def plot_all_PSTHs(data_dir, PRE_STIMULUS_TIME=0, save_fig_dir=None):
# Location of OE db
db_filename = glob.glob(os.path.join(data_dir, '*.db'))[0]
db = OE.open_db('sqlite:///%s' % db_filename)
# Get list of neurons from tetrode block
id_block = get_tetrode_block_id()
block = OE.Block().load(id_block)
neuron_list = block._neurons
big_spiketimes = get_all_spike_times(neuron_list)
# Plot PSTHs
for n_name, spike_time_list in list(big_spiketimes.items()):
plt.figure()
plt.hist(np.array(spike_time_list) - PRE_STIMULUS_TIME, bins=100)
plt.title(n_name)
if save_fig_dir is not None:
plt.savefig(os.path.join(save_fig_dir, 'PSTH_%s.png' % n_name))
plt.close()
else:
plt.show()
def stuff(filename,
db_name,
TIMESTAMPS,
SHOVE_CHANNELS,
pre_slice_len,
post_slice_len,
db_type='sqlite'):
# Load the file and file header
l = ns5.Loader(filename=filename)
l.load_file()
# Audio channel numbers
AUDIO_CHANNELS = l.get_audio_channel_numbers()
# Open connection to OE db and create a block
if db_type is 'postgres':
OE.open_db(url=(
'postgresql://[email protected]/test')) # %s' % db_name))
print('post')
else:
OE.open_db(url=('sqlite:///%s' % db_name))
#OE.open_db(url=('mysql://*****:*****@localhost/%s' % db_name))
block = OE.Block(name='Raw Data',
info='Raw data sliced around trials',
fileOrigin=filename)
id_block = block.save() # need this later
# Convert requested slice lengths to samples
pre_slice_len_samples = int(pre_slice_len * l.header.f_samp)
post_slice_len_samples = int(post_slice_len * l.header.f_samp)
# Add RecordingPoint
ch2rpid = dict()
for ch in SHOVE_CHANNELS:
rp = OE.RecordingPoint(name=('RP%d' % ch),
id_block=id_block,
channel=float(ch))
rp_id = rp.save()
ch2rpid[ch] = rp_id
# Extract each trial as a segment
for tn, trial_start in enumerate(TIMESTAMPS):
# Create segment for this trial
segment = OE.Segment(id_block=id_block,
name=('trial%d' % tn),
info='raw data loaded from good channels')
id_segment = segment.save()
# Create AnalogSignal for each channel
for chn, ch in enumerate(SHOVE_CHANNELS):
# Load
x = np.array(l._get_channel(ch)[trial_start-pre_slice_len_samples:\
trial_start+post_slice_len_samples])
# Convert to uV
x = x * uV_QUANTUM
# Put in AnalogSignal and save to db
sig = OE.AnalogSignal(signal=x,
channel=float(ch),
sampling_rate=l.header.f_samp,
t_start=old_div(
(trial_start - pre_slice_len_samples),
l.header.f_samp),
id_segment=id_segment,
id_recordingpoint=ch2rpid[ch],
name=('Channel %d Trial %d' % (ch, tn)))
# Special processing for audio channels
if ch == AUDIO_CHANNELS[0]:
sig.name = ('L Speaker Trial %d' % tn)
elif ch == AUDIO_CHANNELS[1]:
sig.name = ('R Speaker Trial %d' % tn)
# Save signal to database
sig.save()
# Handle AUDIO CHANNELS only slightly differently
for ch in AUDIO_CHANNELS:
# Load
x = np.array(l._get_channel(ch)[trial_start-pre_slice_len_samples:\
trial_start+post_slice_len_samples])
# Special processing for audio channels
if ch == AUDIO_CHANNELS[0]:
sname = ('L Speaker Trial %d' % tn)
elif ch == AUDIO_CHANNELS[1]:
sname = ('R Speaker Trial %d' % tn)
# Put in AnalogSignal and save to db
sig = OE.AnalogSignal(signal=x,
channel=float(ch),
sampling_rate=l.header.f_samp,
t_start=old_div(
(trial_start - pre_slice_len_samples),
l.header.f_samp),
id_segment=id_segment,
name=sname)
# Save signal to database
sig.save()
# Save segment (with all analogsignals) to db
# Actually this may be unnecessary
# Does saving the signals link to the segment automatically?
segment.save()
return (id_segment, id_block)
def run(db_name, CAR=True, smooth_spikes=True):
"""Filters the data for spike extraction.
db_name: Name of the OpenElectrophy db file
CAR: If True, subtract the common-average of every channel.
smooth_spikes: If True, add an additional low-pass filtering step to
the spike filter.
