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Improve error readability for missing NWP data in PVNet model #69

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Improve error readability for missing NWP data in PVNet model #69

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9199c0c
Improve error readability for missing NWP data in PVNet model
akshay-juneja Sep 14, 2025
9e70fee
Improve NWP error readability
akshay-juneja Sep 15, 2025
a75c5f9
Improve NWP error readability
akshay-juneja Sep 15, 2025
eeafcfc
validate NWP data coverage and timestamps
akshay-juneja Sep 15, 2025
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152 changes: 80 additions & 72 deletions site_forecast_app/models/pvnet/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,19 +6,16 @@
import logging
import os
import shutil
import warnings

import numpy as np
import pandas as pd
import torch
from ocf_data_sampler.numpy_sample.collate import stack_np_samples_into_batch
from ocf_data_sampler.torch_datasets.datasets.site import (
SitesDataset,
)
from ocf_data_sampler.torch_datasets.sample.base import (
batch_to_tensor,
)
from pvnet.models.base_model import BaseModel as PVNetBaseModel
from ocf_data_sampler.torch_datasets.datasets.site import SitesDataset
from ocf_data_sampler.torch_datasets.sample.base import batch_to_tensor

from pvnet.models.base_model import BaseModel as PVNetBaseModel
from site_forecast_app.data.satellite import download_satellite_data

from .consts import (
Expand All @@ -29,6 +26,7 @@
site_netcdf_path,
site_path,
)

from .utils import (
NWPProcessAndCacheConfig,
populate_data_config_sources,
Expand All @@ -38,9 +36,7 @@
set_night_time_zeros,
)

# Global settings for running the model

# Model will use GPU if available
# Setup device
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

log = logging.getLogger(__name__)
Expand Down Expand Up @@ -96,20 +92,18 @@ def predict(self, site_uuid: str, timestamp: dt.datetime) -> dict:

normed_preds = []
with torch.no_grad():

# note this only running ones site
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could you add these comment back in please

samples = self.dataset.valid_t0_and_site_ids
samples_with_same_t0 = samples[samples["t0"] == timestamp]

if len(samples_with_same_t0) == 0:

sample_t0 = samples.iloc[-1].t0
sample_site_id = samples.iloc[-1].site_id

log.warning(
"Timestamp different from the one in the batch: "
f"{timestamp} != {sample_t0} (batch)"
f"The other timestamps are: {samples['t0'].unique()}",
f"The other timestamps are: {samples['t0'].unique()}"
)
else:
sample_t0 = samples_with_same_t0.iloc[0].t0
Expand All @@ -120,7 +114,7 @@ def predict(self, site_uuid: str, timestamp: dt.datetime) -> dict:

if site_uuid != sample_site_id:
log.warning(
f"Site id different from the one in the batch: {site_uuid} != {sample_site_id}",
f"Site id different from the one in the batch: {site_uuid} != {sample_site_id}"
)

# for i, batch in enumerate(self.dataloader):
Expand Down Expand Up @@ -167,7 +161,7 @@ def predict(self, site_uuid: str, timestamp: dt.datetime) -> dict:

# t0 time not included in forecasts
valid_times = pd.to_datetime(
[sample_t0 + dt.timedelta(minutes=15 * (i+1)) for i in range(n_times)],
[sample_t0 + dt.timedelta(minutes=15 * (i + 1)) for i in range(n_times)]
)

# index of the 50th percentile, assumed number of p values odd and in order
Expand All @@ -181,7 +175,7 @@ def predict(self, site_uuid: str, timestamp: dt.datetime) -> dict:
"forecast_power_kw": int(v * capacity_kw),
}
for i, v in enumerate(normed_preds[0, :, middle_plevel_index])
],
]
)
# remove any negative values
values_df["forecast_power_kw"] = values_df["forecast_power_kw"].clip(lower=0.0)
Expand Down Expand Up @@ -220,7 +214,7 @@ def add_probabilistic_values(
# add 10th and 90th percentage
values_df["p10"] = normed_preds[0, :, idx_10] * capacity_kw
values_df["p90"] = normed_preds[0, :, idx_90] * capacity_kw
# change to intergers
# change to integers
values_df["p10"] = values_df["p10"].astype(int)
values_df["p90"] = values_df["p90"].astype(int)
values_df["probabilistic_values"] = values_df[["p10", "p90"]].apply(
Expand All @@ -234,66 +228,79 @@ def _prepare_data_sources(self) -> None:
"""Pull and prepare data sources required for inference."""
log.info("Preparing data sources")

# Create root data directory if not exists
with contextlib.suppress(FileExistsError):
os.mkdir(root_data_path)
# Load remote zarr source
use_satellite = os.getenv("USE_SATELLITE", "true").lower() == "true"
satellite_source_file_path = os.getenv("SATELLITE_ZARR_PATH", None)
satellite_backup_source_file_path = os.getenv("SATELLITE_BACKUP_ZARR_PATH", None)

