From 5600603ef2c71807e8f47e04545b88e358b144e1 Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 18 Aug 2026 14:53:04 +0000 Subject: [PATCH 1/6] stash --- src/cloudcasting_inference/app.py | 102 +++++--- src/cloudcasting_inference/data.py | 382 +++++++++++++++++------------ src/cloudcasting_metrics/app.py | 2 +- 3 files changed, 286 insertions(+), 200 deletions(-) diff --git a/src/cloudcasting_inference/app.py b/src/cloudcasting_inference/app.py index 6051973..d747862 100644 --- a/src/cloudcasting_inference/app.py +++ b/src/cloudcasting_inference/app.py @@ -18,7 +18,8 @@ from safetensors.torch import load_model from loguru import logger -from cloudcasting_inference.data import SatelliteDownloader, sat_path, get_input_data +from cloudcasting_inference.data import SatelliteDownloader +from sat_pred.dataset import SatelliteDataset # Get package version try: @@ -33,40 +34,25 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Model revision on huggingface -REPO_ID = "openclimatefix/cloudcasting_uk" -REVISION = "47643e89000e64e0150f7359ccc0cb6524948712" +REPO_ID = "openclimatefix-models/cloudcasting_uk" +REVISION = "06e7ee93366a0d1dbb52c937840319460b0b51db" -def app(t0=None): - """Inference function for production - - Args: - t0 (datetime): Datetime at which forecast is made - """ - logger.info(f"Using `cloudcasting-app` version: {__version__}", version=__version__) - - # --------------------------------------------------------------------------- - # 0. If inference datetime is None, round down to last 30 minutes +def sanitize_t0(t0: pd.Timestamp | None) -> pd.Timestamp: + """Sanitize the input t0 to be a pandas Timestamp and round down to the nearest 30 minutes.""" if t0 is None: - t0 = pd.Timestamp.now(tz="UTC").replace(tzinfo=None).floor("30min") + t0 = pd.Timestamp.now(tz="UTC").replace(tzinfo=None) else: - t0 = pd.to_datetime(t0).floor("30min") - - logger.info(f"Making forecast for init time: {t0}") + t0 = pd.to_datetime(t0) + return t0.floor("30min") - # --------------------------------------------------------------------------- - # 1. Prepare the input data - logger.info("Downloading satellite data") - satellite_downloader = SatelliteDownloader() - satellite_downloader.prepare_satellite_data(t0) - # --------------------------------------------------------------------------- - # 2. Load model - logger.info("Loading model") +def get_model(repo_id: str, revision: str) -> torch.nn.Module: + """Download the model from huggingface and load it into memory""" hf_download_dir = snapshot_download( - repo_id=REPO_ID, - revision=REVISION, + repo_id=repo_id, + revision=revision, ) with open(f"{hf_download_dir}/model_config.yaml", encoding="utf-8") as f: @@ -82,24 +68,60 @@ def app(t0=None): model.eval() - # --------------------------------------------------------------------------- - # 3. Get inference inputs - logger.info("Preparing inputs") + return model - # Get inputs - ds = xr.open_zarr(sat_path).compute() - X = get_input_data(ds, t0) +def get_input_data(t0: pd.Timestamp, history_mins: int) -> torch.Tensor: + """Get the input data for the model - # Convert to tensor, expand into batch dimension, and move to device - X = X[None, ...].to(device) + Returns: + torch.Tensor: The input data for the model + """ + dataset = SatelliteDataset( + zarr_path=os.environ["PREDICTION_SAVE_DIRECTORY"], + time_periods=[[None, None]], + history_mins=history_mins, + forecast_mins=0, + sample_freq_mins=15, + channels=None, # ChannelConfigInput + preshuffle=False, + ) - # --------------------------------------------------------------------------- - # 4. Make predictions - logger.info("Making predictions") + X, _ = dataset._get_datetime(t0) - with torch.no_grad(): - y_hat = model(X).cpu().numpy() + return X.unsqueeze(0).to(device) + +@torch.no_grad() +def app(t0=None): + """Inference function for production + + Args: + t0 (datetime): Datetime at which forecast is made + """ + logger.info(f"Using `cloudcasting-app` version: {__version__}", version=__version__) + + t0 = sanitize_t0(t0) + logger.info(f"Making forecast for init time: {t0}") + + + logger.info("Downloading satellite data") + SatelliteDownloader( + interval_start=t0-pd.Timedelta(minutes=165), + interval_end=t0, + source_path=os.environ["SATELLITE_ZARR_PATH"], + s3_region=os.environ["S3_REGION"], + destination_path=os.environ["PREDICTION_SAVE_DIRECTORY"], + ).run() + + logger.info("Loading model") + model = get_model(REPO_ID, REVISION) + + logger.info("Preparing inputs") + X = get_input_data(t0, history_mins=model.history_mins) + + + logger.info("Making predictions") + y_hat = model(X).squeeze(0).cpu().numpy() # --------------------------------------------------------------------------- # 5. Save predictions diff --git a/src/cloudcasting_inference/data.py b/src/cloudcasting_inference/data.py index 570d7c1..27ee5d8 100644 --- a/src/cloudcasting_inference/data.py +++ b/src/cloudcasting_inference/data.py @@ -1,212 +1,276 @@ +"""Functions to download and process satellite data.""" + import logging import shutil -import os -import fsspec +import icechunk import numpy as np import pandas as pd -import zarr -import torch import xarray as xr -from ocf_data_sampler.select.geospatial import lon_lat_to_geostationary_area_coords - -xr.set_options(keep_attrs=True) logger = logging.getLogger(__name__) -sat_5_path = "sat_5_min.zarr.zip" -sat_15_path = "sat_15_min.zarr.zip" -sat_path = "sat.zarr" - -lon_min = -16 -lon_max = 10 -lat_min = 45 -lat_max = 70 - -x_size = 614 -y_size = 372 - -channel_order = [ - "IR_016", - "IR_039", - "IR_087", - "IR_097", - "IR_108", - "IR_120", - "IR_134", - "VIS006", - "VIS008", - "WV_062", - "WV_073", -] - -def get_satellite_timestamps(sat_zarr_path: str) -> pd.DatetimeIndex: - """Get the datetimes of the satellite data - - Args: - sat_zarr_path: The path to the satellite zarr - - Returns: - pd.DatetimeIndex: All available satellite timestamps - """ - with zarr.storage.ZipStore(sat_zarr_path) as store: - ds = xr.open_zarr(store) - return pd.to_datetime(ds.time.values) - +# The maximum gap size which will be filled via interpolation +MAXIMUM_INTERPOLATION_GAP = pd.Timedelta("15min") +# The assumed frequency of satellite image inputs required by all models +IMAGE_FREQUENCY = pd.Timedelta("5min") -def crop_input_area(ds: xr.Dataset) -> xr.Dataset: - x_min, y_min = lon_lat_to_geostationary_area_coords(lon_min, lat_min, ds.data.attrs["area"]) +def open_satellite_data(s3_icechunk_path: str, region: str) -> xr.Dataset | None: + """Open the satellite data from the given s3 icechunk path. - # x-axis is expected to be in decreasing order - # y-axis is expected to be in ascending order - assert ds.x_geostationary.values[0] > ds.x_geostationary.values[1] - assert ds.y_geostationary.values[0] < ds.y_geostationary.values[1] - - ds = ds.isel(x_geostationary=slice(None, None, -1)) + Args: + s3_icechunk_path: The s3 path to the icechunk containing the satellite + region: The s3 region where the icechunk is stored + """ + bucket, _, path = s3_icechunk_path.removeprefix("s3://").partition("/") - ds = ( - ds - .sel(x_geostationary=slice(x_min, None), y_geostationary=slice(y_min, None)) - .isel(x_geostationary=slice(0, x_size), y_geostationary=slice(0, y_size)) + store = icechunk.s3_storage( + bucket=bucket, + prefix=path, + from_env=True, + region=region, ) - assert len(ds.x_geostationary)==x_size - assert len(ds.y_geostationary)==y_size + try: + repo = icechunk.Repository.open(store) + session = repo.readonly_session("main") + ds = xr.open_zarr(session.store) + except icechunk.IcechunkError as e: + logger.error(f"Error opening icechunk repository: {e}") + ds = None - return ds.isel(x_geostationary=slice(None, None, -1)) # flip back + return ds -def get_input_data(ds: xr.Dataset, t0: pd.Timestamp) -> torch.Tensor: - """Get the input data required to run the model for init-time t0""" +def fill_1d_bool_gaps(x: np.ndarray, max_gap: int) -> np.ndarray: + """Fill consecutive False elements if their number is less than the gap_size. - # Slice the data - required_timestamps = pd.date_range(t0-pd.Timedelta("165min"), t0, freq="15min") - ds = ds.reindex(time=required_timestamps) - - # Convert to arrays - X = ds.data.values.astype(np.float32) + Args: + x: A 1-dimensional boolean array + max_gap: integer of the maximum gap size which will be filled with True - # Convert NaNs to -1 - X = np.nan_to_num(X, nan=-1) + Returns: + A 1-dimensional boolean array - return torch.Tensor(X) + Examples: + >>> x = np.array([0, 1, 0, 0, 1, 0, 1, 0]) + >>> fill_1d_bool_gaps(x, max_gap=2).astype(int) + array([0, 1, 1, 1, 1, 1, 1, 0]) + >>> x = np.array([1, 0, 0, 0, 1, 0, 1, 0]) + >>> fill_1d_bool_gaps(x, max_gap=2).astype(int) + array([1, 0, 0, 0, 1, 1, 1, 0]) + """ + should_fill = np.zeros(len(x), dtype=bool) -class SatelliteDownloader: + i_start = None - def __init__(self): - self.use_5_minute = None + last_b = False + for i, b in enumerate(x): + if last_b and not b: + i_start = i + elif b and not last_b and i_start is not None: + if i - i_start <= max_gap: + should_fill[i_start:i] = True + i_start = None + last_b = b - def prepare_satellite_data(self, t0: pd.Timestamp) -> None: + return np.logical_or(should_fill, x) - # Download the 5 and/or 15 minutely satellite data - self.download_all_sat_data() - # Select between the 5/15 minute satellite data sources - ds = self.combine_5_and_15_sat_data() +def interpolate_missing_satellite_timestamps(ds: xr.Dataset, max_gap: pd.Timedelta) -> xr.Dataset: + """Linearly interpolate missing satellite timestamps. - # Check the required expected timestamps are available - self.check_required_timestamps_available(ds, t0) + The max gap is inclusive of timestamps either side. E.g. if max gap is 15 minutes and the + satellite includes timestamps 12:00 and 12:15, then 12:05 and 12:10 will be filled. If the max + gap was 10 minutes, then none of the timestamps would be filled. A max gap of 5 minutes will do + nothing since the normal spacing is already 5 minutes. - # Crop the input area to expected - ds = crop_input_area(ds) + Args: + ds: The satellite data + max_gap: The maximum gap size which will be filled via interpolation. + """ + # If any of these times are missing, we will try to interpolate them + dense_times = pd.date_range(ds.time.min().item(), ds.time.max().item(), freq=IMAGE_FREQUENCY) - # Reorder channels - ds = ds.sel(variable=channel_order) + # Create mask array of which timestamps are available + timestamp_available = np.isin(dense_times, ds.time) - # Reshape to (channel, time, height, width) - ds = ds.transpose("variable", "time", "y_geostationary", "x_geostationary") + # If all the requested times are present we avoid running interpolation + if timestamp_available.all(): + logger.info("No gaps in the required satellite sequence - no interpolation run") + return ds - # Resave - ds.to_zarr(sat_path) + # If less than 2 of the buffer requested times are present we cannot infill + elif timestamp_available.sum() < 2: + logger.warning("Cannot run interpolate infilling with less than 2 time steps available") + return ds - def download_all_sat_data(self) -> None: - """Download the sat data""" - # Clean out old files - logger.info("Cleaning out old satellite data") - for loc in [sat_path, sat_5_path, sat_15_path]: - if os.path.exists(loc): - shutil.rmtree(loc) + else: + logger.info("Some requested