def reg_clusters_t_src(mod, IV, factor=None, c1=None, c0=None):
""" Cluster permutation (src x time) t-tests on regression coefficients.
Example: mod='main-VerbGivenWord', IV='LogFre'
"""
dr = '/Volumes/Backup/sufAmb/regression/'
f = dr+'ols_'+mod+'.pickled'
data = load.unpickle(f)
cond = "predictor=='%s'" % IV
data = data.sub(cond) # smaller ds with only the predictor of interest
# cluster permutation parameters
Y='beta'
match='subject'
samples=100
pmin=0.05
tstart=0.13
tstop=0.45
mintime=0.03
minsource=10
if factor is None:
test = testnd.ttest_1samp(Y=Y, ds=data, match=match, samples=samples, pmin=pmin,
tstart=tstart, tstop=tstop, mintime=mintime, minsource=minsource)
elif factor == 'main':
test = testnd.ttest_1samp(Y=Y, ds=data.sub("condition=='main'"), match=match, samples=samples, pmin=pmin,
tstart=tstart, tstop=tstop, mintime=mintime, minsource=minsource)
else:
test = testnd.ttest_rel(Y=Y, ds=data, X=factor, c1=c1, c0=c0, match=match, samples=samples, pmin=pmin,
tstart=tstart, tstop=tstop, mintime=mintime, minsource=minsource)
print "Finished cluster test: mod=%s, IV=%s, factor=%s, c1=%s, c0=%s" % (mod, IV, factor, c1, c0)
path = "/Volumes/BackUp/sufAmb/reg-cluster_time_src/ols_mod-%s_IV-%s_factor-%s_c1-%s_c0-%s.pickled" \
% (mod, IV, factor, c1, c0)
save.pickle(test, path)
return test
# current format: [subject1, stem, stemS, stemEd],
# [subject2, stem, stemS, stemEd],
# ...
#
# new format: [subject1, stem],
# [subject1, stemS],
# ...
# [subject2, stem],
# ...
#===============================================================================
dr = '/Volumes/Backup/sufAmb/pickled/'
IVs = ['VerbGivenWord_nocov', 'VerbGivenWord', 'Ambiguity', 'WrdVerbyWeighted', 'WrdBiasWeighted']
for v in IVs:
fil = dr+'ols_'+v+'.pickled'
data = load.unpickle(fil)
c1, c3 = [], []
c2 = ['all', 'stem', 'stemS', 'stemEd']*data.n_cases
for s in range(data.n_cases):
c1.extend( [ data['subject'][s] ]*4 )
c3.extend( [ data['all'][s], data['stem'][s], data['stemS'][s], data['stemEd'][s] ] )
c1 = Factor(c1)
c2 = Factor(c2)
c3 = combine(c3)
newds = Dataset(('subject',c1), ('Type',c2), ('beta',c3), info=data.info)
save.pickle(newds, fil)
import os
import timeit
import mne
from eelbrain import datasets, save
mne.set_log_level('warning')
fname = 'temp.pickled'
if not os.path.exists(fname):
ds = datasets.get_mne_sample(-0.1, 0.2, src='ico', sub="modality == 'A'")
source = ds['src'].source
y = ds['src'][0].x
save.pickle((y, source), fname)
setup = '''
from itertools import izip
import numpy as np
import scipy as sp
from eelbrain.lab import stats, load
y, source = load.unpickle(%r)
out = np.empty(y.shape, np.uint32)
bin_buff = np.empty(y.shape, np.bool_)
int_buff = np.empty(y.shape, np.uint32)
threshold = 1
tail = 0
struct = sp.ndimage.generate_binary_structure(y.ndim, 1)
struct[::2] = False
import os
import timeit
import mne
from eelbrain import datasets, save
mne.set_log_level("warning")
fname = "temp.pickled"
if not os.path.exists(fname):
ds = datasets.get_mne_sample(-0.1, 0.2, src="ico", sub="modality == 'A'")
source = ds["src"].source
y = ds["src"][0].x
save.pickle((y, source), fname)
setup = (
"""
from itertools import izip
import numpy as np
import scipy as sp
from eelbrain.lab import stats, load
y, source = load.unpickle(%r)
out = np.empty(y.shape, np.uint32)
bin_buff = np.empty(y.shape, np.bool_)
int_buff = np.empty(y.shape, np.uint32)
threshold = 1
tail = 0
struct = sp.ndimage.generate_binary_structure(y.ndim, 1)
def _execute(self):
job = self.generate_job()
if job:
save.pickle(job(), self.path)
pmin=0.15
tstart=0.13
tstop=0.45
mintime=0.03
minsource=10
X = 'Type'
tests = {}
for v in IVs:
print v
testsv = {}
fil = dr+'corr_'+v+'.pickled'
data = load.unpickle(fil)
print 'main'
testsv['main'] = testnd.ttest_1samp(Y=Y, ds=data.sub("Type=='all'"), match=match, samples=samples, pmin=pmin, tstart=tstart, tstop=tstop, mintime=mintime, minsource=minsource)
print 'test 1'
testsv['stemVstemS'] = testnd.ttest_rel(Y=Y, ds=data, X=X, c1='stem', c0='stemS', match=match, samples=samples, pmin=pmin, tstart=tstart, tstop=tstop, mintime=mintime, minsource=minsource)
print 'test 2'
testsv['stemVstemEd'] = testnd.ttest_rel(Y=Y, ds=data, X=X, c1='stem', c0='stemEd', match=match, samples=samples, pmin=pmin, tstart=tstart, tstop=tstop, mintime=mintime, minsource=minsource)
print 'test 3'
testsv['stemSVstemEd'] = testnd.ttest_rel(Y=Y, ds=data, X=X, c1='stemS', c0='stemEd', match=match, samples=samples, pmin=pmin, tstart=tstart, tstop=tstop, mintime=mintime, minsource=minsource)
for t in testsv:
name = v+'_'+t
path = '/Volumes/BackUp/sufAmb/corr_cluster_timespace/corr_%s.pickled' % name
save.pickle(testsv[t], path)
tests[v] = testsv
'''
Created on Aug 18, 2014
@author: andrew
'''
from eelbrain import combine, load, save
dr = '/Volumes/Backup/sufAmb/pickled/'
IVs = ['VerbGivenWord_nocov', 'VerbGivenWord', 'Ambiguity', 'WrdVerbyWeighted', 'WrdBiasWeighted']
for v in IVs:
fil = dr+'ols_'+v+'.pickled'
data = load.unpickle(fil)
for k in data.keys():
if k == 'src~all_VerbGivenWord':
data[k]
save.pickle(data, fil)