Merge branch 'coverage' into 'dev'

ci: implement and visualise test coverage

See merge request aliby!8

.gitlab-ci.yml

@@ -38,7 +38,14 @@ Local Tests:
   stage: tests
   script:
     # - poetry install -vv
-    - poetry run pytest ./tests --ignore ./tests/aliby/network --ignore ./tests/aliby/pipeline
+    - poetry run coverage run -m --branch pytest ./tests --ignore ./tests/aliby/network --ignore ./tests/aliby/pipeline
+    - poetry run coverage report -m
+    - poetry run coverage xml
+  coverage: '/(?i)total.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/'
+# Legacy because we use GitLab 14.2.6
+  artifacts:
+    reports:
+      cobertura: coverage.xml
 
 Network Tools Tests:
   stage: tests

README.md

@@ -4,6 +4,7 @@
 [![PyPI version](https://badge.fury.io/py/aliby.svg)](https://badge.fury.io/py/aliby)
 [![pipeline](https://git.ecdf.ed.ac.uk/swain-lab/aliby/aliby/badges/master/pipeline.svg?key_text=master)](https://git.ecdf.ed.ac.uk/swain-lab/aliby/aliby/-/pipelines)
 [![dev pipeline](https://git.ecdf.ed.ac.uk/swain-lab/aliby/aliby/badges/dev/pipeline.svg?key_text=dev)](https://git.ecdf.ed.ac.uk/swain-lab/aliby/aliby/-/commits/dev)
+[![coverage](https://git.ecdf.ed.ac.uk/swain-lab/aliby/aliby/badges/dev/coverage.svg)](https://git.ecdf.ed.ac.uk/swain-lab/aliby/aliby/-/commits/dev)
 
 End-to-end processing of cell microscopy time-lapses. ALIBY automates segmentation, tracking, lineage predictions, post-processing and report production. It leverages the existing Python ecosystem and open-source scientific software available to produce seamless and standardised pipelines.
 

tests/postprocessor/test_interpolate.py

+#!/usr/bin/env python3
+import numpy as np
+import pandas as pd
+from postprocessor.core.processes.interpolate import (
+    interpolate,
+    interpolateParameters,
+)
+
+
+def dummy_signal_array(n_cells, n_tps):
+    """Creates dummy signal array, i.e. increasing gradient"""
+    signal = np.array([np.linspace(1, 2, n_tps) for _ in range(n_cells)])
+    return signal
+
+
+def test_dummy_signal_array():
+    ds = dummy_signal_array(5, 10)
+    # Check dimensions
+    assert ds.shape[0] == 5
+    assert ds.shape[1] == 10
+
+
+def randomly_add_na(input_array, num_of_na):
+    """Randomly replaces a 2d numpy array with NaNs, number of NaNs specified"""
+    input_array.ravel()[
+        np.random.choice(input_array.size, num_of_na, replace=False)
+    ] = np.nan
+    return input_array
+
+
+def test_interpolate():
+    dummy_array = dummy_signal_array(5, 10)
+    # Poke holes so interpolate can fill
+    holey_array = randomly_add_na(dummy_array, 15)
+
+    dummy_signal = pd.DataFrame(dummy_array)
+    holey_signal = pd.DataFrame(holey_array)
+
+    interpolate_runner = interpolate(interpolateParameters.default())
+    interpolated_signal = interpolate_runner.run(holey_signal)
+
+    subtr = interpolated_signal - dummy_signal
+    # Check that interpolated values are the ones that exist in the dummy
+    assert np.nansum(subtr.to_numpy()) == 0
+    # TODO: Check that if there are NaNs remaining after interpolation, they
+    # are at the ends