def form_string_all_trials_perf(self, translated_trial_matrix):
"""Form a string with side perf and anova for all trials"""
side2perf_all = count_hits_by_type_from_trials_info(
translated_trial_matrix, split_key='rewside')
string_perf_by_side = self.form_string_perf_by_side(side2perf_all)
if len(translated_trial_matrix
) > self.cached_anova_len2 or self.cached_anova_text2 == '':
numericated_trial_matrix = TrialMatrix.numericate_trial_matrix(
translated_trial_matrix)
anova_stats = TrialMatrix.run_anova(numericated_trial_matrix)
self.cached_anova_text2 = anova_stats
self.cached_anova_len2 = len(translated_trial_matrix)
else:
anova_stats = self.cached_anova_text2
return 'All: ' + string_perf_by_side + '. Biases: ' + anova_stats
def form_string_all_trials_perf(self, translated_trial_matrix):
"""Form a string with side perf and anova for all trials"""
side2perf_all = count_hits_by_type_from_trials_info(
translated_trial_matrix,
split_key='rewside')
string_perf_by_side = self.form_string_perf_by_side(side2perf_all)
if len(translated_trial_matrix) > self.cached_anova_len2 or self.cached_anova_text2 == '':
numericated_trial_matrix = TrialMatrix.numericate_trial_matrix(
translated_trial_matrix)
anova_stats = TrialMatrix.run_anova(numericated_trial_matrix)
self.cached_anova_text2 = anova_stats
self.cached_anova_len2 = len(translated_trial_matrix)
else:
anova_stats = self.cached_anova_text2
return 'All: ' + string_perf_by_side + '. Biases: ' + anova_stats
def form_string_recent_trials_perf(self, translated_trial_matrix):
"""Form a string with side perf and anova for recent trials
cached in cached_anova_text3 and cached_anova_len3
"""
side2perf = count_hits_by_type_from_trials_info(
translated_trial_matrix.iloc[-60:], split_key='rewside')
string_perf_by_side = self.form_string_perf_by_side(side2perf)
if len(translated_trial_matrix) > self.cached_anova_len3 or self.cached_anova_text3 == '':
numericated_trial_matrix = TrialMatrix.numericate_trial_matrix(
translated_trial_matrix.iloc[-60:])
anova_stats = TrialMatrix.run_anova(numericated_trial_matrix)
self.cached_anova_text3 = anova_stats
self.cached_anova_len3 = len(translated_trial_matrix)
else:
anova_stats = self.cached_anova_text3
return 'Recent: ' + string_perf_by_side + '. Biases: ' + anova_stats
def form_string_recent_trials_perf(self, translated_trial_matrix):
"""Form a string with side perf and anova for recent trials
cached in cached_anova_text3 and cached_anova_len3
"""
side2perf = count_hits_by_type_from_trials_info(
translated_trial_matrix.iloc[-60:], split_key='rewside')
string_perf_by_side = self.form_string_perf_by_side(side2perf)
if len(translated_trial_matrix
) > self.cached_anova_len3 or self.cached_anova_text3 == '':
numericated_trial_matrix = TrialMatrix.numericate_trial_matrix(
translated_trial_matrix.iloc[-60:])
anova_stats = TrialMatrix.run_anova(numericated_trial_matrix)
self.cached_anova_text3 = anova_stats
self.cached_anova_len3 = len(translated_trial_matrix)
else:
anova_stats = self.cached_anova_text3
return 'Recent: ' + string_perf_by_side + '. Biases: ' + anova_stats
def form_string_unforced_trials_perf(self, translated_trial_matrix):
"""Exactly the same as form_string_all_trials_perf, except that:
We drop all trials where bad is True.
We use cached_anova_len1 and cached_anova_text1 instead of 2.
"""
side2perf = count_hits_by_type_from_trials_info(
translated_trial_matrix[~translated_trial_matrix.bad],
split_key='rewside')
string_perf_by_side = self.form_string_perf_by_side(side2perf)
if len(translated_trial_matrix) > self.cached_anova_len1 or self.cached_anova_text1 == '':
numericated_trial_matrix = TrialMatrix.numericate_trial_matrix(
translated_trial_matrix[~translated_trial_matrix.bad])
anova_stats = TrialMatrix.run_anova(numericated_trial_matrix)
self.cached_anova_text1 = anova_stats
self.cached_anova_len1 = len(translated_trial_matrix)
else:
anova_stats = self.cached_anova_text1
return 'UF: ' + string_perf_by_side + '. Biases: ' + anova_stats
def form_string_unforced_trials_perf(self, translated_trial_matrix):
"""Exactly the same as form_string_all_trials_perf, except that:
We drop all trials where bad is True.
