def bin_flat_spike_data2(fsd, trial_counter=None, F_SAMP=30e3, n_bins=75,
t_start=-.25, t_stop=.5, split_on=None, include_trials='hits',
split_on_filter=None):
"""Bins in time over trials, splitting on split_on.
fsd : a flat array of spike times, with replaced stimulus names
split_on : REQUIRED, how to split fsd, eg ['session', 'unit']
split_on_filter : list of keys to be included, after splitting
if None, then everything is included
It will be separately binned over sound.
"""
if split_on is None:
split_on = []
# iterate over the groups and bin each one
rec_l = []
for key, df in fsd.groupby(split_on):
if split_on_filter is not None and key not in split_on_filter:
continue
for sound_name in ['lehi', 'rihi', 'lelo', 'rilo']:
for block_name in ['LB', 'PB']:
# subframe
subdf = df[(df.sound == sound_name) & (df.block == block_name)]
# get session name
session_l = np.unique(np.asarray(df.session))
assert len(session_l) == 1
session = session_l[0]
# histogramming
counts, t_vals = myutils.times2bins(
np.asarray(subdf.adj_time), f_samp=F_SAMP,
t_start=t_start, t_stop=t_stop, bins=n_bins,
return_t=True)
# count trials
n_trials = trial_counter(session=session, block=block_name,
sound=sound_name, include_trials=include_trials)
# comment this out, because user might request subset of
# original trial set
#if n_trials < len(np.unique(np.asarray(subdf.trial))):
# raise ValueError("counted more trials than exist")
# Add in the keyed info (session etc), plus
# n_counts, n_trials, and bin
this_frame = [list(key) +
[sound_name, block_name, count, n_trials, t_val]
for count, t_val in zip(counts, t_vals)]
# append to growing list
rec_l += this_frame
# convert to new data frame, using same keyed columns plus our new ones
cols = split_on + ['sound', 'block', 'counts', 'trials', 'time']
newdf = pandas.DataFrame(rec_l, columns=cols)
return newdf
def calc_effect_size_by_sound(fdf, trial_lister=None,
comp_meth='ttest', p_adj_meth='BH', split_on=None, split_on_filter=None,
t_start=-.25, t_stop=.5, bins=75, mag_meth='diff'):
"""Calculates a heat map of effect size for each sound, masked by p-value.
Groups spikes by :split_on:, eg nid. Then compares across blocks,
using compare_rasters(comp_meth, p_adj_meth).
fdf : flat spike times
trial_list : SpikeSorter.list_trials_by_type
fig : figure to plot into
comp_meth : how to compare the binned spike times across blocks
Returns
mag_d : dict of effect magnitude by sound name
p_d : dict of p-value by sound name
names : list of values of :split_on:
t : time bins
"""
names, mag_d, p_d = [], {}, {}
if split_on is None:
g = {None: fdf}.items()
else:
g = fdf.groupby(split_on)
# Iterate over groups
n_incl = 0
for key, df in g:
if split_on_filter is not None and key not in split_on_filter:
continue
else:
n_incl += 1
# get session name
session_l = np.unique(np.asarray(df.session))
if len(session_l) != 1:
print "error: must be exactly one session!"
1/0
session = session_l[0]
names.append(key)
# iterate over sound * block
g1 = df.groupby(['sound', 'block'])
for n, sound_name in enumerate(['lehi', 'rihi', 'lelo', 'rilo']):
# Keep a running list in each sound
if sound_name not in mag_d:
mag_d[sound_name] = []
p_d[sound_name] = []
# iterate over blocks and get folded spike times
fsdict = {}
for block_name in ['LB', 'PB']:
# get spikes form this sound * block
try:
x = df.ix[g1.groups[sound_name, block_name]]
except KeyError:
# no spikes of this sound * block!
x = []
# fold by trial
folded_spikes = fold(x, trial_lister(session,
sound=sound_name, block=block_name))
# convert to bins
ns, t = myutils.times2bins(folded_spikes, return_t=True,
t_start=t_start, t_stop=t_stop, bins=bins, f_samp=30000.)
fsdict[block_name] = ns.transpose()
# Do the comparison
mag, p = compare_rasters(fsdict['LB'], fsdict['PB'],
meth=comp_meth, p_adj_meth=p_adj_meth, mag_meth=mag_meth)
mag_d[sound_name].append(mag)
p_d[sound_name].append(p)
for key in mag_d:
mag_d[key] = np.array(mag_d[key])
p_d[key] = np.array(p_d[key])
if split_on_filter is not None and len(split_on_filter) != n_incl:
print "warning: %d in filter but only found %d" % (len(split_on_filter),
n_incl)
return mag_d, p_d, names, t
def get_binned_spikes3(self, spike_filter=None, trial_filter=None):
"""Generic binning function operating on self._fsd
spike_filter : dataframe describing how to split fsd
The columns are the hierarchy to split on:
eg ['session', 'unit']
The items are the ones to include.
If no items, then everything is included.
If None, then bin over everything except 'adj_time' or 'spike_time'
Here we delineate every combination because it's not separable
over session and unit (usually).
trial_filter :
How to do this filtering?
For instance: hits only, stim_numbers 1-12, expressed as dicts
In this case I wouldn't want to enumerate every combination
because I can just take intersection over stim numbers and outcome.
Although note we might want to combine errors and wrongports,
for instance.
It's implicit that we want to do this for each session in
spike_filter.
