[pre-commit.ci] auto fixes from pre-commit.com hooks

doc/source/tutorials/notebooks/DCSC_txnn.ipynb

@@ -47,19 +47,19 @@
  }
  ],
  "source": [
- "import datetime\n",
  "import sys\n",
  "from pathlib import Path\n",
  "\n",
  "import cartopy.crs as ccrs\n",
  "import cftime\n",
- "import icclim\n",
  "import matplotlib.pyplot as plt\n",
  "import numpy as np\n",
  "import pandas as pd\n",
  "import xarray as xr\n",
  "import xclim\n",
- "from xclim.core.calendar import select_time \n",
+ "from xclim.core.calendar import select_time\n",
+ "\n",
+ "import icclim\n",
  "from icclim.frequency import FrequencyRegistry\n",
  "\n",
  "print(\"python: \", sys.version)\n",
@@ -68,7 +68,7 @@
  "print(\"pandas: \", pd.__version__)\n",
  "print(\"icclim: \", icclim.__version__)\n",
  "print(\"cftime: \", cftime.__version__)\n",
- "print(\"xclim: \", xclim.__version__)\n"
+ "print(\"xclim: \", xclim.__version__)"
  ]
  },
  {
@@ -664,7 +664,9 @@
  ],
  "source": [
  "historical_tas = xr.open_mfdataset(historical_files).tas\n",
- "filtered_tas = select_time(historical_tas, month=FrequencyRegistry.AMJJAS.indexer[\"month\"], drop=True)\n",
+ "filtered_tas = select_time(\n",
+ " historical_tas, month=FrequencyRegistry.AMJJAS.indexer[\"month\"], drop=True\n",
+ ")\n",
  "normal = filtered_tas.mean(dim=\"time\", keep_attrs=True)\n",
  "normal"
  ]
@@ -1291,7 +1293,7 @@
  "source": [
  "icclim.dcsc.txnd(\n",
  " in_files=studied_files[0:1],\n",
- " normal = normal,\n",
+ " normal=normal,\n",
  " var_name=\"tas\",\n",
  " slice_mode=FrequencyRegistry.AMJJAS,\n",
  " out_file=out_f,\n",

doc/source/tutorials/notebooks/averaged_tas_anomaly.ipynb

@@ -125,13 +125,13 @@
  "import sys\n",
  "from pathlib import Path\n",
  "\n",
- "import icclim\n",
- "\n",
  "# provides cftime axis in matplotlib\n",
  "import numpy as np\n",
  "import pandas as pd\n",
  "import xarray as xr\n",
  "\n",
+ "import icclim\n",
+ "\n",
  "print(\"python: \", sys.version)\n",
  "print(\"numpy: \", np.__version__)\n",
  "print(\"xarray: \", xr.__version__)\n",

doc/source/tutorials/notebooks/cold_spell_duration.ipynb

@@ -333,9 +333,6 @@
  "# CDS API\n",
  "import cdsapi\n",
  "\n",
- "# icclim package for calculating climate indices\n",
- "import icclim\n",
- "\n",
  "# Libraries for plotting and visualising data\n",
  "import matplotlib.pyplot as plt\n",
  "\n",
@@ -346,6 +343,9 @@
  "import urllib3\n",
  "import xarray as xr\n",
  "\n",
+ "# icclim package for calculating climate indices\n",
+ "import icclim\n",
+ "\n",
  "# To add specific units spelling\n",
  "\n",
  "urllib3.disable_warnings()\n",

doc/source/tutorials/notebooks/custom_freezing_tas.ipynb

@@ -129,12 +129,13 @@
  "\n",
  "import cartopy.crs as ccrs\n",
  "import cftime\n",
- "import icclim\n",
  "import matplotlib.pyplot as plt\n",
  "import numpy as np\n",
  "import pandas as pd\n",
  "import xarray as xr\n",
  "\n",
+ "import icclim\n",
+ "\n",
  "print(\"python: \", sys.version)\n",
  "print(\"numpy: \", np.__version__)\n",
  "print(\"xarray: \", xr.__version__)\n",

