Add code coverage

.github/workflows/pytest.yaml

@@ -43,4 +43,8 @@ jobs:
         pip install --timeout=300 ${{ matrix.env }}
     - name: Test with pytest ${{ matrix.env }}
       run: |
-        pytest
+        python -m pytest --cov=./ --cov-report=xml
+    - name: Upload coverage reports to Codecov
+      uses: codecov/codecov-action@v4
+      env:
+        CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

README.md

@@ -1,11 +1,14 @@
 <img src="./docs/sphinx/source/_images/logo_horizontal_highres.png" width="500" alt="RdTools logo"/>
 
-Master branch: 
-[![Build Status](https://github.com/NREL/rdtools/workflows/pytest/badge.svg?branch=master)](https://github.com/NREL/rdtools/actions?query=branch%3Amaster)  
+Master branch:
+[![Build Status](https://github.com/NREL/rdtools/workflows/pytest/badge.svg?branch=master)](https://github.com/NREL/rdtools/actions?query=branch%3Amaster)
 
-Development branch: 
+Development branch:
 [![Build Status](https://github.com/NREL/rdtools/workflows/pytest/badge.svg?branch=development)](https://github.com/NREL/rdtools/actions?query=branch%3Adevelopment)
 
+Code coverage:
+[![codecov](https://codecov.io/gh/NREL/rdtools/graph/badge.svg?token=K2HDjFkBws)](https://codecov.io/gh/NREL/rdtools)
+
 RdTools is an open-source library to support reproducible technical analysis of
 time series data from photovoltaic energy systems. The library aims to provide
 best practice analysis routines along with the building blocks for users to
@@ -57,11 +60,11 @@ appropriate:
    Detection Techniques in AC Power Time Series," 2021 IEEE 48th Photovoltaic
    Specialists Conference (PVSC), pp. 1638-1643 2021, DOI: [10.1109/PVSC43889.2021.9518733](https://doi.org/10.1109/PVSC43889.2021.9518733).
 
-  
+
 ## References
 The clear sky temperature calculation, `clearsky_temperature.get_clearsky_tamb()`, uses data
-from images created by Jesse Allen, NASA’s Earth Observatory using data courtesy of the MODIS Land Group.  
-https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTD_CLIM_M  
+from images created by Jesse Allen, NASA’s Earth Observatory using data courtesy of the MODIS Land Group.
+https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTD_CLIM_M
 https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTN_CLIM_M
 
 Other useful references which may also be consulted for degradation rate methodology include:

rdtools/test/interpolate_test.py

@@ -2,6 +2,7 @@
 import numpy as np
 from rdtools import interpolate
 import pytest
+import warnings
 
 
 @pytest.fixture
@@ -132,9 +133,9 @@ def test_interpolate_warning(test_df, df_target_index, df_expected_result):
         interpolate(test_df, df_target_index, pd.to_timedelta('15 minutes'),
                     warning_threshold=0.1)
 
-    with pytest.warns(None) as record:
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
         interpolate(test_df, df_target_index, pd.to_timedelta('15 minutes'),
                     warning_threshold=0.5)
-        if 'Fraction of excluded data' in ';'.join([str(x.message) for x in record.list]):
-            pytest.fail("normalize.interpolate raised a warning about "
-                        "excluded data even though the threshold was high")
+        warnings.filterwarnings("error", message='Fraction of excluded data')
+        # if this test fails, it means a warning was raised that was not expected

setup.py

@@ -33,6 +33,7 @@
 
 TESTS_REQUIRE = [
     'pytest >= 3.6.3',
+    'pytest-cov',
     'coverage',
     'flake8',
     'nbval==0.9.6',  # https://github.com/computationalmodelling/nbval/issues/194