def _navigate_tree(self, ids, direction='down', return_indices=False):
"""
Private method to navigate the tree and get all related objects either up, down or along the branch.
By convention the provided id is returned in the list of regions
:param ids: array or single allen id (int32)
:param direction: 'up' returns ancestors, 'down' descendants
:param return indices: Bool (False), if true returns a second argument with indices mapping
to the current br object
:return: Bunch
"""
indices = ismember(self.id, ids)[0]
count = np.sum(indices)
while True:
if direction == 'down':
indices |= ismember(self.parent, self.id[indices])[0]
elif direction == 'up':
indices |= ismember(self.id, self.parent[indices])[0]
else:
raise ValueError("direction should be either 'up' or 'down'")
if count == np.sum(
indices): # last iteration didn't find any match
break
else:
count = np.sum(indices)
if return_indices:
return self.get(self.id[indices]), np.where(indices)[0]
else:
return self.get(self.id[indices])
def _mapping_from_regions_list(self, new_map, lateralize=False):
"""
From a vector of regions id, creates a mapping such as
newids = self.mapping
:param new_map: np.array: vector of regions id
"""
I_ROOT = 1
I_VOID = 0
# to lateralize we make sure all regions are represented in + and -
new_map = np.unique(np.r_[-new_map, new_map])
assert np.all(np.isin(new_map, self.id)), \
"All mapping ids should be represented in the Allen ids"
# with the lateralization, self.id may have duplicate values so ismember is necessary
iid, inm = ismember(self.id, new_map)
iid = np.where(iid)[0]
mapind = np.zeros_like(
self.id) + I_ROOT # non assigned regions are root
# TO DO should root be lateralised?
mapind[iid] = iid # regions present in the list have the same index
# Starting by the higher up levels in the hierarchy, assign all descendants to the mapping
for i in np.argsort(self.level[iid]):
descendants = self.descendants(self.id[iid[i]]).id
_, idesc, _ = np.intersect1d(self.id,
descendants,
return_indices=True)
mapind[idesc] = iid[i]
mapind[0] = I_VOID # void stays void
# to delateralize the regions, assign the positive index to all mapind elements
if lateralize is False:
_, iregion = ismember(np.abs(self.id), self.id)
mapind = mapind[iregion]
return mapind
def multiple_spike_trains(firing_rates=None, rec_len_secs=1000, cluster_ids=None,
amplitude_noise=20 * 1e-6):
"""
:param firing_rates: list or np.array of firing rates (spikes per second)
:param rec_len_secs: recording length in seconds
:return: spike_times, spike_amps, spike_clusters
"""
if firing_rates is None:
firing_rates = np.random.randint(150, 600, 10)
if cluster_ids is None:
cluster_ids = np.arange(firing_rates.size)
ca = np.exp(np.random.normal(5.5, 0.5, firing_rates.size)) / 1e6 # output is in V
st = np.empty(0)
sc = np.empty(0)
for i, firing_rate in enumerate(firing_rates):
t = generate_spike_train(firing_rate=firing_rate, rec_len_secs=rec_len_secs)
st = np.r_[st, t]
sc = np.r_[sc, np.zeros(t.size, dtype=np.int32) + cluster_ids[i]]
ordre = st.argsort()
st = st[ordre]
sc = np.int32(sc[ordre])
_, isc = ismember(sc, cluster_ids) # clusters ids may be arbitrary: re-index
sa = np.maximum(ca[isc] + np.random.randn(st.size) * amplitude_noise, 25 * 1e-6)
return st, sa, sc
def check_up_to_date(subj_path, df):
"""
Check which sessions on local file system are missing from the computed training table
:param subj_path:
:return:
"""
session_dates = subj_path.glob('*')
df_session = pd.DataFrame()
for sess_date in session_dates:
sess_paths = list(sess_date.glob('00*'))
date = sess_date.stem
if len(sess_paths) > 0:
for sess in sess_paths:
if is_session_path(sess):
df_session = pd.concat([
df_session,
pd.DataFrame({
'date': date,
'session_path': str(sess)
},
index=[0])
],
ignore_index=True)
if df is None:
return df_session
