def run_wn(
rs=None,
output_dir="/media/dendrite",
all_channels_file="../DO1_ALL_CHANNELS",
channel_groups_file="../DO1_CHANNEL_GROUPS",
analog_channels_file="../ANALOG_7_8",
ns5_filename=None,
remove_from_TC=None,
soft_time_limits=(-2.0, 2.0),
hard_time_limits=(-0.04, 0.15),
do_timestamps=True,
force_put_neural_data=False,
do_avg_plots=True,
do_extract_spikes=True,
CAR=True,
save_to_klusters=True,
do_MUA_grand_plot=True,
do_run_klustakwik=True,
group_multiplier=100,
**kwargs
):
if len(kwargs) > 0:
print "unexpected kwargs"
print kwargs
# Make session
if rs is None:
printnow("creating recording session %s" % ns5_filename)
rsm = rswrap.RecordingSessionMaker(
data_analysis_dir=output_dir,
all_channels_file=all_channels_file,
channel_groups_file=channel_groups_file,
analog_channels_file=analog_channels_file,
)
if remove_from_TC is None:
remove_from_TC = []
rs = rsm.make_session(ns5_filename=ns5_filename, remove_from_TC=remove_from_TC)
rs.write_time_limits(soft_time_limits, hard_time_limits)
printnow("RS %s" % rs.full_path)
# add timestamps
if do_timestamps:
printnow("adding timestamps")
# write timestamps to directory
times, numbers = rswrap.add_timestamps_to_session(rs, verbose=True, meth="audio_onset")
# put in neural db (does nothing if exists unless forced)
printnow("putting neural data")
rs.put_neural_data_into_db(verbose=True, force=force_put_neural_data)
# plot averages
if do_avg_plots:
printnow("avg plots")
rswrap.plot_avg_lfp(rs, savefig=True)
rswrap.plot_avg_audio(rs, savefig=True)
# spike extract
if do_extract_spikes:
printnow("extracting spikes")
rs.generate_spike_block(CAR=CAR, smooth_spikes=False, verbose=True)
rs.run_spikesorter(save_to_db=True, save_to_klusters=save_to_klusters)
rs.spiketime_dump()
# plot MUA stuff
if do_MUA_grand_plot:
printnow("mua grand psths")
rswrap.plot_all_spike_psths(rs, savefig=True)
# spike sort
if do_run_klustakwik:
printnow("running klustakwik")
rs.group_multiplier = group_multiplier
rs.run_klustakwik()
rs.prep_for_klusters(verbose=True)
return rs
def run_behaving(
rs=None,
output_dir="/media/dendrite",
all_channels_file="../DO1_ALL_CHANNELS",
channel_groups_file="../DO1_CHANNEL_GROUPS",
analog_channels_file="../ANALOG_7_8",
ns5_filename=None,
remove_from_TC=None,
soft_time_limits=(-2.0, 2.0),
hard_time_limits=(-0.04, 0.15),
do_add_bcontrol=True,
bcontrol_folder="/media/hippocampus/TRWINRIG_DATA/Data/chris/CR20B",
do_timestamps=True,
do_add_btrial_numbers=True,
force_put_neural_data=False,
do_avg_plots=True,
do_extract_spikes=True,
CAR=True,
save_to_klusters=True,
do_MUA_grand_plot=True,
do_MUA_plot_by_stimulus=True,
do_run_klustakwik=True,
do_SUA_plot_by_stimulus=True,
group_multiplier=100,
psth_time_limits=(None, None),
lfp_time_limits="hard",
**kwargs
):
if len(kwargs) > 0:
print "unexpected kwargs"
print kwargs
# Make session
if rs is None:
printnow("creating recording session %s" % ns5_filename)
rsm = rswrap.RecordingSessionMaker(
data_analysis_dir=output_dir,
all_channels_file=all_channels_file,
channel_groups_file=channel_groups_file,
analog_channels_file=analog_channels_file,
)
if remove_from_TC is None:
remove_from_TC = []
rs = rsm.make_session(ns5_filename=ns5_filename, remove_from_TC=remove_from_TC)
rs.write_time_limits(soft_time_limits, hard_time_limits)
rs.group_multiplier = group_multiplier
printnow("RS %s" % rs.full_path)
# add bcontrol
if do_add_bcontrol:
printnow("adding bcontrol")
rswrap.add_bcontrol_data_to_session(bcontrol_folder, rs, verbose=True)
# add timestamps
if do_timestamps:
printnow("adding timestamps")
# write timestamps to directory
times, numbers = rswrap.add_timestamps_to_session(rs, verbose=True, meth="digital_trial_number")
# put in neural db (does nothing if exists unless forced)
printnow("putting neural data")
rs.put_neural_data_into_db(verbose=True, force=force_put_neural_data)
# adding btrial numbers
if do_add_btrial_numbers:
printnow("adding btrial numbers")
if len(numbers) == 0:
print "warning: no numbers?"
