def run(self):
experiment = self.pipeline.experiment
events = self.get_passed_object(experiment+'_events')
tal_info = self.get_passed_object('tal_info')
prob_pre = self.get_passed_object('prob_pre')
prob_diff = self.get_passed_object('prob_diff')
ctrl_prob_pre = self.get_passed_object('ctrl_prob_pre')
ctrl_prob_diff0 = self.get_passed_object('ctrl_prob_diff0')
ctrl_prob_diff250 = ctrl_prob_diff500 = ctrl_prob_diff1000 = None
try:
ctrl_prob_diff250 = self.get_passed_object('ctrl_prob_diff250')
except:
pass
try:
ctrl_prob_diff500 = self.get_passed_object('ctrl_prob_diff500')
except:
pass
try:
ctrl_prob_diff1000 = self.get_passed_object('ctrl_prob_diff1000')
except:
pass
sessions = np.unique(events.session)
self.pass_object('NUMBER_OF_SESSIONS', len(sessions))
self.pass_object('NUMBER_OF_ELECTRODES', len(tal_info))
session_data = []
all_durs_ev = np.array([])
all_amps_ev = np.array([])
all_freqs_ev = np.array([])
all_burfs_ev = np.array([])
all_durs = np.array([])
all_amps = np.array([])
all_freqs = np.array([])
all_burfs = np.array([])
session_summary_array = []
prob_array_idx = 0
for session in sessions:
session_summary = SessionSummary()
session_summary.sess_num = session
session_events = events[events.session == session]
n_sess_events = len(session_events)
timestamps = session_events.mstime
first_time_stamp = np.min(timestamps)
last_time_stamp = np.max(timestamps)
session_length = '%.2f' % ((last_time_stamp - first_time_stamp) / 60000.0)
session_date = time.strftime('%d-%b-%Y', time.localtime(last_time_stamp/1000))
session_data.append([session, session_date, session_length])
session_name = 'Sess%02d' % session
stim_tag = session_events[0].stimAnodeTag + '-' + session_events[0].stimCathodeTag
isi_min = np.nanmin(session_events.isi)
isi_max = np.nanmax(session_events.isi)
isi_mid = (isi_max+isi_min) / 2.0
isi_halfrange = isi_max - isi_mid
print 'Session =', session_name, ' StimTag =', stim_tag, ' ISI =', isi_mid, '+/-', isi_halfrange
durs_ev = session_events.pulse_duration
amps_ev = session_events.amplitude
freqs_ev = session_events.pulse_frequency
burfs_ev = session_events.burst_frequency
all_durs_ev = np.hstack((all_durs_ev, durs_ev))
all_amps_ev = np.hstack((all_amps_ev, amps_ev))
all_freqs_ev = np.hstack((all_freqs_ev, freqs_ev))
all_burfs_ev = np.hstack((all_burfs_ev, burfs_ev))
durs = np.unique(durs_ev)
amps = np.unique(amps_ev)
freqs = np.unique(freqs_ev)
burfs = np.unique(burfs_ev)
session_summary.name = session_name
session_summary.length = session_length
session_summary.date = session_date
session_summary.stimtag = stim_tag
session_summary.isi_mid = isi_mid
session_summary.isi_half_range = isi_halfrange
session_prob_pre = prob_pre[prob_array_idx:prob_array_idx+n_sess_events]
session_prob_diff = prob_diff[prob_array_idx:prob_array_idx+n_sess_events]
session_ctrl_prob_pre = ctrl_prob_pre
session_ctrl_prob_diff = ctrl_prob_diff1000 if durs[-1]==1000 else ctrl_prob_diff500 if durs[-1]==500 else ctrl_prob_diff250
all_durs = np.hstack((all_durs, durs))
all_amps = np.hstack((all_amps, amps))
all_freqs = np.hstack((all_freqs, freqs))
all_burfs = np.hstack((all_burfs, burfs))
ev_vals = None
param_grid = None
if experiment == 'PS1':
ev_vals = [freqs_ev, durs_ev]
