Support xclim v0.48.0

.github/workflows/main.yml

@@ -53,7 +53,7 @@ jobs:
           - python-version: "3.9"
           - python-version: "3.10"
           - python-version: "3.11"
-#          - python-version: "3.12"
+          - python-version: "3.12"
     defaults:
       run:
         shell: bash -l {0}
@@ -71,10 +71,11 @@ jobs:
           create-args: >-
             mamba
             python=${{ matrix.python-version }}
-      - name: Downgrade intake-esm (for xscen)
+      - name: Downgrade intake-esm (for xscen) and xclim
         if: matrix.python-version == '3.9'
         run: |
           micromamba install -y intake-esm=2023.11.10
+          micromamba install -y xclim=0.47.0
       - name: Conda and Mamba versions
         run: |
           mamba --version

environment-dev.yml

@@ -11,7 +11,7 @@ dependencies:
   - spotpy
   - statsmodels
   - xarray
-  - xclim >=0.47.0  # FIXME: Remove pin once our dependencies (xclim, xscen) support pandas 2.2.0
+  - xclim >=0.47.0
   - xscen >=0.7.1
   - pip
   - pip:

environment.yml

@@ -2,7 +2,7 @@ name: xhydro
 channels:
   - conda-forge
 dependencies:
-  - python >=3.9,<3.12
+  - python >=3.9,<3.13
   # Don't forget to sync changes between environment.yml, environment-dev.yml, and pyproject.toml!
   # Main packages
   - numpy
@@ -11,7 +11,7 @@ dependencies:
   - spotpy
   - statsmodels
   - xarray
-  - xclim >=0.47.0  # FIXME: Remove pin once our dependencies (xclim, xscen) support pandas 2.2.0
+  - xclim >=0.47.0
   - xscen >=0.7.1
   - pip
   - pip:

pyproject.toml

@@ -29,7 +29,7 @@ classifiers = [
   "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
-  # "Programming Language :: Python :: 3.12",
+  "Programming Language :: Python :: 3.12",
   "Programming Language :: Python :: Implementation :: CPython",
   "Topic :: Scientific/Engineering :: GIS",
   "Topic :: Scientific/Engineering :: Hydrology"

tests/test_calibration.py

@@ -5,7 +5,7 @@
 import pytest
 
 from xhydro.modelling.calibration import perform_calibration
-from xhydro.modelling.hydrological_modelling import _dummy_model
+from xhydro.modelling.hydrological_modelling import _dummy_model  # noqa
 from xhydro.modelling.obj_funcs import get_objective_function, transform_flows
 
 

tests/test_indicators.py

@@ -1,5 +1,7 @@
 import numpy as np
 import pytest
+from packaging.version import parse
+from xclim import __version__ as __xclim_version__
 from xclim.testing.helpers import test_timeseries as timeseries
 
 import xhydro as xh
@@ -25,10 +27,14 @@ def test_compute_volume(self, freq):
         mult = 86400 if freq == "D" else 86400 * 365
         np.testing.assert_array_equal(out, da * mult)
         assert out.attrs["long_name"] == "Foo"
-        assert out.attrs["units"] == "m^3"
         assert out.attrs["cell_methods"] == "time: sum"
         assert out.attrs["description"] == "Volume of water"
 
+        if parse(__xclim_version__) < parse("0.48.0"):
+            assert out.attrs["units"] == "m^3"
+        else:
+            assert out.attrs["units"] == "m3"
+
     def test_units(self):
         da = timeseries(
             np.tile(np.arange(1, 366), 3),
@@ -38,9 +44,15 @@ def test_units(self):
         )
 
         out_m3 = xh.indicators.compute_volume(da)
-        assert out_m3.attrs["units"] == "m^3"
         out_hm3 = xh.indicators.compute_volume(da, out_units="hm3")
-        assert out_hm3.attrs["units"] == "hm^3"
+
+        if parse(__xclim_version__) < parse("0.48.0"):
+            assert out_m3.attrs["units"] == "m^3"
+            assert out_hm3.attrs["units"] == "hm^3"
+        else:
+            assert out_m3.attrs["units"] == "m3"
+            assert out_hm3.attrs["units"] == "hm3"
+
         np.testing.assert_array_equal(out_m3 * 1e-6, out_hm3)
 