We use cached_anova_len1 and cached_anova_text1 instead of 2.
"""
side2perf = count_hits_by_type_from_trials_info(
translated_trial_matrix[~translated_trial_matrix.bad],
split_key='rewside')
string_perf_by_side = self.form_string_perf_by_side(side2perf)
if len(translated_trial_matrix
) > self.cached_anova_len1 or self.cached_anova_text1 == '':
numericated_trial_matrix = TrialMatrix.numericate_trial_matrix(
translated_trial_matrix[~translated_trial_matrix.bad])
anova_stats = TrialMatrix.run_anova(numericated_trial_matrix)
self.cached_anova_text1 = anova_stats
self.cached_anova_len1 = len(translated_trial_matrix)
else:
anova_stats = self.cached_anova_text1
return 'UF: ' + string_perf_by_side + '. Biases: ' + anova_stats
res['STPPOS'] = np.random.randint(1, 10)
res['SRVPOS'] = np.random.randint(1, 10)
res['ITI'] = np.random.randint(10000)
return res
## Main loop
last_released_trial = 0
try:
while True:
# Update chatter
chatter.update(echo_to_stdout=True)
# Check log
splines = TrialSpeak.load_splines_from_file(logfilename)
trial_matrix = TrialMatrix.make_trials_info_from_splines(splines)
# Switch on which trial, and whether it's been released and/or completed
if trial_matrix is None: # or if splines is empty?
# It's the first tiral
# Send each initial param
for param_name, param_val in initial_params.items():
chatter.write_to_device(
TrialSpeak.command_set_parameter(
param_name, param_val))
# Release
chatter.write_to_device(TrialSpeak.command_release_trial())
elif 'response' not in trial_matrix or trial_matrix['response'].isnull().irow(-1):
# Trial has not completed, keep waiting
continue
def choose_scheduler_main_body(self, translated_trial_matrix):
# Main body of session
this_trial = len(translated_trial_matrix)
# Do nothing if we've changed recently
if this_trial < self.last_changed_trial + self.n_trials_sticky:
return
# Check whether we've had at least 10 random in the last 50
recents = translated_trial_matrix['isrnd'].values[-self.
n_trials_recent_win:]
recent_randoms = recents.sum()
if len(recents) == self.n_trials_recent_win and \
recent_randoms < self.n_trials_recent_random_thresh:
# Set to occasional random
self.current_sub_scheduler = self.sub_schedulers['RandomStim']
self.last_changed_trial = this_trial
self.params['status'] = 'randchk' + str(this_trial)
return
# Run the anova on all trials (used for checking for stay bias)
numericated_trial_matrix = TrialMatrix.numericate_trial_matrix(
translated_trial_matrix)
#~ recent_ntm = numericated_trial_matrix.iloc[
#~ -self.n_trials_recent_for_side_bias:]
aov_res = TrialMatrix._run_anova(numericated_trial_matrix)
if aov_res is None:
self.current_sub_scheduler = self.sub_schedulers['RandomStim']
self.last_changed_trial = this_trial
self.params['status'] = 'an_none' + str(this_trial)
return
# Also calculate the side bias in all recent trials
recent_ttm = translated_trial_matrix.iloc[
-self.n_trials_recent_for_side_bias:]
