[Git][debian-gis-team/flox][upstream] New upstream version 0.6.7
Antonio Valentino (@antonio.valentino)
gitlab at salsa.debian.org
Wed Jan 18 07:46:05 GMT 2023
Antonio Valentino pushed to branch upstream at Debian GIS Project / flox
Commits:
314ead24 by Antonio Valentino at 2023-01-18T07:36:12+00:00
New upstream version 0.6.7
- - - - -
9 changed files:
- .github/workflows/ci-additional.yaml
- .github/workflows/ci.yaml
- .pre-commit-config.yaml
- README.md
- ci/environment.yml
- codecov.yml
- flox/aggregate_flox.py
- flox/xrdtypes.py
- tests/test_core.py
Changes:
=====================================
.github/workflows/ci-additional.yaml
=====================================
@@ -71,7 +71,15 @@ jobs:
conda list
- name: Run doctests
run: |
- python -m pytest --doctest-modules flox --ignore flox/tests
+ python -m pytest --doctest-modules flox --ignore flox/tests --cov=./ --cov-report=xml
+ - name: Upload code coverage to Codecov
+ uses: codecov/codecov-action at v3.1.1
+ with:
+ file: ./coverage.xml
+ flags: unittests
+ env_vars: RUNNER_OS
+ name: codecov-umbrella
+ fail_ci_if_error: false
mypy:
name: Mypy
@@ -97,12 +105,12 @@ jobs:
uses: mamba-org/provision-with-micromamba at v14
with:
environment-file: ${{env.CONDA_ENV_FILE}}
- environment-name: xarray-tests
+ environment-name: flox-tests
extra-specs: |
python=${{env.PYTHON_VERSION}}
cache-env: true
cache-env-key: "${{runner.os}}-${{runner.arch}}-py${{env.PYTHON_VERSION}}-${{env.TODAY}}-${{hashFiles(env.CONDA_ENV_FILE)}}"
- - name: Install xarray
+ - name: Install flox
run: |
python -m pip install --no-deps -e .
- name: Version info
@@ -115,4 +123,13 @@ jobs:
- name: Run mypy
run: |
- python -m mypy --install-types --non-interactive
+ python -m mypy --install-types --non-interactive --cobertura-xml-report mypy_report
+
+ - name: Upload mypy coverage to Codecov
+ uses: codecov/codecov-action at v3.1.1
+ with:
+ file: mypy_report/cobertura.xml
+ flags: mypy
+ env_vars: PYTHON_VERSION
+ name: codecov-umbrella
+ fail_ci_if_error: false
=====================================
.github/workflows/ci.yaml
=====================================
@@ -89,7 +89,15 @@ jobs:
python -m pip install --no-deps -e .
- name: Run tests
run: |
- python -m pytest -n auto
+ python -m pytest -n auto --cov=./ --cov-report=xml
+ - name: Upload code coverage to Codecov
+ uses: codecov/codecov-action at v3.1.1
+ with:
+ file: ./coverage.xml
+ flags: unittests
+ env_vars: RUNNER_OS
+ name: codecov-umbrella
+ fail_ci_if_error: false
upstream-dev:
name: upstream-dev
=====================================
.pre-commit-config.yaml
=====================================
@@ -3,24 +3,25 @@ ci:
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.3.0
+ rev: v4.4.0
hooks:
+ - id: check-yaml
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-docstring-first
- repo: https://github.com/psf/black
- rev: 22.8.0
+ rev: 22.12.0
hooks:
- id: black
- repo: https://github.com/PyCQA/flake8
- rev: 5.0.4
+ rev: 6.0.0
hooks:
- id: flake8
- repo: https://github.com/PyCQA/isort
- rev: 5.10.1
+ rev: 5.11.4
hooks:
- id: isort
=====================================
README.md
=====================================
@@ -1,4 +1,4 @@
-[](https://github.com/xarray-contrib/flox/actions)
+[](https://github.com/xarray-contrib/flox/actions)
[](https://results.pre-commit.ci/latest/github/xarray-contrib/flox/main)
[](https://codecov.io/gh/xarray-contrib/flox)
[](https://flox.readthedocs.io/en/latest/?badge=latest)
=====================================
ci/environment.yml
=====================================
@@ -9,6 +9,7 @@ dependencies:
- netcdf4
- pandas
- numpy>=1.20
+ - lxml # for mypy coverage report
- matplotlib
- pip
- pytest
=====================================
codecov.yml
=====================================
@@ -5,6 +5,7 @@ codecov:
comment: false
ignore:
+ - 'benchmarks/*.py'
- 'tests/*.py'
- 'setup.py'
=====================================
flox/aggregate_flox.py
=====================================
@@ -107,7 +107,8 @@ def mean(group_idx, array, *, axis=-1, size=None, fill_value=None, dtype=None):
if fill_value is None:
fill_value = 0
out = sum(group_idx, array, axis=axis, size=size, dtype=dtype, fill_value=fill_value)
- out /= nanlen(group_idx, array, size=size, axis=axis, fill_value=0)
+ with np.errstate(invalid="ignore", divide="ignore"):
+ out /= nanlen(group_idx, array, size=size, axis=axis, fill_value=0)
return out
@@ -115,5 +116,6 @@ def nanmean(group_idx, array, *, axis=-1, size=None, fill_value=None, dtype=None
if fill_value is None:
fill_value = 0
out = nansum(group_idx, array, size=size, axis=axis, dtype=dtype, fill_value=fill_value)
- out /= nanlen(group_idx, array, size=size, axis=axis, fill_value=0)
+ with np.errstate(invalid="ignore", divide="ignore"):
+ out /= nanlen(group_idx, array, size=size, axis=axis, fill_value=0)
return out
=====================================
flox/xrdtypes.py
=====================================
@@ -152,28 +152,3 @@ def get_neg_infinity(dtype, min_for_int=False):
def is_datetime_like(dtype):
"""Check if a dtype is a subclass of the numpy datetime types"""
return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)
-
-
-def result_type(*arrays_and_dtypes):
- """Like np.result_type, but with type promotion rules matching pandas.
-
- Examples of changed behavior:
- number + string -> object (not string)
- bytes + unicode -> object (not unicode)
-
- Parameters
- ----------
- *arrays_and_dtypes : list of arrays and dtypes
- The dtype is extracted from both numpy and dask arrays.
-
- Returns
- -------
- numpy.dtype for the result.
- """
- types = {np.result_type(t).type for t in arrays_and_dtypes}
-
- for left, right in PROMOTE_TO_OBJECT:
- if any(issubclass(t, left) for t in types) and any(issubclass(t, right) for t in types):
- return np.dtype(object)
-
- return np.result_type(*arrays_and_dtypes)
=====================================
tests/test_core.py
=====================================
@@ -140,7 +140,7 @@ def test_groupby_reduce(
elif func == "sum":
expected_result = np.array(expected, dtype=dtype)
elif func == "count":
- expected_result = np.array(expected, dtype=np.int64)
+ expected_result = np.array(expected, dtype=np.intp)
result, groups, = groupby_reduce(
array,
@@ -284,7 +284,7 @@ def test_groupby_reduce_count():
array = np.array([0, 0, np.nan, np.nan, np.nan, 1, 1])
labels = np.array(["a", "b", "b", "b", "c", "c", "c"])
result, _ = groupby_reduce(array, labels, func="count")
- assert_equal(result, np.array([1, 1, 2], dtype=np.int64))
+ assert_equal(result, np.array([1, 1, 2], dtype=np.intp))
def test_func_is_aggregation():
@@ -393,12 +393,12 @@ def test_groupby_agg_dask(func, shape, array_chunks, group_chunks, add_nan, dtyp
kwargs["expected_groups"] = [0, 2, 1]
with raise_if_dask_computes():
actual, groups = groupby_reduce(array, by, engine=engine, **kwargs, sort=False)
- assert_equal(groups, np.array([0, 2, 1], dtype=np.intp))
+ assert_equal(groups, np.array([0, 2, 1], dtype=np.int64))
assert_equal(expected, actual[..., [0, 2, 1]])
with raise_if_dask_computes():
actual, groups = groupby_reduce(array, by, engine=engine, **kwargs, sort=True)