else:
# recorded_session_paths = df['session_path'].values
isin, _ = ismember(df_session.date.unique(), df.date.unique())
missing_dates = df_session.date.unique()[~isin]
return df_session[df_session['date'].isin(missing_dates)].sort_values(
'date')
def _run(self):
"""runs for initiated PID, streams data, destripe and check bad channels"""
assert self.pid
self.eqcs = []
T0 = 60 * 30
SNAPSHOT_LABEL = "raw_ephys_bad_channels"
output_files = list(self.output_directory.glob(f'{SNAPSHOT_LABEL}*'))
if len(output_files) == 4:
return output_files
self.output_directory.mkdir(exist_ok=True, parents=True)
if self.location != 'server':
self.histology_status = self.get_histology_status()
electrodes = self.get_channels('electrodeSites',
f'alf/{self.pname}')
if 'atlas_id' in electrodes.keys():
electrodes['ibr'] = ismember(electrodes['atlas_id'],
self.brain_regions.id)[1]
electrodes['acronym'] = self.brain_regions.acronym[
electrodes['ibr']]
electrodes['name'] = self.brain_regions.name[electrodes['ibr']]
electrodes['title'] = self.histology_status
else:
electrodes = None
sr, t0 = stream(self.pid, T0, nsecs=1, one=self.one)
raw = sr[:, :-sr.nsync].T
else:
electrodes = None
ap_file = next(
self.session_path.joinpath('raw_ephys_data',
self.pname).glob('*ap.*bin'), None)
if ap_file is not None:
sr = spikeglx.Reader(ap_file)
raw = sr[int((sr.fs * T0)):int((sr.fs *
(T0 + 1))), :-sr.nsync].T
else:
return []
channel_labels, channel_features = voltage.detect_bad_channels(
raw, sr.fs)
_, eqcs, output_files = ephys_bad_channels(
raw=raw,
fs=sr.fs,
channel_labels=channel_labels,
channel_features=channel_features,
channels=electrodes,
title=SNAPSHOT_LABEL,
destripe=True,
save_dir=self.output_directory,
br=self.brain_regions,
pid_info=self.pid_label)
self.eqcs = eqcs
return output_files
def remap(self, region_ids, source_map='Allen', target_map='Beryl'):
"""
Remap atlas regions ids from source map to target map
:param region_ids: atlas ids to map
:param source_map: map name which original region_ids are in
:param target_map: map name onto which to map
:return:
"""
_, inds = ismember(region_ids, self.id[self.mappings[source_map]])
return self.id[self.mappings[target_map][inds]]
def prepare_lr_data(acronyms_lh, values_lh, acronyms_rh, values_rh):
"""
Prepare data in format needed for plotting when providing different region values per hemisphere
:param acronyms_lh: array of acronyms on left hemisphere
:param values_lh: values for each acronym on left hemisphere
:param acronyms_rh: array of acronyms on right hemisphere
:param values_rh: values for each acronym on left hemisphere
:return: combined acronyms and two column array of values
"""
acronyms = np.unique(np.r_[acronyms_lh, acronyms_rh])
values = np.nan * np.ones((acronyms.shape[0], 2))
_, l_idx = ismember(acronyms_lh, acronyms)
_, r_idx = ismember(acronyms_rh, acronyms)
values[l_idx, 0] = values_lh
values[r_idx, 1] = values_rh
return acronyms, values
def test_reorder_data(self):
acronyms = np.array(['AUDp1', 'AUDpo1', 'AUDv1', 'SSp-m1', 'SSp-n1'])
values = np.array([0, 1, 2, 3, 4])
_, idx = ismember(acronyms, self.brs.acronym)
expected_acronyms = acronyms[np.argsort(self.brs.order[idx])]
expected_values = values[np.argsort(self.brs.order[idx])]
values = np.array([0, 1, 2, 3, 4])
acronnyms_ordered, values_ordered = reorder_data(acronyms, values)
assert np.array_equal(acronnyms_ordered, expected_acronyms)
assert np.array_equal(values_ordered, expected_values)
def test_clusters_metrics():
np.random.seed(54)
rec_length = 1000
frs = np.array([3, 100, 80, 40]) # firing rates
cid = [0, 1, 3, 4] # here we make sure one of the clusters has no spike
t, a, c = multiple_spike_trains(firing_rates=frs, rec_len_secs=rec_length, cluster_ids=cid)
d = np.sin(2 * np.pi * c / rec_length * t) * 100 # sinusoidal shift where cluster id drives f
