def get_frametimings(self):
try:
filter_length, frametimings = asc.ft_nblinks(
self.exp, self.stimnr, self.param_file.get('Nblinks', None),
self.refresh_rate)
except ValueError as e:
if str(e).startswith('Unexpected value for nblinks'):
frametimings = asc.readframetimes(self.exp, self.stimnr)
filter_length = None
frametimings = frametimings[:self.maxframes]
self.filter_length = filter_length
self.frametimings = frametimings
self.frame_duration = np.ediff1d(frametimings).mean()
def savenpztomat(exp_name, savedir=None):
"""
Convert frametime files in .npz to .mat for interoperability
with MATLAB users.
savedir
"""
exp_dir = iof.exp_dir_fixer(exp_name)
_, metadata = asc.read_spikesheet(exp_dir)
monitor_delay = metadata['monitor_delay(s)']
for i in range(1, 100):
print(i)
try:
ft_on, ft_off = asc.readframetimes(exp_name, i, returnoffsets=True)
except ValueError as e:
if str(e).startswith('No frametimes'):
break
else:
raise
# Convert to milliseconds b/c that is the convertion in MATLAB scripts
ft_on = (ft_on - monitor_delay) * 1000
ft_off = (ft_off - monitor_delay) * 1000
stimname = iof.getstimname(exp_dir, i)
if savedir is None:
savedir = pjoin(exp_dir, 'frametimes')
savename = pjoin(savedir, stimname)
print(savename)
scipy.io.savemat(savename + '_frametimings', {
'ftimes': ft_on,
'ftimes_offsets': ft_off
},
appendmat=True)
def onoffstepsanalyzer(exp_name, stim_nrs):
"""
Analyze onoffsteps data, plot and save it. Will make a directory
/data_analysis/<stimulus_name> and save svg [and pdf in subfolder.].
Parameters:
exp_name:
Experiment name.
stim_nr:
Order of the onoff steps stimulus.
"""
exp_dir = iof.exp_dir_fixer(exp_name)
exp_name = os.path.split(exp_dir)[-1]
if isinstance(stim_nrs, int):
stim_nrs = [stim_nrs]
for stim_nr in stim_nrs:
stim_nr = str(stim_nr)
stimname = iof.getstimname(exp_dir, stim_nr)
clusters, metadata = asc.read_spikesheet(exp_dir, cutoff=4)
clusterids = plf.clusters_to_ids(clusters)
parameters = asc.read_parameters(exp_dir, stim_nr)
refresh_rate = metadata['refresh_rate']
# Divide by the refresh rate to convert from number of
# frames to seconds
pars_stim_duration = parameters['Nframes'] / refresh_rate
pars_preframe_duration = parameters.get('preframes', 0) / refresh_rate
if pars_preframe_duration == 0:
nopreframe = True
nr_periods = 2
else:
nopreframe = False
nr_periods = 4
# The first trial will be discarded by dropping the first four frames
# If we don't save the original and re-initialize for each cell,
# frametimings will get smaller over time.
frametimings_original = asc.readframetimes(exp_dir, stim_nr)
trial_durs = stim_prefr_durations_frametimes(frametimings_original,
nr_per=nr_periods)
avg_trial_durs = trial_durs.mean(axis=0)
if not nopreframe:
stim_duration = avg_trial_durs[1::2].mean()
preframe_duration = avg_trial_durs[::2].mean()
else:
stim_duration = avg_trial_durs.mean()
preframe_duration = 0
warnings.warn('On-off steps analysis with no preframes'
'is not tested, proceed with caution.')
contrast = parameters['contrast']
total_cycle = avg_trial_durs.sum()
# Set the bins to be 10 ms
tstep = 0.01
bins = int(total_cycle / tstep) + 1
t = np.linspace(0, total_cycle, num=bins)
# Setup for onoff bias calculation
onbegin = preframe_duration
onend = onbegin + stim_duration
offbegin = onend + preframe_duration
offend = offbegin + stim_duration
# Determine the indices for each period
a = []
for i in [onbegin, onend, offbegin, offend]:
yo = np.asscalar(np.where(np.abs(t - i) < tstep / 1.5)[0][-1])
a.append(yo)
# To exclude stimulus offset affecting the bias, use
# last 1 second of preframe period
prefs = []
for i in [onbegin - 1, onbegin, offbegin - 1, offbegin]:
yo = np.asscalar(np.where(np.abs(t - i) < tstep / 1.5)[0][-1])
prefs.append(yo)
onper = slice(a[0], a[1])
offper = slice(a[2], a[3])
pref1 = slice(prefs[0], prefs[1])
pref2 = slice(prefs[2], prefs[3])
onoffbias = np.empty(clusters.shape[0])
baselines = np.empty(clusters.shape[0])
savedir = os.path.join(exp_dir, 'data_analysis', stimname)
os.makedirs(os.path.join(savedir, 'pdf'), exist_ok=True)
# Collect all firing rates in a list
all_frs = []
for i in range(len(clusters[:, 0])):
spikes = asc.read_raster(exp_dir, stim_nr, clusters[i, 0],
clusters[i, 1])
frametimings = frametimings_original
# Discard all the spikes that happen after the last frame
spikes = spikes[spikes < frametimings[-1]]
# Discard the first trial
spikes = spikes[spikes > frametimings[4]]
frametimings = frametimings[4:]
# Find which trial each spike belongs to, and subtract one
# to be able to use as indices
trial_indices = np.digitize(spikes, frametimings[::4]) - 1
rasterplot = []
# Iterate over all the trials, create an empty array for each
for j in range(int(np.ceil(frametimings.max() / total_cycle))):
rasterplot.append([])
# plt.eventplot requires a list containing spikes in each
# trial separately
for k in range(len(spikes)):
trial = trial_indices[k]
rasterplot[trial].append(spikes[k] - frametimings[::4][trial])
