Merge pull request #172 from pfizer-opensource/development

Version 0.16.9

meson.build

@@ -1,7 +1,7 @@
 project(
     'scikit-digital-health',
     'c',
-    version: '0.16.8',
+    version: '0.16.9',
     license: 'MIT',
     meson_version: '>=1.1',
 )

src/skdh/io/empatica.py

@@ -50,7 +50,7 @@ def get_accel(self, raw_accel_dict, results_dict, key):
             Name for the results in `results_dict`.
         """
         # sampling frequency
-        fs = round(raw_accel_dict["samplingFrequency"], decimals=3)
+        fs = raw_accel_dict["samplingFrequency"]
 
         # timestamp start
         ts_start = raw_accel_dict["timestampStart"] / 1e6  # convert to seconds

src/skdh/utility/internal.py

@@ -399,18 +399,21 @@ def fill_data_gaps(time, fs, fill_info, **kwargs):
     >>> print(data_rs.keys())
     dict_keys(['accel', 'temp'])
     """
-    # round-about way, but need to prevent start>>>>>>>>>step
-    time_rs = arange(0, (time[-1] - time[0]) + 0.5 / fs, 1 / fs) + time[0]
-
     # get the first location of gaps in the data - add 1 so that the index reflects
     # the first value AFTER the gap
     gaps = nonzero(diff(time) > (1.5 / fs))[0] + 1
+    if gaps.size == 0:
+        return time, kwargs
+
     # create sequences of data with no gaps
     seqs = zeros((gaps.size + 1, 2), dtype=int_)
     seqs[1:, 0] = gaps
     seqs[:-1, 1] = gaps
     seqs[-1, 1] = time.size
 
+    # round-about way, but need to prevent start>>>>>>>>>step
+    time_rs = arange(0, (time[-1] - time[0]) + 0.5 / fs, 1 / fs) + time[0]
+
     # iterate over the datastreams
     ret = {}
     for name, data in kwargs.items():