Merge pull request #23 from martinvonk/dev

Update main to v0.3.2

.github/workflows/tests.yml

@@ -39,6 +39,10 @@ jobs:
             python: "3.9"
             os: ubuntu-latest
             toxenv: lint
+          - name: Codacy Coverage Report
+            python: "3.9"
+            os: ubuntu-latest
+            toxenv: coverage
 
     name: ${{ matrix.name }}
     env:
@@ -55,16 +59,27 @@ jobs:
       PYTEST_ADDOPTS: "--color=yes"
     steps:
       - uses: actions/checkout@v3
+
       - name: Set up Python ${{ matrix.python }}
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python }}
           check-latest: true
+
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
           pip install tox
+
       - name: Setup tox environment
         run: tox -e ${{ matrix.toxenv }} --notest
+
       - name: Test
         run: tox -e ${{ matrix.toxenv }} --skip-pkg-install
+
+      - name: Run codacy-coverage-reporter
+        if: ${{ matrix.toxenv == 'coverage' && github.repository == 'martinvonk/spei' && success() }}
+        uses: codacy/codacy-coverage-reporter-action@master
+        with:
+          project-token: ${{ secrets.CODACY_PROJECT_TOKEN }}
+          coverage-reports: coverage.xml

.gitignore

@@ -11,4 +11,5 @@
 /.mypy_cache
 /.tox
 /.pytest_cache
-/htmlcov
+/htmlcov
+coverage.xml

README.md

@@ -3,6 +3,8 @@
 ![PyPI](https://img.shields.io/pypi/v/spei?style=flat-square) ![PyPi Supported Python Versions](https://img.shields.io/pypi/pyversions/spei?style=flat-square) ![Code Size](https://img.shields.io/github/languages/code-size/martinvonk/spei?style=flat-square) ![PyPi Downloads](https://img.shields.io/pypi/dm/spei?style=flat-square) ![License](https://img.shields.io/pypi/l/spei?style=flat-square)
 
 ![Tests](https://img.shields.io/github/actions/workflow/status/martinvonk/spei/tests.yml?style=flat-square)
+![CodacyGrade](https://img.shields.io/codacy/grade/908b566912314666b84e1add22ea7d66?style=flat-square)
+![CodacyCoverage](https://img.shields.io/codacy/coverage/908b566912314666b84e1add22ea7d66?style=flat-square)
 ![MyPy](https://img.shields.io/badge/%20type_checker-mypy-%231674b1?style=flat-square)
 ![Black](https://img.shields.io/badge/code%20style-black-black?style=flat-square)
 
@@ -32,11 +34,11 @@ To get the development version download the GitHub code to your computer. Use cd
 `pip install -e .`
 
 ## Literature
-  1.  B. Lloyd-Hughes and M.A. Saunders (2002) - A Drought Climatology for Europe. DOI: 10.1002/joc.846
-  2.  S.M. Vicente-Serrano, S. Beguería and J.I. López-Moreno (2010) - A Multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index. DOI: 10.1175/2009JCLI2909.1
-  3.  J.P.Bloomfield and B.P. Marchant, B. P. (2013) - Analysis of groundwater drought building on the standardised precipitation index approach. DOI: 10.5194/hess-17-4769-2013
-  4.  A. Babre, A. Kalvāns, Z. Avotniece, I. Retiķe, J. Bikše, K.P.M. Jemeljanova, A. Zelenkevičs and A. Dēliņa (2022) - The use of predefined drought indices for the assessment of groundwater drought episodes in the Baltic States over the period 1989–2018. DOI: 10.1016/j.ejrh.2022.101049
-  5.  E. Tijdeman, K. Stahl and L.M. Tallaksen (2020) - Drought characteristics derived based on the Standardized Streamflow Index: A large sample comparison for parametric and nonparametric methods. DOI: 10.1029/2019WR026315
+1.  B. Lloyd-Hughes and M.A. Saunders (2002) - A Drought Climatology for Europe. DOI: 10.1002/joc.846
+2.  S.M. Vicente-Serrano, S. Beguería and J.I. López-Moreno (2010) - A Multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index. DOI: 10.1175/2009JCLI2909.1
+3.  J.P.Bloomfield and B.P. Marchant, B. P. (2013) - Analysis of groundwater drought building on the standardised precipitation index approach. DOI: 10.5194/hess-17-4769-2013
+4.  A. Babre, A. Kalvāns, Z. Avotniece, I. Retiķe, J. Bikše, K.P.M. Jemeljanova, A. Zelenkevičs and A. Dēliņa (2022) - The use of predefined drought indices for the assessment of groundwater drought episodes in the Baltic States over the period 1989–2018. DOI: 10.1016/j.ejrh.2022.101049
+5.  E. Tijdeman, K. Stahl and L.M. Tallaksen (2020) - Drought characteristics derived based on the Standardized Streamflow Index: A large sample comparison for parametric and nonparametric methods. DOI: 10.1029/2019WR026315
 
