Merge branch 'split_from_xclim' of github.com:Ouranosinc/xsdba into s…

…plit_from_xclim

src/xsdba/base.py

@@ -5,18 +5,19 @@
 
 from __future__ import annotations
 
+import datetime as pydt
 from collections.abc import Sequence
 from inspect import _empty, signature  # noqa
 from typing import Callable
 
+import cftime
 import dask.array as dsk
 import jsonpickle
 import numpy as np
 import xarray as xr
 from boltons.funcutils import wraps
+
 from xsdba.options import OPTIONS, SDBA_ENCODE_CF
-import datetime as pydt
-import cftime
 
 
 # ## Base class for the sdba module
@@ -94,6 +95,7 @@ def set_dataset(self, ds: xr.Dataset) -> None:
         self.ds = ds
         self.ds.attrs[self._attribute] = jsonpickle.encode(self)
 
+
 # XC put here to avoid circular import
 def uses_dask(*das: xr.DataArray | xr.Dataset) -> bool:
     """Evaluate whether dask is installed and array is loaded as a dask array.

src/xsdba/testing.py

@@ -1,21 +1,21 @@
 """Testing utilities for xsdba."""
 
-import warnings
+import collections
 import hashlib
 import logging
 import os
+import warnings
 from pathlib import Path
 from urllib.error import HTTPError, URLError
-from urllib.request import urlopen, urlretrieve
 from urllib.parse import urljoin, urlparse
+from urllib.request import urlopen, urlretrieve
 
 import pandas as pd
 import xarray as xr
 from platformdirs import user_cache_dir
 from xarray import open_dataset as _open_dataset
-import collections 
 
-__all__ = ["test_timeseries", "test_timelonlatseries"]
+__all__ = ["test_timelonlatseries", "test_timeseries"]
 
 # keeping xclim-testdata for now, since it's still this on gitHub
 _default_cache_dir = Path(user_cache_dir("xclim-testdata"))
@@ -47,6 +47,7 @@
 except ImportError:
     SocketBlockedError = None
 
+
 def test_timelonlatseries(values, name, start="2000-01-01"):
     """Create a DataArray with time, lon and lat dimensions."""
     coords = collections.OrderedDict()
@@ -81,6 +82,7 @@ def test_timelonlatseries(values, name, start="2000-01-01"):
         attrs=attrs,
     )
 
+
 # XC
 def test_timeseries(
     values,
@@ -113,7 +115,8 @@ def file_md5_checksum(f_name):
         hash_md5.update(f.read())
     return hash_md5.hexdigest()
 
-# XC 
+
+# XC
 def audit_url(url: str, context: str = None) -> str:
     """Check if the URL is well-formed.
 
@@ -134,6 +137,7 @@ def audit_url(url: str, context: str = None) -> str:
         raise URLError(msg)
     return url
 
+
 # XC (oh dear)
 def _get(
     fullname: Path,

src/xsdba/utils.py

@@ -9,7 +9,6 @@
 from typing import Callable
 from warnings import warn
 
-
 import numpy as np
 import xarray as xr
 from boltons.funcutils import wraps
@@ -95,8 +94,6 @@ def ecdf(x: xr.DataArray, value: float, dim: str = "time") -> xr.DataArray:
     return (x <= value).sum(dim) / x.notnull().sum(dim)
 
 
-
-
 # XC
 def ensure_chunk_size(da: xr.DataArray, **minchunks: int) -> xr.DataArray:
     r"""Ensure that the input DataArray has chunks of at least the given size.

tests/conftest.py

@@ -21,7 +21,7 @@
 
 from xsdba.testing import TESTDATA_BRANCH
 from xsdba.testing import open_dataset as _open_dataset
-from xsdba.testing import test_timeseries, test_timelonlatseries
+from xsdba.testing import test_timelonlatseries, test_timeseries
 
 # import xclim
 # from xclim import __version__ as __xclim_version__
@@ -93,6 +93,7 @@ def _open_session_scoped_file(file: str | os.PathLike, branch: str = TESTDATA_BR
 def timelonlatseries():
     return test_timelonlatseries
 
+
 @pytest.fixture
 def lat_series():
     def _lat_series(values):

tests/test_nbutils.py

@@ -3,6 +3,7 @@
 import numpy as np
 import pytest
 import xarray as xr
+
 from xsdba import nbutils as nbu
 
 

tests/test_utils.py

@@ -10,7 +10,6 @@
 from xsdba.base import Grouper
 
 
-
 def test_ecdf(timelonlatseries, random):
     dist = norm(5, 2)
     r = dist.rvs(10000, random_state=random)
@@ -66,9 +65,7 @@ def test_equally_spaced_nodes():
     np.testing.assert_almost_equal(x[0], 0.5)
 