- assert_equal(groups, np.array([0, 1, 2], np.intp))
+ assert_equal(groups, np.array([0, 1, 2], np.int64))
assert_equal(expected, actual)
@@ -408,29 +408,29 @@ def test_numpy_reduce_axis_subset(engine):
array = np.ones_like(by, dtype=np.int64)
kwargs = dict(func="count", engine=engine, fill_value=0)
result, _ = groupby_reduce(array, by, **kwargs, axis=1)
- assert_equal(result, np.array([[2, 3], [2, 3]], dtype=np.int64))
+ assert_equal(result, np.array([[2, 3], [2, 3]], dtype=np.intp))
by = np.broadcast_to(labels2d, (3, *labels2d.shape))
array = np.ones_like(by)
result, _ = groupby_reduce(array, by, **kwargs, axis=1)
- subarr = np.array([[1, 1], [1, 1], [0, 2], [1, 1], [1, 1]], dtype=np.int64)
+ subarr = np.array([[1, 1], [1, 1], [0, 2], [1, 1], [1, 1]], dtype=np.intp)
expected = np.tile(subarr, (3, 1, 1))
assert_equal(result, expected)
result, _ = groupby_reduce(array, by, **kwargs, axis=2)
- subarr = np.array([[2, 3], [2, 3]], dtype=np.int64)
+ subarr = np.array([[2, 3], [2, 3]], dtype=np.intp)
expected = np.tile(subarr, (3, 1, 1))
assert_equal(result, expected)
result, _ = groupby_reduce(array, by, **kwargs, axis=(1, 2))
- expected = np.array([[4, 6], [4, 6], [4, 6]], dtype=np.int64)
+ expected = np.array([[4, 6], [4, 6], [4, 6]], dtype=np.intp)
assert_equal(result, expected)
result, _ = groupby_reduce(array, by, **kwargs, axis=(2, 1))
assert_equal(result, expected)
result, _ = groupby_reduce(array, by[0, ...], **kwargs, axis=(1, 2))
- expected = np.array([[4, 6], [4, 6], [4, 6]], dtype=np.int64)
+ expected = np.array([[4, 6], [4, 6], [4, 6]], dtype=np.intp)
assert_equal(result, expected)
@@ -447,11 +447,11 @@ def test_dask_reduce_axis_subset():
axis=1,
expected_groups=[0, 2],
)
- assert_equal(result, np.array([[2, 3], [2, 3]], dtype=np.int64))
+ assert_equal(result, np.array([[2, 3], [2, 3]], dtype=np.intp))
by = np.broadcast_to(labels2d, (3, *labels2d.shape))
array = np.ones_like(by)
- subarr = np.array([[1, 1], [1, 1], [123, 2], [1, 1], [1, 1]], dtype=np.int64)
+ subarr = np.array([[1, 1], [1, 1], [123, 2], [1, 1], [1, 1]], dtype=np.intp)
expected = np.tile(subarr, (3, 1, 1))
with raise_if_dask_computes():
result, _ = groupby_reduce(
@@ -464,7 +464,7 @@ def test_dask_reduce_axis_subset():
)
assert_equal(result, expected)
- subarr = np.array([[2, 3], [2, 3]], dtype=np.int64)
+ subarr = np.array([[2, 3], [2, 3]], dtype=np.intp)
expected = np.tile(subarr, (3, 1, 1))
with raise_if_dask_computes():
result, _ = groupby_reduce(
@@ -672,7 +672,7 @@ def test_groupby_bins(chunk_labels, chunks, engine, method) -> None:
engine=engine,
method=method,
)
- expected = np.array([3, 1, 0], dtype=np.int64)
+ expected = np.array([3, 1, 0], dtype=np.intp)
for left, right in zip(groups, pd.IntervalIndex.from_arrays([1, 2, 4], [2, 4, 5]).to_numpy()):
assert left == right
assert_equal(actual, expected)
@@ -801,7 +801,7 @@ def test_cohorts_map_reduce_consistent_dtypes(method, dtype, labels_dtype):
actual, actual_groups = groupby_reduce(array, labels, func="count", method=method)
assert_equal(actual_groups, np.arange(6, dtype=labels.dtype))
- assert_equal(actual, repeats.astype(np.int64))
+ assert_equal(actual, repeats.astype(np.intp))
actual, actual_groups = groupby_reduce(array, labels, func="sum", method=method)
assert_equal(actual_groups, np.arange(6, dtype=labels.dtype))
@@ -955,7 +955,7 @@ def test_group_by_datetime(engine, method):
def test_factorize_values_outside_bins():
-
+ # pd.factorize returns intp
vals = factorize_(
(np.arange(10).reshape(5, 2), np.arange(10).reshape(5, 2)),
axis=(0, 1),
@@ -967,7 +967,7 @@ def test_factorize_values_outside_bins():
