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Cleanup of ttest and anova
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@bazilinskyy
bazilinskyy committed Jan 1, 2025
1 parent 6204a29 commit 0f8e54b
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Showing 3 changed files with 79 additions and 38 deletions.
96 changes: 66 additions & 30 deletions trust/analysis/analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1992,7 +1992,7 @@ def plot_kp_slider_videos(self, df, y: list, y_legend=None, x=None, events=None,
orientation='v', xaxis_slider_title='Stimulus', yaxis_slider_show=False,
yaxis_slider_title=None, show_text_labels=False, name_file=None, save_file=True,
fig_save_width=1320, legend_x=0.7, legend_y=0.95, fig_save_height=680, font_family=None,
font_size=None, ttest_signals=None):
font_size=None, ttest_signals=None, anova_signals=None):
"""Plot keypresses with multiple variables as a filter and slider questions for the stimuli.
Args:
Expand Down Expand Up @@ -2026,7 +2026,8 @@ def plot_kp_slider_videos(self, df, y: list, y_legend=None, x=None, events=None,
fig_save_height (int, optional): height of figures to be saved.
font_family (str, optional): font family to be used across the figure. None = use config value.
font_size (int, optional): font size to be used across the figure. None = use config value.
ttest_signals (list, optional): signals to compare with ttest. None = compare all with all.
ttest_signals (list, optional): signals to compare with ttest. None = do not compare.
anova_signals (dict, optional): signals to compare with ANOVA. None = do not compare.
"""
logger.info('Creating figure keypress+slider for {}.', df.index.tolist())
# calculate times
Expand Down Expand Up @@ -2137,23 +2138,32 @@ def plot_kp_slider_videos(self, df, y: list, y_legend=None, x=None, events=None,
text=text,
textposition='auto'), row=1, col=2)
# output ttest
for signals in ttest_signals:
# smoothen signal
if self.smoothen_signal:
signal_1 = self.smoothen_filter(signals['signal_1'])
signal_2 = self.smoothen_filter(signals['signal_2'])
# receive significance values
# considering 0.02s is the response input
significance = self.ttest(signal_1=signal_1,
signal_2=signal_2,
paired=signals['paired'])
if ttest_signals:
for signals in ttest_signals:
# # smoothen signal
# if self.smoothen_signal:
# signal_1 = self.smoothen_filter(signals['signal_1'])
# signal_2 = self.smoothen_filter(signals['signal_2'])
# receive significance values
[p_values, significance] = self.ttest(signal_1=signals['signal_1'],
signal_2=signals['signal_2'],
paired=signals['paired'])
# add to the plot
# todo: @Shadab, plot those stars here based on significance
# todo: @Shadab, adjust the ylim with yaxis_kp_range

# output anova
# self.anova(y)
# output anova
# output ANOVA
if anova_signals:
# # smoothen signal
# if self.smoothen_signal:
# signal_1 = self.smoothen_filter(signals['signal_1'])
# signal_2 = self.smoothen_filter(signals['signal_2'])
# receive significance values
[p_values, significance] = self.anova(signal_1=anova_signals['signal_1'],
signal_2=anova_signals['signal_2'],
signal_3=anova_signals['signal_3'])
# add to the plot
# todo: @Shadab, plot those pluses here based on significance
# todo: @Shadab, adjust the ylim with yaxis_kp_range
# update axis
fig.update_xaxes(title_text=xaxis_slider_title, row=1, col=2)
fig.update_yaxes(title_text=yaxis_slider_title, row=1, col=2)
Expand Down Expand Up @@ -2760,26 +2770,52 @@ def smoothen_filter(self, signal, type_flter='OneEuroFilter'):
return -1

def ttest(self, signal_1, signal_2, type="two-sided", paired=True):
# Convert to numpy arrays if signal_1 and signal_2 are lists
"""Summary
Args:
signal_1 (TYPE): Description
signal_2 (TYPE): Description
type (str, optional): Description
paired (bool, optional): Description
Returns:
TYPE: Description
"""
# todo: @Shadab, finish the doctring above.
# convert to numpy arrays if signal_1 and signal_2 are lists
signal_1 = np.asarray(signal_1)
signal_2 = np.asarray(signal_2)