times are missing - running interpolation") - sat_5_dl_path = os.getenv("SATELLITE_ZARR_PATH") - sat_15_dl_path = os.getenv("SATELLITE_15_ZARR_PATH") + # Run the interpolation to all 5-minute timestamps between the first and last + ds_interp = ds.interp(time=dense_times, method="linear", assume_sorted=True) - for remote_path, local_path, label in [ - (sat_5_dl_path, sat_5_path, "5-min"), - (sat_15_dl_path, sat_15_path, "15-min"), - ]: - if remote_path is not None: - fs, _ = fsspec.core.url_to_fs(remote_path) - if fs.exists(remote_path): - logger.info(f"Downloading {label} satellite data") - fs.get(remote_path, local_path) - else: - logger.info(f"No {label} data available to download") + # Find the timestamps which are within max gap size + max_gap_steps = int(max_gap / IMAGE_FREQUENCY) - 1 + valid_fill_times = fill_1d_bool_gaps(timestamp_available, max_gap_steps) - def combine_5_and_15_sat_data(self) -> xr.Dataset: - """Select and/or combine the 5 and 15-minutely satellite data""" - # Check which satellite data exists - exists_5_minute = os.path.exists(sat_5_path) - exists_15_minute = os.path.exists(sat_15_path) + # Mask the timestamps outside the max gap size + valid_fill_times_xr = xr.zeros_like(ds_interp.time, dtype=bool) + valid_fill_times_xr.values[:] = valid_fill_times + ds_sat_filtered = ds_interp.where(valid_fill_times_xr, drop=True) - if not (exists_5_minute or exists_15_minute): - raise FileNotFoundError("Neither 5- nor 15-minutely data was found.") + time_was_filled = np.logical_and(valid_fill_times_xr, ~timestamp_available) - # Find the delay in the 5- and 15-minutely data - if exists_5_minute: - datetimes_5min = get_satellite_timestamps(sat_5_path) + if time_was_filled.any(): + infilled_times = time_was_filled.where(time_was_filled, drop=True) logger.info( - f"Latest 5-minute timestamp is {datetimes_5min.max()}. " - f"All the datetimes are: \n{datetimes_5min}", + f"The following times were filled by interpolation:\n{infilled_times.time.values}", ) - if exists_15_minute: - datetimes_15min = get_satellite_timestamps(sat_15_path) + if not valid_fill_times_xr.all(): + not_infilled_times = valid_fill_times_xr.where(~valid_fill_times_xr, drop=True) logger.info( - f"Latest 5-minute timestamp is {datetimes_15min.max()}. " - f"All the datetimes are: \n{datetimes_15min}", + "After interpolation the following times are still missing:\n" + f"{not_infilled_times.time.values}", ) - # If both 5- and 15-minute data exists, use the most recent - if exists_5_minute and exists_15_minute: - use_5_minute = datetimes_5min.max() > datetimes_15min.max() - else: - # If only one exists, use that - use_5_minute = exists_5_minute + return ds_sat_filtered - # Store the choice in satellite data - self.use_5_minute = use_5_minute - # Move the selected data to the expected path - if use_5_minute: - logger.info("Using 5-minutely data.") - selected_path = sat_5_path - else: - logger.info("Using 15-minutely data.") - selected_path = sat_15_path +def satellite_inputs_available( + interval_start: pd.Timestamp, + interval_end: pd.Timestamp, + sat_datetimes: pd.DatetimeIndex | None, +) -> bool: + """Checks whether the model can be run given the current satellite delay. - # Open and return the satellite data - with zarr.storage.ZipStore(selected_path) as store: - ds = xr.open_zarr(store).compute() + Args: + interval_start: The init-time of the forecast + interval_end: The end-time of the forecast + sat_datetimes: The available satellite timestamps - return ds + Returns: + bool: Whether the satellite data satisfies that specified in the config + """ - @staticmethod - def check_required_timestamps_available(ds: xr.Dataset, t0: pd.Timestamp) -> None: - available_timestamps = pd.to_datetime(ds.time.values) + # In case no satellite is available + if sat_datetimes is None: + return False - # Need 12 timestamps of 15 minutely data up to and including time t0 - expected_timestamps = pd.date_range(t0-pd.Timedelta("165min"), t0, freq="15min") + else: + expected_datetimes = pd.date_range(interval_start, interval_end, freq=IMAGE_FREQUENCY) - timestamps_available = np.isin(expected_timestamps, available_timestamps) + # Check if any of the expected datetimes are missing + missing_time_steps = np.setdiff1d(expected_datetimes, sat_datetimes, assume_unique=True) - if not timestamps_available.all(): - missing_timestamps = expected_timestamps[~timestamps_available] - raise Exception( - "Some required timestamps missing\n" - f"Required timestamps: {expected_timestamps}\n" - f"Available timestamps: {timestamps_available}\n" - f"Missing timestamps: {missing_timestamps}", - ) + available = len(missing_time_steps) == 0 + + if len(missing_time_steps) > 0: + logger.info( + f"Some satellite timesteps in interval {interval_start}-{interval_end} missing:" + f"\n{missing_time_steps}") + + return available + + + +class SatelliteDownloader: + """Class to download and process satellite data.""" + + def __init__( + self, + interval_start: pd.Timestamp, + interval_end: pd.Timestamp, + source_path: str, + s3_region: str, + destination_path: str, + ) -> None: + """Class to download and process satellite data.""" + self.interval_start = interval_start + self.interval_end = interval_end + self.source_path = source_path + self.s3_region = s3_region + self.destination_path = destination_path + + def process(self, ds: xr.Dataset) -> xr.Dataset: + """Apply all processing steps to the satellite data in order to match the training data. + + Args: + ds: The satellite data + + Returns: + xr.Dataset: The processed satellite data + """ + # Interpolate missing satellite timestamps + ds = interpolate_missing_satellite_timestamps(ds, max_gap=MAXIMUM_INTERPOLATION_GAP) + + return ds + + def resave(self, ds: xr.Dataset) -> None: + """Resave the satellite data to the destination path.""" + # Overwrite the old data + shutil.rmtree(self.destination_path, ignore_errors=True) + + save_chunk_dict = { + "x_geostationary": 100, + "y_geostationary": 100, + "time": 6, + "channel": -1, + } + + # Clear old encoding + for v in list(ds.variables.keys()): + ds[v].encoding.clear() + + ds.chunk(save_chunk_dict).to_zarr(self.destination_path) + + def run(self) -> None: + """Download, process, and save the satellite data.""" + + + ds = open_satellite_data( + s3_icechunk_path=self.source_path, + region=self.s3_region, + ) + + logger.info( + f"Satellite data contains times:" + f"\n...\n{ds.time.values[-24:]}", + ) + + # We slice the data to the required time window for the model, plus a buffer to allow for + # interpolation of missing timestamps + start_dt = self.interval_start - MAXIMUM_INTERPOLATION_GAP + end_dt = self.interval_end + MAXIMUM_INTERPOLATION_GAP + + ds = ( + ds + .sortby("time") + .drop_duplicates("time", keep="last") + .sel(time=slice(start_dt, end_dt)) + # Filter out unused variables + [["data"]] + # Load into memory for processing + .load() + ) + + if len(ds.time) == 0: + logger.warning("No satellite data available in recent window.") + return + + ds = self.process(ds) + + if satellite_inputs_available( + interval_start=self.interval_start, + interval_end=self.interval_end, + sat_datetimes=ds.time.to_index(), + ): + self.resave(ds) + else: + raise ValueError("Satellite data is not available for the required time window.") + + \ No newline at end of file diff --git a/src/cloudcasting_metrics/app.py b/src/cloudcasting_metrics/app.py index 46e858f..7c2c95c 100644 --- a/src/cloudcasting_metrics/app.py +++ b/src/cloudcasting_metrics/app.py @@ -34,7 +34,7 @@ def open_icechunk(path: str) -> xr.Dataset: Args: path: The path to the local or s3 icechunk store """ - + assert path.startswith("s3://"), path if path.startswith("s3://"): bucket, _, path = path.removeprefix("s3://").partition("/") store = icechunk.s3_storage( From d9b9c9ed410afab0bd8a5805cc565c534765e82a Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 18 Aug 2026 15:34:20 +0000 Subject: [PATCH 2/6] stash --- pyproject.toml | 1 + src/cloudcasting_inference/app.py | 182 ++++++++++++++++++------- src/cloudcasting_inference/settings.py | 33 +++++ src/cloudcasting_metrics/app.py | 21 ++- src/cloudcasting_metrics/settings.py | 28 ++++ uv.lock | 25 ++++ 6 files changed, 230 insertions(+), 60 deletions(-) create mode 100644 src/cloudcasting_inference/settings.py create mode 100644 src/cloudcasting_metrics/settings.py diff --git a/pyproject.toml b/pyproject.toml index b13eef4..1ba04bf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,6 +28,7 @@ dependencies = [ "numpy", "ocf-data-sampler==0.5.27", "pandas", + "pydantic-settings", "s3fs", "safetensors", "sat_pred @ git+https://github.com/openclimatefix/sat_pred.git@main", diff --git a/src/cloudcasting_inference/app.py b/src/cloudcasting_inference/app.py index d747862..eae3ead 100644 --- a/src/cloudcasting_inference/app.py +++ b/src/cloudcasting_inference/app.py @@ -1,11 +1,13 @@ """The main script for running the cloudcasting model in production -This app expects these environmental variables to be available: - SATELLITE_ZARR_PATH (str): The path of the input satellite data - PREDICTION_SAVE_DIRECTORY (str): The path of the directory to save the predictions to +The runtime configuration is loaded from environment variables - see +`cloudcasting_inference.settings.AppSettings` for the full list. """ +import contextlib import os +import tempfile +from collections.abc import Iterator from importlib.metadata import PackageNotFoundError, version import fsspec @@ -17,11 +19,13 @@ from huggingface_hub import snapshot_download from safetensors.torch import load_model from loguru import logger +from numpy.typing import NDArray from cloudcasting_inference.data import SatelliteDownloader +from cloudcasting_inference.settings import AppSettings from sat_pred.dataset import SatelliteDataset +from sat_pred.channels import ChannelConfig, parse_channel_config -# Get package version try: __version__ = version("cloudcasting-inference") except PackageNotFoundError: @@ -38,6 +42,10 @@ REVISION = "06e7ee93366a0d1dbb52c937840319460b0b51db" +# Filename of the downloaded satellite data within the scratch directory +sat_path = "sat.zarr" + + def sanitize_t0(t0: pd.Timestamp | None) -> pd.Timestamp: """Sanitize the input t0 to be a pandas Timestamp and round down to the nearest 30 minutes.""" if t0 is None: @@ -47,8 +55,14 @@ def sanitize_t0(t0: pd.Timestamp | None) -> pd.Timestamp: return t0.floor("30min") -def get_model(repo_id: str, revision: str) -> torch.nn.Module: - """Download the model from huggingface and load it into memory""" +def get_model(repo_id: str, revision: str) -> tuple[torch.nn.Module, dict]: + """Download the model from huggingface and load it into memory + + Returns: + The model, and the config of the data it was trained on. The data config gives the + history, forecast horizon, time resolution and channels the model expects, so the inputs + built here always match what the model was trained to read. + """ hf_download_dir = snapshot_download( repo_id=repo_id, @@ -58,6 +72,9 @@ def get_model(repo_id: str, revision: str) -> torch.nn.Module: with open(f"{hf_download_dir}/model_config.yaml", encoding="utf-8") as f: model = hydra.utils.instantiate(yaml.safe_load(f)) + with open(f"{hf_download_dir}/data_config.yaml", encoding="utf-8") as f: + data_config = yaml.safe_load(f) + model = model.to(device) load_model( @@ -68,88 +85,157 @@ def