Simplify function

scintillation/Calibrationlib.py

@@ -241,14 +241,13 @@ def move_mean_sq(a, window_arr, out):
 
 
 def window_stdv_mean_numba(arr: numpy.ndarray, window):
-    nfreq = numpy.int32(arr.shape[1])
-    std_arr = numpy.empty_like(arr)
-    mean_arr = numpy.empty_like(arr)
+    arr = numpy.swapaxes(arr, 0, 1)
     start_index = window // 2
-    for s in range(nfreq):
-        view: numpy.ndarray = arr[:, s]
-        mean_arr[:, s] = move_mean(view, window)
-        std_arr[:, s] = numpy.sqrt(numpy.abs(move_mean_sq(view, window) - numpy.power(mean_arr[:, s], 2)))
+    mean_arr = move_mean(arr, window)
+    std_arr = numpy.sqrt(numpy.abs(move_mean_sq(arr, window) - numpy.power(mean_arr, 2)))
+
+    mean_arr = np.swapaxes(mean_arr, 0, 1)
+    std_arr = np.swapaxes(std_arr, 0, 1)
 
     return std_arr[start_index: -start_index + 1], mean_arr[start_index: -start_index + 1]
 
@@ -268,8 +267,8 @@ def getS4Numba(data, window_size, skip_sample_time=65, axis=1, has_nan=False):
     # make sure axis to sample = 0-th axis:
     tmpdata = np.swapaxes(data, 0, axis)
     stddata, avgdata = window_stdv_mean_numba(tmpdata, window_size)
-    # S4 = stddata[::skip_sample_time] / avgdata[::skip_sample_time]
-    # return np.swapaxes(S4, 0, axis)
+    S4 = stddata[::skip_sample_time] / avgdata[::skip_sample_time]
+    return np.swapaxes(S4, 0, axis)
 
 
 def getS4Naive(data, window_size, skip_sample_time=65, axis=1, has_nan=False):