Add reference data to output plot and plot diffs

reframe_tests/utilities/data.py

@@ -1,7 +1,9 @@
 import logging
+from typing import Tuple
 
 import h5py
 import numpy as np
+import numpy.typing as npt
 
 from gprMax.utilities.logging import logging_config
 
@@ -12,7 +14,7 @@ logging_config(name=__name__)
 FIELD_COMPONENTS_BASE_PATH = "/rxs/rx1/"
 
 
-def get_data_from_h5_file(h5_filepath):
+def get_data_from_h5_file(h5_filepath: str) -> Tuple[npt.NDArray, npt.NDArray]:
     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())
@@ -23,7 +25,9 @@ def get_data_from_h5_file(h5_filepath):
         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)
+            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])):
@@ -36,18 +40,21 @@ def get_data_from_h5_file(h5_filepath):
     return time, data
 
 
-def calculate_diffs(test_data, ref_data):
+def calculate_diffs(test_data: npt.NDArray, ref_data: npt.NDArray) -> npt.NDArray:
     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
+            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
+        # diffs[:, i][np.invert(np.isfinite(diffs[:, i]))] = 0
 
     return diffs