doc/source/tutorials/notebooks/deltaT_deltaP_anomaly.ipynb

@@ -131,12 +131,13 @@
  "from pathlib import Path\n",
  "\n",
  "import cftime\n",
- "import icclim\n",
  "import matplotlib.pyplot as plt\n",
  "import numpy as np\n",
  "import pandas as pd\n",
  "import xarray as xr\n",
  "\n",
+ "import icclim\n",
+ "\n",
  "print(\"python: \", sys.version)\n",
  "print(\"numpy: \", np.__version__)\n",
  "print(\"xarray: \", xr.__version__)\n",

doc/source/tutorials/notebooks/su_summer_days__subset_and_plot.ipynb

@@ -129,12 +129,13 @@
  "\n",
  "import cartopy.crs as ccrs\n",
  "import cftime\n",
- "import icclim\n",
  "import matplotlib.pyplot as plt\n",
  "import numpy as np\n",
  "import pandas as pd\n",
  "import xarray as xr\n",
  "\n",
+ "import icclim\n",
+ "\n",
  "print(\"python: \", sys.version)\n",
  "print(\"numpy: \", np.__version__)\n",
  "print(\"xarray: \", xr.__version__)\n",

doc/source/tutorials/notebooks/tg90p_calculate__subset_and_plot.ipynb

@@ -133,12 +133,13 @@
  "import cartopy.crs as ccrs\n",
  "import cftime\n",
  "import dask\n",
- "import icclim\n",
  "import matplotlib.pyplot as plt\n",
  "import numpy as np\n",
  "import pandas as pd\n",
  "import xarray as xr\n",
  "\n",
+ "import icclim\n",
+ "\n",
  "print(\"python: \", sys.version)\n",
  "print(\"numpy: \", np.__version__)\n",
  "print(\"pandas: \", pd.__version__)\n",

tests/test_cf_calendar.py

@@ -4,6 +4,7 @@
 import pandas as pd
 import pytest
 import xarray as xr
+
 from icclim._core.model.cf_calendar import CfCalendarRegistry
 from icclim.exception import InvalidIcclimArgumentError
 

tests/test_dcsc.py

@@ -1,5 +1,4 @@
 from icclim.dcsc import txnd
-
 from tests.testing_utils import stub_tas
 
 

tests/test_ecad_indices.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import pytest
+
 from icclim._core.model.standard_index import StandardIndex
 from icclim.ecad.registry import EcadIndexRegistry
 from icclim.exception import InvalidIcclimArgumentError

tests/test_frequency.py

@@ -4,9 +4,9 @@
 import numpy as np
 import pandas as pd
 import pytest
+
 from icclim.exception import InvalidIcclimArgumentError
 from icclim.frequency import FrequencyRegistry, get_seasonal_time_updater
-
 from tests.testing_utils import stub_tas
 
 

tests/test_generated_api.py

@@ -4,9 +4,10 @@
 from typing import TYPE_CHECKING
 from unittest.mock import MagicMock, patch
 
-import icclim
 import numpy as np
 import pytest
+
+import icclim
 from icclim._core.constants import QUANTILE_BASED, REFERENCE_PERIOD_INDEX
 from icclim._core.legacy.user_index.calc_operation import (
  CalcOperation,
@@ -20,7 +21,6 @@
 from icclim.generic.registry import GenericIndicatorRegistry
 from icclim.logger import VerbosityRegistry
 from icclim.threshold.factory import build_threshold
-
 from tests.testing_utils import stub_tas
 