 
@@ -57,9 +69,9 @@ class TestGetYearlyOp:
     def test_get_yearly_op(self, op):
         timeargs = {
             "annual": {},
-            "winterdate": {"date_bounds": ["12-01", "02-28"], "freq": "AS-DEC"},
-            "winterdoy": {"doy_bounds": [335, 59], "freq": "AS-DEC"},
-            "winterdjf": {"season": ["DJF"], "freq": "AS-DEC"},
+            "winterdate": {"date_bounds": ["12-01", "02-28"], "freq": "YS-DEC"},
+            "winterdoy": {"doy_bounds": [335, 59], "freq": "YS-DEC"},
+            "winterdjf": {"season": ["DJF"], "freq": "YS-DEC"},
             "summer": {"doy_bounds": [200, 300]},
         }
 
@@ -227,7 +239,7 @@ def test_errors(self):
                 op="max",
                 timeargs={"annual": {"season": ["DJF"], "doy_bounds": [200, 300]}},
             )
-        with pytest.warns(UserWarning, match="The frequency is not AS-DEC"):
+        with pytest.warns(UserWarning, match="The frequency is not YS-DEC"):
             xh.indicators.get_yearly_op(
                 self.ds, op="max", timeargs={"annual": {"season": ["DJF"]}}
             )

tests/test_local.py

@@ -4,7 +4,10 @@
 from xclim.testing.helpers import test_timeseries as timeseries
 
 import xhydro.frequency_analysis as xhfa
-from xhydro.frequency_analysis.local import _get_plotting_positions, _prepare_plots
+from xhydro.frequency_analysis.local import (  # noqa
+    _get_plotting_positions,
+    _prepare_plots,
+)
 
 
 class TestFit:
@@ -29,10 +32,12 @@ def test_fit(self):
 
         np.testing.assert_array_almost_equal(
             params.streamflow,
-            [
-                [9.95357815e00, np.nan, -3.07846650e01, 1.56498193e01],
-                [np.nan, -2.25674044e-05, 1.25012261e02, 4.74238877e01],
-            ],
+            np.array(
+                [
+                    [9.95357815e00, np.nan, -3.07846650e01, 1.56498193e01],
+                    [np.nan, -2.25674044e-05, 1.25012261e02, 4.74238877e01],
+                ]
+            ),
         )
 
     def test_default(self):
@@ -73,9 +78,11 @@ def test_min_years(self, miny):
         np.testing.assert_array_almost_equal(
             params.streamflow,
             (
-                [[9.95357815e00, -3.07846650e01, 1.56498193e01]]
-                if miny == 10
-                else [[np.nan, np.nan, np.nan]]
+                np.array(
+                    [[9.95357815e00, -3.07846650e01, 1.56498193e01]]
+                    if miny == 10
+                    else [[np.nan, np.nan, np.nan]]
+                )
             ),
         )
 
@@ -110,7 +117,7 @@ def test_quantiles(mode):
         assert rp.streamflow.attrs["cell_methods"] == "dparams: ppf"
         assert rp.streamflow.attrs["mode"] == mode
 
-        ans = (
+        ans = np.array(
             [[190.66041057, 214.08102761], [185.78830382, 203.01731036]]
             if mode == "max"
             else [[66.00067153, 53.58658639], [64.23598869, 47.00660287]]
@@ -150,10 +157,12 @@ def test_criteria():
 
     np.testing.assert_array_almost_equal(
         crit.streamflow,
-        [
-            [118.19066549, 118.58856076, 118.63510993],
-            [118.12140939, 118.51930466, 118.56585383],
-        ],
+        np.array(
+            [
+                [118.19066549, 118.58856076, 118.63510993],
+                [118.12140939, 118.51930466, 118.56585383],
+            ]
+        ),
     )
 
