def test_process_trials_returns_dataframe(self):
"Makes sure dataframe is returned"
output = ProcessTrials(seg=self.segment,
name=self.name,
startoftrial=self.startoftrial,
epochs=self.epochs,
typedf=self.typeframe)
self.assertIsInstance(output, pd.core.frame.DataFrame)
def test_trials_dataframe_has_name_trials(self):
"Makes sure dataframe has the correct name"
output = ProcessTrials(seg=self.segment,
name=self.name,
startoftrial=self.startoftrial,
epochs=self.epochs,
typedf=self.typeframe,
firsttrial=False)
self.assertEqual(output.name, 'trials')
def test_correct_columns(self):
"Makes sure dataframe has the correct columns"
output = ProcessTrials(seg=self.segment,
name=self.name,
startoftrial=self.startoftrial,
epochs=self.epochs,
typedf=self.typeframe,
firsttrial=False)
self.assertTrue(all(output.columns == self.columns))
def test_process_trials_starts_with_first_trial(self):
"Makes sure dataframe can have events before first trial if firsttrail=False"
output = ProcessTrials(seg=self.segment,
name=self.name,
startoftrial=self.startoftrial,
epochs=self.epochs,
typedf=self.typeframe,
firsttrial=False)
self.assertEqual(output.trial_idx.min(), 0)
def test_process_trials_starts_with_first_trial_default(self):
"Makes sure dataframe only has events from first trial if firsttrail=True"
output = ProcessTrials(seg=self.segment,
name=self.name,
startoftrial=self.startoftrial,
epochs=self.epochs,
typedf=self.typeframe,
firsttrial=True)
self.assertEqual(output.trial_idx.min(), 1)
def test_dataframe_appended_to_segment(self):
"Makes sure dataframe is appended to dataframe part of segment object"
df = ProcessTrials(seg=self.segment,
name=self.name,
startoftrial=self.startoftrial,
epochs=self.epochs,
typedf=self.typeframe,
firsttrial=False,
returndf=True)
pd.testing.assert_frame_equal(df, self.segment.dataframes[0])
def test_dataframes_attribute_added_to_segment(self):
"Makes sure dataframes attribute is added to segment object"
ProcessTrials(seg=self.segment,
name=self.name,
startoftrial=self.startoftrial,
epochs=self.epochs,
typedf=self.typeframe,
firsttrial=False,
returndf=False)
self.assertTrue(hasattr(self.segment, 'dataframes'))
evtframe=evtframe,
name=processed_event_ch_name)
print('Done!')
# Takes processed events and segments them by trial number. Trial start
# is determined by events in the list 'start' from LoadEventParams. This
# can be set in the event_params.json. Additionally, the result of the
# trial is set by matching the epoch type to the typeframe dataframe
# (also from LoadEventParams). Example of epochs are 'correct', 'omission',
# etc.
# The result of this process is a dataframe with each event and their
# timestamp in chronological order, with the trial number and trial outcome
# appended to each event/timestamp.
PrintNoNewLine('\nProcessing trials...')
trials = ProcessTrials(seg=segment,
name=processed_event_ch_name,
startoftrial=start,
epochs=epochs,
typedf=typeframe,
appendmultiple=False)
print('Done!')
# With processed trials, we comb through each epoch ('correct', 'omission'
# etc.) and find start/end times for each trial. Start time is determined
# by the earliest 'start' event in a trial. Stop time is determined by
# 1) the earliest 'end' event in a trial, 2) or the 'last' event in a trial
# or the 3) 'next' event in the following trial.
PrintNoNewLine('\nCalculating epoch times and durations...')
GroupTrialsByEpoch(seg=segment,
startoftrial=start,
endoftrial=end,
endeventmissing=how_trial_ends)
print('Done!')
def main(file: str) -> None:
"""Runs flexible imaging analysis based on inputs from a parameter file"""
sns.set_style('darkgrid')
############## PART 1 Preprocess data ##########################
##################### Kazu/Mike Section ###############################
print(f"LOADING PARAMETERS FROM {file}")
params = json.load(open(file, 'r'))
mode = params.get("mode")
dpaths = params.get("dpaths")
baseline_window_unaligned = params.get("baseline_window_unaligned")
offsets_list = params.get("offsets_list")
offset_events = params.get("offset_events")
before_alignment = params.get("before_alignment")
signal_channel = params.get("signal_channel")
reference_channel = params.get("reference_channel")
analysis_blocks = params.get("analysis_blocks")
path_to_ttl_event_params = params.get("path_to_ttl_event_params")
path_to_social_excel = params.get("path_to_social_excel")
####################### PREPROCESSING DATA ###############################
print(('\n\n\n\nRUNNING IN MODE: %s \n\n\n' % mode))
for dpath_ind, dpath in enumerate(dpaths):
# Reads data from Tdt folder
PrintNoNewLine(
'\nCannot find processed pkl object, reading TDT folder instead...'
