diff --git a/Dockerfile b/Dockerfile index c715484..f80cf1c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -37,6 +37,7 @@ FROM build-deps AS build-app # * .git: Required for setuptools-git-versioning 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 # --- Runtime image (use distroless if feasible for 100MB saving) --- # @@ -49,5 +50,24 @@ COPY --from=build-app --chown=app:app /opt/app/.venv /opt/app/.venv ENV _TYPER_STANDARD_TRACEBACK=1 -ENTRYPOINT ["/opt/app/.venv/bin/cloudcasting-app"] +COPY < diff --git a/pyproject.toml b/pyproject.toml index 04246bf..b13eef4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,23 +21,23 @@ classifiers = [ ] dependencies = [ "fsspec==2025.7.0", - "huggingface-hub==0.28.1", - "hydra-core==1.3.2", - "loguru == 0.7.3", - "numpy==2.1.2", + "huggingface-hub", + "hydra-core", + "icechunk", + "loguru", + "numpy", "ocf-data-sampler==0.5.27", - "pandas==2.2.3", - "s3fs==2025.7.0", - "safetensors==0.5.2", + "pandas", + "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'", - "typer==0.15.1", - "xarray==2025.7.1", - "zarr==3.1.1", + "xarray", + "zarr", ] [dependency-groups] @@ -52,11 +52,15 @@ dev = [ [project.scripts] # Put entrypoints in here -cloudcasting-app = "cloudcasting_app.app:main" +cloudcasting-inference = "cloudcasting_inference.app:app" +cloudcasting-metrics = "cloudcasting_metrics.app:app" [project.urls] repository = "https://github.com/openclimatefix/cloudcasting-app" +[tool.setuptools.packages.find] +where = ["src"] + [tool.setuptools] include-package-data = false @@ -122,18 +126,6 @@ line-ending = "auto" docstring-code-format = true docstring-code-line-length = 100 -# --- DOCUMENTATION CONFIGURATION --- # - -[tool.pydoctor] -add-package = ["src/cloudcasting_app"] -project-base-dir = "src/cloudcasting_app" -docformat = "google" -html-output = "docs" -theme = "classic" -privacy = [ - "HIDDEN:**.test_*", -] - [[tool.uv.index]] url = "https://pypi.org/simple" diff --git a/src/cloudcasting_inference/README.md b/src/cloudcasting_inference/README.md new file mode 100644 index 0000000..97fcc67 --- /dev/null +++ b/src/cloudcasting_inference/README.md @@ -0,0 +1,31 @@ +# cloudcasting-inference + +This package is used to run inference on the model. This model takes previous frames of EUMETSAT +satellite images and forecasts the future frames to come. + +The repo associated with training the models run here is https://github.com/openclimatefix/sat_pred + +The model checkpoints are hosted at: +https://huggingface.co/openclimatefix/cloudcasting_uk + +## Environment Variables + +The following environment variables are used in the app: + +- `SATELLITE_ZARR_PATH`: The path to the satellite data in Zarr format. +- `PREDICTION_SAVE_DIRECTORY`: The directory where predictions will be saved. + +### 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` + +## Example usage + +### Running the app locally + +It is possible to run the app locally by setting the required environment variables listed at the +top of the [inference app](src/cloudcasting_inference/app.py), these should point to the relevant +paths for loading satellite data and saving predicitons. + +You can then run the app using `uv run cloudcasting-inference`.s diff --git a/src/cloudcasting_app/__init__.py b/src/cloudcasting_inference/__init__.py similarity index 90% rename from src/cloudcasting_app/__init__.py rename to src/cloudcasting_inference/__init__.py index 57ed5c0..917f83f 100644 --- a/src/cloudcasting_app/__init__.py +++ b/src/cloudcasting_inference/__init__.py @@ -43,9 +43,4 @@ def structured_formatter(record: "loguru.Record") -> str: loguru.logger.add( sys.stdout, format=structured_formatter, backtrace=True, level=os.getenv("LOGLEVEL", "INFO").upper(), - ) - -# Uncomment and change the list to quieten external libraries -# for logger in ["aiobotocore", "cfgrib"]: -# logging.getLogger(logger).setLevel(logging.WARNING) - + ) \ No newline at end of file diff --git a/src/cloudcasting_app/app.py b/src/cloudcasting_inference/app.py similarity index 89% rename from src/cloudcasting_app/app.py rename to src/cloudcasting_inference/app.py index f8816f5..6051973 100644 --- a/src/cloudcasting_app/app.py +++ b/src/cloudcasting_inference/app.py @@ -2,7 +2,7 @@ This app expects these environmental variables to be available: SATELLITE_ZARR_PATH (str): The path of the input satellite data - OUTPUT_PREDICTION_DIRECTORY (str): The path of the directory to save the predictions to + PREDICTION_SAVE_DIRECTORY (str): The path of the directory to save the predictions to """ import os @@ -12,18 +12,17 @@ import hydra import pandas as pd import torch -import typer import xarray as xr import yaml from huggingface_hub import snapshot_download from safetensors.torch import load_model from loguru import logger -from cloudcasting_app.data import SatelliteDownloader, sat_path, get_input_data +from cloudcasting_inference.data import SatelliteDownloader, sat_path, get_input_data # Get package version try: - __version__ = version("cloudcasting-app") + __version__ = version("cloudcasting-inference") except PackageNotFoundError: __version__ = "v?" @@ -33,20 +32,18 @@ # Model will use GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - # Model revision on huggingface REPO_ID = "openclimatefix/cloudcasting_uk" REVISION = "47643e89000e64e0150f7359ccc0cb6524948712" - 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__) + logger.info(f"Using `cloudcasting-app` version: {__version__}", version=__version__) # --------------------------------------------------------------------------- # 0. If inference datetime is None, round down to last 30 minutes @@ -123,7 +120,7 @@ def app(t0=None): ds_y_hat.sat_pred.attrs.update(ds.data.attrs) # Save predictions to the latest path and to path with timestring - out_dir = os.environ["OUTPUT_PREDICTION_DIRECTORY"] + out_dir = os.environ["PREDICTION_SAVE_DIRECTORY"] if satellite_downloader.use_5_minute: latest_zarr_path = f"{out_dir}/latest.zarr" @@ -141,11 +138,3 @@ def app(t0=None): fs.rm(path, recursive=True) ds_y_hat.to_zarr(path) - - -def main() -> None: - """Entrypoint to the application.""" - typer.run(app) - -if __name__ == "__main__": - main() diff --git a/src/cloudcasting_app/data.py b/src/cloudcasting_inference/data.py similarity index 100% rename from src/cloudcasting_app/data.py rename to src/cloudcasting_inference/data.py diff --git a/src/cloudcasting_metrics/README.md b/src/cloudcasting_metrics/README.md new file mode 100644 index 0000000..0465f8a --- /dev/null +++ b/src/cloudcasting_metrics/README.md @@ -0,0 +1,14 @@ +# cloudcasting-metrics + +This package is used to score the predictions made by the cloudcasting model and save the scores to +a zarr store at the location `METRIC_ZARR_PATH`. By default, the scoring is run for all init-times +made the day before running. + +## Environment Variables + +The following environment variables are used in the app: + +- `SATELLITE_ICECHUNK_ARCHIVE`: The path to the satellite archive in icechunk formast +- `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/__init__.py b/src/cloudcasting_metrics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/cloudcasting_metrics/app.py b/src/cloudcasting_metrics/app.py new file mode 100644 index 0000000..46e858f --- /dev/null +++ b/src/cloudcasting_metrics/app.py @@ -0,0 +1,188 @@ +"""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. +""" + +import os +import re +import fsspec +import numpy as np +import pandas as pd +from tqdm import tqdm + +import xarray as xr +import icechunk +from loguru import logger + +# --------------------------------------------------------------------------- + +# The forecast produces these horizon steps +FORECAST_STEPS = pd.timedelta_range(start="15min", end="180min", freq="15min") +# The forecast is run at this frequency +FORECAST_FREQ = pd.Timedelta("30min") + + +def open_icechunk(path: str) -> xr.Dataset: + """Open an icechunk store to xarray Dataset + + Args: + path: The path to the local or s3 icechunk store + """ + + if path.startswith("s3://"): + bucket, _, path = path.removeprefix("s3://").partition("/") + store = icechunk.s3_storage( + bucket=bucket, + prefix=path, + from_env=True, + ) + else: + store = icechunk.local_filesystem_storage(path=path) + + repo = icechunk.Repository.open(store) + session = repo.readonly_session("main") + return xr.open_zarr(session.store) + + +def app(date: pd.Timestamp | None = None) -> None: + """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. + """ + + # 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"] + + now = pd.Timestamp.now(tz="UTC").replace(tzinfo=None) + + # Default to yesterday + if date is None: + date = now.floor("1D") - pd.Timedelta("1D") + + start_dt = date.floor("1D") + end_dt = start_dt + pd.Timedelta("1D") + + if now <= end_dt + FORECAST_STEPS.max(): + raise Exception( + f"We cannot score forecast with init-time {end_dt} until after the last valid-time." + ) + + # Open the satellite data store + ds_sat = open_icechunk(path=sat_path) + + # 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 + # - This eliminates any costs of repeatedly streaming data from the bucket + # - It's also faster + ds_sat = ds_sat.compute() + + # Find recent forecasts + date_string = start_dt.strftime("%Y-%m-%d") + remote_path = f"{prediction_dir}/{date_string}*.zarr" + fs, path = fsspec.core.url_to_fs(remote_path) + + file_list = fs.glob(path) + + # Filter forecasts + # - We only score forecasts we have the satellite data for + # - If we are missing one satellite image we will skip scoring all forecasts require that + forecasts_to_score = [] + + 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) + if match is None: + raise Exception(f"Could not derive datetime of file {file}") + + # Check the satellite data required to score it is present + init_time = pd.Timestamp(match.group(0)) + if np.isin(init_time + FORECAST_STEPS, ds_sat.time).all(): + forecasts_to_score.append(file) + else: + logger.warn(f"Cannot score {file} due to missing satellite data") + + ds_mae_list = [] + + 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 + + ds_forecast = ( + ds_forecast + .assign_coords(time=valid_times) + .swap_dims({"step":"time"}) + ) + + ds_sat_sel = ds_sat.sel( + time=ds_forecast.time, + x_geostationary=ds_forecast.x_geostationary, + y_geostationary=ds_forecast.y_geostationary, + variable=ds_forecast.variable, + ) + + da_mae = np.abs( + (ds_sat_sel.data - ds_forecast.sat_pred) + .swap_dims({"time":"step"}) + .drop_vars("time") + ) + + # 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")) + + ds_mae_reductions = xr.Dataset( + { + "mae_step": da_mae_step, + "mae_variable": da_mae_variable, + "mae_spatial": da_mae_spatial, + } + ) + + ds_mae_list.append(ds_mae_reductions) + + # 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") + 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) + + # 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 + } + ) + + # If it exists, open the archive of MAE values and check the coordinates against them + fs, stripped = fsspec.core.url_to_fs(metric_zarr_path) + 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(): + 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 diff --git a/tests/test_cloudcasting_inference/__init__.py b/tests/test_cloudcasting_inference/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_cloudcasting_inference/conftest.py b/tests/test_cloudcasting_inference/conftest.py new file mode 100644 index 0000000..64a0958 --- /dev/null +++ b/tests/test_cloudcasting_inference/conftest.py @@ -0,0 +1,18 @@ +import pandas as pd +import pytest +from tests.utils import make_sat_data + + +@pytest.fixture() +def init_time(): + return pd.Timestamp.now(tz="UTC").replace(tzinfo=None).floor("30min") + + +@pytest.fixture() +def sat_5_data(init_time): + times = pd.date_range( + init_time - pd.Timedelta("3h"), + init_time, + freq=f"5min", + ) + return make_sat_data(times) \ No newline at end of file diff --git a/tests/test_app.py b/tests/test_cloudcasting_inference/test_app.py similarity index 79% rename from tests/test_app.py rename to tests/test_cloudcasting_inference/test_app.py index 950d323..231e074 100644 --- a/tests/test_app.py +++ b/tests/test_cloudcasting_inference/test_app.py @@ -4,17 +4,17 @@ import xarray as xr import zarr -from cloudcasting_app.app import app +from cloudcasting_inference.app import app -def test_app(sat_5_data, tmp_path, test_t0): +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["OUTPUT_PREDICTION_DIRECTORY"] = f"{tmp_path}" + 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) @@ -23,7 +23,7 @@ def test_app(sat_5_data, tmp_path, test_t0): # Check the two output files have been created latest_zarr_path = f"{tmp_path}/latest.zarr" - t0_string_zarr_path = test_t0.strftime(f"{tmp_path}/%Y-%m-%dT%H:%M.zarr") + t0_string_zarr_path = init_time.strftime(f"{tmp_path}/%Y-%m-%dT%H:%M.zarr") assert os.path.exists(latest_zarr_path) assert os.path.exists(t0_string_zarr_path) @@ -37,7 +37,7 @@ def test_app(sat_5_data, tmp_path, test_t0): ) # Make sure all the coords are correct - assert