From 873be0a69814a54e22ffd9e5aaea14ca68e4f912 Mon Sep 17 00:00:00 2001
From: mbergmann <maximilian.bergmann@student.uni-tuebingen.de>
Date: Thu, 24 Oct 2024 10:59:29 +0200
Subject: [PATCH] [useful_functions.py] added contrast_sorting function
---
code/useful_functions.py | 53 ++++++++++++++++++++++++++++++++++++++++
1 file changed, 53 insertions(+)
diff --git a/code/useful_functions.py b/code/useful_functions.py
index 9f56b57..72a58f6 100644
--- a/code/useful_functions.py
+++ b/code/useful_functions.py
@@ -154,6 +154,59 @@ 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