param_grid = [freqs, durs]
session_summary.constant_name = 'Amplitude'
session_summary.constant_value = amps[0]
session_summary.constant_unit = 'mA'
session_summary.parameter1 = 'Pulse Frequency'
session_summary.parameter2 = 'Duration'
elif experiment == 'PS2':
ev_vals = [freqs_ev, amps_ev]
param_grid = [freqs, amps]
session_summary.constant_name = 'Duration'
session_summary.constant_value = durs[-1]
session_summary.constant_unit = 'ms'
session_summary.parameter1 = 'Pulse Frequency'
session_summary.parameter2 = 'Amplitude'
elif experiment == 'PS3':
ev_vals = [freqs_ev, burfs_ev]
param_grid = [freqs, burfs]
session_summary.constant_name = 'Amplitude'
session_summary.constant_value = amps[0]
session_summary.constant_unit = 'mA'
session_summary.parameter1 = 'Pulse Frequency'
session_summary.parameter2 = 'Burst Frequency'
delta_stats = DeltaStats(2, ev_vals, param_grid, session_prob_pre, session_prob_diff, session_ctrl_prob_pre, session_ctrl_prob_diff, 1.0/3.0)
delta_stats.run()
anova = anova_test(ev_vals, param_grid, session_prob_diff)
if anova is not None:
session_summary.anova_fvalues = anova[0]
session_summary.anova_pvalues = anova[1]
data_point_indexes_left = np.arange(1,len(param_grid[0])+2)
data_point_indexes_right = np.arange(1,len(param_grid[1])+2)
# computting y axis limits
min_plot, max_plot = delta_stats.y_range()
ylim = [min_plot-0.1*(max_plot-min_plot), max_plot+0.1*(max_plot-min_plot)]
x_tick_labels = ['CONTROL'] + [x if x>0 else 'PULSE' for x in param_grid[0]]
session_summary.plot_data_dict[(0,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_all[0], yerr=delta_stats.stdev_all[0], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary.plot_data_dict[(1,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_low[0], yerr=delta_stats.stdev_low[0], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary.plot_data_dict[(2,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_high[0], yerr=delta_stats.stdev_high[0], x_tick_labels=x_tick_labels, ylim=ylim)
x_tick_labels = ['CONTROL'] + list(param_grid[1])
session_summary.plot_data_dict[(0,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_all[1], yerr=delta_stats.stdev_all[1], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary.plot_data_dict[(1,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_low[1], yerr=delta_stats.stdev_low[1], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary.plot_data_dict[(2,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_high[1], yerr=delta_stats.stdev_high[1], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary_array.append(session_summary)
prob_array_idx += len(session_events)
self.pass_object('SESSION_DATA', session_data)
self.pass_object('session_summary_array', session_summary_array)
isi_min = np.nanmin(events.isi)
isi_max = np.nanmax(events.isi)
isi_mid = (isi_max+isi_min) / 2.0
isi_halfrange = isi_max - isi_mid
print 'ISI =', isi_mid, '+/-', isi_halfrange
self.pass_object('CUMULATIVE_ISI_MID',isi_mid)
self.pass_object('CUMULATIVE_ISI_HALF_RANGE',isi_halfrange)
durs = np.unique(all_durs)
amps = np.unique(all_amps)
freqs = np.unique(all_freqs)
burfs = np.unique(all_burfs)