def _assertions(dfm, idf, target_cid):
# dfm: qc dataframe, idf: indices of existing clusters in dfm, cid: cluster ids
assert np.allclose(dfm['amp_median'][idf] / np.exp(5.5) * 1e6, 1, rtol=1.1)
assert np.allclose(dfm['amp_std_dB'][idf] / 20 * np.log10(np.exp(0.5)), 1, rtol=1.1)
assert np.allclose(dfm['drift'][idf], np.array(cid) * 100 * 4 * 3.6, rtol=1.1)
assert np.allclose(dfm['firing_rate'][idf], frs, rtol=1.1)
assert np.allclose(dfm['cluster_id'], target_cid)
# check with missing clusters
dfm = quick_unit_metrics(c, t, a, d, cluster_ids=np.arange(5), tbounds=[100, 900])
idf, _ = ismember(np.arange(5), cid)
_assertions(dfm, idf, np.arange(5))
def plot_scalar_on_barplot(acronyms,
values,
errors=None,
order=True,
ylim=None,
ax=None,
brain_regions=None):
br = brain_regions or BrainRegions()
if order:
acronyms, values = reorder_data(acronyms, values, brain_regions)
_, idx = ismember(acronyms, br.acronym)
colours = br.rgb[idx]
if ax:
fig = ax.get_figure()
else:
fig, ax = plt.subplots()
ax.bar(np.arange(acronyms.size), values, color=colours)
return fig, ax
def reorder_data(acronyms, values, brain_regions=None):
"""
Reorder list of acronyms and values to match the Allen ordering
:param acronyms: array of acronyms
:param values: array of values
:param brain_regions: BrainRegions object
:return: ordered array of acronyms and values
"""
br = brain_regions or BrainRegions()
atlas_id = br.acronym2id(acronyms, hemisphere='right')
all_ids = br.id[br.order][:br.n_lr + 1]
ordered_ids = np.zeros_like(all_ids) * np.nan
ordered_values = np.zeros_like(all_ids) * np.nan
_, idx = ismember(atlas_id, all_ids)
ordered_ids[idx] = atlas_id
ordered_values[idx] = values
ordered_ids = ordered_ids[~np.isnan(ordered_ids)]
ordered_values = ordered_values[~np.isnan(ordered_values)]
ordered_acronyms = br.id2acronym(ordered_ids)
return ordered_acronyms, ordered_values
def quick_unit_metrics(spike_clusters,
spike_times,
spike_amps,
spike_depths,
params=METRICS_PARAMS,
cluster_ids=None,
tbounds=None):
"""
Computes single unit metrics from only the spike times, amplitudes, and
depths for a set of units.
Metrics computed:
'amp_max',
'amp_min',
'amp_median',
'amp_std_dB',
'contamination',
'contamination_alt',
'drift',
'missed_spikes_est',
'noise_cutoff',
'presence_ratio',
'presence_ratio_std',
'slidingRP_viol',
'spike_count'
Parameters (see the METRICS_PARAMS constant)
----------
spike_clusters : ndarray_like
A vector of the unit ids for a set of spikes.
spike_times : ndarray_like
A vector of the timestamps for a set of spikes.
spike_amps : ndarray_like
A vector of the amplitudes for a set of spikes.
spike_depths : ndarray_like
A vector of the depths for a set of spikes.
clusters_id: (optional) lists of cluster ids. If not all clusters are represented in the
spikes_clusters (ie. cluster has no spike), this will ensure the output size is consistent
with the input arrays.
tbounds: (optional) list or 2 elements array containing a time-selection to perform the
metrics computation on.
params : dict (optional)
Parameters used for computing some of the metrics in the function:
'presence_window': float
The time window (in s) used to look for spikes when computing the presence ratio.
'refractory_period': float
The refractory period used when computing isi violations and the contamination
estimate.
'min_isi': float
The minimum interspike-interval (in s) for counting duplicate spikes when computing
the contamination estimate.
'spks_per_bin_for_missed_spks_est': int
The number of spikes per bin used to compute the spike amplitude pdf for a unit,
when computing the missed spikes estimate.
'std_smoothing_kernel_for_missed_spks_est': float
The standard deviation for the gaussian kernel used to compute the spike amplitude
pdf for a unit, when computing the missed spikes estimate.
