Split up reframe tests and pytest unit tests

reframe_tests/utilities/data.py

@@ -0,0 +1,53 @@
+import logging
+
+import h5py
+import numpy as np
+
+from gprMax.utilities.logging import logging_config
+
+logger = logging.getLogger(__name__)
+logging_config(name=__name__)
+
+
+FIELD_COMPONENTS_BASE_PATH = "/rxs/rx1/"
+
+
+def get_data_from_h5_file(h5_filepath):
+    with h5py.File(h5_filepath, "r") as h5_file:
+        # Get available field output component names and datatype
+        field_components = list(h5_file[FIELD_COMPONENTS_BASE_PATH].keys())
+        dtype = h5_file[FIELD_COMPONENTS_BASE_PATH + field_components[0]].dtype
+        shape = h5_file[FIELD_COMPONENTS_BASE_PATH + str(field_components[0])].shape
+
+        # Arrays for storing field data
+        if len(shape) == 1:
+            data = np.zeros((h5_file.attrs["Iterations"], len(field_components)), dtype=dtype)
+        else:  # Merged B-scan data
+            data = np.zeros((h5_file.attrs["Iterations"], len(field_components), shape[1]), dtype=dtype)
+        for index, field_component in enumerate(field_components):
+            data[:, index] = h5_file[FIELD_COMPONENTS_BASE_PATH + str(field_component)]
+            if np.any(np.isnan(data[:, index])):
+                logger.exception("Data contains NaNs")
+                raise ValueError
+
+        max_time = (h5_file.attrs["Iterations"] - 1) * h5_file.attrs["dt"] / 1e-9
+        time = np.linspace(0, max_time, num=h5_file.attrs["Iterations"])
+
+    return time, data
+
+
+def calculate_diffs(test_data, ref_data):
+    diffs = np.zeros(test_data.shape, dtype=np.float64)
+    for i in range(test_data.shape[1]):
+        maxi = np.amax(np.abs(ref_data[:, i]))
+        diffs[:, i] = np.divide(
+            np.abs(ref_data[:, i] - test_data[:, i]), maxi, out=np.zeros_like(ref_data[:, i]), where=maxi != 0
+        )  # Replace any division by zero with zero
+
+        # Calculate power (ignore warning from taking a log of any zero values)
+        with np.errstate(divide="ignore"):
+            diffs[:, i] = 20 * np.log10(diffs[:, i])
+        # Replace any NaNs or Infs from zero division
+        diffs[:, i][np.invert(np.isfinite(diffs[:, i]))] = 0
+
+    return diffs