# Perform t-test for each value (treated as an independent bin)
significance = []
p_values = [] # record raw p value for each bin
significance = [] # record binary flag (0 or 1) if p value < tr.common.get_configs('p_value'))
# perform t-test for each value (treated as an independent bin)
for i in range(len(signal_1)):
if paired:
t_stat, p_value = ttest_rel([signal_1[i]], [signal_2[i]], axis=-1, alternative=type)
else:
t_stat, p_value = ttest_ind([signal_1[i]], [signal_2[i]], axis=-1, alternative=type, equal_var=False)

# Determine significance for this value
# record raw p value
p_values.append(p_value)
# determine significance for this value
# todo: double check based on matlab < or <=
significance.append(int(p_value < tr.common.get_configs('p_value')))
# return raw p values and binary flags for significance for output
return [p_values, significance]

return significance

def anova(self, signal_type, signal_ego, signal_kp):
# signal_type = list of int, eg: [1,1,0,0]
# signal_ego = list of int, eg: [1,1,0,0]
# signal_kp = list of lists, eg: [[1,1,1,1], [1,1,1,1], [1,1,1,1], [1,1,1,1]]
# return [0,0,0,0,1,0,0]
return
def anova(self, signal_1, signal_2, signal_3):
"""Summary
Args:
signal_1 (TYPE): Description
signal_2 (TYPE): Description
signal_3 (TYPE): Description
Returns:
TYPE: Description
"""
# convert to numpy arrays if signal_1 and signal_2 are lists
signal_1 = np.asarray(signal_1)
signal_2 = np.asarray(signal_2)
signal_3 = np.asarray(signal_3)
p_values = [] # record raw p value for each bin
significance = [] # record binary flag (0 or 1) if p value < tr.common.get_configs('p_value'))
return [p_values, significance]
9 changes: 3 additions & 6 deletions trust/analysis/heroku.py
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Original file line number Diff line number Diff line change
Expand Up @@ -583,16 +583,13 @@ def process_kp(self, filter_length=True):
# loop through rows in column
for row_index, row in enumerate(col_data):
# consider only videos of allowed length
if (video_dur in self.heroku_data.keys()
and filter_length):
if video_dur in self.heroku_data.keys() and filter_length:
# extract recorded duration
dur = self.heroku_data.iloc[row_index][video_dur]
# check if duration is within limits
if (dur < self.mapping['min_dur'][video_id]
or dur > self.mapping['max_dur'][video_id]):
if dur < self.mapping['min_dur'][video_id] or dur > self.mapping['max_dur'][video_id]:
# increase counter of filtered videos
logger.debug('Filtered keypress data from '
+ 'video {} of detected '
logger.debug('Filtered keypress data from video {} of detected '
+ 'duration of {} for '
+ 'worker {}.',
video_id, dur,
Expand Down
12 changes: 10 additions & 2 deletions trust/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -202,7 +202,7 @@
'end': end,
'annotation': vert_line_annotations[x]})
# prepare pairs of signals to compare with ttest
ttest_signals = [] # list of dictionaries
# ttest_signals = [] # list of dictionaries
# todo: @Shadab, create list of things to compare using ttest here
# 0 and 1 = within (paired): https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_rel.html # noqa: E501
# 0 and 2 = between: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html
Expand Down Expand Up @@ -235,6 +235,13 @@
{'signal_1': df.loc['video_' + str(ids[1])]['kp'], # 1 and 3 = between
'signal_2': df.loc['video_' + str(ids[3])]['kp'],
'paired': False}]
# signal_1 = signal_type = list of int, eg: [1,1,0,0]
# signal_2 = signal_ego = list of int, eg: [1,1,0,0]
# signal_3 = signal_kp = list of lists, eg: [[1,1,1,1], [1,1,1,1], [1,1,1,1], [1,1,1,1]]
# prepare signals to compare with ANOVA
anova_signals = {'signal_1': df.loc['video_' + str(ids[0])]['kp'],
'signal_2': df.loc['video_' + str(ids[0])]['kp'],
'signal_3': df.loc['video_' + str(ids[0])]['kp']}
# plot keypress data and slider questions
analysis.plot_kp_slider_videos(df,
y=['comfort', 'safety', 'expectation'],
Expand All @@ -256,7 +263,8 @@
fig_save_width=1600, # preserve ratio 225x152
fig_save_height=1080, # preserve ratio 225x152
name_file='kp_videos_sliders_'+','.join([str(i) for i in ids]),
ttest_signals=ttest_signals)
ttest_signals=ttest_signals,
anova_signals=anova_signals)
# keypresses of an individual stimulus for an individual pp
analysis.plot_kp_video_pp(mapping,
heroku_data,
Expand Down

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