# Workaround for matplotlib issue #6412.
# https://github.com/matplotlib/matplotlib/issues/6412
# If a cell has no spikes for the first trial i.e. the first
# element of the list is empty, an error is raised due to
# a plt.eventplot bug.
if len(rasterplot[0]) == 0:
rasterplot[0] = [-1]
plt.figure(figsize=(9, 9))
ax1 = plt.subplot(211)
plt.eventplot(rasterplot, linewidth=.5, color='r')
# Set the axis so they align with the rectangles
plt.axis([0, total_cycle, -1, len(rasterplot)])
# Draw rectangles to represent different parts of the on off
# steps stimulus
plf.drawonoff(ax1,
preframe_duration,
stim_duration,
contrast=contrast)
plt.ylabel('Trial')
plt.gca().invert_yaxis()
ax1.set_xticks([])
plf.spineless(ax1)
# Collect all trials in one array to calculate firing rates
ras = np.array([])
for ii in range(len(rasterplot)):
ras = np.append(ras, rasterplot[ii])
# Sort into time bins and count how many spikes happened in each
fr = np.digitize(ras, t)
fr = np.bincount(fr)
# Normalize so that units are spikes/s
fr = fr * (bins / total_cycle) / (len(rasterplot) - 1)
# Equalize the length of the two arrays for plotting.
# np.bincount(x) normally produces x.max()+1 bins
if fr.shape[0] == bins + 1:
fr = fr[:-1]
# If there aren't any spikes at the last trial, the firing
# rates array is too short and plt.plot raises error.
while fr.shape[0] < bins:
fr = np.append(fr, 0)
prefr = np.append(fr[pref1], fr[pref2])
baseline = np.median(np.round(prefr))
fr_corr = fr - baseline
r_on = np.sum(fr_corr[onper])
r_off = np.sum(fr_corr[offper])
if r_on == 0 and r_off == 0:
bias = np.nan
else:
bias = (r_on - r_off) / (np.abs(r_on) + np.abs(r_off))
plt.suptitle(f'{exp_name}\n{stimname}'
f'\n{clusterids[i]} Rating: {clusters[i][2]}\n')
if fr.max() < 20:
bias = np.nan
onoffbias[i] = bias
baselines[i] = baseline
all_frs.append(fr)
ax2 = plt.subplot(212)
plt.plot(t, fr)
for eachslice in [onper, offper]:
ax2.fill_between(t[eachslice],
fr[eachslice],
baseline,
where=fr[eachslice] > baseline,
facecolor='lightgray')
plf.spineless(ax2)
plt.axis([0, total_cycle, fr.min(), fr.max()])
plt.title(f'Baseline: {baseline:2.0f} Hz Bias: {bias:0.2f}')
plt.xlabel('Time[s]')
plt.ylabel('Firing rate[spikes/s]')
# Save as svg for looking through data, pdf for
# inserting into presentations
plt.savefig(
savedir +
'/{:0>3}{:0>2}.svg'.format(clusters[i, 0], clusters[i, 1]),
format='svg',
bbox_inches='tight')
plt.savefig(os.path.join(
savedir, 'pdf', '{:0>3}'
'{:0>2}.pdf'.format(clusters[i, 0], clusters[i, 1])),
format='pdf',
bbox_inches='tight')
plt.close()
keystosave = [
'clusters', 'total_cycle', 'bins', 'tstep', 'stimname',
'stim_duration', 'preframe_duration', 'contrast', 'all_frs', 't',
'exp_name', 'onoffbias', 'baselines'
]
data_in_dict = {}
for key in keystosave:
data_in_dict[key] = locals()[key]
np.savez(os.path.join(savedir, stim_nr + '_data'), **data_in_dict)
print(f'Analysis of {stimname} completed.')
def randomizestripes(label, exp_name='20180124', stim_nrs=6):
exp_dir = iof.exp_dir_fixer(exp_name)
if isinstance(stim_nrs, int):
stim_nrs = [stim_nrs]
for stim_nr in stim_nrs:
stimname = iof.getstimname(exp_name, stim_nr)
clusters, metadata = asc.read_spikesheet(exp_dir)
parameters = asc.read_parameters(exp_dir, stim_nr)
scr_width = metadata['screen_width']
px_size = metadata['pixel_size(um)']
stx_w = parameters['stixelwidth']
stx_h = parameters['stixelheight']
if (stx_h/stx_w) < 2:
raise ValueError('Make sure the stimulus is stripeflicker.')
sy = scr_width/stx_w
# sy = sy*4
sy = int(sy)
nblinks = parameters['Nblinks']
try:
bw = parameters['blackwhite']
except KeyError:
bw = False
try:
seed = parameters['seed']
initialseed = parameters['seed']
except KeyError:
seed = -10000
initialseed = -10000
if nblinks == 1:
ft_on, ft_off = asc.readframetimes(exp_dir, stim_nr,
returnoffsets=True)
# Initialize empty array twice the size of one of them, assign
# value from on or off to every other element.
frametimings = np.empty(ft_on.shape[0]*2, dtype=float)
frametimings[::2] = ft_on
frametimings[1::2] = ft_off
# Set filter length so that temporal filter is ~600 ms.
# The unit here is number of frames.
filter_length = 40
elif nblinks == 2:
frametimings = asc.readframetimes(exp_dir, stim_nr)
filter_length = 20
else:
raise ValueError('Unexpected value for nblinks.')