get_model(repo_id: str, revision: str) -> torch.nn.Module: model.eval() - return model + return model, data_config -def get_input_data(t0: pd.Timestamp, history_mins: int) -> torch.Tensor: +def get_input_tensor(dataset: SatelliteDataset, t0: pd.Timestamp) -> torch.Tensor: """Get the input data for the model + Args: + dataset: The dataset to get the input data from + t0: Datetime at which forecast is made + Returns: torch.Tensor: The input data for the model """ - dataset = SatelliteDataset( - zarr_path=os.environ["PREDICTION_SAVE_DIRECTORY"], - time_periods=[[None, None]], - history_mins=history_mins, - forecast_mins=0, - sample_freq_mins=15, - channels=None, # ChannelConfigInput - preshuffle=False, - ) X, _ = dataset._get_datetime(t0) return X.unsqueeze(0).to(device) + +def prediction_to_dataset_like( + y_hat: NDArray, + t0: pd.Timestamp, + forecast_mins: int, + sample_freq_mins: int, + da: xr.DataArray, + model_address: str +) -> xr.Dataset: + """Convert the model output to a DataArray with the same coordinates as the input""" + + da_y_hat = xr.DataArray( + y_hat, + dims=["init_time_utc", "channel", "step", "y_geostationary", "x_geostationary"], + coords={ + "init_time_utc": [t0], + "channel": da.channel, + "step": pd.timedelta_range( + start=f"{sample_freq_mins}min", + end=f"{forecast_mins}min", + freq=f"{sample_freq_mins}min", + ), + "y_geostationary": da.y_geostationary, + "x_geostationary": da.x_geostationary, + }, + ) + + attrs_dict = dict(da.attrs) + + ds_y_hat = da_y_hat.to_dataset(name="sat_pred") + ds_y_hat.attrs.update(attrs_dict) + ds_y_hat.attrs["model_address"] = model_address + return ds_y_hat + +@contextlib.contextmanager +def scratch_directory(parent_dir: str | None, t0: pd.Timestamp) -> Iterator[str]: + """Create the directory which the downloaded inputs are saved to + + If a parent directory is given, the per-run directory created inside it is named from the + forecast init-time and is left in place after the run. Otherwise a temporary directory is used + and is deleted on exit. + + Args: + parent_dir: The directory to create the per-run directory in, if any + t0: Datetime at which forecast is made + """ + if parent_dir is None: + with tempfile.TemporaryDirectory(prefix="cloudcasting-app-") as scratch_dir: + yield scratch_dir + else: + os.makedirs(parent_dir, exist_ok=True) + yield tempfile.mkdtemp( + prefix=t0.strftime("cloudcasting-app-%Y%m%dT%H%M%SZ-"), + dir=parent_dir, + ) + + @torch.no_grad() def app(t0=None): """Inference function for production + Sets up the scratch directory used for the downloaded inputs, then runs the forecast. + Args: t0 (datetime): Datetime at which forecast is made """ logger.info(f"Using `cloudcasting-app` version: {__version__}", version=__version__) + settings = AppSettings() + t0 = sanitize_t0(t0) logger.info(f"Making forecast for init time: {t0}") + with scratch_directory(settings.scratch_dir, t0) as scratch_dir: + logger.info(f"Using scratch directory: {scratch_dir}") + _run_forecast(settings, t0, scratch_dir) + + +def _run_forecast(settings: AppSettings, t0: pd.Timestamp, scratch_dir: str) -> None: + """Download the inputs, run the model, and save the predictions + + Args: + settings: The application settings + t0: Datetime at which forecast is made + scratch_dir: Directory for downloaded inputs and temporary files + """ + logger.info("Loading model") + model, data_config = get_model(REPO_ID, REVISION) + logger.info("Downloading satellite data") + sat_path = f"{scratch_dir}/rss.zarr" SatelliteDownloader( - interval_start=t0-pd.Timedelta(minutes=165), + interval_start=t0-pd.Timedelta(minutes=data_config["history_mins"]), interval_end=t0, - source_path=os.environ["SATELLITE_ZARR_PATH"], - s3_region=os.environ["S3_REGION"], - destination_path=os.environ["PREDICTION_SAVE_DIRECTORY"], + source_path=settings.satellite_zarr_path, + s3_region=settings.s3_region, + destination_path=sat_path, ).run() - logger.info("Loading model") - model = get_model(REPO_ID, REVISION) - logger.info("Preparing inputs") - X = get_input_data(t0, history_mins=model.history_mins) - + channel_config: ChannelConfig = parse_channel_config(data_config["channels"]) + dataset = SatelliteDataset( + zarr_path=sat_path, + time_periods=[[None, None]], + history_mins=data_config["history_mins"], + forecast_mins=0, + sample_freq_mins=data_config["sample_freq_mins"], + channels=channel_config, + preshuffle=False, + ) + X = get_input_tensor(dataset, t0) logger.info("Making predictions") y_hat = model(X).squeeze(0).cpu().numpy() + y_hat = channel_config.normaliser.denormalise(y_hat) - # --------------------------------------------------------------------------- - # 5. Save predictions logger.info("Saving predictions") - da_y_hat = xr.DataArray( - y_hat, - dims=["init_time", "variable", "step", "y_geostationary", "x_geostationary"], - coords={ - "init_time": [t0], - "variable": ds.variable, - "step": pd.timedelta_range(start="15min", end="180min", freq="15min"), - "y_geostationary": ds.y_geostationary, - "x_geostationary": ds.x_geostationary, - }, - ) - ds_y_hat = da_y_hat.to_dataset(name="sat_pred") - ds_y_hat.sat_pred.attrs.update(ds.data.attrs) + ds_y_hat = prediction_to_dataset_like( + y_hat=y_hat, + t0=t0, + forecast_mins=data_config["forecast_mins"], + sample_freq_mins=data_config["sample_freq_mins"], + da=dataset.da, + model_address=f"{REPO_ID}@{REVISION}" + ) # Save predictions to the latest path and to path with timestring - out_dir = os.environ["PREDICTION_SAVE_DIRECTORY"] + out_dir = settings.prediction_save_directory - if satellite_downloader.use_5_minute: - latest_zarr_path = f"{out_dir}/latest.zarr" - t0_string_zarr_path = t0.strftime(f"{out_dir}/%Y-%m-%dT%H:%M.zarr") - else: - latest_zarr_path = f"{out_dir}/latest_0-deg.zarr" - t0_string_zarr_path = t0.strftime(f"{out_dir}/%Y-%m-%dT%H:%M_0-deg.zarr") + latest_zarr_path = f"{out_dir}/latest.zarr" + t0_string_zarr_path = t0.strftime(f"{out_dir}/%Y-%m-%dT%H:%M.zarr") fs = fsspec.open(out_dir).fs for path in [latest_zarr_path, t0_string_zarr_path]: diff --git a/src/cloudcasting_inference/settings.py b/src/cloudcasting_inference/settings.py new file mode 100644 index 0000000..c02f6e8 --- /dev/null +++ b/src/cloudcasting_inference/settings.py @@ -0,0 +1,33 @@ +"""Runtime configuration loaded from environment.""" + +from pydantic_settings import BaseSettings + + +class AppSettings(BaseSettings): + """Runtime configuration loaded from environment. + + Input data paths + + - SATELLITE_ZARR_PATH: The path of the input satellite data. + - S3_REGION: The AWS region for the satellite data S3 bucket. + + Output data paths + + - PREDICTION_SAVE_DIRECTORY: The path of the directory to save the predictions to. + + Other settings + + - SCRATCH_DIR: If set, this directory is used as the parent for per-run scratch + directories named from the forecast init time. These per-run directories are not cleaned + up by the app. Else, the system temp directory will be used and cleaned up after the run. + """ + + # Input data paths + satellite_zarr_path: str + s3_region: str + + # Output data paths + prediction_save_directory: str + + # Other settings + scratch_dir: str | None = None diff --git a/src/cloudcasting_metrics/app.py b/src/cloudcasting_metrics/app.py index 7c2c95c..ee82698 100644 --- a/src/cloudcasting_metrics/app.py +++ b/src/cloudcasting_metrics/app.py @@ -1,15 +1,9 @@ """Runs metric calculations on cloudcasting for a given input day and appends to zarr store -This app expects these environmental variables to be available: - - SATELLITE_ICECHUNK_ARCHIVE (str): Path at which ground truth satellite data can be found - - PREDICTION_SAVE_DIRECTORY (str): The directory where the cloudcasting forecasts are saved - - METRIC_ZARR_PATH (str): The path where the metric values will be saved - - If the SATELLITE_ICECHUNK_ARCHIVE is an s3 path, then the environment variables - AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_REGION must also be set. +The runtime configuration is loaded from environment variables - see +`cloudcasting_metrics.settings.AppSettings` for the full list. """ -import os import re import fsspec import numpy as np @@ -20,6 +14,8 @@ import icechunk from loguru import logger +from cloudcasting_metrics.settings import AppSettings + # --------------------------------------------------------------------------- # The forecast produces these horizon steps @@ -57,10 +53,11 @@ def app(date: pd.Timestamp | None = None) -> None: date: The day for which the cloudcasting predictions will be scored. """ - # Unpack environmental variables - sat_path = os.environ["SATELLITE_ICECHUNK_ARCHIVE"] - prediction_dir = os.environ["PREDICTION_SAVE_DIRECTORY"] - metric_zarr_path = os.environ["METRIC_ZARR_PATH"] + settings = AppSettings() + + sat_path = settings.satellite_icechunk_archive + prediction_dir = settings.prediction_save_directory + metric_zarr_path = settings.metric_zarr_path now = pd.Timestamp.now(tz="UTC").replace(tzinfo=None) diff --git a/src/cloudcasting_metrics/settings.py b/src/cloudcasting_metrics/settings.py new file mode 100644 index 0000000..cba341c --- /dev/null +++ b/src/cloudcasting_metrics/settings.py @@ -0,0 +1,28 @@ +"""Runtime configuration loaded from environment.""" + +from pydantic_settings import BaseSettings + + +class AppSettings(BaseSettings): + """Runtime configuration loaded from environment. + + Input data paths + + - SATELLITE_ICECHUNK_ARCHIVE: Path at which ground truth satellite data can be found. + - PREDICTION_SAVE_DIRECTORY: The directory where the cloudcasting forecasts are saved. + + Output data paths + + - METRIC_ZARR_PATH: The path where the metric values will be saved. + + If SATELLITE_ICECHUNK_ARCHIVE is an s3 path, then the environment variables + AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_REGION must also be set. These are read + directly from the environment by icechunk rather than through these settings. + """ + + # Input data paths + satellite_icechunk_archive: str + prediction_save_directory: str + + # Output data paths + metric_zarr_path: str diff --git a/uv.lock b/uv.lock index dae182b..84a3048 100644 --- a/uv.lock +++ b/uv.lock @@ -171,6 +171,7 @@ dependencies = [ { name = "numpy" }, { name = "ocf-data-sampler" }, { name = "pandas" }, + { name = "pydantic-settings" }, { name = "s3fs" }, { name = "safetensors" }, { name = "sat-pred" }, @@ -200,6 +201,7 @@ requires-dist = [ { name = "numpy" }, { name = "ocf-data-sampler", specifier = "==0.5.27" }, { name = "pandas" }, + { name = "pydantic-settings" }, { name = "s3fs" }, { name = "safetensors" }, { name = "sat-pred", git = "https://github.com/openclimatefix/sat_pred.git?rev=main" }, @@ -1055,6 +1057,20 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/0d/24/8b11e8b3e2be9dd82df4b11408a67c61bb4dc4f8e11b5b0fc888b38118b5/pydantic_core-2.33.2-cp312-cp312-win_arm64.whl", hash = "sha256:cca3868ddfaccfbc4bfb1d608e2ccaaebe0ae628e1416aeb9c4d88c001bb45ab", size = 1888894, upload-time = "2025-04-23T18:31:51.609Z" }, ] +[[package]] +name = "pydantic-settings" +version = "2.15.