 Note that the method for calculating the drought indices does not come from these articles and SciPy is used for deriving the distribution. However the literature is helpful as a reference to understand the context and application of drought indices.
 

pyproject.toml

@@ -32,7 +32,8 @@ linting = ["flake8"]
 formatting = ["black[jupyter]", "isort"]
 typing = ["mypy", "pandas-stubs"]
 pytesting = ["pytest>=7", "pytest-cov", "pytest-sugar"]
-dev = ["spei[linting,formatting,typing,pytesting]"]
+coveraging = ["coverage"]
+dev = ["spei[linting,formatting,typing,pytesting,coveraging]"]
 
 [tool.setuptools.dynamic]
 version = { attr = "spei._version.__version__" }
@@ -86,4 +87,12 @@ legacy_tox_ini = """
     extras = linting
     commands =
             flake8 src --max-line-length=88
+
+    [testenv:coverage]
+    description = get coverage report xml
+    basepython = 3.9
+    extras = coveraging,pytesting
+    commands =
+            coverage run -m pytest tests
+            coverage xml
 """

src/spei/_typing.py

@@ -1,10 +1,12 @@
 # flake8: noqa
 from typing import Any, Union
 
-from matplotlib.axes import Axes
+from matplotlib.axes import Axes as mplAxes
 from numpy import float64
 from numpy.typing import NDArray
 from scipy.stats._continuous_distns import rv_continuous
 
 ContinuousDist = Union[Any, rv_continuous]
-Axes = Union[Any, Axes]
+Axes = Union[Any, mplAxes]
+NDArrayAxes = NDArray[Axes]
+NDArrayFloat = NDArray[float64]

src/spei/_version.py

@@ -1 +1 @@
-__version__ = "0.3.1"
+__version__ = "0.3.2"

src/spei/plot.py

@@ -7,7 +7,7 @@
 from pandas import Series
 from scipy.stats import gaussian_kde
 
-from ._typing import Axes, ContinuousDist, NDArray, float64
+from ._typing import Axes, ContinuousDist, NDArrayAxes
 from .utils import dist_test, validate_index, validate_series
 
 
@@ -40,9 +40,9 @@ def si(
 
     nmin = -bound
     nmax = bound
-    droughts = si.to_numpy(dtype=float64, copy=True)
+    droughts = si.to_numpy(dtype=float, copy=True)
     droughts[droughts > 0] = 0
-    nodroughts = si.to_numpy(dtype=float64, copy=True)
+    nodroughts = si.to_numpy(dtype=float, copy=True)
     nodroughts[nodroughts < 0] = 0
 
     x, y = meshgrid(si.index, linspace(nmin, nmax, 100))
@@ -64,7 +64,7 @@ def dist(
     cmap: Optional[str] = None,
     figsize: tuple = (8, 10),
     legend: bool = True,
-) -> NDArray[Axes]:
+) -> NDArrayAxes:
     """Plot the (cumulative) histogram and scipy fitted distribution
     for the time series on a monthly basis.
 

