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import pandas as pd
import moss
from scipy import stats
import scipy as sp
import seaborn as sns
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import os.path as op
import os
# for plotting
sns.set(style='ticks', context='poster', font_scale=1.3)
%matplotlib inline
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# R for stats
%load_ext rpy2.ipython
%R require(lme4)
%R require(lmerTest)
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dirs = dict()
dirs['basedir'] = op.join(op.expanduser('~'), 'Experiments/localizer')
dirs['datadir'] = op.join(dirs['basedir'], 'data/')
dirs['analydir'] = op.join(dirs['basedir'], 'analysis')
dirs['subj_info_file'] = op.join(dirs['datadir'], 'subj_info.csv')
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subj_info = pd.read_csv(dirs['subj_info_file'])
subj_info = subj_info[pd.isnull(subj_info.remove)]
subj_info
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dl = pd.DataFrame()
for subid in subj_info.subid:
print subid
# add study file
study_file = op.join(dirs['datadir'], subid, subid + '_behav_localizer.csv')
d = pd.read_csv(study_file)
d['subid'] = subid
dl = dl.append(d, ignore_index=True)
# Merge with subj_info
dl = dl.merge(subj_info, on='subid', how='outer')
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dl.head()
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len(dl.subid.unique())
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dl.groupby(['subid', 'group']).mean().reset_index().groupby('group').count().subid
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dl.loc[dl.resp == 'NR', 'respRT'] = dl.loc[dl.resp == 'NR', 'ISIrespRT'] + .5 # adjust for stimTime
dl.loc[dl.resp == 'NR', 'acc'] = dl.loc[dl.resp == 'NR', 'ISIacc']
dl.loc[dl.resp == 'NR', 'resp'] = dl.loc[dl.resp == 'NR', 'ISIresp']
drop_cols = ['ISIrespRT', 'ISIresp', 'ISIacc', 'remove', 'index']
dl.drop(drop_cols, 1, inplace=True)
# Correct duration of image trials by subtracting ITI (1s fix)
dl['duration_adj'] = dl.duration
dl.ix[dl.cond != 'rest', 'duration_adj'] = dl.ix[dl.cond != 'rest', 'duration_adj'] - 1 # subtract ITI
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dl.head()
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dl['onset_adj'] = dl.onset - 12
dl.head()
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durations = dl.onset - dl.onset.shift(1)
durations[durations > -5].hist(bins=20)
sns.distplot(durations[durations > -5])
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sns.distplot(dl[dl.duration_adj < 5].duration)
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dl_switch = dl[dl.cond != 'rest']
dl_switch['switch'] = dl_switch.cond == dl_switch.cond.shift(1)
dl_switch.switch = dl_switch.switch.apply(lambda x: 0 if x == True else 1)
for increment in [1, 2, 3, 4, 5]:
dl_switch.switch = np.nansum([dl_switch.switch,
dl_switch.switch.apply(lambda x: 0 if x < increment else x+1).shift(1)], axis=0)
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dl_resp = dl_switch[(dl_switch.resp != 'NR') & (dl_switch.switch > 0)]
means = dl_resp.groupby(['subid', 'switch', 'group', 'cond', 'subcond', 'acc']).mean().reset_index()
sns.factorplot(x='switch', hue='group', y='respRT', col='cond',
units='subid', ci=68, dodge=.1,palette=['dodgerblue', 'orange'],
data=means.query('acc == 1'))
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dl_resp = dl_switch[(dl_switch.resp != 'NR') & (dl_switch.switch > 0)]
means = dl_resp.groupby(['subid', 'switch', 'group', 'acc']).mean().reset_index()
means.switch = means.switch.astype(int)
g = sns.factorplot(x='switch', hue='group', y='respRT', aspect=1.5,
