Chore: pre-commit autoupdate

tests/conftest.py

@@ -6,7 +6,6 @@
 import pandas as pd  # type: ignore
 import pytest
 import xarray as xr
-from pytest import approx
 
 from hazard.protocols import OpenDataset, WriteDataset
 from hazard.utilities import zarr_utilities
@@ -37,8 +36,12 @@ class TestSource(OpenDataset):
     def __init__(self, datasets: Dict[Tuple[str, int], xr.Dataset]):
         self.datasets = datasets
 
-    def open_dataset_year(self, gcm: str, scenario: str, quantity: str, year: int, chunks=None) -> xr.Dataset:
-        return self.datasets[(quantity, year)]  # ignore scenario and gcm: we test just a single one
+    def open_dataset_year(
+        self, gcm: str, scenario: str, quantity: str, year: int, chunks=None
+    ) -> xr.Dataset:
+        return self.datasets[
+            (quantity, year)
+        ]  # ignore scenario and gcm: we test just a single one
 
 
 class TestTarget(WriteDataset):
@@ -47,7 +50,9 @@ class TestTarget(WriteDataset):
     def __init__(self):
         self.datasets = {}
 
-    def write(self, path: str, dataset: xr.Dataset, spatial_coords: Optional[bool] = False):
+    def write(
+        self, path: str, dataset: xr.Dataset, spatial_coords: Optional[bool] = False
+    ):
         self.datasets[path] = dataset
 
     def read(self, path: str):
@@ -74,7 +79,9 @@ def _create_test_datasets_tas(
 
 def _create_test_dataset_averaged() -> xr.Dataset:
     """An example 3x3 array that might result from some operation averaging over time."""
-    temperature = np.array([[293.0, 298.0, 310.0], [304.0, 302.0, 300.0], [308.0, 290.0, 294.0]])
+    temperature = np.array(
+        [[293.0, 298.0, 310.0], [304.0, 302.0, 300.0], [308.0, 290.0, 294.0]]
+    )
     lat = np.arange(3.0, 0.0, -1.0)
     lon = np.arange(0.0, 3.0, 1.0)
     ds = xr.Dataset(
@@ -87,7 +94,9 @@ def _create_test_dataset_averaged() -> xr.Dataset:
     return ds
 
 
-def _create_test_dataset_tas(year: int, offset: float = 0, quantity: str = "tasmax") -> xr.Dataset:
+def _create_test_dataset_tas(
+    year: int, offset: float = 0, quantity: str = "tasmax"
+) -> xr.Dataset:
     """Create test xarray Dataset.
     Convention is that data is arranged in image-like way:
     - dimensions are ('latitude', 'longitude')
@@ -97,7 +106,10 @@ def _create_test_dataset_tas(year: int, offset: float = 0, quantity: str = "tasm
     Returns:
        xr.Dataset : test Dataset
     """
-    temperature_t1 = np.array([[293.0, 298.0, 310.0], [304.0, 302.0, 300.0], [308.0, 290.0, 294.0]]) + offset
+    temperature_t1 = (
+        np.array([[293.0, 298.0, 310.0], [304.0, 302.0, 300.0], [308.0, 290.0, 294.0]])
+        + offset
+    )
     temperature_t2 = temperature_t1 + 1.0  # temp at t1 + 1 degree
     temperature_t3 = temperature_t2 + 2.0
     # stack to give 3 time points
@@ -116,7 +128,9 @@ def _create_test_dataset_tas(year: int, offset: float = 0, quantity: str = "tasm
     return ds
 