 
@@ -166,7 +175,7 @@ def test_default(self):
             freq="YS",
             as_dataset=True,
         )
-        expected = [1.16666667, 1.61538462, 2.625, 7.0]
+        expected = np.array([1.16666667, 1.61538462, 2.625, 7.0])
         result = _get_plotting_positions(data)
         np.testing.assert_array_almost_equal(result.streamflow_pp, expected)
         np.testing.assert_array_almost_equal(result.streamflow, data.streamflow)
@@ -184,7 +193,7 @@ def test_nan(self):
             freq="YS",
             as_dataset=True,
         )
-        expected = [1.23076923, np.nan, 2.0, 5.33333333]
+        expected = np.array([1.23076923, np.nan, 2.0, 5.33333333])
         result = _get_plotting_positions(data)
         np.testing.assert_array_almost_equal(result.streamflow_pp, expected)
         np.testing.assert_array_almost_equal(result.streamflow, data.streamflow)
@@ -202,7 +211,7 @@ def test_return_period(self):
             freq="YS",
             as_dataset=True,
         )
-        expected = [0.14285714, 0.38095238, 0.61904762, 0.85714286]
+        expected = np.array([0.14285714, 0.38095238, 0.61904762, 0.85714286])
         result = _get_plotting_positions(data, return_period=False)
         np.testing.assert_array_almost_equal(result.streamflow_pp, expected)
         np.testing.assert_array_almost_equal(result.streamflow, data.streamflow)
@@ -220,7 +229,7 @@ def test_alpha_beta(self):
             freq="YS",
             as_dataset=True,
         )
-        expected = [1.25, 1.66666667, 2.5, 5.0]
+        expected = np.array([1.25, 1.66666667, 2.5, 5.0])
         result = _get_plotting_positions(data, alpha=0, beta=0)
         np.testing.assert_array_almost_equal(result.streamflow_pp, expected)
         np.testing.assert_array_almost_equal(result.streamflow, data.streamflow)
@@ -246,19 +255,23 @@ def test_prepare_plots_default(self):
         assert result.streamflow.shape == (2, 100)
         assert result.return_period.min() == 1
         assert result.return_period.max() == 10000
-        expected = [
-            [-30.78466504, 63.64266447, 78.19229358, 88.62699148, 97.15369501],
-            [-np.inf, 61.08708903, 79.72126025, 92.05411647, 101.59650405],
-        ]
+        expected = np.array(
+            [
+                [-30.78466504, 63.64266447, 78.19229358, 88.62699148, 97.15369501],
+                [-np.inf, 61.08708903, 79.72126025, 92.05411647, 101.59650405],
+            ]
+        )
         np.testing.assert_array_almost_equal(result.streamflow.head(), expected)
 
     def test_prepare_plots_linear(self):
         result = _prepare_plots(self.params, log=False)
 
-        expected = [
-            [-30.78466504, 262.72577254, 281.56238078, 292.27851004, 299.75980757],
-            [-np.inf, 235.6878466, 247.41104463, 253.8825982, 258.32114457],
-        ]
+        expected = np.array(
+            [
+                [-30.78466504, 262.72577254, 281.56238078, 292.27851004, 299.75980757],
+                [-np.inf, 235.6878466, 247.41104463, 253.8825982, 258.32114457],
+            ]
+        )
         np.testing.assert_array_almost_equal(result.streamflow.head(), expected)
 
     def test_prepare_plots_range(self):

tests/test_testing_utils.py

@@ -1,3 +1,5 @@
+from pathlib import Path
+
 import pytest
 
 import xhydro as xh
@@ -7,7 +9,11 @@
 @pytest.mark.requires_docs
 def test_publish_release_notes(tmp_path):
     temp_md_filename = tmp_path.joinpath("version_info.md")
-    xhu.publish_release_notes(style="md", file=temp_md_filename)
+    xhu.publish_release_notes(
+        style="md",
+        file=temp_md_filename,
+        changes=Path(__file__).parent.parent.joinpath("CHANGES.rst"),
+    )
 