# Omit everything that happens before the first 10 seconds
cut_time = 10
frame_duration = np.average(np.ediff1d(frametimings))
total_frames = int(frametimings.shape[0]/4)
all_spiketimes = []
# Store spike triggered averages in a list containing correct
# shaped arrays
stas = []
for i in range(len(clusters[:, 0])):
spikes_orig = asc.read_raster(exp_dir, stim_nr,
clusters[i, 0], clusters[i, 1])
spikesneeded = spikes_orig.shape[0]*1000
spiketimes = np.random.random_sample(spikesneeded)*spikes_orig.max()
spiketimes = np.sort(spiketimes)
spikes = asc.binspikes(spiketimes, frametimings)
all_spiketimes.append(spikes)
stas.append(np.zeros((sy, filter_length)))
if bw:
randnrs, seed = randpy.ran1(seed, sy*total_frames)
# randnrs = mersennetw(sy*total_frames, seed1=seed)
randnrs = [1 if i > .5 else -1 for i in randnrs]
else:
randnrs, seed = randpy.gasdev(seed, sy*total_frames)
stimulus = np.reshape(randnrs, (sy, total_frames), order='F')
del randnrs
for k in range(filter_length, total_frames-filter_length+1):
stim_small = stimulus[:, k-filter_length+1:k+1][:, ::-1]
for j in range(clusters.shape[0]):
spikes = all_spiketimes[j]
if spikes[k] != 0 and frametimings[k]>cut_time:
stas[j] += spikes[k]*stim_small
max_inds = []
spikenrs = np.array([spikearr.sum() for spikearr in all_spiketimes])
quals = np.array([])
for i in range(clusters.shape[0]):
stas[i] = stas[i]/spikenrs[i]
# Find the pixel with largest absolute value
max_i = np.squeeze(np.where(np.abs(stas[i])
== np.max(np.abs(stas[i]))))
# If there are multiple pixels with largest value,
# take the first one.
if max_i.shape != (2,):
try:
max_i = max_i[:, 0]
# If max_i cannot be found just set it to zeros.
except IndexError:
max_i = np.array([0, 0])
max_inds.append(max_i)
quals = np.append(quals, asc.staquality(stas[i]))
# savefname = str(stim_nr)+'_data'
# savepath = pjoin(exp_dir, 'data_analysis', stimname)
#
# exp_name = os.path.split(exp_dir)[-1]
#
# if not os.path.isdir(savepath):
# os.makedirs(savepath, exist_ok=True)
# savepath = os.path.join(savepath, savefname)
#
# keystosave = ['stas', 'max_inds', 'clusters', 'sy',
# 'frame_duration', 'all_spiketimes', 'stimname',
# 'total_frames', 'stx_w', 'spikenrs', 'bw',
# 'quals', 'nblinks', 'filter_length', 'exp_name']
# data_in_dict = {}
# for key in keystosave:
# data_in_dict[key] = locals()[key]
#
# np.savez(savepath, **data_in_dict)
# print(f'Analysis of {stimname} completed.')
clusterids = plf.clusters_to_ids(clusters)
# assert(initialseed.ty)
correction = corrector(sy, total_frames, filter_length, initialseed)
correction = np.outer(correction, np.ones(filter_length))
t = np.arange(filter_length)*frame_duration*1000
vscale = int(stas[0].shape[0] * stx_w*px_size/1000)
for i in range(clusters.shape[0]):
sta = stas[i]-correction
vmax = 0.03
vmin = -vmax
plt.figure(figsize=(6, 15))
ax = plt.subplot(111)
im = ax.imshow(sta, cmap='RdBu', vmin=vmin, vmax=vmax,
extent=[0, t[-1], -vscale, vscale], aspect='auto')
plt.xlabel('Time [ms]')
plt.ylabel('Distance [mm]')
plf.spineless(ax)
plf.colorbar(im, ticks=[vmin, 0, vmax], format='%.2f', size='2%')
plt.suptitle('{}\n{}\n'
'{} Rating: {}\n'
'nrofspikes {:5.0f}'.format(exp_name,
stimname,
clusterids[i],
clusters[i][2],
spikenrs[i]))
plt.subplots_adjust(top=.90)
savepath = os.path.join(exp_dir, 'data_analysis',
stimname, 'STAs_randomized')
svgpath = pjoin(savepath, label)
if not os.path.isdir(svgpath):
os.makedirs(svgpath, exist_ok=True)
plt.savefig(os.path.join(svgpath, clusterids[i]+'.svg'),
bbox_inches='tight')
plt.close()
os.system(f"convert -delay 25 {svgpath}/*svg {savepath}/animated_{label}.gif")
def get_frametimings(self):
frametimings = asc.readframetimes(self.exp,
self.stimnr)[:self.maxframes_i]
self.frametimings = frametimings
def checkerflickerplusanalyzer(exp_name,
stimulusnr,
clusterstoanalyze=None,
frametimingsfraction=None,
cutoff=4):
"""
Analyzes checkerflicker-like data, typically interspersed
stimuli in between chunks of checkerflicker.
e.g. checkerflickerplusmovie, frozennoise
Parameters:
----------
exp_name:
Experiment name.
stimulusnr:
Number of the stimulus to be analyzed.
clusterstoanalyze:
Number of clusters should be analyzed. Default is None.
First N cells will be analyzed if this parameter is given.