)
block = ReadNeoTdt(path=dpath, return_block=True)
seglist = block.segments
print('Done!')
# Trunactes first/last seconds of recording
PrintNoNewLine('Truncating signals and events...')
seglist = TruncateSegments(seglist, start=0, end=10, clip_same=True)
print('Done!')
# Iterates through each segment in seglist. Right now, there is only one segment
for segment in seglist:
segment_name = segment.name
# Extracts the sampling rate from the signal channel
try:
sampling_rate = [
x for x in segment.analogsignals
if x.name == signal_channel
][0].sampling_rate
except IndexError:
raise ValueError(
'Could not find your channels. Make sure you have the right names!'
)
# Appends an analog signal object that is delta F/F. The name of the channel is
# specified by deltaf_ch_name above. It is calculated using the function
# NormalizeSignal in signal_processing.py. As of right now it:
# 1) Lowpass filters signal and reference (default cutoff = 40 Hz, order = 5)
# 2) Calculates deltaf/f for signal and reference (default is f - median(f) / median(f))
# 3) Detrends deltaf/f using a savgol filter (default window_lenght = 3001, poly order = 1)
# 4) Subtracts reference from signal
# NormalizeSignal has a ton of options, you can pass in paramters using
# the deltaf_options dictionary above. For example, if you want it to be mean centered
# and not run the savgol_filter, set deltaf_options = {'mode': 'mean', 'detrend': False}
PrintNoNewLine('\nProcessing signal before event alignment...')
before_alignment_channels = []
if len(before_alignment) > 0:
for step_number, process in enumerate(before_alignment):
if step_number == 0:
input_sig_ch = signal_channel
input_ref_ch = reference_channel
if 'start_from' in list(process['options'].keys()):
start_from = process['options']['start_from']
if start_from == 'list':
start = offsets_list[dpath_ind]
end = start + process['options']['end_from']
else:
start = GetImagingDataTTL(dpath)
end = start + process['options']['end_from']
indices = GetImagingDataIndices(
start, end, sampling_rate.magnitude)
process['options']['period'] = indices
signal, reference = SingleStepProcessSignalData(
data=segment,
process_type=process['type'],
input_sig_ch=input_sig_ch,
input_ref_ch=input_ref_ch,
datatype='segment',
**process['options'])
if process['type'] == 'filter':
input_sig_ch = 'filtered_signal'
input_ref_ch = 'filtered_reference'
elif process['type'] == 'detrend':
input_sig_ch = 'detrended_signal'
input_ref_ch = 'detrended_reference'
elif process['type'] == 'subtract':
input_sig_ch = 'subtracted_signal'
input_ref_ch = None
elif process['type'] == 'measure':
input_sig_ch = 'measure_signal'
input_ref_ch = 'measure_reference'
if input_sig_ch not in before_alignment_channels:
before_alignment_channels.append(input_sig_ch)
if input_ref_ch not in before_alignment_channels:
before_alignment_channels.append(input_ref_ch)
# Appends an Event object that has all event timestamps and the proper label
# (determined by the evtframe loaded earlier). Uses a tolerance (in seconds)
# to determine if events co-occur. For example, if tolerance is 1 second
# and ch1 fires an event, ch2 fires an event 0.5 seconds later, and ch3 fires
# an event 3 seconds later, the output array will be [1, 1, 0] and will
# match the label in evtframe (e.g. 'omission')
print('Done!')