     with open(temp_md_filename) as f:
         changelog = f.read()
@@ -28,7 +34,11 @@ def test_publish_release_notes(tmp_path):
     assert ":pull:`" not in changelog
 
     temp_rst_filename = tmp_path.joinpath("version_info.rst")
-    xhu.publish_release_notes(style="rst", file=temp_rst_filename)
+    xhu.publish_release_notes(
+        style="rst",
+        file=temp_rst_filename,
+        changes=Path(__file__).parent.parent.joinpath("CHANGES.rst"),
+    )
     with open(temp_rst_filename) as f:
         changelog_rst = f.read()
     assert changelog_rst.startswith("=========\nChangelog\n=========")

xhydro/indicators.py

@@ -82,12 +82,12 @@ def get_yearly_op(
         Dictionary of time arguments for the operation.
         Keys are the name of the period that will be added to the results (e.g. "winter", "summer", "annual").
         Values are up to two dictionaries, with both being optional.
-        The first is {'freq': str}, where str is a frequency supported by xarray (e.g. "YS", "AS-JAN", "AS-DEC").
-        It needs to be a yearly frequency. Defaults to "AS-JAN".
+        The first is {'freq': str}, where str is a frequency supported by xarray (e.g. "YS", "YS-JAN", "YS-DEC").
+        It needs to be a yearly frequency. Defaults to "YS-JAN".
         The second is an indexer as supported by :py:func:`xclim.core.calendar.select_time`.
         Defaults to {}, which means the whole year.
         See :py:func:`xclim.core.calendar.select_time` for more information.
-        Examples: {"winter": {"freq": "AS-DEC", "date_bounds": ["12-01", "02-28"]}}, {"jan": {"freq": "YS", "month": 1}}, {"annual": {}}.
+        Examples: {"winter": {"freq": "YS-DEC", "date_bounds": ["12-01", "02-28"]}}, {"jan": {"freq": "YS", "month": 1}}, {"annual": {}}.
     missing : str
         How to handle missing values. One of "skip", "any", "at_least_n", "pct", "wmo".
         See :py:func:`xclim.core.missing` for more information.
@@ -159,7 +159,7 @@ def get_yearly_op(
         "DEC",
     ]
     for i in timeargs:
-        freq = timeargs[i].get("freq", "AS-JAN")
+        freq = timeargs[i].get("freq", "YS-JAN")
         if not xc.core.calendar.compare_offsets(freq, "==", "YS"):
             raise ValueError(
                 f"Frequency {freq} is not supported. Please use a yearly frequency."
@@ -172,10 +172,10 @@ def get_yearly_op(
         if (
             "season" in indexer.keys()
             and "DJF" in indexer["season"]
-            and freq != "AS-DEC"
+            and freq != "YS-DEC"
         ):
             warnings.warn(
-                "The frequency is not AS-DEC, but the season indexer includes DJF. "
+                "The frequency is not YS-DEC, but the season indexer includes DJF. "
                 "This will lead to misleading results."
             )
         elif (
@@ -207,7 +207,7 @@ def get_yearly_op(
             if freq == "YS" or (month_start != month_labels.index(freq.split("-")[1])):
                 warnings.warn(
                     f"The frequency is {freq}, but the bounds are between months {month_start} and {month_end}. "
-                    f"You should use 'AS-{month_labels[month_start - 1]}' as the frequency."
+                    f"You should use 'YS-{month_labels[month_start - 1]}' as the frequency."
                 )
 
         identifier = f"{input_var}{window if window > 1 else ''}_{op}_{i.lower()}"

xhydro/modelling/hydrological_modelling.py

@@ -37,7 +37,7 @@
 import pandas as pd
 import xarray as xr
 
-__all__ = ["run_hydrological_model"]
+__all__ = ["get_hydrological_model_inputs", "run_hydrological_model"]
 
 
 def run_hydrological_model(model_config: dict):

xhydro/modelling/obj_funcs.py

@@ -25,7 +25,7 @@
 import numpy as np
 import xarray as xr
 
-__all__ = ["get_objective_function"]
+__all__ = ["get_objective_function", "transform_flows"]
 
 
 def get_objective_function(