def load_responses(data_dir, stimuli):
psth = np.load(data_dir / 'solo.rsvp.hvm.experiment_psth.npy') # Shaped images x repetitions x time_bins x channels
# Drop first (index 0) and second last session (index 25) since they had only one repetition each
# Actually not, since we're sticking to older protocol re: data cleaning for now
# psth = np.delete(psth, (0, 25), axis=1)
# Select responses from 70-170ms (accounting for ~30ms delay recorded from photodiode signal) and take average
timebase = np.arange(-100, 381, 10) # PSTH from -100ms to 380ms relative to stimulus onset
assert len(timebase) == psth.shape[2]
t_cols = np.where((timebase >= 100) & (timebase < 200))[0] # Delay recorded on photodiode is ~30ms, so adding that
rate = np.mean(psth[:, :, t_cols, :], axis=2) # Shaped images x repetitions x channels
# Load image related meta data (id ordering differs from dicarlo.hvm)
image_id = [x.split()[0][:-4] for x in open(data_dir.parent / 'image-metadata' / 'hvm_map.txt').readlines()]
# Load neuroid related meta data
neuroid_meta = pd.DataFrame(json.load(open(data_dir.parent / 'array-metadata' / 'mapping.json')))
assembly = xr.DataArray(rate,
coords={'repetition': ('repetition', list(range(rate.shape[1]))),
'image_id': ('image', image_id)},
dims=['image', 'repetition', 'neuroid'])
for column_name, column_data in neuroid_meta.iteritems():
assembly = assembly.assign_coords(**{f'{column_name}': ('neuroid', list(column_data.values))})
assembly = assembly.sortby(assembly.image_id)
stimuli = stimuli.sort_values(by='image_id').reset_index(drop=True)
for column_name, column_data in stimuli.iteritems():
assembly = assembly.assign_coords(**{f'{column_name}': ('image', list(column_data.values))})
assembly = assembly.sortby(assembly.id) # Re-order by id to match dicarlo.hvm ordering
# Collapse dimensions 'image' and 'repetitions' into a single 'presentation' dimension
assembly = assembly.stack(presentation=('image', 'repetition')).reset_index('presentation')
assembly = NeuroidAssembly(assembly)
# Filter noisy electrodes
assembly = filter_neuroids(assembly, 0.7)
# Add time info
assembly = assembly.expand_dims('time_bin')
assembly['time_bin_start'] = 'time_bin', [70]
assembly['time_bin_end'] = 'time_bin', [170]
assembly = assembly.transpose('presentation', 'neuroid', 'time_bin')
# Add other experiment and data processing related info
assembly.attrs['experiment_paradigm'] = 'rsvp'
assembly.attrs['sampling_rate_hz'] = 20000
assembly.attrs['image_size_degree'] = 8
assembly.attrs['stim_on_time_ms'] = 100
assembly.attrs['stim_off_time_ms'] = 100
assembly.attrs['stim_on_delay_ms'] = 0
assembly.attrs['inter_trial_interval_ms'] = 500
assembly.attrs['fixation_point_size_degree'] = 0.2
assembly.attrs['fixation_window_size_degree'] = 2
assembly.attrs['threshold_sd'] = 3
assembly.attrs['chunks_for_threshold'] = 10
assembly.attrs['passband_hz'] = [300, 6000]
assembly.attrs['ellip_order'] = 2
return assembly
def load_responses(data_dir, stimuli):
data_dir = data_dir / 'database'
assert os.path.isdir(data_dir)
psth = np.load(data_dir / 'solo.rsvp.things-2.experiment_psth.npy') # Shaped images x repetitions x time_bins x channels
# Compute firing rate for given time bins
timebins = [[70, 170], [170, 270], [50, 100], [100, 150], [150, 200], [200, 250], [70, 270]]
photodiode_delay = 30 # Delay recorded on photodiode is ~30ms
timebase = np.arange(-100, 381, 10) # PSTH from -100ms to 380ms relative to stimulus onset
assert len(timebase) == psth.shape[2]
rate = np.empty((len(timebins), psth.shape[0], psth.shape[1], psth.shape[3]))
for idx, tb in enumerate(timebins):
t_cols = np.where((timebase >= (tb[0] + photodiode_delay)) & (timebase < (tb[1] + photodiode_delay)))[0]
rate[idx] = np.mean(psth[:, :, t_cols, :], axis=2) # Shaped time bins x images x repetitions x channels
assembly = xr.DataArray(rate,
coords={'repetition': ('repetition', list(range(rate.shape[2]))),
'time_bin_id': ('time_bin', list(range(rate.shape[0]))),
'time_bin_start': ('time_bin', [x[0] for x in timebins]),
'time_bin_stop': ('time_bin', [x[1] for x in timebins])},
dims=['time_bin', 'image', 'repetition', 'neuroid'])
# Add neuroid related meta data
neuroid_meta = pd.DataFrame(json.load(open(data_dir.parent / 'array-metadata' / 'mapping.json')))
for column_name, column_data in neuroid_meta.iteritems():
assembly = assembly.assign_coords(**{f'{column_name}': ('neuroid', list(column_data.values))})
# Add stimulus related meta data
for column_name, column_data in stimuli.iteritems():
assembly = assembly.assign_coords(**{f'{column_name}': ('image', list(column_data.values))})
# Collapse dimensions 'image' and 'repetitions' into a single 'presentation' dimension
assembly = assembly.stack(presentation=('image', 'repetition')).reset_index('presentation')
assembly = assembly.drop('image')
assembly = NeuronRecordingAssembly(assembly)
# Filter noisy electrodes
psth = np.load(data_dir / 'solo.rsvp.things-2.normalizer_psth.npy')