 
-def _create_test_dataset_hurs(year: int, offset: float = 0, quantity="hurs") -> xr.Dataset:
+def _create_test_dataset_hurs(
+    year: int, offset: float = 0, quantity="hurs"
+) -> xr.Dataset:
     """Create test xarray Dataset.
     Convention is that data is arranged in image-like way:
     - dimensions are ('latitude', 'longitude')
@@ -126,7 +140,9 @@ def _create_test_dataset_hurs(year: int, offset: float = 0, quantity="hurs") ->
     Returns:
        xr.Dataset : test Dataset
     """
-    hurs_t1 = np.array([[70.0, 72.0, 69.0], [72.0, 71.0, 70.0], [80.0, 74.0, 68.0]]) + offset
+    hurs_t1 = (
+        np.array([[70.0, 72.0, 69.0], [72.0, 71.0, 70.0], [80.0, 74.0, 68.0]]) + offset
+    )
     hurs_t2 = hurs_t1 + 5.0  # hurs at t1 + 5%
     hurs_t3 = hurs_t2 + 10.0
     # stack to give 3 time points

tests/test_drought_indicators.py

@@ -9,8 +9,12 @@
 import zarr  # type: ignore
 
 from hazard.docs_store import DocStore
-from hazard.models.drought_index import DroughtIndicator, LocalZarrWorkingStore, ProgressStore, S3ZarrWorkingStore
-from tests.conftest import TestTarget
+from hazard.models.drought_index import (
+    DroughtIndicator,
+    LocalZarrWorkingStore,
+    ProgressStore,
+    S3ZarrWorkingStore,
+)
 
 
 @pytest.mark.skip(reason="incomplete")
@@ -21,7 +25,9 @@ def test_spei_indicator(test_output_dir, s3_credentials):
     working_store = S3ZarrWorkingStore()
     working_store = LocalZarrWorkingStore(test_output_dir)
     model = DroughtIndicator(working_store)
-    model.calculate_spei(gcm, scenario, progress_store=ProgressStore(test_output_dir, "spei_prog_store"))
+    model.calculate_spei(
+        gcm, scenario, progress_store=ProgressStore(test_output_dir, "spei_prog_store")
+    )
     # target = TestTarget()
     data_chunks = model.get_datachunks()
     test_chunk = data_chunks["Chunk_0255"]
@@ -33,9 +39,9 @@ def test_spei_indicator(test_output_dir, s3_credentials):
     # model.calculate_spei("MIROC6", "ssp585")
     # model.calculate_annual_average_spei("MIROC6", "ssp585", 2080, target)
 
-    ds_tas = model.read_quantity_from_s3_store(gcm, scenario, "tas", lat_min, lat_max, lon_min, lon_max).chunk(
-        {"time": 100000}
-    )
+    ds_tas = model.read_quantity_from_s3_store(
+        gcm, scenario, "tas", lat_min, lat_max, lon_min, lon_max
+    ).chunk({"time": 100000})
     ds_tas_local = ds_tas.compute()
     series_tas = ds_tas_local["tas"][0, 0, :].values
     # ds_pr = model.read_quantity_from_s3_store(gcm, scenario, "pr", lat_min, lat_max, lon_min, lon_max).chunk(
@@ -50,7 +56,9 @@ def test_spei_indicator(test_output_dir, s3_credentials):
 
 
 def test_partial_write_zarr(test_output_dir):
-    zarr_store = zarr.DirectoryStore(os.path.join(test_output_dir, "drought", "hazard.zarr"))
+    zarr_store = zarr.DirectoryStore(
+        os.path.join(test_output_dir, "drought", "hazard.zarr")
+    )
 
     lat = np.arange(-60 + 0.25 / 2, 90 + 0.25 / 2, 0.25)
     lon = np.arange(0.25 / 2, 360 + 0.25 / 2, 0.25)
@@ -70,8 +78,12 @@ def test_partial_write_zarr(test_output_dir):
     ds_spei.to_zarr(store=zarr_store, mode="w", compute=False)
     # see https://docs.xarray.dev/en/stable/user-guide/io.html?appending-to-existing-zarr-stores=#appending-to-existing-zarr-stores # noqa: E501
     sliced = ds_spei.sel(lat=slice(10, 20), lon=slice(30, 40))
-    lat_indexes = np.where(np.logical_and(ds_spei["lat"].values >= 10, ds_spei["lat"].values <= 20))[0]
-    lon_indexes = np.where(np.logical_and(ds_spei["lon"].values >= 30, ds_spei["lon"].values <= 40))[0]
+    lat_indexes = np.where(
+        np.logical_and(ds_spei["lat"].values >= 10, ds_spei["lat"].values <= 20)
+    )[0]
+    lon_indexes = np.where(
+        np.logical_and(ds_spei["lon"].values >= 30, ds_spei["lon"].values <= 40)
+    )[0]
     ds_spei_slice = (
         xr.DataArray(
             coords={
@@ -109,6 +121,8 @@ def test_progress_store(test_output_dir):
 def test_doc_store(test_output_dir, s3_credentials):
     docs_store = DocStore()
     docs_store.write_new_empty_inventory()
-    zarr_store = zarr.DirectoryStore(os.path.join(test_output_dir, "drought", "hazard.zarr"))
+    zarr_store = zarr.DirectoryStore(
+        os.path.join(test_output_dir, "drought", "hazard.zarr")
+    )
     resource = DroughtIndicator(zarr_store).resource
     docs_store.update_inventory([resource])