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pydantic" }, + { name = "python-dotenv" }, + { name = "typing-inspection" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/68/ca/31c57507b13119d7d3cfa1576dad2911a4861e3be07b579395f4e9d393f9/pydantic_settings-2.15.0.tar.gz", hash = "sha256:694b793e84f766ba76a90ebdefc01d0a9a045dab0382bee70393da93712ad117", size = 261253, upload-time = "2026-08-07T09:24:57.419Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/30/a4/2bffa9f8e804325a09867f0e9d30795c80ea9f8d62560bd1b6ad6220eb2f/pydantic_settings-2.15.0-py3-none-any.whl", hash = "sha256:0ba092c291c94baceb5eff768aa0d56400a457585bc0175925a5a5510303da42", size = 69413, upload-time = "2026-08-07T09:24:55.839Z" }, +] + [[package]] name = "pygments" version = "2.19.2" @@ -1163,6 +1179,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, ] +[[package]] +name = "python-dotenv" +version = "1.2.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6a/53/ed9d74092561d4b01a2ef1349d52cdbc135e526c245f366b089cfca6de49/python_dotenv-1.2.3.tar.gz", hash = "sha256:a20a594dabeaa385725aa239d5244871c143ecb356add8a20fcf23773a6c3a35", size = 58945, upload-time = "2026-08-16T16:54:54.067Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0d/17/c5c6b53ddc18f297992099b3d9ec16c855c0ccc83263a21fe4d1c625ec6c/python_dotenv-1.2.3-py3-none-any.whl", hash = "sha256:904552145e8bfed22162c09dab1c2b9b54fefa7b23ba780f4f26ca0316b0f0d9", size = 22780, upload-time = "2026-08-16T16:54:52.473Z" }, +] + [[package]] name = "pytorch-lightning" version = "2.5.5" From 72f213c6969a278a88b243242246bb20339dc01d Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 18 Aug 2026 18:52:20 +0000 Subject: [PATCH 3/6] refresh --- .github/workflows/branch_ci.yml | 116 ----- .github/workflows/merged_ci.yml | 102 ---- .github/workflows/release_on_tag.yml | 11 + .github/workflows/tag.yml | 12 + .github/workflows/test_and_build.yml | 21 + README.md | 38 -- pyproject.toml | 10 +- src/cloudcasting_inference/README.md | 11 +- src/cloudcasting_inference/app.py | 71 ++- src/cloudcasting_inference/data.py | 35 +- src/cloudcasting_inference/settings.py | 5 +- src/cloudcasting_metrics/README.md | 4 +- src/cloudcasting_metrics/app.py | 56 +- src/cloudcasting_metrics/settings.py | 13 +- tests/test_cloudcasting_inference/conftest.py | 5 +- tests/test_cloudcasting_inference/test_app.py | 21 +- tests/test_cloudcasting_metrics/conftest.py | 15 +- tests/test_cloudcasting_metrics/test_app.py | 33 +- tests/utils.py | 7 +- uv.lock | 484 ++++++++++++++++-- 20 files changed, 633 insertions(+), 437 deletions(-) delete mode 100644 .github/workflows/branch_ci.yml delete mode 100644 .github/workflows/merged_ci.yml create mode 100644 .github/workflows/release_on_tag.yml create mode 100644 .github/workflows/tag.yml create mode 100644 .github/workflows/test_and_build.yml diff --git a/.github/workflows/branch_ci.yml b/.github/workflows/branch_ci.yml deleted file mode 100644 index 1a305b8..0000000 --- a/.github/workflows/branch_ci.yml +++ /dev/null @@ -1,116 +0,0 @@ -# Workflow that runs on pushes to non-default branches - -name: Non-Default Branch Push CI (Python) - -on: - push: - branches-ignore: ['main'] - paths-ignore: ['README.md'] - -# Specify concurrency such that only one workflow can run at a time -# * Different workflow files are not affected -concurrency: - group: ${{ github.workflow }}-${{ github.ref }} - cancel-in-progress: true - -# Registry for storing Container images -env: - REGISTRY: ghcr.io - IMAGE_NAME: ${{ github.repository }} - -# Ensure the GitHub token can remove packages -permissions: - packages: write - - -jobs: - - # Job to run unittests - # * Produces a JUnit XML report that can be displayed in the GitHub UI - test-unit: - runs-on: ubuntu-latest - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Setup uv - uses: astral-sh/setup-uv@v3 - with: - enable-cache: true - cache-dependency-glob: "pyproject.toml" - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version-file: "pyproject.toml" - - - name: Install editable package and required dependencies - run: uv sync - - # Run unittests - # * Produce JUnit XML report - - name: Run unit tests - run: uv run python -m pytest --junitxml=ut-report.xml tests - - # Create test summary to be visualised on the job summary screen on GitHub - # * Runs even if previous steps fail - - name: Create test summary - uses: test-summary/action@v2 - with: - paths: "*t-report.xml" - show: "fail, skip" - if: always() - - # Job for building container image - # * Builds and pushes an OCI Container image to the registry defined in the environment variables - # * Only runs if test and lint jobs pass - build-container: - runs-on: ubuntu-latest - permissions: - contents: read - packages: write - needs: ["test-unit"] - - steps: - # Do a non-shallow clone of the repo to ensure tags are present - # * This allows setuptools-git-versioning to automatically set the version - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Set up Buildx - uses: docker/setup-buildx-action@v3 - - - name: Log in to the Container registry - uses: docker/login-action@v3 - with: - registry: ${{ env.REGISTRY }} - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - # Tag the built image according to the event type - # The event is a branch commit, so use the commit sha - - name: Extract metadata (tags, labels) for Container - id: meta - uses: docker/metadata-action@v5 - with: - images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} - tags: type=ref,event=branch - - # Build and push the Container image to the registry - # * Creates a multiplatform-aware image - # * Pulls build cache from the registry - - name: Build and push container image - uses: docker/build-push-action@v6 - with: - context: . - file: Dockerfile - push: true - tags: ${{ steps.meta.outputs.tags }} - labels: ${{ steps.meta.outputs.labels }} - platforms: linux/amd64 - cache-from: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache - cache-to: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache - diff --git a/.github/workflows/merged_ci.yml b/.github/workflows/merged_ci.yml deleted file mode 100644 index e320ca3..0000000 --- a/.github/workflows/merged_ci.yml +++ /dev/null @@ -1,102 +0,0 @@ -# Workflow that runs on closed PRs to the default branch - -name: Default Branch PR Merged CI (Python) - -on: - pull_request: - types: ["closed"] - branches: ["main"] - -# Specify concurrency such that only one workflow can run at a time -# * Different workflow files are not affected -concurrency: - group: ${{ github.workflow }}-${{ github.ref }} - cancel-in-progress: false - -env: - REGISTRY: ghcr.io - IMAGE_NAME: ${{ github.repository }} - - -jobs: - - # Define an autotagger job that creates tags on changes to master - # Use #major #minor in merge commit messages to bump version beyond patch - bump-tag: - runs-on: ubuntu-latest - if: | - github.event_name == 'pull_request' && - github.event.action == 'closed' && - github.event.pull_request.merged == true - permissions: - contents: write - outputs: - tag: ${{ steps.tag.outputs.tag }} - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Bump version and push tag - uses: anothrNick/github-tag-action@1.67.0 - id: tag - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - RELEASE_BRANCHES: main - WITH_V: true - DEFAULT_BUMP: patch - GIT_API_TAGGING: false - - # Job for building container image - # * Builds and pushes an OCI Container image to the registry defined in the environment variables - build-container: - runs-on: ubuntu-latest - needs: bump-tag - permissions: - contents: read - packages: write - - steps: - # Do a non-shallow clone of the repo to ensure tags are present - # * This allows setuptools-git-versioning to automatically set the version - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Set up Buildx - uses: docker/setup-buildx-action@v3 - - - name: Log in to the Container registry - uses: docker/login-action@v3 - with: - registry: ${{ env.REGISTRY }} - username: ${{ github.actor }} - password: ${{ secrets.GITHUB_TOKEN }} - - # Tag the built image according to the event type - # The event is a semver release, so use the version - - name: Extract metadata (tags, labels) for Container - id: meta - uses: docker/metadata-action@v5 - with: - images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} - tags: type=semver,pattern={{version}},value=${{ needs.bump-tag.outputs.tag }} - - # Build and push the Container image to the registry - # * Creates a multiplatform-aware image - # * Pulls build cache from the registry and pushes new cache back - - name: Build and push container image - uses: docker/build-push-action@v6 - with: - context: . - file: Dockerfile - push: true - tags: ${{ steps.meta.outputs.tags }} - labels: ${{ steps.meta.outputs.labels }} - platforms: linux/amd64 - cache-from: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache - cache-to: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache,mode=max - diff --git a/.github/workflows/release_on_tag.yml b/.github/workflows/release_on_tag.yml new file mode 100644 index 0000000..4e49c7f --- /dev/null +++ b/.github/workflows/release_on_tag.yml @@ -0,0 +1,11 @@ +name: Build Versioned Release Artifacts + +on: + push: + tags: + - 'v*.*.*' + +jobs: + tagged-ci: + uses: openclimatefix/.github/.github/workflows/tagged_ci.yml@main + secrets: inherit diff --git a/.github/workflows/tag.yml b/.github/workflows/tag.yml new file mode 100644 index 0000000..bb1531c --- /dev/null +++ b/.github/workflows/tag.yml @@ -0,0 +1,12 @@ +name: Bump Tag + +on: + pull_request: + types: ["closed"] + branches: ["main"] + +jobs: + bump-tag: + if: github.event.pull_request.merged == true + uses: openclimatefix/.github/.github/workflows/bump_tag.yml@main + secrets: inherit diff --git a/.github/workflows/test_and_build.yml b/.github/workflows/test_and_build.yml new file mode 100644 index 0000000..9695a46 --- /dev/null +++ b/.github/workflows/test_and_build.yml @@ -0,0 +1,21 @@ +name: Branch Test and Build + +on: + push: + branches-ignore: ['main'] + paths-ignore: ['README.md'] + pull_request: + types: [opened, reopened, synchronize] + paths-ignore: ['README.md'] + +jobs: + + branch-ci: + uses: openclimatefix/.github/.github/workflows/branch_ci.yml@78c15cc19e7140a3e9a76803a224d85c21c612f6 + secrets: inherit + with: + enable_linting: true + enable_formatting: true + # The codebase does not typecheck cleanly yet + enable_typechecking: false + tests_folder: tests diff --git a/README.md b/README.md index dc63687..cca83ff 100644 --- a/README.md +++ b/README.md @@ -42,44 +42,6 @@ pytest This will run tests for both packages. -## Contributing and community - -[![issues badge](https://img.shields.io/github/issues/openclimatefix/cloudcasting-app?color=FFAC5F)](https://github.com/openclimatefix/cloudcasting-app/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc) - -- PR's are welcome! See the [Organisation Profile](https://github.com/openclimatefix) for details on contributing -- Find out about our other projects in the [here](https://github.com/openclimatefix/.github/tree/main/profile) -- Check out the [OCF blog](https://openclimatefix.org/blog) for updates -- Follow OCF on [LinkedIn](https://uk.linkedin.com/company/open-climate-fix) - -## Contributors - - - - - - - - - - - - - -
James Fulton
James Fulton
💻		devsjc
devsjc
🚇		Peter Dudfield
Peter Dudfield
💻		Sumana Sree Angajala
Sumana Sree Angajala
🚇