# Take the largest significant bias
# Actually, better to take the diff of perf between sides for forced
# side. Although this is a bigger issue than unexplainable variance
# shouldn't be interpreted.
side2perf_all = TrialMatrix.count_hits_by_type(recent_ttm,
split_key='rewside')
if 'left' in side2perf_all and 'right' in side2perf_all:
lperf = side2perf_all['left'][0] / float(side2perf_all['left'][1])
rperf = side2perf_all['right'][0] / float(
side2perf_all['right'][1])
sideperf_diff = rperf - lperf
else:
sideperf_diff = 0
# Decide whether stay, side, or neither bias is critical
if aov_res['pvals']['p_prevchoice'] < 0.05 and aov_res['fit'][
'fit_prevchoice'] > 0:
# Stay bias
self.last_changed_trial = this_trial
self.params['status'] = 'antistay' + str(this_trial)
self.current_sub_scheduler = self.sub_schedulers[
'ForcedAlternation']
elif np.abs(sideperf_diff) > .25:
# Side bias
self.last_changed_trial = this_trial
self.params['status'] = 'antiside' + str(this_trial)
self.current_sub_scheduler = self.sub_schedulers['ForcedSide']
if sideperf_diff > 0:
self.current_sub_scheduler.params['side'] = 'left'
else:
self.current_sub_scheduler.params['side'] = 'right'
else:
# No bias
self.last_changed_trial = this_trial
self.params['status'] = 'good' + str(this_trial)
self.current_sub_scheduler = self.sub_schedulers['RandomStim']
def update(self, filename):
"""Read info from filename and update the plot"""
## Load data and make trials_info
# Check log
lines = TrialSpeak.read_lines_from_file(filename)
splines = TrialSpeak.split_by_trial(lines)
# Really we should wait until we hear something from the arduino
# Simply wait till at least one line has been received
if len(splines) == 0 or len(splines[0]) == 0:
return
# Construct trial_matrix. I believe this will always have at least
# one line in it now, even if it's composed entirely of Nones.
trials_info = TrialMatrix.make_trials_info_from_splines(splines)
## Translate condensed trialspeak into full data
# Put this part into TrialSpeak.py
translated_trial_matrix = TrialSpeak.translate_trial_matrix(
trials_info)
# return if nothing to do
if len(translated_trial_matrix) < 1:
return
# define the "bad" trials
# these are marked differently and discounted from certain ANOVAs
# maybe include user delivery trials too?
if 'isrnd' in translated_trial_matrix:
translated_trial_matrix['bad'] = ~translated_trial_matrix['isrnd']
else:
translated_trial_matrix['bad'] = False
## Define trial types, the ordering on the plot
# Make any updates to trial type parameters (child-class-dependent)
self.update_trial_type_parameters(lines)
# Add type information to trials_info and generate type names
translated_trial_matrix = self.assign_trial_type_to_trials_info(
translated_trial_matrix)
trial_type_names = self.get_list_of_trial_type_names()
## Count performance by type
# Hits by type
typ2perf = count_hits_by_type_from_trials_info(
translated_trial_matrix[~translated_trial_matrix.bad])
typ2perf_all = count_hits_by_type_from_trials_info(
translated_trial_matrix)
# Combined
total_nhit, total_ntot = calculate_nhit_ntot(
translated_trial_matrix[~translated_trial_matrix.bad])
# Turn the typ2perf into ticklabels
ytick_labels = typ2perf2ytick_labels(trial_type_names, typ2perf,
typ2perf_all)
## title string
# number of rewards
title_string = self.form_string_rewards(splines,
translated_trial_matrix)
# This depends on rewside existing, which is only true for 2AC
if 'rewside' in translated_trial_matrix.columns:
title_string += '\n' + self.form_string_all_trials_perf(
translated_trial_matrix)
title_string += '\n' + self.form_string_recent_trials_perf(
translated_trial_matrix)
title_string += '\n' + self.form_string_unforced_trials_perf(
translated_trial_matrix)
## PLOTTING
# plot each outcome
for outcome in ['hit', 'error', 'spoil', 'curr']:
# Get rows corresponding to this outcome
msk = translated_trial_matrix['outcome'] == outcome
# Get the line corresponding to this outcome and set the xdata
# to the appropriate trial numbers and the ydata to the trial types
line = self.graphics_handles['label2lines'][outcome]
line.set_xdata(np.where(msk)[0])
line.set_ydata(translated_trial_matrix['trial_type'][msk].values)