In case of long recordings it might make sense to first
look at a subset of cells before starting to analyze
the whole dataset.
frametimingsfraction:
Fraction of the recording to analyze. Should be a number
between 0 and 1. e.g. 0.3 will analyze the first 30% of
the whole recording.
cutoff:
Worst rating that is wanted for the analysis. Default
is 4. The source of this value is manual rating of each
cluster.
"""
exp_dir = iof.exp_dir_fixer(exp_name)
stimname = iof.getstimname(exp_dir, stimulusnr)
exp_name = os.path.split(exp_dir)[-1]
clusters, metadata = asc.read_spikesheet(exp_dir, cutoff=cutoff)
# Check that the inputs are as expected.
if clusterstoanalyze:
if clusterstoanalyze > len(clusters[:, 0]):
warnings.warn('clusterstoanalyze is larger '
'than number of clusters in dataset. '
'All cells will be included.')
clusterstoanalyze = None
if frametimingsfraction:
if not 0 < frametimingsfraction < 1:
raise ValueError('Invalid input for frametimingsfraction: {}. '
'It should be a number between 0 and 1'
''.format(frametimingsfraction))
scr_width = metadata['screen_width']
scr_height = metadata['screen_height']
refresh_rate = metadata['refresh_rate']
parameters = asc.read_parameters(exp_dir, stimulusnr)
stx_h = parameters['stixelheight']
stx_w = parameters['stixelwidth']
# Check whether any parameters are given for margins, calculate
# screen dimensions.
marginkeys = ['tmargin', 'bmargin', 'rmargin', 'lmargin']
margins = []
for key in marginkeys:
margins.append(parameters.get(key, 0))
# Subtract bottom and top from vertical dimension; left and right
# from horizontal dimension
scr_width = scr_width - sum(margins[2:])
scr_height = scr_height - sum(margins[:2])
nblinks = parameters['Nblinks']
bw = parameters.get('blackwhite', False)
# Gaussian stimuli are not supported yet, we need to ensure we
# have a black and white stimulus
if bw is not True:
raise ValueError('Gaussian stimuli are not supported yet!')
seed = parameters.get('seed', -1000)
sx, sy = scr_height / stx_h, scr_width / stx_w
# Make sure that the number of stimulus pixels are integers
# Rounding down is also possible but might require
# other considerations.
if sx % 1 == 0 and sy % 1 == 0:
sx, sy = int(sx), int(sy)
else:
raise ValueError('sx and sy must be integers')
filter_length, frametimings = asc.ft_nblinks(exp_dir, stimulusnr)
if parameters['stimulus_type'] in [
'FrozenNoise', 'checkerflickerplusmovie'
]:
runfr = parameters['RunningFrames']
frofr = parameters['FrozenFrames']
# To generate the frozen noise, a second seed is used.
# The default value of this is -10000 as per StimulateOpenGL
secondseed = parameters.get('secondseed', -10000)
if parameters['stimulus_type'] == 'checkerflickerplusmovie':
mblinks = parameters['Nblinksmovie']
# Retrivee the number of frames (files) from parameters['path']
ipath = PureWindowsPath(parameters['path']).as_posix()
repldict = iof.config('stimuli_path_replace')
for needle, repl in repldict.items():
ipath = ipath.replace(needle, repl)
ipath = os.path.normpath(ipath) # Windows compatiblity
moviefr = len([
name for name in os.listdir(ipath)
if os.path.isfile(os.path.join(ipath, name))
and name.lower().endswith('.raw')
])
noiselen = (runfr + frofr) * nblinks
movielen = moviefr * mblinks
triallen = noiselen + movielen
ft_on, ft_off = asc.readframetimes(exp_dir,
stimulusnr,
returnoffsets=True)
frametimings = np.empty(ft_on.shape[0] * 2, dtype=float)
frametimings[::2] = ft_on
frametimings[1::2] = ft_off
import math
ntrials = math.floor(frametimings.size / triallen)
trials = np.zeros((ntrials, runfr + frofr + moviefr))
for t in range(ntrials):
frange = frametimings[t * triallen:(t + 1) * triallen]
trials[t, :runfr + frofr] = frange[:noiselen][::nblinks]
trials[t, runfr + frofr:] = frange[noiselen:][::mblinks]
frametimings = trials.ravel()
filter_length = np.int(np.round(.666 * refresh_rate / nblinks))
# Add frozen movie to frozen noise (for masking)
frofr += moviefr
savefname = str(stimulusnr) + '_data'
if clusterstoanalyze:
clusters = clusters[:clusterstoanalyze, :]
print('Analyzing first %s cells' % clusterstoanalyze)
savefname += '_' + str(clusterstoanalyze) + 'cells'
if frametimingsfraction:
frametimingsindex = int(len(frametimings) * frametimingsfraction)
frametimings = frametimings[:frametimingsindex]
print('Analyzing first {}% of'
' the recording'.format(frametimingsfraction * 100))
savefname += '_' + str(frametimingsfraction).replace('.',
'') + 'fraction'
frame_duration = np.average(np.ediff1d(frametimings))
total_frames = frametimings.shape[0]
all_spiketimes = []
# Store spike triggered averages in a list containing correct shaped
# arrays
stas = []
for i in range(len(clusters[:, 0])):
spiketimes = asc.read_raster(exp_dir, stimulusnr, clusters[i, 0],
clusters[i, 1])
spikes = asc.binspikes(spiketimes, frametimings)
all_spiketimes.append(spikes)
stas.append(np.zeros((sx, sy, filter_length)))
# Separate out the repeated parts
all_spiketimes = np.array(all_spiketimes)
mask = runfreezemask(total_frames, runfr, frofr, refresh_rate)
repeated_spiketimes = all_spiketimes[:, ~mask]
run_spiketimes = all_spiketimes[:, mask]
# We need to cut down the total_frames by the same amount
# as spiketimes
total_run_frames = run_spiketimes.shape[1]