numbers = None # will auto-determine if all is well
rswrap.add_behavioral_trial_numbers2(rs, known_trial_numbers=numbers, trial_number_channel=16, verbose=True)
# plot averages
if do_avg_plots:
printnow("avg plots")
if lfp_time_limits == "hard":
lfp_time_limits = rs.read_time_limits()[1]
rswrap.plot_avg_lfp(rs, savefig=True, t_start=lfp_time_limits[0], t_stop=lfp_time_limits[1])
rswrap.plot_avg_audio(rs, savefig=True)
# spike extract
if do_extract_spikes:
printnow("extracting spikes")
rs.generate_spike_block(CAR=CAR, smooth_spikes=False, verbose=True)
rs.run_spikesorter(save_to_db=True, save_to_klusters=save_to_klusters)
rs.spiketime_dump()
# plot MUA stuff
if do_MUA_grand_plot:
printnow("mua grand psths")
rswrap.plot_all_spike_psths(rs, savefig=True)
# plot MUA by stim
if do_MUA_plot_by_stimulus:
printnow("mua by stimulus")
t_start, t_stop = psth_time_limits
rswrap.plot_MUA_by_stim(rs, savefig=True, t_start=t_start, t_stop=t_stop)
# spike sort
if do_run_klustakwik:
printnow("running klustakwik")
rs.group_multiplier = group_multiplier
rs.run_klustakwik()
rs.prep_for_klusters(verbose=True)
# SUA by stim
if do_SUA_plot_by_stimulus:
printnow("SUA plot by stimulus")
t_start, t_stop = psth_time_limits
rswrap.plot_all_spike_psths_by_stim(
rs, savefig=True, t_start=t_start, t_stop=t_stop, skipNoScore=False, override_path=rs.last_klusters_dir()
)
return rs
def run_tones(
rs=None,
output_dir="/media/dendrite",
all_channels_file="../DO1_ALL_CHANNELS",
channel_groups_file="../DO1_CHANNEL_GROUPS",
analog_channels_file="../ANALOG_7_8",
ns5_filename=None,
remove_from_TC=None,
soft_time_limits=(-1.0, 1.0),
hard_time_limits=(-0.04, 0.15),
do_add_bcontrol=True,
bcontrol_folder="/media/hippocampus/TRWINRIG_DATA/Data/SPEAKERCAL/",
bcontrol_files=None,
n_tones=None,
n_tones_per_bout=200,
TR_NDAQ_offset_sec=420,
start_offset=0,
stop_offset=0,
do_timestamps=True,
plot_spectrograms=True,
break_at_spectrograms=False,
force_put_neural_data=False,
do_avg_plots=True,
do_extract_spikes=True,
detection_kwargs=None,
CAR=True,
save_to_klusters=True,
do_MUA_grand_plot=True,
group_multiplier=100,
psth_time_limits=(None, None),
do_tuning_curve=True,
**kwargs
):
"""Daily run script for tones (tuning curve)
rs : RS if it exists. If it doesn't, provide the following:
output_dir, all_channels_file, channel_groups_file,
analog_channels_file, ns5_filename, remove_from_TC, soft_time_limits,
hard_time_limits
do_add_bcontrol: if True, will find bcontrol files, extract information,
write "tones" and "attens" to directory. (If those files already exist,
then this block is skipped, so delete them if you want to force.)