if mode == 'TTL':
# Loading event labeling/combo parameters
path_to_event_params = path_to_ttl_event_params[dpath_ind]
elif mode == 'manual':
# Generates a json for reading excel file events
path_to_event_params = 'imaging_analysis/manual_event_params.json'
GenerateManualEventParamsJson(path_to_social_excel[dpath_ind],
event_col='Bout type',
name=path_to_event_params)
# This loads our event params json
start, end, epochs, evtframe, typeframe = LoadEventParams(
dpath=path_to_event_params, mode=mode)
# Appends processed event_param.json info to segment object
AppendDataframesToSegment(segment, [evtframe, typeframe],
['eventframe', 'resultsframe'])
# Processing events
PrintNoNewLine('\nProcessing event times and labels...')
if mode == 'manual':
manualframe = path_to_social_excel[dpath_ind]
else:
manualframe = None
ProcessEvents(seg=segment,
tolerance=.1,
evtframe=evtframe,
name='Events',
mode=mode,
manualframe=manualframe,
event_col='Bout type',
start_col='Bout start',
end_col='Bout end',
offset_events=offset_events[dpath_ind])
print('Done!')
# Takes processed events and segments them by trial number. Trial start
# is determined by events in the list 'start' from LoadEventParams. This
# can be set in the event_params.json. Additionally, the result of the
# trial is set by matching the epoch type to the typeframe dataframe
# (also from LoadEventParams). Example of epochs are 'correct', 'omission',
# etc.
# The result of this process is a dataframe with each event and their
# timestamp in chronological order, with the trial number and trial outcome
# appended to each event/timestamp.
PrintNoNewLine('\nProcessing trials...')
trials = ProcessTrials(seg=segment,
name='Events',
startoftrial=start,
epochs=epochs,
typedf=typeframe,
appendmultiple=False)
print('Done!')
# With processed trials, we comb through each epoch ('correct', 'omission'
# etc.) and find start/end times for each trial. Start time is determined
# by the earliest 'start' event in a trial. Stop time is determined by
# 1) the earliest 'end' event in a trial, 2) or the 'last' event in a trial
# or the 3) 'next' event in the following trial.
PrintNoNewLine('\nCalculating epoch times and durations...')
GroupTrialsByEpoch(seg=segment,
startoftrial=start,
endoftrial=end,
endeventmissing='last')
print('Done!')
segment.processed = True
################### ALIGN DATA ##########################################
# for segment in seglist:
for block in analysis_blocks:
# Extract analysis block params
epoch_name = block['epoch_name']
event = block['event']
prewindow = block['prewindow']
postwindow = block['postwindow']
downsample = block['downsample']
after_alignment = block['after_alignment']
quantification = block['quantification']
baseline_window = block['baseline_window']
response_window = block['response_window']
save_file_as = block['save_file_as']
heatmap_range = block['plot_paramaters']['heatmap_range']
smoothing_window = block['plot_paramaters']['smoothing_window']
lookup = {}
#############################################################################
######################## PROCESS SIGNALS (IF NECESSARY); PLOT; STATS ######
# Load data
# Checks to see if we have filtered the data before alignment
filter_channel_names = [
x for x in before_alignment_channels if 'filtered' in x
]
if len(filter_channel_names) == 0:
filter_channel_names = [signal_channel, reference_channel]
PrintNoNewLine(
'Centering trials and analyzing for filtered signal...')
for channel in filter_channel_names:
dict_name = epoch_name + '_' + channel
lookup[channel] = dict_name
results = AlignEventsAndSignals(seg=segment,
epoch_name=epoch_name,
analog_ch_name=channel,
event_ch_name='Events',
event=event,
event_type='label',
prewindow=prewindow,
postwindow=postwindow,
window_type='event',
clip=False,
name=dict_name,
to_csv=False,
dpath=dpath)
print('Done!')