src/spei/si.py

@@ -4,7 +4,7 @@
 from pandas import DatetimeIndex, Series
 from scipy.stats import fisk, gamma, genextreme, norm
 
-from ._typing import ContinuousDist, NDArray, float64
+from ._typing import ContinuousDist, NDArrayFloat
 from .utils import validate_index, validate_series
 
 
@@ -42,7 +42,7 @@ def compute_si_ppf(
         series = validate_series(series)
         index = validate_index(series.index)
 
-    si = Series(index=index, dtype=float64)
+    si = Series(index=index, dtype=float)
     for month in range(1, 13):
         data = series[index.month == month].sort_values()
         if not sgi:
@@ -58,16 +58,16 @@ def compute_si_ppf(
 
 
 def compute_cdf(
-    data: Union[Series, NDArray[float64]], dist: ContinuousDist
-) -> NDArray[float64]:
+    data: Union[Series, NDArrayFloat], dist: ContinuousDist
+) -> NDArrayFloat:
     *pars, loc, scale = dist.fit(data, scale=std(data))
     cdf = dist.cdf(data, pars, loc=loc, scale=scale)
     return cdf
 
 
 def compute_cdf_probzero(
-    data: Union[Series, NDArray[float64]], dist: ContinuousDist
-) -> NDArray[float64]:
+    data: Union[Series, NDArrayFloat], dist: ContinuousDist
+) -> NDArrayFloat:
     p0 = (data == 0.0).sum() / len(data)
     *pars, loc, scale = dist.fit(data[data != 0.0], scale=std(data))
     cdf_sub = dist.cdf(data, pars, loc=loc, scale=scale)
@@ -76,7 +76,7 @@ def compute_cdf_probzero(
     return cdf
 
 
-def compute_cdf_nsf(data: Union[Series, NDArray[float64]]) -> NDArray[float64]:
+def compute_cdf_nsf(data: Union[Series, NDArrayFloat]) -> NDArrayFloat:
     """Normal Scores Transform"""
     n = data.size
     cdf = linspace(1 / (2 * n), 1 - 1 / (2 * n), n)

src/spei/utils.py

@@ -15,7 +15,7 @@
     pearson3,
 )
 
-from ._typing import ContinuousDist, NDArray, float64
+from ._typing import ContinuousDist, NDArrayFloat
 
 
 def validate_series(series: Series) -> Series:
@@ -53,7 +53,7 @@ def validate_index(index: Index) -> DatetimeIndex:
 
 
 def dist_test(
-    series: Union[Series, NDArray[float64]],
+    series: Union[Series, NDArrayFloat],
     dist: ContinuousDist,
     N: int = 100,
     alpha: float = 0.05,
@@ -66,7 +66,7 @@ def dist_test(
 
     Parameters
     ----------
-    data : Union[Series, NDArray[float64]]
+    data : Union[Series, NDArray[float]]
         pandas Series or numpy array of floats of observations of random
         variables
     dist: scipy.stats.rv_continuous
@@ -100,7 +100,7 @@ def dist_test(
 
 
 def dists_test(
-    series: Union[Series, NDArray[float64]],
+    series: Union[Series, NDArrayFloat],
     distributions: Optional[List[ContinuousDist]] = None,
     N: int = 100,
     alpha: float = 0.05,
@@ -113,7 +113,7 @@ def dists_test(
 
     Parameters
     ----------
-    series : Union[Series, NDArray[float64]]
+    series : Union[Series, NDArray[float]]
         pandas Series with observations of random variables
     distributions : list of scipy.stats.rv_continuous, optional
         A list of (can be) any continuous distribution from the scipy.stats

tests/test_dist_utils.py

@@ -1,5 +1,3 @@
-from numpy import random
-
 from spei.utils import dists_test
 
 from .fixtures import head

tests/test_plots.py

@@ -1,5 +1,4 @@
 import matplotlib as mpl
-import pytest
 from pandas import Series
 from scipy.stats import pearson3