fastpath=True,
)
actual = vals[0]
- expected = np.array([[-1, -1], [-1, 0], [6, 12], [18, 24], [-1, -1]], np.int64)
+ expected = np.array([[-1, -1], [-1, 0], [6, 12], [18, 24], [-1, -1]], np.intp)
assert_equal(expected, actual)
@@ -991,7 +991,8 @@ def test_multiple_groupers_bins(chunk) -> None:
),
func="count",
)
- expected = np.eye(5, 5, dtype=np.int64)
+ # output from `count` is intp
+ expected = np.eye(5, 5, dtype=np.intp)
assert_equal(expected, actual)
@@ -1020,7 +1021,8 @@ def test_multiple_groupers(chunk, by1, by2, expected_groups) -> None:
if chunk:
by2 = dask.array.from_array(by2)
- expected = np.ones((5, 2), dtype=np.int64)
+ # output from `count` is intp
+ expected = np.ones((5, 2), dtype=np.intp)
actual, *_ = groupby_reduce(
array, by1, by2, axis=(0, 1), func="count", expected_groups=expected_groups
)
@@ -1059,6 +1061,7 @@ def test_validate_expected_groups_not_none_dask() -> None:
def test_factorize_reindex_sorting_strings():
+ # pd.factorize seems to return intp so int32 on 32bit arch
kwargs = dict(
by=(np.array(["El-Nino", "La-Nina", "boo", "Neutral"]),),
axis=-1,
@@ -1066,19 +1069,20 @@ def test_factorize_reindex_sorting_strings():
)
expected = factorize_(**kwargs, reindex=True, sort=True)[0]
- assert_equal(expected, np.array([0, 1, 4, 2], dtype=np.int64))
+ assert_equal(expected, np.array([0, 1, 4, 2], dtype=np.intp))
expected = factorize_(**kwargs, reindex=True, sort=False)[0]
- assert_equal(expected, np.array([0, 3, 4, 1], dtype=np.int64))
+ assert_equal(expected, np.array([0, 3, 4, 1], dtype=np.intp))
expected = factorize_(**kwargs, reindex=False, sort=False)[0]
- assert_equal(expected, np.array([0, 1, 2, 3], dtype=np.int64))
+ assert_equal(expected, np.array([0, 1, 2, 3], dtype=np.intp))
expected = factorize_(**kwargs, reindex=False, sort=True)[0]
- assert_equal(expected, np.array([0, 1, 3, 2], dtype=np.int64))
+ assert_equal(expected, np.array([0, 1, 3, 2], dtype=np.intp))
def test_factorize_reindex_sorting_ints():
+ # pd.factorize seems to return intp so int32 on 32bit arch
kwargs = dict(
by=(np.array([-10, 1, 10, 2, 3, 5]),),
axis=-1,
@@ -1086,18 +1090,18 @@ def test_factorize_reindex_sorting_ints():
)
expected = factorize_(**kwargs, reindex=True, sort=True)[0]
- assert_equal(expected, np.array([6, 1, 6, 2, 3, 5], dtype=np.int64))
+ assert_equal(expected, np.array([6, 1, 6, 2, 3, 5], dtype=np.intp))
expected = factorize_(**kwargs, reindex=True, sort=False)[0]
- assert_equal(expected, np.array([6, 1, 6, 2, 3, 5], dtype=np.int64))
+ assert_equal(expected, np.array([6, 1, 6, 2, 3, 5], dtype=np.intp))
kwargs["expected_groups"] = (np.arange(5, -1, -1),)
expected = factorize_(**kwargs, reindex=True, sort=True)[0]
- assert_equal(expected, np.array([6, 1, 6, 2, 3, 5], dtype=np.int64))
+ assert_equal(expected, np.array([6, 1, 6, 2, 3, 5], dtype=np.intp))
expected = factorize_(**kwargs, reindex=True, sort=False)[0]
- assert_equal(expected, np.array([6, 4, 6, 3, 2, 0], dtype=np.int64))
+ assert_equal(expected, np.array([6, 4, 6, 3, 2, 0], dtype=np.intp))
@requires_dask
View it on GitLab: https://salsa.debian.org/debian-gis-team/flox/-/commit/314ead24d41259d48e73b9163951f2d690a0ccb5
--
View it on GitLab: https://salsa.debian.org/debian-gis-team/flox/-/commit/314ead24d41259d48e73b9163951f2d690a0ccb5
You're receiving this email because of your account on salsa.debian.org.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://alioth-lists.debian.net/pipermail/pkg-grass-devel/attachments/20230118/d05119c3/attachment-0001.htm>
More information about the Pkg-grass-devel
mailing list