tests/postprocessor/test_crosscorr.py

+import numpy as np
+import pandas as pd
+import pytest
+
+from postprocessor.core.multisignal.crosscorr import (
+    crosscorr,
+    crosscorrParameters,
+)
+
+
+def generate_sinusoids_df(
+    time_axis,
+    num_replicates,
+):
+    t = time_axis
+    ts = np.tile(t, num_replicates).reshape((num_replicates, len(t)))
+    s = 3 * np.sin(
+        2 * np.pi * ts + 2 * np.pi * np.random.rand(num_replicates, 1)
+    )
+    s_df = pd.DataFrame(s)
+    return s_df
+
+
+@pytest.mark.parametrize("time_axis", [np.linspace(0, 4, 200)])
+@pytest.mark.parametrize("num_replicates", [333])
+def test_crosscorr(
+    time_axis,
+    num_replicates,
+):
+    """Tests croscorr.
+
+    Tests whether a crosscorr runner can be initialised with default
+    parameters and runs without errors.
+    """
+    dummy_signal1 = generate_sinusoids_df(time_axis, num_replicates)
+    dummy_signal2 = generate_sinusoids_df(time_axis, num_replicates)
+    crosscorr_runner = crosscorr(crosscorrParameters.default())
+    _ = crosscorr_runner.run(dummy_signal1, dummy_signal2)
+
+
+@pytest.mark.parametrize("time_axis", [np.linspace(0, 4, 200)])
+@pytest.mark.parametrize("num_replicates", [333])
+def test_autocorr(
+    time_axis,
+    num_replicates,
+):
+    """Tests croscorr.
+
+    Tests whether a crosscorr runner can be initialised with default
+    parameters and runs without errors, when performing autocorrelation.
+    """
+    dummy_signal1 = generate_sinusoids_df(time_axis, num_replicates)
+    crosscorr_runner = crosscorr(crosscorrParameters.default())
+    _ = crosscorr_runner.run(dummy_signal1, dummy_signal1)