# To be able to use the same code as checkerflicker analyzer,
# convert to list again.
run_spiketimes = list(run_spiketimes)
# Empirically determined to be best for 32GB RAM
desired_chunk_size = 21600000
# Length of the chunks (specified in number of frames)
chunklength = int(desired_chunk_size / (sx * sy))
chunksize = chunklength * sx * sy
nrofchunks = int(np.ceil(total_run_frames / chunklength))
print(f'\nAnalyzing {stimname}.\nTotal chunks: {nrofchunks}')
time = startime = datetime.datetime.now()
timedeltas = []
quals = np.zeros(len(stas))
frame_counter = 0
for i in range(nrofchunks):
randnrs, seed = randpy.ranb(seed, chunksize)
# Reshape and change 0's to -1's
stimulus = np.reshape(randnrs,
(sx, sy, chunklength), order='F') * 2 - 1
del randnrs
# Range of indices we are interested in for the current chunk
if (i + 1) * chunklength < total_run_frames:
chunkind = slice(i * chunklength, (i + 1) * chunklength)
chunkend = chunklength
else:
chunkind = slice(i * chunklength, None)
chunkend = total_run_frames - i * chunklength
for k in range(filter_length, chunkend - filter_length + 1):
stim_small = stimulus[:, :,
k - filter_length + 1:k + 1][:, :, ::-1]
for j in range(clusters.shape[0]):
spikes = run_spiketimes[j][chunkind]
if spikes[k] != 0:
stas[j] += spikes[k] * stim_small
qual = np.array([])
for c in range(clusters.shape[0]):
qual = np.append(qual, asc.staquality(stas[c]))
quals = np.vstack((quals, qual))
# Draw progress bar
width = 50 # Number of characters
prog = i / (nrofchunks - 1)
bar_complete = int(prog * width)
bar_noncomplete = width - bar_complete
timedeltas.append(msc.timediff(time)) # Calculate running avg
avgelapsed = np.mean(timedeltas)
elapsed = np.sum(timedeltas)
etc = startime + elapsed + avgelapsed * (nrofchunks - i)
sys.stdout.flush()
sys.stdout.write('\r{}{} |{:4.1f}% ETC: {}'.format(
'█' * bar_complete, '-' * bar_noncomplete, prog * 100,
etc.strftime("%a %X")))
time = datetime.datetime.now()
sys.stdout.write('\n')
# Remove the first row which is full of random nrs.
quals = quals[1:, :]
max_inds = []
spikenrs = np.array([spikearr.sum() for spikearr in run_spiketimes])
for i in range(clusters.shape[0]):
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '.*true_divide*.')
stas[i] = stas[i] / spikenrs[i]
# Find the pixel with largest absolute value
max_i = np.squeeze(
np.where(np.abs(stas[i]) == np.max(np.abs(stas[i]))))
# If there are multiple pixels with largest value,
# take the first one.
if max_i.shape != (3, ):
try:
max_i = max_i[:, 0]
# If max_i cannot be found just set it to zeros.
except IndexError:
max_i = np.array([0, 0, 0])
max_inds.append(max_i)
print(f'Completed. Total elapsed time: {msc.timediff(startime)}\n' +
f'Finished on {datetime.datetime.now().strftime("%A %X")}')
savepath = os.path.join(exp_dir, 'data_analysis', stimname)
if not os.path.isdir(savepath):
os.makedirs(savepath, exist_ok=True)
savepath = os.path.join(savepath, savefname)
keystosave = [
'clusters', 'frametimings', 'mask', 'repeated_spiketimes',
'run_spiketimes', 'frame_duration', 'max_inds', 'nblinks', 'stas',
'stx_h', 'stx_w', 'total_run_frames', 'sx', 'sy', 'filter_length',
'stimname', 'exp_name', 'spikenrs', 'clusterstoanalyze',
'frametimingsfraction', 'cutoff', 'quals', 'nrofchunks', 'chunklength'
]
datadict = {}
for key in keystosave:
datadict[key] = locals()[key]
np.savez(savepath, **datadict)
t = (np.arange(nrofchunks) * chunklength * frame_duration) / refresh_rate
qmax = np.max(quals, axis=0)
qualsn = quals / qmax[np.newaxis, :]
ax = plt.subplot(111)
ax.plot(t, qualsn, alpha=0.3)
plt.ylabel('Z-score of center pixel (normalized)')
plt.xlabel('Minutes of stimulus analyzed')
plt.ylim([0, 1])
plf.spineless(ax, 'tr')
plt.title(f'Recording duration optimization\n{exp_name}\n {savefname}')
plt.savefig(savepath + '.svg', format='svg')
plt.close()
def stripeflickeranalysis(exp_name, stim_nrs):
exp_dir = iof.exp_dir_fixer(exp_name)
if isinstance(stim_nrs, int):
stim_nrs = [stim_nrs]
for stim_nr in stim_nrs:
stimname = iof.getstimname(exp_name, stim_nr)
clusters, metadata = asc.read_spikesheet(exp_dir)
parameters = asc.read_parameters(exp_dir, stim_nr)
scr_width = metadata['screen_width']
px_size = metadata['pixel_size(um)']
stx_w = parameters['stixelwidth']
stx_h = parameters['stixelheight']
if (stx_h / stx_w) < 2:
raise ValueError('Make sure the stimulus is stripeflicker.')
