denoising/__main__.py

@@ -4,11 +4,8 @@ from pathlib import Path
 import sys
 from scipy.signal import savgol_filter
 import copy
-import plotly.graph_objects as go
-import json
 
 from scipy.interpolate import PchipInterpolator
-
 from denoising import (dataset, outliers)
 
 
@@ -76,7 +73,6 @@ def main():
     # OK !!!
     tE = outliers.get_wavelet_spikes(signal, sfr, wid, L, wname)
 
-
     scor4 = copy.deepcopy(scor3)
 
     for k in range(len(tE)):
@@ -85,12 +81,6 @@ def main():
 
     # dataset.check_values(scor4)
 
-    # Interpolate reference at sensor time
-    reference_interpolator = PchipInterpolator(x=reference_data.values[:, 0], y=reference_data.values[:, 5])
-    refint = reference_interpolator(sensor_data.values[:, 0])
-
-    # dataset.check_values(refint)
-
     # NOTE: Call this scor5 to avoid overwritting scor4 for control porposes
     scor5 = savgol_filter(x=scor4,
                           window_length=5,  # window size used for filtering
@@ -98,6 +88,11 @@ def main():
 
     # dataset.check_values(scor5)
 
+    # Interpolate reference at sensor time
+    reference_interpolator = PchipInterpolator(x=reference_data.values[:, 0], y=reference_data.values[:, 5])
+    refint = reference_interpolator(sensor_data.values[:, 0])
+
+    # dataset.check_values(refint)
 
     corr_matrix = np.corrcoef(refint, scor5)
     corr = corr_matrix[0, 1]

denoising/outliers.py

@@ -81,6 +81,7 @@ def get_wavelet_spikes(signal: np.array, sfr, wid: np.array, L: float,
     is_spike[0, [0, -1]] = False
     ind_ones = np.argmax(is_spike[0, :] == True)
     ind_ones_is_empty = not ind_ones.any()
+    tE = np.empty([])
     if not ind_ones_is_empty:
         # there will be 1 followed by -1 for each spike
         temp = np.diff(is_spike)
@@ -173,7 +174,8 @@ def get_spike_indexes(L, c, nt, nw, option, w):
         Index = np.logical_or(io, Index).astype(int)
         io = Index
 
-    return Index
+    return io
+    # return Index