Prepare v0.16.1 (numpy<2) release (#309)

* Fix and filter warnings, remove old plotting module

* Update CI build

.github/workflows/ci.yml

@@ -51,9 +51,9 @@ jobs:
       - name: Export Environment
         shell: bash -l {0}
         run: |
-          mkdir -p .artifacts
+          mkdir -p artifacts
           filename=env_py${{ matrix.python-version }}_${{ matrix.os }}.yml
-          conda env export --no-builds | grep -v "prefix" > .artifacts/$filename
+          conda env export --no-builds | grep -v "prefix" > artifacts/$filename
       - name: Install package and test
         shell: bash -l {0}
         run: |
@@ -71,22 +71,23 @@ jobs:
         shell: bash -l {0}
         run: |
           git status
-          pip install setuptools_scm
+          pip install setuptools_scm twine
           if [ ${{ matrix.os }} == "windows-latest" ]
           then
             # build whls on windows
             pip install wheel
-            python setup.py bdist_wheel --dist-dir .artifacts/dist
+            python setup.py bdist_wheel --dist-dir artifacts/dist
           else
             # build dist on linux
-            python setup.py sdist --dist-dir .artifacts/dist
+            python setup.py sdist --dist-dir artifacts/dist
           fi
-          ls .artifacts/dist
+          ls artifacts/dist
+          twine check artifacts/dist/*
       - name: Upload Artifacts
         uses: actions/upload-artifact@v4
         with:
           name: Artifacts-${{ matrix.python-version }}-${{ matrix.os }}
-          path: .artifacts/*
+          path: artifacts/*
   coveralls:
     name: Submit Coveralls 👚
     needs: build

CHANGELOG.rst

@@ -4,6 +4,12 @@ Changelog
 
 Unreleased changes in master
 ============================
+
+Version 0.16.1, 2024-11-13
+==========================
+- THIS VERSION IS STILL COMPATIBLE WITH ``NUMPY<2.0``, from v0.17 on pytesmo will require ``numpy>=2.0``
+- Old ``pytesmo.timeseries.plotting`` module was removed
+- Fixed and filtered many warnings that were printed by tests until now
 - Fixed an issue with the intra-annual metrics adapter when an empty time series is passed (PR `#307 <https://github.com/TUW-GEO/pytesmo/pull/307>`_)
 - Metapackage updated (pyscaffold 4.5) (PR `#307 <https://github.com/TUW-GEO/pytesmo/pull/307>`_)
 - C modules were outdated and could not be compiled, therefore rebuilt (PR `#307 <https://github.com/TUW-GEO/pytesmo/pull/307>`_)

environment.yml

@@ -10,7 +10,7 @@ dependencies:
   - numpy<2.0.0
   - numba
   - scipy>=0.12
-  - pandas>=0.11.0,!=0.15.2
+  - pandas>=0.23.0
   - netcdf4>=1.0.1,!=1.6.2
   - cython>=0.29.21
   - scikit-learn
@@ -34,7 +34,7 @@ dependencies:
     - ipykernel
     - sphinx_rtd_theme
     - ascat>=2.0
-    - ismn==1.3.4
+    - ismn==1.5.1
     - pytest
     - pytest-cov
     - pytest-mpl

setup.cfg

@@ -33,7 +33,7 @@ install_requires =
     importlib-metadata; python_version<"3.8"
     numpy>=1.7.0,<2.0.0
     scipy>=0.12
-    pandas>=0.11.0,!=0.15.2
+    pandas>=0.23.0
     matplotlib>=1.2.0
     netCDF4>=1.0.1,!=1.6.2
     pygeogrids
@@ -103,6 +103,7 @@ norecursedirs =
 markers =
     full_framework : marks slow test that use the whole validation framework (deselect with '-m "not full_framework"')
     slow : marks slow tests (deselect with '-m "not slow"')
+    doc_example : marks slow tests that test python code from documentation
 testpaths = tests
 # This removes some of the warnings that show up with pytest but are not an issue
 filterwarnings =
@@ -122,6 +123,13 @@ filterwarnings =
     ignore:`np.bool` is a deprecated alias for the builtin `bool`
     # this comes from the `test_cci` in `test_data_averager`
     ignore: IOError in reading ISMN data
+    # old CDF matching method
+    ignore:Use the new implementation 'cdf_match' instead.:DeprecationWarning
+    # ascat package prints some warnings, doesn't matter for pytesmo
+    ignore::UserWarning:^ascat
+    # ismn package used deprecated version of this, doesn't matter for pytesmo
+    ignore:The 'parallel_process_async' method was renamed to `parallel_process`.:DeprecationWarning
+
 
 [aliases]
 dists = bdist_wheel

src/pytesmo/time_series/grouping.py

@@ -366,6 +366,8 @@ def filter(self, idx: pd.DatetimeIndex):
     # selection = dat.query(" | ".join(cond)).index
 
         if self.yearless_date_ranges is not None:
+            cols = {}
+
             for i, gdrange in enumerate(self.yearless_date_ranges):
                 for y in np.unique(idx.year):
                     start = gdrange[0]
@@ -385,7 +387,11 @@ def filter(self, idx: pd.DatetimeIndex):
 
                     end_dt = end.to_datetime(years=y)
 
-                    mask[f"gen_range{y}-{i}"] = (idx >= start_dt) & (
+                    cols[f"gen_range{y}-{i}"] = (idx >= start_dt) & (
                         idx <= end_dt)
 
+            mask = pd.concat(
+                [mask, pd.DataFrame(index=mask.index, data=cols)],
+                axis=1)
+
         return mask.any(axis=1, bool_only=True)

src/pytesmo/utils.py

@@ -47,7 +47,7 @@ def rootdir() -> Path:
 
 def deprecated(message: str = None):
     """
-    Decorator for classes or functions to mark them as deprecated.
+    Decorator for class methods or functions to mark them as deprecated.
     If the decorator is applied without a specific message (`@deprecated()`),
     the default warning is shown when using the function/class. To specify
     a custom message use it like:
@@ -67,13 +67,11 @@ def decorator(src):
 
         @functools.wraps(src)
         def new_func(*args, **kwargs):
-            warnings.simplefilter('always', DeprecationWarning)
-
             warnings.warn(
                 default_msg if message is None else message,
                 category=DeprecationWarning,
                 stacklevel=2)
-            warnings.simplefilter('default', DeprecationWarning)
+
             return src(*args, **kwargs)
 
         return new_func

src/pytesmo/validation_framework/adapters.py

@@ -699,9 +699,9 @@ def _adapt(self, data: DataFrame) -> DataFrame:
 
         if self.drop_original:
             if self.time_offset_fields is not None:
-                data.drop(columns=self.time_offset_fields, inplace=True)
+                data = data.drop(columns=self.time_offset_fields)
             if self.base_time_field in data.columns:
-                data.drop(columns=[self.base_time_field], inplace=True)
+                data = data.drop(columns=[self.base_time_field])
 
         # Remove NaNs from index, if present
         data = data.loc[data.index.dropna()]