sy = scr_width / stx_w
if sy % 1 == 0:
sy = int(sy)
else:
raise ValueError('sy is not an integer')
nblinks = parameters['Nblinks']
try:
bw = parameters['blackwhite']
except KeyError:
bw = False
try:
seed = parameters['seed']
except KeyError:
seed = -10000
if nblinks == 1:
ft_on, ft_off = asc.readframetimes(exp_dir,
stim_nr,
returnoffsets=True)
# Initialize empty array twice the size of one of them, assign
# value from on or off to every other element.
frametimings = np.empty(ft_on.shape[0] * 2, dtype=float)
frametimings[::2] = ft_on
frametimings[1::2] = ft_off
# Set filter length so that temporal filter is ~600 ms.
# The unit here is number of frames.
filter_length = 40
elif nblinks == 2:
frametimings = asc.readframetimes(exp_dir, stim_nr)
filter_length = 20
else:
raise ValueError('Unexpected value for nblinks.')
# Omit everything that happens before the first 10 seconds
cut_time = 10
frame_duration = np.average(np.ediff1d(frametimings))
total_frames = frametimings.shape[0]
all_spiketimes = []
# Store spike triggered averages in a list containing correct
# shaped arrays
stas = []
for i in range(len(clusters[:, 0])):
spiketimes = asc.read_raster(exp_dir, stim_nr, clusters[i, 0],
clusters[i, 1])
spikes = asc.binspikes(spiketimes, frametimings)
all_spiketimes.append(spikes)
stas.append(np.zeros((sy, filter_length)))
if bw:
randnrs, seed = randpy.ran1(seed, sy * total_frames)
randnrs = [1 if i > .5 else -1 for i in randnrs]
else:
randnrs, seed = randpy.gasdev(seed, sy * total_frames)
stimulus = np.reshape(randnrs, (sy, total_frames), order='F')
del randnrs
for k in range(filter_length, total_frames - filter_length + 1):
stim_small = stimulus[:, k - filter_length + 1:k + 1][:, ::-1]
for j in range(clusters.shape[0]):
spikes = all_spiketimes[j]
if spikes[k] != 0 and frametimings[k] > cut_time:
stas[j] += spikes[k] * stim_small
max_inds = []
spikenrs = np.array([spikearr.sum() for spikearr in all_spiketimes])
quals = np.array([])
for i in range(clusters.shape[0]):
stas[i] = stas[i] / spikenrs[i]
# Find the pixel with largest absolute value
max_i = np.squeeze(
np.where(np.abs(stas[i]) == np.max(np.abs(stas[i]))))
# If there are multiple pixels with largest value,
# take the first one.
if max_i.shape != (2, ):
try:
max_i = max_i[:, 0]
# If max_i cannot be found just set it to zeros.
except IndexError:
max_i = np.array([0, 0])
max_inds.append(max_i)
quals = np.append(quals, asc.staquality(stas[i]))
savefname = str(stim_nr) + '_data'
savepath = pjoin(exp_dir, 'data_analysis', stimname)
exp_name = os.path.split(exp_dir)[-1]
if not os.path.isdir(savepath):
os.makedirs(savepath, exist_ok=True)
savepath = os.path.join(savepath, savefname)
keystosave = [
'stas', 'max_inds', 'clusters', 'sy', 'frame_duration',
'all_spiketimes', 'stimname', 'total_frames', 'stx_w', 'spikenrs',
'bw', 'quals', 'nblinks', 'filter_length', 'exp_name'
]
data_in_dict = {}
for key in keystosave:
data_in_dict[key] = locals()[key]
np.savez(savepath, **data_in_dict)
print(f'Analysis of {stimname} completed.')
def OMSpatchesanalyzer(exp_name, stim_nrs):
"""
Analyze and plot the responses to object motion patches stimulus.
"""
exp_dir = iof.exp_dir_fixer(exp_name)
exp_name = os.path.split(exp_dir)[-1]
if isinstance(stim_nrs, int):
stim_nrs = [stim_nrs]
elif len(stim_nrs) == 0:
return
clusters, metadata = asc.read_spikesheet(exp_dir, cutoff=4)
clusterids = plf.clusters_to_ids(clusters)
all_omsi = np.empty((clusters.shape[0], len(stim_nrs)))
stimnames = []
for stim_index, stim_nr in enumerate(stim_nrs):
stim_nr = str(stim_nr)
stimname = iof.getstimname(exp_dir, stim_nr)
stimnames.append(stimname)
parameters = asc.read_parameters(exp_dir, stim_nr)
refresh_rate = metadata['refresh_rate']
nblinks = parameters.get('Nblinks', 1)
seed = parameters.get('seed', -10000)
stim_duration = parameters.get('stimFrames', 1400)
# The duration in the parameters refers to the total duration of both
# epochs. We divide by two to get the length of a single stim_duration
stim_duration = int(stim_duration / 2)
prefr_duration = parameters.get('preFrames', 100)
frametimings = asc.readframetimes(exp_dir, stim_nr)
# ntrials is the number of trials containing both
ntrials = np.floor((frametimings.shape[0] / (stim_duration + 1))) / 2
ntrials = ntrials.astype(int)
frametimings_rs = frametimings[:ntrials * 2 * (stim_duration + 1)]
frametimings_rs = frametimings_rs.reshape(
(ntrials * 2, stim_duration + 1))
ft_local = frametimings_rs[::2][:, :-1]
ft_global = frametimings_rs[1::2][:, :-1]
localspikes = np.empty((clusters.shape[0], ntrials, stim_duration))
globalspikes = np.empty((clusters.shape[0], ntrials, stim_duration))
for i, cluster in enumerate(clusters):
spikes = asc.read_raster(exp_name, stim_nr, cluster[0], cluster[1])
for j in range(ntrials):
localspikes[i, j, :] = asc.binspikes(spikes, ft_local[j, :])
globalspikes[i, j, :] = asc.binspikes(spikes, ft_global[j, :])
response_local = localspikes.mean(axis=1)
response_global = globalspikes.mean(axis=1)
# Differential and coherent firing rates
fr_d = response_local.mean(axis=1)
fr_c = response_global.mean(axis=1)