tests/test_heat_indicators.py

@@ -1,10 +1,8 @@
-import json
 import os
-from typing import Dict, List
+from typing import List
 
 import fsspec.implementations.local as local  # type: ignore
 import numpy as np
-import pandas as pd  # type: ignore
 import pytest
 import s3fs  # type: ignore
 import xarray as xr
@@ -13,13 +11,17 @@
 from pytest import approx
 
 import hazard.utilities.zarr_utilities as zarr_utilities
-from hazard.docs_store import DocStore, HazardResources  # type: ignore
+from hazard.docs_store import DocStore  # type: ignore
 from hazard.models.degree_days import BatchItem, DegreeDays, HeatingCoolingDegreeDays
 from hazard.models.work_loss import WorkLossIndicator
-from hazard.protocols import OpenDataset, WriteDataset
 from hazard.sources.nex_gddp_cmip6 import NexGddpCmip6
 from hazard.sources.osc_zarr import OscZarr
-from tests.conftest import TestSource, TestTarget, _create_test_dataset_averaged, _create_test_datasets_tas
+from tests.conftest import (
+    TestSource,
+    TestTarget,
+    _create_test_dataset_averaged,
+    _create_test_datasets_tas,
+)
 
 from .conftest import _create_test_datasets_hurs, test_output_dir  # noqa: F401; pylint: disable=unused-variable
 
@@ -32,16 +34,24 @@ def test_degree_days_mocked():
     source = TestSource(_create_test_datasets_tas())
     target = TestTarget()
     # cut down the transform
-    model = DegreeDays(window_years=2, gcms=[gcm], scenarios=[scenario], central_years=[year])
+    model = DegreeDays(
+        window_years=2, gcms=[gcm], scenarios=[scenario], central_years=[year]
+    )
     model.run_all(source, target, debug_mode=True)
     with source.open_dataset_year(gcm, scenario, "tasmax", 2029) as y0:
         with source.open_dataset_year(gcm, scenario, "tasmax", 2030) as y1:
             scale = 365.0 / len(y0.time)
-            deg0 = scale * xr.where(y0.tasmax > (32 + 273.15), y0.tasmax - (32 + 273.15), 0).sum(dim=["time"])
-            deg1 = scale * xr.where(y1.tasmax > (32 + 273.15), y1.tasmax - (32 + 273.15), 0).sum(dim=["time"])
+            deg0 = scale * xr.where(
+                y0.tasmax > (32 + 273.15), y0.tasmax - (32 + 273.15), 0
+            ).sum(dim=["time"])
+            deg1 = scale * xr.where(
+                y1.tasmax > (32 + 273.15), y1.tasmax - (32 + 273.15), 0
+            ).sum(dim=["time"])
             expected = (deg0 + deg1) / 2
     assert expected.values == approx(
-        target.datasets["chronic_heat/osc/v2/mean_degree_days_v2_above_32c_NorESM2-MM_ssp585_2030"].values
+        target.datasets[
+            "chronic_heat/osc/v2/mean_degree_days_v2_above_32c_NorESM2-MM_ssp585_2030"
+        ].values
     )
 