ctrl_prob_diff = ctrl_prob_diff1000 if durs[-1]==1000 else ctrl_prob_diff500 if durs[-1]==500 else ctrl_prob_diff250
ev_vals = None
param_grid = None
if experiment == 'PS1':
ev_vals = [all_freqs_ev, all_durs_ev]
param_grid = [freqs, durs]
self.pass_object('CUMULATIVE_PARAMETER1', 'Pulse Frequency')
self.pass_object('CUMULATIVE_PARAMETER2', 'Duration')
elif experiment == 'PS2':
ev_vals = [all_freqs_ev, all_amps_ev]
param_grid = [freqs, amps]
self.pass_object('CUMULATIVE_PARAMETER1', 'Pulse Frequency')
self.pass_object('CUMULATIVE_PARAMETER2', 'Amplitude')
elif experiment == 'PS3':
ev_vals = [all_freqs_ev, all_burfs_ev]
param_grid = [freqs, burfs]
self.pass_object('CUMULATIVE_PARAMETER1', 'Pulse Frequency')
self.pass_object('CUMULATIVE_PARAMETER2', 'Burst Frequency')
delta_stats = DeltaStats(2, ev_vals, param_grid, prob_pre, prob_diff, ctrl_prob_pre, ctrl_prob_diff, 1.0/3.0)
delta_stats.run()
data_point_indexes_left = np.arange(1,len(param_grid[0])+2)
data_point_indexes_right = np.arange(1,len(param_grid[1])+2)
# computing y axis limits
min_plot, max_plot = delta_stats.y_range()
ylim = [min_plot-0.1*(max_plot-min_plot), max_plot+0.1*(max_plot-min_plot)]
cumulative_plot_data_dict = OrderedDict()
x_tick_labels = ['CONTROL'] + [x if x>0 else 'PULSE' for x in param_grid[0]]
cumulative_plot_data_dict[(0,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_all[0], yerr=delta_stats.stdev_all[0], x_tick_labels=x_tick_labels, ylim=ylim)
cumulative_plot_data_dict[(1,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_low[0], yerr=delta_stats.stdev_low[0], x_tick_labels=x_tick_labels, ylim=ylim)
cumulative_plot_data_dict[(2,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_high[0], yerr=delta_stats.stdev_high[0], x_tick_labels=x_tick_labels, ylim=ylim)
x_tick_labels = ['CONTROL'] + list(param_grid[1])
cumulative_plot_data_dict[(0,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_all[1], yerr=delta_stats.stdev_all[1], x_tick_labels=x_tick_labels, ylim=ylim)
cumulative_plot_data_dict[(1,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_low[1], yerr=delta_stats.stdev_low[1], x_tick_labels=x_tick_labels, ylim=ylim)
cumulative_plot_data_dict[(2,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_high[1], yerr=delta_stats.stdev_high[1], x_tick_labels=x_tick_labels, ylim=ylim)
self.pass_object('cumulative_plot_data_dict', cumulative_plot_data_dict)
def run(self):
subject_id = self.pipeline.subject_id
experiment = self.pipeline.experiment
PostStimBuff = 50 # buffer in ms to leave following stim offset
w_dir = self.get_workspace_dir()
paramsPS = deserialize_single_object_from_matlab_format(join(w_dir,'paramsPS.mat'),'params')
bpFull = deserialize_single_object_from_matlab_format(join(w_dir,'bpFull.mat'),'bpFull')
bp = deserialize_single_object_from_matlab_format(join(w_dir,'bp.mat'),'bp')
Weights = deserialize_single_object_from_matlab_format(join(w_dir,'Weights.mat'),'Weights')
ps_events = deserialize_single_object_from_matlab_format(join(w_dir,'PSEvents.mat'),'PSEvents')
ps_events_size = len(ps_events)
for ev in ps_events:
ev.ISI = np.nan
for i in xrange(1, ps_events_size):
ev_curr = ps_events[i]
ev_prev = ps_events[i-1]
if ev_curr.session == ev_prev.session:
if is_stim_event(ev_curr) and is_stim_event(ev_prev):
