def plot_raster_stim_trial(spikes, trials, clusters,
stim, trial, period, rec, fs, plot_params=None, ax=None):
'''
Plots a spike raster for all cells for a single trial of a single stimulus
'''
nclus = len(clusters)
cluIDs = clusters['cluster'].values
stim_trials = trials[trials['stimulus']==stim]
this_trial = stim_trials.iloc[trial]
stim_start = this_trial['time_samples']
stim_end = this_trial['stimulus_end']
stim_end_seconds = np.unique((stim_end - stim_start)/fs)[0]
window = [period[0], stim_end_seconds+period[1]]
raster_data = []
for clu_num, clu in enumerate(cluIDs):
sptrain = get_spiketrain(rec, stim_start, clu, spikes, window, fs)
raster_data.append(sptrain)
if plot_params == None:
do_raster(raster_data, window, [0, stim_end_seconds], ax)
else:
do_raster(raster_data, window, [0, stim_end_seconds], ax,
spike_linewidth=plot_params['spike_linewidth'],
spike_color=plot_params['spike_color'],
tick_linewidth=plot_params['tick_linewidth'],
tick_color=plot_params['tick_color'])
def plot_trial_raster_emily(spikes,
trials,
clusters,
trialID,
stim,
period,
rec,
fs,
ax=None,
**kwargs):
stim_trials = trials[trials['stimulus'] == stim]
stim_recs = stim_trials['recording'].values
ntrials = len(stim_trials)
stim_starts = stim_trials['time_samples'].values
stim_ends = stim_trials['stimulus_end'].values
stim_start = stim_starts[trialID]
stim_end = stim_ends[trialID]
stim_end_seconds = (stim_end - stim_start) / fs
srec = stim_recs[trialID]
clusterIDs = clusters['cluster'].values
window = [period[0], stim_end_seconds + period[1]]
raster_data = []
for clu in clusterIDs:
sptrain = get_spiketrain(srec, stim_start, clu, spikes, window, fs)
raster_data.append(sptrain)
rasters.do_raster(raster_data, window, [0, stim_end_seconds], ax, **kwargs)
def plot_raster_stim_trial(spikes,
trials,
clusters,
stim,
trial,
period,
rec,
fs,
plot_params=None,
ax=None):
'''
Plots a spike raster for all cells for a single trial of a single stimulus
'''
nclus = len(clusters)
cluIDs = clusters['cluster'].values
stim_trials = trials[trials['stimulus'] == stim]
this_trial = stim_trials.iloc[trial]
stim_start = this_trial['time_samples']
stim_end = this_trial['stimulus_end']
stim_end_seconds = np.unique((stim_end - stim_start) / fs)[0]
window = [period[0], stim_end_seconds + period[1]]
raster_data = []
for clu_num, clu in enumerate(cluIDs):
sptrain = get_spiketrain(rec, stim_start, clu, spikes, window, fs)
raster_data.append(sptrain)
if plot_params == None:
do_raster(raster_data, window, [0, stim_end_seconds], ax)
else:
do_raster(raster_data,
window, [0, stim_end_seconds],
ax,
spike_linewidth=plot_params['spike_linewidth'],
spike_color=plot_params['spike_color'],
tick_linewidth=plot_params['tick_linewidth'],
tick_color=plot_params['tick_color'])
def plot_trial_raster_emily(spikes,
trials,
clusters,
trialID,
stim,
period,
rec,
fs,
ax=None,
**kwargs):
'''
Plots TODO
Parameters
------
spikes : pandas dataframe
spike dataframe from core
trials : pandas dataframe
trials dataframe from events
clusters :
TODO
trialID : int
ID number of the trial you wish to make the raster for
stim : str
Name of the stimulus to plot raster for
period : list of floats
Time window for the raster:
[Seconds_pre_stimulus_onset, Seconds_post_stimulus_end]
fs : float
Sampling rate
ax : Matplotlib axes handle, optional
Axes on which to produce raster. Default gca.
kwargs :
keyword arguments are passed to the do_raster method
'''
stim_trials = trials[trials['stimulus'] == stim]
stim_recs = stim_trials['recording'].values
ntrials = len(stim_trials)
stim_starts = stim_trials['time_samples'].values
stim_ends = stim_trials['stimulus_end'].values
stim_start = stim_starts[trialID]
stim_end = stim_ends[trialID]
stim_end_seconds = (stim_end - stim_start) / fs
srec = stim_recs[trialID]
clusterIDs = clusters['cluster'].values
window = [period[0], stim_end_seconds + period[1]]
raster_data = []
for cluster in clusterIDs:
sptrain = get_spiketrain(srec, stim_start, cluster, spikes, window, fs)
raster_data.append(sptrain)
do_raster(raster_data, window, [0, stim_end_seconds], ax, **kwargs)
def calc_avg_gaussian_psth(spikes, trials, clusterID, stim, period, rec, fs, sigma=0.05, alpha=0.95):
'''
Calculates a gaussian smoothed average psth over all trials of stim for a given cluster.