filter_signal_name = [
x for x in filter_channel_names if 'signal' in x
][0]
filter_reference_name = [
x for x in filter_channel_names if 'reference' in x
][0]
signal = segment.analyzed[
lookup[filter_signal_name]]['all_traces']
reference = segment.analyzed[
lookup[filter_reference_name]]['all_traces']
# check to see if we need to filter after alignment
for process in [
x for x in after_alignment if x['type'] == 'filter'
]:
data = {
filter_signal_name: signal,
filter_reference_name: reference
}
signal, reference = SingleStepProcessSignalData(
data=data,
process_type=process['type'],
input_sig_ch=filter_signal_name,
input_ref_ch=filter_reference_name,
datatype='dataframe',
**process['options'])
filter_signal_name = 'filtered_signal'
filter_reference_name = 'filtered_reference'
lookup[
filter_signal_name] = epoch_name + '_' + filter_signal_name
lookup[
filter_reference_name] = epoch_name + '_' + filter_reference_name
if lookup[filter_signal_name] in list(segment.analyzed.keys()):
segment.analyzed[lookup[filter_signal_name]][
'all_traces'] = signal.copy()
segment.analyzed[lookup[filter_reference_name]][
'all_traces'] = reference.copy()
else:
segment.analyzed[lookup[filter_signal_name]] = {
'all_traces': signal.copy(),
'all_events': results
}
segment.analyzed[lookup[filter_reference_name]] = {
'all_traces': reference.copy(),
'all_events': results
}
# Get plotting read
figure = plt.figure(figsize=(12, 12))
figure.subplots_adjust(hspace=1.3)
ax1 = plt.subplot2grid((6, 2), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((6, 2), (2, 0), rowspan=2)
ax3 = plt.subplot2grid((6, 2), (4, 0), rowspan=2)
ax4 = plt.subplot2grid((6, 2), (0, 1), rowspan=3)
ax5 = plt.subplot2grid((6, 2), (3, 1), rowspan=3)
############################### PLOT AVERAGE EVOKED RESPONSE ######################
PrintNoNewLine(
'Calculating average filtered responses for %s trials...' %
epoch_name)
signal_mean = signal.mean(axis=1)
reference_mean = reference.mean(axis=1)
signal_sem = signal.sem(axis=1)
reference_sem = reference.sem(axis=1)
signal_dc = signal_mean.mean()
reference_dc = reference_mean.mean()
signal_avg_response = signal_mean - signal_dc
reference_avg_response = reference_mean - reference_dc
if smoothing_window is not None:
signal_avg_response = SmoothSignalWithPeriod(
x=signal_avg_response,
sampling_rate=float(sampling_rate) / downsample,
ms_bin=smoothing_window,
window='flat')
reference_avg_response = SmoothSignalWithPeriod(
x=reference_avg_response,
sampling_rate=float(sampling_rate) / downsample,
ms_bin=smoothing_window,
window='flat')
signal_sem = SmoothSignalWithPeriod(
x=signal_sem,
sampling_rate=float(sampling_rate) / downsample,
ms_bin=smoothing_window,
window='flat')
reference_sem = SmoothSignalWithPeriod(
x=reference_sem,
sampling_rate=float(sampling_rate) / downsample,
ms_bin=smoothing_window,
window='flat')
curr_ax = ax1
curr_ax.plot(signal_avg_response.index,
signal_avg_response.values,
color='b',
linewidth=2)
curr_ax.fill_between(signal_avg_response.index,
(signal_avg_response - signal_sem).values,
(signal_avg_response + signal_sem).values,
color='b',
alpha=0.05)
# Plotting reference
curr_ax.plot(reference_avg_response.index,
reference_avg_response.values,
color='g',
linewidth=2)
curr_ax.fill_between(
reference_avg_response.index,
(reference_avg_response - reference_sem).values,
(reference_avg_response + reference_sem).values,
color='g',
alpha=0.05)
# Plot event onset
curr_ax.axvline(0, color='black', linestyle='--')
curr_ax.set_ylabel('Voltage (V)')
curr_ax.set_xlabel('Time (s)')
curr_ax.legend(['465 nm', '405 nm', event])
curr_ax.set_title(
'Average Lowpass Signal $\pm$ SEM: {} Trials'.format(
signal.shape[1]))
print('Done!')
################################################################################################################
############################# Calculate detrended signal #################################
# # Detrending
# PrintNoNewLine('Detrending signal...')
# fits = np.array([np.polyfit(reference.values[:, i],signal.values[:, i],1) for i in xrange(signal.shape[1])])
# Y_fit_all = np.array([np.polyval(fits[i], reference.values[:,i]) for i in np.arange(reference.values.shape[1])]).T
# Y_df_all = signal.values - Y_fit_all
# # detrended_signal = pd.DataFrame(Y_df_all, index=signal.index)
# Checks to see if we have detrended the data before alignment
detrend_channel_names = [
x for x in before_alignment_channels if 'detrended' in x
]
if len(detrend_channel_names) == 0:
detrend_channel_names = [
filter_signal_name, filter_reference_name
]
PrintNoNewLine(
'Centering trials and analyzing for detrended signal...')