# plot vert bars where bad trials occurred
msk = translated_trial_matrix['bad']
line = self.graphics_handles['label2lines']['bad']
line.set_xdata(np.where(msk)[0])
line.set_ydata(translated_trial_matrix['trial_type'][msk])
## PLOTTING axis labels and title
ax = self.graphics_handles['ax']
f = self.graphics_handles['f']
# Use the ytick_labels calculated above
ax.set_yticks(range(len(trial_type_names)))
ax.set_yticklabels(ytick_labels, size='small')
# The ylimits go BACKWARDS so that trial types are from top to bottom
ymax = np.max(ax.get_yticks())
ymin = np.min(ax.get_yticks())
ax.set_ylim((ymax + .5, ymin - .5))
# The xlimits are a sliding window of size TRIAL_PLOT_WINDOW_SIZE
ax.set_xlim(
(len(translated_trial_matrix) - self.trial_plot_window_size,
len(translated_trial_matrix)))
# title set above
#~ ax.set_title(title_string, size='small')
self.graphics_handles['suptitle'].set_text(title_string)
## plot division between L and R
line = self.graphics_handles['label2lines']['divis']
line.set_xdata(ax.get_xlim())
line.set_ydata([np.mean(ax.get_yticks())] * 2)
## PLOTTING finalize
plt.show()
plt.draw()
def choose_scheduler_main_body(self, translated_trial_matrix):
# Main body of session
this_trial = len(translated_trial_matrix)
# Do nothing if we've changed recently
if this_trial < self.last_changed_trial + self.n_trials_sticky:
return
# Check whether we've had at least 10 random in the last 50
recents = translated_trial_matrix['isrnd'].values[
-self.n_trials_recent_win:]
recent_randoms = recents.sum()
if len(recents) == self.n_trials_recent_win and \
recent_randoms < self.n_trials_recent_random_thresh:
# Set to occasional random
self.current_sub_scheduler = self.sub_schedulers['RandomStim']
self.last_changed_trial = this_trial
self.params['status'] = 'randchk' + str(this_trial)
return
# Run the anova on all trials (used for checking for stay bias)
numericated_trial_matrix = TrialMatrix.numericate_trial_matrix(
translated_trial_matrix)
#~ recent_ntm = numericated_trial_matrix.iloc[
#~ -self.n_trials_recent_for_side_bias:]
aov_res = TrialMatrix._run_anova(numericated_trial_matrix)
if aov_res is None:
self.current_sub_scheduler = self.sub_schedulers['RandomStim']
self.last_changed_trial = this_trial
self.params['status'] = 'an_none' + str(this_trial)
return
# Also calculate the side bias in all recent trials
recent_ttm = translated_trial_matrix.iloc[
-self.n_trials_recent_for_side_bias:]
# Take the largest significant bias
# Actually, better to take the diff of perf between sides for forced
# side. Although this is a bigger issue than unexplainable variance
# shouldn't be interpreted.
side2perf_all = TrialMatrix.count_hits_by_type(
recent_ttm, split_key='rewside')
if 'left' in side2perf_all and 'right' in side2perf_all:
lperf = side2perf_all['left'][0] / float(side2perf_all['left'][1])
rperf = side2perf_all['right'][0] / float(side2perf_all['right'][1])
sideperf_diff = rperf - lperf
else:
sideperf_diff = 0
# Decide whether stay, side, or neither bias is critical
if aov_res['pvals']['p_prevchoice'] < 0.05 and aov_res['fit']['fit_prevchoice'] > 0:
# Stay bias
self.last_changed_trial = this_trial
self.params['status'] = 'antistay' + str(this_trial)
self.current_sub_scheduler = self.sub_schedulers['ForcedAlternation']
elif np.abs(sideperf_diff) > .25:
# Side bias
self.last_changed_trial = this_trial
self.params['status'] = 'antiside' + str(this_trial)
self.current_sub_scheduler = self.sub_schedulers['ForcedSide']
if sideperf_diff > 0:
self.current_sub_scheduler.params['side'] = 'left'
else:
self.current_sub_scheduler.params['side'] = 'right'
else:
# No bias
self.last_changed_trial = this_trial
self.params['status'] = 'good' + str(this_trial)
self.current_sub_scheduler = self.sub_schedulers['RandomStim']
res['STPPOS'] = np.random.randint(1, 10)
res['SRVPOS'] = np.random.randint(1, 10)
res['ITI'] = np.random.randint(10000)
return res
## Main loop
last_released_trial = 0
try:
while True:
# Update chatter
chatter.update(echo_to_stdout=True)
# Check log
splines = TrialSpeak.load_splines_from_file(logfilename)
trial_matrix = TrialMatrix.make_trials_info_from_splines(splines)
# Switch on which trial, and whether it's been released and/or completed
if trial_matrix is None: # or if splines is empty?