- - - - - - - - - - - - - ---- *Part of the [Open Climate Fix](https://github.com/orgs/openclimatefix/people) community.* diff --git a/pyproject.toml b/pyproject.toml index 1ba04bf..66b2623 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ dependencies = [ "icechunk", "loguru", "numpy", - "ocf-data-sampler==0.5.27", + "ocf-data-sampler==1.0.23", "pandas", "pydantic-settings", "s3fs", @@ -88,11 +88,11 @@ plugins = [ [tool.ruff] line-length = 100 indent-width = 4 -exclude = ["__init__.py"] +# Scratch notebooks are not part of the packages and are not linted +exclude = ["__init__.py", "*.ipynb"] [tool.ruff.lint] fixable = ["ALL"] -ignore = ["ANN101", "ANN102"] select = [ "F", # pyflakes "E", # pycodestyle @@ -103,7 +103,6 @@ select = [ "S", # flake8 bandit "B", # flake8 bugbear "C4", # flake8 comprehensions - "COM", # flake8 commas "T20", # flake8 print "SIM", # flake8 simplify "ARG", # flake8 unused arguments @@ -115,7 +114,8 @@ select = [ ] [tool.ruff.lint.per-file-ignores] -"test*" = ["D", "ANN"] +# Tests are not documented or annotated, and `assert` is how they assert +"tests/*" = ["D", "ANN", "S101"] [tool.ruff.lint.pydocstyle] convention = "google" diff --git a/src/cloudcasting_inference/README.md b/src/cloudcasting_inference/README.md index 97fcc67..7a3d2c8 100644 --- a/src/cloudcasting_inference/README.md +++ b/src/cloudcasting_inference/README.md @@ -12,13 +12,18 @@ https://huggingface.co/openclimatefix/cloudcasting_uk The following environment variables are used in the app: -- `SATELLITE_ZARR_PATH`: The path to the satellite data in Zarr format. +- `SATELLITE_ICECHUNK_PATH`: The s3 path to the icechunk store holding the satellite data. +- `S3_REGION`: The AWS region of the bucket the satellite icechunk store is in. - `PREDICTION_SAVE_DIRECTORY`: The directory where predictions will be saved. +The satellite store is read with the credentials in the environment, so `AWS_ACCESS_KEY_ID` and +`AWS_SECRET_ACCESS_KEY` must also be set. + ### Optional Environment Variables -- `SATELLITE_15_ZARR_PATH`: The path to the 15 minute satellite data in Zarr format. If -this is not set then the `SATELLITE_ZARR_PATH` is used by `.zarr` is repalced with `_15.zarr` +- `SCRATCH_DIR`: If set, the satellite data downloaded for each run is saved to a directory inside +this one named from the forecast init-time, and is left in place after the run. Otherwise the +system temp directory is used and is cleaned up when the run finishes. ## Example usage diff --git a/src/cloudcasting_inference/app.py b/src/cloudcasting_inference/app.py index eae3ead..02fd2ec 100644 --- a/src/cloudcasting_inference/app.py +++ b/src/cloudcasting_inference/app.py @@ -1,4 +1,4 @@ -"""The main script for running the cloudcasting model in production +"""The main script for running the cloudcasting model in production. The runtime configuration is loaded from environment variables - see `cloudcasting_inference.settings.AppSettings` for the full list. @@ -8,29 +8,26 @@ import os import tempfile from collections.abc import Iterator -from importlib.metadata import PackageNotFoundError, version +from importlib.metadata import version import fsspec import hydra +import numpy as np import pandas as pd import torch import xarray as xr import yaml from huggingface_hub import snapshot_download -from safetensors.torch import load_model from loguru import logger from numpy.typing import NDArray +from safetensors.torch import load_model +from sat_pred.channels import ChannelConfig, parse_channel_config +from sat_pred.dataset import SatelliteDataset from cloudcasting_inference.data import SatelliteDownloader from cloudcasting_inference.settings import AppSettings -from sat_pred.dataset import SatelliteDataset -from sat_pred.channels import ChannelConfig, parse_channel_config - -try: - __version__ = version("cloudcasting-inference") -except PackageNotFoundError: - __version__ = "v?" +__version__ = version("cloudcasting-app") # --------------------------------------------------------------------------- @@ -42,28 +39,20 @@ REVISION = "06e7ee93366a0d1dbb52c937840319460b0b51db" -# Filename of the downloaded satellite data within the scratch directory -sat_path = "sat.zarr" - - def sanitize_t0(t0: pd.Timestamp | None) -> pd.Timestamp: """Sanitize the input t0 to be a pandas Timestamp and round down to the nearest 30 minutes.""" - if t0 is None: - t0 = pd.Timestamp.now(tz="UTC").replace(tzinfo=None) - else: - t0 = pd.to_datetime(t0) + t0 = pd.Timestamp.now(tz="UTC").replace(tzinfo=None) if t0 is None else pd.to_datetime(t0) return t0.floor("30min") def get_model(repo_id: str, revision: str) -> tuple[torch.nn.Module, dict]: - """Download the model from huggingface and load it into memory + """Download the model from huggingface and load it into memory. Returns: The model, and the config of the data it was trained on. The data config gives the history, forecast horizon, time resolution and channels the model expects, so the inputs built here always match what the model was trained to read. """ - hf_download_dir = snapshot_download( repo_id=repo_id, revision=revision, @@ -89,19 +78,18 @@ def get_model(repo_id: str, revision: str) -> tuple[torch.nn.Module, dict]: def get_input_tensor(dataset: SatelliteDataset, t0: pd.Timestamp) -> torch.Tensor: - """Get the input data for the model + """Get the input data for the model. Args: dataset: The dataset to get the input data from t0: Datetime at which forecast is made Returns: - torch.Tensor: The input data for the model + torch.Tensor: The input data for the model, with a leading batch dimension of 1 """ - X, _ = dataset._get_datetime(t0) - return X.unsqueeze(0).to(device) + return torch.from_numpy(X).unsqueeze(0).to(device) def prediction_to_dataset_like( @@ -110,10 +98,9 @@ def prediction_to_dataset_like( forecast_mins: int, sample_freq_mins: int, da: xr.DataArray, - model_address: str + model_address: str, ) -> xr.Dataset: - """Convert the model output to a DataArray with the same coordinates as the input""" - + """Convert the model output to a DataArray with the same coordinates as the input.""" da_y_hat = xr.DataArray( y_hat, dims=["init_time_utc", "channel", "step", "y_geostationary", "x_geostationary"], @@ -137,9 +124,10 @@ def prediction_to_dataset_like( ds_y_hat.attrs["model_address"] = model_address return ds_y_hat + @contextlib.contextmanager def scratch_directory(parent_dir: str | None, t0: pd.Timestamp) -> Iterator[str]: - """Create the directory which the downloaded inputs are saved to + """Create the directory which the downloaded inputs are saved to. If a parent directory is given, the per-run directory created inside it is named from the forecast init-time and is left in place after the run. Otherwise a temporary directory is used @@ -161,15 +149,15 @@ def scratch_directory(parent_dir: str | None, t0: pd.Timestamp) -> Iterator[str] @torch.no_grad() -def app(t0=None): - """Inference function for production +def app(t0: pd.Timestamp | None = None) -> None: + """Inference function for production. Sets up the scratch directory used for the downloaded inputs, then runs the forecast. Args: - t0 (datetime): Datetime at which forecast is made + t0: Datetime at which forecast is made. Defaults to the current time """ - logger.info(f"Using `cloudcasting-app` version: {__version__}", version=__version__) + logger.info(f"Using `cloudcasting-app` version: {__version__}") settings = AppSettings() @@ -182,7 +170,7 @@ def app(t0=None): def _run_forecast(settings: AppSettings, t0: pd.Timestamp, scratch_dir: str) -> None: - """Download the inputs, run the model, and save the predictions + """Download the inputs, run the model, and save the predictions. Args: settings: The application settings @@ -192,13 +180,17 @@ def _run_forecast(settings: AppSettings, t0: pd.Timestamp, scratch_dir: str) -> logger.info("Loading model") model, data_config = get_model(REPO_ID, REVISION) - logger.info("Downloading satellite data") + # ocf_data_sampler.find_contiguous_time_periods which SatelliteDataset uses internally discards + # any time periods which aren't <= the history_mins, so we need to download a bit more than that + # to ensure we get enough data. This can be removed once the new version of ocf_data_sampler + # is released and SatelliteDataset is updated to use it. + history_mins = data_config["history_mins"] + data_config["sample_freq_mins"] sat_path = f"{scratch_dir}/rss.zarr" SatelliteDownloader( - interval_start=t0-pd.Timedelta(minutes=data_config["history_mins"]), + interval_start=t0 - pd.Timedelta(minutes=history_mins), interval_end=t0, - source_path=settings.satellite_zarr_path, + source_path=settings.satellite_icechunk_path, s3_region=settings.s3_region, destination_path=sat_path, ).run() @@ -217,8 +209,10 @@ def _run_forecast(settings: AppSettings, t0: pd.Timestamp, scratch_dir: str) -> X = get_input_tensor(dataset, t0) logger.info("Making predictions") + # The normaliser works on (channel, time, y, x) samples, so denormalise the single sample + # before putting the init-time axis back on y_hat = model(X).squeeze(0).cpu().numpy() - y_hat = channel_config.normaliser.denormalise(y_hat) + y_hat = channel_config.normaliser.denormalise(y_hat)[np.newaxis] logger.info("Saving predictions") @@ -228,7 +222,7 @@ def _run_forecast(settings: AppSettings, t0: pd.Timestamp, scratch_dir: str) -> forecast_mins=data_config["forecast_mins"], sample_freq_mins=data_config["sample_freq_mins"], da=dataset.da, - model_address=f"{REPO_ID}@{REVISION}" + model_address=f"{REPO_ID}@{REVISION}", ) # Save predictions to the latest path and to path with timestring @@ -239,7 +233,6 @@ def _run_forecast(settings: AppSettings, t0: pd.Timestamp, scratch_dir: str) -> fs = fsspec.open(out_dir).fs for path in [latest_zarr_path, t0_string_zarr_path]: - # Remove the path if it exists already if fs.exists(path): logger.info(f"Removing path: {path}") diff --git a/src/cloudcasting_inference/data.py b/src/cloudcasting_inference/data.py index 27ee5d8..616038c 100644 --- a/src/cloudcasting_inference/data.py +++ b/src/cloudcasting_inference/data.py @@ -1,15 +1,12 @@ """Functions to download and process satellite data.""" -import logging import shutil import icechunk import numpy as np import pandas as pd import xarray as xr - - -logger = logging.getLogger(__name__) +from loguru import logger # The maximum gap size which will be filled via interpolation MAXIMUM_INTERPOLATION_GAP = pd.Timedelta("15min") @@ -156,7 +153,6 @@ def satellite_inputs_available( Returns: bool: Whether the satellite data satisfies that specified in the config """ - # In case no satellite is available if sat_datetimes is None: return False @@ -172,12 +168,12 @@ def satellite_inputs_available( if len(missing_time_steps) > 0: logger.info( f"Some satellite timesteps in interval {interval_start}-{interval_end} missing:" - f"\n{missing_time_steps}") + f"\n{missing_time_steps}" + ) return available - class SatelliteDownloader: """Class to download and process satellite data.""" @@ -230,16 +226,16 @@ def resave(self, ds: xr.Dataset) -> None: def run(self) -> None: """Download, process, and save the satellite data.""" - - ds = open_satellite_data( s3_icechunk_path=self.source_path, region=self.s3_region, ) + if ds is None: + raise ValueError(f"Could not open the satellite data at {self.source_path}") + logger.info( - f"Satellite data contains times:" - f"\n...\n{ds.time.values[-24:]}", + f"Satellite data contains times:\n...