try:
for channel in detrend_channel_names:
dict_name = epoch_name + '_' + channel
lookup[channel] = dict_name
results = AlignEventsAndSignals(seg=segment,
epoch_name=epoch_name,
analog_ch_name=channel,
event_ch_name='Events',
event=event,
event_type='label',
prewindow=prewindow,
postwindow=postwindow,
window_type='event',
clip=False,
name=dict_name,
to_csv=False,
dpath=dpath)
print('Done!')
except:
print('No detrending before alignment...')
detrend_signal_name = [
x for x in detrend_channel_names if 'signal' in x
][0]
detrend_reference_name = [
x for x in detrend_channel_names if 'reference' in x
][0]
detrended_signal = segment.analyzed[
lookup[detrend_signal_name]]['all_traces']
detrended_reference = segment.analyzed[
lookup[detrend_reference_name]]['all_traces']
# check to see if we need to filter after alignment
for process in [
x for x in after_alignment if x['type'] == 'detrend'
]:
data = {
detrend_signal_name: detrended_signal,
detrend_reference_name: detrended_reference
}
detrended_signal, detrended_reference = SingleStepProcessSignalData(
data=data,
process_type=process['type'],
input_sig_ch=detrend_signal_name,
input_ref_ch=detrend_reference_name,
datatype='dataframe',
**process['options'])
detrend_signal_name = 'detrended_signal'
detrend_reference_name = 'detrended_reference'
lookup[
detrend_signal_name] = epoch_name + '_' + detrend_signal_name
lookup[
detrend_reference_name] = epoch_name + '_' + detrend_reference_name
if lookup[detrend_signal_name] in list(
segment.analyzed.keys()):
segment.analyzed[lookup[detrend_signal_name]][
'all_traces'] = detrended_signal.copy()
segment.analyzed[lookup[detrend_reference_name]][
'all_traces'] = detrended_reference.copy()
else:
segment.analyzed[lookup[detrend_signal_name]] = {
'all_traces': detrended_signal.copy(),
'all_events': results
}
segment.analyzed[lookup[detrend_reference_name]] = {
'all_traces': detrended_reference.copy(),
'all_events': results
}
# if downsample > 0:
# detrended_signal.reset_index(inplace=True)
# detrended_reference.reset_index(inplace=True)
# sample = (detrended_signal.index.to_series() / downsample).astype(int)
# detrended_signal = detrended_signal.groupby(sample).mean()
# detrended_reference = detrended_reference.groupby(sample).mean()
# detrended_signal = detrended_signal.set_index('index')
# detrended_reference = detrended_reference.set_index('index')
################# PLOT DETRENDED SIGNAL ###################################
detrended_signal_mean = detrended_signal.mean(axis=1)
detrended_signal_sem = detrended_signal.sem(axis=1)
if smoothing_window is not None:
detrended_signal_mean = SmoothSignalWithPeriod(
x=detrended_signal_mean,
sampling_rate=float(sampling_rate) / downsample,
ms_bin=smoothing_window,
window='flat')
detrended_signal_sem = SmoothSignalWithPeriod(
x=detrended_signal_sem,
sampling_rate=float(sampling_rate) / downsample,
ms_bin=smoothing_window,
window='flat')
# Plotting signal
# current axis
curr_ax = ax2
# # curr_ax = axs[1, 0]
#curr_ax = plt.axes()
if any([x['type'] == 'measure' for x in before_alignment]):
pass
else:
z_score_window = [
x['options']['period'] for x in after_alignment
if x['type'] == 'measure'
][0]
zscore_start = detrended_signal[
z_score_window[0]:z_score_window[1]].index[0]
zscore_end = detrended_signal[
z_score_window[0]:z_score_window[1]].index[-1]
zscore_height = detrended_signal[
z_score_window[0]:z_score_window[1]].mean(
axis=1).min()
if zscore_height < 0:
zscore_height = zscore_height * 1.3
else:
zscore_height = zscore_height * 0.7
curr_ax.plot([zscore_start, zscore_end],
[zscore_height, zscore_height],
color='.1',
linewidth=3)
curr_ax.plot(detrended_signal_mean.index,
detrended_signal_mean.values,
color='b',
linewidth=2)
curr_ax.fill_between(
detrended_signal_mean.index,
(detrended_signal_mean - detrended_signal_sem).values,
(detrended_signal_mean + detrended_signal_sem).values,
color='b',
alpha=0.05)
# Plot event onset
if any([x['type'] == 'measure' for x in before_alignment]):
pass
else:
curr_ax.legend(['z-score window'])
curr_ax.axvline(0, color='black', linestyle='--')
curr_ax.set_ylabel('Voltage (V)')
curr_ax.set_xlabel('Time (s)')
curr_ax.set_title('465 nm Average Detrended Signal $\pm$ SEM')
print('Done!')
