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Storing raw KP values in bins
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@bazilinskyy
bazilinskyy committed Jan 2, 2025
1 parent e5ef2c1 commit 8b652bf
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Showing 3 changed files with 40 additions and 25 deletions.
2 changes: 1 addition & 1 deletion trust/analysis/analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2174,7 +2174,7 @@ def plot_kp_slider_videos(self, df, y: list, y_legend=None, x=None, events=None,
paired=signals['paired'])
# add to the plot
signal_length = len(signals['signal_1']) # get the length of 'signal_1'
significance = [random.randint(0, 1) for _ in range(signal_length)] # generate random list
# significance = [random.randint(0, 1) for _ in range(signal_length)] # generate random list
# plot stars based on random lists
marker_x = [] # x-coordinates for stars
marker_y = [] # y-coordinates for stars
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39 changes: 27 additions & 12 deletions trust/analysis/heroku.py
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Expand Up @@ -562,22 +562,28 @@ def process_kp(self, filter_length=True):
logger.info('Processing keypress data with res={} ms.', self.res)
# array to store all binned rt data
mapping_rt = []
# array to store all raw binned rt data
# array to store all raw binned rt data per pp
mapping_rt_raw = []
# counter of videos filtered because of length
counter_filtered = 0
# loop through all stimuli
for num in tqdm(range(self.num_stimuli)):
video_kp = []
video_kp_raw = []
# video ID
video_id = 'video_' + str(num)
# extract video length
video_len = self.mapping.loc[video_id]['video_length']
# add new row to df with raw data
video_kp = []
video_kp_raw = []
# df to store keypresses in bins per pp for this individual stimulus
pp_kp = pd.DataFrame(0,
index=list(range(self.res, video_len + self.res, self.res)),
columns=self.heroku_data.index)
# go over repetitions
for rep in range(self.num_repeat):
# add suffix with repetition ID
video_rt = 'video_' + str(num) + '-rt-' + str(rep)
video_dur = 'video_' + str(num) + '-dur-' + str(rep)
# extract video length
video_len = self.mapping.loc[video_id]['video_length']
rt_data = []
counter_data = 0
for (col_name, col_data) in self.heroku_data.items():
Expand Down Expand Up @@ -606,42 +612,51 @@ def process_kp(self, filter_length=True):
# if list contains only one value, append to rt_data
if len(row) == 1:
rt_data.append(row[0])
# record raw value for pp
for rt_bin in range(self.res, video_len + self.res, self.res):
if rt_bin - self.res < row[0] <= rt_bin:
pp_kp.loc[rt_bin, [self.heroku_data.index[row_index]]] = 1
# if list contains more then one value, go through list to remove keyholds
elif len(row) > 1:
for j in range(1, len(row)):
# if time between 2 stimuli is more than 35 ms, add to array (no hold)
if row[j] - row[j - 1] > 35:
# append buttonpress data to rt array
# append button press data to rt array
rt_data.append(row[j])
# record raw value for pp
for rt_bin in range(self.res, video_len + self.res, self.res):
if rt_bin - self.res < row[0] <= rt_bin:
pp_kp.loc[rt_bin, [self.heroku_data.index[row_index]]] = 1
# if all data for one video was found, divide them in bins
kp = []
kp_raw = []
# loop over all bins, dependent on resolution
bin_counter = 0 # record number of rt values found within bin
bin_id = 0 # record current bin
for rt in range(self.res, video_len + self.res, self.res):
bin_counter = 0
for data in rt_data:
# go through all video data to find all data within specific bin
if rt - self.res < data <= rt:
# if data is found, up bin counter
bin_counter = bin_counter + 1
kp_raw.append(data)
# kp_raw[bin_id].append(1)
if counter_data:
percentage = bin_counter / counter_data
kp.append(round(percentage * 100))
else:
kp.append(0)
kp_raw.append(0)
bin_id = bin_id + 1 # increment bin id
# store keypresses from repetition
video_kp.append(kp)
# store raw data from repetition
video_kp_raw.append(kp_raw)
print(video_kp_raw)
# todo: fix extra [] added to results
video_kp_raw.append(pp_kp.values.tolist()[0])
break
# calculate mean keypresses from all repetitions
kp_mean = [*map(mean, zip(*video_kp))]
# kp_mean_raw = [*map(mean, zip(*video_kp_raw))]
# append data from one video to the mapping array
mapping_rt.append(kp_mean)
# todo: raw data does not take multiple repetitions into account
mapping_rt_raw.append(video_kp_raw)
if filter_length:
logger.info('Filtered out keypress data from {} videos with unexpected length.', counter_filtered)
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24 changes: 12 additions & 12 deletions trust/run.py
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Expand Up @@ -217,28 +217,28 @@
# ttest_signals.append({'signal_1': df.loc['video_' + str(ids[0])]['kp'],
# 'signal_2': df.loc['video_' + str(stim)]['kp'],
# 'paired': True})
ttest_signals = [{'signal_1': df.loc['video_' + str(ids[0])]['kp'], # 0 and 1 = within
'signal_2': df.loc['video_' + str(ids[1])]['kp'],
ttest_signals = [{'signal_1': df.loc['video_' + str(ids[0])]['kp_raw'], # 0 and 1 = within
'signal_2': df.loc['video_' + str(ids[1])]['kp_raw'],
'label': 'ttest(0, 1)',
'paired': True},
{'signal_1': df.loc['video_' + str(ids[0])]['kp'], # 0 and 2 = between
'signal_2': df.loc['video_' + str(ids[2])]['kp'],
{'signal_1': df.loc['video_' + str(ids[0])]['kp_raw'], # 0 and 2 = between
'signal_2': df.loc['video_' + str(ids[2])]['kp_raw'],
'label': 'ttest(0, 2)',
'paired': False},
{'signal_1': df.loc['video_' + str(ids[0])]['kp'], # 0 and 3 = between
'signal_2': df.loc['video_' + str(ids[3])]['kp'],
{'signal_1': df.loc['video_' + str(ids[0])]['kp_raw'], # 0 and 3 = between
'signal_2': df.loc['video_' + str(ids[3])]['kp_raw'],
'label': 'ttest(0, 3)',
'paired': False},
{'signal_1': df.loc['video_' + str(ids[1])]['kp'], # 1 and 2 = between
'signal_2': df.loc['video_' + str(ids[2])]['kp'],
{'signal_1': df.loc['video_' + str(ids[1])]['kp_raw'], # 1 and 2 = between
'signal_2': df.loc['video_' + str(ids[2])]['kp_raw'],
'label': 'ttest(1, 2)',
'paired': False},
{'signal_1': df.loc['video_' + str(ids[2])]['kp'], # 2 and 3 = within
'signal_2': df.loc['video_' + str(ids[3])]['kp'],
{'signal_1': df.loc['video_' + str(ids[2])]['kp_raw'], # 2 and 3 = within
'signal_2': df.loc['video_' + str(ids[3])]['kp_raw'],
'label': 'ttest(2, 3)',
'paired': True},
{'signal_1': df.loc['video_' + str(ids[1])]['kp'], # 1 and 3 = between
'signal_2': df.loc['video_' + str(ids[3])]['kp'],
{'signal_1': df.loc['video_' + str(ids[1])]['kp_raw'], # 1 and 3 = between
'signal_2': df.loc['video_' + str(ids[3])]['kp_raw'],
'label': 'ttest(1, 3)',
'paired': False}]
# signal_1 = signal_type = list of int, eg: [1,1,0,0]
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