def plot_logMUA_estimation(asig, logMUA_asig, highpass_freq, lowpass_freq,
t_start, t_stop, channel):
asig = time_slice(asig, t_start, t_stop)
logMUA_asig = time_slice(logMUA_asig, t_start, t_stop)
filt_asig = butter(asig, highpass_freq=highpass_freq,
lowpass_freq=lowpass_freq)
sns.set(style='ticks', palette="deep", context="notebook")
fig, ax = plt.subplots()
ax.plot(asig.times,
zscore(asig.as_array()[:,channel]),
label='original signal')
ax.plot(filt_asig.times,
zscore(filt_asig.as_array()[:,channel]) + 10,
label=f'signal [{highpass_freq}-{lowpass_freq}]',
alpha=0.5)
ax.plot(logMUA_asig.times,
zscore(logMUA_asig.as_array()[:,channel]) + 20,
label='logMUA')
ax.set_title('Channel {}'.format(channel))
ax.set_xlabel('time [{}]'.format(asig.times.units.dimensionality.string))
ax.set_yticklabels([])
plt.legend()
return ax
CLI.add_argument("--time_slice",
nargs='?',
type=none_or_float,
default=None,
help="length of time_slice in seconds.")
args = CLI.parse_args()
block = load_neo(args.data)
evts = [ev for ev in block.segments[0].events
if ev.name == 'Wavefronts'][0]
if args.time_slice is not None:
asig = block.segments[0].analogsignals[0]
t_stop = asig.t_start.rescale('s') + args.time_slice * pq.s
evts = time_slice(evts, t_start=asig.t_start, t_stop=t_stop)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
N = len(np.unique(evts.labels))
cmap = sns.husl_palette(N - 1, h=.5, l=.6)
cmap = random.sample([c for c in cmap], N - 1)
cmap = ListedColormap(['k'] + cmap)
ax.scatter(evts.times,
evts.array_annotations['x_coords'],
evts.array_annotations['y_coords'],
c=[int(label) for label in evts.labels],
cmap=cmap,
s=2)
try:
block = load_neo(args.data, try_signal_grouping=True)
except Exception as e:
print(e)
block = load_neo(args.data, try_signal_grouping=True)
asigs = block.segments[0].analogsignals
if len(asigs) > 1:
print(f'Merging {len(asigs)} AnalogSignals into one.')
asig = merge_analogsingals(asigs)
else:
asig = asigs[0]
asig = time_slice(asig, args.t_start, args.t_stop)
# add metadata
kwargs = parse_string2dict(args.kwargs)
channels = asig.array_annotations[kwargs['ELECTRODE_ANNOTATION_NAME']]
coords = np.array([
kwargs['NAME2COORDS'][str(channel)] for channel in channels.astype(int)
])
asig.array_annotations.update(x_coords=coords[:, 0])
asig.array_annotations.update(y_coords=coords[:, 1])
# locations = []
# for channel in channels:
# locations.append([loc for loc in kwargs['ELECTRODE_LOCATION'].keys()
help="start time in seconds")
CLI.add_argument("--t_stop",
nargs='?',
type=float,
default=10,
help="stop time in seconds")
CLI.add_argument("--channel",
nargs='?',
type=int,
default=0,
help="channel to plot")
args = CLI.parse_args()
orig_asig = load_neo(args.original_data, 'analogsignal', lazy=True)
orig_asig = time_slice(orig_asig,
t_start=args.t_start,
t_stop=args.t_stop,
lazy=True,
channel_indexes=args.channel)
proc_asig = load_neo(args.processed_data, 'analogsignal', lazy=True)
proc_asig = time_slice(proc_asig,
t_start=args.t_start,
t_stop=args.t_stop,
lazy=True,
channel_indexes=args.channel)
plot_traces(orig_asig, proc_asig, args.channel)
save_plot(args.output)
print('vlock', block)
print('seg', block.segments[0])
block.segments[0].imagesequences.append(imageSequences)
# change data orientation to be top=ventral, right=lateral
imgseq = block.segments[0].imagesequences[0]
imgseq = flip_image(imgseq, axis=-2)
imgseq = rotate_image(imgseq, rotation=-90)