ev_curr.ISI = ev_curr.mstime - ev_prev.mstime
ps_events = [ev for ev in ps_events if is_stim_event(ev)]
print 'stim events size = ', len(ps_events)
ps_sessions = np.unique([e.session for e in ps_events])
# print 'ps_sessions=',ps_sessions
self.pass_object('NUMBER_OF_SESSIONS',len(ps_sessions))
self.pass_object('NUMBER_OF_ELECTRODES',len(bp))
session_data = []
all_durs_ev = np.array([])
all_amps_ev = np.array([])
all_freqs_ev = np.array([])
all_burfs_ev = np.array([])
all_durs = np.array([])
all_amps = np.array([])
all_freqs = np.array([])
all_burfs = np.array([])
ProbPostAllSessions = np.array([])
ProbPreAllSessions = np.array([])
ProbDiffAllSessions = np.array([])
session_summary_array = []
for SessNum in ps_sessions:
session_summary = SessionSummary() # object that contains all the report related information for a given session
SessEv = [ev for ev in ps_events if ev.session == SessNum]
timestamps = [ev.mstime for ev in SessEv]
firstTimestamp = min(timestamps)
lastTimestamp = max(timestamps)
SessLength = (lastTimestamp-firstTimestamp)/60000.0
SessDate = time.strftime('%Y-%m-%d', time.localtime(lastTimestamp/1000))
session_data.append([SessNum, SessDate, SessLength])
SessName = 'Sess%02d' % SessNum
StimTag = SessEv[0].stimAnodeTag + '-' + SessEv[0].stimCathodeTag
#self.pipeline.add_object_to_pass('STIMTAG',StimTag)
ISI_min = np.nanmin([ev.ISI for ev in SessEv])
ISI_max = np.nanmax([ev.ISI for ev in SessEv])
ISI_mid = (ISI_max+ISI_min) / 2.0
ISI_halfrange = ISI_max - ISI_mid
print 'Session =', SessName, ' StimTag =', StimTag, ' ISI =', ISI_mid, '+/-', ISI_halfrange
durs_ev = [s.pulse_duration for s in SessEv]
amps_ev = [s.amplitude for s in SessEv]
freqs_ev = [s.pulse_frequency for s in SessEv]
burfs_ev = [s.burst_frequency for s in SessEv]
all_durs_ev = np.hstack((all_durs_ev, durs_ev))
all_amps_ev = np.hstack((all_amps_ev, amps_ev))
all_freqs_ev = np.hstack((all_freqs_ev, freqs_ev))
all_burfs_ev = np.hstack((all_burfs_ev, burfs_ev))
durs = np.unique(durs_ev)
amps = np.unique(amps_ev)
freqs = np.unique(freqs_ev)
burfs = np.unique(burfs_ev)
session_summary.name = SessName
session_summary.length = SessLength
session_summary.date = SessDate
session_summary.stimtag = StimTag
session_summary.isi_mid = ISI_mid
session_summary.isi_half_range = ISI_halfrange
all_durs = np.hstack((all_durs, durs))
all_amps = np.hstack((all_amps, amps))
all_freqs = np.hstack((all_freqs, freqs))
all_burfs = np.hstack((all_burfs, burfs))
StimOnBin = np.ones(len(SessEv), dtype=np.int)*paramsPS.pow.onsetInd
PreStimInds = npm.repmat(paramsPS.pow.baseBins-1,len(SessEv),1);
PostStimBin = np.empty_like(StimOnBin, dtype=np.int)
PostStimInds = np.empty((len(PostStimBin),len(paramsPS.pow.baseBins)), dtype=np.int)
for iEv in xrange(len(SessEv)):
dur = SessEv[iEv].pulse_duration
if dur == -999:
dur = durs[-1]
PostStimBin[iEv] = indices(paramsPS.pow.timeBins[:,0], lambda x: x <= dur+PostStimBuff)[-1]
PostStimInds[iEv,:] = range(PostStimBin[iEv], PostStimBin[iEv]+len(paramsPS.pow.baseBins))
DataMat_PostStim = np.empty((50,len(bp),len(SessEv)))
DataMat_PreStim = np.empty((50,len(bp),len(SessEv)))
workspace_dir = self.get_workspace_dir()
for iElec in xrange(len(bp)):