'min_num_bins_for_missed_spks_est': int
The minimum number of bins used to compute the spike amplitude pdf for a unit,
when computing the missed spikes estimate.
Returns
-------
r : bunch
A bunch whose keys are the computed spike metrics.
Notes
-----
This function is called by `ephysqc.unit_metrics_ks2` which is called by `spikes.ks2_to_alf`
during alf extraction of an ephys dataset in the ibl ephys extraction pipeline.
Examples
--------
1) Compute quick metrics from a ks2 output directory:
>>> from ibllib.ephys.ephysqc import phy_model_from_ks2_path
>>> m = phy_model_from_ks2_path(path_to_ks2_out)
>>> cluster_ids = m.spike_clusters
>>> ts = m.spike_times
>>> amps = m.amplitudes
>>> depths = m.depths
>>> r = bb.metrics.quick_unit_metrics(cluster_ids, ts, amps, depths)
"""
metrics_list = [
'cluster_id', 'amp_max', 'amp_min', 'amp_median', 'amp_std_dB',
'contamination', 'contamination_alt', 'drift', 'missed_spikes_est',
'noise_cutoff', 'presence_ratio', 'presence_ratio_std',
'slidingRP_viol', 'spike_count'
]
if tbounds:
ispi = between_sorted(spike_times, tbounds)
spike_times = spike_times[ispi]
spike_clusters = spike_clusters[ispi]
spike_amps = spike_amps[ispi]
spike_depths = spike_depths[ispi]
if cluster_ids is None:
cluster_ids = np.unique(spike_clusters)
nclust = cluster_ids.size
r = Bunch({k: np.full((nclust, ), np.nan) for k in metrics_list})
r['cluster_id'] = cluster_ids
# vectorized computation of basic metrics such as presence ratio and firing rate
tmin = spike_times[0]
tmax = spike_times[-1]
presence_ratio = bincount2D(spike_times,
spike_clusters,
xbin=params['presence_window'],
ybin=cluster_ids,
xlim=[tmin, tmax])[0]
r.presence_ratio = np.sum(presence_ratio > 0,
axis=1) / presence_ratio.shape[1]
r.presence_ratio_std = np.std(presence_ratio, axis=1)
r.spike_count = np.sum(presence_ratio, axis=1)
r.firing_rate = r.spike_count / (tmax - tmin)
# computing amplitude statistical indicators by aggregating over cluster id
camp = pd.DataFrame(np.c_[spike_amps, 20 * np.log10(spike_amps),
spike_clusters],
columns=['amps', 'log_amps', 'clusters'])
camp = camp.groupby('clusters')
ir, ib = ismember(r.cluster_id, camp.clusters.unique())
r.amp_min[ir] = np.array(camp['amps'].min())
r.amp_max[ir] = np.array(camp['amps'].max())
# this is the geometric median
r.amp_median[ir] = np.array(10**(camp['log_amps'].median() / 20))
r.amp_std_dB[ir] = np.array(camp['log_amps'].std())
# loop over each cluster to compute the rest of the metrics
for ic in np.arange(nclust):
# slice the spike_times array
ispikes = spike_clusters == cluster_ids[ic]
if np.all(~ispikes): # if this cluster has no spikes, continue
continue
ts = spike_times[ispikes]
amps = spike_amps[ispikes]
depths = spike_depths[ispikes]
# compute metrics
r.contamination_alt[ic] = contamination_alt(
ts, rp=params['refractory_period'])
r.contamination[ic], _ = contamination(ts,
tmin,
tmax,
rp=params['refractory_period'],
min_isi=params['min_isi'])
r.slidingRP_viol[ic] = slidingRP_viol(
ts,
bin_size=params['bin_size'],
thresh=params['RPslide_thresh'],
acceptThresh=params['acceptable_contamination'])
r.noise_cutoff[ic] = noise_cutoff(
amps,
quartile_length=params['nc_quartile_length'],
n_bins=params['nc_bins'],
n_low_bins=params['nc_n_low_bins'])
r.missed_spikes_est[ic], _, _ = missed_spikes_est(
amps,
spks_per_bin=params['spks_per_bin_for_missed_spks_est'],
sigma=params['std_smoothing_kernel_for_missed_spks_est'],
min_num_bins=params['min_num_bins_for_missed_spks_est'])
# wonder if there is a need to low-cut this
r.drift[ic] = np.sum(np.abs(np.diff(depths))) / (tmax - tmin) * 3600
r.label = compute_labels(r)
return r
def plot_swanson(acronyms=None,
values=None,
ax=None,
hemisphere=None,
br=None,
orientation='landscape',
annotate=False,
**kwargs):
"""
Displays the 2D image corresponding to the swanson flatmap.