# ########### Calculate z-scores ###############################################
measure_channel_names = [
x for x in before_alignment_channels if 'measure' in x
]
if len(measure_channel_names) == 0:
measure_channel_names = [
detrend_signal_name, detrend_reference_name
]
PrintNoNewLine(
'Centering trials and analyzing for z scores...')
try:
for channel in measure_channel_names:
dict_name = epoch_name + '_' + channel
lookup[channel] = dict_name
results = AlignEventsAndSignals(seg=segment,
epoch_name=epoch_name,
analog_ch_name=channel,
event_ch_name='Events',
event=event,
event_type='label',
prewindow=prewindow,
postwindow=postwindow,
window_type='event',
clip=False,
name=dict_name,
to_csv=False,
dpath=dpath)
except:
print('No z scores before alignment...')
print('Done!')
measure_signal_name = [
x for x in measure_channel_names if 'signal' in x
][0]
measure_reference_name = [
x for x in measure_channel_names if 'reference' in x
][0]
measure_signal = segment.analyzed[
lookup[measure_signal_name]]['all_traces']
measure_reference = segment.analyzed[
lookup[measure_reference_name]]['all_traces']
# check to see if we need to filter after alignment
for process in [
x for x in after_alignment if x['type'] == 'measure'
]:
data = {
measure_signal_name: measure_signal,
measure_reference_name: measure_reference
}
measure_signal, measure_reference = SingleStepProcessSignalData(
data=data,
process_type=process['type'],
input_sig_ch=measure_signal_name,
input_ref_ch=measure_reference_name,
datatype='dataframe',
**process['options'])
measure_signal_name = 'measure_signal'
measure_reference_name = 'measure_reference'
lookup[
measure_signal_name] = epoch_name + '_' + measure_signal_name
lookup[
measure_reference_name] = epoch_name + '_' + measure_reference_name
if lookup[measure_signal_name] in list(
segment.analyzed.keys()):
segment.analyzed[lookup[measure_signal_name]][
'all_traces'] = measure_signal.copy()
segment.analyzed[lookup[measure_reference_name]][
'all_traces'] = measure_reference.copy()
else:
segment.analyzed[lookup[measure_signal_name]] = {
'all_traces': measure_signal.copy(),
'all_events': results
}
segment.analyzed[lookup[measure_reference_name]] = {
'all_traces': measure_reference.copy(),
'all_events': results
}
# if downsample > 0:
# measure_signal.reset_index(inplace=True)
# measure_reference.reset_index(inplace=True)
# sample = (measure_signal.index.to_series() / downsample).astype(int)
# measure_signal = measure_signal.groupby(sample).mean()
# measure_reference = measure_reference.groupby(sample).mean()
# measure_signal = measure_signal.set_index('index')
# measure_reference = measure_reference.set_index('index')
zscores = measure_signal.copy()
############################ Make rasters #######################################
PrintNoNewLine('Making heatmap for %s trials...' % event)
# indice that is closest to event onset
# curr_ax = axs[0, 1]
curr_ax = ax4
# curr_ax = plt.axes()
# Plot nearest point to time zero
zero = np.concatenate([
np.where(zscores.index == np.abs(zscores.index).min())[0],
np.where(zscores.index == -1 *
np.abs(zscores.index).min())[0]
]).min()
for_hm = zscores.T.copy()
for_hm.reset_index(drop=True, inplace=True)
# for_hm.index = for_hm.index + 1
for_hm.columns = np.round(for_hm.columns, 1)
try:
sns.heatmap(for_hm.iloc[::-1],
center=0,
robust=True,
ax=curr_ax,
cmap='bwr',
xticklabels=int(for_hm.shape[1] * .15),
yticklabels=int(for_hm.shape[0] * .15),
vmin=heatmap_range[0],
vmax=heatmap_range[1])
except:
sns.heatmap(for_hm.iloc[::-1],
center=0,
robust=True,
ax=curr_ax,
cmap='bwr',
xticklabels=int(for_hm.shape[1] * .15),
vmin=heatmap_range[0],
vmax=heatmap_range[1])
curr_ax.axvline(zero,
linestyle='--',
color='black',
linewidth=2)
curr_ax.set_ylabel('Trial')
curr_ax.set_xlabel('Time (s)')
if any([x['type'] == 'measure' for x in before_alignment]):
period = [
x['options']['period'] for x in before_alignment
if x['type'] == 'measure'
][0]
sampling_per = segment.analogsignals[0].sampling_period
curr_ax.set_title(
'Z-Score Heat Map \n Baseline Window: {} to {} Seconds'
.format(round(period[0] * sampling_per),
round(period[1] * sampling_per)))
else:
curr_ax.set_title(
'Z-Score Heat Map \n Baseline Window: {} to {} Seconds'
.format(z_score_window[0], z_score_window[1]))
print('Done!')