block.segments[0].imagesequences[0] = imgseq
# Transform into analogsignals
block.segments[0].analogsignals = []
block = ImageSequence2AnalogSignal(block)
block.segments[0].analogsignals[0] = time_slice(
block.segments[0].analogsignals[0], args.t_start, args.t_stop)
if args.annotations is not None:
block.segments[0].analogsignals[0].annotations.\
update(parse_string2dict(args.annotations))
block.segments[0].analogsignals[0].annotations.update(
orientation_top=args.orientation_top)
block.segments[0].analogsignals[0].annotations.update(
orientation_right=args.orientation_right)
# ToDo: add metadata
block.name = args.data_name
block.segments[0].name = 'Segment 1'
block.segments[0].description = 'Loaded from mat file. '\
.format(neo.__version__)
type=none_or_float,
default=10,
help="stop time in seconds")
args = CLI.parse_args()
block = load_neo(args.data)
asig = block.segments[0].analogsignals[0]
args.plot_tstart = asig.t_start if args.plot_tstart is None else args.plot_tstart
args.plot_tstop = asig.t_stop if args.plot_tstop is None else args.plot_tstop
transition_event = detect_transitions(asig, args.transition_phase)
block.segments[0].events.append(transition_event)
write_neo(args.output, block)
if args.plot_channels[0] is not None:
for channel in args.plot_channels:
plot_hilbert_phase(asig=time_slice(asig, args.plot_tstart,
args.plot_tstop),
event=time_slice(transition_event,
args.plot_tstart,
args.plot_tstop),
channel=int(channel))
output_path = os.path.join(
args.img_dir,
args.img_name.replace('_channel0', f'_channel{channel}'))
save_plot(output_path)
CLI.add_argument("--output",
nargs='?',
type=str,
required=True,
help="path of output file")
CLI.add_argument("--t_start",
nargs='?',
type=float,
default=0,
help="new starting time in s")
CLI.add_argument("--t_stop",
nargs='?',
type=float,
default=10,
help="new stopping time in s")
args = CLI.parse_args()
block = load_neo(args.data)
for i, asig in enumerate(block.segments[0].analogsignals):
block.segments[0].analogsignals[i] = time_slice(asig,
t_start=args.t_start,
t_stop=args.t_stop)
for i, evt in enumerate(block.segments[0].events):
block.segments[0].events[i] = time_slice(evt,
t_start=args.t_start,
t_stop=args.t_stop)
write_neo(args.output, block)
CLI.add_argument("--output",
nargs='?',
type=str,
required=True,
help="path of output file")
CLI.add_argument("--t_start",
nargs='?',
type=float,
default=0,
help="new starting time in s")
CLI.add_argument("--t_stop",
nargs='?',
type=float,
default=10,
help="new stopping time in s")
args = CLI.parse_args()
block = load_neo(args.data)
for i, asig in enumerate(blk.segments[0].analogsignals):
blk.segments[0].analogsignals[i] = time_slice(asig,
t_start=t_start,
t_stop=t_stop)
for i, evt in enumerate(blk.segments[0].events):
blk.segments[0].events[i] = time_slice(evt,
t_start=t_start,
t_stop=t_stop)
write_neo(args.output, block)
CLI.add_argument("--t_start",
nargs='?',
type=float,
default=0,
help="start time in seconds")
CLI.add_argument("--t_stop",
nargs='?',
type=float,
default=10,
help="stop time in seconds")
CLI.add_argument("--channels",
nargs='+',
type=none_or_int,
default=0,
help="list of channels to plot")
args = CLI.parse_args()
asig = load_neo(args.data, 'analogsignal', lazy=True)
channels = parse_plot_channels(args.channels, args.data)
asig = time_slice(asig,
t_start=args.t_start,
t_stop=args.t_stop,
lazy=True,
channel_indexes=channels)
fig = plot_traces(asig, channels)
save_plot(args.output)