\n{ds.time.values[-24:]}", ) # We slice the data to the required time window for the model, plus a buffer to allow for @@ -248,19 +244,20 @@ def run(self) -> None: end_dt = self.interval_end + MAXIMUM_INTERPOLATION_GAP ds = ( - ds - .sortby("time") + ds.sortby("time") .drop_duplicates("time", keep="last") - .sel(time=slice(start_dt, end_dt)) - # Filter out unused variables - [["data"]] + .sel(time=slice(start_dt, end_dt))[ + # Filter out unused variables + ["data"] + ] # Load into memory for processing .load() ) if len(ds.time) == 0: - logger.warning("No satellite data available in recent window.") - return + raise ValueError( + f"No satellite data available between {start_dt} and {end_dt}.", + ) ds = self.process(ds) @@ -272,5 +269,3 @@ def run(self) -> None: self.resave(ds) else: raise ValueError("Satellite data is not available for the required time window.") - - \ No newline at end of file diff --git a/src/cloudcasting_inference/settings.py b/src/cloudcasting_inference/settings.py index c02f6e8..7a5ffe5 100644 --- a/src/cloudcasting_inference/settings.py +++ b/src/cloudcasting_inference/settings.py @@ -8,7 +8,8 @@ class AppSettings(BaseSettings): Input data paths - - SATELLITE_ZARR_PATH: The path of the input satellite data. + - SATELLITE_ICECHUNK_PATH: The s3 path of the icechunk store holding the input + satellite data. - S3_REGION: The AWS region for the satellite data S3 bucket. Output data paths @@ -23,7 +24,7 @@ class AppSettings(BaseSettings): """ # Input data paths - satellite_zarr_path: str + satellite_icechunk_path: str s3_region: str # Output data paths diff --git a/src/cloudcasting_metrics/README.md b/src/cloudcasting_metrics/README.md index 0465f8a..9e7d9d5 100644 --- a/src/cloudcasting_metrics/README.md +++ b/src/cloudcasting_metrics/README.md @@ -8,7 +8,9 @@ made the day before running. The following environment variables are used in the app: -- `SATELLITE_ICECHUNK_ARCHIVE`: The path to the satellite archive in icechunk formast +- `SATELLITE_ICECHUNK_PATH`: The path to the satellite archive in icechunk formast +- `S3_REGION`: The AWS region of the bucket the satellite icechunk store is in. Unused if + `SATELLITE_ICECHUNK_PATH` is a local path. - `PREDICTION_SAVE_DIRECTORY`: The directory where the cloudcasting predictions are saved. i.e. set to the same as `PREDICTION_SAVE_DIRECTORY` in `cloudcasting_inference`. - `METRIC_ZARR_PATH`: Where to save metrics zarr diff --git a/src/cloudcasting_metrics/app.py b/src/cloudcasting_metrics/app.py index ee82698..d1467d1 100644 --- a/src/cloudcasting_metrics/app.py +++ b/src/cloudcasting_metrics/app.py @@ -1,18 +1,18 @@ -"""Runs metric calculations on cloudcasting for a given input day and appends to zarr store +"""Runs metric calculations on cloudcasting for a given input day and appends to zarr store. The runtime configuration is loaded from environment variables - see `cloudcasting_metrics.settings.AppSettings` for the full list. """ import re + import fsspec +import icechunk import numpy as np import pandas as pd -from tqdm import tqdm - import xarray as xr -import icechunk from loguru import logger +from tqdm import tqdm from cloudcasting_metrics.settings import AppSettings @@ -24,19 +24,20 @@ FORECAST_FREQ = pd.Timedelta("30min") -def open_icechunk(path: str) -> xr.Dataset: - """Open an icechunk store to xarray Dataset - +def open_icechunk(path: str, region: str) -> xr.Dataset: + """Open an icechunk store to xarray Dataset. + Args: path: The path to the local or s3 icechunk store + region: The s3 region the store is in. Unused for a local store """ - assert path.startswith("s3://"), path if path.startswith("s3://"): bucket, _, path = path.removeprefix("s3://").partition("/") store = icechunk.s3_storage( bucket=bucket, prefix=path, from_env=True, + region=region, ) else: store = icechunk.local_filesystem_storage(path=path) @@ -47,15 +48,14 @@ def open_icechunk(path: str) -> xr.Dataset: def app(date: pd.Timestamp | None = None) -> None: - """Runs metric calculations on cloudcasting for a given input day and appends to zarr store + """Runs metric calculations on cloudcasting for a given input day and appends to zarr store. Args: date: The day for which the cloudcasting predictions will be scored. """ - settings = AppSettings() - sat_path = settings.satellite_icechunk_archive + sat_path = settings.satellite_icechunk_path prediction_dir = settings.prediction_save_directory metric_zarr_path = settings.metric_zarr_path @@ -64,8 +64,8 @@ def app(date: pd.Timestamp | None = None) -> None: # Default to yesterday if date is None: date = now.floor("1D") - pd.Timedelta("1D") - - start_dt = date.floor("1D") + + start_dt = date.floor("1D") end_dt = start_dt + pd.Timedelta("1D") if now <= end_dt + FORECAST_STEPS.max(): @@ -74,9 +74,9 @@ def app(date: pd.Timestamp | None = None) -> None: ) # Open the satellite data store - ds_sat = open_icechunk(path=sat_path) + ds_sat = open_icechunk(path=sat_path, region=settings.s3_region) - # Slice to only the timesteps we need for scoring + # Slice to only the timesteps we need for scoring ds_sat = ds_sat.sel(time=slice(start_dt, end_dt + FORECAST_STEPS.max())) # It is better to preload if we have the RAM space @@ -98,7 +98,7 @@ def app(date: pd.Timestamp | None = None) -> None: for file in file_list: # Find the datetime of this forecast - match = re.search(r'\d{4}-\d{2}-\d{2}T\d{2}:\d{2}', file) + match = re.search(r"\d{4}-\d{2}-\d{2}T\d{2}:\d{2}", file) if match is None: raise Exception(f"Could not derive datetime of file {file}") @@ -116,11 +116,7 @@ def app(date: pd.Timestamp | None = None) -> None: valid_times = pd.Timestamp(ds_forecast.init_time.item()) + ds_forecast.step - ds_forecast = ( - ds_forecast - .assign_coords(time=valid_times) - .swap_dims({"step":"time"}) - ) + ds_forecast = ds_forecast.assign_coords(time=valid_times).swap_dims({"step": "time"}) ds_sat_sel = ds_sat.sel( time=ds_forecast.time, @@ -130,9 +126,7 @@ def app(date: pd.Timestamp | None = None) -> None: ) da_mae = np.abs( - (ds_sat_sel.data - ds_forecast.sat_pred) - .swap_dims({"time":"step"}) - .drop_vars("time") + (ds_sat_sel.data - ds_forecast.sat_pred).swap_dims({"time": "step"}).drop_vars("time") ) # Create reductions of the full MAE matrix @@ -158,13 +152,7 @@ def app(date: pd.Timestamp | None = None) -> None: # Chunk the data ready for saving ds_all_maes = ds_all_maes.chunk( - { - "x_geostationary": -1, - "y_geostationary": -1, - "step": -1, - "variable": -1, - "init_time": 48 - } + {"x_geostationary": -1, "y_geostationary": -1, "step": -1, "variable": -1, "init_time": 48} ) # If it exists, open the archive of MAE values and check the coordinates against them @@ -174,12 +162,12 @@ def app(date: pd.Timestamp | None = None) -> None: if np.isin(ds_all_maes.init_time, ds_maes_archive.init_time).any(): raise Exception("init-times in new MAEs already exist in MAE store") - + for coord in ["variable", "step", "x_geostationary", "y_geostationary"]: if not ds_maes_archive[coord].identical(ds_all_maes[coord]): raise Exception("Found differences in coord: {coord}") - + ds_all_maes.to_zarr(metric_zarr_path, mode="a-", append_dim="init_time") else: - ds_all_maes.to_zarr(metric_zarr_path, mode="w") \ No newline at end of file + ds_all_maes.to_zarr(metric_zarr_path, mode="w") diff --git a/src/cloudcasting_metrics/settings.py b/src/cloudcasting_metrics/settings.py index cba341c..3b72912 100644 --- a/src/cloudcasting_metrics/settings.py +++ b/src/cloudcasting_metrics/settings.py @@ -8,20 +8,23 @@ class AppSettings(BaseSettings): Input data paths - - SATELLITE_ICECHUNK_ARCHIVE: Path at which ground truth satellite data can be found. + - SATELLITE_ICECHUNK_PATH: Path at which ground truth satellite data can be found. + - S3_REGION: The AWS region for the satellite data S3 bucket. Unused if + SATELLITE_ICECHUNK_PATH is a local path. - PREDICTION_SAVE_DIRECTORY: The directory where the cloudcasting forecasts are saved. Output data paths - METRIC_ZARR_PATH: The path where the metric values will be saved. - If SATELLITE_ICECHUNK_ARCHIVE is an s3 path, then the environment variables - AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_REGION must also be set. These are read - directly from the environment by icechunk rather than through these settings. + If SATELLITE_ICECHUNK_PATH is an s3 path, then the environment variables + AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY must also be set. These are read directly from + the environment by icechunk rather than through these settings. """ # Input data paths - satellite_icechunk_archive: str + satellite_icechunk_path: str + s3_region: str prediction_save_directory: str # Output data paths diff --git a/tests/test_cloudcasting_inference/conftest.py b/tests/test_cloudcasting_inference/conftest.py index 64a0958..7f36555 100644 --- a/tests/test_cloudcasting_inference/conftest.py +++ b/tests/test_cloudcasting_inference/conftest.py @@ -1,5 +1,6 @@ import pandas as pd import pytest + from tests.utils import make_sat_data @@ -13,6 +14,6 @@ def sat_5_data(init_time): times = pd.date_range( init_time - pd.Timedelta("3h"), init_time, - freq=f"5min", + freq="5min", ) - return make_sat_data(times) \ No newline at end of file + return make_sat_data(times) diff --git a/tests/test_cloudcasting_inference/test_app.py b/tests/test_cloudcasting_inference/test_app.py index 231e074..e3237fa 100644 --- a/tests/test_cloudcasting_inference/test_app.py +++ b/tests/test_cloudcasting_inference/test_app.py @@ -12,7 +12,7 @@ def test_app(sat_5_data, tmp_path, init_time): os.chdir(tmp_path) # In production sat zarr is zipped - os.environ["SATELLITE_ZARR_PATH"] = "temp_sat.zarr.zip" + os.environ["SATELLITE_ICECHUNK_PATH"] = "temp_sat.zarr.zip" os.environ["PREDICTION_SAVE_DIRECTORY"] = f"{tmp_path}" @@ -30,17 +30,20 @@ def test_app(sat_5_data, tmp_path, init_time): # Load the predictions and check them ds_y_hat = xr.open_zarr(latest_zarr_path) - assert "sat_pred" in ds_y_hat - assert ( - list(ds_y_hat.sat_pred.dims)== - ["init_time", "variable", "step", "y_geostationary", "x_geostationary"] - ) + assert "sat_pred" in ds_y_hat + assert list(ds_y_hat.sat_pred.dims) == [ + "init_time", + "variable", + "step", + "y_geostationary", + "x_geostationary", + ] # Make sure all the coords are correct assert ds_y_hat.init_time == init_time - assert len(ds_y_hat.step)==12 - assert (ds_y_hat.x_geostationary==sat_5_data.x_geostationary).all() - assert (ds_y_hat.y_geostationary==sat_5_data.y_geostationary).all() + assert len(ds_y_hat.step) == 12 + assert (ds_y_hat.x_geostationary == sat_5_data.x_geostationary).all() + assert (ds_y_hat.y_geostationary == sat_5_data.y_geostationary).all() # Make sure all of the predictions are finite assert np.isfinite(ds_y_hat.sat_pred).all() diff --git a/tests/test_cloudcasting_metrics/conftest.py b/tests/test_cloudcasting_metrics/conftest.py index fb22c74..3faa6a2 100644 --- a/tests/test_cloudcasting_metrics/conftest.py +++ b/tests/test_cloudcasting_metrics/conftest.py @@ -1,10 +1,11 @@ +import icechunk import pandas as pd import pytest -from cloudcasting_metrics.app import FORECAST_STEPS, FORECAST_FREQ -from tests.utils import get_sat_shell, make_sat_data -import icechunk from icechunk.xarray import to_icechunk +from cloudcasting_metrics.app import FORECAST_FREQ, FORECAST_STEPS +from tests.utils import get_sat_shell, make_sat_data + @pytest.fixture() def today(): @@ -25,9 +26,9 @@ def forecast_directory(tmp_path_factory, init_times_tuple) -> str: all_init_times = [t for ts in init_times_tuple for t in ts] for init_time in