########################## Plot Z-score waveform ##########################
PrintNoNewLine('Plotting Z-Score waveforms...')
zscores_mean = zscores.mean(axis=1)
zscores_sem = zscores.sem(axis=1)
if smoothing_window is not None:
zscores_mean = SmoothSignalWithPeriod(
x=zscores_mean,
sampling_rate=float(sampling_rate) / downsample,
ms_bin=smoothing_window,
window='flat')
zscores_sem = SmoothSignalWithPeriod(
x=zscores_sem,
sampling_rate=float(sampling_rate) / downsample,
ms_bin=smoothing_window,
window='flat')
# Plotting signal
# current axis
# curr_ax = axs[1, 1]
curr_ax = ax3
#curr_ax = plt.axes()
# Plot baseline and response
legend = []
if baseline_window_unaligned is False:
baseline_start = zscores[
baseline_window[0]:baseline_window[1]].index[0]
baseline_end = zscores[
baseline_window[0]:baseline_window[1]].index[-1]
baseline_height = zscores[
baseline_window[0]:baseline_window[1]].mean(
axis=1).min() - 0.5
curr_ax.plot([baseline_start, baseline_end],
[baseline_height, baseline_height],
color='.6',
linewidth=3)
legend.append('baseline window')
response_start = zscores[
response_window[0]:response_window[1]].index[0]
response_end = zscores[
response_window[0]:response_window[1]].index[-1]
response_height = zscores[
response_window[0]:response_window[1]].mean(
axis=1).max() + .5
curr_ax.plot([response_start, response_end],
[response_height, response_height],
color='r',
linewidth=3)
legend.append('response window')
curr_ax.plot(zscores_mean.index,
zscores_mean.values,
color='b',
linewidth=2)
curr_ax.fill_between(zscores_mean.index,
(zscores_mean - zscores_sem).values,
(zscores_mean + zscores_sem).values,
color='b',
alpha=0.05)
# Plot event onset
curr_ax.axvline(0, color='black', linestyle='--')
curr_ax.set_ylabel('Z-Score')
curr_ax.set_xlabel('Time (s)')
curr_ax.legend(legend)
curr_ax.set_title('465 nm Average Z-Score Signal $\pm$ SEM')
print('Done!')
##################### Quantification #################################
PrintNoNewLine(
'Performing statistical testing on baseline vs response periods...'
