6fiXCSlJ7CQszOTZ8xi6cL5+K5YhrlFPvp5DqFG7TpA0pDJf1684OD+vYSFhpIrd24KFXZm4bJV5M+fn9dZmGwCoGXbDsTGxDB35tSkh98WLsK0OQsVjuPD+3cKz/YJCvxIjw4pDZQtG9ayZcNa3NyLM3uR4iM1sqNVuw7ExETjPX2qvGxnzFukWLbv3yFL9XewiKsbYyZMYeXSRaxauggLS0vGTppC4VRlGxkRwfJFC/j44T05c+WmYpWqdO3VBw2N9EeH1KpRndDQMJYuX8nHwEAcHRxYOM87Vb0M/Kpe5mPRvDlMn+XNpi3bkurlkEFUr5bycOAtW7cTFxfHoKHDFfbVs3tXevfoLn9/8dJlAt69o2GD+vwXfD2CQ8gamZSZmywEQRAyKTY04wlKfjYtgzxouXf+2WGkK/bGKnrKbH92GBlaIr1gw43MT5n9s7R2tyQmPOsPP/03aecyIv6G6mcB/So03Gv98mUJSeX5MVz1g59/Baa5dLPckPoZLI30/8feXYdFlfUBHP8OHSIxICEopSLYgWB3u3aLrevardixdqKuHWAX1rp2YIuF2LqrYoJKq0jP+wc6ODBDCLvo6/k8z30euHPOub97ZubOPfecey7Bkaofrvu9sDTSJ+5DVF6HkSHtfMrv+c0r1xrnzmRcFQ6pfjbg/zPRIyUIgiAIgiAIPyGJmGwiR0RDShD+I8+fP8c5g5tT7927R6FCqp+NIQiCIAiCkJvUfuL7m3KDaEgJwn/EyspKYUZBZa8LgiAIgiAIPwbRkBKE/4iGhgaOjo55HYYgCIIgCAIgHsibU6IhJQiCIAiCIAg/IXGPVM6IhpQgCIIgCIIg/ITEPVI5I5qhgiAIgiAIgiAI2SR6pARBEARBEAThJyQRD+TNEdGQEgRBEARBEISfkJq4RypHRO0JgiAIgiAIgiBkk+iREgRBEARBEISfkJj+PGdEQ0oQBEEQBEEQfkJi+vOcEbUnCIIgCIIgCIKQTaJHShAEQRAEQRB+QhI10aeSE6IhJQiCIAiCIAg/ITFrX86IhpQgCIIgCIIg/ITEPVI5I5HJZLK8DkIQBEEQBEEQhP/WPwPb5Uo5jst25ko5PxrRIyUIQq56Gvo+r0PIlJ2pAbGHVuR1GBnSafwbWwNe5nUYmepU1pp+Etu8DiNTK2VBvIuOyeswMmSWX48LVavldRiZqnL+3Hdfl5BSnzGfYvM6jAzp6eoQNKZbXoeRKds5PsSd2JDXYWRKu24P4t5H5nUYGdI2MMrrEBSIHqmcEQ0pQRAEQRAEQfgJickmckbUniAIgiAIgiAIQjaJHilBEARBEARB+AlJ1NXzOoQfmmhICYIgCIIgCMJPSNwjlTOi9gRBEARBEARBELJJ9EgJgiAIgiAIwk9ITUw2kSOi9gRBEARBEAThJyRRV8uV5VssX74cOzs7dHR0KF++POfOnVOZ1s/PD4lEkm558OCBQjpfX1+cnZ3R1tbG2dmZvXv3flNsWSUaUoIgCIIgCILwE8qrhtSOHTsYOnQo48ePJyAggGrVqtGoUSOeP3+eYb6HDx8SHBwsX4oUKSJ/7dKlS7Rv3x4PDw8CAwPx8PCgXbt2+Pv7Zzu+rBINKUEQBEEQBEEQ/jMLFy6kV69e9O7dm+LFi7N48WJsbGxYsWJFhvkKFCiAhYWFfFH/atbBxYsXU69ePTw9PXFycsLT05M6deqwePHif20/RENKEARBEARBEH5CEjW1XFni4uKIjo5WWOLi4pRuMz4+nuvXr1O/fn2F9fXr1+fixYsZxlu2bFksLS2pU6cOp0+fVnjt0qVL6cps0KBBpmXmhGhICYIgCIIgCMJPKLeG9s2aNQtDQ0OFZdasWUq3GRoaSlJSEubm5grrzc3NCQkJUZrH0tKS1atX4+vry549eyhWrBh16tTh7Nmz8jQhISHZKjM3iFn7BEEQBEEQBEH4Zp6engwfPlxhnba2doZ5JBKJwv8ymSzdui+KFStGsWLF5P+7u7vz4sUL5s+fT/Xq1b+pzNwgeqSE/zvdu3enRYsWeR1GttWsWZOhQ4d+N+UIgiAIgvD/Lbd6pLS1tcmfP7/CoqohZWpqirq6erqeordv36brUcqIm5sbf//9t/x/CwuLHJeZXaJHSvhP1KxZkzJlymTrhr9vyfMz8fPzo1atWkRERGBkZCRfv2fPHjQ1NfMusAzIZDI2r1/N4f17+fD+PcVcXBgwfAy29g4q8wQ9ecymtSv5++ED3oYE8+vg4bRs30khzcG9uzm4dzdvg4MBKGRnT+cevanoXuWb4txxPhDv09cJjf6Ig4WU0S1qUM6hoNK0N568wuvP8zx9G0FsQgKWxvlp414Sj5rllKY/fOMhYzcdplYJexb3+uWb4vtCJpNxZvdGrp/6i9gP7ynoWJzGPQdTwMZWZZ77V85xbt9WwkNekZyUhIlFQdybtKV09XpK05/bt5VT29dRqVErGnYbkKN4M+JYzZX6o/pSqHxJjKzMWdGiL4H7j/1r25PJZKxfs4oDe315//49zi4lGD7aE3sH1Z9FAL9TJ1i7cjmvXr6koLU1fX4bSI1atRXSvHv7lhVLvbh86QJxsXHYFCrE2ImTcSruDEBMTAwrly3h3JnTREVFYWlpRZv2Hejbs3u298OiZQsKduyIllRKTFAQT72WEH3rltK0juPGYd64Ubr1MU+fEuDRNdvb/uJ7rMsWrduydOlStu/YwfvoaEqUKImnpycOjo4ZxnTixAmWL/+Dly9eYG1jw8CBA6ldu45Cmp07duDj401oaCgODg6MHDWacuVSv+8xMTEs8VrM6dMpMVlZWdGhYyfatWsnT9O7Vy+uX7+mUG6NglLGVSyisM7ArTb5azRGw8CQ+DevCf9zC3FBj5TGrmPvhMWvnunWv5o/loR3KcdGPZfyGNZuhqa0AKhrkBgaQtTZI3wMyNk9JNvP3sD7hD+hUR9wsDRldJu6lHe0UZr2xj8vWLzfj6dvwoiNT8TSJD9tq5bBo7arPM3uCzf50/8O/7x+B4BzIQsG/1KDkrZWWY9p1268N20mNDQMB3s7Ro8YRvmyZVWmv3b9BvMWLebxk6eYmZnSw8ODdm1ayV8/ceo0azd48+LFSxISEylcyIaunTvRrEljeZrExERWrF7LX0eOEBYWjqmplOZNm9C3V8/v9nlNat84dXlOaGlpUb58eY4fP07Lli3l648fP07z5s2zXE5AQACWlpby/93d3Tl+/DjDhg2Trzt27BiVK1fOncCVEA0pQfg/Y2JiktchqLRriw97t29l+PjJWBcqxDbvdYwbOoC123zR09dXmicuLhYLK2uq1a7LqiULlaYxNStAz34DsbJO+eE+cfggU8eOYNmGLRk20pQ5EvCQufvOML5NbcrYWbH74i36r97H3rEeWBrnT5deV0uTDtVKU8TSDF1tDQKevGb6rpPoamnSpnJJhbSvw6NZeOAc5eyVN8qy68KB7Vw6tJsWv41GamnN2T2b2TRzNAMXeqOtq6c0j66+AdVadMa0oA3q6po8unGJ/Svnom9ohGPpigppXz1+wI2Tf2FeyD5X4s2Itr4eLwPvc3HDLvrtWfWvb2/LRm92bN3M+ElTsSlUGJ/1axg2sB/bdu9T+Vm8cyuQyePG0vvX36heqzZnT59ikucYlq9dj0uJlPc6Ojqa33p3p1z5isz3WoaxsQmvXr7AwMBAXs7ShfO5cf0aE6fNwNLSiiuXL7Fw7izsC1mjm419MK1dG7vBg3myYCHRt29j0fwXnOfP44aHB/Fv3qZL/9TLi2crV8r/l6irU8Z7A6FpbtjOru+xLu/evsW5M35MnTaNwoULs2bNGvr91o99+/ajryKmwMBAxo4ZzW/9B1C7dm1OnTrFmNGjWb9hAyVLlgLg6NEjzJs3F89x4ylTpgy+u3czcEB/fPfslZ/QzZ83j2vXrjJjxkysrKy4dOkSs2bNxMzMjFq1asm316pVa4YPH8aL3wcDoJ3mhFavlCsmzToTtm8jcc8eYVCpFuY9R/BqoSdJkeEq34+X80Yji42V/5/0MVr+d/Knj0Sd+pOEd6+RJSahW7w0pm17k/QxmthHd1SWmZEj1+8zd/cJxrdvQFmHguw6f5P+f+xk38TeWJoYpkuvq61JhxrlKWplhq62JgGPXzJt21F0tbRoU7UMANcePadRBWfK2BVEW1ODDccv02/ZDvZM6I25kUG6MtPFdOw4cxcsYvzY0ZQtXYpde/bSf/Aw9u3ajqWFRbr0L1+9pv+QYbRu2ZxZ06cSEHiLGbPnYmxsRL06KY17w/z56dOzB3a2hdHU1OTMufNMmvY7JiYmVHF3A2C9zyZ2+e7h96mTcLC35+69+0ya9jv58uWjS8cO31S//6+GDx+Oh4cHFSpUwN3dndWrV/P8+XP69esHpAwVfPXqFRs3bgRSZuSztbXFxcWF+Ph4Nm/ejK+vL76+vvIyhwwZQvXq1ZkzZw7Nmzdn//79nDhxgvPnz/9r+/F9No+F/yvdu3fnzJkzeHl5yR+gFhQUxJkzZ3B1dUVbWxtLS0vGjh1LYmJihnmSkpLo1asXdnZ26OrqUqxYMby8vL45tpo1azJw4EAGDhyIkZERUqmUCRMmIJPJ5GkiIiLo2rUrxsbG6Onp0ahRI4WuZG9vb4yMjNi3bx9FixZFR0eHevXq8eLFC4U6SDvccOjQodSsWVNlbJs3b6ZChQoYGBhgYWFBp06dePs25eQoKChI/oNsbGyMRCKhe/fu8n36emhfVuM/evQoxYsXJ1++fDRs2JDgz707uUUmk7F35zY6dOtB1Zq1sbV3ZMSEqcTFxXL6+BGV+YoVd6HPwCHUrNsATU0tpWncqlbHtXJVrAsVxrpQYbr/OgAdXT0e3L2d7Tg3+d2gZSUXWrmVwN7chNEta2JhlI+dF5Rf5S9uXYBG5ZxwtJRS0MSQphWKU7lYYW48eaWQLik5Gc/NR/itoRvW0vQNsuySyWT4H95DtRadKO5ajQI2drToP4aEuFhuXzipMp+tSxmKu1bFrGBhTCyscGvcGvNC9jx/oHgSFR/7iT1LZ9Ks73B09DM/ccmpu0f8ODBxATf3Hv3XtyWTydi1bStde/SiRu062Ds6Mn7KdOJiYzl29LDKfDu3baWCayU8evSisK0dHj16Ub6iKzu3bZGn2eKzgQLmFoybPBVnlxJYWllRwbUSBa1Tr87fuX2LRk2aUq58BSytrGjeqjUORYpy5072TmStOrTnzcG/eHPwIJ+ePePpkqXEvX2LZYuWStMnffxIQni4fMnn5ISGgQFv/zqUre1+7XusS3vHIpw5fYp+/fpRp05dHB2LMH3678R+iuXwYdX7unXLZiq5ucl/Y3r16oWrqytbtqTGtHnTJlq0bEmrVq2wt7dn1OjRWFhYsGvXTnmaW7cCadqsGRUqVsSqYEFat2lD0aJFuXfvrsL2dHR0MDMzw0RHCxMdLfQ1Fa9tG1ZryPurZ/lw9QwJb4MJ/3MriVHhGLgp9pCllfzhPUkfouQLX/2exT55QMzd6yS8DSYx/C3vLxwnPuQFOrZFMywzIxtPXqGle2laVymNvYUpY9rUxcI4PzvPBShNX9zGgsYVnHG0MqOg1IimriWoUtyOG/+k/mbO7vELHaqXw8nGHDsLKZM7NyJZJsP/YVDWYtqyjZbNf6F1i+bY29kxZsRwLMzN2bnbV2n6Xb57sLSwYMyI4djb2dG6RXNa/tIMn82p733FCuWpU6sm9nZ22Fhb06VjB4o4OhJw86Y8za3bt6lVozrVq1aloJUV9evWwb2SK/fu3c9S3Hkht2bty6727duzePFipk2bRpkyZTh79iyHDh2icOHCAAQHBys8Uyo+Pp6RI0dSqlQpqlWrxvnz5/nrr79o1Sq117By5cps376dDRs2UKpUKby9vdmxYweVKlXKeUWpIBpSwr/Oy8sLd3d3+vTpI3+AmqamJo0bN6ZixYoEBgayYsUK1q1bx++//64yj42NDcnJyVhbW7Nz507u3bvHpEmTGDduHDt37swkCtV8fHzQ0NDA39+fJUuWsGjRItauXSt/vXv37ly7do0DBw5w6dIlZDIZjRs3JiEhQZ4mJiaGGTNm4OPjw4ULF4iOjqZDh5xdfYqPj2f69OkEBgayb98+nj59Km8s2djYyK/CfHk4naoGZVbjnz9/Pps2beLs2bM8f/6ckSNH5ij+tEJevyIiLIxyrm7ydVpaWpQsU477t5U3Ur5FUlISfieOEhf7ieIlSmUrb0JiEvdfvsW9WGGF9e7FChMYlLWG5f2XbwkMCqaCo7XC+lVH/THOp0srtxLZikmVyLfBfIgMx6FUBfk6DU0tbIuX5uWjuxnkTCWTyXhy+wZhwS8pXFyx9+zQei+KlHXDvmT5XIn3e/L61SvCwkJxdXOXr9PS0qJMufLcuRWoMt+d27cU8gBUcndXyHPh3BmcijszYewomtavTY/OHTiwd49CnlJlynD+7BnevX2LTCbjxrWrvHj+jKpVq2Z5HyQaGuQrWpTIq1cU1kdevYpBiax9xsybNiHy2jXi3rzJ8nbT+h7r8vmzID59+qRQn1paWpSvUJ7Am6pjunXrFu5pYnJ3r0xgYEqehIQE7t+/j7u7Yho3N3d5GoAyZctyxu8Mb9+8QSaTcfXqFZ49e5ZueNGhw4eoVKkSfU4GsvrOM2ISklJfVFdHq6AtsX8rNq5jH91Bp3DGwxMth0zDerwX5n1Go2PvlGFaHQdnNM0siX36MMN0qiQkJnH/RQiVi9sqrHcvbsvNNBeTVLn/IoSbT15RvkghlWli4xNITErGUC/zPtuEhATuP3hAZTfFk2d3N1du3lJ+cS3w9m3c3VwV1lV2d+PevfskfL7A+zWZTMblK1cJevZMYbhg2TKl8b96jaBnKQ2Ah48eERAYSNUq/97QspzKqwfyAvTv35+goCDi4uK4fv26wqQR3t7e+Pn5yf8fPXo0//zzD58+fSI8PJxz587RuHHjdGW2adOGBw8eEB8fz/379xUaWv8GMbRP+NcZGhqipaWFnp4eFp+71MePH4+NjQ3Lli1DIpHg5OTE69evGTNmDJMmTVKaB0BdXZ2pU6fK/7ezs+PixYvs3LlTYfx5dtjY2LBo0SIkEgnFihXj9u3bLFq0iD59+vD3339z4MABLly4IP8R3LJlCzY2Nuzbt4+2bdsCKQfuZcuWya96+Pj4ULx4ca5cuYKrq6vKbWekZ8+e8r/t7e1ZsmQJrq6ufPjwgXz58smH8BUoUEDhHqmvZSf+lStX4vD5noaBAwcybdq0b4pblYjwMACMjaUK641NpLwJyXnv19PH/zDs1x7Ex8ejq6vLxJnzKGyXvSFpER8/kZQsQ2qgOCxOaqBHaHRMhnnrTVlLxIdPJCUn06+hm0KDKeDJa/b632XnyM7ZiicjHyIjAMhnaKywXt/QmKjQjE+MY2M+sPC39iQlJiBRU6NJzyEKDbI7F08R/PQf+sxYnmvxfk/Cw0KB9MNgM/sshoeFYmyS/vMbHhYm///1q1fs891F+05d6NqjF/fu3mHxgrloamnSqEkzAIaOHMOcGdNo2aQB6uoaqKlJGDNhEhUqVOBCFvdB09AQiYYGCeERCusTwiPQkmY+vFdTKsW4UiUe5vB7/j3WZaeu3fFZtwapVLF8qYmU4ODXKmMKDQ1Nn0cqJSw0ZR8jIiJISkrCxER1GoAxY8YybepUGjSoj4aGBhKJhEmTJ1O2bOp9VI0bN8aqYEGsC1pxceow1t97wZOoGGZXKQ6Aup4BEnX1lB6lryR9iELdIP1wOYDE6EhCfdcT/zIIiYYG+uWqYN5nDCGrZxP3VUNJoqOLzbjFSDQ0IDmZsH0bif07axdf0or4EJNyzMyvOFxSaqBPaPTHDPPWHf9HSv6kZH5rUpXWVUqrTLt4/xkKGObDzck285giI0lKSkKa5jMpNZESGnpZaZ6wsDCkad9XExMSk5KIjIzEzNQUgPcfPlC3UVMS4uNRU1dn/JhRuH/VYOvZrSsfPnygeZt2qKupkZSczKD+/WjcsEGmceeVb20ECSlEQ0rIE1+u6n09JWWVKlX48OEDL1++pFAh1VemVq5cydq1a3n27BmfPn0iPj6eMmXKfHMsbm5uCnG4u7uzYMECkpKSuH//PhoaGgrdwlKplGLFinH/fmpXvYaGBhUqpJ6IOjk5YWRkxP3797+5IRUQEMCUKVO4efMm4eHhJCcnA/D8+XOcnZ2zVEZW49fT05M3oiDleQ1fhhGqEhcXl+5he1/P0HPq6GGWzJsp/3/avMUpf/xLU5NaFyrMcu+tfHj/nvN+p1gwYwpzl63OdmNKSYjIlKxLa8OgtnyKS+DWs2C8Dl6gkKkhjco58TE2nnFbjjC5fR2M82XnDhhFt86f4OCaRfL/O42ZqTRYGbJMg9XW0aPfnNXEx37iyZ0bHN20AuMClti6lCEq9C1HfP6gy7i5aGgpH0r5owkxUKNs2bLyIbtzFy1JeSHdGy0DMq67dFWb5vObnJyMU3Fnfh0wCICixZwIevKYfb675Cf/u7Zv4+7t28xesBgLS0sCA26wYM4sHApZk21fDdtKCRCFocmqFGjUiMQPHwg/ey5bm7sUG8vA76wur1y+hJaWFhKJhOTkZLZt3vi5/G841mQhT2blbtu6ldu3b7HYywtLSytu3LjOrJkzMTU1w80tpUe+VevWAOjp6qBlbUrBfDoM9LvD35EfKWKk/3XhaQNUsi5FYmgIH0JTZyyLe/4YDUMTDKs34u1XDSlZXCyvvSaipqWDjqMzJk07khj+jtgnDzKumwxISHscyvyY6T2sMzFx8dwKeo3Xfj9szIxpXCH9b9v645c5fO0e64d2Qlsz66et2X3/03+EU+r5633T19Nj19ZNxMR8wv/qVeYv8sK6YEEqVkjpuT9y7DgHDx9h9u/TcHCw5+HDR8xduAgzMzOaN22S5diFH4doSAl5QtkBTX7QyuBAt3PnToYNG8aCBQtwd3fHwMCAefPm4e/v/6/FqWp9Zj+uX69TU1NLV9bXQ+vS+vjxI/Xr16d+/fps3rwZMzMznj9/ToMGDYiPj8/1+NPO8ieRSDI9GZs1a5ZC7yDA5MmT6TZwBJBy35KTS2qvzJe4I8JDkX6+ugcQGRGOsXHOJ8jQ1NSUTzZRtLgzjx7cY9+ubQwZPT7LZRjr66KuJknX+xT+PiZdL1Va1tKUq8RFrEwJex/DiiP+NCrnxIuwSF6HRzN47QF52uTPdVtuhBf7PbthY2qUaWzFylfG2rG4/P/Ez5+fD5HhGHzVyxcTFUk+w4zLk6ipYWKRMuGFha0joa+ec37/NmxdyhD89BEfoyJZ7dlPnl6WnMyzB7e4cnQfEzYfQU1NPdN4vyemH5Lx2reP8A+fAIiPT6m78LAwTE3N5OkiIsIxyaA3x0RqqtBj8iWP8VdXvqWmptjaKzbeC9va4Xcq5b61uNhYVi9fysx5C6lctRoAjkWK8vejh6xbt46eZE1CVBSyxEQ008SraWycrpdKGfMmjXl39BgyJcOWMlJGS4u223Z/V3U5afpMin71fJkVS704c/oUoaGh5DNIvR8xPCI8XW/S10xNTRV6lgDCw8Mx+dxLZWxsjLq6OmFhqtPExsaydOkSFi5cRLXPw5SKFi3Kw4cP2bTRR96QSsvRUB8NiYRXHz5RxEifpJj3yJKSUDcwUkinni8/SR+ilZahTNzzx+iXSzOsTCYjMSzlQll88HM0C1hhWKvpNzWkjPPpfT5mflBYH/7+I1ID5ZN6fGH9+bhXtGABwqM/suKv8+kaUt4n/Fl39BKrB3WgaMECWYvJyAh1dXVC03y+wiPCkar4TEqlUiXpI9BQV8fQKLUHUE1NjUI2Kb8zTsWK8uRpEOu8feQNqYVLltKrW1caNaifsm+OjgQHh7Bug89325D6lvubhFSi9oT/hJaWFklJqeO/nZ2duXjxosLJ+sWLFzEwMKBgwYJK8wCcO3eOypUr079/f8qWLYujoyOPHz/OUWyXL19O93+RIkVQV1fH2dmZxMREhYZaWFgYjx49onjxr05qExO5di11KtuHDx8SGRmJk1PK+HQzM7N0kzfc/OoG1bQePHhAaGgos2fPplq1ajg5OaXrIdL63FuQto6+ltX4v4WnpydRUVEKi6dn6tS7evr6WFnbyJfCdvYYS6UEXE2NJSEhgds3b1C8ZPbuZcoSmYyEeNWNVWU0NdQpbl2Ay4+eK6y//Og5pW0tVeRSsmmQj6u3K2DC7tFd2DGys3yp6WJPRUcbdozsjEUWZqAC0NbVw8SioHwxsy5MPiMTnty+Lk+TlJhA0P1ArIu6ZDlWSGlYf2mY2ZUox2/z1tJvzmr5YmVfjFJV6tBvzuofrhEFoCGDwoULY21TCGubQtjZ2yOVmnLVP/W7n5CQwM0b1ylRSvXwohIlSynkAbhy+ZJCnpKly/D82TOFNC+eP8fCIuXzk5iYSGJiYroLL2pq6lnqSfpClpjIh0ePMKqoONOiUYWKvM9k0or8Zcuga2PDm4MHs7y9L3TV1L67utTV1ZXHY21TiPyGRmhqanLhQupAyYSEBK5fu07pMqpjKlWqVLrfg0uXL1G6dEoeTU1NihcvzuVLaX4z/C/L08jf3zQnp+pqavJRBco8e/+JRJkMqc7nXuCkJOJfBaFTRPG7rFPEhdhn/6gsJy2tgoVJio7MOJEEJOrfdl1dU0Od4jYWXHoQpLD+8oMgymRjdtKvj5lfbDjuz+rDF1k+oB0uhbN+/NXU1KS4kxOX/BXvH7zsf4UypUoqzVO6ZEkup0l/8bI/zs7F0dTIoG5kMvnFBEhpSKd979XU1ZDJVL/3eU1NXT1Xlp+VaEgJ/wlbW1v8/f0JCgoiNDSU/v378+LFCwYNGsSDBw/Yv38/kydPZvjw4fJnLaTNk5ycjKOjI9euXePo0aM8evSIiRMncvXq1RzF9uLFC4YPH87Dhw/Ztm0bS5cuZciQIQAUKVKE5s2b06dPH86fP09gYCBdunShYMGCCs860NTUZNCgQfj7+3Pjxg169OiBm5ubfFhf7dq1uXbtGhs3buTvv/9m8uTJGc7QVahQIbS0tFi6dClPnjzhwIEDTJ8+XSFN4cKFkUgkHDx4kHfv3vHhw4d05WQ1/m+RnYfvQUovV8t2Hdm+cQMXzpwm6Mk/LJgxBW1tHWrVayhPN2/6JNavWCb/PyEhgcePHvL40UMSExIIffeOx48e8vpl6gxPG1b+wZ2bAYQEv+bp43/wXvUHtwKuU7t+Q7LLo2Y59ly+w17/uzx5E868vWcIjnhP28opjT2vg+cZvyV1Zrnt5wPxu/OEZ+8iePYugn3+d9l4+jpNKqQ0VLU1NShiaaqwGOhqo6+tSRFLUzQ1vu0HSCKRUKlRK87t28r9K+d5++Ip+5bPRVNbh5JVUmf12vvHbE5sS5085dy+rTy+dY2IN68JffWcS3/t4ta545SqlpJHW1ePAjZ2Coumtg66BvkpYGP3TbFmhba+HtalnbEunXJF2tTOBuvSzhjbZP25MVklkUho27ETmzas48zpUzz55x9mTJ2Eto4O9RukPmdp+uQJrFy2RP5/2w4duep/mc0+G3gW9JTNPhu4duUK7Tqm3vvWvmMX7t6+zcYN63j54jnHjhzmwF5fWrVtD4B+vnyUKVee5UsWc+P6NV6/esWhPw9w5NBB6tatm639eL19B+ZNm1KgSWN0CxfGbtAgtM0LELJvHwCFf/2VIhPS98iaN2nK+7t3iXn6NFvbU+Z7rMujh//CrXIVVq1axalTJ/nnn7+ZNHEiOro6NGqUenP6hAnjWbIkdZKejp06c/nyJTZsWM/Tp0/ZsGE9V/z96dw5NaYuHh7s3buHffv28uTJE+bPm0dIcDBt2qTcb5ovXz7Kl6/A4kULuXb1Kq9eveTA/v0cPHiQWp+fR/XixQtWrVrJ3bt3efnyJVdCIvj9yiMcDfVwlqZeWIk6dwSDijXIV6EamgUsMW7aCQ0jKe8vnwLAqGFbTNv1lafPX7U+es7l0JCao2leEKOGbdEvWZH3F0/I0xjWbIpOERc0TMzQNLMkf7UG5CtXhQ8Bl77xEwBd67iy52Igey8G8iQklLm7TxAcHk3bqimTMHjt92Ocz5/y9NvPXMfv9t88exvOs7fh7Lt0C58TV2jimjqCYf3xyyw7eJapXRpR0MSQ0KgPhEZ9ICY2ayMyunbuyJ59+9m7/wBPnj5l7oJFBIe8oW3rlIkHvJb9wbhJU+Tp27ZuxevgEOYtXMyTp0/Zu/8Ae/cfoFuX1Pd+7QZvLl325+XLVzwNCmLj5q38+dchmjRO/Z2pUa0aa9Zv4Oz587x6/ZqTp/3YtGUbtTOYoVf4sYmhfcJ/YuTIkXTr1g1nZ2c+ffrE06dPOXToEKNGjaJ06dKYmJjQq1cvJkyYkGGefv36cfPmTdq3b49EIqFjx47079+fw4dVT7Obma5du/Lp0ydcXV1RV1dn0KBB9O2b+uO0YcMGhgwZQtOmTYmPj6d69eocOnRIYTicnp4eY8aMoVOnTrx8+ZKqVauyfv16+esNGjRg4sSJjB49mtjYWHr27EnXrl25fVv5DEJmZmZ4e3szbtw4lixZQrly5Zg/fz6//JL6ANeCBQsydepUxo4dS48ePejatSve3t7pyspK/P+Vtp27ERcXx7IFs/nw/j1OziWYuXiZwrNm3r4JQSJJvcYTFvqOAT1Sf8x8t23Cd9smSpYtx7xlqwGIiAhj7vRJRISFoqefDzvHIvy+YInCDIFZ1bBsMaI+xrL66GXeRcfgaCnlj77NsTJJGSIUGv2RkIivnsuSLGPJXxd4FR6Fhpoa1lJDhjStQhv3f6GXLY0qv3QgMT6eQ+u9+PTxPdaOxfEYN0fhGVJRoW8Vej8S4mI5tH4J0WHv0NDSxtTKhpYDPClRuZayTfxnClcoxXC/7fL/2y6aCMAl79349MjdGSQBOnftTlxcHAvnzOL9+2icXUqwaOkKhc/im5AQ1L76LJYsXYYpM2axZsVy1q5cTkFrG6bNnC1/7hFAcRcXZs5bwKo/luK9djWWVgUZPHwU9b86gZ86Yzar/ljKtInjiI6OxsLCkr6/DaBjx45cXL4iy/sQeuoUGob5senePeWBvE+fcm/UaPksfJpSKdrm5gp51PX1kdaswVOvJcqK/CbfY12269iZnZs2MGvmTKKjoylRsiQrVqxQeIZUSLBiTGXKlGHW7Dks/2MZy//4AxsbG2bPmSN/hhRAgwYNiYqMYvWq1YSGvsPR0ZGly/7Ayiq1wT97zhyWLvFi3DhPoqOjsbS0ZMDAgfLJfTQ1Nbly5Qrbtm4lJiYGUy01Kpkb09nJGvWvvqsxt64QrpcPozrNUc9vRHzIK95sWEhSZMoQNA0DQzSMvhqqpq6BcZMOqBsaI0uIJ+HNK96sX8Cnh6mzokq0tJG26Iq6oUlKmnfBvNu+iphbir0x2dGwfHEiP35i1eELvIv+iKOlKX/0b4vV5+HO76I+KB4zZTK89p/hVdjnY6aZEUOa15A3vAB2nr1BQmISI9buU9hWv8ZV6N+kWuYx1a9HZFQUq9au511oKI4O9vzhtQirz8/6ehcaRkhI6qQ81gWtWO61iLkLF7N9127MzEwZO3KE/BlSAJ8+xTJjzlzevH2HtrY2draFmTl9Kg3rpz7M3HPUCJatXMWM2fMIj4jAzNSUNq1a0q9Pr+xV6n9ITDaRMxJZdsYSCML/mZo1a1KmTBkWL178zWV4e3szdOhQIiMjcy2uH9nT0Pd5HUKm7EwNiD2U9RPWvKDT+De2BrzM6zAy1amsNf0ktnkdRqZWyoJ4l8nMi3nNLL8eF6pmfpKY16qcP/fd1yWk1GfMp9jME+YhPV0dgsZ0y+swMmU7x4e4ExvyOoxMadftQdz7yLwOI0Paae57y2tR6yZknigLDHv9nivl/GhEM1QQBEEQBEEQBCGbxNA+4f9WZtOE37t37z+MRhAEQRAE4fsiZu3LGdGQEv5vWVlZZTgznpWVlcJTs79V9+7d6d69e47LEQRBEARB+C+Je6RyRjSkhP9bGhoaODo65nUYgiAIgiAI3yXRkMoZUXuCIAiCIAiCIAjZJHqkBEEQBEEQBOEnJO6RyhnRkBIEQRAEQRCEn5BE7dseCi+kEM1QQRAEQRAEQRCEbBI9UoIgCIIgCILwMxI9UjkiGlKCIAiCIAiC8DMS90jliKg9QRAEQRAEQRCEbBI9UoIgCIIgCILwE5Koi6F9OSEaUoIgCIIgCILwMxL3SOWIGNonCIIgCIIgCIKQTaJHShAEQRAEQRB+RqJHKkckMplMltdBCIIgCIIgCILw34o9tCJXytFp/FuulPOjET1SgiDkqsTrf+V1CJnSKN+EyA8xeR1Ghozy6REXHZ7XYWRKO78J76K/77oEMMuvRz+JbV6HkaGVsiDiPr7P6zAypa1v8N3XJaTUZ26dJP5bdBr/9sN8f64+j8jrMDJVsZAxCSGP8zqMDGlaOOR1CIpEj1SOiHukBEEQBEEQBEEQskn0SAmCIAiCIAjCz0j0SOWIaEgJgiAIgiAIwk9IoiYGp+WEqD1BEARBEARBEIRsEj1SgiAIgiAIgvAzEkP7ckQ0pARBEARBEAThZyQaUjkihvYJgiAIgiAIgiBkk+iREgRBEARBEISfkERd9EjlhGhICYIgCIIgCMLPSMzalyOiISUIgiAIgiAIPyNxj1SOiGaoIAiCIAiCIAhCNomGlCAIgiAIgiD8hCRq6rmyfIvly5djZ2eHjo4O5cuX59y5cyrT7tmzh3r16mFmZkb+/Plxd3fn6NGjCmm8vb2RSCTpltjY2G+KLytEQ+oziUTCvn37spzez88PiURCZGRkjrYVFBSERCLh5s2bWc7v7e2NkZFRtrebE1OmTKFMmTL/6TYFQRAEQRCEf5GaWu4s2bRjxw6GDh3K+PHjCQgIoFq1ajRq1Ijnz58rTX/27Fnq1avHoUOHuH79OrVq1aJZs2YEBAQopMufPz/BwcEKi46OzjdVTVaIhtRnwcHBNGrUKFfLzErjw8bGhuDgYEqUKJGr2+7evTstWrTI1TL/H4SHhzNo0CCKFSuGnp4ehQoVYvDgwURFRSmki4iIwMPDA0NDQwwNDfHw8EjXaB4yZAjly5dHW1tb6fv8pZGcdjly5EiWYs3K1RcAX19fnJ2d0dbWxtnZmb179yq8PmvWLCpWrIiBgQEFChSgRYsWPHz4UOV2f/31VyQSCYsXL85SnNmx7fgF6g/5nbLdRtN23EKuP3iiMu3xK7foPXMlVX+diGsvTzpN8uJ84AOFNHvPXMGl0/B0S1x8QrbikslkrFm1kiYN6lG9shu/9e3Nk8ePM8136uQJ2rdpRVU3V9q3aYXfqVMq03qvX0el8mVZOH+eyjSzZvxOpfJl2bZ1S7rXtu/ypWHzVlSoUoP2Ht25HnAzw9iuXb9Be4/uVKhSg0bNW7PTd4/KtIePHadURXeGjByjMs3aDT6UqujOnAWLMtyuTCZj3eqVNG9Uj9pV3Rj4a9bq0u/UCbq0a0Wtyq50adeKM6fT1+W7t2+ZNnE8jevWpE5Vd7p3as+D+/fkr8fExLBw7mxaNmlA7apudG7bir27d2a67axyrOZK/wNrmf3Kn5WyIEo3r59rZSuzfecuGjb9hQpulWnfqQvXbwRkmP7a9eu079SFCm6VadSsOTt371Z4/Z/Hjxk2chQNmzSjVLkKbNqyNV0ZiYmJLP1jOQ2b/kJF9yo0ataclavXkJycnKv7Bv99faa143wgjaavp+KopXRYsJUbj1+pTHvjySu6ee2g+viVuI5eSvNZPmzyu6Ey/eEbDyk9bDFD1x3IVkx5+f352tyZv1O1Yll2KjkWqYrbd+MaBrZvSo8mNfh9xG+8DFJ9fAc4fWgf04b9St+W9ejbsh6zRg/k8YO7CmkObPNh4oAe9P6lNv3bNmLR5NG8fvEsSzEps33vQRq070G5es1p12cw1wPvqEz7Liyc0dPm0LRLH0rWbMLspavSpdn95xG6DhxF5SbtqNykHb2Hj+P2fdW/r0KqhQsX0qtXL3r37k3x4sVZvHgxNjY2rFixQmn6xYsXM3r0aCpWrEiRIkWYOXMmRYoU4c8//1RIJ5FIsLCwUFj+TaIh9ZmFhQXa2tr/+XbV1dWxsLBAQ0PM+/FfeP36Na9fv2b+/Pncvn0bb29vjhw5Qq9evRTSderUiZs3b3LkyBGOHDnCzZs38fDwUEgjk8no2bMn7du3z3CbJ06cULgyUrt27SzFmpWrL5cuXaJ9+/Z4eHgQGBiIh4cH7dq1w9/fX57mzJkzDBgwgMuXL3P8+HESExOpX78+Hz9+TLfNffv24e/vj5WVVZZizI7DlwKYvXEffVvUZffMEZRzsuPXOat5HRqhNP21B49xL1mUFaP7sOv34bg6OzJg/jruB71USJdPVwe/5VMUFm0tzWzFtsnHm61bNjNyzFg2bNyMiVTKoP79lNbRF7dvBTLBcyyNGjdh87YdNGrchHFjx3Dn9u10ae/dvcu+vXtwLFJEZXlnTp/m7p3bmJmZpXvtyLETzF24mD49urNzsw/lypSm/5DhBIeEKC3r5avX9B86gnJlSrNzsw+9e3Rj9vxFHD91Ol3a18HBLPBaSrmyZVTGdufuPXbv20/RIo4q03yxZaM3O7ZuZviosaz13oxUKmXYwH7EZFCXd24FMnncWBo0aoL31h00aNSESZ5juHsntS6jo6P5rXd3NDQ0mO+1jM07fRk4dDgGBgbyNEsXzsf/0kUmTpvBlp17aNexM4vnz+XcmfT7/S209fV4GXif7QMn5Up5GTly9Bhz5y+gT6+e7Ny6hXJly9J/0GCCg1W956/oP2gI5cqmnPz27tmD2XPnc/zkSXma2NhYrAtaM2TwQExNpUrLWe/twy5fX8aNGc0+310MGzII742b2Lp9R67v439Zn2kdCXjI3H1n6FPPlR0jO1PO3or+q/cRHBGtNL2uliYdqpVm/cC27B3blT71XFl2+CK7L6b/vr8Oj2bhgXOUsy+Y7bjy8vvzxVm/09y7cxtTJcciVQ7u2MRh3210GziCacvWY2QiZfaYwXyKUR33/cAbuNeqx/h5fzDFaw3SAhbMGTuE8NC3qWluBVDvl9ZMWbKWMbOXkJSUxJyxQ4j99CnLsX1x+NQZZi9bTR+P9uxas5RypVzoN2YSwW/eKk0fH5+AsZEhfbp0oJiDndI0V2/eonGdGqxfPIvNyxdgUcCMviMn8OZdaLbjyyu5NbQvLi6O6OhohSUuLk7pNuPj47l+/Tr16ytePKlfvz4XL17MUtzJycm8f/8eExMThfUfPnygcOHCWFtb07Rp03Q9Vrntp2lI1axZk8GDBzN69GhMTEywsLBgypQp8tfTDu27ePEiZcqUQUdHhwoVKrBv3z6lQ/CuX79OhQoV0NPTo3LlyvIr/d7e3kydOpXAwEB5T4S3t3e6uJQN7Ttw4ABFihRBV1eXWrVq4ePjo3QY4dGjRylevDj58uWjYcOGBAcHAyk9YT4+Puzfv1++bT8/v0zr6OXLl3To0AETExP09fWpUKGCwgk5wKZNm7C1tcXQ0JAOHTrw/v17+WtHjhyhatWqGBkZIZVKadq0KY+/upL2ZV/37NlDrVq10NPTo3Tp0ly6dElhG2vWrMHGxgY9PT1atmzJwoUL0w1l/PPPPylfvjw6OjrY29szdepUEhMTM93HEiVK4OvrS7NmzXBwcKB27drMmDGDP//8U57//v37HDlyhLVr1+Lu7o67uztr1qzh4MGDCj05S5YsYcCAAdjb22e4TalUqnBlREtLK9M4IWtXXxYvXky9evXw9PTEyckJT09P6tSpo9CbdOTIEbp3746LiwulS5dmw4YNPH/+nOvXryts79WrVwwcOJAtW7agqZm9hkhW+Bw6Q+ualWhTyw2HguZ4dm2JpdSIHScuKE3v2bUlvZrVpqRDIQpbmjG0QxMKW5hy+obiFUuJBMyM8iss2SGTydi+dSs9evaiVu06ODg6MnnqdGJjYzl65LDKfNu3bsW1UiW69+yFrZ0d3Xv2oqKrK9u3KV7BjYmJYdKEcYybMJH8+ZXH9vbtW+bNnc2032cqvaiyces2WjZvRusWv2BvZ8uYEcOwMC/Azt3Ke5l27dmLpYU5Y0YMw97OltYtfqHlL03x2azYA5GUlITnxCn079sbaxWN55iYGDwnTWHKuLHkV3LS9TWZTMaubVvp2qMXNWrXwd7RkfFTphMXG8uxo6rrcue2rVRwrYRHj14UtrXDo0cvyld0ZedXdbnFZwMFzC0YN3kqzi4lsLSyooJrJQpa28jT3Ll9i0ZNmlKufAUsraxo3qo1DkWK8uCe8qvu2XX3iB8HJi7g5t70PcO5beOWLbRs0ZzWLVtgb2/HmFEjsDA3T9fL9MWu3b5YWlgwZtQI7O3taN2yBS2b/4LPxs3yNCVcXBgxbAiNGjRAS1P5cejWrdvUqlGD6tWqUtDKivp16+LuVol7uVSHX/sv6zOtTX43aFnJhVZuJbA3N2F0y5pYGOVj54VbStMXty5Ao3JOOFpKKWhiSNMKxalcrDA3nij2YiUlJ+O5+Qi/NXTDWpr9Y1Fefn8gpddq0bzZTJqu/FikKu4je3fQvGN3KlarhY2dA7+OmkR8XCwXTx1Tma+/5zTq/dKGwo5FsSpkS+9hniTLkrkbcE2eZsysxVRv0BRrW3sKOxSh78gJhL0NIejvByrLVWXjzr20alyfNk0b4mBbiLGDfsXCzIzt+/9Smr6gpTmeg/vRvGEd8uXTV5pmzsTRdGjZFKciDtgXtmHqqMEkJydz+XpgtuPLM2rqubLMmjVLPornyzJr1iylmwwNDSUpKQlzc3OF9ebm5oSouECY1oIFC/j48SPt2rWTr3NycsLb25sDBw6wbds2dHR0qFKlCn///fe3108mfpqGFICPjw/6+vr4+/szd+5cpk2bxvHjx9Ole//+Pc2aNaNkyZLcuHGD6dOnM2aM8iEv48ePZ8GCBVy7dg0NDQ169uwJQPv27RkxYgQuLi7ynojMei4gpbHRpk0bWrRowc2bN/n1118ZP358unQxMTHMnz+fTZs2cfbsWZ4/f87IkSMBGDlyJO3atZM3roKDg6lcuXKG2/3w4QM1atTg9evXHDhwgMDAQEaPHq0wnOPx48fs27ePgwcPcvDgQc6cOcPs2bPlr3/8+JHhw4dz9epVTp48iZqaGi1btkw3JGT8+PGMHDmSmzdvUrRoUTp27ChvxFy4cIF+/foxZMgQbt68Sb169ZgxY4ZC/qNHj9KlSxcGDx7MvXv3WLVqFd7e3unSZVVUVBT58+eX/2hcunQJQ0NDKlWqJE/j5uaGoaFhlq+UfO2XX36hQIECVKlShd0qToKyQtnVl0uXLqW7otOgQYMM4/wyjPHrcpKTk/Hw8GDUqFG4uLh8c4yqxCcmcu/pSyqXKqqwvnLJYtx8FJSlMpKTk/kYG4ehvp7C+pjYeOoOnk7tgVPpP29tuh6rzLx+9YqwsFAqubnL12lpaVG2fHluB6r+Mbx965ZCHgA3d/d0eebNnkWVqtVwreSmcr+mTJxAF49u2Ds4pHs9ISGB+w8eUrmSq8J690qVuHkr/dVwgMDbd3D/6vMLUNmtEvfu3SfhqwsOK9eux9jYiFbNf1G5nzPmzqdalcq4pdm+Ml/q0jVNXZYpV547t1TX5Z3btxTyAFRyd1fIc+HcGZyKOzNh7Cia1q9Nj84dOLBXsSFZqkwZzp89w7u3b5HJZNy4dpUXz5/h6p7x8e97k5CQwP37D6jspviZcXd342ag8hP9wFu3cXdXTF/Z3Z179++RkJD5RaYvypYtg/+VqwQ9Sxk+9fDRIwJuBlK1apVs7sX3KyExifsv3+JerLDCevdihQkMCs5SGfdfviUwKJgKjtYK61cd9cc4ny6t3LI/XD+vvz/JyclMnzyBjl2UH4tUeRfymqjwMEpWSD3maGpp4VSqLH/fU36MUiYuLpakxCTyGahugMZ8/ACAfgZplElISODeo3+oXLGcwvrKFcsSeOd+tsrKSGxcHImJSRjmz5drZf4oPD09iYqKUlg8PT0zzCORSBT+l8lk6dYps23bNqZMmcKOHTsoUKCAfL2bmxtdunShdOnSVKtWjZ07d1K0aFGWLl36bTuVBT/VeLJSpUoxefJkAIoUKcKyZcs4efIk9erVU0i3ZcsWJBIJa9asQUdHB2dnZ169ekWfPn3SlTljxgxq1KgBwNixY2nSpAmxsbHo6uqSL18+NDQ0sjU+c+XKlRQrVox581LuoyhWrBh37txJ10hISEhg5cqVOHw+2A0cOJBp06YBkC9fPnR1dYmLi8vytrdu3cq7d++4evWq/ATb0VFxGE9ycjLe3t7yoQAeHh6cPHlSHlvr1q0V0q9bt44CBQpw7949hXvARo4cSZMmTQCYOnUqLi4u/PPPPzg5ObF06VIaNWokbxQWLVqUixcvcvDgQXn+GTNmMHbsWLp16waAvb0906dPZ/To0fL3N6vCwsKYPn06v/76q3xdSEiIwhfziwIFCmT5SgmkvA8LFy6kSpUqqKmpceDAAdq3b4+Pjw9dunTJVpyg/OpLSEhItq7oyGQyhg8fTtWqVRXekzlz5qChocHgwYOzHVdWRL7/SFJyMlJDxR4NqaEBoVHvVeRS5P2XH5/i4mnoVka+zt6qADP6daCIjSUfP8Wx6chZukxZyp5ZIylsmbVhKWFhKUMwTKSKwwNMTKSEBKs+qQoLC8XERHF4lImJlLCwMPn/x44e4eGDB2zYtDltdrmN3htQV1enfceOSl+PiIwkKSkJaZrhC1KpMaFh4SpiC0MqNVZMb2JCYlISkZGRmJmaEhAYyN4Df7Jry0aVsR0+dpz7Dx6yzWe9yjRfC/9Sl2liNTaR8iZEdV2Gh4VinKYujU2khH9Vl69fvWKf7y7ad+pC1x69uHf3DosXzEVTS5NGTZoBMHTkGObMmEbLJg1QV9dATU3CmAmTKF2mbJbi/17I3/M0n0mpiQmhYcqHDIWFhSn5jJiQmPj5PTczzdK2e3bvxocPH2jeqg3q6mokJSUzaEB/Gjds+G078x2K+PiJpGQZUgPFizJSAz1Co2MyzFtvyloiPnwiKTmZfg3dFBpMAU9es9f/LjtHdv6muPL6+7PFJ+VY1LaD8mORKpHhKdsxNFKM29DYhNA3Wf/N3LF2OcamZriUq6j0dZlMxpaVXhQtURobu6w39AAioqJJSkpGamKksF5qbExouPLh5d9i0aoNFDCT4l7+Bzrm5NIDebW1tbN8i4ypqSnq6urpzlXevn2b7pwmrR07dtCrVy927dpF3bp1M0yrpqZGxYoV/9UeqZ+uIfU1S0tL3r5NPzb24cOHlCpVSmGWD1dX5Vdjvy7T0tISSPkgFCpU6JtifPjwIRUrKh5ElG1bT09P3oj6sm1l+5JVN2/epGzZsukO4F+ztbVVGE+ddpuPHz9m4sSJXL58mdDQUHlP1PPnzxVO2lXVmZOTEw8fPqRly5YK23V1dVVoSF2/fp2rV68qNC6TkpKIjY0lJiYGPT3FH0dVoqOjadKkCc7OzukaYMquiGT1SskXpqamDBs2TP5/hQoViIiIYO7cudluSH25+rJ///50jbzsXNEZOHAgt27d4vz58/J1169fx8vLixs3bmRr/+Li4tKNf9bW1iajSVAlpIkVWbp1yvx18QbL9xxj6fCeCo2x0kVsKV3EVv5/2aK2tBm/kC3HzjGuWyulZR08f52pvScgk8kAWOi1RGlsZOX9TpclNc+bkBAWzp/Hkj+Wq/xxuX//Hju2b2Pjlq2Zbiv9+5wyrFF1aOnr+sv6jx8/4jlpKpPHeWKsYgbQkJA3zFmwiFVLvVTG/9fho0yfPVdel3MXLfkSrGJCmYx0lZU23rQvp6n/5ORknIo78+uAQQAULeZE0JPH7PPdJT8R3LV9G3dv32b2gsVYWFoSGHCDBXNmIZWa0rhe1u5N/J6kew8z+UwqOxakrM/6No8cO8bBQ4eZPfN3HOwdePjwIXMXLMTMzIzmzZpmvaAfQLqPqZJ1aW0Y1JZPcQncehaM18ELFDI1pFE5Jz7GxjNuyxEmt6+DcT7dLG3/r+sP+H182e/i+/Pg/j12bd/G+s2ZH4sunDzC+sVz5P+P/H2B0iBkmR2kvnJwxyYu+R1n/Pw/0NJSfrzxWTqfF0//YeKi1VkqUxmlv0HZ+YJkYP3WXRw6eYYNXnPQ1s7aEP7vgUT9v38gr5aWFuXLl+f48eMK53zHjx+nefPmKvNt27aNnj17sm3bNvkF+YzIZDJu3rxJyZIlcyVuZX6qhlTa+z4kEonSmYiU/Vh9OdBlVOaXPDmZ3Sir21a2L6pizApd3cwP/JnVX7NmzbCxsWHNmjVYWVmRnJxMiRIliI+PV1lO2jrLyv4nJyczdepUWrVKf6Kc1Sku379/T8OGDcmXLx979+5ViMnCwoI3b96ky/Pu3btMr5Rkxs3NjbVr12YrT0ZXXywsLLJ8RWfQoEEcOHCAs2fPYm2dOhzl3Llz6Rr/SUlJjBgxgsWLFxMUFKQ0rlmzZjF16lSFdZMnT2ZCs/RXE40M9FFXUyM0SvFG7vCoD0gNMx4CcfhSAJNW72DhkG64lyyaYVo1NTVK2NvwLET1jb61yrtQtkU3omNSblZO+DzDX1hYmMLN1eER4RleWJBKTRWu+AJEfJXnwf37RISH071L6tXppKQkAm7cYPfOHZy75M/NgAAiwsNp3qSxQpolixaya/s2Du/bjbGREerq6oSm2VZ4eES6HojU2KTpeqvCwyPQUFfH0MiQx4+f8Pp1MINHjJK//uU7WNatKgd2b+fvfx4THh5Bh649FGK7HnCT7bt8uXbhDDWrV6W8W2XCP6TUZfznugwPC8PUNLUuIyLC0/X4fc1ERV0af7V/UlNTbNPcj1jY1g6/UymTKcTFxrJ6+VJmzltI5arVAHAsUpS/Hz1k2+ZNP1RDSuV7HhGBNE3Pwxcp73n6z4iGhjqGhkZZ3vbCxUvo1b0bjRo0AKBoEUeCQ4JZt2HD/01DylhfF3U1Sbrep/D3Mel6qdKylhoCUMTKlLD3Maw44k+jck68CIvkdXg0g9emztKX/Pm3q9wIL/Z7dsPG1EihrJou9lToOvy7+P7cCgggIiKc1s0Uj0XLvBayc/sWznx1n3U592o4OKUOAU9MSIk7KiIMY2lqz2d0ZASGxqrj/uKvXVs4sM2HsXOWUshe+YQ8Psvmc+PyOSYsWInULP2IkcwYG+ZHXV0tXe9TeEQkUmOjbJeX1obtvqzZspM1C2aonJjiu/WNz4DKqeHDh+Ph4UGFChVwd3dn9erVPH/+nH79+gEpQwVfvXrFxo0poya2bdtG165d8fLyws3NTX7uo6uri6Fhyvdy6tSpuLm5UaRIEaKjo1myZAk3b97kjz/++Nf246dqSGWVk5MTW7ZsIS4uTn4l9tq1a5nkSk9LS4ukpKRsb/vQoUMK6/6LbZcqVYq1a9cSHp7xyaMqYWFh3L9/n1WrVlGtWspJzNe9Hlnl5OTElStXFNal3f9y5crx8OHDdEMPsyo6OpoGDRqgra3NgQMH0jW+3N3diYqK4sqVK/LeQH9/f6KiojK91ywzAQEB8l64rMjs6ou7uzvHjx9X6Pk6duyYQpwymYxBgwaxd+9e/Pz8sLNTPMh7eHika6A1aNAADw8PevTogSqenp4MHz5cYZ22tjbcOZEurZaGBs521ly8/Yi6FVN7JC/eeUTt8qrvyfrr4g0mrtrOvIEe1CjrrDLdFzKZjAfPXlPURnUd6+vqYFi4MJEfYuR5pFJTrvhfppiTE5AydDbg+nUGDB6ispySpUrh73+Zjp1Texf9L1+iZOnSAFRwdWXrjl0KeaZPnUxhWzu6duuOuro6jRs3wdVV8V6mIQP706hxEzp2SLmnUlNTk+JOxbjkf5U6tWrK012+coVa1aspja10yRKcOaf4/bvofwVn5+JoamhgZ1sY322Kww2XrVzNx48fP09kYY6JsXG6NJOmzcDOtjA9unZBXV0dfX19TCxN0ItWrMur/pcpWiy1Lm/euE6/QarrskTJUlz1v0z7Tql1eeXyJUqUKi3/v2TpMjx/pjjt8Yvnz7GwSHmvExMTSUxMTHchRk1NHZks96fu/jdpampSvLgTl/z9qVO7lnz95cv+1KpZQ2me0qVKcuas4sMsL16+jHNxZzQ1s/5THxsbiyTNUB81NXVkyd9+oe57o6mhTnHrAlx+9Jw6pVJ/Ry4/ek7NEhlPHvQ1GcjvObQrYMLu0YojDf44dJGPcQmMblkDC6P0E7Xo62ghLVz4u/j+NGjchAppjkXDB/enQaMmNGmm2EOgq6ePrl7q5AsymQxDEyl3rl/B1rEYkNK4enArgPa9B6iMG+Dgzs3s37KBMbO8sC9WPN3rMpmMjcsWcO3CGcbP/4MClt82o6ympibORR25dC2AutVTfx8vXQugVlXl969m1fptu1m9aTur5v1OCaeML/YJqdq3b09YWBjTpk2TPwbo0KFDFC6ccu9icHCwwjOlVq1aRWJiIgMGDGDAgNTPVbdu3eSTuUVGRtK3b19CQkIwNDSkbNmynD17VuWostwgGlJKdOrUifHjx9O3b1/Gjh3L8+fPmT9/PqB8yJcqtra2PH36lJs3b2JtbY2BgUGm40d//fVXFi5cyJgxY+jVqxc3b96Uf0Cyu+2jR4/y8OFDpFIphoaGGc7E1rFjR2bOnEmLFi2YNWsWlpaWBAQEYGVlhbu7u8p8XxgbGyOVSlm9ejWWlpY8f/6csWPHZjneLwYNGkT16tVZuHAhzZo149SpUxw+fFhh3ydNmkTTpk2xsbGhbdu2qKmpcevWLW7fvs3vv/+eYfnv37+nfv36xMTEsHnzZvkUnQBmZmaoq6tTvHhxGjZsSJ8+fVi1KuW5EX379qVp06YUK1ZMXtY///zDhw8fCAkJ4dOnT/KZF52dndHS0sLHxwdNTU3Kli2Lmpoaf/75J0uWLGHOnDnp4lImK1dfhgwZQvXq1ZkzZw7Nmzdn//79nDhxQqERO2DAALZu3cr+/fsxMDCQl2NoaIiuri5SqRSpVPEqt6amJhYWFgr7m5aq8dCqbmvv1rgGY5dvpYS9DaWL2LLr1CWCQyNoXyflR23R9oO8DY9mVv9OQEojatyKrYzt2pJSRQrzLjLlfdLR0sRAL6UHdbnvUUo5FqawhRkfPsWy5eg5Hj57xYTuyof1KSORSOjQqRPe69dhY1MIm0KF8F6/Dh0dHRo0TH223JRJEzAzK8CAQSn3kbXv2JF+fXqz0XsD1WvU5OwZP674X2H1upT7ifT19XFI09j/8t59WW9oZIRhmqF1GhoamJiaYm9vT1x0Sq9S104dGTd5Ki7OTpQuWZLde/cRHPKGtq1ThkR4LVvOm3fvmDk1ZYhq21Yt2bZzN/MWedG6RXMCb99m7/4/mTMj5T5KbW1tijgq3mNgkC+lZ/DLek1NzXRpdHV1MDTMn27913XZtmMnNm1Yh7VNIWxsCrHRex3aOjrUb5Bal9Mnp9Rlv4Epddm2Q0cG/tqbzT4bqFajJufO+HHtyhWWr029N6t9xy7069WdjRvWUbtuPe7dvcuBvb6MHjcxpb7z5aNMufIsX7IYbR0dLCwsuXnjOkcOHWTQUMUG/7fS1tfDzNFW/r+pnQ3WpZ35GB5JxIvXubKNL7p27sy4iZNwKV6c0qVKsXvPHoJDQmj7+V5Ur6XLePP2LTOnp7ynbdu0ZtuOncxbsJDWLVsSeOsWe/ftZ86s1CHQCQkJPH7yRP7327fvePDwIXq6ehQqlDJ7W43q1Vizbj2WFhY4ONjz4MFDNm3eQosMJiT5Vv9lfablUbMc47ccxdnGnNK2lvhevE1wxHvaVk650ON18Dxvoz4yo3NKz9z284FYGBlgZ55y72HAk9dsPH2djtXKpOyLpgZFLBXvQzPQTTk+pl2vSl5+f1Qdi6RSUwrZ2mYad8OW7TmwzQfzgjZYFLThwDYftLR1qFw7dTKklXOmYmxqRvte/YGU4Xy7fVbT33MqphaW8nutdHR10dFN6Rn0XjqPS6eOMWzqXHT09OVp9PT10dLO3kNWu7ZrieeMBbgUK0JpFyd2HzxC8Nt3tP8lpRdu0eoNvH0XxqzxI+V5HvydMvNwzKdPRERG8eDvx2hqauJgmzKCY/3WXSxdv4m5E0dT0KKAfCSAnq4uenpZG+KZ5/KoRwqgf//+9O/fX+lraWe6zsrs04sWLWLRooyfdZjbRENKifz58/Pnn3/y22+/UaZMGUqWLMmkSZPo1KlTtp6O3Lp1a/lU35GRkWzYsIHu3btnmMfOzo7du3czYsQIvLy8cHd3Z/z48fz222/Zes5Vnz598PPzo0KFCnz48IHTp09Ts2ZNlem1tLQ4duwYI0aMoHHjxiQmJuLs7Jzl7lA1NTW2b9/O4MGDKVGiBMWKFWPJkiUZblOZKlWqsHLlSqZOncqECRNo0KABw4YNY9myZfI0DRo04ODBg0ybNo25c+eiqamJk5MTvXv3zrT869evy6d0T9uj9fTpU2w//2Bs2bKFwYMHy2fE++WXXxRiAOjduzdnzpyR/1+2bNl05fz+++88e/YMdXV1ihYtyvr167N8f1RWrr5UrlyZ7du3M2HCBCZOnIiDgwM7duxQmHHwy8Pt0r4XWfk85qZG7mWJ/BDDij3HeBcZTRFrS1aO7oOVWUoP6LvI9wSHpQ672HXyEolJyfy+wZffN/jK1zevXpGZ/VJuho6O+cSUdbsIjYzGQE8Xp8IF8Zk4kFKOirNxZcajW3fi4uKYO3sW799H41KiBEv+WIG+fupV1zchIahJUq/UlypdhukzZ7Fq+XJWrViOtbUNM2bPpsS/MBa7Yf26REZFsWrtet6FhuHoYM8fixdg9bl3811oGCEhqcNRrQtasXzxAuYu8mL7Ll/MzEwZO3IY9b7q3fi3dO6aUpcL56TUpbNLCRYtXYFeBnVZsnQZpsyYxZoVy1m7cjkFrW2YNnM2LiVS67K4iwsz5y1g1R9L8V67GkurggwePor6jVKHIk2dMZtVfyxl2sRxREdHY2FhSd/fBtCiddtc2bfCFUox3G+7/P+2i1JOQi9578anx0hV2b5Jwwb1U97zNWt5FxqKo4MDfyzxwsrqy3seqjCs17pgQZYv9WLugoVs37kLMzMzxo4eSb06deRp3r57R7uOqUNNfTZtwmfTJiqUL8f6NSn3nXiOHsWy5SuZMWs24RERmJmZ0qZ1K/r1TT/ZUk79l/WZVsOyxYj6GMvqo5d5Fx2Do6WUP/o2x8okZTa40OiPhHz1TKnkZBlL/rrAq/AoNNTUsJYaMqRpFdq4l1K1iW+Sl9+fnGja3oP4+Di8l84j5v17HJxcGDPbS6HnKvRtiMJF0RN/+pKYkMCSaeMUymrp0YvWXVM+byf/TJlZcMZIxZPtviMnUL1B9oaaNqpdg6io96zcuJV3YeEUsbNlxZypWFmkDIUPDYsg+O07hTxteg+S/33v4T/8dcIPK4sCHNvhDcD2/X+RkJDIsEkzFfL91r0TA3pkf1KpvJC2B1rIHoksJzfW/ES2bNlCjx49iIqKytL9RLlpxowZrFy5khcvXvyn2/1e9OnThwcPHnDu3LnMEwt5LvG68mdyfE80yjeRD+37Xhnl05P3SH3PtPOb8C6Tmc6+B2b59egnsc3rMDK0UhZE3MeszWKZl7T1Db77uoSU+ow9tCKvw8iQTuPffpjvz9XnuTe73b+lYiFjEkIeZ54wD2laZG/GwX9b8iPlz3LMLrWi/z+PSMgO0SOlwsaNG7G3t6dgwYIEBgYyZswY2rVr9580opYvX07FihWRSqVcuHCBefPmMXDgwH99u9+L+fPnU69ePfT19Tl8+DA+Pj4sX748r8MSBEEQBEH4/5KHQ/v+H4j+PBVCQkLo0qULxYsXZ9iwYbRt25bVq799ys3s+Pvvv2nevDnOzs5Mnz6dESNGMGXKlByVOXPmTPLly6d0adSoUeYF/IeuXLlCvXr1KFmyJCtXrmTJkiVZGrYHKT2Hqvbz33jQbE64uLiojHXLli2ZFyAIgiAIgpATErXcWX5SYmjfTyI8PJzwcOXDhHR1dSlYsOB/HNG/4/3790qnLoeUm+e/zAbzPXj27BkJn6eNTcvc3FzhmV0/EjG0L3eIoX25Swztyz1iaF/uEUP7cpcY2pd9yY+vZJ4oC9Qc/r2Z8b5nYmjfT8LExOSbpjX/0RgYGPwwDZDvqVEnCIIgCMJP6CfuTcoNoiElCIIgCIIgCD8hmWhI5YhoSAmCIAiCIAjCz0g0pHJE1J4gCIIgCIIgCEI2iR4pQRAEQRAEQfgZffWQZCH7RENKEARBEARBEH5GamJwWk6I2hMEQRAEQRAEQcgm0SMlCIIgCIIgCD8hMWtfzoiGlCAIgiAIgiD8jERDKkdE7QmCIAiCIAiCIGST6JESBEEQBEEQhJ+R6JHKEdGQEgRBEARBEISfkWhI5YhoSAmCIAiCIAjCT0hMNpEzEplMJsvrIARBEARBEARB+G8lhDzOlXI0LRxypZwfjeiREgQhV4W/j8nrEDJlYqDHuSdheR1GhqrZS0kMOJLXYWRKo2xDLlStltdhZKrK+XPEfXyf12FkSFvfgH4S27wOI1MrZUHffV1CSn1Gfvi+j0dG+fQInjUgr8PIlKXnHz/M8Sg+8m1eh5EhLaMCeR2CItEjlSOiISUIgiAIgiAIPyOJJK8j+KGJZqggCIIgCIIgCEI2iR4pQRAEQRAEQfgZiaF9OSIaUoIgCIIgCILwExKz9uWMqD1BEARBEARBEIRsEj1SgiAIgiAIgvAzUhN9KjkhGlKCIAiCIAiC8DMSQ/tyRNSeIAiCIAiCIAhCNokeKUEQBEEQBEH4GYkeqRwRDSlBEARBEARB+BmJhlSOiIaUIAiCIAiCIPyExPTnOSNqTxAEQRAEQRAEIZtEQ0r4LtWsWZOhQ4d+c/6goCAkEgk3b94EwM/PD4lEQmRkpMo83t7eGBkZyf+fMmUKZcqU+eYYBEEQBEEQvmsStdxZflJiaJ/wf8nGxobg4GBMTU2/uYyRI0cyaNCgXIzqx+Dt7c3QoUMzbHRmlUwmY93qVezf60v0+/e4uJRg5BhP7B0cMsx3+uQJVq9czquXLylobc2v/QdSs1Zt+etrV61k3ZpVCnlMpFL+OnpC/r/fqZPs2+PLg/v3iYqKxGfLdooWK5bluA9sWcfZwweI+RCNXTEXOg8YQcHC9irznD28n0snj/Dq2RMACjsWo2X3ftgXc5an2b95LX9uWa+QL7+xCQu3HsxSXF/bduwcG/48xbvIaBytLRjbtRXliyuv1+NXAtlx/DwPgl4Rn5iIo7Ul/ds0pGrp4vI0e/38mbBya7q8NzbOR1tLM9vxZcSiZQsKduyIllRKTFAQT72WEH3rltK0juPGYd64Ubr1MU+fEuDR9Zu2v33nLrw3biI0NBQHe3tGjxxB+XJlVaa/dv068xYs4vGTJ5iZmdGjmwft2rSRv/7P48f8sWIl9+8/4HVwMKNGDMejcyeFMhITE1mxajV/HT5CWFgYpqamNG/WlL69e6GWy89xcazmSv1RfSlUviRGVuasaNGXwP3HcnUbX/tR6lMmk7F29Sr27fHl/fv3uJQowagsHI9OnTzBqhWpx6Pf+g+kZu3aStN6r1/Hij+W0b5jJ4aPHKXw2tOnT/hjiRc3rt9AJkvGzt6BZUuXIMlg23rlqqFfqS7q+QxJfBdM1IndJLx8nGG8AJoF7ZF2GUriu2BC189KfUFNjXzuDdAtWQl1AyMSw97w3m8/cU/uZVpmRn6U49H23Xvx3ryNd2FhONjZMmbYYMqXLa0y/dUbAcxbvIzHT4MwM5XS06MT7Vq1UEgT/f49S1as4aTfGaLff6CglSUjBw+gehX3b47zPyXJ6BMoZObnbUIK/9fU1dWxsLBAQ+PbrxXky5cPqVSai1H9fDb7eLNt62ZGjB7Lep/NSKVShgzox8ePH1XmuX0rkInjxtKwcRM2bttBw8ZNmDB2DHfv3FZIZ2/vwMEjx+XL5u07FV7/9OkTJUuXpv83NIaP7NrM8T3b6dR/OBO81mFobMLCcUOJjVEd98NbAbjWrMvI2UvxXLgKEzNzFo0fSkToO4V0VoXtWLDlT/kydfmmbMd3+OINZvvspW/L+uyePYpyTg78Onslr0PDlaa/dv8x7iWdWDH2V3bNHImrsyMD5q7h/tOXCuny6ergt3K6wpLbjSjT2rWxGzyYlxs3cbNnL6IDA3GePw8t8wJK0z/18uLKL83ly9WWrUiIiiL09Olv2v6Ro8eYO38BfXr1ZOfWLZQrW5b+gwYTHByiNP3LV6/oP2gI5cqWZefWLfTu2YPZc+dz/ORJeZrY2FisC1ozZPBATE2VHzPWe/uwy9eXcWNGs893F8OGDMJ74ya2bt/xTfuREW19PV4G3mf7wEm5XnZaP1J9bvLxZuuWzYwcM5YNGzdjIpUyqH/mx6MJnmNp1LgJm7ftoFHjJowbO4Y7t2+nS3vv7l327d2DY5Ei6ff7xQv69upJYVs7Vqxew+ZtO+jZuw/a2toqt61TvBz567bhw8WjhK6fRfzLfzBpPwC1/MYq8wBItHUwataV+KCH6V4zqN4MvbJViT6+i3drphMTcB7jVn3QMLfOsMyM/CjHoyPHTzJn0RL69PBg18Z1lC9Tmt+GjSI45I3S9C9fv2bAsNGUL1OaXRvX0ae7B7MWeHH8lJ88TUJCAn0HDed1cDALZ03nz51bmOI5GnMzs2+O82eyfPly7Ozs0NHRoXz58pw7dy7D9GfOnKF8+fLo6Ohgb2/PypUr06Xx9fXF2dkZbW1tnJ2d2bt3778VPiAaUsJ3LDExkYEDB2JkZIRUKmXChAnIZDIAJBIJ+/btU0hvZGSEt7c3kH5onzLe3t4UKlQIPT09WrZsSVhYmMLraYf2de/enRYtWjB//nwsLS2RSqUMGDCAhIQEeZrg4GCaNGmCrq4udnZ2bN26FVtbWxYvXpylfY6MjKRv376Ym5ujo6NDiRIlOHgwtbfC19cXFxcXtLW1sbW1ZcGCBQr5s1ove/bsoVatWujp6VG6dGkuXboEpAyB7NGjB1FRUUgkEiQSCVOmTMlS7GnJZDJ2bNtK9x69qFm7Dg6OjkycOp3Y2FiOHTmsMt+ObVupWKkS3Xr0wtbWjm49elHB1ZUdW7copFPXUEdqaipfjI1NFF5v1KQpvfr8SkVXt2zHfWLfTpp06Eb5KjUpaOtAzxETiY+Lxd/vuMp8fcZMoVbT1hRyKIqljS3dhoxFlpzM/ZvXFONW18DQRCpfDIwyPilSxucvP1rXcqNNbXccClrg2a0VllJjdhy/oDS9Z7dW9PqlDiUdClPYsgBDOzajsKUZp2/cUUgnkUgwM8qvsOQ2qw7teXPwL94cPMinZ894umQpcW/fYtmipdL0SR8/khAeLl/yOTmhYWDA278OfdP2N27ZQssWzWndsgX29naMGTUCC3Nzdu7erTT9rt2+WFpYMGbUCOzt7WjdsgUtm/+Cz8bN8jQlXFwYMWwIjRo0QEtTS2k5t27dplaNGlSvVpWCVlbUr1sXd7dK3LuXs54AZe4e8ePAxAXc3Hs018tO60epT5lMxvatW+nRsxe1Ph+PJn8+Hh3N4Hi0fetWXCtVonvPXtja2dG9Zy8qurqyfZvi8SgmJoZJE8YxbsJE8udP/71ZsXwZlatUZdCQoRRzcqKgtTVVq1XL8GKdvmsdYgIv8SnwIolhb4g+4UtydAT6ZaupzANg2LAjn+5dI/7V03Sv6ZZw5cPFo8Q9vktSZBgxAeeIe3qffK51MiwzIz/K8Wjjth20+qUJrZs3w97OljHDB2NhXoAdvspPtHfu2Y+FhTljhg/G3s6W1s2b0bJZE7y3bJen2fvnX0RFR+M1bxZlS5fCytKCcmVKUayoY45i/U/l0dC+HTt2MHToUMaPH09AQADVqlWjUaNGPH/+XGn6p0+f0rhxY6pVq0ZAQADjxo1j8ODB+Pr6ytNcunSJ9u3b4+HhQWBgIB4eHrRr1w5/f/9vrp7MiIaU8N3y8fFBQ0MDf39/lixZwqJFi1i7dm2ulO3v70/Pnj3p378/N2/epFatWvz++++Z5jt9+jSPHz/m9OnT+Pj44O3tLW+kAHTt2pXXr1/j5+eHr68vq1ev5u3bt1mKKTk5mUaNGnHx4kU2b97MvXv3mD17Nurq6gBcv36ddu3a0aFDB27fvs2UKVOYOHGiwvazavz48YwcOZKbN29StGhROnbsSGJiIpUrV2bx4sXkz5+f4OBggoODGTlyZLbLB3j96hVhYaG4uqUOb9DS0qJsufLcvhWoMt+dW7dwraQ4JKKSm3u6PC+eP6dZw3q0+qUJEz3H8Oql4tXMbxUa8pqoiDBcyrnK12lqaVGsZBn+uZf+KrQq8XGxJCUlom+g+OP/5tULRnT+hbHdW7Nq1kTeBb/KVnzxiYnce/qCyqUUhylWLlWMm4/Snzgpk5yczMdPsRjq6ymsj4mNo+7AKdTuP4n+c1alu0KcUxINDfIVLUrk1SsK6yOvXsWgRIkslWHetAmR164R90b5VeSMJCQkcP/+Ayq7KTau3d3duBmofGhh4K3buLsrpq/s7s69+/dISEjM8rbLli2D/5WrBD17BsDDR48IuBlI1apVsrkX348fqT6/HI8qpT0elS/P7UDVx6Pbt24p5AFwc3dPl2fe7FlUqVoN10rpL9wkJydz8fx5ChUqxOAB/WlYtzY9u3pwJqNeVTV1NC1siHt6X2F13NP7aFqrHmKsW9INdWMzPpxTfqFBoqGBLDFBYZ0sMQFN64yHN6ryoxyPEhISuPfgEZUruSqsr+xakZu37yjNE3j7LpVdKyqsq+Lmyr37D0hITPmsnj57gdIlXZgxdyE1Gv5Cy45dWeO9kaSkpG+O9b8mk6jlypJdCxcupFevXvTu3ZvixYuzePFibGxsWLFihdL0K1eupFChQixevJjixYvTu3dvevbsyfz58+VpFi9eTL169fD09MTJyQlPT0/q1KmT5YvZ30LcIyV8t2xsbFi0aBESiYRixYpx+/ZtFi1aRJ8+fXJctpeXFw0aNGDs2LEAFC1alIsXL3LkyJEM8xkbG7Ns2TLU1dVxcnKiSZMmnDx5kj59+vDgwQNOnDjB1atXqVChAgBr166liJJhHsqcOHGCK1eucP/+fYoWLQqAvX3qD+bChQupU6cOEydOlMd879495s2bR/fu3bO1/yNHjqRJkyYATJ06FRcXF/755x+cnJwwNDREIpFgYWGRrTLTCgsLBcBEqthTZCKVEhIcnGE+kzRXaU2kUoUeQ5cSJZg0dTo2hQsTHhaG97q19O3Vna07dmP41YQh3yIqImU4Sv40PVz5jUwIe6t8uJIyvhtWYCQ1w7lsBfk6+2Iu9Bo5EfOChYiODOfgNm9mjfiVaSu3kC+/YZbKjYz+SFJyMlJDxQaa1NCA0Mj3WSrD+6/TfIqLp6F76n0s9gXNmfFbJ4oUsuJjTCybDp+hy+TF7JkzmsKWyofdZZemoSESDQ0SwiMU1ieER6CV5nOiNL9UinGlSjycNu2bth8RGUlSUhLSNNuSmpgQ+vnzmlZYWBhSkzTppSYkJiYRGRmJmVnW7sPs2b0bHz58oHmrNqirq5GUlMygAf1p3LDhN+3L9+BHqk+VxyOTLByPTNIcj0wUj0fHjh7h4YMHbNi0OW12ACLCw4mJiWGj9wb69R/AwMFDuHTxAmNGjcDS3AwbJXnU9PIhUVMn+WO0wvqkj+/R1lfeM6NubIZBreaEbV4EsmSlaeKe3EfftQ7xL/4hKSIULdti6BQp9c33yfwox6OIyKiUz6qJ4ggAqdSYsMvKhyCGhYUhlSo2vKQmxiQmff6smpry8vVrrlwPoUmDeixfNI/nL14wY94iEhOT+K13j2zH+bOIj4/n+vXr8nOwL+rXr8/FixeV5rl06RL169dXWNegQQPWrVtHQkICmpqaXLp0iWHDhqVLIxpSwk/Jzc0NyVcHd3d3dxYsWJArV3ru379Py5aKQ4nc3d0zbUi5uLjIe4gALC0tuf15rPzDhw/R0NCgXLly8tcdHR0xNs7a0K2bN29ibW0tb0Qpi7l58+YK66pUqcLixYtJSkpSiCszpUqVUtgHgLdv3+Lk5JTlMuLi4oiLi5P/f/jwYWbNSr2pef7iJQAK7yGkDLHJ7Ec73asymeJnoUrV1Ncci1CyVGnatGjGoYN/0rGLR5b3AeDyqaNsWjpX/v/gqZ+vbqWNm8zj/uLwrs34+x1n1Nw/0NRKvQeiZMWvr2w74FC8BJ4923LxxCHqt+qYrbjThiJTsk6Zvy5cZ/nuIywd2RupoYF8fekitpQuYiv/v2wxO9p4zmfL0XOM6946W7Fl6vMQXTkJ8mG7GSnQqBGJHz4QfjbjcfSZkZD+M5n2c6qQXtlnmOydex45doyDhw4ze+bvONg78PDhQ+YuWIiZmRnNmzXNekHfoe+xPv86dJjpM2fJy17otURprGmPLcoDTpslNc+bkBAWzp/Hkj+Wq7zfKflzo6Z6jZp07NwFgKLFinH7ViDbt29nVDZup0nZrJLvikSCUfMefDj3F0nhqkdBRB/fjWHjTpj1nQTISIoIJebWJfRK5WxihB/meJTus5dxoMo+2ynFpKyXJSdjYmzEZM9RqKur41K8GG9DQ/HevO3HaUjl0ox7ac8JALS1tZV+L0JDQ0lKSsLc3Fxhvbm5OSEhyi9YhoSEKE2fmJhIaGgolpaWKtOoKjM3iIaU8EOSSCTpTry+vlcpM1k5aVNGU1PxRleJREJycnKGZWZ1W7q6uhm+ruzkJG3ZWa2Xr/fjS5lf9iOrZs2axdSpUxXKGTZsGF17pfQYJsSnbDcsNAxT09QzhYjwcExMVPc+SKWm6e5XC88kj66uLg4Ojrx4oXxsdUbKuFXFzslF/n9iQjwA0eFhGJmkXh1/HxlBfqPMe02O7t7KoR0bGTHTCxu7jMfJa+voUtDWgTevsj5kxSi/PupqaoRGKl6pDo96r3AioszhizeYtGobC4f2wL1kxjMYqqmpUcKhEM+C32WYLjsSoqKQJSaimaZXQNPYOF0vlTLmTRrz7ugxZIlZHwL2NWMjI9TV1QlN+/mKiEBqovxeFalUmj59eAQaGuoYGhpledsLFy+hV/duNGrQAICiRRwJDglm3YYNP2xD6nuuz5o1qlPetRLRMZ+Ar45HYWGYfjURQHhE5sej8DTxRnyV58H9+0SEh9O9S2f560lJSQTcuMHunTs4d8kfIyNj1NU1sLNXHJJna2fPnVuBYJa+rpJjPiBLTkItTe+Tmp4BSR/T9/RItHTQsiyMprk1+eu3+7xSgkSihsWYJYRvX0b8s0ckf/pAhO9qUNdATVef5A9RGNRsTmJkWLoys+JHOR4ZGxmirq5OWJhi71N4eES6XqovUj6radJHRKKhro6hYcoIAlNTKRoaGgoXMu1tbQkNC5f3knzvZLk0a9/sNOcEAJMnT87wPmtl5zTfdhFGkmGaTC+W5IC4R0r4bl2+fDnd/0WKFEFdXR0zMzOCvxqO8ffffxMTE5Plsp2dnZWWnxNOTk4kJiYSEBAgX/fPP/9keRrxUqVK8fLlSx49eqT0dWdnZ86fP6+w7uLFixQtWlR+EM9pvUDKfQNZ6fXz9PQkKipKvkRGRjJz5kxsbAphY1MIO3t7pFJTrvqn1mtCQgIBN65TspTq6WZLlCqlkAfgiv+lDPPEx8cTFPQU6TdMd6+jp4+5lbV8sSpkh6GxlLsBV+VpEhMSeHj7Jo7OJTMs68juLRzctoGh0xdiW7R4hmkBEuLjCXkehJGKk05ltDQ0cLaz4eJtxRm5Lt5+SJmidirz/XXhOuNXbGXuoK7UKOeiMt0XMpmMB0EvMTPOvQknZImJfHj0CKOKivcdGFWoyPs7yu9T+CJ/2TLo2tjw5mD2p4r/QlNTk+LFnbiU5sbjy5f9KVO6lNI8pUuV5PJlxfQXL1/GubgzmppZvxYZGxuLJM203Gpq6siSv+2izvfge65PfX19ChcunO54dCXt8ej6dUqWVn1sKVmqFP5pjkf+ly/J81RwdWXrjl1s2rpdvhR3dqZBo8Zs2roddXV1NDU1cXZx5tnn+7m+eP7sGQULFlS+4eQkEkJeoG2nOEpAy86JhJdP0iWXxcXybs3vhK6bJV9iAs6TGBZC6LpZJLwOUsyQlEjyhyhQU0PHqSxxfyu/py0zP8rxSFNTE2enoly6clVh/aUrVylTUvn9maVLuqRLf9H/Cs7FndD8PCNw2VIlefHylcKFyGfPX2BmKv0hGlGQ0iuXG0vac4KoqCg8PT2VbtPU1BR1dfV0PUVv375N16P0hYWFhdL0Ghoa8klbVKVRVWZuEA0p4bv14sULhg8fzsOHD9m2bRtLly5lyJAhANSuXZtly5Zx48YNrl27Rr9+/bJ10Bo8eDBHjhxh7ty5PHr0iGXLlmU6rC8zTk5O1K1bl759+3LlyhUCAgLo27cvurq6WboaUqNGDapXr07r1q05fvw4T58+5fDhw/K4RowYwcmTJ5k+fTqPHj3Cx8eHZcuWKUwGkdN6AbC1teXDhw+cPHmS0NBQlQ0xbW1t8ufPr7B83YUvkUho37ETPhvW4Xf6FI//+YfpUyaho6ND/YapzwWaOmkCy5ctkf/frkNHrvhfZpP3BoKCnrLJewNX/a/QvlPqFd8lixdy4/o1Xr96xd07txk3ZhQfP36kcdNm8jRRUVE8eviQp09Snrny/FkQjx4+JCxU+b0bX8ddt0U7Du3YyI0LZ3gV9Jj1C39HS1uHSjXrydOtmz8N3w2pN8Ue3rWZfT6r6T5sHKbmlkSFhxEVHkbsp9T627lmKQ9vBfAu5DVPHtxlxYzxfIr5SOW66Z+TlJFuTWrie+oye05f5vGrEGb77CE4NIL2dVNutF+07U88/0i9X+OvC9cZt3wzozyaU6qILe8io3kXGc37z1frAZbvPsz5wPu8eBPK/aCXTFy1jYfPXtGubu5OhvB6+w7MmzalQJPG6BYujN2gQWibFyDk82yThX/9lSITxqfLZ96kKe/v3iXmadZuYFela+fO7Nm7j7379vPkyVPmzl9AcEgIbVunDBfyWrqMcRNTpw1v26Y1r4ODmbdgIU+ePGXvvv3s3befbl27yNMkJCTw4OFDHjx8SEJCAm/fvuPBw4c8f/5CnqZG9WqsWbees+fO8+r1a06eOs2mzVuoXatmjvZHGW19PaxLO2NdOuUZZqZ2NliXdsbYxirXt/Wj1KdEIqFDp054r1+H36mU49G0ySnHowZfHY+mTJrAH0tTj0ftO3bkyuXLbPTeQNDTp2z03sAV/yt06JhyPNLX18fB0VFh0dXVxdDQEAfH1B7pLh7dOHHsKPv27OHFi+fs2rGd8+fO0rGj6iG9H6+cRK90ZXRLuaMhNcegTmvU85sQE5ByQc2gxi8YNv3yLDUZiaHBCkvyx/fIEhNJDA1G9rmnXdPKFp2ipVE3kqJp7YBJ+4GAhA+XVc9Impkf5XjUtWN7fPcfZO+Bv3jyNIg5i5YQ/Oat/LlQi/9YybgpqZNOtWvVnOCQN8xdvJQnT4PYe+Av9hz4i+6dO8jTtG/dgsioKGYv9CLo+XPOnr/IGu9NdGjT6pvj/FFldk7wNS0tLcqXL8/x44qfu+PHj1O5cmWledzd3dOlP3bsGBUqVJCf56hKo6rM3CCG9gnfra5du/Lp0ydcXV1RV1dn0KBB9O3bF4AFCxbQo0cPqlevjpWVFV5eXly/fj3LZbu5ubF27Vp5t3PdunWZMGEC06dPz1HMGzdupFevXlSvXh0LCwtmzZrF3bt30dHRyVJ+X19fRo4cSceOHfn48SOOjo7Mnj0bgHLlyrFz504mTZrE9OnTsbS0ZNq0aQoTTeS0XgAqV65Mv379aN++PWFhYZl2zWekS7fuxMXFMX/2LN6/j8a5RAkWL1uBvr6+PM2bkBCFB2iWKl2GaTNmsWrFclavXE5Baxt+nzUblxKpvUHv3rxh8nhPIiMjMTI2pkSJkqzd4IOlZeqJ4vmzZ/h96mT5/xPHpdzU2qvPr4weOTzDuBu27UJ8fBxb/pjPxw/vsS/mzPAZi9DRS4077O0bJF+NLfc7uIfExARWzFBsBDTr3JPmXXoDEBH6ltVzJvMhOhIDQyPsnUowbtEapOaWWarPLxpVLkfkh4+s8D3Ku8goithYsnLsr1iZpQw3ehcRTXBo6lC5XScukpiUzO/rd/P7+tRpqZtXd2Vm/5QTwuiPn5iyZgehkdEY6OniZGuNz+TBlHIsnK3YMhN66hQahvmx6d495YG8T59yb9Ro+Sx8mlIp2mmuHqrr6yOtWYOnXkuUFZktDRvUJzIqilVr1vIuNBRHBwf+WOKFlVXKe/AuNFThiqZ1wYIsX+rF3AUL2b5zF2ZmZowdPZJ6dVKni3777h3tOqY29H02bcJn0yYqlC/H+jWrAfAcPYply1cyY9ZswiMiMDMzpU3rVvTrm/PJc9IqXKEUw/1Sp2huuyhlgppL3rvx6fFts3Cq8iPVp8fn49Hcz8cjlxIlWPKHkuORRPF4NH3mLFYtX86qFcuxtrZhxuzZlCiZce90WjVr12bMuPH4bFjPwvlzKVS4MLPmzqNChQoEH9+gNE/s/RtE6+qTr0oj1PPlJ/FdMBE7l5MUnTLcTC2fIeqZPFMqLYm6BvlqNEPDyBRZfByxj+8S+acPsrhPmWdW4Uc5HjWsV4fIqGhWrvfmXWgYjvZ2LF80FyvLlImV3oWFEfzVbKDWVlb8sWgu8xYvZfvuvRQwNcVzxBDq1a4pT2Nhbs6qJQuZt2gprTv3oICZKV06tKGnR+e0m/9uJX/jrQ45NXz4cDw8PKhQoQLu7u6sXr2a58+f069fPyClh+vVq1ds3LgRgH79+rFs2TKGDx9Onz59uHTpEuvWrWPbtm3yMocMGUL16tWZM2cOzZs3Z//+/Zw4cSLdaJ7cJJF9680igiBk6uXLl9jY2HDixAnqfHWi8P8s/H32hhLmBRMDPc49+bZ7Av4r1eylJAbkrJf0v6BRtiEXqmb8XJvvQZXz54hTcm/J90Rb34B+Etu8DiNTK2VB331dQkp9Rn74vo9HRvn0CJ41IK/DyJSl5x8/zPEoPjJrjxzJK1pGuTMLam75uicwJwz0Mr7PW5nly5czd+5cgoODKVGiBIsWLaJ69epAyrM7g4KC8PPzk6c/c+YMw4YN4+7du1hZWTFmzBh5w+uL3bt3M2HCBJ48eYKDgwMzZsygVat/r4dQ9EgJQi46deoUHz58oGTJkgQHBzN69GhsbW3lBwZBEARBEAQB+vfvT//+/ZW+puwZmTVq1ODGjRsZltmmTRvatGmTG+FlibhHShByUUJCAuPGjcPFxYWWLVtiZmaGn58fmpqabNmyhXz58ildXFwyv9lWEARBEAQhNyXLcmf5WYkeKUHIRQ0aNKDB5+l40/rll1+oVKmS0td+lNl9BEEQBEH4/yHu8MkZ0ZAShP+IgYEBBgYZP1dDEARBEARB+DGIhpQgCIIgCIIg/IR+5mF5uUE0pARBEARBEAThJyTaUTkjGlKCIAiCIAiC8BMSPVI5I2btEwRBEARBEARByCbRIyUIgiAIgiAIPyExa1/OiIaUIAiCIAiCIPyEkvM6gB+