t_cols = np.where((timebase >= (70 + photodiode_delay)) & (timebase < (170 + photodiode_delay)))[0]
rate = np.mean(psth[:, :, t_cols, :], axis=2)
normalizer_assembly = xr.DataArray(rate,
coords={'repetition': ('repetition', list(range(rate.shape[1]))),
'image_id': ('image', list(range(rate.shape[0]))),
'id': ('image', list(range(rate.shape[0])))},
dims=['image', 'repetition', 'neuroid'])
for column_name, column_data in neuroid_meta.iteritems():
normalizer_assembly = normalizer_assembly.assign_coords(
**{f'{column_name}': ('neuroid', list(column_data.values))})
normalizer_assembly = normalizer_assembly.stack(presentation=('image', 'repetition')).reset_index('presentation')
normalizer_assembly = normalizer_assembly.drop('image')
normalizer_assembly = normalizer_assembly.transpose('presentation', 'neuroid')
normalizer_assembly = NeuronRecordingAssembly(normalizer_assembly)
filtered_assembly = filter_neuroids(normalizer_assembly, 0.7)
assembly = assembly.sel(neuroid=np.isin(assembly.neuroid_id, filtered_assembly.neuroid_id))
assembly = assembly.transpose('presentation', 'neuroid', 'time_bin')
# Add other experiment and data processing related info
assembly.attrs['image_size_degree'] = 8
assembly.attrs['stim_on_time_ms'] = 100
return assembly
def load_responses(data_dir, stimuli):
psth = np.load(data_dir / 'solo.rsvp.hvm.experiment_psth.npy'
) # Shaped images x repetitions x time_bins x channels
# Drop first (index 0) and second last session (index 25) since they had only one repetition each
# Actually not, since we're sticking to older protocol re: data cleaning for now
# psth = np.delete(psth, (0, 25), axis=1)
# Compute firing rate for given time bins
timebins = [[70, 170], [170, 270], [50, 100], [100, 150], [150, 200],
[200, 250], [70, 270]]
photodiode_delay = 30 # Delay recorded on photodiode is ~30ms
timebase = np.arange(
-100, 381, 10) # PSTH from -100ms to 380ms relative to stimulus onset
assert len(timebase) == psth.shape[2]
rate = np.empty(
(len(timebins), psth.shape[0], psth.shape[1], psth.shape[3]))
for idx, tb in enumerate(timebins):
t_cols = np.where((timebase >= (tb[0] + photodiode_delay))
& (timebase < (tb[1] + photodiode_delay)))[0]
rate[idx] = np.mean(
psth[:, :, t_cols, :],
axis=2) # Shaped time bins x images x repetitions x channels
# Load image related meta data (id ordering differs from dicarlo.hvm)
image_id = [
x.split()[0][:-4] for x in open(data_dir.parent / 'image-metadata' /
'hvm_map.txt').readlines()
]
# Load neuroid related meta data
neuroid_meta = pd.DataFrame(
json.load(open(data_dir.parent / 'array-metadata' / 'mapping.json')))
assembly = xr.DataArray(
rate,
coords={
'repetition': ('repetition', list(range(rate.shape[2]))),
'time_bin_id': ('time_bin', list(range(rate.shape[0]))),
'time_bin_start': ('time_bin', [x[0] for x in timebins]),
'time_bin_stop': ('time_bin', [x[1] for x in timebins]),
'image_id': ('image', image_id)
},
dims=['time_bin', 'image', 'repetition', 'neuroid'])
for column_name, column_data in neuroid_meta.iteritems():
assembly = assembly.assign_coords(
**{f'{column_name}': ('neuroid', list(column_data.values))})
assembly = assembly.sortby(assembly.image_id)
stimuli = stimuli.sort_values(by='image_id').reset_index(drop=True)
for column_name, column_data in stimuli.iteritems():
assembly = assembly.assign_coords(
**{f'{column_name}': ('image', list(column_data.values))})
assembly = assembly.sortby(
assembly.id) # Re-order by id to match dicarlo.hvm ordering
# Collapse dimensions 'image' and 'repetitions' into a single 'presentation' dimension
assembly = assembly.stack(
presentation=('image', 'repetition')).reset_index('presentation')
assembly = NeuronRecordingAssembly(assembly)
# Filter noisy electrodes
psth = np.load(data_dir / 'solo.rsvp.hvm.normalizer_psth.npy')
t_cols = np.where((timebase >= (70 + photodiode_delay))
& (timebase < (170 + photodiode_delay)))[0]
rate = np.mean(psth[:, :, t_cols, :], axis=2)
normalizer_assembly = xr.DataArray(
rate,
coords={
'repetition': ('repetition', list(range(rate.shape[1]))),
'image_id': ('image', list(range(rate.shape[0]))),
'id': ('image', list(range(rate.shape[0])))
},
dims=['image', 'repetition', 'neuroid'])
for column_name, column_data in neuroid_meta.iteritems():
normalizer_assembly = normalizer_assembly.assign_coords(
**{f'{column_name}': ('neuroid', list(column_data.values))})
normalizer_assembly = normalizer_assembly.stack(
presentation=('image', 'repetition')).reset_index('presentation')
normalizer_assembly = normalizer_assembly.drop('image')
normalizer_assembly = normalizer_assembly.transpose(
'presentation', 'neuroid')
normalizer_assembly = NeuronRecordingAssembly(normalizer_assembly)
filtered_assembly = filter_neuroids(normalizer_assembly, 0.7)
assembly = assembly.sel(
neuroid=np.isin(assembly.neuroid_id, filtered_assembly.neuroid_id))
assembly = assembly.transpose('presentation', 'neuroid', 'time_bin')
# Add other experiment related info
assembly.attrs['image_size_degree'] = 8
assembly.attrs['stim_on_time_ms'] = 100
return assembly