recording.get_demo_recordings(signals_dir)
pkl_file = signals_dir + "/TAR010c-18-1.pkl"
# ----------------------------------------------------------------------------
# LOAD AND FORMAT RECORDING DATA
with open(pkl_file, 'rb') as f: # Python 3: open(..., 'rb')
cellid, recname, fs, X_est, Y_est, X_val, Y_val = pickle.load(f)
epochs = None
stimchans = [str(x) for x in range(X_est.shape[0])]
# borrowed from recording.load_recording_from_arrays
# est recording - for model fitting
resp = RasterizedSignal(fs, Y_est, 'resp', recname, chans=[cellid])
stim = RasterizedSignal(fs, X_est, 'stim', recname, chans=stimchans)
signals = {'resp': resp, 'stim': stim}
est = recording.Recording(signals)
# val recording - for testing predictions
resp = RasterizedSignal(fs, Y_val, 'resp', recname, chans=[cellid])
stim = RasterizedSignal(fs, X_val, 'stim', recname, chans=stimchans)
signals = {'resp': resp, 'stim': stim}
val = recording.Recording(signals)
# ----------------------------------------------------------------------------
# INITIALIZE MODELSPEC
#
# GOAL: Define the model that you wish to test
def load_recording_from_arrays(arrays,
rec_name,
fs,
sig_names=None,
signal_kwargs={}):
'''
Generates a recording object, and the signal objects it contains,
from a list of array-like structures of the form channels x time
(see signal.py for more details about how arrays are represented
by signals).
If any of the arrays are more than 2-dimensional,
an error will be thrown. Also pay close attention to any
RuntimeWarnings from the signal class regarding improperly-shaped
arrays, which may indicate that an array was passed as
time x channels instead of the reverse.
Arguments:
----------
arrays : list of array-like
The data to be converted to a recording of signal objects.
Each item should be 2-dimensional and convertible to a
numpy ndarray via np.array(x). No constraints are enforced
on the dtype of the arrays, but in general float values
are expected by most native NEMS functions.
rec_name : str
The name to be given to the new recording object. This will
also be assigned as the recording attribute of each new signal.
fs : int or list of ints
The frequency of sampling of the data arrays - used to
interconvert between real time and time bins (see signal.py).
If int, the same fs will be assigned to each signal.
If list, the length must match the length of arrays.
sig_names : list of strings (optional)
Name to attach to the signal created from
each array. The length of this list should match that of
arrays.
If not specified, the signals will be given the generic
names: ['signal1', 'signal2', ...].
signal_kwargs : list of dicts
Keyword arguments to be passed through to
each signal object. The length of this list should
match the length of arrays, and may be padded with empty
dictionaries to ensure this constraint.
For example:
[{'chans': ['1 kHz', '3 kHz']}, {'chans': ['one', 'two']}, {}]
Would assign channel names '1 kHz' and '3 kHz' to the signal
for the first array, 'one' and 'two' for the second array,
and no channel names (or any other arguments) for the third array.
Valid keyword arguments are: chans, epochs, meta,
and safety_checks
Returns:
--------
rec : recording object
New recording containing a signal for each array.
'''
# Assemble and validate lists for signal construction
arrays = [np.array(a) for a in arrays]
for i, a in enumerate(arrays):
if len(a.shape) != 2:
raise ValueError("Arrays should have shape chans x time."
"Array {} had shape: {}".format(i, a.shape))
n = len(arrays)
recs = [rec_name] * len(arrays)
if sig_names:
if not len(sig_names) == n:
raise ValueError("Length of sig_names must match"
"the length of arrays.\n"
"Got sig_names: {} and arrays: {}".format(
len(sig_names), n))
else:
sig_names = ['sig%s' % i for i in range(n)]
if isinstance(fs, int):
fs = [fs] * n
else:
if not len(fs) == n:
raise ValueError("Length of fs must match"
"the length of arrays.\n"
"Got fs: {} and arrays: {}".format(len(fs), n))
if not signal_kwargs:
signal_kwargs = [{}] * n
else:
if not len(signal_kwargs) == n:
raise ValueError("Length of signal_kwargs must match"
"the length of arrays.\n"
"Got signal_kwargs: {} and arrays: {}".format(
len(signal_kwargs), n))
# Construct the signals
to_sigs = zip(fs, arrays, sig_names, recs, signal_kwargs)
signals = [
