code/useful_functions.py

@@ -154,59 +154,6 @@ def calculate_integral(freq, power, point, delta = 2.5):
     local_mean = np.mean([l_integral, r_integral])
     return integral, local_mean, p_power
 
-def contrast_sorting(sams, con_1 = 20, con_2 = 10, con_3 = 5, stim_count = 3, stim_dur = 2):
-    '''
-    sorts the sams into three contrasts
-
-    Parameters
-    ----------
-    sams : ReproRuns
-        The sams to be sorted.
-    con_1 : int, optional
-        the first contrast. The default is 20.
-    con_2 : int, optional
-        the second contrast. The default is 10.
-    con_3 : int, optional
-        the third contrast. The default is 5.
-    stim_count : int, optional
-        the amount of stimuli per sam in a good sam. The default is 3.
-    stim_dur : int, optional
-        The stimulus duration. The default is 2.
-
-    Returns
-    -------
-    contrast_sams : dictionary
-        A dictionary containing all sams sorted to the contrasts.
-
-    '''
-    # dictionary for the contrasts
-    contrast_sams = {con_1 : [],
-                     con_2 : [],
-                     con_3 : []}
-    # loop over all sams
-    for sam in sams:
-        # get the contrast
-        avg_dur, contrast, _, _, _, _, _ = sam_data(sam)
-        # check for valid trails
-        if np.isnan(contrast):
-            continue
-        elif sam.stimulus_count < stim_count: #aborted trials
-            continue
-        elif avg_dur < (stim_dur * 0.8):
-            continue
-        else:
-            contrast = int(contrast) # get integer of contrast
-            # sort them accordingly
-            if contrast == con_1:
-                contrast_sams[con_1].append(sam)
-            elif contrast == con_2:
-                contrast_sams[con_2].append(sam)
-            elif contrast == con_3:
-                contrast_sams[con_3].append(sam)
-            else:
-                continue
-    return contrast_sams
-
 def extract_stim_data(stimulus):
     '''
     extracts all necessary metadata for each stimulus