# only load nwp that we need
nwp_configs = []
nwp_keys = self.config["input_data"]["nwp"].keys()
if "ecmwf" in nwp_keys:

nwp_configs.append(
NWPProcessAndCacheConfig(
source_nwp_path=os.environ["NWP_ECMWF_ZARR_PATH"],
dest_nwp_path=nwp_ecmwf_path,
source="ecmwf",
),
)
if "mo_global" in nwp_keys:
nwp_configs.append(
NWPProcessAndCacheConfig(
source_nwp_path=os.environ["NWP_MO_GLOBAL_ZARR_PATH"],
dest_nwp_path=nwp_mo_global_path,
source="mo_global",
),
try:
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Soryr this wont do it. You need to actually look at the configuration and see if that data is available in the NWP data / timestamps

# Create root data directory if not exists
with contextlib.suppress(FileExistsError):
os.mkdir(root_data_path)

# Load remote zarr source
use_satellite = os.getenv("USE_SATELLITE", "true").lower() == "true"
satellite_source_file_path = os.getenv("SATELLITE_ZARR_PATH", None)
satellite_backup_source_file_path = os.getenv("SATELLITE_BACKUP_ZARR_PATH", None)

# only load nwp that we need
nwp_configs = []
nwp_keys = self.config["input_data"]["nwp"].keys()
if "ecmwf" in nwp_keys:
nwp_configs.append(
NWPProcessAndCacheConfig(
source_nwp_path=os.environ["NWP_ECMWF_ZARR_PATH"],
dest_nwp_path=nwp_ecmwf_path,
source="ecmwf",
),
)
if "mo_global" in nwp_keys:
nwp_configs.append(
NWPProcessAndCacheConfig(
source_nwp_path=os.environ["NWP_MO_GLOBAL_ZARR_PATH"],
dest_nwp_path=nwp_mo_global_path,
source="mo_global",
),
)

# Remove local cached zarr if already exists
for nwp_config in nwp_configs:
# Process/cache remote zarr locally
process_and_cache_nwp(nwp_config)
if use_satellite and "satellite" in self.config["input_data"]:
download_satellite_data(
satellite_source_file_path,
satellite_path,
self.satellite_scaling_method,
satellite_backup_source_file_path,
)

log.info("Preparing Site data sources")
# Clear local cached site data if already exists
shutil.rmtree(site_path, ignore_errors=True)
os.mkdir(site_path)

# Save generation data as netcdf file
generation_xr = self.generation_data["data"]

forecast_timesteps = pd.date_range(
start=self.t0 - pd.Timedelta("52h"),
periods=int(4 * 24 * 4.5),
freq="15min",
)

# Remove local cached zarr if already exists
for nwp_config in nwp_configs:
# Process/cache remote zarr locally
process_and_cache_nwp(nwp_config)
if use_satellite and "satellite" in self.config["input_data"]:
download_satellite_data(satellite_source_file_path,
satellite_path,
self.satellite_scaling_method,
satellite_backup_source_file_path)

log.info("Preparing Site data sources")
# Clear local cached site data if already exists
shutil.rmtree(site_path, ignore_errors=True)
os.mkdir(site_path)

# Save generation data as netcdf file
generation_xr = self.generation_data["data"]

forecast_timesteps = pd.date_range(
start=self.t0 - pd.Timedelta("52h"),
periods=4 * 24 * 4.5,
freq="15min",
)
generation_xr = generation_xr.reindex(time_utc=forecast_timesteps, fill_value=0.00001)
log.info(forecast_timesteps)

generation_xr = generation_xr.reindex(time_utc=forecast_timesteps, fill_value=0.00001)
log.info(forecast_timesteps)
generation_xr.to_netcdf(site_netcdf_path, engine="h5netcdf")

generation_xr.to_netcdf(site_netcdf_path, engine="h5netcdf")
# Save metadata as csv
self.generation_data["metadata"].to_csv(site_metadata_path, index=False)

# Save metadata as csv
self.generation_data["metadata"].to_csv(site_metadata_path, index=False)
except Exception as e:
error_message = (
"Could not run the forecast because there wasn't enough NWP data. "
"Please check your NWP input files and time range."
)
log.error(error_message)
log.error(f"Underlying error: {e}")
warnings.warn(error_message)
raise RuntimeError(error_message) from e

def _get_config(self) -> None:
"""Setup dataloader with prepared data sources."""
Expand All @@ -308,6 +315,7 @@ def _get_config(self) -> None:
)

# Populate the data config with production data paths

populated_data_config_filename = "data/data_config.yaml"
log.info(populated_data_config_filename)
# if the file already exists, remove it
Expand Down