RasterizedSignal(fs, a, name, rec, **kw)
for fs, a, name, rec, kw in to_sigs
]
signals = {s.name: s for s in signals}
# Combine into recording and return
return Recording(signals)
rec['resp']=rec['resp'].extract_channels([cellid])
est, val = rec.split_using_epoch_occurrence_counts(epoch_regex="^STIM_")
est=preproc.average_away_epoch_occurrences(est, epoch_regex="^STIM_")
val=preproc.average_away_epoch_occurrences(val, epoch_regex="^STIM_")
elif load_method==1:
# method 1: load from a pkl datafile that contains full stim+response data
# along with metadata (fs, stimulus epoch list)
datafile = signals_dir / 'TAR010c-18-1.pkl'
with open(datafile, 'rb') as f:
#cellid, recname, fs, X, Y, X_val, Y_val = pickle.load(f)
cellid, recname, fs, X, Y, epochs = pickle.load(f)
# create NEMS-format recording objects from the raw data
resp = RasterizedSignal(fs, Y, 'resp', recname, chans=[cellid], epochs=epochs)
stim = RasterizedSignal(fs, X, 'stim', recname, epochs=epochs)
# create the recording object from the signals
signals = {'resp': resp, 'stim': stim}
rec = recording.Recording(signals)
est, val = rec.split_using_epoch_occurrence_counts(epoch_regex="^STIM_")
est=preproc.average_away_epoch_occurrences(est, epoch_regex="^STIM_")
val=preproc.average_away_epoch_occurrences(val, epoch_regex="^STIM_")
elif load_method==2:
# method 2: load from CSV files - one per response, stimulus, epochs
# X is a frequency X time spectrgram, sampled at 100 Hz
# Y is a neuron X time PSTH, aligned with X. Ie, same number of time bins
# epochs is a list of STIM events with start and stop time of each event
# in seconds
def from_nwb_pupil(nwb_file, nwb_format,fs=20,with_pupil=False,running_speed=False,as_dict=True):
#def from_nwb(cls, nwb_file, nwb_format,with_pupil=False,fs=20):
"""
The NWB (Neurodata Without Borders) format is a unified data format developed by the Allen Brain Institute.
Data is stored as an HDF5 file, with the format varying depending how the data was saved.
References:
- https://nwb.org
- https://pynwb.readthedocs.io/en/latest/index.html
:param nwb_file: path to the nwb file
:param nwb_format: specifier for how the data is saved in the container
:param int fs: will match for all signals
:param bool with_pupil, running speed: whether to return pupil, speed signals in recording
:param bool as_dict: return a dictionary of recording objects, each corresponding to a single unit/neuron
else a single recording object w/ each unit corresponding to a channel in pointprocess signal
:return: a recording object
"""
#log.info(f'Loading NWB file with format "{nwb_format}" from "{nwb_file}".')
# add in supported nwb formats here
assert nwb_format in ['neuropixel'], f'"{nwb_format}" not a supported NWB file format.'
nwb_filepath = Path(nwb_file)
if not nwb_filepath.exists():
raise FileNotFoundError(f'"{nwb_file}" could not be found.')
if nwb_format == 'neuropixel':
"""
In neuropixel ecephys nwb files, data is stored in several attributes of the container:
- units: individual cell metadata, a dataframe
- epochs: timing of the stimuli, series of arrays
- lab_meta_data: metadata about the experiment, such as specimen details
Spike times are saved as arrays in the 'spike_times' column of the units dataframe as xarrays.
The frequency defaults to match pupil - if no pupil data retrieved, set to chosen value (previous default 1250).
Refs:
- https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html
- https://allensdk.readthedocs.io/en/latest/_static/examples/nb/ecephys_quickstart.html
- https://allensdk.readthedocs.io/en/latest/_static/examples/nb/ecephys_data_access.html
"""
try:
from pynwb import NWBHDF5IO
from allensdk.brain_observatory.ecephys import nwb # needed for ecephys format compat
except ImportError:
m = 'The "allensdk" library is required to work with neuropixel nwb formats, available on PyPI.'
#log.error(m)
raise ImportError(m)
session_name = nwb_filepath.stem
with NWBHDF5IO(str(nwb_filepath), 'r') as nwb_io:
nwbfile = nwb_io.read()
units = nwbfile.units
epochs = nwbfile.epochs
spike_times = dict(zip(units.id[:].astype(str), units['spike_times'][:]))
# extract the metadata and convert to dict
metadata = nwbfile.lab_meta_data['metadata'].to_dict()
metadata['uri'] = str(nwb_filepath) # add in uri
#add invalid times data to meta as df if exist - includes times and probe id?