 if TYPE_CHECKING:
@@ -137,7 +137,7 @@ def test_txx__season_slice_mode() -> None:
  res = icclim.txx(tas, slice_mode=["season", [11, 12, 1, 2]]).compute()
  # THEN
  # missing values for nov, dec of first period
- np.testing.assert_array_equal(res.TXx.isel(time=0), np.NAN)
+ np.testing.assert_array_equal(res.TXx.isel(time=0), np.nan)
  np.testing.assert_array_equal(res.TXx.isel(time=1), 30.0)
  np.testing.assert_array_equal(
  res.time_bounds.isel(time=0),
@@ -151,7 +151,7 @@ def test_txx__months_slice_mode() -> None:
  tas.loc[{"time": "2042-01-01"}] = 303.15 # 30°C 273.15
  res = icclim.txx(tas, slice_mode=["months", [11, 1]]).compute()
  np.testing.assert_array_equal(res.TXx.isel(time=0), 30)
- np.testing.assert_array_equal(res.TXx.isel(time=1), np.NAN)
+ np.testing.assert_array_equal(res.TXx.isel(time=1), np.nan)
  np.testing.assert_almost_equal(res.TXx.sel(time="2042-11"), 21.85)
  np.testing.assert_array_equal(
  res.time_bounds.isel(time=0),
@@ -192,8 +192,8 @@ def test_custom_index__season_slice_mode(
  },
  ).compute()
  # missing values algo applied for first and last years
- np.testing.assert_almost_equal(res.pouet.isel(time=0), np.NAN)
- np.testing.assert_almost_equal(res.pouet.isel(time=-1), np.NAN)
+ np.testing.assert_almost_equal(res.pouet.isel(time=0), np.nan)
+ np.testing.assert_almost_equal(res.pouet.isel(time=-1), np.nan)
  np.testing.assert_almost_equal(res.pouet.isel(time=1), expectation_year_1)
  np.testing.assert_almost_equal(res.pouet.isel(time=2), expectation_year_2)
 
@@ -225,8 +225,8 @@ def test_custom_index_run_algos__season_slice_mode(
  },
  )
  # missing values algo applied for first and last years
- np.testing.assert_almost_equal(res.pouet.isel(time=0), np.NAN)
- np.testing.assert_almost_equal(res.pouet.isel(time=-1), np.NAN)
+ np.testing.assert_almost_equal(res.pouet.isel(time=0), np.nan)
+ np.testing.assert_almost_equal(res.pouet.isel(time=-1), np.nan)
  np.testing.assert_almost_equal(res.pouet.isel(time=1), expectation_year_1)
  np.testing.assert_almost_equal(res.pouet.isel(time=2), expectation_year_2)
 
@@ -280,12 +280,12 @@ def test_custom_index_anomaly__datetime_ref_period() -> None:
  },
  ).compute()
  # missing values algo applied for first and last years
- np.testing.assert_almost_equal(res.anomaly.sel(time="2041"), np.NAN)
+ np.testing.assert_almost_equal(res.anomaly.sel(time="2041"), np.nan)
  np.testing.assert_almost_equal(res.anomaly.sel(time="2042"), 0)
  np.testing.assert_almost_equal(res.anomaly.sel(time="2043"), 0)
  np.testing.assert_almost_equal(res.anomaly.sel(time="2044"), 4.80645161)
  np.testing.assert_almost_equal(res.anomaly.sel(time="2045"), 0)
- np.testing.assert_almost_equal(res.anomaly.sel(time="2046"), np.NAN)
+ np.testing.assert_almost_equal(res.anomaly.sel(time="2046"), np.nan)
 
 
 def test_custom_index_anomaly__groupby_and_resample_month() -> None:

tests/test_generic_functions.py

@@ -4,7 +4,6 @@
 from icclim._core.model.standard_variable import StandardVariableRegistry
 from icclim.frequency import FrequencyRegistry
 from icclim.threshold.factory import build_threshold
-
 from tests.testing_utils import stub_tas
 