You can specify explicitly, or else it will search.
bcontrol_files : a list of bcontrol files that you've selected.
bcontrol_folder : If bcontrol_files is None, then will look here
for them. Will try to guess from ns5 time which are appropriate.
It will keep grabbing files until it find at least `n_tones`
tones. If n_tones is None, uses the number of timestamps.
In either case, the mat files are copied into the directory, and then
the `tones` and `attens` files are written. Those files are used
for all subsequent analyses.
plot_spectrograms: if True, will plot spectrograms of the audio stimulus
for every 200 tones, in order to check that the tones and attens
are correctly lined up.
break_at_spectrograms : if True, errors immediately after plotting
spectrograms, for debugging
do_tuning_curve : if True, plots tuning curve
n_tones_per_bout : int, or list of ints
Number of tones expected in each bout.
This is used for calculating timestamps of each tone, from timestamps
of each trial (which corresponds to a single bcontrol file).
Other parameters should be same as other Dailies.
"""
if len(kwargs) > 0:
print "unexpected kwargs"
print kwargs
# Make session
if rs is None:
printnow("creating recording session %s" % ns5_filename)
rsm = rswrap.RecordingSessionMaker(
data_analysis_dir=output_dir,
all_channels_file=all_channels_file,
channel_groups_file=channel_groups_file,
analog_channels_file=analog_channels_file,
)
if remove_from_TC is None:
remove_from_TC = []
rs = rsm.make_session(ns5_filename=ns5_filename, remove_from_TC=remove_from_TC)
rs.write_time_limits(soft_time_limits, hard_time_limits)
rs.group_multiplier = group_multiplier
printnow("RS %s" % rs.full_path)
# add timestamps
if do_timestamps:
printnow("adding timestamps")
# write timestamps to directory
# have to force, otherwise will sub-time it twice
times, numbers = rswrap.add_timestamps_to_session(rs, verbose=True, force=True, meth="digital_trial_number")
# Right now one time per trial (bcontrol file)
# Need to decimate by number of trials per bout
# First figure out whether int or list of ints
if not hasattr(n_tones_per_bout, "__len__"):
n_tones_per_bout = [n_tones_per_bout] * len(times)
# Now decimate each bout
# This command works for 200 tones, extrapolate correct formula from it
# subtimes = np.rint(np.linspace(3300, 1194210, 200)).astype(np.int)
alltimes = []
for time, n_tones in zip(times, n_tones_per_bout):
istart = 3300
istop = istart + np.rint((n_tones - 1) * 5984.5).astype(np.int)
subtimes = np.rint(np.linspace(istart, istop, n_tones)).astype(np.int)
alltimes.append(time + subtimes)
alltimes = np.concatenate(alltimes)
rs.add_timestamps(alltimes)
# add bcontrol
tone_filename = os.path.join(rs.full_path, "tones")
atten_filename = os.path.join(rs.full_path, "attens")
if do_add_bcontrol and (not os.path.exists(tone_filename) or not os.path.exists(atten_filename)):
printnow("adding bcontrol")
# First find out how many tones there probably are
if n_tones is None:
n_tones = len(rs.read_timestamps())
if bcontrol_files is None:
# Guess by ns5 filetime
ns5_stoptime = gettime(rs.get_ns5_filename())
ns5_startime = ns5_stoptime - datetime.timedelta(
seconds=rs.get_ns5_loader().header.n_samples / rs.get_sampling_rate()
)
ns5_stoptime += datetime.timedelta(seconds=TR_NDAQ_offset_sec)
ns5_startime += datetime.timedelta(seconds=TR_NDAQ_offset_sec)
mintime = ns5_startime + datetime.timedelta(seconds=start_offset)
maxtime = ns5_stoptime + datetime.timedelta(seconds=stop_offset)
# Find the bcontrol files that were saved during the recording
# And sort by time
allfiles = np.asarray(glob.glob(os.path.join(bcontrol_folder, "speakercal*.mat")))
bcontrol_filetimes = np.asarray(map(gettime, allfiles))
sidxs = np.argsort(bcontrol_filetimes)
bcontrol_filetimes = bcontrol_filetimes[sidxs]
allfiles = allfiles[sidxs]
# Choose the files within the window
check_idxs = np.where((bcontrol_filetimes > mintime) & (bcontrol_filetimes < maxtime))[0]
# Iterate through the found files until a sufficient number
# of tones have been found
n_found_tones = 0
found_files = []
for check_idx in check_idxs:
# Load file
filename = allfiles[check_idx]
tl = myutils.ToneLoader(filename)
# Skip if WN
if not np.all(tl.tones == 0):
found_files.append(filename)
n_found_tones += len(tl.tones)
# Break if enough found
if n_found_tones >= n_tones:
break
# Output debugging info
print "I found %d tones in %d files" % (n_found_tones, len(found_files))
if n_found_tones < n_tones:
print "insufficient tones found ... try increasing start delta"
# More debugging info about first file
print "Using general offset of " + str(TR_NDAQ_offset_sec) + " ...."