all_init_times: - ds_pred = make_sat_data(times=init_time+FORECAST_STEPS) + ds_pred = make_sat_data(times=init_time + FORECAST_STEPS) ds_pred = ds_pred.assign_coords(step=("time", FORECAST_STEPS)) - ds_pred = ds_pred.swap_dims({"time":"step"}).drop_vars("time") + ds_pred = ds_pred.swap_dims({"time": "step"}).drop_vars("time") ds_pred = ds_pred.expand_dims({"init_time": [init_time]}) ds_pred = ds_pred.rename({"data": "sat_pred"}) @@ -43,9 +44,9 @@ def sat_icechunk_path(tmp_path, init_times_tuple) -> str: all_init_times = [t for ts in init_times_tuple for t in ts] - sat_times = set([t for init_time in all_init_times for t in (init_time+FORECAST_STEPS)]) + sat_times = {t for init_time in all_init_times for t in (init_time + FORECAST_STEPS)} sat_times = pd.to_datetime(sorted(sat_times)) - + ds_sat = make_sat_data(sat_times) store = icechunk.local_filesystem_storage(sat_icechunk_path) diff --git a/tests/test_cloudcasting_metrics/test_app.py b/tests/test_cloudcasting_metrics/test_app.py index c2ec140..e359e36 100644 --- a/tests/test_cloudcasting_metrics/test_app.py +++ b/tests/test_cloudcasting_metrics/test_app.py @@ -1,31 +1,32 @@ import os + import pandas as pd import xarray as xr -from cloudcasting_metrics.app import app -from cloudcasting_metrics.app import FORECAST_STEPS, FORECAST_FREQ +from cloudcasting_metrics.app import FORECAST_FREQ, FORECAST_STEPS, app def test_app(tmp_path, forecast_directory, sat_icechunk_path, today, init_times_tuple, sat_shell): mae_path = str(tmp_path / "mae.zarr") - os.environ["SATELLITE_ICECHUNK_ARCHIVE"] = sat_icechunk_path + os.environ["SATELLITE_ICECHUNK_PATH"] = sat_icechunk_path + os.environ["S3_REGION"] = "eu-west-1" os.environ["PREDICTION_SAVE_DIRECTORY"] = forecast_directory os.environ["METRIC_ZARR_PATH"] = mae_path # Run once for "2 days ago" - this creates an MAE zarr - app(date=today-pd.Timedelta("2D")) + app(date=today - pd.Timedelta("2D")) # Check the expected init-times are there ds_mae = xr.open_zarr(mae_path).compute() expected_init_times = pd.date_range( - today-pd.Timedelta("2D"), - today-pd.Timedelta("1D"), - freq=FORECAST_FREQ, + today - pd.Timedelta("2D"), + today - pd.Timedelta("1D"), + freq=FORECAST_FREQ, inclusive="left", ) - assert (ds_mae.init_time.values==expected_init_times).all() + assert (ds_mae.init_time.values == expected_init_times).all() # Check that the expected init-times are not NaN and the others are NaN ds_nan = ds_mae.isnull().mean(dim=("variable", "x_geostationary", "y_geostationary", "step")) @@ -36,17 +37,17 @@ def test_app(tmp_path, forecast_directory, sat_icechunk_path, today, init_times_ assert ds_nan[v].sel(init_time=nan_init_times).all() # Run once for "1 days ago" - this appends to the MAE zarr - app(date=today-pd.Timedelta("1D")) + app(date=today - pd.Timedelta("1D")) # Check the expected init-times are there ds_mae = xr.open_zarr(mae_path).compute() expected_init_times = pd.date_range( - today-pd.Timedelta("2D"), - today, - freq=FORECAST_FREQ, + today - pd.Timedelta("2D"), + today, + freq=FORECAST_FREQ, inclusive="left", ) - assert (ds_mae.init_time.values==expected_init_times).all() + assert (ds_mae.init_time.values == expected_init_times).all() # Check that the expected init-times are not NaN and the others are NaN ds_nan = ds_mae.isnull().mean(dim=("variable", "x_geostationary", "y_geostationary", "step")) @@ -57,8 +58,8 @@ def test_app(tmp_path, forecast_directory, sat_icechunk_path, today, init_times_ assert ds_nan[v].sel(init_time=nan_init_times).all() # Check the other coordinates and data_vars - assert set(ds_mae.data_vars)=={"mae_step", "mae_variable", "mae_spatial"} - assert (ds_mae.step.values==FORECAST_STEPS).all() + assert set(ds_mae.data_vars) == {"mae_step", "mae_variable", "mae_spatial"} + assert (ds_mae.step.values == FORECAST_STEPS).all() for coord in ["x_geostationary", "y_geostationary", "variable"]: - assert 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+++++++++++-------- tests/test_cloudcasting_inference/conftest.py | 11 ++++--- tests/test_cloudcasting_inference/test_app.py | 28 ++++++++--------- tests/test_cloudcasting_metrics/conftest.py | 17 ++--------- tests/test_cloudcasting_metrics/test_app.py | 21 ++++++------- tests/utils.py | 20 ++++++++++++- 7 files changed, 85 insertions(+), 70 deletions(-) diff --git a/src/cloudcasting_inference/data.py b/src/cloudcasting_inference/data.py index 616038c..c4142fa 100644 --- a/src/cloudcasting_inference/data.py +++ b/src/cloudcasting_inference/data.py @@ -14,21 +14,23 @@ IMAGE_FREQUENCY = pd.Timedelta("5min") -def open_satellite_data(s3_icechunk_path: str, region: str) -> xr.Dataset | None: - """Open the satellite data from the given s3 icechunk path. +def open_satellite_data(icechunk_path: str, region: str) -> xr.Dataset | None: + """Open the satellite data from the given icechunk path. Args: - s3_icechunk_path: The s3 path to the icechunk containing the satellite - region: The s3 region where the icechunk is stored + icechunk_path: The local or s3 path to the icechunk store holding the satellite data + region: The s3 region the store is in. Unused for a local store """ - bucket, _, path = s3_icechunk_path.removeprefix("s3://").partition("/") - - store = icechunk.s3_storage( - bucket=bucket, - prefix=path, - from_env=True, - region=region, - ) + if icechunk_path.startswith("s3://"): + bucket, _, prefix = icechunk_path.removeprefix("s3://").partition("/") + store = icechunk.s3_storage( + bucket=bucket, + prefix=prefix, + from_env=True, + region=region, + ) + else: + store = icechunk.local_filesystem_storage(path=icechunk_path) try: repo = icechunk.Repository.open(store) @@ -227,7 +229,7 @@ def resave(self, ds: xr.Dataset) -> None: def run(self) -> None: """Download, process, and save the satellite data.""" ds = open_satellite_data( - s3_icechunk_path=self.source_path, + icechunk_path=self.source_path, region=self.s3_region, ) diff --git a/src/cloudcasting_metrics/app.py b/src/cloudcasting_metrics/app.py index d1467d1..b5fa1f7 100644 --- a/src/cloudcasting_metrics/app.py +++ b/src/cloudcasting_metrics/app.py @@ -114,7 +114,7 @@ def app(date: pd.Timestamp | None = None) -> None: for file in tqdm(forecasts_to_score): ds_forecast = xr.open_zarr(fs.get_mapper(file)).compute() - valid_times = pd.Timestamp(ds_forecast.init_time.item()) + ds_forecast.step + valid_times = pd.Timestamp(ds_forecast.init_time_utc.item()) + ds_forecast.step ds_forecast = ds_forecast.assign_coords(time=valid_times).swap_dims({"step": "time"}) @@ -122,7 +122,7 @@ def app(date: pd.Timestamp | None = None) -> None: time=ds_forecast.time, x_geostationary=ds_forecast.x_geostationary, y_geostationary=ds_forecast.y_geostationary, - variable=ds_forecast.variable, + channel=ds_forecast.channel, ) da_mae = np.abs( @@ -130,14 +130,14 @@ def app(date: pd.Timestamp | None = None) -> None: ) # Create reductions of the full MAE matrix - da_mae_step = da_mae.mean(dim=("x_geostationary", "y_geostationary", "variable")) - da_mae_variable = da_mae.mean(dim=("x_geostationary", "y_geostationary", "step")) - da_mae_spatial = da_mae.mean(dim=("step", "variable")) + da_mae_step = da_mae.mean(dim=("x_geostationary", "y_geostationary", "channel")) + da_mae_channel = da_mae.mean(dim=("x_geostationary", "y_geostationary", "step")) + da_mae_spatial = da_mae.mean(dim=("step", "channel")) ds_mae_reductions = xr.Dataset( { "mae_step": da_mae_step, - "mae_variable": da_mae_variable, + "mae_channel": da_mae_channel, "mae_spatial": da_mae_spatial, } ) @@ -146,13 +146,19 @@ def app(date: pd.Timestamp | None = None) -> None: # Concat all the MAE scores and in-fill missing init times with NaNs # - Filling with NaNs makes the chunking easier - ds_all_maes = xr.concat(ds_mae_list, dim="init_time") + ds_all_maes = xr.concat(ds_mae_list, dim="init_time_utc") expected_init_times = pd.date_range(start_dt, end_dt, freq=FORECAST_FREQ, inclusive="left") - ds_all_maes = ds_all_maes.reindex(init_time=expected_init_times, method=None) + ds_all_maes = ds_all_maes.reindex(init_time_utc=expected_init_times, method=None) # Chunk the data ready for saving ds_all_maes = ds_all_maes.chunk( - {"x_geostationary": -1, "y_geostationary": -1, "step": -1, "variable": -1, "init_time": 48} + { + "x_geostationary": -1, + "y_geostationary": -1, + "step": -1, + "channel": -1, + "init_time_utc": 48, + } ) # If it exists, open the archive of MAE values and check the coordinates against them @@ -160,14 +166,14 @@ def app(date: pd.Timestamp | None = None) -> None: if fs.exists(stripped): ds_maes_archive = xr.open_zarr(metric_zarr_path) - if np.isin(ds_all_maes.init_time, ds_maes_archive.init_time).any(): + if np.isin(ds_all_maes.init_time_utc, ds_maes_archive.init_time_utc).any(): raise Exception("init-times in new MAEs already exist in MAE store") - for coord in ["variable", "step", "x_geostationary", "y_geostationary"]: + for coord in ["channel", "step", "x_geostationary", "y_geostationary"]: if not ds_maes_archive[coord].identical(ds_all_maes[coord]): raise Exception("Found differences in coord: {coord}") - ds_all_maes.to_zarr(metric_zarr_path, mode="a-", append_dim="init_time") + ds_all_maes.to_zarr(metric_zarr_path, mode="a-", append_dim="init_time_utc") else: ds_all_maes.to_zarr(metric_zarr_path, mode="w") diff --git a/tests/test_cloudcasting_inference/conftest.py b/tests/test_cloudcasting_inference/conftest.py index 7f36555..db2d3c3 100644 --- a/tests/test_cloudcasting_inference/conftest.py +++ b/tests/test_cloudcasting_inference/conftest.py @@ -1,7 +1,7 @@ import pandas as pd import pytest -from tests.utils import make_sat_data +from tests.utils import make_sat_data, write_icechunk @pytest.fixture() @@ -10,10 +10,13 @@ def init_time(): @pytest.fixture() -def sat_5_data(init_time): +def sat_icechunk_path(tmp_path, init_time) -> str: + # The model needs 165 minutes of history, and `SatelliteDataset` discards a run of timestamps + # exactly as long as the sample it needs, so the app asks for one 15-minute step more than + # that. 4 hours covers it with room to spare times = pd.date_range( - init_time - pd.Timedelta("3h"), + init_time - pd.Timedelta("4h"), init_time, freq="5min", ) - return make_sat_data(times) + return write_icechunk(make_sat_data(times), str(tmp_path / "sat.icechunk")) diff --git a/tests/test_cloudcasting_inference/test_app.py b/tests/test_cloudcasting_inference/test_app.py index e3237fa..b399b33 100644 --- a/tests/test_cloudcasting_inference/test_app.py +++ b/tests/test_cloudcasting_inference/test_app.py @@ -2,22 +2,16 @@ import numpy as np import xarray as xr -import zarr from cloudcasting_inference.app import app +from tests.utils import get_sat_shell -def test_app(sat_5_data, tmp_path, init_time): +def test_app(sat_icechunk_path, tmp_path, init_time): - os.chdir(tmp_path) - - # In production sat zarr is zipped - os.environ["SATELLITE_ICECHUNK_PATH"] = "temp_sat.zarr.zip" - - os.environ["PREDICTION_SAVE_DIRECTORY"] = f"{tmp_path}" - - with zarr.storage.ZipStore("temp_sat.zarr.zip", mode="x") as store: - sat_5_data.to_zarr(store) + os.environ["SATELLITE_ICECHUNK_PATH"] = sat_icechunk_path + os.environ["S3_REGION"] = "eu-west-1" + os.environ["PREDICTION_SAVE_DIRECTORY"] = str(tmp_path) app() @@ -32,18 +26,20 @@ def test_app(sat_5_data, tmp_path, init_time): assert "sat_pred" in ds_y_hat assert list(ds_y_hat.sat_pred.dims) == [ - "init_time", - "variable", + "init_time_utc", + "channel", "step", "y_geostationary", "x_geostationary", ] # Make sure all the coords are correct - assert ds_y_hat.init_time == init_time + sat_shell = get_sat_shell() + assert ds_y_hat.init_time_utc == init_time assert len(ds_y_hat.step) == 12 - assert (ds_y_hat.x_geostationary == sat_5_data.x_geostationary).all() - assert (ds_y_hat.y_geostationary == sat_5_data.y_geostationary).all() + assert (ds_y_hat.channel == sat_shell.channel).all() + assert (ds_y_hat.x_geostationary == sorted(sat_shell.x_geostationary)).all() + assert (ds_y_hat.y_geostationary == sorted(sat_shell.y_geostationary)).all() # Make sure all of the predictions are finite assert np.isfinite(ds_y_hat.sat_pred).all() diff --git a/tests/test_cloudcasting_metrics/conftest.py b/tests/test_cloudcasting_metrics/conftest.py index 3faa6a2..536cf51 100644 --- a/tests/test_cloudcasting_metrics/conftest.py +++ b/tests/test_cloudcasting_metrics/conftest.py @@ -1,10 +1,8 @@ -import icechunk import pandas as pd import pytest -from icechunk.xarray import to_icechunk from cloudcasting_metrics.app import FORECAST_FREQ, FORECAST_STEPS -from tests.utils import get_sat_shell, make_sat_data +from tests.utils import get_sat_shell, make_sat_data, write_icechunk @pytest.fixture() @@ -29,7 +27,7 @@ def forecast_directory(tmp_path_factory, init_times_tuple) -> str: ds_pred = make_sat_data(times=init_time + FORECAST_STEPS) ds_pred = ds_pred.assign_coords(step=("time", FORECAST_STEPS)) ds_pred = ds_pred.swap_dims({"time": "step"}).drop_vars("time") - ds_pred = ds_pred.expand_dims({"init_time": [init_time]}) + ds_pred = ds_pred.expand_dims({"init_time_utc": [init_time]}) ds_pred = ds_pred.rename({"data": "sat_pred"}) zarr_path = init_time.strftime(f"{pred_dir}/%Y-%m-%dT%H:%M.zarr") @@ -47,16 +45,7 @@ def sat_icechunk_path(tmp_path, init_times_tuple) -> str: sat_times = {t for init_time in all_init_times for t in (init_time + FORECAST_STEPS)} sat_times = pd.to_datetime(sorted(sat_times)) - ds_sat = make_sat_data(sat_times) - - store = icechunk.local_filesystem_storage(sat_icechunk_path) - repo = icechunk.Repository.create(store) - session = repo.writable_session(branch="main") - - to_icechunk(ds_sat, session) - session.commit("Commit test data") - - yield sat_icechunk_path + yield write_icechunk(make_sat_data(sat_times), sat_icechunk_path) @pytest.fixture() diff --git a/tests/test_cloudcasting_metrics/test_app.py b/tests/test_cloudcasting_metrics/test_app.py index e359e36..4349fb8 100644 --- a/tests/test_cloudcasting_metrics/test_app.py +++ b/tests/test_cloudcasting_metrics/test_app.py @@ -14,6 +14,7 @@ def test_app(tmp_path, forecast_directory, sat_icechunk_path, today, init_times_ os.environ["S3_REGION"] = "eu-west-1" os.environ["PREDICTION_SAVE_DIRECTORY"] = forecast_directory os.environ["METRIC_ZARR_PATH"] = mae_path + os.environ["S3_REGION"] = "eu-west-1" # Run once for "2 days ago" - this creates an MAE zarr app(date=today - pd.Timedelta("2D")) @@ -26,15 +27,15 @@ def test_app(tmp_path, forecast_directory, sat_icechunk_path, today, init_times_ freq=FORECAST_FREQ, inclusive="left", ) - assert (ds_mae.init_time.values == expected_init_times).all() + assert (ds_mae.init_time_utc.values == expected_init_times).all() # Check that the expected init-times are not NaN and the others are NaN - ds_nan = ds_mae.isnull().mean(dim=("variable", "x_geostationary", "y_geostationary", "step")) + ds_nan = ds_mae.isnull().mean(dim=("channel", "x_geostationary", "y_geostationary", "step")) non_nan_init_times = init_times_tuple[0] nan_init_times = [t for t in expected_init_times if t not in non_nan_init_times] for v in ds_nan.data_vars: - assert not ds_nan[v].sel(init_time=non_nan_init_times).any() - assert ds_nan[v].sel(init_time=nan_init_times).all() + assert not ds_nan[v].sel(init_time_utc=non_nan_init_times).any() + assert ds_nan[v].sel(init_time_utc=nan_init_times).all() # Run once for "1 days ago" - this appends to the MAE zarr app(date=today - pd.Timedelta("1D")) @@ -47,19 +48,19 @@ def test_app(tmp_path, forecast_directory, sat_icechunk_path, today, init_times_ freq=FORECAST_FREQ, inclusive="left", ) - assert (ds_mae.init_time.values == expected_init_times).all() + assert (ds_mae.init_time_utc.values == expected_init_times).all() # Check that the expected init-times are not NaN and the others are NaN - ds_nan = ds_mae.isnull().mean(dim=("variable", "x_geostationary", "y_geostationary", "step")) + ds_nan = ds_mae.isnull().mean(dim=("channel", "x_geostationary", "y_geostationary", "step")) non_nan_init_times = [t for ts in init_times_tuple for t in ts] nan_init_times = [t for t in expected_init_times if t not in non_nan_init_times] for v in ds_nan.data_vars: - assert not ds_nan[v].sel(init_time=non_nan_init_times).any() - assert ds_nan[v].sel(init_time=nan_init_times).all() + assert not ds_nan[v].sel(init_time_utc=non_nan_init_times).any() + assert ds_nan[v].sel(init_time_utc=nan_init_times).all() # Check the other coordinates and data_vars - assert set(ds_mae.data_vars) == {"mae_step", "mae_variable", "mae_spatial"} + assert set(ds_mae.data_vars) == {"mae_step", "mae_channel", "mae_spatial"} assert (ds_mae.step.values == FORECAST_STEPS).all() - for coord in ["x_geostationary", "y_geostationary", "variable"]: + for coord in ["x_geostationary", "y_geostationary", "channel"]: assert (ds_mae[coord].values == sat_shell[coord].values).all() diff --git a/tests/utils.py b/tests/utils.py index c54ae8f..7d230c3 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -1,9 +1,11 @@ from pathlib import Path +import icechunk import numpy as np import pandas as pd import xarray as xr import zarr +from icechunk.xarray import to_icechunk xr.set_options(keep_attrs=True) @@ -14,7 +16,11 @@ def get_sat_shell(): ds = xr.open_zarr(store) # Remove original time dim - return ds.drop_vars("time") + ds = ds.drop_vars("time") + + # The shell was saved with the channels under `variable`, but the production satellite store + # names that dimension `channel` - see `sat_pred.dataset.open_sat_data` + return ds.rename({"variable": "channel"}) def make_sat_data(times: pd.DatetimeIndex) -> xr.Dataset: @@ -40,3 +46,15 @@ def make_sat_data(times: pd.DatetimeIndex) -> xr.Dataset: ds[v].encoding.clear() return ds + + +def write_icechunk(ds: xr.Dataset, path: str) -> str: + """Write a dataset to a new local icechunk store, as the apps expect to read it.""" + store = icechunk.local_filesystem_storage(path) + repo = icechunk.Repository.create(store) + session = repo.writable_session(branch="main") + + to_icechunk(ds, session) + session.commit("Commit test data") + + return path From 09a435dad5eb5bc934f494efad2402fe61e64607 Mon Sep 17 00:00:00 2001 From: James Fulton Date: Tue, 18 Aug 2026 19:24:33 +0000 Subject: [PATCH 5/6] save as float16 --- src/cloudcasting_inference/app.py | 3 ++- tests/test_cloudcasting_inference/test_app.py | 3 +++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/src/cloudcasting_inference/app.py b/src/cloudcasting_inference/app.py index 02fd2ec..db99142 100644 --- a/src/cloudcasting_inference/app.py +++ b/src/cloudcasting_inference/app.py @@ -212,7 +212,8 @@ def _run_forecast(settings: AppSettings, t0: pd.Timestamp, scratch_dir: str) -> # The normaliser works on (channel, time, y, x) samples, so denormalise the single sample # before putting the init-time axis back on y_hat = model(X).squeeze(0).cpu().numpy() - y_hat = channel_config.normaliser.denormalise(y_hat)[np.newaxis] + # Saved as float16, matching the backtest store these forecasts are scored against + y_hat = channel_config.normaliser.denormalise(y_hat)[np.newaxis].astype(np.float16) logger.info("Saving predictions") diff --git a/tests/test_cloudcasting_inference/test_app.py b/tests/test_cloudcasting_inference/test_app.py index b399b33..30c47ee 100644 --- a/tests/test_cloudcasting_inference/test_app.py +++ b/tests/test_cloudcasting_inference/test_app.py @@ -43,3 +43,6 @@ def test_app(sat_icechunk_path, tmp_path, init_time): # Make sure all of the predictions are finite assert np.isfinite(ds_y_hat.sat_pred).all() + + # The predictions are stored as float16 + assert ds_y_hat.sat_pred.dtype == np.float16 From e8d82afa0f34a77de1f6bebf3142fb1ecb088261 Mon Sep 17 00:00:00 2001 From: James Fulton Date: Wed, 19 Aug 2026 09:19:34 +0000 Subject: [PATCH 6/6] Improve torch pin alignment --- Dockerfile | 5 +- pyproject.toml | 23 +- uv.lock | 752 ++++++++++++++++++++++++++++++++++++------------- 3 files changed, 570 insertions(+), 210 deletions(-) diff --git a/Dockerfile b/Dockerfile index f80cf1c..ff414c4 100644 --- a/Dockerfile +++ b/Dockerfile @@ -23,8 +23,7 @@ ENV UV_COMPILE_BYTECODE=1 \ # * --no-install-project: Only install dependencies # * --no-editable: Copy the source code into site-packages RUN mkdir src && \ - uv sync --no-dev --no-install-project --no-editable \ - --index-strategy unsafe-best-match + uv sync --no-dev --no-install-project --no-editable # Remove tests (Pandas ship loads, for instance) # * Remove this line if causing problems @@ -38,7 +37,7 @@ FROM build-deps AS build-app COPY src /opt/app/src COPY .git /opt/app/.git COPY uv.lock /opt/app/uv.lock -RUN uv sync --no-dev --no-editable --index-strategy unsafe-best-match +RUN uv sync --no-dev --no-editable # --- Runtime image (use distroless if feasible for 100MB saving) --- # FROM python:3.12-slim-bookworm diff --git a/pyproject.toml b/pyproject.toml index 66b2623..167124b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,7 @@ name = "cloudcasting-app" dynamic = ["version"] # Set automtically using git: https://setuptools-git-versioning.readthedocs.io/en/stable/ description = "An app for running the OCF cloudcasting model in production" readme = {file = "README.md", content-type = "text/markdown"} -requires-python = ">=3.12.0,<3.13" +requires-python = ">=3.12.0,<3.14" license = {text = "MIT License"} authors = [ { name = "Open Climate Fix Team", email = "info@openclimatefix.org"} @@ -31,12 +31,13 @@ dependencies = [ "pydantic-settings", "s3fs", "safetensors", - "sat_pred @ git+https://github.com/openclimatefix/sat_pred.git@main", - # Since torch distributes CPU only packages as wheels, have to specify the target platform in order to pull the wheel compiled for that specific platform - "torch @ https://download.pytorch.org/whl/cpu/torch-2.3.1-cp312-none-macosx_11_0_arm64.whl ; platform_system == 'Darwin' and platform_machine == 'arm64'", - "torchvision @ https://download.pytorch.org/whl/cpu/torchvision-0.18.1-cp312-cp312-macosx_11_0_arm64.whl ; platform_system == 'Darwin' and platform_machine == 'arm64'", - "torch @ https://download.pytorch.org/whl/cpu/torch-2.3.1%2Bcpu-cp312-cp312-linux_x86_64.whl ; platform_system == 'Linux' and platform_machine == 'x86_64'", - "torchvision @ https://download.pytorch.org/whl/cpu/torchvision-0.18.1%2Bcpu-cp312-cp312-linux_x86_64.whl ; platform_system == 'Linux' and platform_machine == 'x86_64'", + # Pinned to a tag rather than a branch, so advancing it is a deliberate edit rather than + # something an incidental re-lock does silently + "sat_pred @ git+https://github.com/openclimatefix/sat_pred.git@v0.0.2", + # pytorch is deliberately unconstrained: we inherit the version according to the version pin in + # sat_pred. Inheriting the pin means models are always run with the same version of torch they + # we trained with. 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