This case is different from the others in the sense that only a region maps to another regions, there
is no correspondency from the spatial 3D coordinates.
:param acronyms:
:param values:
:param hemisphere: hemisphere to display, options are 'left', 'right', 'both' or 'mirror'
:param br: ibllib.atlas.BrainRegions object
:param ax: matplotlib axis object to plot onto
:param orientation: 'landscape' (default) or 'portrait'
:param annotate: (False) if True, labels regions with acronyms
:param kwargs: arguments for imshow
:return:
"""
mapping = 'Swanson'
br = BrainRegions() if br is None else br
s2a = swanson()
# both hemishpere
if hemisphere == 'both':
_s2a = s2a + np.sum(br.id > 0)
_s2a[s2a == 0] = 0
_s2a[s2a == 1] = 1
s2a = np.r_[s2a, np.flipud(_s2a)]
mapping = 'Swanson-lr'
elif hemisphere == 'mirror':
s2a = np.r_[s2a, np.flipud(s2a)]
if orientation == 'portrait':
s2a = np.transpose(s2a)
if acronyms is None:
regions = br.mappings[mapping][s2a]
im = br.rgba[regions]
else:
user_aids = br.parse_acronyms_argument(acronyms)
# if the user provided inputs are higher level than swanson propagate down
swaids = br.id[np.unique(s2a)]
maids = np.setdiff1d(user_aids,
swaids) # those are the indices not in Swanson
for i, maid in enumerate(maids):
if maid <= 1:
continue
childs_in_sw = np.intersect1d(
br.descendants(maid)['id'][1:], swaids)
if childs_in_sw.size > 0:
user_aids = np.r_[user_aids, childs_in_sw]
values = np.r_[values, values[i] + childs_in_sw * 0]
# the user may have input non-unique regions
df = pd.DataFrame(dict(aid=user_aids,
value=values)).groupby('aid').mean()
aids, vals = (df.index.values, df['value'].values)
# apply mapping and perform another round of aggregation
_, _, ibr = np.intersect1d(aids, br.id, return_indices=True)
ibr = br.mappings['Swanson-lr'][ibr]
df = pd.DataFrame(dict(ibr=ibr, value=vals)).groupby('ibr').mean()
ibr, vals = (df.index.values, df['value'].values)
# we now have the mapped regions and aggregated values, map values onto swanson map
iswan, iv = ismember(s2a, ibr)
im = np.zeros_like(s2a, dtype=np.float32)
im[iswan] = vals[iv]
im[~iswan] = np.nan
if not ax:
ax = plt.gca()
ax.set_axis_off() # unless provided we don't need scales here
ax.imshow(im, **kwargs)
# overlay the boundaries if value plot
imb = np.zeros((*s2a.shape[:2], 4), dtype=np.uint8)
imb[s2a == 0] = 255
# imb[s2a == 1] = np.array([167, 169, 172, 255])
imb[s2a == 1] = np.array([0, 0, 0, 255])
ax.imshow(imb)
if annotate:
annotate_swanson(ax=ax, orientation=orientation, br=br)
# provides the mean to see the region on axis
def format_coord(x, y):
acronym = br.acronym[s2a[int(y), int(x)]]
return f'x={x:1.4f}, y={x:1.4f}, {acronym}'
ax.format_coord = format_coord
return ax
def __init__(self,
res_um=25,
scaling=np.array([1, 1, 1]),
mock=False,
hist_path=None):
"""
:param res_um: 10, 25 or 50 um
:param scaling: scale factor along ml, ap, dv for squeeze and stretch ([1, 1, 1])
:param mock: for testing purpose
:param hist_path
:return: atlas.BrainAtlas
"""
par = one.params.get(silent=True)
FLAT_IRON_ATLAS_REL_PATH = PurePosixPath('histology', 'ATLAS',
'Needles', 'Allen')
LUT_VERSION = "v01" # version 01 is the lateralized version
regions = BrainRegions()
xyz2dims = np.array([1, 0, 2]) # this is the c-contiguous ordering
dims2xyz = np.array([1, 0, 2])
# we use Bregma as the origin
self.res_um = res_um
ibregma = (ALLEN_CCF_LANDMARKS_MLAPDV_UM['bregma'] / self.res_um)
dxyz = self.res_um * 1e-6 * np.array([1, -1, -1]) * scaling