Parameters
------
spikes : dataframe
spike data
trials : dataframe
trial data
clusterID : int
cluster ID to compute
stim : str
Name of stimulus to calculate psth for
period : list of floats
Time window for the raster:
[Seconds_pre_stimulus_onset, Seconds_post_stimulus_end]
rec : int
recording id
fs : float
sampling rate
sigma : float
stand deviation for gaussian
alpha : float
confidence level
Returns
------
avg_psth : numpy array
the average gaussian psth
std_psth :
standard deviation of the psth
conf_ints :
confidence intervals
times :
times for the signals
'''
stim_trials = trials[trials['stimulus'] == stim]
ntrials = len(stim_trials)
stim_starts = stim_trials['time_samples'].values
stim_ends = stim_trials['stimulus_end'].values
stim_end_seconds = np.unique((stim_ends - stim_starts) / fs)[0]
window = [period[0], stim_end_seconds + period[1]]
npts = np.floor(1.0 * (window[1] - window[0]) * fs)
times = np.linspace(window[0], window[1], npts)
psths = np.zeros((ntrials, npts))
for trial, start in enumerate(stim_starts):
sptrain = get_spiketrain(rec, start, clusterID, spikes, window, fs)
psths[trial, :] = gaussian_psth_func(times, sptrain, sigma)
avg_psth = np.mean(psths, 0)
std_psth = np.std(psths, 0)
conf_ints = stats.t.interval(alpha, df=ntrials - 1, loc=avg_psth, scale=std_psth / np.sqrt(ntrials))
return (avg_psth, std_psth, conf_ints, times)
def calc_avg_gaussian_psth(spikes, trials, clusterID, stim, period, rec, fs, sigma=0.05, alpha=0.95):
'''
Calculates a gaussian smoothed average psth over all trials of stim for a given cluster.
Parameters
------
spikes : dataframe
spike data
trials : dataframe
trial data
clusterID : int
cluster ID to compute
stim : str
Name of stimulus to calculate psth for
period : list of floats
Time window for the raster:
[Seconds_pre_stimulus_onset, Seconds_post_stimulus_end]
rec : int
recording id
fs : float
sampling rate
sigma : float
stand deviation for gaussian
alpha : float
confidence level
Returns
------
avg_psth : numpy array
the average gaussian psth
std_psth :
standard deviation of the psth
conf_ints :
confidence intervals
times :
times for the signals
'''
stim_trials = trials[trials['stimulus'] == stim]
ntrials = len(stim_trials)
stim_starts = stim_trials['time_samples'].values
stim_ends = stim_trials['stimulus_end'].values
stim_end_seconds = np.unique((stim_ends - stim_starts) / fs)[0]
window = [period[0], stim_end_seconds + period[1]]
npts = np.floor(1.0 * (window[1] - window[0]) * fs)
times = np.linspace(window[0], window[1], npts)
psths = np.zeros((ntrials, npts))
for trial, start in enumerate(stim_starts):
sptrain = get_spiketrain(rec, start, clusterID, spikes, window, fs)
psths[trial, :] = gaussian_psth_func(times, sptrain, sigma)
avg_psth = np.mean(psths, 0)
std_psth = np.std(psths, 0)
conf_ints = stats.t.interval(alpha, df=ntrials - 1, loc=avg_psth, scale=std_psth / np.sqrt(ntrials))
return (avg_psth, std_psth, conf_ints, times)
def plot_raster_cell_stim_emily(spikes,
trials,
clusterID,
stim,
period,
rec,
fs,
ax=None,
**kwargs):
'''
Plots a spike raster for a single cell and stimulus
Parameters
------
spikes : pandas dataframe
spike dataframe from core
trials : pandas dataframe
trials dataframe from events
clusterID : int
ID number of the cluster you wish to make the raster for
stim : str
Name of the stimulus you wish to plot cluster's activity for
period : list of floats
Time window for the raster:
[Seconds_pre_stimulus_onset, Seconds_post_stimulus_end]
rec : int
Recording ID
fs : float
Sampling rate
plot_params : dict
Drawing parameters:
'spike_linewidth' - linewidth of ticks for spikes
'tick_linewidth' - linewidth of ticks for event markers
'spike_color' - color of spike ticks
'tick_color' - color of event ticks
ax : Matplotlib axes handle, optional
Axes on which to produce the raster. Default is to use gca
kwargs :
keyword arguments are passed to the do_raster method
'''
stim_trials = trials[trials['stimulus'] == stim]
stim_recs = stim_trials['recording'].values
ntrials = len(stim_trials)
stim_starts = stim_trials['time_samples'].values
stim_ends = stim_trials['stimulus_end'].values
stim_end_seconds = np.unique((stim_ends - stim_starts) / fs)[0]
window = [period[0], stim_end_seconds + period[1]]
raster_data = []
for trial, stpl in enumerate(zip(stim_starts, stim_recs)):
start = stpl[0]
srec = stpl[1]
sptrain = get_spiketrain(srec, start, clusterID, spikes, window, fs)
raster_data.append(sptrain)