 
@@ -58,7 +68,9 @@ def test_work_loss_mocked():
     source = TestSource(test_sets)
     target = TestTarget()
     # cut down the transform
-    model = WorkLossIndicator(window_years=2, gcms=[gcm], scenarios=[scenario], central_years=[year])
+    model = WorkLossIndicator(
+        window_years=2, gcms=[gcm], scenarios=[scenario], central_years=[year]
+    )
     model.run_all(source, target, debug_mode=True)
     expected: List[xr.DataArray] = []
     with source.open_dataset_year(gcm, scenario, "tas", 2029).tas as t0:
@@ -67,20 +79,30 @@ def test_work_loss_mocked():
                 with source.open_dataset_year(gcm, scenario, "hurs", 2030).hurs as h1:
                     tas_c = t0 - 273.15  # convert from K to C
                     # vpp is water vapour partial pressure in kPa
-                    vpp = (h0 / 100.0) * 6.105 * np.exp((17.27 * tas_c) / (237.7 + tas_c))
+                    vpp = (
+                        (h0 / 100.0) * 6.105 * np.exp((17.27 * tas_c) / (237.7 + tas_c))
+                    )
                     wbgt0 = 0.567 * tas_c + 0.393 * vpp + 3.94
                     tas_c = t1 - 273.15
-                    vpp = (h1 / 100.0) * 6.105 * np.exp((17.27 * tas_c) / (237.7 + tas_c))
+                    vpp = (
+                        (h1 / 100.0) * 6.105 * np.exp((17.27 * tas_c) / (237.7 + tas_c))
+                    )
                     wbgt1 = 0.567 * tas_c + 0.393 * vpp + 3.94
 
                     for alpha1, alpha2 in [alpha_light, alpha_medium, alpha_heavy]:
-                        wa0 = 0.1 + 0.9 / (1.0 + (wbgt0 / alpha1) ** alpha2)  # work-ability
+                        wa0 = 0.1 + 0.9 / (
+                            1.0 + (wbgt0 / alpha1) ** alpha2
+                        )  # work-ability
                         wlm0 = 1.0 - wa0.mean(dim=["time"])  # work-loss
-                        wa1 = 0.1 + 0.9 / (1.0 + (wbgt1 / alpha1) ** alpha2)  # work-ability
+                        wa1 = 0.1 + 0.9 / (
+                            1.0 + (wbgt1 / alpha1) ** alpha2
+                        )  # work-ability
                         wlm1 = 1.0 - wa1.mean(dim=["time"])  # work-loss
                         expected.append((wlm0 + wlm1) / 2)
     assert expected[0].values == approx(
-        target.datasets["chronic_heat/osc/v2/mean_work_loss_low_NorESM2-MM_ssp585_2030"].values
+        target.datasets[
+            "chronic_heat/osc/v2/mean_work_loss_low_NorESM2-MM_ssp585_2030"
+        ].values
     )
 