)
if quantification is not None:
if baseline_window_unaligned:
baseline_window = process['options']['period']
baseline_window_values = [
x.magnitude for x in segment.analogsignals
if x.name == 'measure_signal'
][0]
else:
baseline_window_values = zscores
# Generating summary statistics
if quantification == 'AUC':
base = np.trapz(baseline_window_values[
baseline_window[0]:baseline_window[1]],
axis=0)
resp = np.trapz(
zscores[response_window[0]:response_window[1]],
axis=0)
ylabel = 'AUC'
elif quantification == 'mean':
base = np.mean(baseline_window_values[
baseline_window[0]:baseline_window[1]],
axis=0)
resp = np.mean(
zscores[response_window[0]:response_window[1]],
axis=0)
ylabel = 'Z-Score'
elif quantification == 'median':
base = np.median(baseline_window_values[
baseline_window[0]:baseline_window[1]],
axis=0)
resp = np.median(
zscores[response_window[0]:response_window[1]],
axis=0)
ylabel = 'Z-Score'
if isinstance(base, pd.core.series.Series):
base = base.values
if isinstance(resp, pd.core.series.Series):
resp = resp.values
base_sem = np.mean(base) / np.sqrt(base.shape[0])
resp_sem = np.mean(resp) / np.sqrt(resp.shape[0])
# Testing for normality (D'Agostino's K-Squared Test) (N>8)
if base.shape[0] > 8:
normal_alpha = 0.05
base_normal = stats.normaltest(base)
resp_normal = stats.normaltest(resp)
else:
normal_alpha = 0.05
base_normal = [1, 1]
resp_normal = [1, 1]
difference_alpha = 0.05
if baseline_window_unaligned is True:
test = 'One Sample T-Test'
stats_results = stats.ttest_1samp(resp, base[0])
elif (base_normal[1] >= normal_alpha) or (resp_normal[1] >=
normal_alpha):
test = 'Wilcoxon Signed-Rank Test'
stats_results = stats.wilcoxon(base, resp)
else:
test = 'Paired Sample T-Test'
stats_results = stats.ttest_rel(base, resp)
if stats_results[1] <= difference_alpha:
sig = '**'
else:
sig = 'ns'
#curr_ax = plt.axes()
curr_ax = ax5
ind = np.arange(2)
labels = ['baseline', 'response']
bar_kwargs = {
'width': 0.7,
'color': ['.6', 'r'],
'linewidth': 2,
'zorder': 5
}
err_kwargs = {
'zorder': 0,
'fmt': 'none',
'linewidth': 2,
'ecolor': 'k'
}
curr_ax.bar(ind, [base.mean(), resp.mean()],
tick_label=labels,
**bar_kwargs)
curr_ax.errorbar(ind,
[base.mean(), resp.mean()],
yerr=[base_sem, resp_sem],
capsize=5,
**err_kwargs)
x1, x2 = 0, 1
y = np.max([base.mean(), resp.mean()
]) + np.max([base_sem, resp_sem]) * 1.3
h = y * 1.5
col = 'k'
curr_ax.plot([x1, x1, x2, x2], [y, y + h, y + h, y],
lw=1.5,
c=col)
curr_ax.text((x1 + x2) * .5,
y + h,
sig,
ha='center',
va='bottom',
color=col)
curr_ax.set_ylabel(ylabel)
curr_ax.set_title(
'Baseline vs. Response Changes in Z-Score Signal \n {} of {}s'
.format(test, quantification))
print('Done!')
################# Save Stuff ##################################
PrintNoNewLine('Saving everything...')
save_path = os.path.join(dpath, segment_name, save_file_as)
figure.savefig(save_path + '.png', format='png')
# figure.savefig(save_path + '.pdf', format='pdf')
plt.close()
print('Done!')
# Trial z-scores
# Fix columns
zscores.columns = np.arange(1, zscores.shape[1] + 1)
zscores.columns.name = 'trial'
# Fix rows
zscores.index.name = 'time'
if downsample > 0:
zscores = Downsample(zscores, downsample, index_col='time')
zscores.to_csv(save_path + '_zscores_aligned.csv')
if quantification is not None:
# Trial point estimates
point_estimates = pd.DataFrame(
{
'baseline': base,
'response': resp
},
index=np.arange(1, resp.shape[0] + 1)).ffill().bfill()
point_estimates.index.name = 'trial'
point_estimates.to_csv(save_path + '_point_estimates.csv')
# Save meta data
metadata = {
'baseline_window': baseline_window,
'response_window': response_window,
'quantification': quantification,
'original_sampling_rate': float(sampling_rate),
'downsampled_sampling_rate':
float(sampling_rate) / downsample
}
with open(save_path + '_metadata.json', 'w') as fp:
json.dump(metadata, fp)
# Save smoothed data
smoothed_zscore = pd.concat([zscores_mean, zscores_sem],
axis=1)
smoothed_zscore.columns = ['mean', 'sem']
if downsample > 0:
smoothed_zscore = Downsample(smoothed_zscore,
downsample,
index_col='time')
smoothed_zscore.to_csv(save_path + '_smoothed_zscores.csv')
print(('Finished processing datapath: %s' % dpath))