do_raster(raster_data, window, [0, stim_end_seconds], ax, **kwargs)
def plot_raster_cell_stim(spikes, trials, clusterID,
stim, period, rec, fs, ax=None, stim_ref='stim', **kwargs):
'''
Plots a spike raster for a single cell and stimulus
Parameters
------
spikes : pandas dataframe
spike dataframe from core
trials : pandas dataframe
trials dataframe from events
clusterID : int
ID number of the cluster you wish to make the raster for
stim : str
Name of the stimulus you wish to plot cluster's activity for
period : list of floats
Time window for the raster:
[Seconds_pre_stimulus_onset, Seconds_post_stimulus_end]
rec : int
Recording ID
fs : float
Sampling rate
plot_params : dict
Drawing parameters:
'spike_linewidth' - linewidth of ticks for spikes
'tick_linewidth' - linewidth of ticks for event markers
'spike_color' - color of spike ticks
'tick_color' - color of event ticks
ax : Matplotlib axes handle, optional
Axes on which to produce the raster. Default is to use gca
kwargs :
keyword arguments are passed to the do_raster method
'''
stim_trials = trials[trials['stimulus'] == stim]
ntrials = len(stim_trials)
stim_starts = stim_trials['time_samples'].values
stim_ends = stim_trials['stimulus_end'].values
stim_end_seconds = np.unique((stim_ends - stim_starts) / fs)[0]
if stim_ref == 'stim':
window = [period[0], stim_end_seconds + period[1]]
elif stim_ref == 'abs':
window = [period[0], period[1]]
raster_data = []
for trial, start in enumerate(stim_starts):
sptrain = get_spiketrain(rec, start, clusterID, spikes, window, fs)
raster_data.append(sptrain)
ax = do_raster(raster_data, window, [0, stim_end_seconds], ntrials, ax, **kwargs)
return ax
def plot_trial_raster_emily(spikes, trials, clusters, trialID, stim, period, rec, fs, ax=None, **kwargs):
'''
Plots TODO
Parameters
------
spikes : pandas dataframe
spike dataframe from core
trials : pandas dataframe
trials dataframe from events
clusters :
TODO
trialID : int
ID number of the trial you wish to make the raster for
stim : str
Name of the stimulus to plot raster for
period : list of floats
Time window for the raster:
[Seconds_pre_stimulus_onset, Seconds_post_stimulus_end]
fs : float
Sampling rate
ax : Matplotlib axes handle, optional
Axes on which to produce raster. Default gca.
kwargs :
keyword arguments are passed to the do_raster method
'''
stim_trials = trials[trials['stimulus'] == stim]
stim_recs = stim_trials['recording'].values
ntrials = len(stim_trials)
stim_starts = stim_trials['time_samples'].values
stim_ends = stim_trials['stimulus_end'].values
stim_start = stim_starts[trialID]
stim_end = stim_ends[trialID]
stim_end_seconds = (stim_end - stim_start) / fs
srec = stim_recs[trialID]
clusterIDs = clusters['cluster'].values
window = [period[0], stim_end_seconds + period[1]]
raster_data = []
for cluster in clusterIDs:
sptrain = get_spiketrain(srec, stim_start, cluster, spikes, window, fs)
raster_data.append(sptrain)
do_raster(raster_data, window, [0, stim_end_seconds], ax, **kwargs)
def plot_raster_stim_trial(spikes, trials, clusters, stim, trial, period,
rec, fs, ax=None, **kwargs):
'''
Plots a raster of all clusters for a given stimulus for a given trial
'''
stim_trials = trials[trials['stimulus']==stim]
ntrials = len(stim_trials)
stim_starts = stim_trials['time_samples'].values
stim_ends = stim_trials['stimulus_end'].values
stim_end_seconds = np.unique((stim_ends - stim_starts)/fs)[0]
window = [period[0], stim_end_seconds+period[1]]
raster_data = []
assert (trial < ntrials)
start = stim_starts[trial]
for cell in clusters['cluster'].values:
sptrain = get_spiketrain(rec, start, cell, spikes, window, fs)
raster_data.append(sptrain)
do_raster(raster_data, window, [0, stim_end_seconds], ax, **kwargs)
def plot_trial_raster_emily(spikes, trials, clusters, trialID,
stim, period, rec, fs, ax=None, **kwargs):
stim_trials = trials[trials['stimulus']==stim]
stim_recs = stim_trials['recording'].values
ntrials = len(stim_trials)
stim_starts = stim_trials['time_samples'].values
stim_ends = stim_trials['stimulus_end'].values
stim_start = stim_starts[trialID]
stim_end = stim_ends[trialID]
stim_end_seconds = (stim_end - stim_start)/fs
srec = stim_recs[trialID]
clusterIDs = clusters['cluster'].values
window = [period[0], stim_end_seconds+period[1]]
raster_data = []
for clu in clusterIDs:
sptrain = get_spiketrain(srec, stim_start, clu, spikes, window, fs)
raster_data.append(sptrain)
rasters.do_raster(raster_data, window, [0, stim_end_seconds], ax, **kwargs)