power_file_name = abspath(join(workspace_dir,'power',subject_id, SessName,'%d-%d_Pow_bin_zs.mat'%(bp[iElec].channel[0],bp[iElec].channel[1])))
print power_file_name
bp_session_reader = MatlabIO()
bp_session_reader.deserialize(power_file_name)
PowMat = bp_session_reader.PowMat
pattern_PostStim = np.empty((50,len(SessEv)))
pattern_PreStim = np.empty((50,len(SessEv)))
for iEv in xrange(len(SessEv)):
pattern_PostStim[:, iEv] = np.nanmean(PowMat[:, PostStimInds[iEv,:],iEv],1)
pattern_PreStim[:, iEv] = np.nanmean(PowMat[:, PreStimInds[iEv,:],iEv],1)
DataMat_PostStim[:,iElec,:] = pattern_PostStim
DataMat_PreStim[:,iElec,:] = pattern_PreStim
DataMat_PostStim = DataMat_PostStim.reshape(50*len(bp),len(SessEv), order='F')
DataMat_PreStim = DataMat_PreStim.reshape(50*len(bp),len(SessEv), order='F')
W = np.ravel(Weights.MeanCV) # classifier Beta's
# Beta_0 Weights.MeanIntercept
ProbPost = logit(Weights.MeanIntercept, W, DataMat_PostStim)
ProbPre = logit(Weights.MeanIntercept, W, DataMat_PreStim)
ProbDiff = ProbPost - ProbPre
ProbPostAllSessions = np.hstack((ProbPostAllSessions, ProbPost))
ProbPreAllSessions = np.hstack((ProbPreAllSessions, ProbPre))
ProbDiffAllSessions = np.hstack((ProbDiffAllSessions, ProbDiff))
ev_vals = None
param_grid = None
if experiment == 'PS1':
ev_vals = [freqs_ev, durs_ev]
param_grid = [freqs, durs]
session_summary.constant_name = 'Amplitude'
session_summary.constant_value = amps[0]
session_summary.constant_unit = 'mA'
session_summary.parameter1 = 'Pulse Frequency'
session_summary.parameter2 = 'Duration'
elif experiment == 'PS2':
ev_vals = [freqs_ev, amps_ev]
param_grid = [freqs, amps]
session_summary.constant_name = 'Duration'
session_summary.constant_value = durs[-1]
session_summary.constant_unit = 'ms'
session_summary.parameter1 = 'Pulse Frequency'
session_summary.parameter2 = 'Amplitude'
elif experiment == 'PS3':
ev_vals = [freqs_ev, burfs_ev]
param_grid = [freqs, burfs]
session_summary.constant_name = 'Amplitude'
session_summary.constant_value = amps[0]
session_summary.constant_unit = 'mA'
session_summary.parameter1 = 'Pulse Frequency'
session_summary.parameter2 = 'Burst Frequency'
delta_stats = DeltaStats(2, ev_vals, param_grid, ProbPre, ProbDiff, 1.0/3.0)
delta_stats.run()
data_point_indexes_left = np.arange(1,len(param_grid[0])+1)
data_point_indexes_right = np.arange(1,len(param_grid[1])+1)
# computting y axis limits
min_plot, max_plot = delta_stats.y_range()
ylim = [min_plot-0.1*(max_plot-min_plot), max_plot+0.1*(max_plot-min_plot)]
x_tick_labels = [x if x>0 else 'PULSE' for x in param_grid[0]]
session_summary.plot_data_dict[(0,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_all[0], yerr=delta_stats.stdev_all[0], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary.plot_data_dict[(1,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_low[0], yerr=delta_stats.stdev_low[0], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary.plot_data_dict[(2,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_high[0], yerr=delta_stats.stdev_high[0], x_tick_labels=x_tick_labels, ylim=ylim)
x_tick_labels = param_grid[1]