units='subid', ci=68, dodge=.1, palette=['dodgerblue', 'orange'],
data=means.query('acc == 1'))
g.set_xlabels('trial relative to switch')
g.set_ylabels('RT (s)')
plt.savefig('/Users/steph-backup/Dropbox/Stanford/Presentations/AP/behav_loc_RTs_switch_acc.png')
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dl_resp = dl[(dl.resp != 'NR') & (dl.cond != 'rest')]
means = dl_resp.groupby(['subid', 'group', 'cond', 'acc']).mean().reset_index()
sns.factorplot(x='cond', hue='acc', y='respRT', col='group',
units='subid', ci=68, dodge=.1,
data=means)
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dl_resp = dl[(dl.resp != 'NR') & (dl.cond != 'rest')]
means = dl_resp.groupby(['subid', 'group', 'cond', 'subcond', 'acc']).mean().reset_index()
sns.factorplot(x='subcond', aspect=1.5,
order=['female', 'male', 'manmade', 'natural', 'indoor', 'outdoor'],
hue='acc', y='respRT', col='group',
units='subid', ci=68, dodge=.1,
data=means)
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dl_resp = dl[(dl.cond != 'rest')]
means = dl_resp.groupby(['subid', 'group', 'cond']).mean().reset_index()
sns.factorplot(x='cond', y='acc', hue='group',
units='subid', ci=68, dodge=.1, aspect=1.2,
data=means, palette=['dodgerblue', 'orange'])
sns.despine(trim=True)
plt.savefig('/Users/steph-backup/Dropbox/Stanford/Presentations/AP/behav_loc_acc.png')
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dl_resp = dl[(dl.cond == 'place')]
%R -i dl_resp
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%%R
str(dl_resp)
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dl_resp = dl[(dl.cond != 'rest')]
means = dl_resp.groupby(['subid', 'group', 'cond', 'subcond']).mean().reset_index()
sns.factorplot(x='subcond', aspect=1.5,
x_order=['female', 'male', 'manmade', 'natural', 'indoor', 'outdoor'],
y='acc', hue='group', palette=['dodgerblue', 'orange'],
units='subid', ci=68, dodge=.1,
data=means)
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Each csv file must have a column for run, condition, onset, duration, and value
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output_filename = 'localizer_cond.csv'
output_dir = '/Volumes/group/awagner/sgagnon/AP/data'
for subid in subj_info.subid:
print subid
dsub = dl[(dl.subid == subid) & (dl.cond != 'rest')]
ddesign = pd.DataFrame({'run': dsub.run,
'condition': dsub.cond,
'onset': dsub.onset_adj,
'duration': 0.5,
'value': 1})
sub_output_dir = op.join(output_dir, subid.replace('loc', 'ap'), 'design')
if not os.path.exists(sub_output_dir):
os.makedirs(sub_output_dir)
ddesign.to_csv(op.join(sub_output_dir, output_filename), index=False)
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output_filename = 'localizer_cond_dur=RT.csv'
output_dir = '/Volumes/group/awagner/sgagnon/AP/data'
for subid in subj_info.subid:
print subid
dsub = dl[(dl.subid == subid) & (dl.cond != 'rest')]
ddesign = pd.DataFrame({'run': dsub.run,
'condition': dsub.cond,
'onset': dsub.onset_adj,
'duration': dsub.respRT,
'value': 1})
sub_output_dir = op.join(output_dir, subid.replace('loc', 'ap'), 'design')
if not os.path.exists(sub_output_dir):
os.makedirs(sub_output_dir)
ddesign.to_csv(op.join(sub_output_dir, output_filename), index=False)
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output_filename = 'localizer_cond_mvpa.csv'
output_dir = '/Volumes/group/awagner/sgagnon/AP/data'
for subid in subj_info.subid:
print subid
dsub = dl[(dl.subid == subid)] # & (dl.cond != 'rest')
ddesign = pd.DataFrame({'run': dsub.run + 6,
'condition': dsub.cond,
'onset': dsub.onset_adj,
'duration': 0.5,
'value': 1})
ddesign.to_csv(op.join(output_dir, subid.replace('loc', 'ap'),
'design', output_filename), index=False)
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