# Calculate object motion sensitivity index (OMSI) as defined in
# Kühn et al, 2016
# There the first second of each trial is discarded, here it does not
# seem to be very different from the rest.
omsi = (fr_d - fr_c) / (fr_d + fr_c)
# Create a time array for plotting
time = np.linspace(0,
stim_duration * 2 / refresh_rate,
num=stim_duration)
savepath = os.path.join(exp_dir, 'data_analysis', stimname)
if not os.path.isdir(savepath):
os.makedirs(savepath, exist_ok=True)
for i, cluster in enumerate(clusters):
gs = gridspec.GridSpec(2, 1)
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
rastermat = np.vstack(
(localspikes[i, :, :], globalspikes[i, :, :]))
ax1.matshow(rastermat, cmap='Greys')
ax1.axhline(ntrials - 1, color='r', lw=.1)
ax1.plot([0, 0], [ntrials, 0])
ax1.plot([0, 0], [ntrials * 2, ntrials])
ax1.set_xticks([])
ax1.set_yticks([])
plf.spineless(ax1)
ax2.plot(time, response_local[i, :], label='Local')
ax2.plot(time, response_global[i, :], label='Global')
ax2.set_xlabel('Time [s]')
ax2.set_ylabel('Average firing rate [au]')
ax2.set_xlim([time.min(), time.max()])
plf.spineless(ax2, 'tr')
ax2.legend(fontsize='x-small')
plt.suptitle(f'{exp_name}\n{stimname}\n'
f'{clusterids[i]} OMSI: {omsi[i]:4.2f}')
plt.tight_layout()
plt.savefig(os.path.join(savepath, clusterids[i] + '.svg'),
bbox_inches='tight')
plt.close()
keystosave = [
'nblinks', 'refresh_rate', 'stim_duration', 'prefr_duration',
'ntrials', 'response_local', 'response_global', 'fr_d', 'fr_c',
'omsi', 'clusters'
]
datadict = {}
for key in keystosave:
datadict[key] = locals()[key]
npzfpath = os.path.join(savepath, str(stim_nr) + '_data')
np.savez(npzfpath, **datadict)
all_omsi[:, stim_index] = omsi
print(f'Analysis of {stimname} completed.')
# Draw the distribution of the OMSI for all OMSI stimuli
# If there is only one OMS stimulus, draw it in the same folder
# If there are multiple stimuli, save it in the data analysis folder
if len(stim_nrs) == 1:
pop_plot_savepath = os.path.join(savepath, 'omsi_population.svg')
else:
pop_plot_savepath = os.path.split(savepath)[0]
pop_plot_savepath = os.path.join(pop_plot_savepath, 'all_omsi.svg')
plt.figure(figsize=(5, 2 * len(stim_nrs)))
ax2 = plt.subplot(111)
for j, stim_nr in enumerate(stim_nrs):
np.random.seed(j)
ax2.scatter(all_omsi[:, j],
j + (np.random.random(omsi.shape) - .5) / 1.1)
np.random.seed()
ax2.set_yticks(np.arange(len(stim_nrs)))
ax2.set_yticklabels(stimnames, fontsize='xx-small', rotation='45')
ax2.set_xlabel('Object-motion sensitivity index')
ax2.set_title(f'{exp_name}\nDistribution of OMSI')
plf.spineless(ax2, 'tr')
plt.savefig(pop_plot_savepath, bbox_inches='tight')
plt.close()
# Make sure that the number of stimulus pixels are integers
# Rounding down is also possible but might require
# other considerations.
if sx % 1 == 0 and sy % 1 == 0:
sx, sy = int(sx), int(sy)
else:
raise ValueError('sx and sy must be integers')
# If the frame rate of the checkerflicker stimulus is 16 ms, (i.e.
# Nblinks is set to 1), frame timings should be handled differently
# because the state of the pulse can only be changed when a new
# frame is delivered. For this reason, the offsets of the pulses
# also denote a frame change as well as onsets.
if nblinks == 1:
ft_on, ft_off = asc.readframetimes(exp_dir, stimulusnr,
returnoffsets=True)
# Initialize empty array twice the size of one of them, assign
# value from on or off to every other element.
frametimings = np.empty(ft_on.shape[0]*2, dtype=float)
frametimings[::2] = ft_on
frametimings[1::2] = ft_off
# Set filter length so that temporal filter is ~600 ms. The unit
# here is number of frames.
filter_length = 40
elif nblinks == 2:
frametimings = asc.readframetimes(exp_dir, stimulusnr)
filter_length = 20
elif nblinks == 4:
frametimings = asc.readframetimes(exp_dir, stimulusnr)
filter_length = 10
else:
def saccadegratingsanalyzer(exp_name, stim_nr):
"""
Analyze and save responses to saccadegratings stimulus.