First the spikes are grouped over the columns in spike_filter.
For each group, the trials are grouped over the columns in trial_filter.
This cross-result is histogrammed.
All results are concatenated and returned.
The actual histogramming is done by myutils.times2bins using
self.f_samp, t_start, t_stop, bins
"""
input = self._fsd
# default, use all columns and include all data
if spike_filter is None:
col_list = input.columns.tolist()
remove_cols = ['adj_time', 'spike_time']
for col in remove_cols:
if col in col_list:
col_list.remove(col)
spike_filter = pandas.DataFrame(columns=col_list)
# Choose data from `input` by defining the following variables:
# `keylist` : a list of keys to include, each separately binned
# `grouped_data` : a dict from each key in keylist, to the data
# `keynames` : what to call each entry of the key in the result
if len(spike_filter) == 0:
# use all data
keynames = spike_filter.columns.tolist()
keylist = [tuple([myutils.only_one(input[col])
for col in keynames])]
val = input
grouped_data = {keylist[0]: val}
elif len(spike_filter) == 1:
# Optimized for the case of selecting a single unit
d = {}
for col in spike_filter:
d[col] = spike_filter[col][0]
mask = myutils.pick_mask(input, **d)
keylist = spike_filter.to_records(index=False) # length 1
keynames = spike_filter.columns.tolist()
grouped_data = {keylist[0] : input.ix[mask]}
else:
# standard case
g = input.groupby(spike_filter.columns.tolist())
grouped_data = g.groups
keylist = spike_filter.to_records(index=False)
keynames = spike_filter.columns.tolist()
# Now group the trials
att = self.all_trials.reset_index().set_index(['session', 'trial'], drop=False)
g2 = att.groupby(trial_filter.columns.tolist())
#g2 = self.all_trials.groupby(trial_filter.columns.tolist())
# Now iterate through the keys in keylist and the corresponding values
# in grouped_data.
rec_l = []
for key in keylist:
# Take the data from this group
subdf = grouped_data[key]
for g2k, g2v in g2:
# count trials of this type from this session
session = myutils.only_one(subdf.session)
n_trials = len(g2v.ix[session])
if n_trials == 0:
# for example if a possible combination never actually
# occurred
continue
# Join the spikes on the columns of trial filter
subsubdf = subdf.join(g2v[trial_filter.columns],
on=['session', 'trial'], how='inner', rsuffix='rrr')
# check for already-joined columns
for col in trial_filter.columns:
if col+'rrr' in subsubdf.columns:
assert (subsubdf[col] == subsubdf[col+'rrr']).all()
subsubdf.pop(col + 'rrr')
# histogramming
counts, t_vals = myutils.times2bins(
np.asarray(subsubdf.adj_time), return_t=True,
f_samp=self.f_samp, t_start=self.t_start,
t_stop=self.t_stop, bins=self.bins)
# Add in the keyed info (session etc), plus
# n_counts, n_trials, and bin
frame_label = list(key) + list(np.array([g2k]).flatten())
this_frame = [frame_label +
[count, n_trials, t_val, bin]
for bin, (count, t_val) in enumerate(zip(counts, t_vals))]
# append to growing list
rec_l += this_frame
# convert to new data frame, using same keyed columns plus our new ones
cols = keynames + trial_filter.columns.tolist() + [
'counts', 'trials', 'time', 'bin']
newdf = pandas.DataFrame(rec_l, columns=cols)
# drop the combinations that never actually occurred (future trials)
return newdf
def get_binned_spikes_by_trial(self, split_on, split_on_filter=None,
f_samp=30e3, t_start=-.25, t_stop=.5, bins=75, include_trials='hits'):
"""Returns binned data separately for each trial.
There is a variable number of columns bin%d, depending on the number
you request.
Format:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 23202 entries, 0 to 23201
Data columns:
session 23202 non-null values
tetrode 23202 non-null values
sound 23202 non-null values
block 23202 non-null values
trial 23202 non-null values
bin0 23202 non-null values
dtypes: int64(3), object(3)
"""
fsd = self.read_flat_spikes_and_trials(stim_number_filter=range(5,13),
include_trials=include_trials)
replace_stim_numbers_with_names(fsd)
g = fsd.groupby(split_on)
#df = pandas.DataFrame()
dfs = []
for key, val in g:
if split_on_filter is not None and key not in split_on_filter:
continue
for sound_name in ['lehi', 'rihi', 'lelo', 'rilo']:
for block_name in ['LB', 'PB']:
# subframe
subdf = val[(val.sound == sound_name) &
(val.block == block_name)]
# get session name
session_l = np.unique(np.asarray(subdf.session))
if len(session_l) == 0:
continue
elif len(session_l) > 1:
raise "Non-unique sessions, somehow"
else:
session = session_l[0]
trial_list = self.list_trials_by_type(session=session,
sound=sound_name, block=block_name,
include_trials=include_trials)
counts, times = myutils.times2bins(
fold(subdf, trial_list),
f_samp=f_samp, t_start=t_start, t_stop=t_stop, bins=bins,
return_t=True)
this_frame = [list(key) + [sound_name, block_name, trial]
+ list(count)
for count, trial in zip(counts, trial_list)]
dfs.append(pandas.DataFrame(this_frame,
columns=(split_on + ['sound', 'block', 'trial'] +
['bin%d' % n for n in range(counts.shape[1])])))#,
# ignore_index=True)
return pandas.concat(dfs, ignore_index=True)