# It's the first tiral
# Send each initial param
for param_name, param_val in list(initial_params.items()):
chatter.write_to_device(
TrialSpeak.command_set_parameter(
param_name, param_val))
# Release
chatter.write_to_device(TrialSpeak.command_release_trial())
elif 'response' not in trial_matrix or trial_matrix['response'].isnull().irow(-1):
# Trial has not completed, keep waiting
continue
def update(self, filename):
"""Read info from filename and update the plot"""
## Load data and make trials_info
# Check log
lines = TrialSpeak.read_lines_from_file(filename)
splines = TrialSpeak.split_by_trial(lines)
# Really we should wait until we hear something from the arduino
# Simply wait till at least one line has been received
if len(splines) == 0 or len(splines[0]) == 0:
return
# Construct trial_matrix. I believe this will always have at least
# one line in it now, even if it's composed entirely of Nones.
trials_info = TrialMatrix.make_trials_info_from_splines(splines)
## Translate condensed trialspeak into full data
# Put this part into TrialSpeak.py
translated_trial_matrix = TrialSpeak.translate_trial_matrix(trials_info)
# return if nothing to do
if len(translated_trial_matrix) < 1:
return
# define the "bad" trials
# these are marked differently and discounted from certain ANOVAs
# maybe include user delivery trials too?
if 'isrnd' in translated_trial_matrix:
translated_trial_matrix['bad'] = ~translated_trial_matrix['isrnd']
else:
translated_trial_matrix['bad'] = False
## Define trial types, the ordering on the plot
# Make any updates to trial type parameters (child-class-dependent)
self.update_trial_type_parameters(lines)
# Add type information to trials_info and generate type names
translated_trial_matrix = self.assign_trial_type_to_trials_info(translated_trial_matrix)
trial_type_names = self.get_list_of_trial_type_names()
## Count performance by type
# Hits by type
typ2perf = count_hits_by_type_from_trials_info(
translated_trial_matrix[~translated_trial_matrix.bad])
typ2perf_all = count_hits_by_type_from_trials_info(
translated_trial_matrix)
# Combined
total_nhit, total_ntot = calculate_nhit_ntot(
translated_trial_matrix[~translated_trial_matrix.bad])
# Turn the typ2perf into ticklabels
ytick_labels = typ2perf2ytick_labels(trial_type_names,
typ2perf, typ2perf_all)
## title string
# number of rewards
title_string = self.form_string_rewards(splines,
translated_trial_matrix)
# This depends on rewside existing, which is only true for 2AC
if 'rewside' in translated_trial_matrix.columns:
title_string += '\n' + self.form_string_all_trials_perf(
translated_trial_matrix)
title_string += '\n' + self.form_string_recent_trials_perf(
translated_trial_matrix)
title_string += '\n' + self.form_string_unforced_trials_perf(
translated_trial_matrix)
## PLOTTING
# plot each outcome
for outcome in ['hit', 'error', 'spoil', 'curr']:
# Get rows corresponding to this outcome
msk = translated_trial_matrix['outcome'] == outcome
# Get the line corresponding to this outcome and set the xdata
# to the appropriate trial numbers and the ydata to the trial types
line = self.graphics_handles['label2lines'][outcome]
line.set_xdata(np.where(msk)[0])
line.set_ydata(translated_trial_matrix['trial_type'][msk].values)
# plot vert bars where bad trials occurred
msk = translated_trial_matrix['bad']
line = self.graphics_handles['label2lines']['bad']
line.set_xdata(np.where(msk)[0])
line.set_ydata(translated_trial_matrix['trial_type'][msk])
## PLOTTING axis labels and title
ax = self.graphics_handles['ax']
f = self.graphics_handles['f']
# Use the ytick_labels calculated above
ax.set_yticks(range(len(trial_type_names)))
ax.set_yticklabels(ytick_labels, size='small')
# The ylimits go BACKWARDS so that trial types are from top to bottom
ymax = np.max(ax.get_yticks())
ymin = np.min(ax.get_yticks())
ax.set_ylim((ymax + .5, ymin -.5))
# The xlimits are a sliding window of size TRIAL_PLOT_WINDOW_SIZE
ax.set_xlim((
len(translated_trial_matrix) - self.trial_plot_window_size,
len(translated_trial_matrix)))
# title set above
#~ ax.set_title(title_string, size='small')
self.graphics_handles['suptitle'].set_text(title_string)
## plot division between L and R
line = self.graphics_handles['label2lines']['divis']
line.set_xdata(ax.get_xlim())
line.set_ydata([np.mean(ax.get_yticks())] * 2)
## PLOTTING finalize
plt.show()
plt.draw()