cGNonCIIgCIIgCIKQTaJHShAEQRAEQRB+QmJkX86IhpQgCIIgCIIg/ITErH05I4b2CYIgCIIgCIIgZJPokRIEQRAEQRCEn5CYtS9nRENKEARBEARBEH5CYta+nJHIRFNUEARBEARBEH46z8I+5Eo5haX5cqWcH43okRIEIVclXv8rr0PIlEb5JkR+iMnrMDJklE+PuOjwvA4jU9r5TXgX/X3XJYBZfj36SWzzOowMrZQFEffxfV6HkSltfYPvvi4hpT5jD63I6zAypNP4tx/m+3P1eUReh5GpioWMSQh5nNdhZEjTwiGvQxBykWhICYIgCIIgCMJPKFkMTMsR0ZASBEEQBEEQhJ+QaEbljJj+XBAEQRAEQRAEIZtEj5QgCIIgCIIg/ITEA3lzRjSkBEEQBEEQBOEnJG6RyhkxtE8QBEEQBEEQBCGbRI+UIAiCIAiCIPyEksV0EzkiGlKCIAiCIAiC8BMSQ/tyRjSkBEEQBEEQBOEnJCabyBlxj5QgCIIgCIIgCEI2iR4pQRAEQRAEQfgJiaF9OSMaUoIgCIIgCILwExKTTeSMGNoHdO/enRYtWmQrj62tLYsXL87xtmrWrMnQoUOzVYZEImHfvn3Z3va3CgoKQiKRcPPmzf9sm4IgCIIgCILwPRMNKcDLywtvb+9cLTOrjY89e/Ywffr0XN22n58fEomEyMjIXC33/8GaNWuoVq0axsbGGBsbU7duXa5cuZIu3fLly7Gzs0NHR4fy5ctz7tw5hdf37NlDgwYNMDU1Vfk+16xZE4lEorB06NAhS3EGBQXRq1cv7Ozs0NXVxcHBgcmTJxMfH6+Q7vnz5zRr1gx9fX1MTU0ZPHiwQho/Pz+aN2+OpaUl+vr6lClThi1btqjc7oULF9DQ0KBMmTJZijO7th2/QP0hv1O222jajlvI9QdPVKY9fuUWvWeupOqvE3Ht5UmnSV6cD3ygkGbvmSu4dBqebomLT8hWXDKZjDWrVtKkQT2qV3bjt769efL4cab5Tp08Qfs2rajq5kr7Nq3wO3VKZVrv9euoVL4sC+fPU5lm1ozfqVS+LNu2pn+Ptu/ypWHzVlSoUoP2Ht25HnAzw9iuXb9Be4/uVKhSg0bNW7PTd4/KtIePHadURXeGjByjMs3aDT6UqujOnAWLMtyuTCZj3eqVNG9Uj9pV3Rj4a9bq0u/UCbq0a0Wtyq50adeKM6fT1+W7t2+ZNnE8jevWpE5Vd7p3as+D+/fkr8fExLBw7mxaNmlA7apudG7bir27d2a67axyrOZK/wNrmf3Kn5WyIEo3r59rZSuzfecuGjb9hQpulWnfqQvXbwRkmP7a9eu079SFCm6VadSsOTt371Z4/Z/Hjxk2chQNmzSjVLkKbNqyNV0ZiYmJLP1jOQ2b/kJF9yo0ataclavXkJycnKv7Bv99faa143wgjaavp+KopXRYsJUbj1+pTHvjySu6ee2g+viVuI5eSvNZPmzyu6Ey/eEbDyk9bDFD1x3IVkx5+f352tyZv1O1Yll2KjkWqYrbd+MaBrZvSo8mNfh9xG+8DFJ9fAc4fWgf04b9St+W9ejbsh6zRg/k8YO7CmkObPNh4oAe9P6lNv3bNmLR5NG8fvEsSzEps33vQRq070G5es1p12cw1wPvqEz7Liyc0dPm0LRLH0rWbMLspavSpdn95xG6DhxF5SbtqNykHb2Hj+P2/YffHF9ekMlyZ/lZiYYUYMpF6jYAAQAASURBVGhoiJGRUZ5s28TEBAMDgzzZ9s/Iz8+Pjh07cvr0aS5dukShQoWoX78+r16l/oDu2LGDoUOHMn78eAICAqhWrRqNGjXi+fPn8jQfP36kSpUqzJ49O8Pt9enTh+DgYPmyalX6A7EyDx48IDk5mVWrVnH37l0WLVrEypUrGTdunDxNUlISTZo04ePHj5w/f57t27fj6+vLiBEj5GkuXrxIqVKl8PX15datW/Ts2ZOuXbvy559/pttmVFQUXbt2pU6dOlmKMbsOXwpg9sZ99G1Rl90zR1DOyY5f56zmdWiE0vTXHjzGvWRRVozuw67fh+Pq7MiA+eu4H/RSIV0+XR38lk9RWLS1NLMV2yYfb7Zu2czIMWPZsHEzJlIpg/r34+PHjyrz3L4VyATPsTRq3ITN23bQqHETxo0dw53bt9OlvXf3Lvv27sGxSBGV5Z05fZq7d25jZmaW7rUjx04wd+Fi+vTozs7NPpQrU5r+Q4YTHBKitKyXr17Tf+gIypUpzc7NPvTu0Y3Z8xdx/NTpdGlfBwezwGsp5cqWURnbnbv32L1vP0WLOKpM88WWjd7s2LqZ4aPGstZ7M1KplGED+xGTQV3euRXI5HFjadCoCd5bd9CgURMmeY7h7p3UuoyOjua33t3R0NBgvtcyNu/0ZeDQ4QrHz6UL5+N/6SITp81gy849tOvYmcXz53LuTPr9/hba+nq8DLzP9oGTcqW8jBw5eoy58xfQp1dPdm7dQrmyZek/aDDBware81f0HzSEcmVTTn579+zB7LnzOX7ypDxNbGws1gWtGTJ4IKamUqXlrPf2YZevL+PGjGaf7y6GDRmE98ZNbN2+I9f38b+sz7SOBDxk7r4z9Knnyo6RnSlnb0X/1fsIjohWml5XS5MO1UqzfmBb9o7tSp96riw7fJHdF9N/31+HR7PwwDnK2RfMdlx5+f354qzfae7duY2pkmORKgd3bOKw7za6DRzBtGXrMTKRMnvMYD7FqI77fuAN3GvVY/y8P5jitQZpAQvmjB1CeOjb1DS3Aqj3S2umLFnLmNlLSEpKYs7YIcR++pTl2L44fOoMs5etpo9He3atWUq5Ui70GzOJ4DdvlaaPj0/A2MiQPl06UMzBTmmaqzdv0bhODdYvnsXm5QuwKGBG35ETePMuNNvx5ZVkmSxXlp9VthpSNWvWZNCgQQwdOhRjY2PMzc1ZvXo1Hz9+pEePHhgYGODg4MDhw4flee7du0fjxo3Jly8f5ubmeHh4EBqa+gE7cuQIVatWxcjICKlUStOmTXn81dWXLz07e/bsoVatWujp6VG6dGkuXbqUpZi9vb0xMjLi6NGjFC9enHz58tGwYUOCg4PladIOt3v//j2dO3dGX18fS0tLFi1apHQIXkxMDD179sTAwIBChQqxevVq+Wt2dilfurJlyyKRSKhZs6bKOv263ODgYJo0aYKuri52dnZs3bpV6TDC0NBQWrZsiZ6eHkWKFOHAgQPy+qpVqxYAxsbGSCQSunfvnmk9JScnM2fOHBwdHdHW1qZQoULMmDFDIc2TJ09UvgdhYWF07NgRa2tr9PT0KFmyJNu2bUu3r4MHD2b06NGYmJhgYWHBlClTFNI8ePCAqlWroqOjg7OzMydOnEg3lPHVq1e0b98eY2NjpFIpzZs3JygoKNN9BNiyZQv9+/enTJkyODk5sWZNypXWk1+dbCxcuJBevXrRu3dvihcvzuLFi7GxsWHFihXyNB4eHkyaNIm6detmuD09PT0sLCzki6GhYZbibNiwIRs2bKB+/frY29vzyy+/MHLkSPbsSe1VOHbsGPfu3WPz5s2ULVuWunXrsmDBAtasWUN0dMrJwLhx45g+fTqVK1fGwcGBwYMH07BhQ/bu3Ztum7/++iudOnXC3d09SzFml8+hM7SuWYk2tdxwKGiOZ9eWWEqN2HHigtL0nl1b0qtZbUo6FKKwpRlDOzShsIUpp28oXrGUSMDMKL/Ckh0ymYztW7fSo2cvatWug4OjI5OnTic2NpajRw6rzLd961ZcK1Wie89e2NrZ0b1nLyq6urJ9m+IV3JiYGCZNGMe4CRPJn195bG/fvmXe3NlM+30mGhrpb13duHUbLZs3o3WLX7C3s2XMiGFYmBdg527lvUy79uzF0sKcMSOGYW9nS+sWv9Dyl6b4bFbsgUhKSsJz4hT69+2NtZWV0rJiYmLwnDSFKePGkj+Tiz4ymYxd27bStUcvatSug72jI+OnTCcuNpZjR1XX5c5tW6ngWgmPHr0obGuHR49elK/oys6v6nKLzwYKmFswbvJUnF1KYGllRQXXShS0tpGnuXP7Fo2aNKVc+QpYWlnRvFVrHIoU5cE95Vfds+vuET8OTFzAzb1Hc6W8jGzcsoWWLZrTumUL7O3tGDNqBBbm5ul6mb7YtdsXSwsLxowagb29Ha1btqBl81/w2bhZnqaEiwsjhg2hUYMGaGlqKS3n1q3b1KpRg+rVqlLQyor6devi7laJe7lUh1/7L+szrU1+N2hZyYVWbiWwNzdhdMuaWBjlY+eFW0rTF7cuQKNyTjhaSiloYkjTCsWpXKwwN54o9mIlJSfjufkIvzV0w1qa/WNRXn5/IKXXatG82UyarvxYpCruI3t30LxjdypWq4WNnQO/jppEfFwsF08dU5mvv+c06v3ShsKORbEqZEvvYZ4ky5K5G3BNnmbMrMVUb9AUa1t7CjsUoe/ICYS9DSHo7wcqy1Vl4869tGpcnzZNG+JgW4ixg37FwsyM7fv/Upq+oKU5noP70bxhHfLl01eaZs7E0XRo2RSnIg7YF7Zh6qjBJCcnc/l6YLbjE35M2e6R8vHxwdTUlCtXrjBo0CB+++032rZtS+XKlblx4wYNGjTAw8ODmJgYgoODqVGjBmXKlOHatWscOXKEN2/e0K5dO3l5Hz9+ZPjw4Vy9epWTJ0+ipqZGy5Yt0w0jGD9+PCNHjuTmzZsULVqUjh07kpiYmKWYY2JimD9/Pps2beLs2bM8f/6ckSNHqkw/fPhwLly4wIEDBzh+/Djnzp3jxo30XfgLFiygQoUKBAQE0L9/f3777TcePEj5cn8ZLnbixAmCg4MVToAz0rVrV16/fo2fnx++vr6sXr2at2/TXy2ZOnUq7dq149atWzRu3JjOnTsTHh6OjY0Nvr6+ADx8+JDg4GC8vLwy3a6npydz5sxh4sSJ3Lt3j61bt2Jubq6QJqP3IDY2lvLly3Pw4EHu3LlD37598fDwwN/fX6EMHx8f9PX18ff3Z+7cuUybNo3jx48DKY25Fi1aoKenh7+/P6tXr2b8+PEK+WNiYqhVqxb58uXj7NmznD9/Xt44TjvsLStiYmJISEjAxMQEgPj4eK5fv079+opDTOrXr8/FixezXf6WLVswNTXFxcWFkSNH8v79+2yX8UVUVJQ8ToBLly5RokQJrL46AW7QoAFxcXFcv349y+UAbNiwgcePHzN58uRvji8j8YmJ3Hv6ksqliiqsr1yyGDcfBWWpjOTkZD7GxmGor6ewPiY2nrqDp1N74FT6z1ubrscqM69fvSIsLJRKbqkNSC0tLcqWL8/tQNU/hrdv3VLIA+Dm7p4uz7zZs6hStRquldxU7teUiRPo4tENeweHdK8nJCRw/8FDKldyVVjvXqkSN2+lvxoOEHj7Du6VKimsq+xWiXv37pPw1XFz5dr1GBsb0ar5Lyr3c8bc+VSrUhm3NNtX5ktduqapyzLlynPnluq6vHP7lkIegEru7gp5Lpw7g1NxZyaMHUXT+rXp0bkDB/YqHldLlSnD+bNnePf2LTKZjBvXrvLi+TNc3StnGvv3JCEhgfv3H1DZTfEz4+7uxs1A5Sf6gbdu4+6umL6yuzv37t8jISFrv5UAZcuWwf/KVYKepQyfevjoEQE3A6latUo29+L7lZCYxP2Xb3EvVlhhvXuxwgQGBavIpej+y7cEBgVTwdFaYf2qo/4Y59OllVuJbMeV19+f5ORkpk+eQMcuyo9FqrwLeU1UeBglK6QeczS1tHAqVZa/7yk/RikTFxdLUmIS+QxUN0BjPn4AQD+DNMokJCRw79E/VK5YTmF95YplCbxzP1tlZSQ2Lo7ExCQM8+fLtTL/bUnJubP8rLI9a1/p0qWZMGECkHLyPXv2bExNTenTpw8AkyZNYsWKFdy6dYtDhw5Rrlw5Zs6cKc+/fv16bGxsePToEUWLFqV169YK5a9bt44CBQpw7949SpRIPRCNHDmSJk2aACmNCBcXF/755x+cnJwyjTkhIYGVK1fi8PnAMHDgQKZNm6Y07fv37/Hx8WHr1q3yIU4bNmxQOFn9onHjxvTv3x+AMWPGsGjRIvz8/HBycpIPz5FKpVhYWGQaI6T0xpw4cYKrV69SoUIFANauXUsRJcOBunfvTseOHQGYOXMmS5cu5cqVKzRs2FB+klygQIEsDVl8//49Xl5eLFu2jG7dugHg4OBA1apVFdJl9B4ULFhQoXE6aNAgjhw5wq5du6j01QldqVKl5CfrRYoUYdmyZZw8eZJ69epx7NgxHj9+jJ+fn7zOZsyYQb169eT5t2/fjpqaGmvXrkUikQAp74+RkRF+fn7pGkCZGTt2LAULFpT3LIWGhpKUlJSuEWlubk6IimFUqnTu3Bk7OzssLCy4c+cOnp6eBAYGyhuO2fH48WOWLl3KggUL5OtCQkLSxWlsbIyWlpbKWHfv3s3Vq1cVhhj+/fffjB07lnPnzmX5CmR2Rb7/SFJyMlJDxR4NqaEBoVFZa1x6/+XHp7h4GrqVka+ztyrAjH4dKGJjycdPcWw6cpYuU5ayZ9ZICltmbVhKWFhKD7mJVLFxaWIiJSRY9UlVWFgoJiaKw6NMTKSEhYXJ/z929AgPHzxgw6bNabPLbfTegLq6Ou0/f5/TioiMJCkpCWmaxq9UakxoWLiK2MKQSo0V05uYkJiURGRkJGampgQEBrL3wJ/s2rJRZWyHjx3n/oOHbPNZrzLN18K/1GWaWI1NpLz5H3tnHVVV1gXwH9Il8QDpVlFADESxY+zWsXuMsXXszrG7W8DEdpyxC8fC7u4ClAZFkHjfH08ePN6j1Bmdz/Nbi7V45+5zzr7n5j57n33Dsh/LqMgITLKMpYmphKhMYxny+jV7dm6ndbsOdOrajTu3b7Fg7iw0tTSp16ARAIOGjmDm1Mk0a1AHdXUNChRQY8TY8XiVLJUn/b8X5Mc8yzkpMTUlIlJ1yFBkZKSKc8SUlJRPx9zcLE99/9KlM+/evaNJ859RVy9Aamoa/fv2oX7dup+3M98h0e8/kJomRWKoOCkjMdQjIi4hx7q1Jq4h+t0HUtPS6FW3vILBdPVJCLvP32bb0Pafpde3vn42BcjuRS3bqL4XZUdMlKwfI2NFvY1MTIl4k/fn5tY1yzAxM8e9dFmV26VSKZtWLKSIhxd2Tnk39ACiY+NITU1DYmqsUC4xMSEiSnV4+ecwf6UfFuYSfMv8d+45P3JY3tcg329NJUqUkP+vrq6ORCLB09NTXpb+Yvf27VsuX77MiRMnMDBQtswfP35MkSJFePz4MePGjSM4OJiIiAi5J+rFixcKhlTmfq2srOR95MWQ0tPTkxtR6fVVeXlAFr6WnJyMj0/G7KuRkRFFixZVks2sk5qaGpaWltm2mxfu37+PhoYGpUtnzJi4urpiYmKiJJu5b319fQwNDT+777t375KUlJTr2picjkFqaiozZsxg69atvH79mqSkJJKSktDX18+2jfR20vW+f/8+dnZ2CoZn5uMAcPnyZR49eqQU152YmKgQEpoXZs2axZYtWwgKCkJHR0dhW7qRlo5UKlUqy430yQUADw8PChcujLe3N1euXFE4xrkREhJC3bp1admyJd27d89Rz5x0DQoKokuXLqxevRp3d3dAFtrVrl07Jk2aRJEiRZTq5ET6Mc6MtrY26jnUUSPLuCJVKlPFvrNXWLbrMIsH/6JgjHkVdsSrsKP8d6kijvw8Zh6bDp9idOfmKtv66/RlJnUfi/TTw2PewkUqdSMvx1ypSkadN2FhzJszm0VLl6Gtra2y+t27d9gauIX1mzbn2pfyOSkLa8xeNeWxTi9///49o8ZPYsLoUZhkM9kSFvaGmXPns3Lxwmz133fgEFNmzJKP5az5i9KVVRSUSlEarKz6Zt2cZfzT0tJwK1acX/v2B6BIUTeePXnMnp3b5S+C2wO3cPvmTWbMXYCllRXXr15h7szpSCRm1K9VI8f+v0eUjmEu56Sq+5asPO99Hjx8mL/2H2DGtN9xcXbh/v37zJo7D3Nzc5o0apj3hv4DKJ2mKsqy4te/JR+SkrnxPJSFf53B3syIeqXdeJ/4kdGbDjKhdU1MDHTz1P++y/f4fUyp7+L6uXf3DtsDt7BuY+73ojPHDrJuwUz576G/z1WphDS3m1Qm/tq6gXNBRxgzZylaWqrvNwGL5/Dy6SPGzV+lcnteUPkMyuezPTvWbd7O/mMn8Vs4E21t1aGzgvwTHR3NgAED5EtYGjduzOLFi7N1FCQnJzN27Fj279/PkydPMDIy4qeffmLGjBkKTpFq1apx8uRJhbqtW7cmMDAwX/rl25DS1FRcxK2mpqZQln5CpqWlkZaWRqNGjZg5cyZZSX8Rb9SoEXZ2dqxevRpra2vS0tLw8PBQCtPKro/P1VmajQWe8eBR/UDKrd0vyWyUm07/VN+6unm76ed0DObOncv8+fNZsGABnp6e6OvrM2jQoByPY1a982KspKWlUaZMGZWZ51Qt0s+OOXPmMG3aNI4ePapg3JmZmaGurq7k0Xn79q2S9ye/lC5dGk1NTR4+fJhnQyokJITq1avj6+ursAYPwNLSUil0Mjo6muTkZCVdT548SaNGjZg3bx6dOnWSl8fHx3Pp0iWuXr1Kv379ANkYS6VSNDQ0OHz4MDVqqH4BnT59OpMmTVIomzBhAmMbKc8mGhvqo16gABGxigu5o2LfITHKOQTiwLmrjF+1lXkDO+PrmbOxV6BAATyc7Xgelv1C3+pl3CnVtDNxCbLFysmfMvxFRkYqLK6Oio5SmhnOjERipjDjCxCdqc69u3eJjoqiS4eM2enU1FSuXrnCjm1bOXXuPNeuXiU6KoomDeoryCyaP4/tgVs4sGcHJsbGqKurE5Glr6ioaCUPRIZuEiVvVVRUNBrq6hgZG/H48RNCQkIZMGSYfHv6dViqfCX27gjk4aPHREVF06ZTVwXdLl+9RuD2nVw6c5JqVSpRpnwFot7JxvLjp7GMiozEzCxjLKOjo5Q8fpkxzWYsTTLtn8TMDEdnZwUZB0cngo7L1jcmJSayatlips2eR4VKlQFwLVyEhw/us2Xjhv+UIZXtMY+ORmKqOkmE7JgrnyMaGuoYGRnnue95CxbRrUtn6tWpA0CRwq6EhoWy1s/v/8aQMtHXRb2AmpL3KSo+QclLlRVbiWyda2FrMyLjE1h+8Dz1SrvxMjKGkKg4BqzJyNKXPtNfeshC/hjVGTszY4W2qrk7491p8Hdx/dy4epXo6ChaNFK8Fy1ZOI9tgZs4GRQkLy/tWxkXN3f575Rkmd6x0ZGYSDI8n3Ex0RiZZK93Ovu2b2LvlgBGzlyMvbPqhDwBS+ZwJfgUY+euQGJukWubWTExKoi6egEl71NUdAwSE+N8t5cVv8CdrN60jdVzp2abmOJ7JfU790i1a9eOV69ecfDgQQD50hFVSbNAtmTjypUrjBs3Di8vL6Kjoxk0aBCNGzfm0qVLCrI9evRQiFDL6/twZv7RD/KWLl2anTt34ujoqDJkKDIykrt377Jy5UoqV5Y9+E6fPv1PqpQrLi4uaGpqcuHCBezsZIsw4+LiePjwIVWrVs1zO1pastmI1NTUPNdxc3MjJSWFq1evUqZMGQAePXqU7zTm+e27cOHC6OrqcuzYMSWPR145deoUTZo0oUOHDoDspezhw4cUK1Ysz224ubnx4sUL3rx5IzcELl68qCBTunRptm7dioWFRbYL93Nj9uzZ/P777xw6dEgeQpmOlpYWZcqU4ciRIzRr1kxefuTIEZo0afJZ/aVz+/ZtkpOT5ZMIufH69WuqV69OmTJl8PPzo0ABxSWNvr6+TJ06ldDQUHmbhw8fRltbW37+gMwT1bBhQ2bOnEnPnj0V2ihYsCA3s2SZW7ZsGcePH2fHjh3ypCmqGDVqFIMHD1Yo09bWhltHlWS1NDQo7mTL2ZsP+KlshuF69tYDapRxV5JPZ9/ZK4xbGcjsfh2pWqp4tnLpSKVS7j0PoYhd9mOsr6uDkYMDMe8S5HUkEjMunA+m6CcPd3JyMlcvX6bvgIHZtuNZogTnzwfTtn0Hedn54HN4enkB4O3jw+at2xXqTJk0AQdHJzp17oK6ujr16zfAx0dxLdPAfn2oV78Bbdu0BmSTD8XcinLu/EVqVq8mlwu+cIHqVSqr1M3L04OTpxTvpWfPX6B48WJoamjg5OjAzi2K4YZLVsgSB8kSWRTC1MRESWb85Kk4OTrQtVMH1NXV0dfXx9TKFL04xbG8eD6YIkUzxvLalcv06p/9WHp4luDi+WBat8sYywvB5/Ao4SX/7elVkhfPFdMev3zxAktL2bFOSUkhJSVFaTKmQAF1pNL/VgC/pqYmxYq5ce78eWrWqC4vDw4+T/Vqqp9DXiU8Ofm34mcazgYHU7xYcTQ18/6oT0xMRC3LvaZAAXWkad/3y1Z+0NRQp5itBcEPXlCzREYmyuAHL6jm4ZxDTUWkIF9z6GRhyo7hHRS2L91/lvdJyQxvVhVLY+VELfo6WkgcHL6L66dO/QZ4Z7kXDR7Qhzr1GtCgkeKzT1dPH129jGgTqVSKkamEW5cv4Ogqi95JSU7m3o2rtO7eN1u9Af7atpE/NvkxYvpCnIsqvy9IpVLWL5nLpTMnGTNnKRZWqpPi5IampibFi7hy7tJVfqqSsWby3KWrVK+kev1qXlm3ZQerNgSycvbveLjlL7Lje+B7Du27e/cuBw8eJDg4WL5MZPXq1fj6+nL//n2V0WJGRkZKSygWL16Mj48PL168wN7eXl6enhDsS/hHDam+ffuyevVq2rZty7BhwzAzM+PRo0cEBgayevVqeda1VatWYWVlxYsXLxg5cuQ/qVKuGBoa0rlzZ4YNG4apqSkWFhZMmDCBAgUK5Mv9a2Fhga6uLgcPHsTW1hYdHZ1cM7a5ubnx008/0bNnT5YvX46mpiZDhgxBV1c3X307ODigpqbGX3/9Rf369dHV1VUZXpmOjo4OI0aMYPjw4WhpaVGxYkXCw8O5ffs23bp1y1Ofrq6u7Ny5k7Nnz2JiYsK8efMICwvLlyFVq1YtXFxc6Ny5M7NmzSI+Pl6ebCJ9/9u3b8/s2bNp0qQJkydPxtbWlhcvXrBr1y6GDRuGra1tTl0wa9Ysxo0bJ8+GmO55MjAwkI/R4MGD6dixI97e3nJP0IsXL+jVq5e8naioKF68eEFISAggC0sE5Nn5Hj9+zKZNm6hfvz5mZmbcuXOHIUOGUKpUKSpWzH3RdkhICNWqVcPe3p45c+YQHh4u35Z+0deuXZvixYvTsWNHZs+eTVRUFEOHDqVHjx5yIzMoKIgGDRowcOBAWrRoId9fLS0tTE1NZR4cD8VF0RYWFujo6CiVZ0VbW1tlyFd2y9o716/KyGWb8XC2w6uwI9uPnyM0IprWNWUPtfmBf/E2Ko7pfdoBMiNq9PLNjOzUjBKFHQiPkXmzdLQ0MdSTzRot23mIEq4OOFia8+5DIpsOneL+89eM7aI6rE8VampqtGnXDv91a7Gzs8fO3h7/dWvR0dGhTt16crmJ48dibm5B3/4DAGjdti29enRnvb8fVapW4++TQVw4f4FVa2XrifT19XFxVUwXrquri5GRkbzcyNgYoyzhCRoaGpiameHs7ExSnMyr1KldW0ZPmIR7cTe8PD3ZsXsPoWFvaNlCZuwvXLKMN+HhTJskW3/Ysnkztmzbwez5C2nRtAnXb95k9x9/MnOqbOZNW1ubwq6KawwMP53/6eWamppKMrq6OhgZFVQqzzyWLdu2Y4PfWmzt7LGzs2e9/1q0dXSoXSdjLKdMkI1lr36ysWzZpi39fu3OxgA/KletxqmTQVy6cIFlazLWZrVu24Fe3bqw3m8tNX6qxZ3bt9m7eyfDR4+TjbeBASVLl2HZogVo6+hgaWnFtSuXObj/L/oPUjT4PxdtfT3MXR3lv82c7LD1Ks77qBiiX4Z8lT7S6dS+PaPHjce9WDG8SpRgx65dhIaF0fLTuuKFi5fw5u1bpk2RHdOWP7dgy9ZtzJ47jxbNmnH9xg127/mDmdMzsq8mJyfz+MkT+f9v34Zz7/599HT1sLeXTRxWrVKZ1WvXYWVpiYuLM/fu3WfDxk00zSEhyefyb45nVjpWK82YTYcoblcIL0crdp69SWh0PC0ryCZ6Fv51mrex75naXuaZCzx9HUtjQ5wKyULtrz4JYf2Jy7StXFK2L5oaFLZSXIdmqCu7P2Ytz45vef1kdy+SSMywd3TMVe+6zVqzd0sAhWzssLSxY++WALS0dahQI2Pd8oqZkzAxM6d1N9na8r+2bmBHwCr6jJqEmaWVfK2Vjq4uOroyz6D/4tmcO36Y3ybNQkdPXy6jp6+PlrYO+aFTq2aMmjoX96KF8XJ3Y8dfBwl9G07rxjIv3PxVfrwNj2T6mIz13vceypYMJHz4QHRMLPcePkZTUxMXR9nL+LrN21m8bgOzxg3HxtJCHgmgp6uLnl7+vRvfgu85UcS5c+cwMjJSWGtfvnx5jIyMOHv2rEpDShWxsbGoqakphQNu2rSJjRs3UqhQIerVq8eECRPy/Umif9SQsra25syZM4wYMUKeTczBwYG6devKDZPAwEAGDBiAh4cHRYsWZdGiRdmmCv+3mDdvHr169aJhw4YULFiQ4cOH8/LlS6V1NDmhoaHBokWLmDx5MuPHj6dy5coEZXKNZ8f69evp1q0bVapUwdLSkunTp3P79u189W1jY8OkSZMYOXIkXbt2pVOnTrl+cHjcuHFoaGgwfvx4QkJCsLKyUjAccmPcuHE8ffqUOnXqoKenR8+ePWnatCmxsbF5bkNdXZ09e/bQvXt3ypYti7OzM7Nnz6ZRo0by/dfT0+Pvv/9mxIgRNG/enPj4eGxsbKhZs2aePFTLli3j48eP/PzzzwrlEyZMkKdib926NZGRkUyePJnQ0FA8PDzYv38/Dg4ZGZ727t1L164ZIU/pH9pNb0dLS4tjx46xcOFC3r17h52dHQ0aNGDChAmoq+e0ikjG4cOHefToEY8ePVIyDtNDPdXV1dm3bx99+vShYsWK6Orq0q5dO+bMmSOX9ff3JyEhgenTpzN9+nR5edWqVfN0Pn5N6vmWIuZdAst3HSY8Jo7CtlasGN4Da3NZ6Ed4TDyhkRlhF9uPnSMlNY3f/Xbyu99OeXmTKmWZ1ku2GDou4QMT124nIiYOQz1d3BxsCBjXjxKuitm4cqNj5y4kJSUxa8Z04uPjcPfwYNHS5Qpr/N6EhVFALWOmvoRXSaZMm87KZctYuXwZtrZ2TJ0xA49Ma0a/FnVr/0RMbCwr16wjPCISVxdnli6Yi/UnT2R4RCRhYW/k8rY21ixbMJdZ8xcSuH0n5uZmjBz6G7UyeTf+Kdp3ko3lvJmysSzu7sH8xcvRy2EsPb1KMnHqdFYvX8aaFcuwsbVj8rQZuHtkjGUxd3emzZ7LyqWL8V+zCitrGwYMHkbtehmhSJOmzmDl0sVMHjeauLg4LC2t6Nm7L01btPwq++bgXYLBQRnx8y3ny15Cz/nvIKBr9plgP4e6dWrLjvnqNYRHRODq4sLSRQuxtk4/5hEKIci2NjYsW7yQWXPnEbhtO+bm5owcPpRamda+vg0Pp1XbjFDTgA0bCNiwAe8ypVm3WhY6PGr4MJYsW8HU6TOIio7G3NyMn1s0p1fPjDWfX4t/czyzUrdUUWLfJ7LqUDDhcQm4WklY2rMJ1qay50hE3HvCMn1TKi1NyqJ9Z3gdFYtGgQLYSowY2LAiP/uWyK6Lz+JbXj9fQsPWHfn4MQn/xbNJiI/Hxc2dETMWKniuIt6GKUwKH/1zJynJySyaPFqhrWYdu9Gik+x8O/anLLPg1KF9FGR6Dh1LlTr5CzWtV6MqsbHxrFi/mfDIKAo7ObJ85iSsLWURMBGR0YS+DVeo83P3/vL/79x/xL6jQVhbWnB4qz8AgX/sIzk5hd/GT1Oo17tLO/p2VfRQ/r+T3brp7NbY5oWwsDAsLJRDOS0sLPKcACwxMZGRI0fSrl07hffEr5UQTE2a3cIcgZz3799jY2PD3Llz8+yh+Vq8evUKOzs7jh49+o99KPV75syZM1SqVIlHjx4pJAwRfL+kXFb9TY7vCY0yDeShfd8rxgZ6co/U94x2QVPCc8l09j1gXlCPXmqO31qNHFkhfUbS+8//RMK/hba+4Xc/liAbz8T9y3MX/Ibo1O/9n7l+Lr74etnt/inK2puQHJa/xFP/NpqW39e7zMnHX+fjwSc2LFG5bjrr90IBJk6cqCSblYsXL3L48GECAgLkUT/pFC5cmG7duuUaxZacnEzLli158eIFQUFBOU64X758GW9vby5fvpyvhGD/qEfqv8rVq1e5d+8ePj4+xMbGyheifekambxw/Phx3r17h6enJ6GhoQwfPhxHR0eqVKnyj/f9PbB7924MDAwoXLgwjx49YuDAgVSsWFEYUQKBQCAQCARfma+VbCLbddMq6NevnzySJzscHR25ceMGb968UdoWHh6eawKw5ORkWrVqxdOnTzl+/HiuUUufkxAM/g8MqXr16nHq1CmV20aPHs3o0aNVbsuNOXPmcP/+fXnygVOnTmFmlrc45y8hOTmZ0aNH8+TJEwwNDalQoQKbNm1SynaXH168eEHx4tkv1L9z547C4rtvSXx8vDyU0szMjJ9++knh20m5kdNasAMHDsiTmnxrpk2bpvB9tcxUrlyZAwey/4q9QCAQCAQCwfdEfsL4zMzM8vRO7evrS2xsLBcuXJB/Duf8+fPExsZSoUL2H1pPN6IePnzIiRMnkEhUZzvNTH4TgqXznw/te/36NR8+fFC5zdTUNMf0xT8KKSkpPHv2LNvt2WVV/C/y6NGjbLfZ2Nh8VmrLf4KoqCiiolSHbenq6mJjY/Mva/T1EKF9XwcR2vd1EaF9Xw8R2vf1EKF9XxcR2pd/jj4Mz10oD/xUOO+foMkP9erVIyQkhJUrVwKy9OcODg4K6c/d3NyYPn06zZo1IyUlhRYtWnDlyhX++usvBc+VqakpWlpa2SYE09XV5eLFi3lay57Of/7t+b/8wvlvoaGhgWuW7GH/r/xX9lMY+QKBQCAQCL41qd/5pw02bdrEgAEDqF1blgGycePGLFmyREHm/v378uRmr169kn+8t2TJkgpyJ06coFq1al+cECwz/3lDSiAQCAQCgUAgEPz/YWpqysaNG3OUyRxc5+joSG7BdnZ2dpw8efKr6CcMKYFAIBAIBAKB4Afke/4g738BYUgJBAKBQCAQCAQ/IKnCjvoihCElEAgEAoFAIBD8gAiP1JdRIHcRgUAgEAgEAoFAIBBkRnikBAKBQCAQCASCH5DvPWvf944wpAQCgUAgEAgEgh8QEdr3ZYjQPoFAIBAIBAKBQCDIJ8IjJRAIBAKBQCAQ/ICIrH1fhjCkBAKBQCAQCASCHxAR2vdliNA+gUAgEAgEAoFAIMgnalKpMEUFAoFAIBAIBIIfjc1XX32VdtqVsv0q7fzXEKF9AoHgq5IUF/WtVcgV7YKmfIwO+9Zq5IiWiSXhcQnfWo1cMS+oR8KHxG+tRq7o6eqQuH/5t1YjR3Tq9ybm3fd/zI0N9L77sQTZePZSc/zWauTICukzkt7Ffms1ckXbwIj4hA/fWo1cMdTTJSk+5lurkSPahsbfWgUFxBqpL0MYUgKBQCAQCAQCwQ+IWCP1ZYg1UgKBQCAQCAQCgUCQT4RHSiAQCAQCgUAg+AFJFR6pL0IYUgKBQCAQCAQCwQ9IWpowpL4EEdonEAgEAoFAIBAIBPlEeKQEAoFAIBAIBIIfEJG178sQhpRAIBAIBAKBQPADIrL2fRkitE8gEAgEAoFAIBAI8onwSAkEAoFAIBAIBD8gImvflyEMKYFAIBAIBAKB4AckVWTt+yKEISUQCAQCgUAgEPyACEPqyxBrpAQCgUAgEAgEAoEgnwhDKgeqVavGoEGDAHB0dGTBggX/aH9dunShadOmX609f39/jI2Nv0pb/8b+CwQCgUAgEAj+PVLTpF/l70dFGFJ55OLFi/Ts2VP+W01NjT179nzVPhYuXIi/v/9XbVOgSFBQEE2aNMHKygp9fX1KlizJpk2blOROnjxJmTJl0NHRwdnZmRUrVihsv337Ni1atMDR0RE1NTWVRubEiRNRU1NT+LO0tMyTnsnJyYwYMQJPT0/09fWxtramU6dOhISEKMglJSXRv39/zMzM0NfXp3Hjxrx69Uq+/dmzZ3Tr1g0nJyd0dXVxcXFhwoQJfPz4UWW/kZGR2NraoqamRkxMTJ50zYnA7Tup26Q53hWr0rpjFy5fvZaj/KXLV2jdsQveFatSr0kLtu3cla3sgcNHKFHWl4FDRyhte/P2LaPGTaTyT3XwqVSNlu06cefuPdU67thN3WatKVOlFq069+Dytes56njxyjVade5BmSq1qNu8Ddt2/aEkExcfz++z51O9QTPKVKlF49Yd+ftssHz71p17aN6+K+Vr1KN8jXq0796bU5m2q0IqlbJ21Qqa1KtFjUrl6fdrd548fpxjHYCg40fp0Ko51Sv40KFVc06eOK4kE/72LZPHjaH+T9WoWcmXLu1ac+/uHfn2tatW0O7nZvxU2Ze6NaowsM+v3L51U67XiuXLqVXrJ8qX86F7t248fvQoV72OHj1K8+bN8CnrTfPmzTh+/JiSzLatW2lQvx7lfMrSrm0brly5orA9ISGBGdOnUad2LcqX86F5s6Zs27ZNQaZ7t26UKulF0aJF8fptAV6/LWD4+v1KfW09fZ16U9ZRdthi2szdzJXHr7PV/cqT13ReuJUqY1bgM3wxTaYHsCHoSrbyB67cx+u3BQxauze3YVFAKpWyeuUKGtSpRZUK5endM2/H/Pixo7T+uTmVyvvQ+ufmBB1XPubp+K9bS7kypZg3Z7bStqdPnzD0t4HUqFKZ6pUr8kvnToSFhuba//c4ll+Ca2Uf+uxdw4zX51khfYZXk9r/WF+B23ZQt1ETvH0r0bp9Jy5fvZqj/KXLV2jdvhPevpWo17gp23bsVNi+Y9ceOnfrQcVqNalYrSY9evfl5q3b2ba3Zp0/Jcr4MHPOvBz7lUqlrFyxnLq1alGxfDl6du/G48e5X/fHjh6lZfPm+PqUpWXz5pzIcm7u2LaNNq1aUrVSRapWqkjXTp04c/q0gszxY8fo16c3NatXw7tUSe7fz+bevn0HdRs3xbtCZVp3yONYduiEd4XK1GvSjG07FJ8/R4+foE3HzlSsVhOfSlVp2a4Df+5TvJfUbdSUEt7llP6mzpyV69h8K4Qh9WUIQyqPmJubo6en94/2YWRk9NU8SALVnD17lhIlSrBz505u3LjBL7/8QqdOnfjzzz/lMk+fPqV+/fpUrlyZq1evMnr0aAYMGMDOnRkPqISEBJydnZkxY0aOxpG7uzuhoaHyv5s3b+ZJz4SEBK5cucK4ceO4cuUKu3bt4sGDBzRu3FhBbtCgQezevZvAwEBOnz7Nu3fvaNiwIampqQDcu3ePtLQ0Vq5cye3bt5k/fz4rVqxg9OjRKvvt1q0bJUqUyJOOuXHw8FFmzVtAj65d2LYxgNIlvegzcDChYWEq5V+9DqHPoCGULunFto0BdO/amRlz5nPk+Akl2ZDQUOYuXEzpUiWVtsXFxdG5+69oaGiwbOE8dm/bwpBB/TE0NFDW8chxZi5YQo8uHdkesJoyJUvQ+7cRhIa9Ua1jSCh9B4+gTMkSbA9YTY/OHZg+bxFHjp+UyyQnJ9NzwBBCQsOYN20yf27dwMRRwyhkbiaXKWRhzqC+vxLov4pA/1WUK1OaAcPH8OjJ02zHc9N6f7Zu3sjgYSNZ478RiUTCb/16kfD+fbZ1bt24zoTRI6lTrwH+m7dSp14Dxo8aITeC0serd/cuaGhoMGfhEjZu20m/QYMxNDSUy9jZO/DbsBEEbNnOstV+WFlbM7hfH6Kjo1i9ejUbN25g5MiRbNy0CYmZhF69e/E+B72uX7/OyBHDadCgIVu3badBg4aMGD6cmzdvyGUOHTrI7Nmz6Na9B1sCt1KqVGn69e1DaKYX+TmzZ3P27FmmTp3Grl27ad++A7NmzuDECcVzpnnzFpw+fZpjk3pwbFIPxrWsqbD94NX7zNpzkh61fNg6tD2lna3ps2oPodFxKvXX1dKkTWUv1vVrye6RnehRy4clB86y46zy9R0SFce8vaco7WyT7Xhkx4YAfzZv2sjQESPxW78RU4mE/n1yHtubN64zdtRI6tVvwMYtW6lXvwGjR47glop7z53bt9mzexeuhQsrbXv18iU9u/2Cg6MTy1etZuOWrfzSvQda2to56vy9juWXoK2vx6vrdwnsN/4f7efg4SPMmjuPHr90ZdvmDZQuVZI+/QcRGprdPfM1fQYMonSpkmzbvIHuXbswY/ZcjhzLME4uXb5MvTp1WLtyORv91mJlaUmvvv158/atUnu3bt9hx+7dFCnsmquuAf7+bN64keEjRxKwcRMSiRl9e/XO8dy8cf06o0eOoH6DBmzZuo36DRowcsRwhXPTolAh+vUfwPpNm1m/aTPePmUZ8tsgBSPtw4cPeHmVpH//Adn2JRvL+bKx3LReNpYDfsv5+TPwN9lYblovG8s5imNpVLAgPX7pyga/NewM3ESTRg0ZP/l3zpzLmATbvN6P4wf3y/9WLV0MQO2aNZX6FPx/IAypPJI5tM3R0RGAZs2aoaamJv+dExMnTqRkyZKsXLkSOzs79PT0aNmypcKsf+bQvvDwcCwtLZk2bZp8+/nz59HS0uLw4cMAfPz4keHDh2NjY4O+vj7lypUjKCjos/dx7969eHt7o6Ojg5mZGc2bN1fYnpCQwC+//IKhoSH29vasWrVKYfuIESMoUqQIenp6ODs7M27cOJKTk5XGYMOGDTg6OmJkZESbNm2Ij4+Xy8THx9O+fXv09fWxsrJi/vz5CiGWX7rfo0ePZsqUKVSoUAEXFxcGDBhA3bp12b17t1xmxYoV2Nvbs2DBAooVK0b37t355ZdfmDNnjlymbNmyzJ49mzZt2qCdw4uFhoYGlpaW8j9zc/M86WlkZMSRI0do1aoVRYsWpXz58ixevJjLly/z4sULAGJjY1m7di1z587lp59+olSpUmzcuJGbN29y9OhRAOrWrYufnx+1a9fG2dmZxo0bM3ToUHbtUvb0LF++nJiYGIYOHZonHXNj/eYtNGvSiBZNG+Ps5MiIIb9hWchCaZYvne27dmNlWYgRQ37D2cmRFk0b06xxQwI2blaQS01NZdS4ifTp2R1ba2uldtYFbKRQoUJMmTAWT3d3bKytKO9TFjtbW2Udt2yjeaP6tGjSUKbjb/2xtDBnqwovE8C2XX9gWciCEb/1l+nYpCHNGtXHf3OgXGb3n/uJjYtn4ayplPLyxNrKktIlS1A008tJtcoVqVKhPI72djja2zGgdw/09HS5ceuOqm6RSqVs37KZTl27UbVGTZxdXRkzcQpJiYkcPnRAZR2AbVs24+1Tjo5du+Hg6ETHrt0oU9aHbVsyvLCbAvywKGTJ6AmTKO7ugZW1Nd4+5bCxtZPL1K5bj7LlymNja4uziwv9Bw3h/ft3PHrwgPXr19Ote3dq1vwJV9fCTJnyO4kfEjlwQNnrk87mTRspV7683FvarVs3fHx8FLzDGzdsoGmzZjRv3hxnZ2eGDR+OpaUl27dneJxu3LhOw0aN8C5bFmsbG1r8/DNFihThzh3FGXcdHR3Mzc0xK6iPWUF9DHUVr9kNQVdoVs6d5uU9cC5kyvBm1bA0NmDbmRuoopitBfVKu+FqJcHG1IiG3sWoUNSBK08UPS+paWmM2niQ3nXLYyspmO14qEIqlRK4eTNdf+lG9Ro1cXF1ZcKkKSQmJnLoYPbHPHDzZnzKlaPLL91wdHKiyy/dKOvjQ+AWRc97QkIC48eOZvTYcRQsqKzb8mVLqFCxEv0HDqKomxs2trZUqlwZU1PTHPX+HsfyS7l9MIi94+Zybfehf7Sf9Rs306xJY1o0a4qzkxMjhg7GslAhJS9TOtt37sLK0pIRQwfj7OREi2ZNadakEQEbNsplZkydQptWP+NWtAhOTo5MGDuaNKmU8xcuKrSVkJDAqLHjmDh2jMrzITNSqZQtmzfRtVt3atSsiaurK5OmTCEx8QMHD2R/bm7ZvIly5crTtZvs3Oz66brfnOm6r1K1KpUqV8bBwQEHBwf69uuPnp4eN29kGFsNGjakx6+/4lO+XPZjuWmLbCybNpGN5ZA8juWQT2PZtAnNGjciYGOGbmW9y1CzejWcnZyws7WlQ9s2FHZ15eq1a3IZUxMTzMwk8r+Tp09jZ2uLd5nSOY7pt0R4pL4MYUh9Bhcvym5Afn5+hIaGyn/nxqNHj9i2bRt//vknBw8e5Nq1a/Tt21elrLm5OevWrWPixIlcunSJd+/e0aFDB/r06UPt2rKwgq5du3LmzBkCAwO5ceMGLVu2pG7dujx8+DDf+7Rv3z6aN29OgwYNuHr1KseOHcPb21tBZu7cuXh7e3P16lX69OlD7969uXcvw6VuaGiIv78/d+7cYeHChaxevZr58+crtPH48WP27NnDX3/9xV9//cXJkyeZMWOGfPvgwYM5c+YMe/fu5ciRI5w6dUopnOdr7jfIDJLMLwfnzp2Tj3E6derU4dKlSwqGYV54+PAh1tbWODk50aZNG548efJZOqbrqaamJvdaXr58meTkZAVdra2t8fDw4OzZszm2k/Vl6M6dO0yePJn169dToMCX3xaSk5O5e+8+Fcr5KJT7livHtRuqvXLXb97Ct5zig7FC+XLcuXOX5JQUedmKNeswMTGmeZPGWZsAIOjUKdyLuTFk5Giq1q5Pq/ad2LFb2TBKTk7mzv0HVChXVrHPcmW5dvOWah1v3VaSr1iuLHfu3pfreOLUGbw83Jk6ez5V6zWlWbsurPbfIPcSZiU1NZUDR47x4UMiXp7uKmVCXr8mMjICn/K+8jItLS1Kli7DrRvZhyLeunlDoQ5AOV9fhTpnTp3ErVhxxo4cRsPaNejavg17d2cfUpmcnMwfu3dhYGCAYUFDwsPD8fVV1KuMdxmu5xAieePGDXyz6OXrW4Hr16/L+7h7965CuwDly/vKZQBKlirFyaCTvH3zBqlUysWLF3j+/DkVKlRQqLf/wH7KlStHsxnrmfvH37xPzAhtTU5J5e6rt/gWdVDUp6gD15/lHsYGcPfVW64/C8XbVdFYX3noPCYGujQv75GndjKTfszLZTnmpcqU4eb17Mf25o0bCnUAyvv6KtWZPWM6FStVxqdceaU20tLSOHv6NPb29gzo24e6P9Xgl04dOXlC2Tucme91LP8LyO6Z96iQxTjwLV+OazdUG6HXb9zEt3zWe2Z52T0zOUVlncTERFJSUjDKYixNnTGLypUqUj7LPVsVr1+/JjIigvJZrvvSZby5cf1atvVu3LhBOV/F8628ry83sjmfU1NTOXTwIB8+fMhXpET2Y+mTw/PnJr7lFfe9gm95pedPOlKplOALF3n2/DllSpXKVo99+w/StHEj1NTU8qz/v40wpL4Mkf78M0j3KhgbG+d5zQvIbmABAQHYfpoZX7x4MQ0aNGDu3Lkq26lfvz49evSgffv2lC1bFh0dHbnR8fjxY7Zs2cKrV6+w/jQrP3ToUA4ePIifn5+CJysvTJ06lTZt2jBp0iR5mZeXl5I+ffr0AWTep/nz5xMUFISbmxsAY8eOlcs6OjoyZMgQtm7dyvDhw+XlaWlp+Pv7y8OGOnbsyLFjx5g6dSrx8fEEBASwefNman5yg/v5+cn375/Y7x07dnDx4kVWrlwpLwsLC6NQoUIKcoUKFSIlJYWIiAisrKzy1Ha5cuVYv349RYoU4c2bN/z+++9UqFCB27dvI5FI8qVnYmIiI0eOpF27dvLZwrCwMLS0tDAxMVHSNSyb8IXHjx+zePFi5s6dKy9LSkqibdu2zJ49G3t7+y8y9tKJjokhNTUVSRaDTSIxISIySmWdyMhIJBLFfZGYmpKSmkpMTAzmZmZcvX6d3Xv/ZPum9dn2/ep1CNt27qZjuzZ079qZW7fvMHPuPLS0NGncoH4mHWNV62hqQmS2OkYhMc1Ox1jMzSS8CgnlwuWrNKjzE8vmz+TFy1dMnb2AlNRUenfrIq/34NFjOvToy8ePH9HT1WXBzN9xcXJU2W9UZASAkgFsYirhTVj2L6hRkRGYmCqeayamEqIiI+W/Q16/Zs/O7bRu14FOXbtx5/YtFsydhaaWJvUaNJLLnTn1NxPHjCQxMRGJmRnzl6wg+WPyJ70U+5CYSggNVVzPl5mIiAila0AikRAZIdvP6OhoUlNTldvNJAMwYsRIJk+aRJ06tdHQ0EBNTY3xEyZQqlTG7G/9+vWxtrHB1saa29uWsWjfGR6ERLCyt8zjHv3+A6lpUiSGiqHbEkM9IuISst0HgFoT1xD97gOpaWn0qlte4SX/6pMQdp+/zbah7XNsIzsi04+5RPGYm5pKclynFBkZoTRupqYSIjMd88OHDnL/3j38MnkuMhMdFUVCQgLr/f3o1acv/QYM5NzZM4wYNoRlK1dRo2oV1fW+07H8LyC/ZypdF6ZEZDp2mZHdM7PeYyUZ98xM4cTpLFi8FAtzcwWD6cChw9y9d58tG/zzpGv6Nah8fzdVCL1VVU/ldR8ZoVD26OFDunbuxMePH9HV1WX23Hk4u7jkSTfI4fljKiEiQvVa1MjISCRK9zHF5w9A/Lt3/FSvIckfP1JAXZ0xI4YpGbPpHA86Sfy7dzRp1CDPugv+ewhD6l/E3t5ebkQB+Pr6kpaWxv3797M1yObMmYOHhwfbtm3j0qVL6OjoAHDlyhWkUilFihRRkE9KSsr3SzrAtWvX6NGjR44ymWeE0hMnvM0UZ71jxw4WLFjAo0ePePfuHSkpKUohAo6OjgprL6ysrORtPHnyhOTkZHx8Mm7wRkZGFC1aVP77a+53UFAQXbp0YfXq1bi7K3oCss4eST99+Ts/s0r16tWT/+/p6Ymvry8uLi4EBAQwePDgPLeTnJxMmzZtSEtLY9myZbnKS6VSlXqGhIRQt25dWrZsSffu3eXlo0aNolixYnTo0CHPOoFszJOSkhTKsoY5Ko8j5DSEamSRRyovf//+PaPGT2LC6FGY5LCWMC0tDfdibgzs2xuAYkWL8vjJU7bt3K1gSGXqVEnHnJTM/tz49DstDVMTYyaMHIq6ujrubkV5Gx6B/6ZABUPKycGeHevXEP/uHUdO/M3YydPwW74IFydH/jp4hCmz5snbnjV/EQqdKCqbra6qqpDl/EhLS8OtWHF+7dsfgCJF3Xj25DF7dm5XMKRKe5elW89erF6xjOioKHp27cjUWXOzHZNcr5U81Mmt3S2bN3Pz5g0WLFyIlZU1V65cZvq0aZiZmVO+vGzmu3mLFgDo6epgX7ooDubGtJ23hbsv31LMziLbccrlNADAr39LPiQlc+N5KAv/OoO9mRH1SrvxPvEjozcdZELrmpgY6ObcyCf2Xb7H72NKyY/5vIWyY571msh6/FSiVCWjzpuwMObNmc2ipcuyDUtOk6YBUKVqNdq2l90XihQtys0b19m1c0e2hpS8+288lv9llC9xqfI5kFk+6z0zy/0oM+sC1nPg0GHWrVouP/ZhYW+YOWceK5cuyvZ82Lf/IFOmz5C3vWDR4k99fMZ1r0LfrHUcHB3ZHLiV+Ph4jh87xsTx41m1Zk2+jKnP0U/V2Ms0ztigr6fH9s0bSEj4wPmLF5kzfyG2NjaU9S6j1N7uP/ZSsYIvFnkM6f9W/MjepK+BMKS+IekXdE4X9pMnTwgJCSEtLY3nz5/LjZm0tDTU1dW5fPky6urqCnUMDJQX1eeGrm7uDyhNTU2F32pqaqSlyR64wcHBco9WnTp1MDIyIjAwUMHzkVsb2Rkr6eXw9fb75MmTNGrUiHnz5tGpUyeFbZaWlkoenbdv36KhofFZRmo6+vr6eHp65isEMTk5mVatWvH06VOOHz+uYJhaWlry8eNHoqOjFbxSb9++VQptCgkJoXr16vj6+iqtbTt+/Dg3b95kx44dQMZ4m5mZMWbMGAUvZWamT5+utG3ChAmMGjwAE2Nj1NXVlWZSo6KilWYJ05FIJEreqqioaDTU1TEyNuLx4yeEhIQyYMgw+fb0c6dU+Urs3RGIna0t5mZmODs7KbTj5OjI0SxJK0yMjVBXV1fyPkVFRyt5nTJ0NFXWMfqTjkZGAJiZSdBQ11A4P50dHYiIjCI5OVl+DWhqamJvJ5tYcS/mxq0799i4dQcTRg6leuWKlKlQhah3HwD4+MnzExUZiZlZxkM5OjpKyWORGVOJmYL3Kb2OSaZjIDEzw9HZWUHGwdGJoCxZ9HR1dWncrAWVqlYDYFCfX7l6WRbWHBkZobD+Lyo6SskrkhkzMzMFzxJAVFQUpp+uLxMTk0/HJnuZxMREFi9exLx586lcRfZiX6RIEe7fv8+G9QFyQyorxWwt0FAvwPOIaIrZWWCir4t6ATUlj0lUfIKSZyUrthLZMS9sbUZkfALLD56nXmk3XkbGEBIVx4A1GZnl0j5dV6WHLOSPUZ2xMzNWaKuauzPenQYTlyA75unevsjISMyUxjb7Yy7J5pin17l39y7RUVF06ZDh3UlNTeXqlSvs2LaVU+fOY2xsgrq6Bk5ZzgtHJ2euX8s+89n3Mpb/ReT3zAgV98xsrnHZPTOrfNSn+5GxQrn/+o2sXefPquVLKJIpucidu3eJioqiTYfO8rLU1FQuX7lK4LbtXDp3mmpVK1OmXHnef0gE4GOyLDQ2Iuu5GRWd87lpZqb6ms5yr9DU1MTO3h6A4u7u3Ll9my1bNjNm7Lhs285Mts+f6Kj8jWV0xvMnnQIFCmBvJ1s/6la0CE+ePmOtf4CSIRUSGkrwhYvMnzWD7x1hSH0ZwpD6TDQ1NbNd85AdL168ICQkRB6Sdu7cOQoUKKDkXUnn48ePtG/fntatW+Pm5ka3bt24efMmhQoVolSpUqSmpvL27VsqV678xftTokQJjh07RteuXT+r/pkzZ3BwcGDMmDHysufPn+erDRcXFzQ1Nblw4QJ2n25UcXFxPHz4kKpVqwJ8lf0OCgqiYcOGzJw5UyGlfTq+vr4KWfwADh8+jLe3t5IhmB+SkpK4e/dunvVON6IePnzIiRMnlIy4MmXKoKmpKU9KARAaGsqtW7eYNSsj1err16+pXr06ZcqUwc/PT2kN1M6dO/nw4YP898WLF/nll184deoULjnMAI4aNUrJs6atrQ1J79HU1KSYW1HOnb9IzerV5NuDL1ygehXV++/l6cHJU4ppbs+ev0Dx4sXQ1NDAydGBnVsUQ5GWrFjF+/fvPyWykIVjlvTy5NnzFwpyz1+8wCqL11dTU5PiRYtw7sIlalbLmGE/d+ES1atUUq2jhzsnTyuuPzt7/iLFixVFU0N2Oy1VwoP9h46RlpYmH+vnL19hbibJ5fyRyg0mfX09TEws0fv0QiqVSpFIzLh4PpgiRWWhtMnJyVy7cple/Qdm26KHZwkung+mdbsMb+OF4HN4lMgI2/X0KsmLLNfqyxcvsLRUDmHV09dHT18fAHUNTfQNDDE3Nyf4XDBubsXkel2+dJmBg7LXq0SJEgQHB9OhY0d52bngc/JwYk1NTYoVK0bwuWBq1MjIdhV8Pphq1aoBkJKSQkpKCmpZzmf1AgXkBrYqHoVFkpKahnlB2X5oaqhTzNaC4AcvqFkiIyFI8IMXVPNwzq4ZJaQgX0vhZGHKjuGKHt6l+8/yPimZ4c2qYmlsqFRfX0cLiYMDMe8Uj/mF88EUdcs45lcvX6bvgOzH1rNECc6fD5Z7kgDOB5/D89PYevv4sHnrdoU6UyZNwMHRiU6du6Curo66ujrF3Ysr3cNfPH+u8rxI53sZy/8isnumG+fOX6Bmjery8uDzF6iejQfQq4QnJ//Ocs8MPi+7Z2pmvN75rd/A6jXrWL50Ee7FiyvIl/Mpy86tWxTKxk+ajJOjI107d0JdXR19fX1MCxkR/8nIl0qlSMzMOB98Th7an5yczJXLl+g/cFC2+1iiRAnOBwfTvkPGdX/+XDAlsiwjyIoUKcnZfLJDFQpjWb2avDzHsfT05OSpUwpl8rHUyOFVWZpx387Mnr1/YWpiQuVKFfOs97dCGFJfhjCkPhNHR0eOHTtGxYoV0dbWVlqnogodHR06d+7MnDlziIuLY8CAAbRq1SrbsL4xY8YQGxvLokWLMDAw4MCBA3Tr1o2//vqLIkWK0L59ezp16sTcuXMpVaoUERERHD9+HE9PT+rXVxHClAMTJkygZs2auLi40KZNG1JSUjhw4IDC+qaccHV15cWLFwQGBlK2bFn27dunkAkvLxgaGtK5c2eGDRuGqakpFhYWTJgwgQIFCsi9VF+630FBQTRo0ICBAwfSokULuedJS0tLPpPWq1cvlixZwuDBg+nRowfnzp1j7dq1bNmS8bD5+PEjd+7ckf//+vVrrl27hoGBAa6usheIoUOH0qhRI+zt7Xn79i2///67LDV3587kRkpKCj///DNXrlzhr7/+IjU1Va6rqakpWlpaGBkZ0a1bN4YMGYJEIsHU1JShQ4fi6enJTz/9BMg8UdWqVcPe3p45c+YQHh4u7yP9vMtqLEV88hQUK1Ysx3T82traKkNBkpJk6W87tWvL6AmTcC/uhpenJzt27yE07A0tWzQDYOGSZbwJD2fapAkAtGzejC3bdjB7/kJaNG3C9Zs32f3Hn8ycOlneX2FXRV0NP3khM5d3bNuGTt16strPnzo/1eTm7Tvs2P0HE0aPVNK1U9tWjJo0FfdiRfHycGf7H38R+uYtrZrJElksWLaKt+HhTJsgmyBo1bwJgTt2M2vBEn5u0pDrt26z68/9zJqckRa5dfOmbN6+ixnzFtGuVQtevHzFav+NtG/VQi6zcPkqKvmWw9LCgvcJCRw8cpyLV66xfL7qb42oqanRsm07NvitxdbOHjs7e9b7r0VbR4fadTJCSKdMGIu5uQW9+snSArds05Z+v3ZnY4AflatW49TJIC5duMCyNesy9G3bgV7durDeby01fqrFndu32bt7J8NHy2Z/P3z4wPp1a6hYpSpmZmbExsaye8c2wt++ocZPtZEYGbJy5SrsHeyxt7dn7Zq16OjqUK9exrU4duwYLCwsGPDJAGjbrj3du/2Cn986qlWrTlDQCS6cP886Pz95nQ4dOzJ2zBiKuxenRAkvdu3cSVhoKD//3BKQeaDLlPFmwfx56GhrY2VtxeVLl/nrr78YPESWefLly5fs37+PSpUqY2VZiLt3njL3j79xszGnpFPG2suO1UozZtMhitsVwsvRip1nbxIaHU/LCrIIgIV/neZt7Humtq8DQODp61gaG+JUSHbfv/okhPUnLtO2ckkAtDU1KGyluD4lPVNg1vLsUFNTo027dvivW4udnT129vb4r1uLjo4OdepmHPOJ42XHvO+nVNCt27alV4/urPf3o0rVavx9MogL5y+waq3smOvr6+PiqpjeWldXFyMjI4XyDh07M2bUCEqVKk2Zst4Enz3L6VN/s2zl6hz1/h7H8kvR1tfD3NVR/tvMyQ5br+K8j4oh+mX2awHzS6cO7Rg9bgLuxYvhVcKTHbt2ExoWRsufZev5Fi5eypvwt0ybLIsEaNmiOVu2bmf2vPm0aNaU6zdusvuPvcyc9ru8zXUB61m6fCUzpk7BxspKfn/X09NDT08PfX19pftq+vmQtTwdNTU12rZrj9/atdjbO2Bnb4/f2jXo6OhSN1NI+/ixY7GwsKDfANm52aZtO3p274a/nx/VqlUjKCiI8xfOs3ZdxnW/dPEiKlSsRCHLQiS8T+DQoYNcvnSJRUuXymViY2MJCwsl/K3sefb8mczgl0jMMLSXTcJ2at+W0eMn4l7M7dNYpj9/Po3lkqW8eRvOtMkTM8Zy23Zmz1tAi2ZNMsZy6hR5v2v8/HEvVgw7W1uSU5I5dfosf+7bz5hRit8yTEtL448//6JxwwZo5GSECf4vEEf4M5k7dy6DBw9m9erV2NjY8OzZs1zruLq60rx5c+rXr09UVBT169fPds1LUFAQCxYs4MSJE/Jwrg0bNlCiRAmWL19O79698fPz4/fff2fIkCG8fv0aiUSCr69vvo0ogGrVqrF9+3amTJnCjBkzKFiwIFWq5BwHn5kmTZrw22+/0a9fP5KSkmjQoAHjxo1j4sSJ+dJj3rx59OrVi4YNG1KwYEGGDx/Oy5cv5WvDgC/ab39/fxISEpg+fTrTp0+Xl1etWlWeQt3JyYn9+/fz22+/sXTpUqytrVm0aBEtWmS8CIeEhFAqU6aeOXPmMGfOHIV2Xr16Rdu2bYmIkIU9lS9fnuDgYBwcFDNaqeLVq1fs3SsLZSlZsqTCthMnTshn5efPn4+GhgatWrXiw4cP1KxZE39/f3lY2eHDh3n06BGPHj1SWJ8HiiGT/wR1a/9ETGwsK9esIzwiElcXZ5YumIv1p2Qd4RGRhGX6XpOtjTXLFsxl1vyFBG7fibm5GSOH/katTLOzecHDvTjzZ89g4dLlrFzjh421FcMHD6JBvTrKOtaqQUxsLCvWric8MhJXZyeWzZuJtZWlXMfQsIx1gLbWViydN5PZC5YQuHMPFmYSRg0eQK0aVeUyloUsWLlwDrMXLKVFh1+wMDejQ+sW/NKxnVwmMiqa0ROnER4ZiaGBPoVdXFg+f5ZSRsDMtO/UhaSkJObNnE58fBzF3T2Yv3i53EMEsvUvBdQyPDSeXiWZOHU6q5cvY82KZdjY2jF52gzcPTzlMsXc3Zk2ey4rly7Gf80qrKxtGDB4GLU/GUIFChTg+bNnHNj3J7ExMRQ0MqJYcXeWrlqHs4sLPiU9ePfuPdOnTSMuLg4PT0+WL1+Ofia9wkIV9SpZsiTTZ8xk2dIlLFu6FDs7O2bMnImnZ8Y6zDp16hIbE8uqlauIiAjH1dWVxUuWKiSfmTFzJosXLWT06FHExcVhZWVF3379aNlSZmyle7i3bN5MQkIClkZ6VC7mRK865VHP5MmqW6oose8TWXUomPC4BFytJCzt2QRrU9m9NyLuPWGZvoOUliZl0b4zvI6KRaNAAWwlRgxsWJGffb/ON9jS6dhZdsxnzZAdc3cPDxYtVRzbrMe8hFdJpkybzsply1i5fBm2tnZMnTEDD09PVV1kS7UaNRgxegwBfuuYN2cW9g4OTJ81m5LZZChL53sdyy/BwbsEg4MyPnHQcr5skuGc/w4Cun6dz0UA1K1di5iYWFauXkt4RASuLi4sXTQ/0z0zIss904ZlixYwa+58ArftkN0zhw2hVs0acplt23eSnJzMkOGKE0m9enanz6/KERl5pXOXLiQlJTJj+jTi4+Lw8PBkSdbrPiyUAgUyQvW9SpZk6vQZLF+2lBXLlmJrZ8f0GTMVzs3IyCjGjx1DREQEBgYGFC5chEVLl1I+UybKv08GMWnCBPnv0SNlhkyPX39l6Kcoibq1a2V6/kTInj8L5+f8/Fk4n1nzFhC4/dNYDlUcyw8fEpk6cxZv3oajra2Nk6MD06ZMom7tWgpjE3zhAqFhYTRt3Ij/AsIj9WWoSf/ptykBIPuG0p49e7iW6XsDgtx5//49NjY2zJ07l27dun1rdQR5IClOdca77wntgqZ8jFad2fB7QcvEkvBcMp19D5gX1CPh09qJ7xk9XR0S9y//1mrkiE793vLQvu8ZYwO9734sQTaevdQcv7UaObJC+oykd7HfWo1c0TbICO37njHU0yUpPuZbq5Ej2obG31oFBQb/ofpzH/llXpN/5tME0dHRDBgwQD653LhxYxYvXpxjxEyXLl0ICAhQKCtXrhzBwRlZG5OSkhg6dChbtmyRT0QvW7ZMadI5N8R3pATfFVevXmXLli08fvyYK1eu0L69bEF0kyZNvrFmAoFAIBAIBIJ/k3bt2nHt2jUOHjwo/wZrx0zra7Ojbt26hIaGyv/271f8SPygQYPYvXs3gYGBnD59mnfv3tGwYcN85z8QoX1fCXd392yTK2T+RtG3Ijf90g2W74E5c+Zw//592Yc9y5Th1KlTmJnlLRa+Xr16nMqyYDSd0aNHM3r06K+p6mdz6tQphfToWXn37t2/qI1AIBAIBIIfke85tO/u3bscPHiQ4OBgypWTfa9r9erV+Pr6cv/+fYXP42RFW1s72xwEsbGxrF27lg0bNsjXlG/cuBE7OzuOHj1KnTrKSwGyQxhSX4n9+/eTnKycuQVkH0g1NDTM93qhr0lu+n0vlCpVisuXL392/TVr1ihkoMtMTmlZ/228vb1FmKdAIBAIBIJvyvdsSJ07dw4jIyO5EQVQvnx5jIyMOHv2bI6GVFBQEBYWFhgbG1O1alWmTp2KhYXs24GXL18mOTmZ2rVry+Wtra3x8PDg7NmzwpD6FuQlgcC35HvX72thY2PzrVXIE7q6uvLsfgKBQCAQCAT/ZZKSkkhKSlIoyy67b14JCwuTGz+ZsbCwUPreZ2bq1atHy5YtcXBw4OnTp4wbN44aNWpw+fJltLW1CQsLQ0tLSynjdqFChXJsVxVijZRAIBAIBAKBQPADkiqVfpW/6dOnY2RkpPCXOTtyZiZOnIiamlqOf5cuXQKQf/4mM1KpVGV5Oq1bt6ZBgwZ4eHjQqFEjDhw4wIMHD9i3b1+OY5Fbu6oQHimBQCAQCAQCgeAH5GuF9o0aNYrBn9LPp5OdN6pfv360adMmx/YcHR25ceMGb968UdoWHh6er2UpVlZWODg48PDhQ0D2Hc2PHz8SHR2t4JV6+/YtFSpUyHO7IAwpgUAgEAgEAoFA8AXkJ4zPzMwsT0nEfH19iY2N5cKFC/j4+ABw/vx5YmNj82XwREZG8vLlS6w+fUesTJkyaGpqcuTIEVq1agVAaGgot27dYtasWXluF0Ron0AgEAgEAoFA8EOSmib9Kn//BMWKFaNu3br06NGD4OBggoOD6dGjBw0bNlRINOHm5sbu3bsBWdbjoUOHcu7cOZ49e0ZQUBCNGjXCzMyMZs2aAWBkZES3bt0YMmQIx44d4+rVq3To0AFPT095Fr+8IjxSAoFAIBAIBALBD8j3nLUPYNOmTQwYMECeYa9x48YsWbJEQeb+/fvExso+bK2urs7NmzdZv349MTExWFlZUb16dbZu3YqhoaG8zvz589HQ0KBVq1byD/L6+/ujrq6eL/2EISUQCAQCgUAgEPyApKalfWsVcsTU1JSNGzfmKCOVZhiDurq6HDp0KNd2dXR0WLx4MYsXL/4i/URon0AgEAgEAoFAIBDkE+GREggEAoFAIBAIfkC+99C+7x1hSAkEAoFAIBAIBD8gwpD6MkRon0AgEAgEAoFAIBDkE+GREggEAoFAIBAIfkBShEfqixCGlEAgEAgEAoFA8AMiQvu+DDVp5pyBAoFAIBAIBAKB4Ieg6Zrgr9LOnu7lv0o7/zWER0ogEHxVgp9HfWsVcqW8gynJb55+azVyRLOQE6+i3n1rNXLF1tSAZyM6f2s1csVxZgDhcQnfWo0cMS+oR+j0vt9ajVyxGrX0ux9LkI1n0rvYb61GjmgbGNFLzfFbq5ErK6TPeNj752+tRq4UXr6Dj7ER31qNHNEyMvvWKiggPFJfhjCkBAKBQCAQCASCHxBhSH0ZwpASCAQCgUAgEAh+QIQh9WWI9OcCgUAgEAgEAoFAkE+ER0ogEAgEAoFAIPgBER6pL0MYUgKBQCAQCAQCwQ+IVBhSX4QI7RMIBAKBQCAQCASCfCI8UgKBQCAQCAQCwQ9ImvBIfRHCkBIIBAKBQCAQCH5ApFJhSH0JIrRPIBAIBAKBQCAQCPKJ8EgJBAKBQCAQCAQ/ICLZxJchDCmBQCAQCAQCgeAHRKyR+jKEISUQCAQCgUAgEPyASNO+tQb/bcQaKcFXoUuXLjRt2vSrtaempsaePXvyLB8UFISamhoxMTFf1NezZ89QU1Pj2rVrea7v7++PsbFxvvsVCAQCgUAgEPx3ER6p/2OqVatGyZIlWbBgwT9a558gNDQUExOTr9rmxIkT2bNnT45Gkp2dHaGhoZiZmX3Vvrt06UJMTEyejcNnz54xZcoUjh8/TlhYGNbW1nTo0IExY8agpaUll3vx4gV9+/bl+PHj6Orq0q5dO+bMmSOXCQoKYv78+Vy4cIG4uDgKFy7MsGHDaN++vcp+z5w5Q9WqVfHw8MiXMZlXpFIpezasJWj/H7x/F4eLmzsd+w3F1tE52zpB+//gzNEDvHr2BADHwkX5uWsvXNzc5TLH/tzF8b92EfEmFAAbB2eatP8FLx/fr6J34O4/8duyg/CoKFwdHRjRvxdlvDxUyoZHRDJ72Wru3H/I81chtG/RhJEDen0VPXJDKpWyfu0q9v2xi/i4eIq5ezBg6AgcnV2yrfPsyWP8V6/gwb27vAkLpc/AIbRo0+6r6WRYvgYFq9ZHw9CIj29CiPpzE0nPHqiU1XF2w/LXUUrlr+eMJDlcdmz13MtgVKMRmhILUNcgJSKM2L8P8v7q2XzpJZVKWbd6JXt37yQ+Pp7i7h4MHj4KZ5fsxwog6PhR1qxYxutXr7CxtaVH735UrV5DQSb87VuWL15I8LkzJCUmYWdvz8hxE3ArVlypvVnTfmfv7p0M+G0ofXv1yLFvvdKV0S/3E+oGRqSEhxJ7dAfJrx7nuq+aNs5IOgwiJTyUiHXTMzYUKICBbx10PcuhbmhMSuQb4oP+IOnJnVzbzMz3OJat2ine4wK37cB/wwYiIiJxcXZm+NDfKFOqVLa6Xbp8hdnzFvD4yRPMzc3o2qkjrX5uId++Y9ce/ty3j0ePZfel4sXcGNC3D54e7irbW7POn0VLl9G+bRtGDB2c47h8Dq6Vfag9rCf2ZTwxti7E8qY9uf7H4a/eT3YYVamDSa3GqBuZ8DH0JeHb/Ul8dFelrG5hd2wHT1IqfzZxAMlvQuS/jWs0wKhKbTRMzEh9F8+7q8FE7tmENCU5TzoF7tiF/4bNhEdG4uLsxIjfBlCmVMls5S9eucrsBYt5/OQp5mZm/NKxHa1aNJNv3/PXPsZNnqZU79Kp42hrawOwxn89R0+c5Onz5+hoa+Pl6clv/Xvj5OCQJ52/BSJr35chDCnBd4mlpeU36VddXf2b9Z2Ze/fukZaWxsqVK3F1deXWrVv06NGD9+/fM2fOHABSU1Np0KAB5ubmnD59msjISDp37oxUKmXx4sUAnD17lhIlSjBixAgKFSrEvn376NSpEwULFqRRo0YKfcbGxtKpUydq1qzJmzdv/pH92r9tIwd3baHH0HFY2tixd7M/s0cOZMa6QHT19FWPxfUrlK9WC1d3TzQ1tdi/fSNzRg1i6upNmJpZAGBqZk6rbn0oZG0LwOkj+1k4cTiTlwXkaKTlhQPHTjJj8UrGDu5LKQ93tu/dT6/hY9m7fhVWhSyU5D8mJ2NiZESPjm3ZsH33F/WdXwI3BrBjyyaGj5uIrZ09G/3XMnxgH/wDd6Gnr3p8ExMTsbK2oUqNn1i+cO5X1UevhA+mjdoTuWc9Sc8fYFiuOoV+GcLreaNIjYnKtt6r2cORJibKf6e+j5P/n/bhPbHH/yQ5PARpSiq6xbwwa9md1PdxJD64lWfdNq33Z+vmjYwZPwk7ewcC1q3mt3692LJjT7ZjdevGdSaMHkn3X3tTpXoN/j5xnPGjRrBszTrcPTwBiIuLo3f3LpQuU5Y5C5dgYmLK61cvMTQ0VGrv76AT3Ll1EzNz81z11SlWmoI//Uzsoa0kv3qMXqlKmLbuS/jqKaTFRWdbT01bB+NGnfj47D4F9AsqbDOs0ghdDx9iD2wmJTIMbafimDTvQcSGuaS8eZWrTul872N58PARZs2dx5iRwylV0ovtO3fTp/8g9mzfipWV8v3+1evX9BkwiBbNmjL990lcvXadqTNmYWJiQq2aMkPv0uXL1KtTh5JeJdDW0sJv/QZ69e3Pru2BFLJQvC/cun2HHbt3U6Swa57HNL9o6+vx6vpdzvptp9eulf9YP6owKFMB85ZdeBu4hg+P72FUuRY2fUfzfPJvpERHZFvv2YT+pCV+kP9Ojc+4zg3LVkbStD1vNyzjw+P7aBWyplCnvgBE7PDPVaeDR44yc95Cxg4fQimvEmzfvYfeg4byx9aNWKl4xr96HULfQUNp0bQRMyaN5+r1G/w+ay4mJsbUqlE9Y1/19flz+xaFuulGFMClK9do07I5HsWKkZqayqLlq/i1/2/s2boJPV3dXPX+Fog1Ul+GCO37P6VLly6cPHmShQsXoqamhpqaGs+ePePkyZP4+Pigra2NlZUVI0eOJCUlJcc6qampdOvWDScnJ3R1dSlatCgLFy78bN2qVavGgAEDGD58OKamplhaWjJx4kQFmayhfWfPnqVkyZLo6Ojg7e3Nnj17VIbgXb58GW9vb/T09KhQoQL3798HZOF3kyZN4vr16/J98/f3V9JNVWjf3r17KVy4MLq6ulSvXp2AgACVYYSHDh2iWLFiGBgYULduXUJDZTPoEydOJCAggD/++EPed1BQUI5jVLduXfz8/KhduzbOzs40btyYoUOHsmvXLrnM4cOHuXPnDhs3bqRUqVL89NNPzJ07l9WrVxMXJ3sgjR49milTplChQgVcXFwYMGAAdevWZfdu5Rf8X3/9lXbt2uHr+3W8OFmRSqUc2r2Vxm274F2pGrZOLvQYNo6PSYkEH89+5rTXqEnUbNwCB5ciWNs78sugUaRJ07hz9ZJcppRvZbx8KmBpa4+lrT0/d+2Fjq4uj+/m/cU6O9Zv20XzBnX4uWE9XBztGTmgF5bm5gTu+UulvI2VJaMG9qZJ3Z8w0Nf74v7zilQqZdfWzbTr8guVq9XAycWVEeMmkZiYyLHDB7Ot51bcnV/7D6JGrTpoamplK/c5GFWuS/zFv3l38STJb0OJ+nMzKbFRGJavmWO9tHfxpL6Llf+RacY08ck9Em5fJvltKClRb4k/c4SPYS/RcSySZ72kUinbt2ymU9duVK1RE2dXV8ZMnEJSYiKHDx3Itt62LZvx9ilHx67dcHB0omPXbpQp68O2LZvkMpsC/LAoZMnoCZMo7u6BlbU13j7lsLG1U2gr/O1b5s+ewfgp09DQyH1OU9+nJgnXz/Hh+llSIt8Qd3QnaXHR6JeqnGM9o7pt+XDnEh9fP1Xapuvhw7uzh0h6fJvUmEgSrp4i6eldDHxyPj6Z+S+M5fqNm2nWpDEtmjXF2cmJEUMHY1moENt27FSp2/adu7CytGTE0ME4OznRollTmjVpRMCGjXKZGVOn0KbVz7gVLYKTkyMTxo4mTSrl/IWLCm0lJCQwauw4Jo4dQ8GCBbN29dW4fTCIvePmcm33oX+sj+wwqdmI2LPHiTtzjOSw10Rs9yclOhKjKrVzrJcaH0tqXIz8L/NiHR3nIiQ+vk/8xdOkRIWTcPc68ZdOo+OQs5cznfWbt9K8cUNaNG2Ms5MjIwYPwrKQBVt3qp7c2rZrD5aWhRgxeBDOTo60aNqYZo0a4L9R0WhSU1PDzEyi8JeZFYvm0bRhA1xdnClapDBTxo8mNOwNd+7ez5Pegv8ewpD6P2XhwoX4+vrSo0cPQkNDCQ0NRVNTk/r161O2bFmuX7/O8uXLWbt2Lb///nu2dezs7EhLS8PW1pZt27Zx584dxo8fz+jRo9m2bdtn6xcQEIC+vj7nz59n1qxZTJ48mSNHjqiUjY+Pp1GjRnh6enLlyhWmTJnCiBEjVMqOGTOGuXPncunSJTQ0NPjll18AaN26NUOGDMHd3V2+b61bt85Vz2fPnvHzzz/TtGlTrl27xq+//sqYMWOU5BISEpgzZw4bNmzg77//5sWLFwwdOhSAoUOH0qpVK7lxFRoaSoUKFfI6VHJiY2MxNTWV/z537hweHh5YW1vLy+rUqUNSUhKXL1/OczsAfn5+PH78mAkTJuRbr7wSHhZCbFQkHmV85GWaWloULVGKh3du5rmdpKREUlNSMDBU/VKSlppK8IkjJCUm4lrc84t0Tk5O5s6Dh1QoW1qhvELZ0ly/pTps5VsRGvKaqMhIvH3Ky8u0tLTwKlWG2zev//sKqaujZeNI4kNFYzbxwS10HHKembcaOBnbMQsp1GM4Os5uOcrquBRH09yKxKd5f1EJef2ayMgIfMpnTBpoaWlRsnQZbt3Ifqxu3byhUAegnK+vQp0zp07iVqw4Y0cOo2HtGnRt34a9u3cp1ElLS2PKhLG07dA51/A3AAqoo2lpR9JTxXMu6eldNG2z97jqepZH3cScd6f2q9yupqGhFCYlTUlG0zZvL6vw/Y9lcnIyd+/do0L5cgrlvuXLce3GDZW6Xb9xE98s8hXKl+fOnbskJ6eorJOYmEhKSgpGWYylqTNmUblSRcqX81FZ7z+Pugba9s4k3FE81u/vXkfHuWiOVe1Hz8ZpxmpsBk5At4hiSOSHx/fQtndG+9O9QsPMAn330ry/mf2zLZ3k5GTu3LtPhSxjXqGcD9duqJ5cu37zlpJ8xfLluHP3HskpGcc84cMHajduTs2GTen72zDu3lcdppzOu3fvATAy+ueM6C9Fmib9Kn8/KiK07/8UIyMjtLS00NPTk4eqjRkzBjs7O5YsWYKamhpubm6EhIQwYsQIxo8fr7IOyMLdJk3KiGd2cnLi7NmzbNu2jVatWn2WfiVKlJC/tBcuXJglS5Zw7NgxatWqpSS7adMm1NTUWL16NTo6OhQvXpzXr1/To4fyeoKpU6dStWpVAEaOHEmDBg1ITExEV1cXAwMDNDQ08hW6t2LFCooWLcrs2bMBKFq0KLdu3WLq1KkKcsnJyaxYsQKXTw/yfv36MXnyZAAMDAzQ1dUlKSnps8MGHz9+zOLFi5k7NyP0KiwsjEKFCinImZiYoKWlRVhYmMp2duzYwcWLF1m5MiP04+HDh4wcOZJTp07laWb8c4mNigSgoImiEVfQ2JTIt6r1VcX2tcswMTOneOmyCuUvnz5iysCeJH/8iI6uLgMmzMDGwemLdI6OjSM1NQ1JlvV6ElMTIqKyD037FkRHysbXxFRxhtTE1JQ3YaH/uj7qeoaoqavLPEqZSH0Xi7qhkco6KXExROxcx8dXz1DT0EC/dEUK9RhB2KoZJGUylNR0dLEbvQA1DQ1ISyNyz3oSH97Os25RkbJwo6wTCiamkhzHKioyQsX4Soj6NPYgMyz27NxO63Yd6NS1G3du32LB3FloamlSr4EsnHZTgB/q6uq0bNM2T/oW0DNArYA6aZlCHAFS38ejra/6BU3dxBzD6k2I3Dg/27RcSU/uou9Tk48vH5EaHYGWY1F0CpcANbU86QXf/1hGx8SQmpqKRKLYl0RiSkSmvjITGRmJRGKaRV5CSmoqMTExmJsrr6FdsHgpFubmCgbTgUOHuXvvPls2+Gc7Dv911A0+XefxWa7z+Fg0jIxV1kmJi+bNxhUkvXiMmoYmhuWqYDNwAq/mT5Cvq3p36QzqBgWxGzoF1NRQU9cg5uRBog/vyVWnjGOe5RiamhCZ7TGPQmKa5T4vMc045mZmODk4MGX8GIq4OPPu/Xs2bd1Op+692LEpAAd7O6U2pVIpsxcsorRXCQq7fFmI+T/Jj2wEfQ2EIfUDcffuXXx9fVHL9JCsWLEi796949WrV9jb22dbd8WKFaxZs4bnz5/z4cMHPn78SMmSJT9blxIlSij8trKy4u3btypl79+/T4kSJdDR0ZGX+fiont3L3K6VlRUAb9++zXHfcuL+/fuULav4wq6qbz09PbkRld53dvuTX0JCQqhbty4tW7ake/fuCtvUVLzwSKVSleVBQUF06dKF1atX4+4um/1LTU2lXbt2TJo0iSJF8h4aBZCUlERSUpJCWeZY8bPHDuG/cKb89+DfZWu71MiqmzTP7237tm0kOOgII2cvQ0tLW2Gbla0DU5YHkPD+HRdPnWD17CmMmrPsi40pUH6vzG6M/02OHtrP/JkZC5+nzZGF26rUVWnM/0WUFjKrqSiTkRIRxruIDKM66cVjNIxMMapSj7eZDClpUiIhC8dRQEsHHdfimDZsS0pUOIlP7qls9/jLCBaXKiVfVD1r/qJPqigNlky/HFA67FnOhbS0NNyKFefXvv0BKFLUjWdPHrNn53bqNWjEvbt32B64hXUbN3/xOSSrrmIs1dQwbtKVd6f2kRqV/X0o7sgOjOq3w7zneEBKanQECTfOoVci+/DeI49Dmf8fHMv8XhdZt6Xvr6pu1gWs58Chw6xbtVx+DwwLe8PMOfNYuXSRwn3x/xZV13Q27+fJb0IUkkokPn2AhokZJrUaE/rJkNIt7I5p3ea8DVxD4tOHaJpbYt6qK6mxMUQd2JFHpbIeQ3KcJMh6DsmP+ad2vDw98PLMSDJUyqsErTp2ZfO2HYwa+ptSe1Nnz+PBo8cErFqeR30F/0WEIfUDoerlL+PhkP3NZdu2bfz222/MnTsXX19fDA0NmT17NufPn/9sXTQ1NRV+q6mpkZametY0J71zaje9Tnbt5oW89q1qf75GJpyQkBCqV6+Or68vq1atUthmaWmpdAyio6NJTk5W8lSdPHmSRo0aMW/ePDp16iQvj4+P59KlS1y9epV+/foBsvGSSqVoaGhw+PBhatRQzKKVzvTp0xU8lQATJkygbtcBAJTyrYSLW0ZmreRkWQhRbHQkxpKMGd24mGgKGivOHKpi//ZN/LUlgOEzF2HvrBwapqGpSSEb2aygU5FiPH1wl8O7t9J10Mhc284OE6OCqKsXICJKcTF/VHSMkpfq36ZCpaoUyxS6mJz8EYCoyEgkZhkL7mOiozE2zX18vzapCfFIU1NRNzRWKFc3KEjquzjVlVSQ9OIx+qWzhMJKpaREygyEj6Ev0LSwxqh6w2wNqfKWJtScvoyod7KF7R8/ys7FqMhIzDKNVXR0FKaS7MfKVGKm4DFJr2OSaXwlZmY4OivOPjs4OhF0/BgAN65eJTo6ihaN6su3p6amsmThPHZu28KmWsWU+k1LeIc0LVUpWUQBPUNS38cryatp6aBl5YBmIVsK1v4UNaCmhppaASxHLCIqcAkfnz8g7cM7oneuAnUNCujqk/YuFsNqTUiJUT1rD1DR3pyq4+Z/92O5LXATJ4OCMDE2Rl1dnYgIxb6ioqKVPBbyfiUSJW9VVFQUGurqGGXxsviv38jadf6sWr6EIoULy8vv3L1LVFQUbTp0VtDt8pWrBG7bzqVzp1FXV89ueP4zpL77dJ0XNFYoVzc0IiUuJs/tJD59QEGfKvLfksZtiL/wN3FnZMf6Y8gLCmhrY9G+F1EHd2Y7GQPIj3lW71NUdDSSbO6FMg+lYpRBVFS07Jgbq/agFyhQAI/ixXj+Ujkxy7TZ8wj6+zT+K5diqSIp0fdEmsja90UIQ+r/GC0tLVJTU+W/ixcvzs6dOxWMg7Nnz2JoaIiNjY3KOgCnTp2iQoUK9OnTR172+HHuKXe/Fm5ubmzatImkpCT5zN6lS5dyqaWMqn3LS9/79yuuL/i3+n79+jXVq1enTJky+Pn5UaCA4pJGX19fpk6dSmhoqNz7dvjwYbS1tSlTpoxcLigoiIYNGzJz5kx69uyp0EbBggW5eVNxfdKyZcs4fvw4O3bswMkpe2/OqFGjGDxYMY2vtrY2V8NkMeG6evoKmfikUilGphJuXbmIg6ssdj4lOZn7N67SqlsfcmL/to3s3ezP0OkLcCqi/KKpEqmUlOS8pcnNDk1NTYoXKcy5S1f5qUpFefm5S1epXql8DjX/efT09RUyokmlUkwlEi5fPE/horJ1RcnJyVy/epkefQb8+wqmpvLx9TN0CruTcDtjXYNOYXcS7lzNczNaNg6yheg5oQZq6tk/