session_summary.plot_data_dict[(0,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_all[1], yerr=delta_stats.stdev_all[1], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary.plot_data_dict[(1,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_low[1], yerr=delta_stats.stdev_low[1], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary.plot_data_dict[(2,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_high[1], yerr=delta_stats.stdev_high[1], x_tick_labels=x_tick_labels, ylim=ylim)
session_summary_array.append(session_summary)
self.pass_object('SESSION_DATA',session_data)
self.pass_object('session_summary_array',session_summary_array)
ISI_min = np.nanmin([ev.ISI for ev in ps_events])
ISI_max = np.nanmax([ev.ISI for ev in ps_events])
ISI_mid = (ISI_max+ISI_min) / 2.0
ISI_halfrange = ISI_max - ISI_mid
print 'ISI =', ISI_mid, '+/-', ISI_halfrange
self.pass_object('CUMULATIVE_ISI_MID',ISI_mid)
self.pass_object('CUMULATIVE_ISI_HALF_RANGE',ISI_halfrange)
durs = np.unique(all_durs)
amps = np.unique(all_amps)
freqs = np.unique(all_freqs)
burfs = np.unique(all_burfs)
ev_vals = None
param_grid = None
if experiment == 'PS1':
ev_vals = [all_freqs_ev, all_durs_ev]
param_grid = [freqs, durs]
self.pass_object('CUMULATIVE_PARAMETER1', 'Pulse Frequency')
self.pass_object('CUMULATIVE_PARAMETER2', 'Duration')
elif experiment == 'PS2':
ev_vals = [all_freqs_ev, all_amps_ev]
param_grid = [freqs, amps]
self.pass_object('CUMULATIVE_PARAMETER1', 'Pulse Frequency')
self.pass_object('CUMULATIVE_PARAMETER2', 'Amplitude')
elif experiment == 'PS3':
ev_vals = [all_freqs_ev, all_burfs_ev]
param_grid = [freqs, burfs]
self.pass_object('CUMULATIVE_PARAMETER1', 'Pulse Frequency')
self.pass_object('CUMULATIVE_PARAMETER2', 'Burst Frequency')
delta_stats = DeltaStats(2, ev_vals, param_grid, ProbPreAllSessions, ProbDiffAllSessions, 1.0/3.0)
delta_stats.run()
data_point_indexes_left = np.arange(1,len(param_grid[0])+1)
data_point_indexes_right = np.arange(1,len(param_grid[1])+1)
# computing y axis limits
min_plot, max_plot = delta_stats.y_range()
ylim = [min_plot-0.1*(max_plot-min_plot), max_plot+0.1*(max_plot-min_plot)]
cumulative_plot_data_dict = OrderedDict()
x_tick_labels = [x if x>0 else 'PULSE' for x in param_grid[0]]
cumulative_plot_data_dict[(0,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_all[0], yerr=delta_stats.stdev_all[0], x_tick_labels=x_tick_labels, ylim=ylim)
cumulative_plot_data_dict[(1,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_low[0], yerr=delta_stats.stdev_low[0], x_tick_labels=x_tick_labels, ylim=ylim)
cumulative_plot_data_dict[(2,0)] = PlotData(x=data_point_indexes_left, y=delta_stats.mean_high[0], yerr=delta_stats.stdev_high[0], x_tick_labels=x_tick_labels, ylim=ylim)
x_tick_labels = param_grid[1]
cumulative_plot_data_dict[(0,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_all[1], yerr=delta_stats.stdev_all[1], x_tick_labels=x_tick_labels, ylim=ylim)
cumulative_plot_data_dict[(1,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_low[1], yerr=delta_stats.stdev_low[1], x_tick_labels=x_tick_labels, ylim=ylim)
cumulative_plot_data_dict[(2,1)] = PlotData(x=data_point_indexes_right, y=delta_stats.mean_high[1], yerr=delta_stats.stdev_high[1], x_tick_labels=x_tick_labels, ylim=ylim)
self.pass_object('cumulative_plot_data_dict', cumulative_plot_data_dict)
