def get_concatenated_response_matrix(self,
dtype=np.float,
sampling_rate=1000.,
truncate=None):
"""Loads spike files from disk, returns concatenated responses.
You must run transform_all_stimuli first, or otherwise set self.t_list,
so that I know how to bin the spikes.
truncate : if a value, throw away all spikes greater than thi
if None, throw away all spikes beyond the end of the stimulus
for this response
Returns in shape (1, N_timepoints)
"""
# Set list of filenames and error check
self._set_list_of_files()
# load each one and histogram
concatenated_psths = []
for respfile, bin_centers in zip(self.spike_file_list, self.t_list):
# store responses
#~ try:
#~ # flatten() handles the case of only one value
#~ st = np.loadtxt(respfile).flatten()
#~ except IOError:
#~ # this handles the case of no data
#~ st = np.array([])
#~ st = st / 1000.0
s = file(respfile).readlines()
st = []
for line in s:
tmp = myutils.parse_space_sep(line, dtype=np.float)
tmp = np.asarray(tmp) / sampling_rate
if truncate:
tmp = tmp[tmp <= truncate]
else:
tmp = tmp[tmp <= bin_centers.max()]
st.append(tmp)
# convert bin centers to bin edges
bin_edges = bin_centers[:-1] + 0.5 * np.diff(bin_centers)
bin_edges = np.concatenate([[-np.inf], bin_edges, [np.inf]])
# now histogram
counts = []
for line in st:
counts.append(np.histogram(line, bin_edges)[0])
counts = np.mean(counts, axis=0)
# Append to growing list and check that size matches up trial-by-trial
concatenated_psths.append(counts)
assert len(counts) == len(bin_centers)
# Return a concatenated array of response from this recording
self.psth_list = concatenated_psths
return np.concatenate(concatenated_psths).astype(dtype)[np.newaxis, :]
def get_concatenated_response_matrix(self, dtype=np.float,
sampling_rate=1000., truncate=None):
"""Loads spike files from disk, returns concatenated responses.
You must run transform_all_stimuli first, or otherwise set self.t_list,
so that I know how to bin the spikes.
truncate : if a value, throw away all spikes greater than thi
if None, throw away all spikes beyond the end of the stimulus
for this response
Returns in shape (1, N_timepoints)
"""
# Set list of filenames and error check
self._set_list_of_files()
# load each one and histogram
concatenated_psths = []
for respfile, bin_centers in zip(self.spike_file_list, self.t_list):
# store responses
#~ try:
#~ # flatten() handles the case of only one value
#~ st = np.loadtxt(respfile).flatten()
#~ except IOError:
#~ # this handles the case of no data
#~ st = np.array([])
#~ st = st / 1000.0
s = file(respfile).readlines()
st = []
for line in s:
tmp = myutils.parse_space_sep(line, dtype=np.float)
tmp = np.asarray(tmp) / sampling_rate
if truncate:
tmp = tmp[tmp <= truncate]
else:
tmp = tmp[tmp <= bin_centers.max()]
st.append(tmp)
# convert bin centers to bin edges
bin_edges = bin_centers[:-1] + 0.5 * np.diff(bin_centers)
bin_edges = np.concatenate([[-np.inf], bin_edges, [np.inf]])
# now histogram
counts = []
for line in st:
counts.append(np.histogram(line, bin_edges)[0])
counts = np.mean(counts, axis=0)
# Append to growing list and check that size matches up trial-by-trial
concatenated_psths.append(counts)
assert len(counts) == len(bin_centers)
# Return a concatenated array of response from this recording
self.psth_list = concatenated_psths
return np.concatenate(concatenated_psths).astype(dtype)[np.newaxis,:]
def get_tetrode_filter(ratname=None):
fn_d = {
'CR12B': '/media/STELLATE/20111208_CR12B_allsessions_sorted/data_params_CR12B.csv',
'CR17B': '/media/STELLATE/20110907_CR17B_allsessions_sorted/data_params_CR17B.csv',
'CR13A': '/media/STELLATE/20110816_CR13A_allsessions_sorted/data_params_CR13A.csv'
}
if ratname is None:
l = []
for r in fn_d.keys():
l += get_tetrode_filter(r)
return l
dp = mlab.csv2rec(fn_d[ratname])
tetrode_filter = []
for row in dp:
if row['session_type'] != 'behaving':
continue
for t in myutils.parse_space_sep(row['auditory_tetrodes']):
tetrode_filter.append((row['session_name'], t))
return sorted(tetrode_filter)