zvQ01XFwcEAvLgGQjZVEYsbF88EUyTRW165cplf/gdm24+FZgovng2ndroO87ELwOTxKeMl/e3qV5MXz5wr1Xr54gaWl7BqtU78B3j6K628GD+hDnXoN6NC2NWxVkTkxLZXksJdoO7mR9CBjLYqWkxtJD5TX+UiTEglf/bviGJSpgrZDEaJ3rSE1NouhlJpC2rtYKFAAHbdSJN69ku0Y6GlqYPUfGMsGjZoAsmu4mJsb585foGam7GvB5y9QvWoVVOFVwpOTf59WKDsbfJ7ixYuhqZlxnvmt38DqNetYvnQR7sUV07GX8ynLzq2KiQrGT5qMk6MjXTt3+r8wogBITSHpxRP0ipXg/fUL8mLZ74s5VFRE286JlEzZJ9W0tJRCzqTySVE1snV38em+7VaUcxcuUrN6VXn5uQsXqV6lkso6Xp4enDx9RqHs7PkLFC/mhmY2Ie9SqZR7Dx5S2NVFoWzanHkcD/qbdcuXYGtjrbLu94QI7fsyhCH1f4yjoyPnz5/n2bNnGBgY0KdPHxYsWED//v3p168f9+/fZ8KECQwePFj+kp61jqmpKa6urqxfv55Dhw7h5OTEhg0buHjxYo4v2V+Tdu3aMWbMGHr27MnIkSN58eKFPAV4fkJjHB0defr0KdeuXcPW1hZDQ8NcQy5+/fVX5s2bx4gRI+jWrRvXrl2TZ/vLb9+HDh3i/v37SCQSjIyMlLxYmQkJCaFatWrY29szZ84cwsPD5dvS11nVrl2b4sWL07FjR2bPnk1UVBRDhw6lR48e8uxQQUFBNGjQgIEDB9KiRQv52iktLS1MTU1lM2oeit9DsrCwQEdHR6k8K9ra2tmM33uV8mpqatRp1pq/tgRQyNoWSxs7/gwMQEtbh/I1MrI7rZw1CROJudy42rdtI7sCVtFr5CTMClkR82mtlY6uLjq6sqx429ctp0RZX0zNC5H44T3ng45y98ZVhk6dn+M+5IVOrZozaups3IsWxsu9GDv+PEDo27e0btIAgPkr1/E2IpLpY4bJ69x7KJtoSPiQSHRMLPcePkZTUwMXx3/uWyJqamo0b92OzQHrsLW1w8bOns0B69DR0aFm7bpyuRmTxmNmbk73PrJwqeTkZJ4/lX0LJyUlmYjwtzx6cB9dXT1s7JTj/vND7KmDmLf+lY+vnpL04hEGPtXRMJYQH3wcAOO6LdEoaELENpm3tWCl2qRERfDxzWvZGqlSFdD3LMvb9YvkbRpVa0jS66ekRL5FTV0DXbcSGJSuSOTu9fkaq5Zt27HBby22dvbY2dmz3n8t2jo61K5TTy43ZcJYzM0t6NVPZoi2bNOWfr92Z2OAH5WrVuPUySAuXbjAsjXr5HVat+1Ar25dWO+3lho/1eLO7dvs3b2T4aPHyfQ3NsYoywe8NTQ0kEjMcHZ2JrtVRe8vHMO4UWeSQ1+Q/PoJuiUroV7QlISrshd+w6qNKWBoTOxf6wEpKRGKLaW9j0eakqJQrmntiLqBEclvX1HAwBjDyg0ANd4Fq078818aS3tHR3lZpw7tGD1uAu7Fi+FVwpMdu3YTGhZGy5+bA7Bw8VLehL9l2mSZh71li+Zs2bqd2fPm06JZU67fuMnuP/Yyc1qGcbouYD1Ll69kxtQp2FhZEREhWyump6eHnp4e+vr6Ci/YALq6uhgZGSmVfw209fUwd83YZzMnO2y9ivM+KobolyHZV/wKRB/7E8su/Ul6/oQPT+9jVKkWmiZmxJ6SZWOVNGmHhrGENwGyz3IY12hAcuRbPoa8RE1DA0OfKhiW9iVk5Wx5m+9vXMa4ZkOSXj0l8elDtMwtkTRqw/sbl7Jd85eZTu1aM2rCFNyLueHl6cH23X8QGvaGVs1l34VasHQ5b99GMG2S7Fxq1bwpgdt3Mmv+In5u2pjrN2+xa+9fzPp9orzN5avXUcLDHXt7W96/f8+mrTu4/+AhY4YPkctMnTWX/YeOsHDODPT19OSeUAMDA3R0vs8QT2FIfRnCkPo/ZujQoXTu3JnixYvz4cMHnj59yv79+xk2bBheXl6YmprSrVs3xo4dm2OdXr16ce3aNVq3bo2amhpt27alT58+HDiQfWrbr0nBggX5888/6d27NyVLlsTT05Px48fTrl07hXVTudGiRQt27dpF9erViYmJwc/Pjy5duuRYx8nJiR07djBkyBB5VsMxY8bQu3fvfMW99+jRg6CgILy9vXn37h0nTpygWrVq2cofPnyYR48e8ejRI2xtbRW2pYcMqqurs2/fPvr06UPFihUVPsibjr+/PwkJCUyfPp3p0zM+xFm1atVcU7D/E9Rv1YGPSUmsXzKHhPh4nN2KM2z6AgXPVdTbNxRQy/C+Hf9zJynJySyZMlqhraYdutGsk2zNWFx0FKtmTSImKhJdPQPsnF0YOnW+QobAz6VezarExsWxImAT4ZHRFHZyYPnMKVhbysInIyKjCH2juA7l52595f/fuf+QfUdPYG1pweFteX/Z/xzadOjMx6QkFs6ZQXx8PMWKezBzwVIFz9XbN2GoFciYBIiMCOfXzhkf4N22eQPbNm/Aq1QZ5i1TDCfNLwk3LhClZ4BxzSaoFzTmY9hr3vjNI/VT6JiGoREamcM61TUwadAGdSMTpMkfSX7zmjfr5vLhfobXRU1LG0nTTqgbmcpkwkMJD1xJwo0LWbvPkfadupCUlMS8mdOJj4+juLsH8xcvVxirN2FhCueip1dJJk6dzurly1izYhk2tnZMnjZD/t0jgGLu7kybPZeVSxfjv2YVVtY2DBg8jNr16vMlJN69QpyuPgYV66FuUJCU8FCity0jNU4WjlTAwAj1gvkLN1VT18CgaiM0jM2Qfkwi8fFtYv4MQJr0IffKmfjex7Ju7VrExMSycvVawiMicHVxYemi+Vh/8uSHR0QQFpbx7TxbGxuWLVrArLnzCdy2A3NzM0YOGyL/hhTAtu07SU5OZshwxdDhXj270+dXRc//v4GDdwkGBwXKf7ecLzMQzvnvIKDr0H+073eXzxKub4hpg59RL2jCx9AXvF46jZQomXGpYWSChmlGOLeaugZmzTuhYSy7hj+GvuL1kqkk3M7wVMvWQUmRNGqDhrEpqe/ieH/zMpF/bM6TTnVr/URMbBwr1voRHhGJq4szy+bPwfrTd8PCIyIJfZP5mFuzdMEcZs9fROCOXViYmTFqyCCFb0jFxcczafpMIiKjMDTQx61IEfxWLsPTPcMbmZ5e/Zde/RT0mTJ+NE0bNsjjiAr+S6hJxSeNBf9BNm3aRNeuXYmNjUX3X/7I3dSpU1mxYgUvX778V/v9rxD8/PvKZqeK8g6mJL9R/q7O94RmISdeRb371mrkiq2pAc9GdM5d8BvjODOA8E/haN8r5gX1CJ3eN3fBb4zVqKXf/ViCbDyTsmSN/N7QNjCil5rjt1YjV1ZIn/Gw98/fWo1cKbx8Bx9js/8I8PeAlpFy1sdvSakxX2dS/OrUerkL/R8iPFKC/wTr16/H2dkZGxsbrl+/zogRI2jVqtW/YkQtW7aMsmXLIpFIOHPmDLNnz5YnZhAIBAKBQCD4ryL8KV+GMKQEX5UXL15QPMui28zcuXPns1KRh4WFMX78eMLCwrCysqJly5ZK33L6p3j48CG///47UVFR2NvbM2TIEEaNGvVFbU6bNo1p06ap3Fa5cuV/LWxSIBAIBAKBQPB5CENK8FWxtrbm2rVrOW7/HIYPH87w4cM/U6svY/78+cyf/+VJCzLTq1evbD9m/G+HKgoEAoFAIPgxyUPuDkEOCENK8FXR0NDA1VX5Gz8CRUxNTTH9Bt/2EQgEAoFAIEgn7TvP2hcdHc2AAQPYu3cvAI0bN2bx4sUYZ8nYmZnssirPmjWLYcNkGXarVavGyZMnFba3bt2awMBAVVWzRRhSAoFAIBAIBAKB4LujXbt2vHr1ioMHDwLQs2dPOnbsyJ9//pltndBQxc8/HDhwgG7dutGiRQuF8h49ejB58mT578+JCBKGlEAgEAgEAoFA8APyPX9H6u7duxw8eJDg4GDKlZN9fHv16tX4+vpy//59ihYtqrJe+vc20/njjz+oXr06zs7OCuV6enpKsvmlQO4iAoFAIBAIBAKB4P8NaZr0q/wlJSURFxen8JeUlPRFup07dw4jIyO5EQVQvnx5jIyMOHv2bJ7aePPmDfv27aNbt25K2zZt2oSZmRnu7u4MHTqU+Pj4fOsoDCmBQCAQCAQCgeAHJE0q/Sp/06dPx8jISOFv+vTpX6RbWFgYFhYWSuUWFhaEhYXlqY2AgAAMDQ1p3ry5Qnn79u3ZsmULQUFBjBs3jp07dyrJ5AUR2icQCAQCgUAgEAg+m1GjRjF48GCFMm1tbZWyEydOZNKkSTm2d/HiRUB14gipVJptQomsrFu3jvbt26Ojo6NQ3qNHD/n/Hh4eFC5cGG9vb65cuULp0qXz1DYIQ0ogEAgEAoFAIPgh+VprpLS1tbM1nLLSr18/2rRpk6OMo6MjN27c4M2bN0rbwsPDKVSoUK79nDp1ivv377N169ZcZUuXLo2mpiYPHz4UhpRAIBAIBAKBQCDImW+RbMLMzAwzM7Nc5Xx9fYmNjeXChQv4+PgAcP78eWJjY6lQoUKu9deuXUuZMmXw8vLKVfb27dskJydjZWWV+w5kQqyREggEAoFAIBAIBN8VxYoVo27duvTo0YPg4GCCg4Pp0aMHDRs2VMjY5+bmxu7duxXqxsXFsX37drp3767U7uPHj5k8eTKXLl3i2bNn7N+/n5YtW1KqVCkqVqyYLx2FISUQCAQCgUAgEPyApKVJv8rfP8WmTZvw9PSkdu3a1K5dmxIlSrBhwwYFmfv37xMbG6tQFhgYiFQqpW3btkptamlpcezYMerUqUPRokUZMGAAtWvX5ujRo6irq+dLPxHaJxAIBAKBQCAQ/IBIpd/vd6QATE1N2bhxY44yqvahZ8+e9OzZU6W8nZ0dJ0+e/Cr6CY+UQCAQCAQCgUAgEOQT4ZESCAQCgUAgEAh+QL5Fson/J9Sk37tPTyAQCAQCgUAgEHx1nHru+CrtPF3181dp57+G8EgJBIKvStM1wd9ahVzZ0708yeEvvrUaOaJpbk9ozPtvrUauWBnrk3TU71urkSvaP3Xl4ovob61GjpS1NyHl6sFvrUauaJSq+92PJcjGMz7hw7dWI0cM9XR52Pv7fwEtvHwHvdQcv7UaubJC+owXUe++tRo5Ym9q8K1VEHxFhCElEAgEAoFAIBD8gEjTUr+1Cv9phCElEAgEAoFAIBD8gAhD6ssQhpRAIBAIBAKBQPADIgypL0OkPxcIBAKBQCAQCASCfCI8UgKBQCAQCAQCwQ+INFV4pL4EYUgJBAKBQCAQCAQ/ICK078sQoX0CgUAgEAgEAoFAkE+ER0ogEAgEAoFAIPgBER6pL0MYUgKBQCAQCAQCwQ+IMKS+DBHaJxAIBAKBQCAQCAT5RHikBAKBQCAQCASCHxDhkfoyhCElEAgEAoFAIBD8gAhD6ssQoX2C75IuXbrQtGnTfNVxdHRkwYIFX9xXtWrVGDRoUL7aUFNTY8+ePfnuWyAQCAQCgeBbkZaW+lX+flSER0rwXbJw4UKkUulXbfPZs2c4OTlx9epVSpYsma3crl270NTU/Kp9BwUFUb16daKjozE2Ns5TnenTp7Nr1y7u3buHrq4uFSpUYObMmRQtWlQuI5VKmTRpEqtWrSI6Oppy5cqxdOlS3N3dAYiKimLChAkcPnyYly9fYmZmRtOmTZkyZQpGRkZKfSYlJVGuXDmuX7+e6zh9Lm1K21K7qAX62ho8DH/HyjNPeRnzIVv5GoXNGVDVRam8pd95klNl58iq1qWwMNRWktl/J4xVZ5/lW8fAXXvx27Kd8MhIXB0dGTGwN2W8PLOVv3j1OrMXr+TRs2dYSCR0bd+K1k0bybcnp6SwZsMW/jhwhLcRETja2TG4d3cqlS+bb90yI5VK8V+zkr/27CI+Pp5i7h4MGjYSJ2fl8crMyePHWLdyGSGvX2FtY0v33n2pXK2GfHvC+/esXbmM0ydPEB0dTeEiRek/eBhuxd3zrWPg31fwP3qeiNh3uFiZMfznnyjjaqdS9sqjlyz4I4inbyJJ/JiClWlBWlYqSccaPnKZHWeu8ef5WzwKCQeguL0lAxpXxdPROt+6ZUYqlbJrwxpO7PuD9+/icXErTpf+w7B1dM62zon9ezh15ACvnj0BwKlwUVr90hsXt4xx2rslgIungwh9+RwtbW0KF/ekdfe+WNs55FvHLYdP4ffnccJj4nC1tWRkp+aUKab6WB+5cJ2tR05z79lrPqak4GprRZ+f61LJq5hcZnfQecau2KxU98r6OWhrff498HsdS6lUyqqVK9i9cxfx8XG4e3gwYtQoXFxcc6x37OhRVixbxqtXL7G1taNPv35Ur5FxvezYto0dO7YTGhICgLOzC9179qRipUpymePHjrFr5w7u3r1LbEwMmwIDKVrULVedjarUwaRWY9SNTPgY+pLw7f4kPrqrUla3sDu2gycplT+bOIDkNyHy38Y1GmBUpTYaJmakvovn3dVgIvdsQpqSnKs+X4prZR9qD+uJfRlPjK0LsbxpT67/cfgf7zcdqVTKhrWr2PfHLt7FxePm7kH/oSNwzOGe+ezJYwJWr+Dhvbu8CQul98AhNG/TTkFmS8A6Tp88wcvnz9DW1qa4Zwm69xmAnYPjP7xHgm+N8EgJvkuMjIzybHB8bUxNTTE0NPwmfWfm5MmT9O3bl+DgYI4cOUJKSgq1a9fm/fv3cplZs2Yxb948lixZwsWLF7G0tKRWrVrEx8cDEBISQkhICHPmzOHmzZv4+/tz8OBBunXrprLP4cOHY239ZS+kOdGshDWNPSxZde4pw/64SXTCRybVK4aOZs63ovcfU+iy6bLCX7oRBTD0j5sK28bvvwPA2adR+dbxwLEgZixaTo9Obdm+bjmlvTzoNXQ0oWFvVcq/Cgmlz7CxlPbyYPu65XTv1JbpC5ZxJOiUXGbxKj+2/7GP0b/15Y8Na2nVtCEDR0/k7oNH+dYvM1s2BLB98yYGDh3BCr8NmJpKGNq/NwmZzpGs3L55nUljR1K7XgPWbAykdr0GTBw9kju3bsplZk+bzOUL5xk9cQrrNm3Fu1x5hvTrTfhb1WOQHQcv32XWjqP0qFOBbaO6UtrVjj5LtxEaFatSXldbkzZVy+A3qD17xnWnZ90KLP7zFDtOX5PLXHrwgnrexVk7sB0bh3bCyqQgvZZs5U1MfL50y8pfWzdwYOcWOvcbwuQl6zA2lTBjxAA+JGQ/lnevX8G3ei3GzF7KxIWrkVhYMnPkQKIiMsbp7o2r1GrcgomL1jBixiJSU1OZOXIgiR+ynzxQxYGzV5gRsJuezWqzY8YwSru58OuMFYREqD7HL919jK+nG8tH/sr2aUPxKe5K31mrufv0lYKcga4OQSumKPx9iREF3+9YBvj7s3njRoaPHEnAxk1IJGb07dVb4Z6alRvXrzN65AjqN2jAlq3bqN+gASNHDOfWzYzrxaJQIfr1H8D6TZtZv2kz3j5lGfLbIB4/zri+P3z4gJdXSfr3H5AnXQEMylTAvGUXog7u4sW0YXx4dBebvqPRMDHLsd6zCf15MqK7/C/5bZh8m2HZykiatidq33aeTxrE243LMSxTAUnT9nnW60vQ1tfj1fW7BPYb/6/0l5WtGwPYuWUT/YaMYMm69ZhKJIwY2CfHe2ZSYiJW1jZ069MfU4lEpcyNq1do3KIli1b7M2PhMlJTUhk5qC8f8nmdfwukaalf5e9HRRhS/3GqVatG//79GTRoECYmJhQqVIhVq1bx/v17unbtiqGhIS4uLhw4cEBe586dO9SvXx8DAwMKFSpEx44diYiIkG8/ePAglSpVwtjYGIlEQsOGDXn8+LF8+7Nnz1BTU2PXrl1Ur14dPT09vLy8OHfuXJ509vf3x9jYmEOHDlGsWDEMDAyoW7cuoaGhcpms4Xbx8fG0b98efX19rKysmD9/vsoQvISEBH755RcMDQ2xt7dn1apV8m1OTk4AlCpVCjU1NapVq5btmGZuNzQ0lAYNGqCrq4uTkxObN29WGUYYERFBs2bN0NPTo3Dhwuzdu1c+XtWrVwfAxMQENTU1unTpkus4HTx4kC5duuDu7o6Xlxd+fn68ePGCy5cvA7KZtQULFjBmzBiaN2+Oh4cHAQEBJCQksHmzbJbZw8ODnTt30qhRI1xcXKhRowZTp07lzz//JCUlRaG/AwcOcPjwYebMmZOrbp9LIw9Ltl8LIfhZNC+iP7Dw5GO0NQpQxSXnFwOkEPMhWeEvM3GJKQrbytqbEBqbyK3QuHzruD5wJ80b1uXnRvVxcXRg5MA+WFqYE7jnT5Xy2/b8hWUhc0YO7IOLowM/N6pPswZ18N+yXS7z56Gj9OjYliq+5bCzsaJNs0ZULOeNf+COfOuXjlQqZUfgZjp07UaV6jVxdnFl1ITJJCYmcvTQgWzr7QjcjLdPOdp3+QUHRyfad/mF0mXLsiNQds4kJSZy8sRxfu03EK9SZbC1s6drj15YWlvzx67t2barivXHLtDM14sWFb1wtjRjxM8/YWlSkG2nrqqUL2ZnSX3v4rham2MjMaahjwcVizlx5dFLucyMro1pU6U0bnaFcLKUMKF9PdKkUs7ff5Yv3TIjlUo5uHsrTdp2oWzl6tg5ufDrsPF8TErk7PHsZ8v7jJpMrcY/4+BaBGt7R7r/Noo0aRq3r16Sy4yYvoAqdRpi6+iMg0theg4dS+TbMJ49vJcvHQP2BdGienl+ruGLi40lozo3x0piwtYjZ1TKj+rcnG6Na+Lp4oCDlQWD2jbCwcqcE1duKcipqalhblxQ4e9L+F7HUiqVsmXzJrp2606NmjVxdXVl0pQpJCZ+4OCB7K+XLZs3Ua5cebp264ajkxNdu3XDx8eHzZs2yWWqVK1KpcqVcXBwwMHBgb79+qOnp8fNGxnGVoOGDenx66/4lC+Xq67pmNRsROzZ48SdOUZy2GsitvuTEh2JUZXaOdZLjY8lNS5G/oc0Tb5Nx7kIiY/vE3/xNClR4STcvU78pdPoOOTsxf5a3D4YxN5xc7m2+9C/0l9mpFIpu7dupm2XX6hcrQZOLq4MGzeJpMREjh8+mG29osXd6dl/ENVr1UFTU0ulzPQFS6jToDGOzi64FC7C0LETeRsWxsN7qr2H3xPCkPoyhCH1f0BAQABmZmZcuHCB/v3707t3b1q2bEmFChW4cuUKderUoWPHjiQkJBAaGkrVqlUpWbIkly5d4uDBg7x584ZWrVrJ23v//j2DBw/m4sWLHDt2jAIFCtCsWTPS0tIU+h0zZgxDhw7l2rVrFClShLZt2yq9nGdHQkICc+bMYcOGDfz999+8ePGCoUOHZis/ePBgzpw5w969ezly5AinTp3iypUrSnJz587F29ubq1ev0qdPH3r37s29e7KH7IULFwA4evQooaGh7Nq1K0+6durUiZCQEIKCgti5cyerVq3irYqZ+UmTJtGqVStu3LhB/fr1ad++PVFRUdjZ2bFz504A7t+/T2hoKAsXLsxT35mJjZXN4puamgLw9OlTwsLCqF0746Gqra1N1apVOXv2bI7tFCxYEA2NjMjeN2/e0KNHDzZs2ICenl6+dcsLhQy1MdXT4trrGHlZSpqUW2FxuFnk7AHU0VRnVetSrGlbijG1i+IkyV5HjQJqVHU149iD/HlPAJKTk7nz4AEVypZRKK9QtgzXb91WWef67btK8hV9vLl97wHJn66Hj8nJaGkrPoC1tbS4ekPxpTY/hIa8JioygrLlysvLtLS0KFmqDLdv3si23u2bNxXqAPiU9+X2zesApKamkpaaqqyvtjY3r1/Ls37JKancfRlGhWKOCuW+xRy59uR1ntq4+zKMa09eU6awfbYyiR+TSUlNw0hPN8+6ZSU8LITYqEg8vTNecjW1tHArUYqHd27mUFORpKREUlNSMTDM3hhJeP8OAP0cZLLyMSWFO09fUqFEUYXyCiWKcu3B0zy1kZaWxvsPiRjpK147CYlJ/NRvIjX6jKfPzJVKHqv88r2O5evXr4mMiKC8r6+8TEtLi9JlvLmRw3l948YNyvkqXi/lfX25cf26SvnU1FQOHTzIhw8fKFGiRK56ZYu6Btr2ziTcUezn/d3r6DgXzaaSDPvRs3GasRqbgRPQLaIYjvvh8T207Z3RdpCFM2qYWaDvXpr3Ny9/vq7/EcJCXhMVGYm3j+I9s0SpMty5qfp4fi7v38nOTcOCXzYxIfj+EWuk/g/w8vJi7NixAIwaNYoZM2ZgZmZGjx49ABg/fjzLly/nxo0b7N+/n9KlSzNt2jR5/XXr1mFnZ8eDBw8oUqQILVq0UGh/7dq1WFhYcOfOHTw8POTlQ4cOpUGDBoDMiHB3d+fRo0e4ueUe952cnMyKFStwcZHNgvXr14/JkyerlI2PjycgIIDNmzdTs2ZNAPz8/FSGoNWvX58+ffoAMGLECObPn09QUBBubm6Ym5sDIJFIsLS0zFVHgHv37nH06FEuXryIt7c3AGvWrKFw4cJKsl26dKFt27YATJs2jcWLF3PhwgXq1q0rN34sLCw+K2RRKpUyePBgKlWqJD8GYWGycI1ChQopyBYqVIjnz5+rbCcyMpIpU6bw66+/KrTdpUsXevXqhbe3N8+ePcu3fnnBWFcWLpTVmxT7IRlzA+X1Tem8ivnAor8f8zwqAT1NdRp6WDKjkTuDdt0kNC5RSb6cgwn6Whocexiebx2jY2NJTU1DYmqiUC4xNSEiMlplnYjIKCTlvJXkU1JTiYmJxdxMQkUfb9YH7sTbyxM7G2uCL1/lxOlzpGaZnMgPUZGRAJiYKoaamJia8iYsVFWVT/UiVNSRyNvT09fH3bME69etwcHRGRNTU44dPsjd27ewtcveoMlK9LsEUtOkSArqK5RLDPWJiMs+jAbgpzFLZfVT0+jdoBItKnplK7vgj5NYGBlQ3s0xz7plJSZKtu9GxqYK5UYmpkS8CVNVRSVb1yzDxMwc99Kq175JpVI2rVhIEQ8v7Jzy7gGIiXtPaloaEiPFlzKJkSEReQxp9N93gg9JH6nrW0pe5mxTiKm921HY3pr3CYlsOHCSDhMWsGvmcBysLPKsn4Ku3+lYRn6KupCYKuolkZgqREOoqifJEs4lkUiIjIxQKHv08CFdO3fi48eP6OrqMnvuPJxdPt/Lo25giJq6OqnximGwqfGxaBgZq6yTEhfNm40rSHrxGDUNTQzLVcFm4ARezZ8gX1f17tIZ1A0KYjd0CqipoaauQczJg0Qf3vPZuv5XSL/HGefznplfpFIpKxbNw8OrJE65rL/7HpCm/rjepK+BMKT+D8g866Wuro5EIsHTM2NhfPqL9tu3b7l8+TInTpzAwMBAqZ3Hjx9TpEgRHj9+zLhx4wgODiYiIkLuiXrx4oWCIZW5XysrK3kfeTGk9PT05EZUen1VXh6AJ0+ekJycjI9PxoJzIyMjhaQLqnRSU1PD0tIy23bzwv3799HQ0KB06dLyMldXV0xMTJRkM/etr6+PoaHhF/WdmX79+nHjxg1Onz6ttE1NTU3ht1QqVSoDiIuLo0GDBhQvXpwJEybIyxcvXkxcXByjRo3Kl05JSUkkJSUplGlrZxhEVVwk9K6Usbj890Ofwm9U5BDJKa/Ig/B3PAh/J/99900885p50sC9EGvOKRuMPxW14MqrGKITPn/hdF7HNCf5zOUjB/Zh4qz5NGrfDTU1sLO2pmn92uzZn/dF1kcO7mfujKny3zPmLfrUh6KcVFVhVn1RoW+mOqMnTmHW75P4uWEdCqirU6SoGzXr1OXhvfyFo6nsK3f18P+tPQlJH7nxLISFfwRhZ25Cfe/iSnLrjgRz4NId1g1qh7Zm3h9nZ44dZN2CmfLfQ3+f+0nZnMclJ/7auoFzQUcYM2cpWlqqJwYCFs/h5dNHjJu/SuX23FB1rPOi3r4zl1m24yCLh3ZHYpTh/fUq7IhXYUf571JFnfh51Bw2HTrF6C4tVLSkzPc6lmeOHaTnolnya3HBosWf1MrftS0j9zoOjo5sDtxKfHw8x48dY+L48axas+aLjKlPnakoUy2a/CZEIalE4tMHaJiYYVKrMaGfDCndwu6Y1m3O28A1JD59iKa5JeatupIaG0PUgc8PNf4eOXZoPwtmZkwa/z5HFgmidB1JpUr3qS9h8ZyZPH30kPkr1361Nv9JfuSwvK+BMKT+D8iaYU5NTU2hLP2Gn5aWRlpaGo0aNWLmzJlkJd0YatSoEXZ2dqxevRpra2vS0tLw8PDg48eP2fabuY/P1Tm7LH1ZX0qzlufWbl51yqnvvJR/7b7T6d+/P3v37uXvv//G1tZWXp7uVQsLC5MfO5AZs1m9VPHx8dStWxcDAwN2796toOvx48cJDg5WMIIAvL29ad++PQEBASr1mj59OpMmKWaImjBhAtjWBeDCi2ge7M4IMdMsIIskNtbTJDqTV8pIV1PJS5UTUuBh+DusCiqHcpkbaFHC2oiZRx/kub3MmBgZoa5egIhIxQX8UdExSEyNVdYxk5iqlNdQV8fokwfB1MSYRdMnkZT0kZi4OCzMJMxfvgYbq7x5RgEqVq5KMfeMiYzkZNmYRUVGIjEzl5fHREVhaqp6QTSAqcSMqCjF2fSY6Ci51xTAxtaOhSvW8OHDBxLev0NiZs6kMSOwsrbJs74mBnqoF1AjIu6dQnlU/HskhvrZ1JJha2YMQBEbC6Li3rN832klQ8r/6HnWHjrHqv5tKGKTP+9Jad/KCtngUj6NZWx0JCaSjPV6cTHRGJmYKtXPyr7tm9i7JYCRMxdj76zsrQYIWDKHK8GnGDt3BRLz/OlrXFAf9QIFiIhRXPMXFRuvYBip4sDZK4xfuYV5g7ri65lzSFiBAgXwcLHneWjevbnf61iW9q1MsxoVeP9B5rX+mCx7fkVERmJmnnG9REVFK5z7WZGYmSl5n6JUXGOamprY2cs8tsXd3blz+zZbtmxmzNhxueyxalLfxSNNTUW9oLFCubqhESlxMXluJ/HpAwr6VJH/ljRuQ/yFv4k7cwyAjyEvKKCtjUX7XkQd3JnzrNZ/DN9KVXErnjGpnPzpHIjOes+MjsYkh3MgPyyZO4vg038zd/lqzC0K5V5B8J9HrJH6wShdujS3b9/G0dERV1dXhT99fX0iIyO5e/cuY8eOpWbNmhQrVozoaNUhTf8WLi4uaGpqytc4gcy78vDhw3y1o6UlW/ORmg83tpubGykpKVy9mrE4/tGjR8TExPzjfUulUvr168euXbv4H3t3HRbF9v8B/L10d4kgaaECtqhXxcDGutfAwBYLRbE7QEURsbABCxu7A5QwkLJBJAxAQkAQpM7vj70sDLsg3K+XGX73vJ5nn8vOzO6+nY07Z+acz7l//76gWEYZIyMj6Ojo4M6dO4JlhYWFCAwMROfOnQXLcnJyYGNjAykpKVy+fBkyMjKM59m5cyeioqIQGRmJyMhIXL9+HQBw+vRpuLi4oCrLli1DdnY241bxqlZBUSlScn4Kbh+z8pH5oxCWDcvLrkuI8dBSRwlvv9au4pqRujy+/SgUWt6riRayC4oQ9vGffWYlJSVh1qQJQp8xx9+FhoXDoqXo0t8WLZojNIy5fciz52jRrAkkJZjnqqSlpaCtqYHikhLcCQyC9R9WqCk5eXno6TcS3AyNjKGmroGwp48F2xQVFSEy4jlatKp6bEaLVq0Q9uQJY9mzJ4/RopVw9zlZWVmoa2jie04Onj4ORZdu3WucV1JCHM31dRD6NoGx/PHbBFga17xBRgDBWLMy3nee4MCNEOydPRItDBqIfmA1ZOXkodNQX3BraGAEZTV1vHxe/htTXFSEt9ERaGxWddl7ALh65jguHj+Cxa47YNy0udB6Qgh8d21DWFAglrvthlaD2lfFlJKQgJmRPkJevGMsD3nxDpZNjKp4FP9K1Aqvk3CbOwHd2/y6dD0hBG8TPkFTtebjOri6L2Xl5GFgYAD9Ro2g36gRjI1NoK6hgSePy4siFRUVIfx5GMwtLKt8HnNzczx5/Jix7EnoY5hbVN3dFAAICIoKhX+jaqykGD+TPkCuOfO7LNfcHAUf3lXxIGHS+kYozin/PeRJSYGUMhtLRHDC7/ddleECOXl5NNTXF9wMjIyhpq6O58/Kf/+KiooQHfEcZiJ+/2qDEIJd27YgKOA+3Hbvq9VJJ7bRYhP/G3pF6j9m9uzZOHjwIMaMGYNFixZBQ0MD79+/x6lTp3Dw4EGoqqpCXV0dBw4cQIMGDZCUlISlS5eymllRURH29vZYtGgR1NTUoKWlhTVr1kBMTKwGXTLKaWlpQVZWFjdv3oSenh5kZGREzqVUUbNmzdC7d29Mnz4dXl5ekJSUxMKFCyErK1ur1zYwMACPx8PVq1cxYMAAyMrKiuxeWdHs2bNx8uRJXLp0CYqKioIxUcrKyoLXnz9/PlxdXdG4cWM0btwYrq6ukJOTg50df46L79+/w8bGBj9+/MDx48eRk5ODnBz+WW1NTU2Ii4ujUSPmuJeyXCYmJowrYJVJS0sLXcX6lSsvU/CnRUN8yS5Ack4B/rRoiJ/FpXgYV37Gd153E2TkFeJ4GL9S26jWDfHuay6ScwogJymOgS10YKQuhwMhzEH2PPDnnHoQm4bS/+Gk6oTRI7Bswxa0aNYEFi2b49zl60hO/YpRQwcBADz2HcbXtHRsWrUEADBy6CD4XbgMt137MGJwf0S9fIMLV29i69rlgueMfvUGqenpaGZqiq/p6dh75ChIaSkm2436xzl5PB7+HG2H4z5HoKffCA31G+GEzxHIyMigd9/+gu1c166ChqYWps+eCwAYMcoOjg5TcfKoD7p0647gh4F4/vQpdh0o74by9HEICCFoZGCIzx8/wmvXDjQyMET/wba125e9OmC57xW0aKQDC+OGOBcUieTMHPzVlT9Ox/NSAFKzvsPVnj/n1qnA59BRU4KRNv9sf0TcJ/jefYoxPcqLeRy58xh7rj7C5omD0VBNGenZ/CtectJSkJMRXVHrV3g8HvoNG4XLfr7Q/rtBcNnPF1LSMujcs7yYy74t66CqoYlRU/jjMK+ePoZzvgcwa9k6aOg0EIwPkpGVhYwsv6iDz66tCL1/G07r3CAjJy/YRk5eHlLSMqgp+4E9sHTPcbQ0bgSLJoY4ezcEyenfMKp3FwCAh98VfM3MxqbZ4wDwG1HL9x7HUvvhMG9siLS/r2bJSElC8e/CHHvP3YB5Y0MY6GgiN78AJ24+xLvEz1g5+a9/tB+5vC95PB7G2I2F9+HDaNSI38DyPnwIMjKy6Ne//PuyeuVKaGlpYY4jv0z56DF2mD51Cny8vdGjRw8EBATgydMnOHzEW/CYPbt2onOXrtDW0caPvB+4desmnoeFYeeePYJtsrOzkZKSjLSv/Kt9iQn8bskG1fzGfrt3BToT5+Jn4gfkx7+Dctc+kFTVQPYjfpdg9SF2kFBRR6ovv9uiSs+BKMr4isIvH8GTkIBih25QbGOFL/u3Cp4zL/o5VHoNws9P8SiIj4WUpg7UB49GXnQYo7rfv0VaXg6apoaC+xpG+tCzMENeZha+ffxS9QN/Ax6Ph2Gj7ODnewQN9fTRUL8R/HyPQFpGBj1t+gm227JuNTQ0NTFlFv83s6ioCInx/PnNioqLkJ72Fe9j3kFWVg4N9flz4u3athn3b9/Eui3bIScnh8y/r2LKyytAWqbm33M2/JcbQb8DbUj9x+jq6iI4OBhLlixB37598fPnTxgYGKBfv36ChsmpU6fg6OiIli1bomnTpti5c2eVpcLryvbt2+Hg4IBBgwZBSUkJixcvxsePH4WurlRHQkICO3fuxPr167F69Wr88ccfCAgI+OXjjh49iilTpqBbt27Q0dHBpk2b8OrVq1q9dsOGDbFu3TosXboUkyZNwoQJE+Dj41PtY7y8vABAaN97e3sLyqcvXrwY+fn5mDVrlmBC3tu3bwvmwXr+/Dme/H31wdSUOeg1Pj4ehoaGNf43/A7+0V8gLSGGGV2MoCAlgZi0XKy9+QYFReX/A9dUkGb0LpGXksCsrsZQlZNEXmEJ4jPysOLqa8SmMQsWWDRUhpaiNO69q32RiYr69+qB7Owc7PM5jrSMTDQ2MoTXVhfo6vC7aaRnZCA5tXzsm55uA+zduhFuu/bB78JlaGmoY9n8WejT4w/BNj8LC7HroA8+fUmGnKws/ujUAZtWLYGSYvWN6V8ZM94eP38WwMNtM75/z4FZi5bYunMv5OTLu86lpqaAJ1be+aCluQVWb9iEw/v34sj+vdDV08Mal00wa1l+tSAvNxcH9+5G2tdUKCopo5t1T0ydORsSErWbX6hf2+bIysvH/hvBSMvJg2kDDeyZ9Rd01fknMNKyc5Hyrby7Wikh8LwUiM8Z2ZAQE4OepgrmDekuaHgBwJmH4SgqLsHCQxcZr+UwoAtmDfwD/9SgUeNRWPgTPru24sf37zBp1gJLNntCVq58X6Z/TWGcQLl75TyKi4qwc/1yxnMNGz8FIybwi/3cu8KvDuriPIuxzXTnlejWd1CN8/Xv3AZZuXnwOn8LaVnZaKzfAPuWzoCuJr9LUtq3HCSnl195OHs3BMUlpdh45Bw2Hikf+zKkWwe4zuLPGZSTl4+1B08jPSsHinKyaGaoB981jjA3rf1kwRVxdV/aT5yInz8LsHmTK77n5KBly1bY7eUF+Qrfl5SUZIiJleeysLSEy6bN8Nq7B/v27oGevj42bd6ClhXGIWdkZGL1yhVIT0+HgoICGjdugp179qBTp/Irzg8DA7CuwtjU5Uv5J2LmzJmD8kN4ptznIUiTV4TawD8hrqSKwuQkfN7jiuK/u+ZKKKtCQq28+yRPXAIawydAQkUNpKgQhcmf8Hm3C368Ku9RwR8HRaA+eDQkVNRQkpuDvBfPkXFJeGLmf4NBO3MsCDgluP+XB7/rY6jPOfhOqrpy7+8yapw9Cn/+xK5tm/H9+3c0M2uJzTv2MH4zv6amgFfhM5CRnoaZ9uUT8J49eQxnTx6Deeu2cN/LH6N35QL/O+Y8ezrj9ZxXrkHfgbU7AVXXyG8YgvBfxiNVDQKhKA7Ly8tDw4YN4e7uXuXksv+WT58+QV9fH3fv3hVUEaTKDT30+Ncbsezi1E4oSktiO0a1JDUbITmr+up2XNBARR4/73r/ekOWSfeehGdJ7HZT/pX2jVRRHFH1fDZcIdG6H+f3JcDfn99/cHtCVEU5WcTO/JPtGL/U2OscHHiGbMf4pX0kAUmZub/ekEWN1P63E2i/m3LP5b/eqAay77v+eqP/h+gVKapeiIiIwNu3b9GhQwdkZ2cLSqUPGTLkX3/t+/fvIzc3F61atUJycjIWL14MQ0NDdOvW7dcPpiiKoiiK4ijate9/Q4tNUL9d//79oaCgIPJWcf6q2tq2bRssLCzQu3dv5OXl4dGjR9DQ0Pj1A/9HRUVFWL58OVq0aIFhw4ZBU1MTAQEBQlX6aiMpKanKfaSgoICkJG5fLaEoiqIoqv6jxSb+N/SKFPXbHTrEL5ssSnVlZqvTunVrPH/Ozszrffv2Rd++fX/rc+rq6iIyMrLa9RRFURRFURR30YYU9ds1bFh/yn6yRUJCQqj4A0VRFEVRVF0q/Q9fTfodaEOKoiiKoiiKov6DSC3mt6SE0TFSFEVRFEVRFEVRtUSvSFEURVEURVHUf9B/uVDE70AbUhRFURRFURT1H0QbUv8b2pCiKIqiKIqiqP8g2pD639AxUhRFURRFURRFUbVEr0hRFEVRFEVR1H8QvSL1v6ENKYqiKIqiKIr6DyqMOMJ2hHqNdu2jKIqiKIqiKIqqJdqQoiiKoiiKoiiKqiXakKIoiqIoiqIoiqolHiGEsB2CoiiKoiiKoiiqPqHFJiiK+q2Kn19jO8IvSbQdiO8/8tmOUS1FOVn8zM1mO8YvSSso4+f3LLZj/JK0ogqKUuLYjlEtSR0TFGZ9ZTvGL0mpaHF+XwL8/cn1z6a0ogoKs9PZjvFLUsoaSMrMZTvGLzVSU4ADz5DtGNXaRxLYjkD9RrRrH0VRFEVRFEVRVC3RhhRFURRFURRFUVQt0YYURVEURVEURVFULdGGFEVRFEVRFEVRVC3RhhRFURRFURRFUVQt0YYURVEURVEURVFULdGGFEVRFEVRFEVRVC3RhhRFURRFURRFUVQt0YYURVEURVEURVFULdGGFEVRFEVRFEVRVC3RhhRFURRFURRFUVQt0YYURVEURVEURVFULdGG1L+gR48emD9/PgDA0NAQO3bs+Fdfb+LEiRg6dOhvez4fHx+oqKj8tuejKIqiKIqiqP9vJNgO8P/ds2fPIC8vL7jP4/Hg7+//Wxs+np6eIIT8tuf7L+nRowcsLS3/9cZubUVFRWHz5s0ICgpCeno6DA0N4eDggHnz5jG2e/HiBebMmYOnT59CTU0NM2bMwKpVq8Dj8QAAFy5cgJeXFyIjI/Hz50+0aNECa9euRd++fUW+7qlTpzBmzBgMGTIEFy9e/O3/Lr87wfC++gBpWTkwbaiDpROGom0zY5Hb3nkajdN3Q/A28TMKi4th2lAHs0b0RVeLZoztcvLy4XnmOu4+i0ZOXj70NNWwaKwturU2q3EuQggO7N8H//MX8P17Dlq0bIkly5bBxMS02sfdu3sX+/buxadPH6Gnp49Zc+bAumdPwXrvw4fx4P49JCQkQFpaGuYWFpg7bz4MDQ0F27RrbSnyuRctWoTxo/8S3D915hx8jh1DenoGTIyNsdjZCW1bt64yW9jzcGzdvgNxHz5AU1MDkyaMx8g/RwjWn7twEVeuXcP7uA8AALPmzeA4exZatWwh2KbfoCH4kpws9Nyj/voTK5YuFvm6p86eg8+x43/nNMLihTXI6bEDcR/i+TnHj8fIP4cL1t+9/wCHvH3w8eMnFBUXw6CRPiaMtcPggQME2xQXF8PrwCFcu3kTGRmZ0NBQx5BBAzF9ymSIidXsfOEp/6vwPnUeaZmZMDU0wJI509HWoqXIbdMyMrF1z0G8jnmPxE9fMHaELZbOncHY5tyVm7h86x7exycCAMyammLeNHu0at60RnlEZjznD5/jfkjLyICJkSGWODmibWuLKrd/Fh6BrTt2Iy4+AZoa6pg83g4jhw9lbJPz/Tt2eh3EvYBA5HzPRUPdBnB2nI1uXaz+eU4O7ks2Ppf9Bg+t4vszAiuWVPH9OXcBPsdO8t9jY6O/32PLKnPy3+Nd/JwaGvz3eMQwwfqLV69h1XpX4X/fo/uQlpYGABzyOYq7DwIRn5gIGWlpWLRqBae5M2FkYFDl61ZGCMGxwwdw7dIF5OZ8R7MWLTHXeQkMjU2qfEzChzj4HtyH2LdvkJqSjJnzFmL4aDvGNn6+RxAU+AAfE/m/oWatzDF1liP0DQxrnO2fMP2jA2wWTUejtq2goqsNr6HTEXXp9r/6mlT9RK9I/cs0NTUhJyf3r76GsrLy/7srSEVFRWxHYNXz58+hqamJ48eP49WrV1ixYgWWLVuG3bt3C7bJyclBnz59oKuri2fPnmHXrl3Ytm0btm/fLtjm4cOH6NOnD65fv47nz5/D2toagwcPRkREhNBrJiYmwtnZGX/88ce/8m+6ERqBzUcvYvrQ3jjnuhBtmhlhxpYD+JL+TeT2YW/jYNWqCbwWT8PZjQvQwcwUs7cdxpuET4JtCouLMXXTPnxJy4THvIm4tm0p1k4dCS015Vpl8/Xxwcnjx7F46VL4Hj8BdXUNzHaYiby8vCofEx0VheVLl2DAwIHwO30GAwYOxNIli/HyxQvBNuHhz/HXqFHwPnoUe7z2oaSkBHNmzkR+fr5gm5t37jJuq9euBY/HYzR2b96+Azf37Zg2eRLOnDyGNq0tMWvufCQnp4jM9unzZ8xynI82rS1x5uQxTJ00EZu3uuPOvfvl+/f5c/Tv2xeH93vhuPdhNNDRgcPsuUj9+lWwzcljPrh/67rgdmAv//Nn07uXyNfl5/Tg5zxxlJ/T0QnJKVXl/IJZ85z4OU8c5efcxsyprKSEaZMn4Zj3IZw/dQJDBg/C6vUbERz6WLDNEd9jOHv+ApYvdsbFs6fgNHcOfI6dwMnTZ0S+bmU37gdi8+4DmDZ+FM4e3IU25i3gsGQ1klO/ity+sLAIqirKmDZuNJqaGInc5llkNAb06o4jOzbh+F536GhpYrrzSqSmpdcoU2U379zDFo+dmDZpPM4ePYy2lhaY6bQIySmpIrf/9OULZjstRltLC5w9ehjTJo7HJndP3LkfINimqKgI0+cuwJfkZGzftAFXzpzA2mWLoa2p+Y8yAtzcl2x9Lk8e9cb9m9cFtwN7dgEAbHpV8f25cxdbtnti2qQJOHvMG20tzTFzvnO1OWfPd0ZbS3OcPeb993u8A3fuP2BspyAvjwfXLzNuZY0oAAgLj8Tov4bjxOEDOLBrB0pKSjBjrhN+VPid+pXTx31x3u8E5ixcgt1HjkJNXR1L5s3Cj2p+Q38WFKCBbkNMmTUXaurqIreJjgiH7Yi/sPOgDzZ77kVJcQmWzp/N+A39N0jLy+FT1BucmrP6X30dqv6jDal/WcWufWVnoYcNGwYej8c4K12VtWvXwtLSEvv374e+vj7k5OTw119/ISsrS7BNxa59aWlp0NHRgatr+RmoJ0+eQEpKCrdv88+mFBYWYvHixWjYsCHk5eXRsWNHBAQE/KN/X03yAYC3tzeaN28OGRkZNGvWDHv37hWsS0hIAI/Hw5kzZ9CjRw/IyMjg+PHjAIAjR46gRYsWkJaWRoMGDTBnzhzB47KzszF9+nRoaWlBSUkJPXv2RFRUlFC2Y8eOwdDQEMrKyhg9ejS+f/8u2G+BgYHw9PQEj8cDj8dDQkICSkpKMGXKFBgZGUFWVhZNmzaFp6cn499TXFwMR0dHqKioQF1dHUuWLIG9vT3jSiMhBG5ubjA2NoasrCwsLCxw7ty5Gu3XyZMnY+fOnejevTuMjY0xbtw4TJo0CRcuXBBsc+LECRQUFMDHxwctW7bE8OHDsXz5cmzfvl1whXLHjh1YvHgx2rdvj8aNG8PV1RWNGzfGlStXGK9XUlKCsWPHYt26dTA2Fn2F6H/lez0QI3p0xJ/WnWDSUBvLJgxDA3UVnL4bLHL7ZROGYcrgnmhl0ggGDTQxf/RAGOho4EH4K8E2/gFPkZP7AzsXTEabpkbQ1VRD22bGaGbQsMa5CCHwO3kCk6ZMRc9evWBqaop1GzagoCAfN2/cqPJxfidPoGPHTpg0ZQoMjYwwacoUdOjQASdPnBBss2vPXgy2HQITE1M0adoUa9auQ0pKMt68fi3YRkNDg3ELDAhAu/btoa+vL9jm6PGTGDbEFiOGDYWxkRGWOC+AjrY2zpw7LzLb2fMX0EBHB0ucF8DYyAgjhg3FsCGD4XvsuGCbzS4bMHrkn2jWtAmMjAyxZuVylBKCJ0+fCbZRU1VlZnsUBH09PbRr20bk6x494cfPOXQIP+fCGuZc+HfOoUMwzHYwfI+X78P27dqil3UPGBsZQV9PD+PGjEZjU1NEREYKtol+8QLW3buhW9euaKirC5vevWDVsQNev34j+s2rnPuMP4YPsMGfg/rBxLARls6dAR1NTZy6dE3k9g0baGOZowOG9OsFBQV5kdtsWbUYo4cNQrPGJjA20Me6RY4oLS3F4+dRIrf/ZUa/0xhuOxAjhgyGsZEhlixwhI62Fk6f9xe5/ZkLl6Cjo40lCxxhbGSIEUMGY9jggfA5cUqwjf+Va8jOyYHn1k1obWEO3QY6aGNpjqZNqr8SW21ODu5Ltj6X/O+PuuAWGPSL78/J0xhuOwgjhtr+/R7P/8V7fPHv93g+/z0east/j4/7Mbbj8XiMHBoazEbLvp3bMXTQQJiaGKNpk8bYsHo5klNS8frNuxrsXf5vqP/pkxgzcTL+6NETRiamWLRqHX4WFOD+7ZtVPq6pWQtMnzsf1n36QlJSSuQ2m3bsRt+BtjA0NoFJ4yZwXrkWX1NSEPu2Zt/tf+rVzQBcXuWOSP9b/+rrUPUfbUjVoWfP+Aco3t7eSE5OFtz/lffv3+PMmTO4cuUKbt68icjISMyePVvktpqamjhy5AjWrl2LsLAw5ObmYty4cZg1axZsbGwAAJMmTUJwcDBOnTqF6Oho/PXXX+jXrx9iY2P/0b/rV/kOHjyIFStWwMXFBW/evIGrqytWrVoFX19fxvMsWbIEjo6OePPmDfr27QsvLy/Mnj0b06dPx4sXL3D58mWYmvL/B08IwcCBA5GSkiK42tKmTRv06tULmZmZgueMi4vDxYsXcfXqVVy9ehWBgYHYvHkzAH6XSCsrK0ybNg3JyclITk6Gvr4+SktLoaenhzNnzuD169dYvXo1li9fjjNnys9ub9myBSdOnIC3tzeCg4ORk5Mj1BVu5cqV8Pb2hpeXF169egUnJyeMGzcOgYGB/2g/Z2dnQ01NTXA/NDQU3bt3Z5xZ7Nu3L758+YKEhASRz1FaWorv378zngcA1q9fD01NTUyZMuUfZfuVwuJivI7/hM7mTRjLO7dqisgY0VkrKy0tRV7BTyjLl1/hffD8JSwaG2Cj93l0c1iNIYvdcODiXZSUltY42+fPn5GRno5OVuVdmaSkpNCmbTtER0VW+bjo6Gh0tOrEWNbJygrRUVUf3OXm5gIAlJRFXzHLyMhAUFAQhlRokBcVFeHN27fo3KkjY1urTh0RGR0t8nmiol/AqtL2nTt1wuvXb1BUVCzyMQUFBSguLoaykpLI9UVFRbh2/QaGDhks6Dpaeb3onB0QGf1CaHsAiHrxAladOjBzWv2ds1g4JyEEj58+Q0JiIqNbVmtLCzx5FoaExCQAwLuYGERERaFrl84iX7dy7tcx79G5PfPgtnP71oh6+fsO1gp+/kRxcQmUlRRq/diioiK8fhuDzh0r7asO7RH54qXIx0S9eIXOHdozlnXp1AGv37wV7NsHD4Nh0aoFXNy2o3s/WwwbMwEHfY6ipKSk1hkFOTm2L9n8XFbOce36TQy1rfr78/rtO+H3uGMHREZX9R6/FNq+S6eOjPcYAH7k58PGdjh6DRqK2U6L8OZdjMjnK5Oby7+KpKws+regspQvn5GZkYF2Hcp/D6WkpGDeui1ev/hnJw6qkvf3b6hiFb9TFFXX6BipOqT5d3cJFRUV6Ojo1PhxBQUF8PX1hZ6eHgBg165dGDhwINzd3UU+z4ABAzBt2jSMHTsW7du3h4yMjKDxEBcXBz8/P3z69Am6uroAAGdnZ9y8eRPe3t6MK1m/K9+GDRvg7u6O4cP5/cuNjIzw+vVr7N+/H/b29oLnmT9/vmAbANi4cSMWLlzIGBfUvj3/wODBgwd48eIFvn79KmhIbNu2DRcvXsS5c+cwffp0APyDbx8fHygqKgIAxo8fj3v37sHFxQXKysqQkpKCnJwcYz+Ki4tj3bp1gvtGRkYICQnBmTNnMHLkSMG/cdmyZRg2jN8Xfffu3bh+/brgMXl5edi+fTvu378Pq78P0I2NjREUFIT9+/eje/futdrHoaGhOHPmDK5dKz+rm5KSInRVU1tbW7DOyEi4m4y7uzvy8vIE/w4ACA4OxuHDhxFZ4Uzq75b1PQ8lpaVQV1ZkLFdXVkR69vcaPYfPtQDk/yxEv06WgmWfvmbiyev3GNSlDbwWT0NiSjo2+pxHcWkJZg0XPQ6ssox0fvcg9UqNS3V1NSSLGN9Q8XHqlbqjqKurIyNDdHcjQgi2u7vDsnVrwQmByq5euQx5OTlY9yzv+vMtKwslJSUiXksN6RkZorNlZEBdvfK/Rx3FJSXIysqCpqaG0GN27NoDLU1NdKp0YFbm/oMAfM/NxZDBg0SuF+SsvB/V1JGe/ljkYzIyMqCuVunfpaZWnlODn/N7bi569x+EosJCiImLY8WSRYyG4mT7CcjNzcWQP0dCXEwMJaWlmDvLAQP6/foz8C07ByUlpVBXU2HmUFVFeqbobqf/hMd+b2hpqsOqbdXjcqryLSv7732ryliurq6KjMeZIh/D/www30t1NVXGvv305QuePk/BwL59sNdjK5I+foTLVg8UF5dg5tRJtc/JwX3J5ueyovsBgX9/fwZWn7Py91ZNFRlVfs8zRXwmmDmNDAywYfUKNDExRm5eHk6cPosJUx1w7oQvDBrpCz0nIQRbd+xEGwtzNDapWe+EzL/zqVTaZ6pqakhNqfo3tLYIIdi3cztaWljC6BfjVymqrtCGVD3QqFEjQSMFAKysrFBaWop3795V2SDbtm0bWrZsiTNnziAsLAwyMjIAgPDwcBBC0KQJ88rAz58/hQ7Ufkc+cXFxfPz4EVOmTMG0adME2xQXF0O50ln5du3aCf7++vUrvnz5gl5V9CV//vw5cnNzhTLn5+cjLi5OcN/Q0FDQiAKABg0a4OtX0X31K9q3bx8OHTqExMRE5Ofno7CwEJaWlgD4V4ZSU1PRoUP5QYq4uDjatm2L0r+vhLx+/RoFBQXo06cP43kLCwvRupoBzqK8evUKQ4YMwerVq4Wer/KZzbIufaLOePr5+WHt2rW4dOkStLS0AADfv3/HuHHjcPDgQWhoCB9cV+fnz5/4+fMnY5m0tDTEq3kMD5XygggtE+VaSDj2XriNXQsmMxpjpYRATUkBa6fyD6BbGOvj67dseF97UGVD6mrQc6yburK8++NO/rgFUftS1H6s/C+q6WPcNm/C+9gYHPL2qfLZLl+6hH79BzCuMgpeqdLTElL9vhPa14LPhvC2R3yP4sat2zhywEvkawOA/6XL6NLZClq/GD9T2/0o6t9VOb+8nBzOnjyGHz/y8eTZM2zz8IRew4Zo364tAP4YmKs3bmLzxvUwMTHGu3cxcNvuAU1NTQwZJPrAVSiHqM/mL9//mjly8iyu3wuEt+cWSEuL7sJUI0L7VngZY/MqPwP85aS0FGqqKlizbBHExcXRonlTfE1Ph89xv3/UkKrydTmwL9n4XFZU0++P8G+KiDCMnFW8x38/j0WrlrBoVV7oo7WFOUaOn4STZ85hmbOT0PO5bN2OmPdx8D3gVeVr3rt1HTu2lJ903bjN8+8szO1+9RtVW7u2bUH8+1h47D/8256Tov5XtCFVD5X9cFb3P4EPHz7gy5cvKC0tRWJiIszNzQHwr9CIi4vj+fPnEBdnHvIqKNS+y8mv8pU1LA4ePIiOHZln6iq/fsXqhrKystW+RmlpKRo0aCBybFfFwhuSkpJC2Up/0e3rzJkzcHJygru7O6ysrKCoqIitW7fiyZMnQs9VUcXKiWWvce3aNTRsyByvU9WBqiivX79Gz549MW3aNKxcuZKxTkdHBymVBiGXNRLLrkyVOX36NKZMmYKzZ8+id+/eguVxcXFISEjA4MGDhbJLSEjg3bt3MDERXXVp06ZNjCt3ALBmzRqsHNxeaFsVRXmIi4khPTuHsTwzOxfqytV/7m6ERmD1gdPYPs8eVq2YJwA0VRQhIS4O8QqV2UwaaiM96zsKi4shJSH8E2fdtgVaD7VHXn4BAKCwqBAAkJ6RAY0KBzmZmd+EukBWpK6hIXT1KTMzE2pqwick3DZvxsPAQBw4fETovSkTER6OxIQEbNq8hbFcVUUF4uLiSE9nnpXOzPwmdPZakE1dXehqVWZmJiTExaGsrMJY7nP0OA4f8cEBr91o0rixyOf7kpyMx0+fwWPrFpHrGTkrv+63zNrl/PaNn1Ol/ESLmJgYGv09ZqxZ0yb4EJ+Awz6+ggPW7Tt3YYr9BPTvy+++3MTUFMnJKTjs7fvLhpSqshLExcWErphkfsuCuqpKtY+tCe9T53HwxBkcdHepspjCr6iqKENcXBwZGcyrT5mZ34SuSJTh79tK23/L+vszwN+3GhrqkJCQYPwWGxsaIj0jE0VFRUK/n7/MycF9yebnsozg++O2+Zc5K199yvz2TehqWnlONeH3OFM4Z0ViYmJoadYciR8/Ca1z3bodAQ+D4LN/D3S0tarMatW1O5qZtRLcL/r7N/RbRgbUNcp/Q7O+fYNqNb+htbHb3Q2Pgx7C3esgNLVE/4ZSFBvoGKk6JikpWev+50lJSfjy5YvgfmhoKMTExISuKpUpLCzE2LFjMWrUKGzcuBFTpkxBaiq/slPr1q1RUlKCr1+/wtTUlHGrTXfDmubT1tZGw4YN8eHDB6HXE9X1rIyioiIMDQ1x7949kevbtGmDlJQUSEhICD1vba6sSElJCb0fjx49QufOnTFr1iy0/rsbVsWrXMrKytDW1sbTp08Fy0pKShiV8MzMzCAtLY2kpCShfBWLCFTn1atXsLa2hr29PVxcXITWW1lZ4eHDhygsLBQsu337NnR1dRld/vz8/DBx4kScPHkSAwcyDyqbNWuGFy9eIDIyUnCztbWFtbU1IiMjq826bNkyZGdnM27Lli0Tua2UhATMjPQQ8oLZNz/kZQwsmxiKfAzAvxK1Yp8f3GaPQ3cR5cxbNzFCUmo6o3GckJwGTRUlkY0oAJCXlYGBgQH0GzWCfqNGMDY2gbqGBp48DhVsU1RUhPDnYTC3sKwym7m5OZ48ZnYNehL6GOYW5eWoCSHYsnkTHty/B6/9B4Qa1RVduuiP5s3N0KQps6SzpKQkmjdrhtAnTxnLHz95Csu/T5BUZmHeCo8rbR/y+AnMzJpDUrJ8v3gfPYYDhw5j725PtDCrulz8xctXoKaqij+6dqlym+pzthL5GItW1eSs4v0DABCCwsLyyp4FBQXgVSpzLiYuBkJ+PVZOUlISZk1MERrGrGQZGhYBi5bNf/n46hzxO4f9R/2wz20DWjYT/XtdE5KSkjBr1gShT5njakOfPoNlK9FlxS1atRDaPuTJU5g1bybYt63NW+Hjp8+M709i0kdoaqjXuhElyMmxfcnm57LMxctXa/T9MWvWVPR7bF7Ve9zyl++xcESCtzGx0KxQcIIQApet7rgXEIjDe3dCr6FulTkBQE5eHg319QU3AyNjqKmr4/mz8pONRUVFiI54DrNWVZfnrwlCCHZt24KggPtw270PDXRrXkiIouoCbUjVsbLGQUpKCr59q1mfcRkZGdjb2yMqKgqPHj2Co6MjRo4cWWXDZ8WKFcjOzsbOnTuxePFiNG/eXFBEoEmTJhg7diwmTJiACxcuID4+Hs+ePcOWLVsYY3xq41f51q5di02bNsHT0xMxMTF48eIFvL29GWW6RVm7di3c3d2xc+dOxMbGIjw8HLt28bth9e7dG1ZWVhg6dChu3bqFhIQEhISEYOXKlQgLC6txdkNDQzx58gQJCQlIT+cfkJuamiIsLAy3bt1CTEwMVq1aJVQYZO7cudi0aRMuXbqEd+/eYd68efj27ZvgKpWioiKcnZ3h5OQEX19fxMXFISIiAnv27BEqsiFKWSOqT58+WLBgAVJSUpCSkoK0tDTBNnZ2dpCWlsbEiRPx8uVL+Pv7w9XVFQsWLBDk8PPzw4QJE+Du7o5OnToJnic7OxsA/71r2bIl46aiogJFRUW0bNkSUlJVd52RlpaGkpIS41bd1Tb7Ad1x/sETXAh4grjPqdh87CKS079hVC9+QQCPU1exbO9JwfbXQsKx3OskFo0bAvPGBkjLykFaVg6+/ygvezuqT2dk5f7ApqMXkZD8FYERr3Hw0l2Msan6gKUyHo+HMXZj/57z6T7ev3+PtatXQUZGFv369xdst3rlSuzeuVNwf/QYOzx5/Bg+3t5IiI+Hj7c3njx9AruxYwXbbNnkihvXrmGj6ybIycsjPT0d6enpKCgoYGTIzc3F3Tt3MGTYMIgyYZwdLly8BP9Ll/EhPh5u7tuRnJKCv/6e18Zz1x4sX71GsP1fI4bjS3Iytm73wIf4ePhfugz/S5dhP36cYJsjvkexe+8+rFuzCg0bNBBk+/HjB+O1S0tLcenyVdgOGgiJ6g4iAUwYO6ZSTg8kp6TirxF/59y9B8tXr62UMwVbt+9g5hxXvg8Pefsg9PETfPr0GfEJCTh6/CSuXLuOgQP6Cbbp/scfOHjEGw+DgvD5yxfcexCAYyf80LNHj2rzCnKPHIbz127hwrXbiEtIwpbdB5D8NQ2jbPlzAnkc8MYyl22Mx7yNjcPb2Dj8yM/Ht6xsvI2NQ1xCUvn+PXkWuw4fxYYl89FQRwvpGZlIz8jEjwqf39qYMGYUzl+6Cv/L1/AhPgFbPHYiOfWrYF6oHXv2YfnajYLtRw4fguSUVLjt2IUP8Qnwv3wNFy5fw8SxowXbjBoxFFnZ2di83RMJSUl4GBSCgz7HMLrCfEm1zsnBfcnW5xL4+/tzpYbfH7tROH/pCvwvX+W/x9s9kZySipHD+b8LO/Z4YfmaDYLtRw4fiuTkFLh57Pz7Pb6KC5evYuK4MYJtvA4eQXDoE3z8/BlvY2KweuMmvIuJZcwn5uLmjms3bmPzhrWQl5NDenoG0tMzUFDA7LpdFR6Ph2Gj7PhzPgXcR3zce2zdsAbSMjLoaVO+P7asW43De3cJ7hcVFeF9zDu8j3mHouIipKd9xfuYd/j88aNgm13bNuPeretYts4FcnJyyMxIR2ZGOn5W+g393aTl5aBnYQY9C/4JJg0jfehZmEFVv/pGJvXfQ7v21TF3d3csWLAABw8eRMOGDausrlaRqakphg8fjgEDBiAzMxMDBgxglA+vKCAgADt27MCDBw+g9HdVm2PHjsHc3BxeXl6YOXMmvL29BYUcPn/+DHV1dVhZWWHAgAEin/N/zTd16lTIyclh69atWLx4MeTl5dGqVSvMnz+/2ue1t7dHQUEBPDw84OzsDA0NDfz5558A+D/c169fx4oVKzB58mRB2fdu3bpV2XVKFGdnZ9jb28PMzAz5+fmIj4+Hg4MDIiMjMWrUKP5B9pgxmDVrFm5UKIW9ZMkSpKSkYMKECRAXF8f06dPRt29fRheZDRs2QEtLC5s2bcKHDx+goqKCNm3aYPny5b/MdfbsWaSlpeHEiRM4UaGctoGBgeAzo6ysjDt37mD27Nlo164dVFVVsWDBAixYsECw/f79+1FcXIzZs2czKina29vDx8enxvvpd+hv1RpZuT/gdeE20rJy0FivAfYtngZdTX7Xj7Ss70jOKD+5cPZeKIpLSrHR+zw2epeXKR7SrT1cHfgHCg3UVXFw6QxsOX4Rw5Zug7aqMsb164Yptj1RG/YTJ+LnzwJs3uSK7zk5aNmyFXZ7eTG6m6akJENMrLw7p4WlJVw2bYbX3j3Yt3cP9PT1sWnzFrRsVX6W+9zZswCAGdOmMl5vzbp1GGw7RHD/9q2bIAD69WMehJXpZ9MHWVnZ2H/wMNLS02FqYoI9Oz2g26ABf9+lpyOlwnxCeg0bYu/OHXBz98CpM+egqamBpYsWok+v8v1y5ux5FBUVYeHipYzXcpg+FbNmTBfcf/zkKZJTUjB0yGD8Sj+bPsjKzsb+Q0f+zmmMPZ4Vc2ZUyqmLvZ4ecNu+A6fO/p3TmZkzP78ALlvckPo1DdLS0jAyNIDrhnXoZ1M+XnDZooXYvW8/XDZvRea3b9DU0MCfw4fBYVrNqlD279kd2dnfse/oSaRlZKKxkSG8tqyDrg7/tyQ94xuSv6YxHvPn1LmCv1+/e49rdwOgq6OF26d9AACnLl1DUVExnFYzC/jMnGiH2ZPGobb69emFrOwc7Dvig7T0DJgaG2Gvhxt0G/BPWKVlZCA5tcK+1dXFHg83bN2xC6fO+UNLQwPLFs5Dn549BNvoaGtj/87t2OqxCyPGToKWpgbGjf4Tk8ePrfzyNcbFfcnW5xIAHj/9+/tjW4PvT5/e/Pf4sDf/PTYxxl6PbeXvcXql97ihLvbs2IatHjtx6tyFv9/j+ejT01qwTc7371i3aQvSMzKhqCCPZk2awHv/XrRqUX4Fuqy8+mSH8ulFAGDD6uUYWsMxhqPG2aPw50/s2rYZ379/RzOzlti8Yw/kKvyGfk1NAa/Cb2hGehpm2pdPwHv25DGcPXkM5q3bwn3vAQDAlQv8KUOcZ5f/JgGA88o16DvQtkbZ/gmDduZYEFA+VcBfHqsAAKE+5+A7yflfe12q/uGRigM7KM5Zu3YtLl68+K9WVPtfcD1fXSktLUXz5s0xcuRIbNiw4dcP+H+s+Lno+WK4RKLtQMaVLS5SlJPFz9xstmP8krSCMn5+z2I7xi9JK6qgKCXu1xuySFLHBIVZvy6GwzYpFS3O70uAvz+5/tmUVlRBYfY/m6S5LkkpayApM5ftGL/USE0BDjxDtmNUax9JYDsC9RvRK1IU9Q8kJibi9u3b6N69O37+/Indu3cjPj4ednZ2v34wRVEURVEUVe/RMVIsa9GiBRQUFETeKnbpovm4RUxMDD4+Pmjfvj26dOmCFy9e4O7du2jevGYDqh0cHKrcrw4ODv9yeoqiKIqiKOp/Rbv2sSwxMRFFRcJVfgB+CeuKcyCxgev56quvX78iJydH5DolJSXBPE/1Ee3a93vQrn2/F+3a9/vQrn2/D+3a93vRrn1UXaNd+1hmYGDAdoRqcT1ffaWlpVWvG0sURVEURVH/dbRrH0VRFEVRFEVRVC3RhhRFURRFURRFUVQt0YYURVEURVEURVFULdGGFEVRFEVRFEVRVC3RhhRFURRFURRFUVQt0YYURVEURVEURVFULdGGFEVRFEVRFEVRVC3RhhRFURRFURRFUVQt0YYURVEURVEURVFULdGGFEVRFEVRFEVRVC3RhhRFURRFURRFUVQt0YYURVEURVEURVFUbRGKoiiOKigoIGvWrCEFBQVsR6lSfchICM35O9WHjITQnL9TfchICM35O9WHjBT7eIQQwnZjjqIoSpScnBwoKysjOzsbSkpKbMcRqT5kBGjO36k+ZARozt+pPmQEaM7fqT5kpNhHu/ZRFEVRFEVRFEXVEm1IURRFURRFURRF1RJtSFEURVEURVEURdUSbUhRFMVZ0tLSWLNmDaSlpdmOUqX6kBGgOX+n+pARoDl/p/qQEaA5f6f6kJFiHy02QVEURVEURVEUVUv0ihRFURRFURRFUVQt0YYURVEURVEURVFULdGGFEVRFEVRFEVRVC3RhhRFURRFURRFUVQt0YYURVEURVEURVFULdGGFEVRFEVR1P8DAQEByM/PZzsGRf1n0IYURVGcExgYiMGDB8PU1BSNGzeGra0tHj16xHaseic1NbXKddHR0XWYpGqpqakYP348dHV1ISEhAXFxccaNqp3169fjx48fQsvz8/Oxfv16FhLVTH1pAKxbtw7p6elsx6iSjY0NEhIS2I5RI5MmTcKXL1/YjvFL8fHxKC4uZjsGxVF0HimKojjl+PHjmDRpEoYPH44uXbqAEIKQkBD4+/vDx8cHdnZ2bEcEAAwbNgw8Hk9oOY/Hg4yMDExNTWFnZ4emTZuykI5PS0sLhw4dgq2tLWP5tm3bsGrVKk4cuPbv3x9JSUmYM2cOGjRoILRPhwwZwlKyXzM2NsatW7fQuHFjtqMIiIuLIzk5GVpaWozlGRkZ0NLSQklJCUvJqiclJYWoqCg0b96c7SgAgJycHKFlhBBoamoiKCgIzZo1AwAoKSnVdTQAQJs2bUQuj4yMRLNmzSAjIwMACA8Pr8tYIlV10qZdu3Y4c+YMjI2NAQDm5uZ1GavGuPbZpLhFgu0AFEVRFbm4uMDNzQ1OTk6CZfPmzcP27duxYcMGzjSklJWVcfHiRaioqKBt27YghCAiIgJZWVmwsbHB6dOnsWXLFty7dw9dunRhJeOSJUswatQo2Nvbw8PDA5mZmRg/fjxevXqF06dPs5KpsqCgIDx69AiWlpZsR6nSzp07RS5PSkqCt7c3dHR0AACOjo51GUskQojIBn5UVBTU1NRYSMRUVQOguLgYI0aM4EwDQFVVVeRyQgisrKwE+5mthumLFy/Qu3dvdOrUiZEtKioK1tbWQg1pNllaWoLH40HUefsRI0awvi/LDB8+XOTykpISODo6QlFREQBw4cKFuoxFcRxtSFEUxSkfPnzA4MGDhZbb2tpi+fLlLCQSTUdHB3Z2dti9ezfExPi9pEtLSzFv3jwoKiri1KlTcHBwwJIlSxAUFMRKxoULF6J3794YN24czM3NkZmZiU6dOiE6Ohra2tqsZKpMX19f5AEWl8yfPx8NGzaEhATzf5mlpaU4evQoJCUlwePxWG1IqaqqgsfjgcfjoUmTJozGVElJCXJzc+Hg4MBavjL1pQHQoEEDWFpaYuHChYLvNyEEvXv3xqFDh2BkZMRqvoCAANjb26NDhw5Ys2aNIKOLiwtmz54NMzMzVvNVZG5uDj09PWzbtg2ysrIA+PuycePGuHHjBmeu6F68eBHdunUT+d4qKChAWVmZhVQU5xGKoigOMTExIfv27RNavm/fPmJqaspCItE0NDTIu3fvhJa/e/eOqKurE0IIiY6OJsrKynWcjCknJ4eMGjWKSEhIEAkJCeLj48Nqnspu3bpFbGxsSHx8PNtRqjR9+nRiaWlJXr9+zVguISFBXr16xVIqJh8fH+Lt7U14PB7x9PQkPj4+gtvJkydJSEgI2xEJIYQEBQURExMTsnr1alJSUiJYzqV9SQghGRkZZOjQocTa2pp8+vRJsJxLObOzs8no0aNJhw4dyPv37wkh3MpX5ufPn2TevHnEzMyMhIeHC5ZzLaufnx/R09MjR44cYSznWk6KW2hDiqIoTtm7dy+RkpIiDg4O5OjRo+TYsWNkxowZRFpaWmQDiy0qKirk0qVLQssvXbpEVFRUCCGExMTECP5mQ1BQEDE0NCRt27Ylr1+/JgcPHiSKiorkr7/+IpmZmazlqkhFRYVISUkRMTExoqCgQFRVVRk3rvD39yf6+vpk165dgmVcPMAKCAgghYWFbMeoVn1pABDC/z3S1dUlJ0+eJIRwM+eRI0eIjo4O2b9/P5GUlORcvjLXr18nenp6xNXVlZSUlHByXyYkJJCuXbuS4cOHC34juZiT4g7atY+iKE6ZOXMmdHR04O7ujjNnzgAAmjdvjtOnT3Oq8MD48eMxZcoULF++HO3btwePx8PTp0/h6uqKCRMmAOBXH2zRogVrGXv27AknJyds2LABkpKSaN68OaytrTF+/Hi0atUKnz59Yi1bmR07drAdoUaGDh2K9u3bY8KECbh27Rq8vb3ZjiRS9+7dUVpaipiYGHz9+hWlpaWM9d26dWMpWTklJSX4+fnB29sbXbt2xbp160SO6+KCmTNnonv37rCzs8OVK1fYjiPSpEmT0LVrV4wdO5bT1eX69++PsLAwTJo0CdevX2c7jkgGBgYIDAzEunXrYGFhgYMHD3L2s0lxA63aR1EU9Q+UlJRg8+bN2L17t6DMuLa2NubOnYslS5ZAXFwcSUlJEBMTg56eHisZAwMD0b17d6HlpaWlcHFxwapVq1hIVb8RQrB582bs3LkTaWlpiI6O5tR4lMePH8POzg6JiYlCY8+4MKC/stjYWIwdOxZhYWF4+fIlp/ZlRYWFhVi6dCkePHiACxcusD5GSpTS0lJ8//4dSkpKnD/437lzJx48eIBdu3ax9vv4K8HBwRg/fjwSExPx4sULzn42KXbRhhRFUdT/qKxUMlulkH/l/fv3iIuLQ7du3SArK1tlZTe2lJSU4OLFi3jz5g14PB7MzMxga2vL6Xmknj9/jqCgIEyYMKHKCm9ssLS0RJMmTbBu3TqR5eS5OGC+PjUAqP+W3NxcxMXFoXnz5pCSkmI7DsVBtCFFURTr1NTUEBMTAw0NDUH1sapkZmbWYbL6LSMjAyNHjsSDBw/A4/EQGxsLY2NjTJkyBaqqqti2bRvbEfH+/XsMGDAAnz9/RtOmTUEIQUxMDPT19XHt2jWYmJiwHbFekZeXR1RUFExNTdmOQv3Lrl27Bn9/f6ipqWHy5MmCua0A4Nu3bxgxYgTu37/PYkKm0tJSQXXByss/ffqERo0asZCKov43wp9oiqKoOubh4SGYo8PDw6PaG1ekpqZi/Pjx0NXVhYSEBMTFxRk3LnBycoKkpCSSkpIgJycnWD5q1CjcuHGDxWTlHB0dYWJigo8fPyI8PBwRERFISkqCkZERJ+ZlKpOfn4+goCC8fv1aaF1BQQGOHj3KQiphHTt2xPv379mOUa1r165h6tSpWLx4Md6+fctY9+3bN/Ts2ZOlZOWKioqwePFimJqaokOHDkJj4lJTU1n9np88eRJDhgxBSkoKQkND0bp1a5w4cUKwvrCwEIGBgazlqygnJwcjR46EvLw8tLW1sWbNGkYX07S0NM50lYyKisLGjRuxd+9epKenM9bl5ORg8uTJLCWjOIutKhcURVH1Wb9+/YiZmRnZu3cv8ff3JxcvXmTcuEBbW5tERkYSQghRUFAgcXFxhBBCPnz4QOTl5dmMJiAnJ0eio6OFlkdGRnIm47t374iBgQHh8XhETEyMdO/enXz58kWwPiUlhYiJibGYsNyFCxeImZkZ8fb2JmFhYSQqKopxY9uJEyeIuLg4GThwIOnatSuRkZEhx48fF6znyr5cs2YN0dbWJlu3biUrVqwgysrKZPr06YL1KSkphMfjsZavdevWZOfOnYL7Z8+eJQoKCuTQoUOEEO7sR0IIcXR0JE2aNCFnz54lBw8eJAYGBmTgwIHk58+fhBD292WZW7duESkpKdKiRQvSqFEjoqGhQe7fvy9Yz6V9SnEHbUhRFMUpYmJiJDU1VWh5eno6p/4npqCgQCIiItiOUS0FBQUSExMj+LusIfX06VOipqbGZjQBVVVVEhwcLLQ8KCiIM+XPhw4dSgYNGkTS0tJIbGwsGTx4MDEyMiKJiYmEEG4dYPF4PKGbmJiY4L9sqy8NAFNTU3LlyhXB/ffv35PGjRuTiRMnktLSUtZzysvLkw8fPjCWPXjwgCgqKhIvLy/W81XUqFEj8uDBA8H99PR00rFjR2JjY0MKCgo4k9XKyoosX76cEEJIaWkpcXNzIwoKCuTGjRuEEO58NiluoV37KIriFFLFsM2fP39yarCvvr5+lVm5olu3bowuZzweD6Wlpdi6dSusra1ZTFZu0KBBmD59Op48eQLCP7mHx48fw8HBAba2tmzHAwCEhITA1dUVGhoaMDU1xeXLl9G/f3/88ccf+PDhA9vxGOLj44VuHz58EPyXbTExMRg0aJDg/p9//okrV67AyckJ+/btYzEZ0+fPn9GyZUvBfRMTEwQEBCA0NBTjx49nvfqhkpKSoFpomR49euDKlStYtGgRdu3axVIyYenp6TAwMBDcV1dXx507d/D9+3cMGDAAP378YDFduVevXgm67vF4PCxatAgHDhwQfEYpShQ6jxRFUZywc+dOAPz/gR06dAgKCgqCdSUlJXj48CFjMDXbduzYgaVLl2L//v0wNDRkO45IW7duRY8ePRAWFobCwkIsXrwYr169QmZmJoKDg9mOB4D/vtvb28PKygqSkpIAgOLiYtja2sLT05PldHz5+fmQkGD+73LPnj0QExND9+7dcfLkSZaSCat4wMpFZQ2AimNiyhoAgwYN4sTcZgCgo6ODuLg4xndbV1cX9+/fh7W1Nezt7dkLB6BDhw64ceMGOnXqxFjevXt3wb7kCn19fbx584bxnisqKuL27duwsbHBsGHDWExXTlpaGllZWYxlY8aMgZiYGEaPHg13d3d2glGcRqv2URTFCWX/k01MTISenh5jILeUlBQMDQ2xfv16dOzYka2IDKqqqvjx4weKi4shJycnaASU4Up1wZSUFHh5eeH58+coLS1FmzZtMHv2bDRo0IDtaAyxsbF4+/YtCCEwMzPjVNW5Dh06YO7cuRg/frzQujlz5uDEiRPIyclh/SoFgF8WvSibLJotQ4cOhYWFBdatWye0LiAgAIMGDUJ+fj7r+3Lq1KkghODw4cNC6z5//owePXrgw4cPrOUMDAxESEgIli1bJnJ9QEAAfH19OTFxtKOjI5KTk3H27Fmhdd+/f0efPn3w7Nkz1t9zGxsb2NjYwNnZWWidn58f7O3tUVJSwnpOiltoQ4qiKE6xtrbGhQsXODU3jyi+vr7Vrmf7jDX1+2zatAmPHj3C9evXRa6fNWsW9u3bh9LS0jpOJqzy96aoqAg/fvyAlJQU5OTkWG/g15cGQGJiIt6+fYu+ffuKXJ+cnIzbt2/T73kNfPv2DV++fEGLFi1Ers/NzcXz589FTh5el/z9/fHw4cMqq8P6+fnhwIEDePDgQR0no7iMNqQoiqL+H4mOjq7xtubm5v9ikqotWLAAGzZsgLy8PBYsWFDtttu3b6+jVP9/xcbGYubMmVi0aFGVDQOqfiprhKSkpIDH40FbWxtt27ZldI2mKOrfQ8dIURTFOZ8+fcLly5eRlJSEwsJCxjo2D6xzcnKgpKQk+Ls6ZdvVNUtLS/B4PBBCGBMbl50zq7iMrS4qERERKCoqEvxN/bsaN26MzZs3Y9y4cULzNrGlPjQACCG4e/cuQkJCGDm7dOmCXr16VTtx+L+tqKgIzs7OOHjwIAoKCiAlJQVCCIqKiiAjI4Pp06dj69atQl2O2ZKXl4eTJ0+K3JdjxoyBvLw82xEZEhMTGTm5PvaQYlHdFwqkKIqq2t27d4mcnBxp0aIFkZCQIJaWlkRFRYUoKysTa2trVrNVLM1eVk668o3tMtMJCQmCm7+/PzExMSH79u0TzCO0b98+0rhxY+Lv789axvro6dOnxM7OjhgaGhIZGRkiKytLDA0NiZ2dHXn27Bnb8X4pPDycKCoqsh2DFBYWEkdHRyIrK0t4PB6RlpYmUlJShMfjEVlZWTJv3jxSWFjIdkzy6dMnYmlpScTFxYmFhQWxsbEhffr0IRYWFkRcXJy0adOGfPr0ibV8jo6OpGHDhuTUqVPk27dvguXfvn0jp06dIvr6+mTevHms5avo1atXRFdXl6ioqJAhQ4aQ6dOnk2nTppEhQ4YQFRUV0rBhQ/Lq1Su2YxJCCNm+fTvR09MT/JaX/Z7r6ekRDw8PtuNRHES79lEUxSkdOnRAv379sH79eigqKiIqKgpaWloYO3Ys+vXrh5kzZ7KWLTAwEF26dIGEhAQCAwOr3Zbt/v4Af1+uXbsWAwYMYCy/fv06Vq1ahefPn7OUrNzkyZPh6ekJRUVFxvK8vDzMnTsXR44cYSlZuYsXL2LkyJHo1asX+vbtC21tbRBC8PXrV9y+fRv37t3DmTNnMGTIELaj4vLly4z7hBAkJydj9+7d0NfXx40bN1hKxjdv3jycP38e7u7u6Nu3L1RUVAAAWVlZuHXrFhYtWoThw4djx44drOYcMmQIcnNzcfz4caHCLMnJyRg3bhwUFRVx8eJFVvJpamri9OnT6Nmzp8j19+7dw+jRo5GWllbHyYRZW1tDR0cHvr6+QlNYFBYWYuLEiUhOTmZ97NGGDRuwbds2LF++XOh7fuvWLWzatAnOzs5YuXIlqzkpjmG1GUdRFFWJgoICef/+PSGEEBUVFfLy5UtCCCGRkZHEwMCAxWT1j4yMDHn9+rXQ8tevXxMZGRkWEgmragLmtLQ0Ii4uzkIiYS1atCCbNm2qcv3mzZuJmZlZHSaqmqjJeLW1tcmYMWPIly9f2I5HNDQ0yL1796pcf/fuXaKhoVGHiUSTl5cnkZGRVa4PDw8n8vLydZiISV5enkRFRVW5PiIigtV8FcnKylZ7xenFixdEVla2DhOJpqenV+2V+gsXLhBdXd26C0TVC3SMFEVRnCIvL4+fP38C4M/bEhcXJ6j2lJ6ezma0elHIoaLmzZtj48aNOHz4MGRkZADwJzbeuHEjmjdvzmq2nJwcwQS8379/F+QD+GO3rl+/Di0tLRYTlnv//j2GDx9e5fqhQ4dizZo1dZioalyoHFid/Px8aGhoVLleXV0d+fn5dZhINFlZ2WorHH779g2ysrJ1mIjJ2toaCxYswIkTJ6Ctrc1Yl5qaisWLF1d5taquqaqqIjY2FmZmZiLXv3//nhNVWjMyMtC0adMq1zdp0gTfvn2rw0RUfUAbUhRFcUqnTp0QHBwMMzMzDBw4EAsXLsSLFy9w4cIFockn61pVhRxE4cJcI/v27cPgwYOhr68PCwsLAEBUVBR4PB6uXr3KajYVFRXweDzweDw0adJEaD2PxxM51xAbTExMcPHiRSxevFjk+kuXLsHY2LiOU/0aEVFghG31pQEwevRo2NvbY/v27ejTpw+UlZUBANnZ2bhz5w4WLlwIOzs71vLt3bsXAwYMgJ6eHlq2bAltbW3weDykpKTg5cuXMDMzw7Vr11jLV9G0adNgb2+PlStXok+fPoysd+7cgaurK+bPn892THTo0AEuLi7w8fERmoC7uLgYrq6u6NChA0vpKK6iY6QoiuKUDx8+IDc3F+bm5vjx4wecnZ0RFBQEU1NTeHh4sFo9KTExUfB3REQEnJ2dsWjRIlhZWQEAQkND4e7uDjc3NwwdOpSllEw/fvzA8ePHGZPd2tnZsV4lKzAwEIQQ9OzZE+fPn4eamppgnZSUFAwMDKCrq8tiwnLnz5/H6NGjBRN2Vj4QvH37Nk6dOlXtVau6dPToUWzduhWxsbEA+GfSFy1aJHJC4br28eNHDBgwAG/fvq22AaCnp8dqzsLCQsybNw9HjhxBcXGxYGxPYWEhJCQkMGXKFOzYsUNozE9dKi0txa1bt/D48WOkpKQAAHR0dGBlZQUbGxuIiYmxlq2yLVu2wNPTU1AJD+A39HV0dDB//vwqT1LUpRcvXsDGxgY/f/5E9+7dGZ/Nhw8fQlpaGnfu3KlyPizqv4k2pCiK4oySkhIEBQXB3NycE109qlMfCjnUB4mJidDX1+fUQZ8ooaGh8PT0RGhoqNBB67x58wSNabZt374dq1atwpw5c9ClSxcQQhAcHIw9e/Zg48aNcHJyYjtivWoA5OTkCMq0A/ycbdu2ZW16g/ouPj6esS+NjIxYTsT0/ft3HD9+XORn087Ojr7vlBDakKIoilNkZGTw5s0bzv0PtjJZWVmEh4cLjTV68+YN2rRpw4lxHgAQExODgIAAfP36VWj8zOrVq1lKJezHjx8i5w3jwliz+sTIyAjr1q3DhAkTGMt9fX2xdu1axMfHs5SMoijq/x86RoqiKE5p1aoVPnz4wPmGFJcLOZQ5ePAgZs6cCQ0NDejo6DDGyvB4PE40pNLS0jBp0qQqy3JzYaxZfZKcnIzOnTsLLe/cuTOSk5NZSFS/ffr0CSoqKkITBRcVFSE0NBTdunVjJVdRURFWrFiBCxcuQE1NDTNnzsSkSZME61NTU6Grq8uZ709+fj6eP38ONTU1oaITBQUFOHPmjFDjn6LqA+5cP6coigLg4uICZ2dnXL16FcnJycjJyWHcuGLfvn24e/cu9PX10bt3b/Tu3Rt6enq4c+cO9u3bx3Y8AMDGjRvh4uKClJQUREZGIiIiQnALDw9nOx4AYP78+fj27RseP34MWVlZ3Lx5E76+vmjcuLHQnEhsioqKwsaNG7F3716h6pE5OTmYPHkyS8mYTE1NcebMGaHlp0+fRuPGjVlIxFRUVITFixfD1NQUHTp0gLe3N2N9amoqxMXFWUpXLjk5GR06dICBgQFUVFRgb2+P3NxcwfrMzExYW1uzls/FxQVHjx6Fg4MDbGxs4OTkhBkzZjC24UqHo5iYGDRv3hzdunVDq1at0KNHD0ajPjs7m9EIZNPevXvRu3dvjBw5Evfv32esS09P52RRGYplLJRcpyiKqlLlOXDKbmX3uSQvL4/s37+fODk5kfnz55MDBw6Q3NxctmMJKCoqkri4OLZjVEtHR4c8efKEEMLP++7dO0IIIZcuXSJdunRhM5rArVu3iJSUFGnRogVp1KgR0dDQIPfv3xesT0lJ4cxn89y5c0RcXJz07duXrF+/nmzYsIH07duXSEhIkAsXLrAdj6xZs4Zoa2uTrVu3khUrVhBlZWUyffp0wfqUlBTC4/FYTMg3YcIE0qlTJ/Ls2TNy584d0q5dO9K2bVuSmZlJCGE/p6mpKbly5Yrg/vv370njxo3JxIkTSWlpKac+k0OHDiWDBg0iaWlpJDY2lgwePJgYGRmRxMREQgh3vj+enp5ETk6OzJ49m4wbN45IS0sTV1dXwXqu5KS4hTakKIrilICAgGpvVM1NnjyZeHl5sR2jWoqKiiQ+Pp4QQoiBgQEJCgoihBDy4cMHTkzSSQghVlZWZPny5YQQQkpLS4mbmxtRUFAgN27cIIRw7wArLCyMjB07lrRp04a0bt2ajB07loSHh7MdixBSfxoAurq6ggY+IYQUFBSQIUOGEEtLS5KRkcF6TllZWcH3psznz59J06ZNydixY8nnz585sR8JIURLS4tER0czls2aNYs0atSIxMXFsb4vy5iZmZETJ04I7oeEhBAtLS2yatUqQgj3vucUN9AxUhRFcUr37t1rtN2sWbOwfv36aif3/N1q09XM1tb2X0xSM6ampli1ahUeP36MVq1aQVJSkrHe0dGRpWTlmjZtinfv3sHQ0BCWlpbYv38/DA0NsW/fPjRo0IDteACAV69e4dixYwD4Y8sWLVoEPT09/Pnnn/Dz8+Pc3DJt27bF8ePH2Y4h0ufPn9GyZUvBfRMTEwQEBKBnz54YP3483NzcWExXLjs7m1E5VFpaGufOncNff/0Fa2tr1vevjo4O4uLiYGhoKFimq6uL+/fvw9raGvb29uyFqyQ/P19oXqY9e/ZATEwM3bt3x8mTJ1lKxhQfH88YX2hlZYX79++jV69eKCoq4sRcVxT30IYURVH10vHjx+Hs7FynDamazg3F4/E4Mcj7wIEDUFBQQGBgIAIDAxnreDweJxpS8+fPF4yXWLNmDfr27YsTJ05ASkoKPj4+7Ib7m7S0NLKyshjLxowZAzExMYwePRru7u7sBBPh+vXrEBcXR9++fRnLb926hdLSUvTv35+lZHz1pQFgbGyM6OhoxrgyCQkJnD17Fn/99RcGDRrEYjqgZ8+eOHnyJHr16sVYXrYve/TowU4wEZo1a4awsDChIjy7du0CIYQTJ50AQENDAx8/fmR8Nlu0aIH79++jZ8+e+Pz5M3vhKM6i5c8piqqXFBUVERUVRQf//j/z48cPvH37Fo0aNarTRnJ1yibidXZ2Flrn5+cHe3t7lJSUcKLxbG5ujs2bNwvNb3bz5k0sWbIEUVFRLCXjmzp1KgghOHz4sNC6z58/o0ePHvjw4QPr+3LJkiWIjIzErVu3hNYVFxdjxIgRuHr1Kms5ExMT8fbtW6EGc5nk5GTcvn2bEw3TTZs24dGjR7h+/brI9bNmzcK+ffuEpmeoa3Z2dtDS0sKOHTuE1r169QrW1tbIyMhg/bNJcQttSFEUVS/RhhRVV/z9/fHw4UN4eHiIXO/n54cDBw7gwYMHdZxMmKysLN68ecM4qw4ACQkJaNGiBfLy8tgJ9rf60gAoLi7Gjx8/qpyAtaSkBJ8+fYKBgUEdJ/tnBg4ciEOHDnGmu2x1Pn36BF1d3TqfmDk6OhrPnz+vsoLgq1evcO7cOaxZs6ZOc1HcRhtSFEXVS2w0pHbu3Inp06dDRkYGO3furHZbtrrNLViwABs2bIC8vDwWLFhQ7bbbt2+vo1RMv8pVEVsZ/xd+fn6wtbWFvLx8nb+2jo4OTp48iZ49ezKW3717F3Z2dvj69WudZ/pf1JcGgJKSEiIjIzl7Yqc+nXji+r4sw8Y4XYp76BgpiqKoGvLw8MDYsWMhIyNT5dUJgN3xRxERESgqKhL8XZWKk/PWtepyVcRmxv/FjBkz0LFjR1YOBG1tbTF//nz4+/vDxMQEAPD+/XssXLiQM2NRauPhw4fIz89nO8Yv0XPSv0992ZdsjNOluIc2pCiKomooPj5e5N9cUrF7WU27mtV1VxoudIH7N7F5ILh161b069cPzZo1g56eHgD++/vHH39g27ZtrOWiqP9v6kuDj/p30YYURVH10rhx46ocv0DVjpmZWb3oSkP9mrKyMkJCQnDnzh1ERUVBVlYW5ubm6NatG9vRKIqi/t+hDSmKojjF0NAQkydPxsSJE9GoUaMqt/Py8qrDVMImT55c7fojR47UUZL/HZtnVq2travtwnf//v06TPP/A4/HE1QarEqrVq1w/fp16Ovr12EyiqKo/19oQ4qiKE5ZuHAhfHx8sH79elhbW2PKlCkYNmwYpKWl2Y7G8O3bN8b9oqIivHz5EllZWUID/amqWVpaMu4XFRUhMjISL1++ZL1y2/9nCQkJgrF01P+uvo7n4yK6L6n6hDakKIrilLlz52Lu3LmIiorCkSNH4OjoiFmzZsHOzg6TJ09GmzZt2I4IgF8Su7LS0lLMmjWLdpGrhaqKdqxduxa5ubl1nIai/hm2ruo+fPgQnTt3hoQE83CuuLgYISEhgi6dy5cvh5qaGhsRa42OPaLqk7ot0k9RFFVDFhYW8PT0xOfPn7FmzRocOnQI7du3h4WFBY4cOcLJ/9mKiYnBycmp2op+VM2MGzeuXnWPrMjAwACSkpJsx/h/ob40AG7cuIGGDRvW+etaW1sjMzNTaHl2djasra0F95ctWwYVFZU6TCZs8uTJ+P79u9DyvLw8Rlfp169f14v5ueg4XQqgDSmKojiqqKgIZ86cga2tLRYuXIh27drh0KFDGDlyJFasWIGxY8eyHVGkuLg4FBcXsx2jVrjYlSY0NBQyMjJsx/hHXr58Scce1UJkZCTOnj2LoKAgoRMkXGgAAPyuvDt27MDs2bOxceNGfPz4kbG+a9eurHQ/JoSI/P5mZGSwMo9ZdXx9fUWWss/Pz8fRo0cF9/X19SEuLl6X0YQ8evQI48aNg5WVFT5//gwAOHbsGIKCggTbeHl50dLnFO3aR1EUt4SHh8Pb2xt+fn4QFxfH+PHj4eHhgWbNmgm2sbGxYb0KWeVJZQkhSE5OxrVr1+rd2B42r+4NHz6ccb9sP4aFhWHVqlUspWK6ffs2evbsKeg+dfLkSbi5uSE2NhYNGjSAo6Mja/OG1Td2dnbYv38/FBUVkZubixEjRuDOnTuQlJREUVER2rZtizt37rDeeNLV1cWLFy+grq6O+Ph4dO7cGQC/SMfly5exbds2PH78mPG7VJfKvjc8Hg8TJ05kNOJKSkoQHR0tyMy2nJwcEEJACMH3798ZJ0hKSkpw/fp1aGlpsZiQ6fz58xg/fjzGjh2LiIgI/Pz5EwDw/ft3uLq64vr16ywnpLiER7jYP4aiqP8scXFx9OnTB1OmTMHQoUNFdpHKy8vDnDlz4O3tzUJCvordZgB+tz5NTU307NkTkydPFhqzwKb3798jLi4O3bp1g6ysrNBZ7I8fP0JXV5eVs8CTJk1i3K+4H6urOleXxMXFkZycDC0tLZw/fx6jRo3CrFmz0KlTJ4SHh2P37t3w9vbGmDFj2I5aY4qKioiKiqrz8XwV9+WiRYtw/vx5nDt3Dm3atMHLly8xcuRI9OvXD9u3b6/TXJWJiYkhJSUFWlpaGDNmDFJSUnDt2jXIycnh58+f+PPPPyEjI4OzZ8+ykq/se+Pr64uRI0dCVlZWsE5KSgqGhoaYNm0aJ66YiImJVXvVm8fjYd26dVixYkUdpqpa69at4eTkhAkTJjC+J5GRkejXrx9SUlLYjkhxCG1IURTFKYmJifWif3x9kJGRgVGjRuH+/fvg8XiIjY2FsbExpkyZAhUVFbi7u7MdsV6oeFDdtWtX9OrVC+vWrROs37ZtG86cOYOnT5+ymLJ2Tp48iSFDhtR596+K+7Jly5ZYvXo1Ro4cKVh//fp1zJ8/HzExMXWaq7KKOY2NjXHo0CFGNc4nT57gzz//FOriV9fWrVsHZ2dnznXjqygwMBCEEPTs2RPnz59njHmTkpKCgYEBdHV1WUzIJCcnh9evX8PQ0JDRkPrw4QPMzMxQUFDAdkSKQ7hzypSiKArA169fkZKSgo4dOzKWP3nyBOLi4mjXrh1LyUT7+vUr3r17Bx6PhyZNmnCqi4qTkxMkJCSQlJSE5s2bC5aPGjUKTk5OnGpIhYWF4c2bN+DxeGjevDnatm3LdiSRYmNjsXPnTsYyW1tbbNy4kaVETJWzleHxeJCRkYGpqSm6desGOzu7Ok7GzAIAqampaNmyJWNdixYtWG+clCnL+fPnT2hrazPWaWtrIy0tjY1YDGvWrGE7wi91794dABAfHw99fX2IiXF7eH6DBg3w/v17GBoaMpYHBQXRiqyUENqQoiiKU2bPno3FixcLNaQ+f/6MLVu24MmTJywlY8rOzsacOXPg5+eH0tJSAPxuS6NGjcKePXugrKzMckL+2J5bt25BT0+Psbxx48ZITExkKRXTp0+fMGbMGAQHBwvGxWRlZaFz587w8/PjTNGG169fIyUlBbKysoL3u0xpaSlKSkpYSsbk4eGBtLQ0/PjxA6qqqiCEICsrC3JyclBQUMDXr19hbGyMBw8esLZvV61aBTk5OcFVHzMzM8G69PR0KCgosJKrsl69ekFCQgI5OTmIiYlBixYtBOuSkpI40W0uNTUVzs7OuHfvHr5+/So03pErn0uAX80yKysLT58+xdevX4W+RxMmTGApGdOMGTMwb948HDlyBDweD1++fEFoaCicnZ2xevVqtuNRHEMbUhRFccrr169FzhXVunVrvH79moVEok2bNg2RkZG4du0arKyswOPxEBISgnnz5mHatGk4c+YM2xGRl5cHOTk5oeXp6emcmeB48uTJKCoqwps3b9C0aVMAwLt37zB58mRMmTIFt2/fZjkhX69evQQHqcHBwYwroxEREWjUqBFb0RhcXV1x4MABHDp0CCYmJgD4Y+RmzJiB6dOno0uXLhg9ejScnJxw7ty5Os/XrVs3vHv3DgBgZmaG+Ph4xvrr168zGixsqXylp/L36MqVK/jjjz/qMpJIEydORFJSElatWoUGDRpwsgJnmStXrmDs2LHIy8uDoqIiIyuPx+NMQ2rx4sWC8vEFBQXo1q0bpKWl4ezsjDlz5rAdj+IYOkaKoihOUVdXx9WrV2FlZcVYHhISgoEDB+Lbt28sJWOSl5fHrVu30LVrV8byR48eoV+/fsjLy2MpWbmBAweiTZs22LBhAxQVFREdHQ0DAwOMHj0apaWlrBxIVyYrK4uQkBC0bt2asTw8PBxdunQRWS65rlW+eqegoAB1dXXB/bLSzVw4EDQxMcH58+dhaWnJWB4REYERI0bgw4cPCAkJwYgRI5CcnMxOyGp8+PABUlJSQldRKdEUFRXx6NEjofebi5o0aYIBAwbA1dVV5Akervnx4wdev36N0tJSmJmZceZKKcUt9IoURVGc0qdPHyxbtgyXLl0SdI/LysrC8uXL0adPH5bTlVNXVxfZfU9ZWRmqqqosJBK2detW9OjRA2FhYSgsLMTixYvx6tUrZGZmIjg4mO14AIBGjRqhqKhIaHlxcTErE5yK8qviJ1xoQJVJTk4WOY9ZcXGxoNqYrq6uyIlRuYCOQakdfX19Tk5OLsrnz5/h6OhYLxpRAP8qJNfG5FLcw+0RfxRF/ee4u7vj48ePMDAwgLW1NaytrWFkZISUlBROFUdYuXIlFixYwDirn5KSgkWLFnFm/iMzMzNER0ejQ4cO6NOnD/Ly8jB8+HBEREQIun2xzc3NDXPnzkVYWJjggDAsLAzz5s3Dtm3bWE5XM8XFxUhKSmI7BgB+Wf4ZM2YgIiJCsCwiIgIzZ84UVJ178eIFjIyM2IpYrdTUVKxfv57tGL8UFxfHqOLHlh07dmDp0qVISEhgO8ov9e3bF2FhYWzH+KVhw4Zh+PDhQrcRI0Zg7NixWLNmjaB7KkXRrn0URXFOXl4eTpw4gaioKMjKysLc3BxjxowROadUXWrdujWjX39sbCx+/vwpGB+TlJQEaWlpNG7cGOHh4WzFBAAUFRXBxsYG+/fvR5MmTVjNUh1VVVX8+PEDxcXFgrm3yv6uXNI5MzOTjYi/FBUVhTZt2nBiYH9KSgrGjx+Pe/fuCb4vxcXF6NWrF44dOwZtbW08ePBA8PngGi7ty+qwmVNVVZXxO5SXl4fi4mLIyckJ/Uay/Z25fPmy4O+0tDSsX78ekyZNQqtWrYSy2tra1nU8kSZOnIiLFy9CRUUFbdu2BSEEERERyMrKgo2NDaKiopCQkIB79+6hS5cubMelWEa79lEUxTny8vKYPn062zGEDB06lO0INSYpKYmXL19yevA5wD+jTv0+Ojo6uHPnDt6+fYuYmBgQQtCsWTNBIQ9AeDLpuhQdHV3teq6c6a+qjHyZz58/11ESYfXpOyPqN1PUFUcej8eZxrOOjg7s7Oywe/duQan20tJSzJs3D4qKijh16hQcHBywZMkSBAUFsZyWYhu9IkVRFOfExMQgICBAZInc+lZ+1s/PD7a2tqxMmLlw4UJISkpi8+bNdf7a/5+IqiJZUX5+PmJiYjhzIMhlYmJi4PF4Isf1lC3nwkG1mJgYGjRoACkpKZHrCwsLkZKSwnpO6vfT1NREcHCw0JX8mJgYdO7cGenp6Xjx4gX++OMPZGVlsROS4gx6RYqiKE45ePAgZs6cCQ0NDejo6AiVyK1vDakZM2agY8eOrAyiLywsxKFDh3Dnzh20a9dOqDG3ffv2Os8kSklJCS5evCiYkNfMzAy2trYQFxdnOxoAfkn+0aNHVzmuKDk5GTExMXWcSrSSkhL4+PgI5hWqfCLi/v37LCXjU1dXx5YtW9CrVy+R61+9eoXBgwfXcSphBgYG2LJlC0aOHClyfWRkJCcmjc7JyRG5nMfjQVpausqGIFW14uJivH37Vqgh9fbtW0HDWUZGhvNX+6m6QRtSFEVxysaNG+Hi4oIlS5awHeW3YPOi/8uXLwVXUyof6HPlIOD9+/cYMGAAPn/+jKZNm4IQgpiYGOjr6+PatWucKIrRsmVLdOzYETNnzhS5PjIyEgcPHqzjVKLNmzcPPj4+GDhwIFq2bMmZ97lM27Zt8eXLlyorIWZlZXGiCl3btm3x/PnzKhtSVV1Vq2sqKirVvsd6enqYOHEi1qxZI+imxpaqukvyeDzIyMjA1NQU3bp1Y/0Eyvjx4zFlyhQsX74c7du3B4/Hw9OnT+Hq6iqo0BkYGMiJ+c4o9tGGFEVRnPLt2zf89ddfbMf4f+HBgwdsR/glR0dHmJiY4PHjx1BTUwMAZGRkYNy4cXB0dMS1a9dYTgh07dq12rE7ioqK6NatWx0mqtqpU6dw5swZDBgwgO0oIs2YMaPaOdYaNWoEb2/vOkwk2vr16/Hjx48q14uaTJgNPj4+WLFiBSZOnIgOHTqAEIJnz57B19cXK1euRFpaGrZt2wZpaWksX76c1aweHh5IS0vDjx8/oKqqCkIIsrKyICcnBwUFBXz9+hXGxsZ48OAB9PX1Wc2pra0NNzc3pKamAgC0tbXh5OQkOMFnY2ODfv36sZaR4g46RoqiKE6ZMmUK2rdvDwcHB7aj/BaKioqIioqi8+NUQV5eHo8fP0arVq0Yy6OiotClSxfk5uaylKx+0tXVRUBAAKcrNVK/T69evTBjxgyhK2dnzpzB/v37ce/ePRw7dgwuLi54+/YtSyn5/Pz8cODAARw6dEhwpfn9+/eYMWMGpk+fji5dumD06NHQ0dHhxGThQHnXSSUlJZaTUFxFr0hRFMUppqamWLVqleDgunKJXEdHR5aS1T/W1tbVdvthe7wMAEhLS4ucHDY3N5eO7/gHFi5cCE9PT+zevZtz3frqq5KSEqSnp4PH40FdXZ31rmcVhYaGYt++fULLW7dujdDQUAD8K6pcmOds5cqVOH/+PKO7rqmpKbZt24YRI0bgw4cPcHNzw4gRI1hMyUQbUNSv0IYURVGccuDAASgoKCAwMBCBgYGMdTwejzakasHS0pJxv6ioCJGRkXj58iXs7e3ZCVXJoEGDMH36dBw+fBgdOnQAADx58gQODg6cmVemTGxsLEJCQpCSkgIejwdtbW107twZjRs3ZjuaQFBQEB48eIAbN26gRYsWQiciLly4wFKycp8+fYKXl5fIfeng4MBqt66K/P39sW3bNoSFhaG4uBgAICEhgXbt2mHRokWcmA5BT08Phw8fFqrMefjwYcF+zMjIgKqqKhvxGJKTkwX7saLi4mKkpKQA4F9RFXVipa6dO3cOZ86cQVJSEgoLCxnr2J4jkOIW2pCiKIpTuDDu4FdKSkoQFBQEc3PzXx6gGBgYsDaRsIeHh8jla9eu5UyXuZ07d8Le3h5WVlaMCWRtbW3h6enJcjq+7OxsTJgwAVeuXIGysjK0tLRACEFaWhpycnIwePBgHD16lBNnr1VUVDBs2DC2Y1QpKCgI/fv3h76+PmxsbGBjYwNCCL5+/YqLFy9i165duHHjBusTne7fvx+Ojo6YPHkyFi1aBG1tbUHOW7duYfTo0di1axemTZvGas5t27bhr7/+wo0bNwSFEZ49e4a3b98Kusc9e/YMo0aNYjUnwL9CPmPGDBw6dAitW7cGAERERGDmzJno2bMnAODFixdVVsesKzt37sSKFStgb2+PS5cuYdKkSYiLi8OzZ88we/ZsVrNR3EPHSFEUxUmFhYWIj4+HiYkJJCS4d85HRkYGb968Yf1/+v/E+/fv0aFDB2RmZrIdRSA2NhZv3rwBwB/Ib2pqynKichMmTBBU5uvYsSNj3ZMnTzB9+nRYWlrC19eXpYT1R/v27dG1a9cqG/lOTk4ICgrCs2fP6jgZk6mpKZYtW4YpU6aIXH/kyBG4uLggLi6ujpMJS0hIwL59+xgTMM+YMQOGhoZsR2NISUnB+PHjce/ePcZJk169euHYsWPQ1tbGgwcPUFRUBBsbG9ZyNmvWDGvWrMGYMWMYY1xXr16NzMxM7N69m7VsFPfQhhRFUZzy48cPzJ07V3BQGhMTA2NjYzg6OkJXVxdLly5lOSFf+/btsXnz5irnw+GyY8eOYcmSJfjy5QvbURjK/nfEtbE9KioquHXrllAjqszjx4/Rr18/OjlnDcjKyiIyMhJNmzYVuf7t27do3bo18vPz6zgZU33JWR+9ffuW0eirah+zRU5ODm/evIGBgQG0tLRw584dWFhYIDY2Fp06dUJGRgbbESkO4d5pXoqi/tOWLVuGqKgoBAQEMMrL9u7dG2vWrOFMQ8rFxQXOzs7YsGED2rZtKzTZLRe6eQ0fPpxxnxCC5ORkhIWFYdWqVSylEnb48GF4eHggNjYWANC4cWPMnz8fU6dOZTlZueoad2w3/Nq0aYN79+5BVVUVrVu3rjYP2+M7GjRogJCQkCoPnkNDQ9GgQYM6TiWsRYsWOHDgANzd3UWuP3jwIGvzCEVHR6Nly5YQExNDdHR0tduam5vXUaqaa9asGZo1a8Z2jCrp6OggIyMDBgYGMDAwwOPHj2FhYYH4+HhOzB1GcQttSFEUxSkXL17E6dOn0alTJ8YBoZmZGSe60ZQpa+TZ2toychKsYxO/AABOiElEQVRCwOPxUFJSwlY0ASUlJUY2MTExNG3aFOvXr2e160xFq1atgoeHB+bOnQsrKysA/INpJycnJCQkYOPGjSwnBAYPHoxp06bh8OHDaNeuHWNdWFgY64UxhgwZAmlpaQDgRAGE6jg7O8PBwQHPnz9Hnz59oK2tDR6Ph5SUFNy5cweHDh3Cjh072I4Jd3d3DBw4EDdv3oSNjY1QzsTERFy/fp2VbJaWlkhJSYGWlhYsLS2rnByYC79DCxYswIYNGyAvL48FCxZUu+327dvrKFX1evbsiStXrqBNmzaYMmUKnJyccO7cOYSFhQmdnKIo2rWPoihOkZOTw8uXL2FsbMzonx4VFYVu3bohOzub7YgAIFRRsLLu3bvXUZL6TUNDA7t27cKYMWMYy/38/DB37lykp6ezlKxcVlYWxowZg1u3bkFFRQVaWlrg8XhITU1FdnY2+vbti5MnT0JFRYXtqPXC6dOn4eHhgefPnwsO9MXFxdG2bVssWLBAaE4ktiQkJMDLywuPHz8WVJXT0dGBlZUVHBwcWBuDlJiYiEaNGoHH4yExMbHabQ0MDOoolWjW1tbw9/eHiooKrK2tq9yOx+NxYjoGACgtLUVpaalgbO6ZM2cQFBQEU1NTODg40GkZKAbakKIoilO6d++OP//8E3PnzoWioiKio6NhZGSEOXPm4P3797h58ybbEesNY2NjPHv2DOrq6ozlWVlZaNOmDT58+MBSsnKqqqp4+vSpUAnxmJgYdOjQgVPjjt6+fYvQ0FChg2oud1PisqKiIkFDWUNDg7XqlhRVUVJSEvT19YW6yBJC8PHjRzRq1IilZBQX0YYURVGcEhISgn79+mHs2LHw8fHBjBkz8OrVK4SGhiIwMBBt27ZlO6LAo0ePsH//fnz48AFnz55Fw4YNcezYMRgZGaFr165sx4OYmJigC1BFqampaNSoEX7+/MlSsnJz586FpKSkULceZ2dn5OfnY8+ePSwlqz9UVVVrPE6LS5UaqX/m8uXLNd6Wa3Ox1Qfi4uJITk4W+t3MyMiAlpYW690lKW6hY6QoiuKUzp07Izg4GNu2bYOJiQlu376NNm3aIDQ0FK1atWI7nsD58+cxfvx4jB07FuHh4YJGyffv3+Hq6sra+AmAeaB169YtKCsrC+6XlJTg3r17nCqNfPjwYdy+fRudOnUCwK+C9/HjR0yYMIExroLtMRSlpaUQExMTufzTp0+snamuOKYoIyMDGzduRN++fRljzm7dusWZAiPJycm4d+8e1NTU0Lt3b0ZXqby8PLi7u2P16tUsJuS7du0a/P39oaamhkmTJqF58+aCdd++fcOIESNY6Y5W03FwXBgjVZsxRVyYLBooH+daWW5uLmRkZFhIRHEZvSJFURT1D7Ru3RpOTk6YMGECYyxXZGQk+vXrJ+j+xYayg31Rg9AlJSVhaGgId3d3DBo0iI14DNWNm6iIzTEUOTk5mDp1Kq5cuQIlJSU4ODhg9erVEBcXB8C/wqerq8v6QSsAjBgxAtbW1pgzZw5j+e7du3H37l1cvHiRnWB/e/bsGWxsbFBaWoqioiLo6enB399fUAGPK/vy5MmTmDBhAvr164fs7GyEhYXh0KFDGDt2LKdyct2kSZNqvK23t/e/mOTXyk7aeHp6Ytq0aZCTkxOsKykpwZMnTyAuLo7g4GC2IlIcRK9IURTFupycnBpvy4Wy4gDw7t07dOvWTWi5kpIS6+N6SktLAQBGRkZ49uwZNDQ0WM1TnQcPHtRou0+fPlV5RejftmrVKkRFReHYsWPIysrCxo0b8fz5c1y4cEFwNYUr5yRv3bqFLVu2CC3v27cvJ6YOWL58OYYPH46DBw8iLy8PS5cuRffu3XHnzh20bt2a7XgC27ZtE1STBIBz585h0qRJKCgoqHKSXjYcPXoUo0aNElRtLFNYWIhTp05hwoQJLCXjY7txVBsREREA+N/lFy9eMK6USklJwcLCAs7OzmzFo7iKUBRFsYzH4xExMbEa3bjC2NiY3LlzhxBCiIKCAomLiyOEEOLr60uaN2/OZrT/lxQVFQX7uK41atSIPHjwQHA/PT2ddOzYkdjY2JCCggKSkpLCmc9mo0aNiJubm9ByNzc30qhRIxYSMamqqpJ3794xlm3ZsoWoqqqSp0+fcmZfysvLkw8fPjCWPXjwgCgqKhIvLy/O5BQTEyOpqalCy9PT0zmRryJra2vy7ds3oeXZ2dnE2tq67gNVYeLEiSQ7O5vtGFQ9Qa9IURTFuopXJRISErB06VJMnDiRMcbD19cXmzZtYiuikBkzZmDevHk4cuQIeDwevnz5gtDQUDg7O3NifAcAODo6wtTUFI6Ojozlu3fvxvv37zkxX09NERav+KSnpzPKSKurq+POnTvo27cvBgwYgEOHDrGWrbJ169ZhypQpCAgIEHx/Hj9+jJs3b3ImZ0FBAeP+4sWLISYmBhsbGxw5coSlVExKSkpITU2FkZGRYFmPHj1w5coVDBo0CJ8+fWIxXTlSxXieT58+McZGckFAQAAKCwuFlhcUFODRo0csJBKtPl1Fo9hHG1IURbGu4pxL69evx/bt2xnzCtna2qJVq1Y4cOAA7O3t2YgoZPHixcjOzoa1tTUKCgrQrVs3SEtLw9nZWWh8ClvOnz8vssJX586dsXnz5nrVkGKTvr4+3rx5wzioVlRUxO3bt2FjY4Nhw4axmI5p4sSJaN68OXbu3IkLFy6AEAIzMzMEBwejY8eObMdDy5YtERISAnNzc8ZyZ2dnEEKE5hNjS4cOHXDjxg1BAZQy3bt3FzSm2NS6dWvweDzweDz06tVLMOcRwB/PEx8fL5g0nG3R0dGCv1+/fs0YP1pSUoKbN2+iYcOGbEQTKS8vD5s3b8a9e/fw9etXQVfpMlyYNoLiDtqQoiiKU0JDQ7Fv3z6h5e3atcPUqVNZSFQ1FxcXrFixAq9fv0ZpaSnMzMygoKDAdiyBjIwMkWellZSUODHRbX1hY2MDb29vDBgwgLFcQUEBt27dQp8+fVhKJlrHjh1x4sQJtmOINGHCBAQGBsLBwUFo3aJFi0AIgZeXFwvJmJycnBASEiJyXY8ePXD16lX4+vrWcapyZZX7IiMj0bdvX8bvjpSUFAwNDTFixAiW0jFZWloKGn09e/YUWi8rK4tdu3axkEy0qVOnIjAwEOPHj0eDBg1qPLUA9d9Eq/ZRFMUpTZs2xaBBg+Du7s5YvnDhQly9ehXv3r1jKVnVPn78CB6PBz09PbajMLRs2RIODg5CV8h27doFLy8vvH79mqVktVexMmJd+/btG758+SKoLFdZbm4unj9/zriyWpfqY7GWmgoODka7du2EiilwzebNm+Hg4AAVFZU6fV1fX1+MGjWK02W5ExMTQQiBsbExnj59Ck1NTcE6KSkpaGlpCSpgcoGKigquXbuGLl26sB2FqgdoQ4qiKE65fv06RowYARMTE8a8QnFxcTh//rzQVQG2FBcXY926ddi5cydyc3MB8K9QzJ07F2vWrIGkpCTLCYEjR45gzpw5WLRokeBM8L179+Du7o4dO3Zg2rRpLCesOSUlJURGRrLSkKqtVq1a4fr169DX16+T1xMTE/vlWfOysTT1rVx3fXnf2c5ZWFgoshsaW3Ob1WdGRka4fv06Y94wiqoK7dpHURSnDBgwALGxsdi7dy/evn0LQgiGDBkCBweHOjswrYk5c+bA398fbm5ujKIYa9euRXp6usjuiXVt8uTJ+PnzJ1xcXLBhwwYAgKGhIby8vFgvi1xb9emcX0JCAoqKiurs9WpaQr4+qi/vO1s5Y2NjMXnyZKFuiFxtOMfExCAgIEBko48rRXo2bNiA1atXw9fXlzGXFEWJQq9IURRF/QPKyso4deoU+vfvz1h+48YNjB49GtnZ2SwlEy0tLQ2ysrKcGsNVGx8/foSuri6nugBVhc1uiP/f1Jd9yVbOLl26QEJCAkuXLhU5nsfCwqJO81Tn4MGDmDlzJjQ0NKCjo8PIyuPxEB4ezmK6cq1bt0ZcXBwIITA0NBTqXcCVnBQ30CtSFEVxTlZWFp4+fSryrCVXrqTIyMjA0NBQaLmhoSFjIkeuqDgugW3Dhw+v8bYXLlwAAE5djeSa6OhotGzZEmJiYowKaaJUrpZH1W+RkZF4/vw5mjVrxnaUX9q4cSNcXFywZMkStqNUq6yQB0XVBG1IURTFKVeuXMHYsWORl5cHRUVFobOWXGlIzZ49Gxs2bIC3t7dgIHxZNzqulD83MjKqduwMW2V8uTa/TX1naWmJlJQUaGlpCSqkiepswsWuXtT/xszMrN5U4Pz27Rv++usvtmP80po1a9iOQNUjtCFFURSnLFy4EJMnT4arqyun+6dHRETg3r170NPTE3SfiYqKQmFhIXr16sW46lJ2VaWuzZ8/n3G/qKgIERERuHnzJhYtWsRKJoBOePm7xcfHC644xsfHs5zm96Klp4VVrNK4ZcsWLF68GK6urmjVqpVQNzQuVWn866+/cPv2bZGl77kmKysL586dQ1xcHBYtWgQ1NTWEh4dDW1ubU3NeUeyjDSmKojjl8+fPcHR05HQjCuCXyK08TwvXup/NmzdP5PI9e/YgLCysjtNQ/xYDAwORf/9/UF+Gcf/xxx+QlZWtk9dSUVFhNDAJIejVqxdjG64Um9i5c6fgb1NTU6xatQqPHz8W2ehzdHSs63giRUdHo3fv3lBWVkZCQgKmTZsGNTU1+Pv7IzExEUePHmU7IsUhtNgERVGcMnz4cIwePRojR45kO8pvwcV5cD58+ABLS8tazT/0bzp37hzOnDmDpKQkFBYWMtZxdWB3QUFBlXP3nDx5EkOGDIG8vHwdp8IvD/K40jUW4E8hEBAQgLi4ONjZ2UFRURFfvnyBkpISp4qixMXFwdvbG3FxcfD09ISWlhZu3rwJfX39KucW+zcFBgbWeFu25jYrY2RkVKPteDwea12NK+vduzfatGkDNzc3RhGRkJAQ2NnZISEhge2IFIfQhhRFUZxy+PBhrF+/HpMmTRJ51tLW1palZP8M2/PLiOLm5oa9e/dy4oBg586dWLFiBezt7XHw4EFMmjQJcXFxePbsGWbPng0XFxe2IwqUlpbCxcUF+/btQ2pqKmJiYmBsbIxVq1bB0NAQU6ZMYTsiVFVVGfeLiorw48cPSElJQU5ODpmZmSwlY0pMTES/fv2QlJSEnz9/Cvbl/PnzUVBQwInpAwB+o6V///7o0qULHj58iDdv3sDY2Bhubm54+vQpzp07x3ZE6jdTVlZGeHg4TExMGA2pxMRENG3aFAUFBWxHpDiEdu2jKIpTyiaJXb9+vdA6LnRVqS02z1W1bt1aqAtQSkoK0tLSsHfvXtZyVbR3714cOHAAY8aMga+vLxYvXgxjY2OsXr2aMwf9ZTZu3AhfX1+4ubkxJjNu1aoVPDw8ONGQ+vbtm9Cy2NhYzJw5k9VxcZXNmzcP7dq1Q1RUFNTV1QXLhw0bhqlTp7KYjGnp0qXYuHEjFixYAEVFRcFya2treHp6spiMr6oqjTweDzIyMmjUqBGnrobXBzIyMiKv1r97945T1U8pbqANKYqiOKVyuXPqn6tcxldMTAyampro0aMHZ8olJyUloXPnzgAAWVlZfP/+HQAwfvx4dOrUCbt372YzHsPRo0dx4MAB9OrVizFg3tzcHG/fvmUxWfUaN26MzZs3Y9y4cZzJGRQUhODgYKGpAgwMDPD582eWUgl78eIFTp48KbRcU1MTGRkZLCRiKqvSWBVJSUmMGjUK+/fvr7Iral1ZsGCByOVljT5TU1MMGTIEampqdZyMaciQIVi/fj3OnDkjyJeUlISlS5cKjYulKNqQoiiKs6obh0JVr7i4GIaGhujbty90dHTYjlMlHR0dZGRkwMDAAAYGBnj8+DEsLCwQHx/PuUIDnz9/hqmpqdDy0tJSFBUVsZCo5sTFxfHlyxe2YwiUlpaKvLr86dMnxpUftqmoqCA5OVlorE9ERAQnqrf5+/tjyZIlWLRoETp06ABCCJ49ewZ3d3esWbMGxcXFWLp0KVauXIlt27axmjUiIgLh4eEoKSlB06ZNQQhBbGwsxMXF0axZM+zduxcLFy5EUFAQzMzMWMu5bds2DBgwAFpaWsjPz0f37t2RkpICKysrTnU1priBNqQoiuKUkpISuLq6cnocSn0gISGBmTNn4s2bN2xHqVbPnj1x5coVtGnTBlOmTIGTkxPOnTuHsLCwWk3cWxdatGiBR48eCVXGO3v2LFq3bs1SKqbLly8z7hNCkJycjN27d6NLly4spRLWp08f7NixAwcOHADAP+ufm5uLNWvWYMCAASynK2dnZ4clS5bg7Nmz4PF4KC0tRXBwMJydnTlRuMPFxQWenp7o27evYJm5uTn09PSwatUqPH36FPLy8li4cCHrDamyq03e3t6Csuw5OTmYMmUKunbtimnTpsHOzg5OTk64desWazmVlJQQFBSE+/fvIzw8HKWlpWjTpg169+7NWiaKwwhFURSHrFu3jhgbG5Pjx48TWVlZEhcXRwgh5PTp06RTp04sp6s9RUVFwb+hrvXo0YP4+/uz8to1VVJSQoqKigT3T58+TebOnUs8PT3Jz58/WUwm7PLly0RZWZls3ryZyMnJka1bt5KpU6cSKSkpcvv2bbbjEUII4fF4jJuYmBjR1tYmY8aMIV++fGE7nsDnz59JkyZNSPPmzYmEhATp1KkTUVdXJ02bNiWpqalsxxMoLCwkdnZ2RExMjPB4PCIpKUnExMTIuHHjSHFxMdvxiIyMDHnz5o3Q8jdv3hAZGRlCCCHx8fFEVla2rqMJ0dXVJa9evRJa/vLlS6Krq0sIIeT58+dEXV29rqNR1D9Gq/ZRFMUppqam2L9/P3r16sWomPT27VtYWVmJHEzPZRX/DXXt7NmzWLp0KZycnNC2bVuhctzm5uZ1nqmypKQk6OvrC43zIITg48ePaNSoEUvJRLt16xZcXV3x/PlzwZnq1atXw8bGhu1oQsrGG4qJibGcRLT8/Hz4+fkxzvqPHTu2zuZjqo24uDhERESgtLQUrVu3RuPGjdmOBIBfUMbCwgIHDhwQjDcrKirCtGnTEBUVhYiICAQHB2PcuHGsT9asoKCAq1evokePHozlAQEBGDx4ML5//86JqRkcHR1hamoqNK/V7t278f79e+zYsYOdYBQn0YYURVGcIisri7dv38LAwIDRCHn9+jU6dOiA3NxctiMKcH0eHFEH0DwejzOTdQL8sTvJycnQ0tJiLM/IyICWlhYnMtY3hw8fhoeHB2JjYwHwi03Mnz+fU9XwqN8jJCQEtra2EBMTg7m5OXg8HqKjo1FSUoKrV6+iU6dOOHbsGFJSUliv2jh27FiEhobC3d0d7du3B4/Hw9OnT+Hs7IzOnTvj2LFjOHXqFLZt28bqhOENGzbE5cuX0bZtW8by8PBw2Nra4tOnTywlo7iIjpGiKIpT6sM4FEB4Hpw+ffpAUVERbm5unJkHh+0z0DVR1qirLDc3lxYa+QdWrVoFDw8PzJ07F1ZWVgCA0NBQODk5ISEhARs3bmQ5IV/lsVxlKlZwq+lkrv8mrlea69y5MxISEnD8+HHExMSAEII///xTcGIH4FfA5IL9+/fDyckJo0ePRnFxMQD+WE57e3t4eHgAAJo1a4ZDhw6xGRMZGRlQVlYWWq6kpIT09HQWElFcRq9IURTFKVeuXMH48eOxbNkyrF+/HuvWrcO7d+9w9OhRXL16FX369GE7IgB+aXFFRUUcPnwY6urqgitngYGBmDp1quBqAJsePnyIzp07Q0KCec6suLgYISEh6NatG0vJyg9QPT09MW3aNMjJyQnWlZSU4MmTJxAXF0dwcDBbEYWoqqqKbPRVPKieOHEiJk2axEI6Pg0NDezatQtjxoxhLPfz88PcuXM5cyAoJiYmuDpaUcUrpl27dsXFixeFJhmuS9bW1tVWmnv37h14PB7rlebqk9zcXHz48AGEEJiYmHDi6n1FLVu2hIODA+bMmcNYvmvXLnh5eeH169csJaO4iF6RoiiKUwYPHozTp0/D1dUVPB4Pq1evRps2bXDlyhXONKKA+jEPjrW1tchuc9nZ2bC2tma121xERAQA/hWpFy9eMPajlJQULCws4OzszFY8kVavXg0XFxf079+fUWr65s2bmD17NuLj4zFz5kwUFxczJuytSyUlJWjXrp3Q8rZt2wquAnDBnTt3sGLFCri4uKBDhw4AgKdPn2LlypVYtWoVlJWVMWPGDDg7O+Pw4cOs5eRipbnLly+jf//+kJSUrPLKXhlbW9s6yVQbCgoKnBifWZUFCxZgzpw5SEtLQ8+ePQEA9+7dg7u7Ox0fRQmhV6QoiqL+ATU1NcFZ6IpjuYKCgjBixAikpqayHRFiYmJITU2FpqYmY3lMTAzatWvH6oDuMpMmTYKnp6fgIJXLRowYgT59+jAm4wX4XZZu376N8+fPY9euXThw4ABevHjBSsa5c+dCUlIS27dvZyx3dnZGfn4+9uzZw0quylq2bIkDBw4IJmMuExwcjOnTp+PVq1e4e/cuJk+ejKSkJJZS8sfL3LlzR+hq06tXr2BjY4PPnz8jPDwcNjY2dXa1T0xMDCkpKdDS0qq2kAgXxkEOHz4cPj4+UFJS+uV0BhcuXKijVL/m5eUFFxcXwdxrhoaGWLt2LSdK3lPcQq9IURTFSWFhYXjz5g14PB6aN28uNPCXbVyeB6fsgIXH42HixImQlpYWrCspKUF0dLTQASxbvL29BX9/+vQJPB6PExOdinLr1i1s2bJFaHmvXr2wcOFCAMCAAQOwdOnSOs1VcRwPj8fDoUOHcPv2bXTq1AkA8PjxY3z8+JFTB4FxcXEiG89KSkr48OEDAH6RDLa7ImZnZ+Pr169CDam0tDTBiQgVFRUUFhbWWaayaoyV/+YiZWVlQXdYUeOOuKa4uBgnTpzAsGHDMHPmTKSlpUFWVpZz3Q8p7qANKYqiOOXTp08YM2YMgoODoaKiAgDIyspC586d4efnB319fXYD/s3DwwPW1tYwMzNDQUEB7OzsEBsbCw0NDfj5+bGareyAhRACRUVFRjlpKSkpdOrUibWuZ5WVlpZi48aNcHd3F1RkVFRUxMKFC7FixQpOle5WU1PDlStX4OTkxFh+5coVQbGBvLw8wSD/ulLWTbJM2UmHuLg4AICmpiY0NTXx6tWrOs1VnbZt22LRokU4evSo4IppWloaFi9ejPbt2wMAYmNjoaenx2ZMDBkyBJMnTxZZaW7o0KEA+F0SmzRpwmpOrqp4oqTi31xVeSLzylfzKaoy2pCiKIpTJk+ejKKiIrx58wZNmzYFALx79w6TJ0/GlClTcPv2bZYT8unq6iIyMpIxD86UKVM4MQ9O2QGLoaEhnJ2dheaP4pIVK1bg8OHD2Lx5M7p06QJCCIKDg7F27VoUFBTAxcWF7YgCq1atwsyZM/HgwQN06NBBcFB9/fp1QZXGO3fuoHv37nWa68GDB3X6er/D4cOHMWTIEOjp6QnmEUtKSoKxsTEuXboEgF+UYNWqVazmrA+V5u7du4d79+7h69evQleojhw5wlKq+qtjx46IiIgQqhxLUaLQMVIURXGKrKwsQkJChEqdh4eHo0uXLsjPz2cpWf2Tn58PQoigIl5iYiL8/f1hZmbGmQlkdXV1sW/fPqFB8ZcuXcKsWbM4U7ijTHBwMHbv3o13796BEIJmzZph7ty5nOkqWZ8QQnDr1i1B2e5mzZqhT58+nLoKWYarlebWrVuH9evXo127dmjQoIFQVUl/f3+WkglLTU2Fs7OzoNFX+fCT7fFcZerDROYUd9CGFEVRnNK0aVMcO3ZMUMmrzNOnT2FnZ4f379+zlKzquW9E4UK1LBsbGwwfPhwODg7IyspC06ZNISUlhfT0dGzfvh0zZ85kOyJkZGQQHR0t1DXq3bt3sLS0pA1niqpGgwYN4Obmxpm5oqrTv39/JCUlYc6cOSIbfUOGDGEpGVN9mMic4g7atY+iKE5xc3PD3LlzsWfPHrRt2xY8Hg9hYWGYN28etm3bxmq2sjERv8KV/9mGh4cLuh+dO3cOOjo6iIiIwPnz57F69WpONKQsLCywe/du7Ny5k7F89+7dsLCwYClV1UpLS/H+/XuR3ajYnJerPqoPXdLy8vKwefPmKnOWFcZgS2FhYb25GhoUFIRHjx7B0tKS7SjVqg8TmVPcQRtSFEVxysSJE/Hjxw907NhRMJFscXExJCQkMHnyZEyePFmwbWZmZp1m43qFrMp+/PghKHxw+/ZtDB8+HGJiYujUqRMSExNZTsfn5uaGgQMH4u7du7CysgKPx0NISAg+fvyI69evsx2P4fHjx7Czs0NiYqLIiWS50HiuL37VJY0rpk6disDAQIwfP56TOadOnYqTJ0+yPpasJvT19YW+N1xEx0ZRtUEbUhRFcQqd8PD3MTU1xcWLFzFs2DDcunVLUG3u69evnJm3ycjICDExMdizZw/evn0LQgiGDx+OWbNmcWoCWQBwcHBAu3btcO3aNU4eVNcn+/btg4+PD+e7pN24cQPXrl1Dly5d2I4iUkFBAQ4cOIC7d+/C3NwckpKSjPWV5xNj044dO7B06VLs378fhoaGbMep1rFjx7Bv3z7Ex8cjNDQUBgYG2LFjB4yMjDjTBZHiBjpGiqIo6h+6d+8ePDw8BPNdNWvWDPPnz0fv3r3ZjgaA353Pzs4OJSUl6NWrl6Di4aZNm/Dw4UPcuHGD5YSAuLg4kpOToaWlxViekZEBLS0tTl3lkZeXR1RUFExNTdmOUu+pq6vj6dOnMDExYTtKtYyMjHD9+nU0b96c7SgiWVtbV7mOx+Ph/v37dZimeqqqqvjx4weKi4shJycn1Oir6x4GVfHy8sLq1asxf/58uLi44OXLlzA2NoaPjw98fX3rZZVM6t9DG1IURXFOXFwcvL29ERcXB09PT2hpaeHmzZvQ19dHixYt2I4HgD+Gx8nJCX/++SesrKwA8Lt+nTt3Dtu3b8ecOXNYTsiXkpKC5ORkWFhYCAZRP336FEpKSmjWrBnL6fgDu1NSUoQaUomJiTAzM0NeXh5LyYT17NkTixcvRr9+/diOUu8tWbIECgoKnO+Sdvz4cVy6dAm+vr6C6pf10adPn6Crq8tqRURfX99q19vb29dRkuqZmZnB1dUVQ4cOhaKiIqKiomBsbIyXL1+iR48erE8STXELbUhRFMUpgYGB6N+/P7p06YKHDx/izZs3MDY2hpubG54+fYpz586xHREA0LBhQyxbtkyowbRnzx64uLjgy5cvLCWrHxYsWAAA8PT0xLRp0xgHqSUlJXjy5AnExcURHBzMVkQh/v7+WLlyJRYtWoRWrVoJnVGnZZFrbt68eTh69CjMzc053SWtdevWiIuLAyEEhoaGQjnDw8NZSlY7SkpKiIyMhLGxMdtRfmnz5s1wcHAQTMhe12RlZfH27VsYGBgwGlKxsbEwNzenlUQpBjpGiqIoTlm6dCk2btyIBQsWCAolAPwuLJ6eniwmY8rJyRF5ZcLGxgZLlixhIZFoz549w9mzZ5GUlITCwkLGugsXLrCUCoiIiADAn0voxYsXkJKSEqyTkpKChYUFnJ2d2Yon0ogRIwCAUfCElkX+Z6KjowXV216+fMlYx6WxZzWt1Ml19emcuaurK0aOHMlaQ8rIyAiRkZFCRSdu3LgBMzMzVjJR3EUbUhRFccqLFy9w8uRJoeWamprIyMhgIZFotra28Pf3x6JFixjLL126hMGDB7OUiunUqVOYMGECbGxscOfOHdjY2CA2NhYpKSkYNmwYq9nKxhlMmjQJnp6enCl+UR1aFvn3qS/jTNasWcN2hP8ctht9ixYtwuzZs1FQUABCCJ4+fQo/Pz9s2rQJhw4dYjUbxT20IUVRFKeoqKggOTkZRkZGjOURERFo2LAhS6mENW/eHC4uLggICGCMkQoODsbChQsZ8yI5OjqyktHV1RUeHh6YPfv/2rv3qKjL/A/g7y/IRREQQRIQYRCRVUGxVmW9lFqZmweRQpISL3ndVFbAy3pj3bRcFRWss4maZaYprptu9RMVVAw1UEDwhly8lHJRyCUxL8zM7w8Pk+MMOmjO8/3q+3VO5+DznXN8HwydzzzP5/O8B3t7eyQmJkKlUmHChAlwc3MTkul+69evFx3BZByLTPT0Gz16NOrq6jBjxgzcuHEDkZGR8PDwQGJiIt566y3R8Uhm2CNFRLIyY8YMHD58GCkpKfDz80NOTg4qKioQFRWFqKgo2XxCfH+h1xBJkoRd2mlnZ4eTJ0/C29sbLi4u2LdvHwICAnD69Gn0798fZWVlQnIp3alTp4welQwJCRGUSJnkeuz0Xmq1GitWrMDWrVuN5pTLpLmHubfXR+7klPXq1avQaDQGw3CI6nFHiohkZdGiRRg1ahQ8PDyg1WrRsWNH1NXV4e2338bcuXNFx9NRwjGvli1b4pdffgFwdzjGiRMnEBAQgGvXruHGjRuC0ylPaWkphg4dioKCAl1vFPBbTw97pEwn52On91qwYAHWrl2LmJgYzJs3D3PmzMH58+fx9ddfY/78+aLjmUxOfWdKUVlZicLCQkiSBEmS0KpVK9GRSIbEzcEkIjLCysoKX375JYqKirB161Zs3LgRhYWF+OKLL2BpaSk6nlFarVb4uX5j+vTpgz179gAAhg0bhujoaIwbNw7Dhw/HgAEDBKdTnujoaKhUKlRUVKBZs2Y4efIkMjIy8MILL2D//v2i4ylK/bHTb775BtbW1khMTMTp06cxbNgwtG3bVnQ8nS+//BJr1qxBXFwcmjRpguHDh2Pt2rWYP38+jhw5IjqeyeT495Nc1dTUYMSIEXB3d8eLL76Ivn37wt3dHe+88w7+97//iY5HMsOjfUQkXP0obFPIZSwyAGzYsAFLly5FUVERAMDPzw/Tp0/HiBEjBCe7q7q6Gjdv3oS7uzs0Gg2WLVuG77//Hr6+vpg3bx6cnJxER1QUFxcXpKenIzAwEI6OjsjKykKHDh2Qnp6O2NhY3SRCejilHDu1s7PD6dOn0bZtW7i5ueHbb79Ft27dUFpaiqCgIMW8sf7xxx/h7u4u2w+j7vXnP/8Z69atE9bHOWzYMOTl5WHVqlUIDg6GJEk4dOgQoqOjERgYiK1btwrJRfLEo31EJNz9b0CPHTsGtVqNDh06AADOnj0LS0tLPP/88yLiGbV8+XLMmzcPkydPRq9evaDVapGZmYmJEyfi6tWrmDZtmtB8dXV1+O9//4uBAwcCuHvx7YwZMzBjxgyhuZRMrVajefPmAO4WVZcvX0aHDh3g5eWFwsJCwemURSnHTtu0aYOysjK0bdsWvr6+2L17N7p164bs7GzY2NgIyRQWFmbya+t7zTw9PZ9UnEbRaDQoLi5GZWUlNBqN3rO+ffsCAL777jsR0XS+/fZbpKamonfv3rq1gQMHYs2aNbyMmwywkCIi4e4dhbx8+XLY29vj888/1+2Y/Pzzzxg9ejT69OkjKqKBVatW4V//+heioqJ0a0OGDEGnTp3w97//XXgh1aRJE0yaNAmnT58WmuNp0rlzZ+Tn58PHxwc9evTAkiVLYG1tjeTkZFk0xitJ/bHTgIAA3bHT9PR07NmzR1bHTocOHYq0tDT06NED0dHRGD58ONatW4eLFy8K+xl3dHQU8vs+riNHjiAyMhIXLlwwOGoop3vYnJ2djX6PHR0duYtPBni0j4hkxcPDA7t370anTp301k+cOIFXX30Vly9fFpRMn62tLU6cOAFfX1+99aKiIgQEBODmzZuCkv2mX79+iI6OfmouFRUtNTUVtbW1CAsLQ2lpKQYPHowzZ87A2dkZW7ZsQf/+/UVHVAylHjs9cuQIDh06BF9fX05pbKSuXbvCz88PCxYsgJubm8EADLkUiMnJyUhJScGGDRt0xwvLy8sxcuRIhIWFYcKECYITkpywkCIiWbG3t8eOHTsM3pSmp6djyJAhuuNAonXu3BmRkZGYPXu23vrChQuxZcsWFBQUCEr2m5SUFMyaNQvTpk3D888/Dzs7O73ngYGBgpI9Paqrq+Hk5MSpaEQPYWdnh+PHjxt8+CQ3QUFBKC4uxq1bt3SDTy5evAgbGxu0b99e77U5OTkiIpKM8GgfEcnK0KFDMXr0aCQkJKBnz54A7n4KPH369Eb1BjxpCxYsQEREBDIyMtCrVy9IkoTvv/8eaWlpsmlGjoiIAKB/IXD92G45HaVRspYtW4qOoFim9MvIwdmzZ7F//36jOeUwAn3btm0N3nMlpzf6PXr0QHFxsewLKe7gU2NwR4qIZOXGjRuIi4vDp59+ijt37gC42+/z7rvvYunSpQa7KiIdO3YMK1aswOnTp3V3XsXGxiIoKEh0NADAhQsXHvjcy8vLTEmeDrW1tVi8eDHS0tKMvqkWdfGyEimlX2bNmjWYNGkSXFxc0Lp1a72dR0mShBcqSUlJmDNnDkaOHIk1a9Zg9OjRKCkpQXZ2Nt577z0sWrRIaL57/ec//8HcuXMxffp0BAQEwMrKSu+50nbIN2/ejJCQEFn9m0Tmx0KKiGSptrYWJSUl0Gq18PX15T9WJurWrRvS0tLg5OSEf/zjH4iLi0OzZs1Ex3oqDB8+HAcOHMCIESOM9nhER0cLSqY8SumX8fLywl/+8hfMnDlTdBSj/P39ER8fj+HDh8Pe3h7Hjx+Hj48P5s+fj+rqanz00UeiI+pYWBheXarkHXIHBwfk5eVx0MwzjoUUEdEjKikpwfr161FaWoqVK1fC1dUVu3btgqenp8GwDHNp2rQpioqK0KZNG1haWqKsrAyurq5CsjxtWrRogW+//Ra9evUSHUXxlNIvI/c3y82aNcPp06fh5eUFV1dX7NmzB126dEFRURF69uyJqqoq0RF1nrYd8nsLV3p2sUeKiOgRHDhwAIMGDUKvXr2QkZGBhQsXwtXVFfn5+Vi7di22bdsmJFfXrl0xevRo9O7dG1qtFsuWLdPdfXQ/OfR3KImTkxN7on4nSumXCQ8Px+7duzFx4kTRUYxq3bo1qqqq4OXlBS8vLxw5cgRdunTBuXPnDI5Miqa0QonIFNyRIiJ6BMHBwQgPD0dMTIzeJ5PZ2dkIDQ3FpUuXhOQqLCxEfHw8SkpKkJOTg44dO6JJE8PPzOTQ36E0GzduxI4dO/D555/zuOQjyM/P131dUlIi236ZpKQk3de1tbVYvnw5Xn/9daM57x3kIsLYsWPh6emJ+Ph4fPLJJ4iJiUGvXr1w9OhRhIWFYd26dULzGXPq1CmjgzGUNk6eO1IEsJAiInokzZs3R0FBAVQqld4/qOfPn4e/v78s7pGysLBAeXk5j/Y9hqCgIL3+neLiYmi1Wnh7exu8qWZh+mAWFha6nhhj5NIvo1KpTHqdJEnCB4xoNBpoNBrdhyVbt27V3cc1ceJEWFtbC813r9LSUgwdOhQFBQV6/x/U/3wprUeKhRQBPNpHRPRIWrRogbKyMoM3Xbm5ufDw8BCUSt/9U+Wo8TgK+fdz7tw50RFMopScAPDTTz/B09NT9+thw4Zh2LBh0Gq1+PHHH3X3IMlBdHQ0VCoV9u7dCx8fH2RlZaGqqgqxsbFYtmyZ6HhEj4SFFBHRI4iMjMTMmTORkpICSZKg0WiQmZmJuLg4REVFiY6nI/c7cOQuPj5edISnBntkfn8qlcroQJnq6mqoVCpZ7fIcPnwY6enpaNWqFSwsLGBhYYHevXvjww8/xNSpU5Gbmys6YqN4eXkZ7ErTs4eFFBHRI1i0aBFGjRoFDw8P3R1SdXV1ePvttzF37lzR8QA8/A4cFlKNk52dDY1Ggx49euit//DDD7C0tMQLL7wgKJnyfPjhh3juuecwZswYvfVPP/0UV65ckc248TfffBMvvPACZs2apbe+dOlSZGVlISUlRVCyu+qPQt7v+vXrsLW1FZCoYWq1Wjf4xsXFBZcvX0aHDh3g5eWFwsJCweka78SJE6IjkAywR4qI6DGUlpbi6NGjkCQJQUFBsppCJvc7cJSme/fumDFjBt5880299e3bt+Of//wnfvjhB0HJlMfb2xubNm3Cn/70J731H374AW+99ZZsjte1atUK6enpCAgI0FsvKCjAyy+/jIqKCiG5YmJiAACJiYkYN26c3vATtVqtK+4zMzOF5DOmT58+iI2NRWhoKCIjI/Hzzz9j7ty5SE5OxrFjx4QWJk5OTkYLUmOqq6ufcBpSEu5IERE9onXr1mHFihUoKioCALRv3x5//etfMXbsWMHJ7vr5558RHh4uOsZT49SpU+jWrZvBelBQEE6dOiUgkXKVl5fDzc3NYL1Vq1YoKysTkMi469evGx3YYGVlhZqaGgGJ7qo/BqfValFQUKCX0draGl26dEFcXJyoeEbNnTsXtbW1AICFCxdi8ODB6NOnD5ydnbFlyxah2VauXCn09yflYiFFRPQI5s2bhxUrVmDKlCkIDg4GcLcHYNq0aTh//jwWLlwoOKH878BRGhsbG1RUVBhM6SorKzM6Yp4a5unpiczMTINhLZmZmXB3dxeUylDnzp2xZcsWg2OwX331FTp27CgoFbBv3z4AwOjRo5GYmAgHBwdhWUw1cOBA3dc+Pj44deoUqqurG7Ub9KSMHDlS6O9PysW/+YmIHsG//vUvrFmzBsOHD9ethYSEIDAwEFOmTJFFIeXr64t58+bhyJEjsrwDR2leeeUV/O1vf8OOHTvg6OgIALh27Rpmz56NV155RXA6ZRk7diz++te/4s6dO+jfvz8AIC0tDTNmzEBsbKzgdL+ZN28e3njjDZSUlOjl3Lx5s/D+KABYv3697uuffvoJkiTJZmpoQ4qLi1FSUoK+ffuiZcuWsrs4GLh7z9n69etRUlKCxMREuLq6YteuXfD09ESnTp1ExyMZYY8UEdEjcHJyQlZWFtq3b6+3fvbsWXTv3h3Xrl0TE+weD7oPRw534CjNpUuX0LdvX1RVVSEoKAgAkJeXh+eeew579uzRG0NND6bVajFr1iwkJSXpLma1tbXFzJkzZTcE5dtvv8UHH3yAvLw8NG3aFIGBgYiPj8eLL74oOho0Gg0WLlyIhIQEXL9+HcDd+41iY2MxZ84cWFhYCE74m6qqKgwbNgz79u2DJEkoKiqCj48P3n33XbRo0QIJCQmiIwIADhw4gEGDBqFXr17IyMjA6dOn4ePjgyVLliArKwvbtm0THZFkhIUUEdEjmDJlCqysrLB8+XK99bi4OPz666/4+OOPBSWjJ6m2thZffvkljh8/rntTPXz4cI5BfkTXr1/H6dOn0bRpU7Rv3x42NjZ6z3/66Se4u7vLqiAwZvPmzQgJCYGdnZ1Zf9+//e1vWLduHRYsWIBevXpBq9UiMzMTf//73zFu3DgsWrTIrHkeJCoqCpWVlVi7di3+8Ic/6C6z3b17N6ZNm4aTJ0+KjggACA4ORnh4OGJiYvQu3c3OzkZoaCguXbokOiLJCAspIqJHMGXKFGzYsAGenp7o2bMnAODIkSP48ccfERUVpffG+v5i60mKiYnB+++/Dzs7O91kL2MkSZLNJ8BPm9dffx1r1641OkyBGsfBwQF5eXkGfWlyIyqnu7s7PvnkE4SEhOit79ixA3/5y19k9aa/devWSE1NRZcuXfQKlHPnziEgIEC3oyZa8+bNUVBQAJVKpZfz/Pnz8Pf3x82bN0VHJBlhjxQR0SM4ceKEboJbSUkJgLsTx1q1aqU3xtfcTdS5ubm4c+eO7uuGiG7ufpplZGTg119/FR3jqaCUz3pF5ayuroa/v7/Bur+/v+zGdNfW1uqNaa939epVg51IkVq0aIGysjKDo9G5ubmy7z8j82MhRUT0COqnZsnNvbnkmpGIfh9dunTBRx99hKSkJL31jz76CF26dBGUyri+fftiw4YNeP/99wHc/TBHo9Fg6dKl6Nevn+B0v4mMjMTMmTORkpKiy5iZmYm4uDhERUWJjkcyw0KKiIiISIGWLFmC119/HXv37kVwcDAkScKhQ4fw448/4rvvvhMdT8/SpUvx0ksv4ejRo7h9+zZmzJiBkydPorq6WlYXBy9atAijRo2Ch4cHtFotOnbsCLVajcjISMydO1d0PJIZeXdvEhEREZFRKpUKZ8+exdChQ3Ht2jVUV1cjLCwMhYWF8PLyEh1PT8eOHXH8+HF0794dr7zyCmpraxEWFobc3Fy0a9dOdDwdKysrfPnllygqKsLWrVuxceNGnDlzBl988QUsLS1FxyOZ4bAJIiKi39G9Der0eJQybELUn7mlpSXKysrg6uqqt15VVQVXV1eo1Wqz5nmYmzdvIj8/H5WVldBoNHrP7h+YIcrevXvx8ssvG322evVqTJgwwcyJSM54tI+IiIhkSSmf9Xp5eQkZgd/Q9+f69euwtbU1c5oH27VrF6KiolBVVWWQW5Ik2RR9r7/+OiZPnowPP/wQ1tbWAIArV65gzJgxyMzMZCFFelhIERERmSAjIwN/+tOf0KSJ/j+ddXV1OHToEPr27QsAmD17Nlq2bCkiomKMGTMGiYmJsLe311uvra3FlClT8OmnnwIATp06BXd3dxERG+XeSZ3mUH+1gSRJmD9/vt40PLVajR9++AFdu3Y1a6aHmTx5MsLDwzF//nw899xzouM0KCMjAyNGjMDevXuxadMmnD9/HmPGjNEdTSS6F4/2ERERmUBpx6jkrKHv5dWrV9G6dWvU1dUJSgY4OTmZfD2AqBHj9VPuDhw4gODgYN3OCQBYW1vD29sbcXFxaN++vZB8xjg4OMiuH6ohtbW1mDhxIlJSUqDRaLBw4UJMnz6d10aQAe5IERERmUCr1Rp9I1VVVQU7OzsBiZSnpqYGWq0WWq0Wv/zyi97xM7Vaje+++86guDK3lStXCv39TVF/tcHo0aORmJgIBwcHwYke7s0338T+/fsVUUgVFhYiOzsbbdq0weXLl3HmzBncuHGDP+dkgDtSREREDxAWFgYA2LFjB1577TW9y0PVajXy8/PRoUMH7Nq1S1RExbCwsHjgp/qSJGHBggWYM2eOGVOROdy4cQPh4eFo1aoVAgICDHrKpk6dKiiZvsWLFyM+Ph7jx4/H0qVLUVJSgnfeeQc1NTXYuHEjgoODRUckGeGOFBER0QM4OjoCuLsjZW9vj6ZNm+qeWVtbo2fPnhg3bpyoeIqyb98+aLVa9O/fH//+97/1esmsra3h5eUlu56okpISrF+/HiUlJUhMTISrqyt27doFT09PdOrUSXQ8xdi0aRNSU1PRtGlT7N+/X6+gliRJNoVUYmIivv76awwaNAgA0KlTJ2RlZWH27Nl46aWXcOvWLcEJSU64I0VERGSCBQsWIC4ujsd7fgcXLlyAp6cnLCzkfZ3lgQMHMGjQIPTq1QsZGRk4ffo0fHx8sGTJEmRlZWHbtm2iIypG69atMXXqVMyaNUvWf+5Xr16Fi4uL0WcHDhzAiy++aOZEJGcspIiIiMjsrl27hqysLKN3CkVFRQlKpS84OBjh4eGIiYnRuysqOzsboaGhuHTpkuiIitGyZUtkZ2crokeKyFQspIiIiExQUVGBuLg4pKWlobKy0uAuHE7tM91///tfvP3226itrYW9vb3BMS9R0/Du17x5cxQUFEClUukVUufPn4e/vz9u3rwpOqJiTJs2Da1atcLs2bNFRzEQFhaGzz77DA4ODrqeyIZs377dTKlICdgjRUREZIJRo0bh4sWLmDdvHtzc3DgK+THExsZizJgx+OCDD/TuQJKbFi1aoKysDCqVSm89NzcXHh4eglIpk1qtxpIlS5CamorAwECDYRPLly8XlOxuH2T9z3N9TySRKbgjRUREZAJ7e3scPHhQdhedKpGdnR0KCgrg4+MjOsoDzZgxA4cPH0ZKSgr8/PyQk5ODiooKREVFISoqCvHx8aIjKkb93VfGSJKE9PR0M6Yh+n1wR4qIiMgEnp6eBsf56NEMHDgQR48elX0htWjRIowaNQoeHh7QarXo2LEj1Go1IiMjMXfuXNHxFKX+7iulqKysRGFhISRJgp+fn/D7zUieuCNFRERkgt27dyMhIQGrV6+Gt7e36DiKs3PnTt3XV65cwT/+8Q+MHj3a6J1CISEh5o73QKWlpcjJyYFGo0FQUBDat28vOhI9ITU1NXjvvffw1Vdf6foeLS0tERERgY8//phH/0gPCykiIqIGODk56fVC1dbWoq6uDs2aNTN48y+XAQlyZerIa0mSZDO4Y+/evXj55ZeNPlu9ejUmTJhg5kT0pA0bNgx5eXlYtWoVgoODIUkSDh06hOjoaAQGBmLr1q2iI5KMsJAiIiJqwOeff27ya0eOHPkEk5AINjY2mDx5Mj788ENYW1sDuLubNmbMGGRmZrJ4fgrZ2dkhNTUVvXv31ls/ePAgXnvtNdTW1gpKRnLEHikiIqIGsDh6tmVkZGDEiBHYu3cvNm3ahPPnz2PMmDHo2LEjjh8/LjoePQHOzs5Gj+85OjrCyclJQCKSM+5IERERmaCmpsbouiRJsLGx0e1Y0MMlJSUZXZckCba2tvD19UXfvn1haWlp5mSGamtrMXHiRKSkpECj0WDhwoWYPn06x98/pZKTk5GSkoINGzbAzc0NAFBeXo6RI0ciLCyMxzlJDwspIiIiE1hYWDzwzXObNm0watQoxMfHm9wP9KxSqVS4cuUKbty4AScnJ2i1Wly7dg3NmjVD8+bNUVlZCR8fH+zbtw+enp5Cs+bk5CAyMhJ1dXW4fPky3nrrLaxatQp2dnZCc9GTERQUhOLiYty6dQtt27YFAFy8eBE2NjYGQ0ZycnJERCQZ4dE+IiIiE3z22WeYM2cORo0ahe7du0Or1SI7Oxuff/455s6diytXrmDZsmWwsbHB7NmzRceVtQ8++ADJyclYu3Yt2rVrBwAoLi7GhAkTMH78ePTq1QtvvfUWpk2bhm3btgnLuXjxYsTHx2P8+PFYunQpSkpK8M477yAwMBAbN25EcHCwsGz0ZISGhoqOQArCHSkiIiITDBgwABMmTMCwYcP01rdu3YrVq1cjLS0NX3zxBRYtWoQzZ84ISqkM7dq1w7///W+Dy41zc3PxxhtvoLS0FIcOHcIbb7yBsrIyMSEBuLm54dNPP8WgQYN0a3fu3MHs2bORlJSEW7duCctGYm3evBkhISHcmXzG8ewBERGRCQ4fPoygoCCD9aCgIBw+fBgA0Lt3b1y8eNHc0RSnrKwMdXV1But1dXUoLy8HALi7u+OXX34xdzQ9BQUFekUUAFhZWWHp0qXYvXu3oFQkBxMmTEBFRYXoGCQYCykiIiITtGnTBuvWrTNYX7duna6Pp6qqipO9TNCvXz9MmDABubm5urXc3FxMmjQJ/fv3B3C3iFGpVKIiAgBcXFwafPbiiy+aMQnJDQ90EcAeKSIiIpMsW7YM4eHh+L//+z/88Y9/hCRJyM7OxpkzZ3R9PNnZ2YiIiBCcVP7WrVuHESNG4Pnnn9ddbFxXV4cBAwboitXmzZsjISHB7NnCwsLw2WefwcHBAWFhYQ987fbt282UiojkiIUUERGRCUJCQlBYWIhPPvkEZ8+ehVarxaBBg/D111/D29sbADBp0iSxIRWidevW2LNnD86cOaP7Xvr7+6NDhw661/Tr109INkdHR910RmP3CRER1eOwCSIiIiKiRrC3t8fx48fh4+MjOgoJxB0pIiKiBuTn56Nz586wsLBAfn7+A18bGBhoplTKFBMTg/fffx92dnaIiYl54GuXL19uplSmqaysRGFhISRJgp+fH1xdXUVHIiIZYCFFRETUgK5du6K8vByurq7o2rUrJEky2mQuSRLUarWAhMqRm5uLO3fu6L5uyIMuPTa3mpoavPfee/jqq690f76WlpaIiIjAxx9/zKN/zzAvLy9dfx89u3i0j4iIqAEXLlxA27ZtIUkSLly48MDXenl5mSkVmcuwYcOQl5eHVatWITg4GJIk4dChQ4iOjkZgYCC2bt0qOiL9znx8fJCdnQ1nZ2e99WvXrqFbt24oLS0VlIzkiIUUERERkRF2dnZITU1F79699dYPHjyI1157DbW1tYKS0ZNiYWGh24W+V0VFBdq2bctLmEkPj/YRERE1YOfOnSa/NiQk5AkmUb6HjRK/l1zGijs7Oxs9vufo6Mj7wp4y9/6sp6am6v25q9VqpKWl6aZzEtXjjhQREVEDLCxMu7eePVIPN3r0aJNfu379+ieYxHTJyclISUnBhg0b4ObmBgAoLy/HyJEjERYWhgkTJghOSL+X+p91Y32QVlZW8Pb2RkJCAgYPHiwiHskUCykiIiIiI4KCglBcXIxbt26hbdu2AICLFy/CxsYG7du313ttTk6OiIj0O1OpVMjOzoaLi4voKKQAPNpHRERkgg0bNiAiIgI2NjZ667dv38ZXX32FqKgoQcmUp3///ti+fTtatGiht15TU4PQ0FCkp6eLCXaf0NBQ0RHIzM6dOyc6AikId6SIiIhMYGlpibKyMoMm9KqqKri6uvJoXyM01NBfWVkJDw8P3Zh0pdi8eTNCQkJgZ2cnOgo9gqSkJIwfPx62trZISkp64GunTp1qplSkBCykiIiITGBhYYGKigq0atVKb/348ePo168fqqurBSVTjvpLjbt27Yr09HS0bNlS90ytVmPXrl1YvXo1zp8/Lyjho3FwcEBeXh58fHxER6FHoFKpcPToUTg7O0OlUjX4OkmSOP6c9PBoHxER0QMEBQVBkiRIkoQBAwagSZPf/ulUq9U4d+4cXnvtNYEJlaP+UmNJktC/f3+D502bNsWqVasEJHs8/Exa2e49zsejfdQYLKSIiIgeoL5PJi8vDwMHDkTz5s11z6ytreHt7Y033nhDUDplOXfuHLRaLXx8fJCVlaW3u2dtbQ1XV1dYWloKTEjPopiYGJNeJ0kSEhISnnAaUhIWUkRERA8QHx8PAPD29kZERARsbW0FJ1IuLy8vAIBGoxGchOg3ubm5Jr1OkqQnnISUhj1SREREjXD79m1UVlYaFAP147HJNGfPnsX+/fuNfi/nz58vKNWjsbe3x/Hjx9kjRfSM4Y4UERGRCYqKijBmzBgcOnRIb12r1fJC3kZas2YNJk2aBBcXF7Ru3Vrvk35JkhRXSBHRs4mFFBERkQlGjRqFJk2a4JtvvoGbmxuP+TyGhQsXYtGiRZg5c6boKL8LLy8vWFlZiY5BRGbGo31EREQmsLOzw7Fjx+Dv7y86iuIpZVy4j48PsrOz4ezsrLd+7do1dOvWjaOwiZ5xFqIDEBERKUHHjh1x9epV0TGeCuHh4di9e7foGA91/vx5o0c2b926hUuXLglIRERywqN9REREDaipqdF9/c9//hMzZszABx98gICAAIOjXA4ODuaOpyhJSUm6r319fTFv3jwcOXLE6Pdy6tSp5o6nZ+fOnbqvU1NT4ejoqPu1Wq1GWloavL29BSQjIjnh0T4iIqIGWFhY6PVC1Q+WuBeHTZhGpVKZ9DpJkoQfmbOwsNBluf9tkpWVFby9vZGQkIDBgweLiEdEMsEdKSIiogbs27dPdISnxrlz50RHMFn9OHaVSoXs7Gy4uLgITkREcsQdKSIiIiIiokbijhQREZEJ8vPzja5LkgRbW1u0bdsWNjY2Zk6lTDExMUbX67+Xvr6+GDJkCFq2bGnmZHd7ucaPHw9bW1u9vi5jRPdyEZFY3JEiIiIywf39UvezsrJCREQEVq9eDVtbWzMmU55+/fohJycHarUaHTp0gFarRVFRESwtLeHv74/CwkJIkoTvv/8eHTt2NGs2lUqFo0ePwtnZ+YF9XXLo5SIisVhIERERmWDHjh2YOXMmpk+fju7du0Or1SI7OxsJCQmIj49HXV0dZs2ahYiICCxbtkx0XFlbuXIlDh48iPXr1+umHdbU1ODdd99F7969MW7cOERGRuLXX39Famqq4LRERMaxkCIiIjJB9+7d8f7772PgwIF666mpqZg3bx6ysrLw9ddfIzY2FiUlJYJSKoOHhwf27NljsNt08uRJvPrqq7h06RJycnLw6quvmv3uroaOHd5PkiQkJCQ84TREJGfskSIiIjJBQUEBvLy8DNa9vLxQUFAAAOjatSvKysrMHU1x/ve//6GystKgkLpy5Yru7q4WLVrg9u3bZs+Wm5tr0usedMyTiJ4NLKSIiIhM4O/vj8WLFyM5ORnW1tYAgDt37mDx4sXw9/cHAFy6dAnPPfecyJiKMGTIEIwZMwYJCQn44x//CEmSkJWVhbi4OISGhgIAsrKy4OfnZ/ZsHHlPRKbi0T4iIiITHDp0CCEhIbCwsEBgYCAkSUJ+fj7UajW++eYb9OzZE1988QXKy8sxffp00XFl7fr165g2bRo2bNiAuro6AECTJk0wcuRIrFixAnZ2dsjLywNwd5ePiEiOWEgRERGZ6Pr169i4cSPOnj0LrVYLf39/REZGwt7eXnQ0Rbp+/TpKS0uh1WrRrl07NG/eXHQkIiKTsZAiIiIiIiJqJPZIERERNWDnzp0YNGgQrKyssHPnzge+NiQkxEyplCksLAyfffYZHBwcEBYW9sDXbt++3UypiIgeHQspIiKiBoSGhqK8vByurq66IQjGSJIEtVptvmAK5OjoqJt05+joKDgNEdHj49E+IiIiIiKiRuKOFBERkYnS0tKQlpaGyspKaDQa3bokSVi3bp3AZEREZG4WogMQEREpwYIFC/Dqq68iLS0NV69exc8//6z7r7q6WnQ8RamoqMCIESPg7u6OJk2awNLSUu8/IiIl4NE+IiIiE7i5uWHJkiUYMWKE6CiKN2jQIFy8eBGTJ0+Gm5ubrneq3pAhQwQlIyIyHQspIiIiEzg7OyMrKwvt2rUTHUXx7O3tcfDgQV62S0SKxqN9REREJhg7diw2bdokOsZTwdPTE/wcl4iUjsMmiIiITHDz5k0kJydj7969CAwMhJWVld7z5cuXC0qmPCtXrsSsWbOwevVqeHt7i45DRPRIeLSPiIjIBP369WvwmSRJSE9PN2MaZXNycsKNGzdQV1eHZs2aGRSlHN5BRErAHSkiIiIT7Nu3T3SEp8bKlStFRyAiemzckSIiIiJZWrx4MSZOnIgWLVqIjkJEZICFFBEREcmSg4MD8vLy4OPjIzoKEZEBTu0jIiIiWeJnvUQkZyykiIiIiIiIGomFFBERERERUSOxkCIiIiIiImokFlJERERERESNxEKKiIiIZKlPnz5o2rSp6BhEREZx/DkRERGZnUajQXFxMSorK6HRaPSe9e3bV1AqIiLTNREdgIiIiJ4tR44cQWRkJC5cuGAw4lySJKjVakHJiIhMxx0pIiIiMquuXbvCz88PCxYsgJubGyRJ0nvu6OgoKBkRkelYSBEREZFZ2dnZ4fjx4/D19RUdhYjokXHYBBEREZlVjx49UFxcLDoGEdFjYY8UERERmdWUKVMQGxuL8vJyBAQEwMrKSu95YGCgoGRERKbj0T4iIiIyKwsLwwMxkiRBq9Vy2AQRKQZ3pIiIiMiszp07JzoCEdFj444UERERERFRI3FHioiIiIQ4deoULl68iNu3b+uth4SECEpERGQ6FlJERERkVqWlpRg6dCgKCgp0vVEAdPdJsUeKiJSA48+JiIjIrKKjo6FSqVBRUYFmzZrh5MmTyMjIwAsvvID9+/eLjkdEZBL2SBEREZFZubi4ID09HYGBgXB0dERWVhY6dOiA9PR0xMbGIjc3V3REIqKH4o4UERERmZVarUbz5s0B3C2qLl++DADw8vJCYWGhyGhERCZjjxQRERGZVefOnZGfnw8fHx/06NEDS5YsgbW1NZKTk+Hj4yM6HhGRSXi0j4iIiMwqNTUVtbW1CAsLQ2lpKQYPHowzZ87A2dkZW7ZsQf/+/UVHJCJ6KBZSREREJFx1dTWcnJx0k/uIiOSOPVJEREQkRHFxMVJTU/Hrr7+iZcuWouMQETUKCykiIiIyq6qqKgwYMAB+fn7485//jLKyMgDA2LFjERsbKzgdEZFpWEgRERGRWU2bNg1WVla4ePEimjVrpluPiIjArl27BCYjIjIdp/YRERGRWe3evRupqalo06aN3nr79u1x4cIFQamIiBqHO1JERERkVrW1tXo7UfWuXr0KGxsbAYmIiBqPhRQRERGZVd++fbFhwwbdryVJgkajwdKlS9GvXz+ByYiITMfx50RERGRWp06dwksvvYTnn38e6enpCAkJwcmTJ1FdXY3MzEy0a9dOdEQioodiIUVERERmV1ZWhk8++QTHjh2DRqNBt27d8N5778HNzU10NCIik7CQIiIiIrO7efMm8vPzUVlZCY1Go/csJCREUCoiItNxah8RERGZ1a5duxAVFYWqqirc/3muJElQq9WCkhERmY7DJoiIiMisJk+ejPDwcFy+fBkajUbvPxZRRKQUPNpHREREZuXg4IDc3FwOlSAiReOOFBEREZnVm2++if3794uOQUT0WLgjRURERGZ148YNhIeHo1WrVggICICVlZXe86lTpwpKRkRkOhZSREREZFZr167FxIkT0bRpUzg7O0OSJN0zSZJQWloqMB0RkWlYSBEREZFZtW7dGlOnTsWsWbNgYcEuAyJSJv7tRURERGZ1+/ZtREREsIgiIkXj32BERERkViNHjsSWLVtExyAieiy8kJeIiIjMSq1WY8mSJUhNTUVgYKDBsInly5cLSkZEZDr2SBEREZFZ9evXr8FnkiQhPT3djGmIiB4NCykiIiIiIqJGYo8UERERERFRI7GQIiIiIiIiaiQWUkRERERERI3EQoqIiIiIiKiRWEgRERERERE1EgspIiIiIiKiRmIhRURERERE1EgspIiIiIiIiBrp/wFPBJh+iqg0PQAAAABJRU5ErkJggg==", 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", "text/plain": [ "
      " ] @@ -2631,6 +2780,21 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "fb9b2ce3", + "metadata": {}, + "source": [ + "
      \n", + "

      📝 Marking Notes for Part-2-EDA

      \n", + "
        \n", + "
      • Heatmap: 10
        • \n", + "
      • Interesting Correlations: 5
        • \n", + "
      • NTL Trends: 10
        • \n", + "
      \n", + "
      " + ] + }, { "cell_type": "markdown", "id": "4c703224", @@ -2641,7 +2805,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 20, "id": "383af240", "metadata": {}, "outputs": [], @@ -2657,7 +2821,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 21, "id": "da8ed78d", "metadata": {}, "outputs": [ @@ -2670,8 +2834,8 @@ "Dep. Variable: total_population R-squared: 0.540\n", "Model: OLS Adj. R-squared: 0.538\n", "Method: Least Squares F-statistic: 281.3\n", - "Date: Thu, 04 Sep 2025 Prob (F-statistic): 0.00\n", - "Time: 11:58:08 Log-Likelihood: -20322.\n", + "Date: Fri, 19 Sep 2025 Prob (F-statistic): 0.00\n", + "Time: 13:00:30 Log-Likelihood: -20322.\n", "No. Observations: 2163 AIC: 4.066e+04\n", "Df Residuals: 2153 BIC: 4.072e+04\n", "Df Model: 9 \n", @@ -2708,7 +2872,7 @@ "lit_pixel_change_2015_2024 lit_pixel_change_2015_2024 500.780863 \n", "\n", " P-value Abs_t_stat \n", - "building_count 1.425042e-199 33.622678 \n", + "building_count 1.425043e-199 33.622678 \n", "total_nightlight_2024 1.601139e-27 11.022217 \n", "lit_pixel_change_2015_2024 8.066012e-03 2.651763 \n", "\n", @@ -2718,8 +2882,8 @@ "Dep. Variable: total_population R-squared: 0.534\n", "Model: OLS Adj. R-squared: 0.534\n", "Method: Least Squares F-statistic: 825.2\n", - "Date: Thu, 04 Sep 2025 Prob (F-statistic): 0.00\n", - "Time: 11:58:08 Log-Likelihood: -20337.\n", + "Date: Fri, 19 Sep 2025 Prob (F-statistic): 0.00\n", + "Time: 13:00:30 Log-Likelihood: -20337.\n", "No. Observations: 2163 AIC: 4.068e+04\n", "Df Residuals: 2159 BIC: 4.070e+04\n", "Df Model: 3 \n", @@ -2746,7 +2910,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
      " ] @@ -2805,7 +2969,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 22, "id": "6460ee22", "metadata": {}, "outputs": [ @@ -2841,7 +3005,7 @@ " building_count\n", " building_count\n", " 2.927238\n", - " 1.425042e-199\n", + " 1.425043e-199\n", " 33.622678\n", " \n", " \n", @@ -2869,12 +3033,12 @@ "lit_pixel_change_2015_2024 lit_pixel_change_2015_2024 500.780863 \n", "\n", " P-value Abs_t_stat \n", - "building_count 1.425042e-199 33.622678 \n", + "building_count 1.425043e-199 33.622678 \n", "total_nightlight_2024 1.601139e-27 11.022217 \n", "lit_pixel_change_2015_2024 8.066012e-03 2.651763 " ] }, - "execution_count": 39, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2884,12 +3048,19 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "16d4af8b", + "cell_type": "markdown", + "id": "ca721f58", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
      \n", + "

      📝 Marking Notes for Part-2-Modelling

      \n", + "
        \n", + "
      • Variable Selection: 10
        • \n", + "
      • Model definition and fittting: 15
        • \n", + "
      • Top variable selection and scatter scatterplot: 15
        • \n", + "
      \n", + "
      " + ] } ], "metadata": { @@ -2908,7 +3079,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.7" } }, "nbformat": 4,

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