Merge pull request #7 from mluacnunes/main

Minor changes

.gitignore

@@ -118,3 +118,7 @@ dmypy.json
 
 *.pdf
 
+# documentation
+docs/_twed.py
+docs/_lcss.py
+docs/_dtw.py

tests/test_segmentation.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from tssearch import load_ecg_example, get_distance_dict, time_series_segmentation, get_distances_by_type
+from tssearch import load_ecg_example, get_distance_dict, time_series_segmentation
 
 
 def segmentation():

tssearch/distances/distances.json

@@ -7,7 +7,7 @@
       "parameters": {
         "nu": 1e-3,
         "lmbda": 0,
-        "degree": 2,
+        "p": 2,
         "time": "true"
       },
       "use": "yes"

tssearch/distances/elastic_distances.py

@@ -229,7 +229,7 @@ def twed(x, y, tx, ty, nu=0.001, lmbda=1.0, p=2, report="distance"):
             # Keep data points in both time series
             C[2] = (
                 ac[i - 1, j - 1]
-                + dlp(query[i], sequence[j], degree)
+                + dlp(query[i], sequence[j], p)
                 + dlp(query[i - 1], sequence[j - 1], p)
                 + nu * (abs(tq[i] - ts[j]) + abs(tq[i - 1] - ts[j - 1]))
             )

tssearch/utils/visualisation.py

@@ -66,17 +66,6 @@ def plot_alignment(ref_signal, estimated_signal, path, **kwargs):
         for i in range(len(path[0]))[::step]
     ]
 
-    ref_pks = ni.peakdelta(copy_ref, 0.5)
-    est_pks = ni.peakdelta(estimated_signal, 0.5)
-
-    if len(ref_pks[0]) > 0 and len(ref_pks[0]) == len(est_pks[0]):
-        for i in range(len(ref_pks[0])):
-            plt.plot([ref_pks[0][i, 0], est_pks[0][i, 0]], [ref_pks[0][i, 1], est_pks[0][i, 1]], "red")
-
-    if len(ref_pks[1]) > 0 and len(ref_pks[1]) == len(est_pks[1]):
-        for i in range(len(ref_pks[1])):
-            plt.plot([ref_pks[1][i, 0], est_pks[1][i, 0]], [ref_pks[1][i, 1], est_pks[1][i, 1]], "red")
-
 
 def plot_costmatrix(matrix, path):
     """