if nwbfile.invalid_times is not None:
invalid_times = nwbfile.invalid_times
invalid_times = np.array([invalid_times[col][:] for col in invalid_times.colnames])
metadata['invalid_times'] = pd.DataFrame(invalid_times.transpose(),columns=['start_time', 'stop_time', 'tags'])
# build the units metadata
units_data = {
col.name: col.data for col in units.columns
if col.name not in ['spike_times', 'spike_times_index', 'spike_amplitudes',
'spike_amplitudes_index', 'waveform_mean', 'waveform_mean_index']
}
# needs to be a dict
units_meta = pd.DataFrame(units_data, index=units.id[:])
#add electrode info to units meta
electrodes=nwbfile.electrodes
e_data = {col.name: col.data for col in electrodes.columns}
e_meta = pd.DataFrame(e_data,index=electrodes.id[:])
units_meta=pd.merge(units_meta,e_meta,left_on=units_meta.peak_channel_id,right_index=True,
suffixes=('_unit','_channel')).drop(['key_0','group'],axis=1).to_dict('index')# needs to be a dict
# build the epoch dataframe
epoch_data = {
col.name: col.data for col in epochs.columns
if col.name not in ['tags', 'timeseries', 'tags_index', 'timeseries_index']
}
epoch_df = pd.DataFrame(epoch_data, index=epochs.id[:]).rename({
'start_time': 'start',
'stop_time': 'end',
'stimulus_name': 'name'
}, axis='columns')
#rename epochs to correspond to different nat scene/movie frames -
epoch_df.loc[epoch_df['frame'].notna(),'name'] = epoch_df.loc[epoch_df['frame'].notna(),'name'] + '_' + \
epoch_df[epoch_df['frame'].notna()].iloc[:]['frame'].astype(int).astype(str)
#drop extra columns
metadata['epochs']=epoch_df #save extra stim info to meta
epoch_df=epoch_df.drop([col for col in epoch_df.columns if col not in ['start','end','name']],axis=1)
# #rename natural scene epochs to work w/demo
df_copy = epoch_df[epoch_df.name.str.contains('natural_scene')].copy()
df_copy.loc[:,'name']='REFERENCE'
epoch_df=epoch_df.append(df_copy,ignore_index=True)
#expand epoch bounds epochs will overlap to test evoked potential
# to_adjust=epoch_df.loc[:,['start','end']].to_numpy()
# epoch_df.loc[:,['start','end']] = nems.epoch.adjust_epoch_bounds(to_adjust,-0.1,0.1)
# save the spike times as a point process signal frequency set to match other signals
pp = PointProcess(fs, spike_times, name='resp', recording=session_name, epochs=epoch_df,
chans=[str(c) for c in nwbfile.units.id[:]],meta=units_meta)
#dict to pass to recording
#signal_dict = {pp.name: pp}
# log.info('Successfully loaded nwb file.')
from scipy.interpolate import interp1d
#save pupil data as rasterized signal
if with_pupil:
try:
pupil = nwbfile.modules['eye_tracking'].data_interfaces['pupil_ellipse_fits']
t = pupil['timestamps'][:]
pupil = pupil['width'][:].reshape(1,-1) #only 1 dimension - or get 'height'
#interpolate to set sampling rate
f = interp1d(t,pupil,bounds_error=False,fill_value=np.nan)
new_t = np.arange(0.0,(t.max()+1/fs),1/fs)#pupil data starting at timepoint 0.0 (nan filler)
pupil = f(new_t)
pupil_signal = RasterizedSignal(fs=fs,data=pupil,recording=session_name,name='pupil',
epochs=epoch_df,chans=['pupil']) #for all data list(pupil_data.colnames[0:5])
#if no pupil data for session - still get spike data
except KeyError:
print(session_name + ' has no pupil data.')
if running_speed:
running = nwbfile.modules['running'].data_interfaces['running_speed']
t = running.timestamps[:][1]#data has start and end timestamps, here only end used
running = running.data[:].reshape(1,-1)
f = interp1d(t,running)
#new_t = np.arange(np.min(t),np.max(t),1/fs)
new_t = np.arange(epoch_df.start.min(),epoch_df.end.max(),1/fs)
running = f(new_t)
running=RasterizedSignal(fs=fs,data=running,name='running',recording=session_name,epochs=epoch_df)
if as_dict:
#each unit has seperate recording in dict
rec_dict={}
for c in pp.chans:
unit_signal=pp.extract_channels([c])
rec=Recording({'resp':unit_signal},meta=metadata)
if with_pupil:
rec.add_signal(pupil_signal)
if running_speed:
rec.add_signal(running)
rec_dict[c]=rec
return rec_dict
else:
rec=Recording({'resp':pp},meta=metadata)
if with_pupil:
rec.add_signal(pupil_signal)
if running_speed:
rec.add_signal(running)
return rec
recording.get_demo_recordings(signals_dir)
datafile = signals_dir + "/TAR010c-18-1.pkl"
batch = 271
# ----------------------------------------------------------------------------
# LOAD AND FORMAT RECORDING DATA
with open(datafile, 'rb') as f:
cellid, recname, fs, X, Y, epochs = pickle.load(f)
stimchans = [str(x) for x in range(X.shape[0])]
# borrowed from recording.load_recording_from_arrays
resp = RasterizedSignal(fs, Y, 'resp', recname, epochs=epochs, chans=[cellid])
stim = RasterizedSignal(fs, X, 'stim', recname, epochs=epochs, chans=stimchans)
signals = {'resp': resp, 'stim': stim}
rec = recording.Recording(signals)
#est, val = rec.split_at_time(0.2)
est, val = rec.split_using_epoch_occurrence_counts(epoch_regex="^STIM_")
est = preproc.average_away_epoch_occurrences(
est, epoch_regex="^STIM_").apply_mask()
val = preproc.average_away_epoch_occurrences(
val, epoch_regex="^STIM_").apply_mask()
sr_Hz = est['resp'].fs
time_win_sec = 0.1
n_feats = est['stim'].shape[0]