"""
# Open connection to the database
OE.open_db(url=('sqlite:///%s' % db_name))
# Check that I haven't already run
id_blocks, = OE.sql(
"SELECT block.id FROM block WHERE block.name='CAR Tetrode Data'")
if len(id_blocks) > 0:
print("CAR Tetrode Data already exists, no need to recompute")
return
# Find the block
id_blocks, = OE.sql(
"SELECT block.id FROM block WHERE block.name='Raw Data'")
assert (len(id_blocks) == 1)
id_block = id_blocks[0]
raw_block = OE.Block().load(id_block)
# Define spike filter
# TODO: fix so that doesn't assume all sampling rates the same!
fixed_sampling_rate = OE.AnalogSignal().load(1).sampling_rate
FILTER_B, FILTER_A = define_spike_filter(fixed_sampling_rate)
# If requested, define second spike filter
if smooth_spikes is True:
FILTER_B2, FILTER_A2 = define_spike_filter_2(fixed_sampling_rate)
# Find TETRODE_CHANNELS file in data directory of db
data_dir = path.split(db_name)[0]
TETRODE_CHANNELS = get_tetrode_channels(
path.join(data_dir, 'TETRODE_CHANNELS'))
N_TET = len(TETRODE_CHANNELS)
# For convenience, flatten TETRODE_CHANNELS to just get worthwhile channels
GOOD_CHANNELS = [item for sublist in TETRODE_CHANNELS for item in sublist]
# Create a new block for referenced data, and save to db.
car_block = OE.Block(\
name='CAR Tetrode Data',
info='Raw neural data, now referenced and ordered by tetrode',
fileOrigin=db_name)
id_car_block = car_block.save()
# Make RecordingPoint for each channel, linked to tetrode number with `group`
# Also keep track of link between channel and RP with ch2rpid dict
ch2rpid = dict()
for tn, ch_list in enumerate(TETRODE_CHANNELS):
for ch in ch_list:
rp = OE.RecordingPoint(name=('RP%d' % ch),
id_block=id_car_block,
trodness=len(ch_list),
channel=float(ch),
group=tn)
rp_id = rp.save()
ch2rpid[ch] = rp_id
# Find all segments in the block of raw data
id_segments, = OE.sql('SELECT segment.id FROM segment ' + \
'WHERE segment.id_block = :id_block', id_block=id_block)
# For each segment in this block, load each AnalogSignal listed in
# TETRODE channels and average
# to compute CAR. Then subtract from each AnalogSignal.
for id_segment in id_segments:
# Create a new segment in the new block with the same name
old_seg = OE.Segment().load(id_segment)
car_seg = OE.Segment(
name=old_seg.name,
id_block=id_car_block,
)
id_car_seg = car_seg.save()
# Find all AnalogSignals in this segment
id_sigs, = OE.sql('SELECT analogsignal.id FROM analogsignal ' + \
'WHERE analogsignal.id_segment = :id_segment', id_segment=id_segment)
# Compute average of each
running_car = 0
n_summed = 0
for id_sig in id_sigs:
sig = OE.AnalogSignal().load(id_sig)
if sig.channel not in GOOD_CHANNELS:
continue
running_car = running_car + sig.signal
n_summed = n_summed + 1
# Zero out CAR if CAR is not wanted
# TODO: eliminate the actual calculation of CAR above in this case
# For now, just want to avoid weird bugs
if CAR is False:
running_car = np.zeros(running_car.shape)
# Put the CAR into the new block
# not assigning channel, t_start, sample_rate, maybe more?
car_sig = OE.AnalogSignal(
name='CAR',
signal=old_div(running_car, n_summed),
info='CAR calculated from good channels for this segment',
id_segment=id_segment)
car_sig.save()
# Put all the substractions in id_car_seg
for id_sig in id_sigs:
# Load the raw signal (skip bad channels)
sig = OE.AnalogSignal().load(id_sig)
if sig.channel not in GOOD_CHANNELS:
continue
# Subtract the CAR
referenced_signal = sig.signal - car_sig.signal
# Filter!
filtered_signal = scipy.signal.filtfilt(FILTER_B, FILTER_A,
referenced_signal)
if smooth_spikes is True:
filtered_signal = scipy.signal.filtfilt(
FILTER_B2, FILTER_A2, filtered_signal)
# Check for infs or nans
if np.isnan(filtered_signal).any():
print("ERROR: Filtered signal contains NaN!")
if np.isinf(filtered_signal).any():
print("ERROR: Filtered signal contains Inf!")