"""
exp_dir = iof.exp_dir_fixer(exp_name)
exp_name = os.path.split(exp_dir)[-1]
stimname = iof.getstimname(exp_dir, stim_nr)
clusters, metadata = asc.read_spikesheet(exp_dir)
clusterids = plf.clusters_to_ids(clusters)
refresh_rate = metadata['refresh_rate']
parameters = asc.read_parameters(exp_name, stim_nr)
if parameters['stimulus_type'] != 'saccadegrating':
raise ValueError('Unexpected stimulus type: '
f'{parameters["stimulus_type"]}')
fixfr = parameters.get('fixationframes', 80)
sacfr = parameters.get('saccadeframes', 10)
barwidth = parameters.get('barwidth', 40)
averageshift = parameters.get('averageshift', 2)
# The seed is hard-coded in the Stimulator
seed = -10000
ftimes = asc.readframetimes(exp_dir, stim_nr)
ftimes.resize(int(ftimes.shape[0] / 2), 2)
nfr = ftimes.size
# Re-generate the stimulus
# Amplitude of the shift and the transition type (saccade or grey is
# determined based on the output of ran1
randnrs = np.array(randpy.ran1(seed, nfr)[0])
# Separate the amplitude and transitions into two arrays
stimpos = (4 * randnrs[::2]).astype(int)
# Transition variable, determines whether grating is moving during
# the transion or only a grey screen is presented.
trans = np.array(randnrs[1::2] > 0.5)
# Record before and after positions in a single array and remove
# The first element b/c there is no before value
stimposx = np.append(0, stimpos)[:-1]
stimtr = np.stack((stimposx, stimpos), axis=1)[1:]
trans = trans[:-1]
saccadetr = stimtr[trans, :]
greytr = stimtr[~trans, :]
# Create a time vector with defined temporal bin size
tstep = 0.01 # Bin size is defined here, unit is seconds
trialduration = (fixfr + sacfr) / refresh_rate
nrsteps = int(trialduration / tstep) + 1
t = np.linspace(0, trialduration, num=nrsteps)
# Collect saccade beginning time for each trial
trials = ftimes[1:, 0]
sacftimes = trials[trans]
greyftimes = trials[~trans]
sacspikes = np.empty((clusters.shape[0], sacftimes.shape[0], t.shape[0]))
greyspikes = np.empty((clusters.shape[0], greyftimes.shape[0], t.shape[0]))
# Collect all the psth in one array. The order is
# transision type, cluster index, start pos, target pos, time
psth = np.zeros((2, clusters.shape[0], 4, 4, t.size))
for i, (chid, clid, _) in enumerate(clusters):
spiketimes = asc.read_raster(exp_dir, stim_nr, chid, clid)
for j, _ in enumerate(sacftimes):
sacspikes[i, j, :] = asc.binspikes(spiketimes, sacftimes[j] + t)
for k, _ in enumerate(greyftimes):
greyspikes[i, k, :] = asc.binspikes(spiketimes, greyftimes[k] + t)
# Sort trials according to the transition type
# nton[i][j] contains the indexes of trials where saccade was i to j
nton_sac = [[[] for _ in range(4)] for _ in range(4)]
for i, trial in enumerate(saccadetr):
nton_sac[trial[0]][trial[1]].append(i)
nton_grey = [[[] for _ in range(4)] for _ in range(4)]
for i, trial in enumerate(greytr):
nton_grey[trial[0]][trial[1]].append(i)
savedir = os.path.join(exp_dir, 'data_analysis', stimname)
os.makedirs(savedir, exist_ok=True)
for i in range(clusters.shape[0]):
fig, axes = plt.subplots(4,
4,
sharex=True,
sharey=True,
figsize=(8, 8))
for j in range(4):
for k in range(4):
# Start from bottom left corner
ax = axes[3 - j][k]
# Average all transitions of one type
psth_sac = sacspikes[i, nton_sac[j][k], :].mean(axis=0)
psth_grey = greyspikes[i, nton_grey[j][k], :].mean(axis=0)
# Convert to spikes per second
psth_sac = psth_sac / tstep
psth_grey = psth_grey / tstep
psth[0, i, j, k, :] = psth_sac
psth[1, i, j, k, :] = psth_grey
ax.axvline(sacfr / refresh_rate * 1000,
color='red',
linestyle='dashed',
linewidth=.5)
ax.plot(t * 1000, psth_sac, label='Saccadic trans.')
ax.plot(t * 1000, psth_grey, label='Grey trans.')
ax.set_yticks([])
ax.set_xticks([])
# Cosmetics
plf.spineless(ax)
if j == k:
ax.set_facecolor((1, 1, 0, 0.15))
if j == 0:
ax.set_xlabel(f'{k}')
if k == 3:
ax.legend(fontsize='xx-small', loc=0)
if k == 0:
ax.set_ylabel(f'{j}')
# Add an encompassing label for starting and target positions
ax0 = fig.add_axes([0.08, 0.08, .86, .86])
plf.spineless(ax0)
ax0.patch.set_alpha(0)
ax0.set_xticks([])
ax0.set_yticks([])
ax0.set_ylabel('Start position')
ax0.set_xlabel('Target position')
plt.suptitle(f'{exp_name}\n{stimname}\n{clusterids[i]}')
plt.savefig(os.path.join(savedir, f'{clusterids[i]}.svg'))
plt.close()
# Save results
keystosave = [
'fixfr', 'sacfr', 't', 'averageshift', 'barwidth', 'seed', 'trans',
'saccadetr', 'greytr', 'nton_sac', 'nton_grey', 'stimname',
'sacspikes', 'greyspikes', 'psth', 'nfr', 'parameters'
]
data_in_dict = {}
for key in keystosave:
data_in_dict[key] = locals()[key]
np.savez(os.path.join(savedir, str(stim_nr) + '_data'), **data_in_dict)
print(f'Analysis of {stimname} completed.')