 

tests/test_index_group.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import pytest
+
 from icclim._core.model.index_group import IndexGroupRegistry
 from icclim.exception import InvalidIcclimArgumentError
 

tests/test_input_parsing.py

@@ -8,6 +8,7 @@
 import pandas as pd
 import pytest
 import xarray as xr
+
 from icclim._core.constants import UNITS_KEY
 from icclim._core.input_parsing import (
  PercentileDataArray,

tests/test_main.py

@@ -6,20 +6,20 @@
 from unittest.mock import MagicMock, patch
 
 import cftime
-import icclim
 import numpy as np
 import pandas as pd
 import pint
 import pytest
 import xarray as xr
+
+import icclim
 from icclim import __version__ as icclim_version
 from icclim._core.constants import PART_OF_A_WHOLE_UNIT, REFERENCE_PERIOD_ID, UNITS_KEY
 from icclim._core.model.index_group import IndexGroupRegistry
 from icclim.ecad.registry import EcadIndexRegistry
 from icclim.exception import InvalidIcclimArgumentError
 from icclim.frequency import FrequencyRegistry
 from icclim.threshold.factory import build_threshold
-
 from tests.testing_utils import K2C, stub_pr, stub_tas
 
 
@@ -38,7 +38,7 @@ def test_deprecated_indice(log_mock: MagicMock, index_mock: MagicMock) -> None:
 HEAT_INDICES = ["SU", "TR", "WSDI", "TG90p", "TN90p", "TX90p", "TXx", "TNx", "CSU"]
 
 
-@pytest.mark.slow()
+@pytest.mark.slow
 class TestIntegration:
  """
  Integration tests.
@@ -282,7 +282,7 @@ def test_index_su__djf_cf_time(self) -> None:
  out_file=self.OUTPUT_FILE,
  slice_mode=FrequencyRegistry.DJF,
  )
- np.testing.assert_array_equal(res.SU.isel(time=0), np.NAN)
+ np.testing.assert_array_equal(res.SU.isel(time=0), np.nan)
  np.testing.assert_array_equal(res.SU.isel(time=1), 0)
  # "+ 1" because DJF sampling create a december month with nans before first year
  np.testing.assert_array_equal(

tests/test_rechunk.py

@@ -6,6 +6,7 @@
 import pandas as pd
 import pytest
 import xarray as xr
+
 from icclim import create_optimized_zarr_store
 from icclim._core.constants import UNITS_KEY
 from icclim.exception import InvalidIcclimArgumentError

tests/test_threshold.py

@@ -9,6 +9,9 @@
 import pint
 import pytest
 import xarray as xr
+from xclim.core.calendar import percentile_doy
+from xclim.core.units import units as xc_units
+
 from icclim._core.constants import UNITS_KEY
 from icclim._core.generic.threshold.basic import BasicThreshold
 from icclim._core.generic.threshold.bounded import BoundedThreshold
@@ -18,8 +21,6 @@
 from icclim._core.model.operator import OperatorRegistry
 from icclim.exception import InvalidIcclimArgumentError
 from icclim.threshold.factory import build_threshold
-from xclim.core.calendar import percentile_doy
-from xclim.core.units import units as xc_units
 
 
 def test_value_error() -> None:

tests/test_user_index.py

@@ -1,14 +1,14 @@
 from __future__ import annotations
 
+from xclim.core.calendar import build_climatology_bounds
+
 import icclim
 from icclim._core.constants import (
  UNITS_KEY,
  USER_INDEX_PRECIPITATION_STAMP,
  USER_INDEX_TEMPERATURE_STAMP,
 )
 from icclim._core.model.operator import OperatorRegistry
-from xclim.core.calendar import build_climatology_bounds
-
 from tests.testing_utils import stub_tas
 
 

tests/testing_utils.py

@@ -3,6 +3,7 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
+
 from icclim._core.constants import UNITS_KEY
 
 VALUE_COUNT = 365 * 5 + 1 # 5 years of data (with 1 leap year)