 
@@ -89,7 +111,9 @@ def test_zarr_read_write(test_output_dir):  # noqa: F811
     read from the zarr array alone using attributes and ignoring coordinates.
     """
     ds = _create_test_dataset_averaged()
-    store = zarr.DirectoryStore(os.path.join(test_output_dir, "hazard_test", "hazard.zarr"))
+    store = zarr.DirectoryStore(
+        os.path.join(test_output_dir, "hazard_test", "hazard.zarr")
+    )
     source = OscZarr(store=store)
     source.write("test", ds.tasmax)
     # ds.to_zarr(store, compute=True, group="test", mode="w", consolidated=False)
@@ -106,12 +130,16 @@ def test_degree_days(test_output_dir):  # noqa: F811
     download_test_datasets(test_output_dir, gcm, scenario, years)
     # source: read downloaded datasets from local file system
     fs = local.LocalFileSystem()
-    source = NexGddpCmip6(root=os.path.join(test_output_dir, NexGddpCmip6.bucket), fs=fs)
+    source = NexGddpCmip6(
+        root=os.path.join(test_output_dir, NexGddpCmip6.bucket), fs=fs
+    )
     # target: write zarr to load fine system
     store = zarr.DirectoryStore(os.path.join(test_output_dir, "hazard", "hazard.zarr"))
     target = OscZarr(store=store)
     # cut down the model and run
-    model = HeatingCoolingDegreeDays(window_years=1, gcms=[gcm], scenarios=[scenario], central_years=[years[0]])
+    model = HeatingCoolingDegreeDays(
+        window_years=1, gcms=[gcm], scenarios=[scenario], central_years=[years[0]]
+    )
     # model = DegreeDays(window_years=1, gcms=[gcm], scenarios=[scenario], central_years=[years[0]])
     model.run_all(source, target)
     # check one point...
@@ -136,15 +164,21 @@ def test_work_loss(test_output_dir):  # noqa: F811
     gcm = "NorESM2-MM"
     scenario = "ssp585"
     years = [2028, 2029, 2030]
-    download_test_datasets(test_output_dir, gcm, scenario, years, indicators=["tas", "hurs"])
+    download_test_datasets(
+        test_output_dir, gcm, scenario, years, indicators=["tas", "hurs"]
+    )
     # source: read downloaded datasets from local file system
     fs = local.LocalFileSystem()
-    source = NexGddpCmip6(root=os.path.join(test_output_dir, NexGddpCmip6.bucket), fs=fs)
+    source = NexGddpCmip6(
+        root=os.path.join(test_output_dir, NexGddpCmip6.bucket), fs=fs
+    )
     # target: write zarr to load fine system
     store = zarr.DirectoryStore(os.path.join(test_output_dir, "hazard", "hazard.zarr"))
     target = OscZarr(store=store)
     # cut down the model and run
-    model = WorkLossIndicator(window_years=3, gcms=[gcm], scenarios=[scenario], central_years=[years[1]])
+    model = WorkLossIndicator(
+        window_years=3, gcms=[gcm], scenarios=[scenario], central_years=[years[1]]
+    )
     # resources = list(model.inventory())
     # models = HazardResources(resources=resources)
     # json_str = json.dumps(models.dict(), indent=4)  # pretty print
@@ -170,9 +204,13 @@ def test_example_run_degree_days():
     scenario = "ssp585"
     year = 2030
     source = NexGddpCmip6()
-    target = OscZarr(prefix="hazard_test")  # test prefix is "hazard_test"; main one "hazard"
+    target = OscZarr(
+        prefix="hazard_test"
+    )  # test prefix is "hazard_test"; main one "hazard"
     # cut down the transform
-    model = DegreeDays(window_years=1, gcms=[gcm], scenarios=[scenario], central_years=[year])
+    model = DegreeDays(
+        window_years=1, gcms=[gcm], scenarios=[scenario], central_years=[year]
+    )
 
     docs_store.update_inventory(model.inventory())
 
@@ -181,7 +219,9 @@ def test_example_run_degree_days():
     assert True
 
 
-def download_test_datasets(test_output_dir, gcm, scenario, years, indicators=["tasmax"]):  # noqa: F811
+def download_test_datasets(
+    test_output_dir, gcm, scenario, years, indicators=["tasmax"]
+):  # noqa: F811
     store = NexGddpCmip6()
     s3 = s3fs.S3FileSystem(anon=True)
     for year in years:

tests/test_inventory.py

@@ -3,7 +3,7 @@
 import fsspec.implementations.local as local  # type: ignore
 import pytest
 
-from hazard.docs_store import DocStore, HazardResources
+from hazard.docs_store import DocStore
 from hazard.models.days_tas_above import DaysTasAboveIndicator
 from hazard.models.degree_days import DegreeDays, HeatingCoolingDegreeDays
 from hazard.models.drought_index import DroughtIndicator
@@ -17,8 +17,6 @@
 from hazard.onboard.wri_aqueduct_water_risk import WRIAqueductWaterRisk
 from hazard.utilities import zarr_utilities  # type: ignore
 
-from .conftest import test_output_dir
-
 
 def test_create_inventory(test_output_dir):  # noqa: F811
     """Create inventory for all indicators and write into this repo."""