 
-@pytest.mark.parametrize(
-    "interp,expi", [("nearest", 2.9), ("linear", 2.95), ("cubic", 2.95)]
-)
+@pytest.mark.parametrize("interp,expi", [("nearest", 2.9), ("linear", 2.95), ("cubic", 2.95)])
 @pytest.mark.parametrize("extrap,expe", [("constant", 4.4), ("nan", np.NaN)])
 def test_interp_on_quantiles_constant(interp, expi, extrap, expe):
     quantiles = np.linspace(0, 1, num=25)
@@ -95,9 +92,7 @@ def test_interp_on_quantiles_constant(interp, expi, extrap, expe):
     yq = yq.expand_dims(lat=[1, 2, 3])
     newx = newx.expand_dims(lat=[1, 2, 3])
 
-    out = u.interp_on_quantiles(
-        newx, xq, yq, group="time", method=interp, extrapolation=extrap
-    )
+    out = u.interp_on_quantiles(newx, xq, yq, group="time", method=interp, extrapolation=extrap)
 
     if np.isnan(expe):
         assert out.isel(time=0).isnull().all()
@@ -108,9 +103,7 @@ def test_interp_on_quantiles_constant(interp, expi, extrap, expe):
 
     xq = xq.where(xq != 220)
     yq = yq.where(yq != 3)
-    out = u.interp_on_quantiles(
-        newx, xq, yq, group="time", method=interp, extrapolation=extrap
-    )
+    out = u.interp_on_quantiles(newx, xq, yq, group="time", method=interp, extrapolation=extrap)
 
     if np.isnan(expe):
         assert out.isel(time=0).isnull().all()
@@ -124,21 +117,15 @@ def test_interp_on_quantiles_monthly(random):
     t = xr.cftime_range("2000-01-01", "2030-12-31", freq="D", calendar="noleap")
     ref = xr.DataArray(
         (
-            -20 * np.cos(2 * np.pi * t.dayofyear / 365)
-            + 2 * random.random(t.size)
-            + 273.15
-            + 0.1 * (t - t[0]).days / 365
+            -20 * np.cos(2 * np.pi * t.dayofyear / 365) + 2 * random.random(t.size) + 273.15 + 0.1 * (t - t[0]).days / 365
         ),  # "warming" of 1K per decade,
         dims=("time",),
         coords={"time": t},
         attrs={"units": "K"},
     )
     sim = xr.DataArray(
         (
-            -18 * np.cos(2 * np.pi * t.dayofyear / 365)
-            + 2 * random.random(t.size)
-            + 273.15
-            + 0.11 * (t - t[0]).days / 365
+            -18 * np.cos(2 * np.pi * t.dayofyear / 365) + 2 * random.random(t.size) + 273.15 + 0.11 * (t - t[0]).days / 365
         ),  # "warming" of 1.1K per decade
         dims=("time",),
         coords={"time": t},
@@ -155,15 +142,11 @@ def test_interp_on_quantiles_monthly(random):
     af = u.get_correction(hist_q, ref_q, "+")
 
     for interp in ["nearest", "linear", "cubic"]:
-        afi = u.interp_on_quantiles(
-            sim, hist_q, af, group="time.month", method=interp, extrapolation="constant"
-        )
+        afi = u.interp_on_quantiles(sim, hist_q, af, group="time.month", method=interp, extrapolation="constant")
         assert afi.isnull().sum("time") == 0, interp
 
 
-@pytest.mark.parametrize(
-    "interp,expi", [("nearest", 2.9), ("linear", 2.95), ("cubic", 2.95)]
-)
+@pytest.mark.parametrize("interp,expi", [("nearest", 2.9), ("linear", 2.95), ("cubic", 2.95)])
 @pytest.mark.parametrize("extrap,expe", [("constant", 4.4), ("nan", np.NaN)])
 def test_interp_on_quantiles_constant_with_nan(interp, expi, extrap, expe):
     quantiles = np.linspace(0, 1, num=30)
@@ -190,9 +173,7 @@ def test_interp_on_quantiles_constant_with_nan(interp, expi, extrap, expe):
     yq = yq.expand_dims(lat=[1, 2, 3])
     newx = newx.expand_dims(lat=[1, 2, 3])
 
-    out = u.interp_on_quantiles(
-        newx, xq, yq, group="time", method=interp, extrapolation=extrap
-    )
+    out = u.interp_on_quantiles(newx, xq, yq, group="time", method=interp, extrapolation=extrap)
 
     if np.isnan(expe):
         assert out.isel(time=0).isnull().all()
@@ -203,9 +184,7 @@ def test_interp_on_quantiles_constant_with_nan(interp, expi, extrap, expe):
 
     xq = xq.where(xq != 220)
     yq = yq.where(yq != 3)
-    out = u.interp_on_quantiles(
-        newx, xq, yq, group="time", method=interp, extrapolation=extrap
-    )
+    out = u.interp_on_quantiles(newx, xq, yq, group="time", method=interp, extrapolation=extrap)
 
     if np.isnan(expe):
         assert out.isel(time=0).isnull().all()