if mock:
image, label = [
np.zeros((528, 456, 320), dtype=np.int16) for _ in range(2)
]
label[:, :, 100:
105] = 1327 # lookup index for retina, id 304325711 (no id 1327)
else:
path_atlas = Path(par.CACHE_DIR).joinpath(FLAT_IRON_ATLAS_REL_PATH)
file_image = hist_path or path_atlas.joinpath(
f'average_template_{res_um}.nrrd')
# get the image volume
if not file_image.exists():
_download_atlas_allen(file_image, FLAT_IRON_ATLAS_REL_PATH,
par)
# get the remapped label volume
file_label = path_atlas.joinpath(f'annotation_{res_um}.nrrd')
if not file_label.exists():
_download_atlas_allen(file_label, FLAT_IRON_ATLAS_REL_PATH,
par)
file_label_remap = path_atlas.joinpath(
f'annotation_{res_um}_lut_{LUT_VERSION}.npz')
if not file_label_remap.exists():
label = self._read_volume(file_label).astype(dtype=np.int32)
_logger.info("computing brain atlas annotations lookup table")
# lateralize atlas: for this the regions of the left hemisphere have primary
# keys opposite to to the normal ones
lateral = np.zeros(label.shape[xyz2dims[0]])
lateral[int(np.floor(ibregma[0]))] = 1
lateral = np.sign(
np.cumsum(lateral)[np.newaxis, :, np.newaxis] - 0.5)
label = label * lateral.astype(np.int32)
# the 10 um atlas is too big to fit in memory so work by chunks instead
if res_um == 10:
first, ncols = (0, 10)
while True:
last = np.minimum(first + ncols, label.shape[-1])
_logger.info(
f"Computing... {last} on {label.shape[-1]}")
_, im = ismember(label[:, :, first:last], regions.id)
label[:, :, first:last] = np.reshape(
im, label[:, :, first:last].shape)
if last == label.shape[-1]:
break
first += ncols
label = label.astype(dtype=np.uint16)
_logger.info("Saving npz, this can take a long time")
else:
_, im = ismember(label, regions.id)
label = np.reshape(im.astype(np.uint16), label.shape)
np.savez_compressed(file_label_remap, label)
_logger.info(f"Cached remapping file {file_label_remap} ...")
# loads the files
label = self._read_volume(file_label_remap)
image = self._read_volume(file_image)
super().__init__(image,
label,
dxyz,
regions,
ibregma,
dims2xyz=dims2xyz,
xyz2dims=xyz2dims)
def _find_inds(self, values, all_values):
if not isinstance(values, list) and not isinstance(values, np.ndarray):
values = np.array([values])
_, inds = ismember(np.array(values), all_values)
return inds
def find_trial_ids(trials,
side='all',
choice='all',
order='trial num',
sort='idx',
contrast=(1, 0.5, 0.25, 0.125, 0.0625, 0),
event=None):
"""
Finds trials that match criterion
:param trials: trials object. Must contain attributes contrastLeft, contrastRight and
feedbackType
:param side: stimulus side, options are 'all', 'left' or 'right'
:param choice: trial choice, options are 'all', 'correct' or 'incorrect'
:param contrast: contrast of stimulus, pass in list/tuple of all contrasts that want to be
considered e.g [1, 0.5] would only look for trials with 100 % and 50 % contrast
:param order: how to order the trials, options are 'trial num' or 'reaction time'
:param sort: how to sort the trials, options are 'side' (split left right trials), 'choice'
(split correct incorrect trials), 'choice and side' (split left right and correct incorrect)
:param event: trial event to align to (in order to remove nan trials for this event)
:return: np.array of trial ids, list of dividers to indicate how trials are sorted
"""
if event:
idx = ~np.isnan(trials[event])
nan_idx = np.where(idx)[0]
else:
idx = np.ones_like(trials['feedbackType'], dtype=bool)
