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updated ttest
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@Shaadalam9
Shaadalam9 committed Dec 31, 2024
1 parent 8a2a712 commit 6204a29
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Showing 3 changed files with 44 additions and 60 deletions.
3 changes: 1 addition & 2 deletions default.config
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,5 @@
"dcutoff": 1.0,
"font_family": "Open Sans, verdana, arial, sans-serif",
"font_size": 12,
"p_value": 0.001,
"time_ttest": 0.1
"p_value": 0.001
}
55 changes: 20 additions & 35 deletions trust/analysis/analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2146,8 +2146,7 @@ def plot_kp_slider_videos(self, df, y: list, y_legend=None, x=None, events=None,
# considering 0.02s is the response input
significance = self.ttest(signal_1=signal_1,
signal_2=signal_2,
paired=signals['paired'],
bins=int(len(signal_1)*0.02/tr.common.get_configs('time_ttest')))
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
Expand Down Expand Up @@ -2184,16 +2183,16 @@ def plot_kp_slider_videos(self, df, y: list, y_legend=None, x=None, events=None,
# use value from config file
fig.update_layout(font=dict(size=tr.common.get_configs('font_size')))
# save file
# if save_file:
# if not name_file:
# self.save_plotly(fig, 'kp_videos_sliders', self.folder, remove_margins=True, width=fig_save_width,
# height=fig_save_height)
# else:
# self.save_plotly(fig, name_file, self.folder, remove_margins=True, width=fig_save_width,
# height=fig_save_height)
# open it in localhost instead
# else:
# fig.show()
if save_file:
if not name_file:
self.save_plotly(fig, 'kp_videos_sliders', self.folder, remove_margins=True, width=fig_save_width,
height=fig_save_height)
else:
self.save_plotly(fig, name_file, self.folder, remove_margins=True, width=fig_save_width,
height=fig_save_height)
# # open it in localhost instead
else:
fig.show()

def plot_kp_variable(self, df, variable, y_legend=None, values=None, xaxis_title='Time (s)',
yaxis_title='Percentage of trials with response key pressed', xaxis_range=None,
Expand Down Expand Up @@ -2760,37 +2759,23 @@ def smoothen_filter(self, signal, type_flter='OneEuroFilter'):
logger.error('Specified filter {} not implemented.', type_flter)
return -1

def ttest(self, signal_1, signal_2, type="two-sided", paired=True, bins=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
signal_1 = np.asarray(signal_1)
signal_2 = np.asarray(signal_2)

if signal_1.shape != signal_2.shape:
raise ValueError("Signals must have the same shape for the selected test type.")

n_time_points = signal_1.shape[-1]
bin_size = n_time_points // bins
significance_per_bin = []

for bin_idx in range(bins):
start = bin_idx * bin_size
end = (bin_idx + 1) * bin_size if bin_idx < bins - 1 else n_time_points

# Slice signals for the current bin
signal_1_bin = signal_1[..., start:end]
signal_2_bin = signal_2[..., start:end]

# Perform t-test for each value (treated as an independent bin)
significance = []
for i in range(len(signal_1)):
if paired:
if signal_1_bin.shape != signal_2_bin.shape:
raise ValueError("Signals must have the same shape for a paired t-test.")
t_stat, p_values = ttest_rel(signal_1_bin, signal_2_bin, axis=-1, alternative=type)
t_stat, p_value = ttest_rel([signal_1[i]], [signal_2[i]], axis=-1, alternative=type)
else:
t_stat, p_values = ttest_ind(signal_1_bin, signal_2_bin, axis=-1, alternative=type, equal_var=False)
t_stat, p_value = ttest_ind([signal_1[i]], [signal_2[i]], axis=-1, alternative=type, equal_var=False)

significance = (p_values < tr.common.get_configs('p_value')).astype(int)
significance_per_bin.append(significance)
# Determine significance for this value
significance.append(int(p_value < tr.common.get_configs('p_value')))

return significance_per_bin
return significance

def anova(self, signal_type, signal_ego, signal_kp):
# signal_type = list of int, eg: [1,1,0,0]
Expand Down
46 changes: 23 additions & 23 deletions trust/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,24 +217,24 @@
# ttest_signals.append({'signal_1': df.loc['video_' + str(ids[0])]['kp'],
# 'signal_2': df.loc['video_' + str(stim)]['kp'],
# 'paired': True})
for i in range(len(ids)):
for j in range(i + 1, len(ids)):
# Add with 'paired' as True
ttest_signals.append({
'signal_1_original': 'video_' + str(ids[i]), # Add metadata for signal_1
'signal_2_original': 'video_' + str(ids[j]), # Add metadata for signal_2
'signal_1': df.loc['video_' + str(ids[i])]['kp'],
'signal_2': df.loc['video_' + str(ids[j])]['kp'],
'paired': True
})
# Add with 'paired' as False
ttest_signals.append({
'video_1_original': 'video_' + str(ids[i]), # Add metadata for signal_1
'video_2_original': 'video_' + str(ids[j]), # Add metadata for signal_2
'signal_1': df.loc['video_' + str(ids[i])]['kp'],
'signal_2': df.loc['video_' + str(ids[j])]['kp'],
'paired': False
})
ttest_signals = [{'signal_1': df.loc['video_' + str(ids[0])]['kp'], # 0 and 1 = within
'signal_2': df.loc['video_' + str(ids[1])]['kp'],
'paired': True},
{'signal_1': df.loc['video_' + str(ids[0])]['kp'], # 0 and 2 = between
'signal_2': df.loc['video_' + str(ids[2])]['kp'],
'paired': False},
{'signal_1': df.loc['video_' + str(ids[0])]['kp'], # 0 and 3 = between
'signal_2': df.loc['video_' + str(ids[3])]['kp'],
'paired': False},
{'signal_1': df.loc['video_' + str(ids[1])]['kp'], # 1 and 2 = between
'signal_2': df.loc['video_' + str(ids[2])]['kp'],
'paired': False},
{'signal_1': df.loc['video_' + str(ids[2])]['kp'], # 2 and 3 = within
'signal_2': df.loc['video_' + str(ids[3])]['kp'],
'paired': True},
{'signal_1': df.loc['video_' + str(ids[1])]['kp'], # 1 and 3 = between
'signal_2': df.loc['video_' + str(ids[3])]['kp'],
'paired': False}]
# plot keypress data and slider questions
analysis.plot_kp_slider_videos(df,
y=['comfort', 'safety', 'expectation'],
Expand All @@ -258,11 +258,11 @@
name_file='kp_videos_sliders_'+','.join([str(i) for i in ids]),
ttest_signals=ttest_signals)
# keypresses of an individual stimulus for an individual pp
# analysis.plot_kp_video_pp(mapping,
# heroku_data,
# pp='R51701197342646JF16777X',
# stimulus='video_2',
# conf_interval=0.95)
analysis.plot_kp_video_pp(mapping,
heroku_data,
pp='R51701197342646JF16777X',
stimulus='video_2',
conf_interval=0.95)
# keypresses of all videos individually
analysis.plot_kp_videos(mapping, show_menu=False, show_title=False)
# keypress based on the type of ego car
Expand Down

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