idx1 = np.where(allfiles == found_files[0])[0]
offsets = bcontrol_filetimes[idx1 - 1 : idx1 + 2] - ns5_startime
poffsets1 = [offset.seconds if offset > datetime.timedelta(0) else -(-offset).seconds for offset in offsets]
print "First file (prev,curr,next) offsets from start: %d %d %d" % (
poffsets1[0],
poffsets1[1],
poffsets1[2],
)
# And last file
idx1 = np.where(allfiles == found_files[-1])[0]
offsets = bcontrol_filetimes[idx1 - 1 : idx1 + 2] - ns5_stoptime
poffsets2 = [offset.seconds if offset > datetime.timedelta(0) else -(-offset).seconds for offset in offsets]
print "Last file (prev,curr,next) offsets from stop: %d %d %d" % (poffsets2[0], poffsets2[1], poffsets2[2])
# Now put in forward order
bcontrol_files = np.asarray(found_files)
# Debugging output
print "Like these results? Here's how to replicate:"
print "<speakercal_files>"
for bcf in bcontrol_files:
print os.path.split(bcf)[1]
print "</speakercal_files>"
print "clock_offset='%d' start_offset='%d %d %d' stop_offset='%d %d %d'" % (
TR_NDAQ_offset_sec,
poffsets1[0],
start_offset,
poffsets1[1],
poffsets2[1],
stop_offset,
poffsets2[2],
)
# Add to RS
if bcontrol_files is not None:
for file in bcontrol_files:
rs.add_file(file)
# Now that we've settled on a canonical bcontrol file ordering,
# dump tones and attens
tls = [myutils.ToneLoader(file) for file in bcontrol_files]
tones = np.concatenate([tl.tones for tl in tls])
attens = np.concatenate([tl.attens for tl in tls])
np.savetxt(tone_filename, tones)
np.savetxt(atten_filename, attens, fmt="%d")
if plot_spectrograms:
tones = np.loadtxt(tone_filename)
attens = np.loadtxt(atten_filename, dtype=np.int)
# verify timestamps
timestamps = rs.read_timestamps()
if len(timestamps) < len(tones):
print "warning not enough timestamps, discarding tones: " + "%d timestamps but %d tones" % (
len(timestamps),
len(tones),
)
tones = tones[: len(timestamps)]
attens = attens[: len(timestamps)]
elif len(timestamps) > len(tones):
print "warning too many timestamps, provide more tones: " + "%d timestamps but %d tones" % (
len(timestamps),
len(tones),
)
# check spectrograms
# plot debugging spectrograms of audio
l = rs.get_ns5_loader()
raw = l.get_chunk_by_channel()
ain135 = raw[135]
ain136 = raw[136]
# Spectrogrammer object
sg = myutils.Spectrogrammer(NFFT=1024, Fs=30e3, max_freq=30e3, min_freq=0, noverlap=512, downsample_ratio=1)
# Fake toneloader to calculate aliased tones
tl = myutils.ToneLoader()
tl.tones = tones
aliased_tones = tl.aliased_tones()
for n in range(0, len(tones) - 5, 200):
ts = timestamps[n]
known_tones = aliased_tones[n : n + 5]
slc1 = ain135[ts : ts + 30e3]
slc2 = ain136[ts : ts + 30e3]
# Transform and plot
Pxx, freqs, t = sg.transform(np.mean([slc1, slc2], axis=0))
myutils.my_imshow(Pxx, t, freqs)
plt.axis("auto")
# Title with known tones
plt.title(
"tl%d %0.1f %0.1f %0.1f %0.1f %0.1f"
% (n, known_tones[0], known_tones[1], known_tones[2], known_tones[3], known_tones[4])
)