# Find trials that have specified contrasts
cont = np.bitwise_or(
ismember(trials['contrastLeft'][idx], np.array(contrast))[0],
ismember(trials['contrastRight'][idx], np.array(contrast))[0])
# Find different permutations of trials
# correct right
cor_r = np.where(
np.bitwise_and(
cont,
np.bitwise_and(trials['feedbackType'][idx] == 1,
np.isfinite(trials['contrastRight'][idx]))))[0]
# correct left
cor_l = np.where(
np.bitwise_and(
cont,
np.bitwise_and(trials['feedbackType'][idx] == 1,
np.isfinite(trials['contrastLeft'][idx]))))[0]
# incorrect right
incor_r = np.where(
np.bitwise_and(
cont,
np.bitwise_and(trials['feedbackType'][idx] == -1,
np.isfinite(trials['contrastRight'][idx]))))[0]
# incorrect left
incor_l = np.where(
np.bitwise_and(
cont,
np.bitwise_and(trials['feedbackType'][idx] == -1,
np.isfinite(trials['contrastLeft'][idx]))))[0]
reaction_time = trials['response_times'][idx] - trials['goCue_times'][idx]
def _order_by(_trials, order):
# Returns subset of trials either ordered by trial number or by reaction time
sorted_trials = np.sort(_trials)
if order == 'trial num':
return sorted_trials
elif order == 'reaction time':
sorted_reaction = np.argsort(reaction_time[sorted_trials])
return sorted_trials[sorted_reaction]
dividers = []
# Find the trial id for all possible combinations
if side == 'all' and choice == 'all':
if sort == 'idx':
trial_id = _order_by(np.r_[cor_r, cor_l, incor_r, incor_l], order)
elif sort == 'choice':
trial_id = np.r_[_order_by(np.r_[cor_l, cor_r], order),
_order_by(np.r_[incor_l, incor_r], order)]
dividers.append(np.r_[cor_l, cor_r].shape[0])
elif sort == 'side':
trial_id = np.r_[_order_by(np.r_[cor_l, incor_l], order),
_order_by(np.r_[cor_r, incor_r], order)]
dividers.append(np.r_[cor_l, incor_l].shape[0])
elif sort == 'choice and side':
trial_id = np.r_[_order_by(cor_l, order),
_order_by(incor_l, order),
_order_by(cor_r, order),
_order_by(incor_r, order)]
dividers.append(cor_l.shape[0])
dividers.append(np.r_[cor_l, incor_l].shape[0])
dividers.append(np.r_[cor_l, incor_l, cor_r].shape[0])
if side == 'left' and choice == 'all':
if sort in ['idx', 'side']:
trial_id = _order_by(np.r_[cor_l, incor_l], order)
elif sort in ['choice', 'choice and side']:
trial_id = np.r_[_order_by(cor_l, order),
_order_by(incor_l, order)]
dividers.append(cor_l.shape[0])
if side == 'right' and choice == 'all':
if sort in ['idx', 'side']:
trial_id = _order_by(np.r_[cor_r, incor_r], order)
elif sort in ['choice', 'choice and side']:
trial_id = np.r_[_order_by(cor_r, order),
_order_by(incor_r, order)]
dividers.append(cor_r.shape[0])
if side == 'all' and choice == 'correct':
if sort in ['idx', 'choice']:
trial_id = _order_by(np.r_[cor_l, cor_r], order)
elif sort in ['side', 'choice and side']:
trial_id = np.r_[_order_by(cor_l, order), _order_by(cor_r, order)]
dividers.append(cor_l.shape[0])
if side == 'all' and choice == 'incorrect':
if sort in ['idx', 'choice']:
trial_id = _order_by(np.r_[incor_l, incor_r], order)
elif sort in ['side', 'choice and side']:
trial_id = np.r_[_order_by(incor_l, order),
_order_by(incor_r, order)]
dividers.append(incor_l.shape[0])
if side == 'left' and choice == 'correct':
trial_id = _order_by(cor_l, order)
if side == 'left' and choice == 'incorrect':
trial_id = _order_by(incor_l, order)
if side == 'right' and choice == 'correct':
trial_id = _order_by(cor_r, order)
if side == 'right' and choice == 'incorrect':
trial_id = _order_by(incor_r, order)
if event:
trial_id = nan_idx[trial_id]
return trial_id, dividers