# Store in db
new_sig = OE.AnalogSignal(\
name=sig.name,
signal=filtered_signal,
info='CAR has been subtracted',
id_segment=id_car_seg,
id_recordingpoint=ch2rpid[sig.channel],
channel=sig.channel,
t_start=sig.t_start,
sampling_rate=sig.sampling_rate)
new_sig.save()
# Finally, copy the audio channel over from the old block
id_audio_sigs, = OE.sql('SELECT analogsignal.id FROM analogsignal ' + \
'WHERE analogsignal.id_segment = :id_segment AND ' + \
"analogsignal.name LIKE '% Speaker %'", id_segment=id_segment)
for id_audio_sig in id_audio_sigs:
old_sig = OE.AnalogSignal().load(id_audio_sig)
OE.AnalogSignal(\
name=old_sig.name,
signal=old_sig.signal,
id_segment=id_car_seg,
channel=old_sig.channel,
t_start=old_sig.t_start,
sampling_rate=old_sig.sampling_rate).save()
def execute(data_dir, PRE_STIMULUS_TIME=0.5):
"""Write spike time data and metadata from OE db.
Writes spike times from all sorted spikes in OE db to KlustaKwik
files in same directory.
Also writes a file metadata.csv with the ntrial number (ordering
of segments in OE db), the btrial number (trial id in matlab struct,
extracted from OE db info field), and the time of stimulus onset.
Time of stimulus onset is calculated as the t_start time of that segment
in OE dB, plus the provided PRE_STIMULUS_TIME parameter. Later scripts
will use the stimulus onset times to find spikes associated with that
trial in the KlustaKwik files.
"""
# Location of data
#data_dir = '/home/chris/Public/20110517_CR12B_FF2B/CR12B_0514_001'
#PRE_STIMULUS_TIME = 0.5
# Location of the Bcontrol file
#bdata_filename = os.path.join(data_dir,
# 'data_@TwoAltChoice_v2_chris_AIR11A_101012a.at')
# Location of OE db
#db_filename = os.path.join(data_dir, 'datafile_CR_CR12B_110507_001.db')
db_filename = glob.glob(os.path.join(data_dir, '*.db'))[0]
# Output of the writers
output_filename = os.path.splitext(db_filename)[0]
# Metadata: trial numbers etc
metadata_filename = os.path.join(data_dir, 'metadata.csv')
# Load db
db = OE.open_db('sqlite:///%s' % db_filename)
# Get list of segments from tetrode block
id_block = 2
block = OE.Block().load(id_block)
neuron_list = block._neurons
# Build a writer
w = KlustaKwikIO(filename=output_filename)
w.write_block(block)
# Also dump metadata: btrial num, t_start in samples
seg_metadata = list()
for seg in block.get_segments():
t_starts1 = [sig.t_start for sig in seg._analogsignals]
t_starts2 = [st.t_start for st in seg._spiketrains]
assert len(np.unique(t_starts1)) == 1
# You can get errors here where some spiketrains have already the right
# t_start and others don't. I think maybe this happens when you re-spike
# sort or something.
#assert len(np.unique(t_starts2)) == 1
#assert np.unique(t_starts1) == np.unique(t_starts2)
# This was a stupid bugfix for a stupid bug that is now breaking things
# Replaced with PRE_STIMULUS_TIME so at least it's up front
t_start = np.rint(
(t_starts1[0] + PRE_STIMULUS_TIME) * 30000.).astype(np.int64)
seg_metadata.append((seg.name, int(seg.info), t_start))
# Convert seg_metadata to recarray and write to disk
r = np.rec.fromrecords(seg_metadata,
dtype=[('ntrial', '<U32'), ('btrial_num', np.int),
('stim_onset', np.int64)])
mlab.rec2csv(r, metadata_filename)