ds_y_hat.init_time == test_t0 + 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() diff --git a/tests/test_cloudcasting_metrics/__init__.py b/tests/test_cloudcasting_metrics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_cloudcasting_metrics/conftest.py b/tests/test_cloudcasting_metrics/conftest.py new file mode 100644 index 0000000..fb22c74 --- /dev/null +++ b/tests/test_cloudcasting_metrics/conftest.py @@ -0,0 +1,63 @@ +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 + + +@pytest.fixture() +def today(): + return pd.Timestamp.now(tz="UTC").replace(tzinfo=None).floor("1D") + + +@pytest.fixture() +def init_times_tuple(today) -> tuple[pd.DatetimeIndex, pd.DatetimeIndex]: + init_times_day1 = pd.date_range(today - pd.Timedelta("2D"), freq=FORECAST_FREQ, periods=3) + init_times_day2 = pd.date_range(today - pd.Timedelta("1D"), freq=FORECAST_FREQ, periods=3) + return init_times_day1, init_times_day2 + + +@pytest.fixture() +def forecast_directory(tmp_path_factory, init_times_tuple) -> str: + pred_dir = str(tmp_path_factory.mktemp("pred_dir")) + + 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 = 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.rename({"data": "sat_pred"}) + + zarr_path = init_time.strftime(f"{pred_dir}/%Y-%m-%dT%H:%M.zarr") + ds_pred.to_zarr(zarr_path) + + yield pred_dir + + +@pytest.fixture() +def sat_icechunk_path(tmp_path, init_times_tuple) -> str: + sat_icechunk_path = str(tmp_path / "sat.icechunk") + + 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 = 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 + + +@pytest.fixture() +def sat_shell(): + return get_sat_shell() diff --git a/tests/test_cloudcasting_metrics/test_app.py b/tests/test_cloudcasting_metrics/test_app.py new file mode 100644 index 0000000..c2ec140 --- /dev/null +++ b/tests/test_cloudcasting_metrics/test_app.py @@ -0,0 +1,64 @@ +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 + + + +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["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")) + + # 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, + inclusive="left", + ) + 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")) + 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() + + # Run once for "1 days ago" - this appends to the MAE zarr + 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, + inclusive="left", + ) + 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")) + 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() + + # 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() + + for coord in ["x_geostationary", "y_geostationary", "variable"]: + assert (ds_mae[coord].values==sat_shell[coord].values).all() diff --git a/tests/conftest.py b/tests/utils.py similarity index 60% rename from tests/conftest.py rename to tests/utils.py index 789735d..8f024fd 100644 --- a/tests/conftest.py +++ b/tests/utils.py @@ -1,35 +1,27 @@ -import os - -import fsspec +from pathlib import Path import numpy as np import pandas as pd -import pytest import xarray as xr import zarr xr.set_options(keep_attrs=True) -@pytest.fixture() -def test_t0(): - return pd.Timestamp.now(tz="UTC").replace(tzinfo=None).floor("30min") - - -def make_sat_data(test_t0, freq_mins): - # Load dataset which only contains coordinates, but no data - shell_path = f"{os.path.dirname(os.path.abspath(__file__))}/test_data/non_hrv_shell.zarr.zip" +def get_sat_shell(): + shell_path = f"{Path(__file__).parent}/test_data/non_hrv_shell.zarr.zip" with zarr.storage.ZipStore(shell_path, mode="r") as store: ds = xr.open_zarr(store) # Remove original time dim - ds = ds.drop_vars("time") + return ds.drop_vars("time") + + +def make_sat_data(times: pd.DatetimeIndex) -> xr.Dataset: + + # Load dataset which only contains coordinates, but no data + ds = get_sat_shell() # Add new times so they lead up to present - times = pd.date_range( - test_t0 - pd.Timedelta("3h"), - test_t0, - freq=f"{freq_mins}min", - ) ds = ds.expand_dims(time=times) # Add data to dataset @@ -50,6 +42,4 @@ def make_sat_data(test_t0, freq_mins): return ds -@pytest.fixture() -def sat_5_data(test_t0): - return make_sat_data(test_t0, freq_mins=5) + diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..dae182b --- /dev/null +++ b/uv.lock @@ -0,0 +1,1745 @@ +version = 1 +revision = 2 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