# Save to RS
plt.savefig(os.path.join(rs.full_path, "tones_%d.png" % n))
plt.close()
if break_at_spectrograms:
1 / 0
# put in neural db (does nothing if exists unless forced)
printnow("putting neural data")
rs.put_neural_data_into_db(verbose=True, force=force_put_neural_data)
# plot averages
if do_avg_plots:
printnow("avg plots")
rswrap.plot_avg_lfp(rs, savefig=True)
rswrap.plot_avg_audio(rs, savefig=True)
# spike extract
if do_extract_spikes:
printnow("extracting spikes")
rs.generate_spike_block(CAR=CAR, smooth_spikes=False, verbose=True)
rs.run_spikesorter(save_to_db=True, save_to_klusters=save_to_klusters, detection_kwargs=detection_kwargs)
rs.spiketime_dump()
# plot MUA stuff
if do_MUA_grand_plot:
printnow("mua grand psths")
rswrap.plot_all_spike_psths(rs, savefig=True)
# make a tuning curve
if do_tuning_curve:
# extract tones and attens from each
tones = np.loadtxt(tone_filename)
attens = np.loadtxt(atten_filename, dtype=np.int)
if len(timestamps) < len(tones):
print "warning not enough timestamps, discarding tones: " + "%d timestamps but %d tones" % (
len(timestamps),
len(tones),
)
tones = tones[: len(timestamps)]
attens = attens[: len(timestamps)]
elif len(timestamps) > len(tones):
print "warning too many timestamps, provide more tones: " + "%d timestamps but %d tones" % (
len(timestamps),
len(tones),
)
# parameters for tuning curve
tc_freqs = 10 ** np.linspace(np.log10(5e3), np.log10(50e3), 15)
tc_attens = np.unique(attens)
# Determine which bin each trial belongs to
tone_freq_bin = np.searchsorted(tc_freqs, tones, side="right") - 1
tone_atten_bin = np.searchsorted(tc_attens, attens, side="right") - 1
# spike count for each trial
group = 5
spike_time_file = os.path.join(rs.last_klusters_dir(), "%s.res.%d" % (rs.session_name, group))
spike_times = np.loadtxt(spike_time_file, dtype=np.int)
timestamps = rs.read_timestamps()
spike_counts = count_within_window(timestamps, spike_times, 0.005 * 30e3, 0.030 * 30e3)
# reshape into tuning curve
tc_mean = np.zeros((len(tc_attens), len(tc_freqs) - 1))
tc_std = np.zeros((len(tc_attens), len(tc_freqs) - 1))
tc_median = np.zeros((len(tc_attens), len(tc_freqs) - 1))
for n, tc_freq in enumerate(tc_freqs[:-1]):
for m, tc_atten in enumerate(tc_attens):
# Which tones go into this bin
tone_idxs = np.where((tone_freq_bin == n) & (tone_atten_bin == m))[0]
if len(tone_idxs) == 0:
print "none in this bin %f %d" % (tc_freq, tc_atten)
continue
tc_mean[m, n] = np.mean(spike_counts[tone_idxs])
tc_median[m, n] = np.median(spike_counts[tone_idxs])
tc_std[m, n] = np.std(spike_counts[tone_idxs])
# plot
np.savez("data", tc_mean=tc_mean, tc_freqs=tc_freqs, tc_attens=tc_attens)
myutils.my_imshow(tc_mean, tc_freqs, tc_attens, cmap=plt.cm.Purples)
plt.axis("tight")
plt.colorbar()
myutils.my_imshow(tc_median, tc_freqs, tc_attens, cmap=plt.cm.Purples)
plt.colorbar()
myutils.my_imshow(tc_std, tc_freqs, tc_attens, cmap=plt.cm.gray)
plt.colorbar()
plt.show()
return rs