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analyse_location_prefs.py
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from __future__ import division
import os,sys,glob
from joblib import Parallel, delayed
import multiprocessing
import numpy as np
import scipy.linalg as la
import scipy.stats as stats
from scipy.signal import fftconvolve, resample
import scipy.io as sio
import nibabel as nib
import pickle
import tables
from Staircase import ThreeUpOneDownStaircase
from tools import two_gamma as hrf
from tools import add_subplot_axes
import ColorTools as ct
from sklearn.linear_model import RidgeCV
import matplotlib.pyplot as plt
import seaborn as sn
sn.set(style='ticks')
from IPython import embed
subs = ['sub-n001','sub-n003','sub-n005']
task = 'location'#'fullfield' # 'ocinterleave'
rois = ['V1']#,'V2','MT','BA3a','BA44','BA45']
locations = [[-1.5, -1.5], [-1.5, 1.5], [1.5, -1.5], [1.5, 1.5]]
ROI = 'V1'
TR = 0.945
US_FACTOR = 10
DS_FACTOR = 10
fit_per_run = False
# mapper location order (from params):
# (T=top,B=bottom,L=left,R=right)
# TR-BR-BL-TL
# exp_location_order = [3, 0, 2, 1]
def bootstrap(data, num_samples, statistic, alpha):
"""Returns bootstrap estimate of 100.0*(1-alpha) CI for statistic."""
n = len(data)
idx = np.random.randint(0, n, (num_samples, n))
samples = data[idx]
stat = np.sort(statistic(samples, 1))
return (stat[int((alpha/2.0)*num_samples)],
stat[int((1-alpha/2.0)*num_samples)])
embed()
# def identify_pref_locs(subid):
all_tvals = []
all_rs = []
all_lc = []
for subid in subs:
print 'Running %s'%(subid)
# Setup directories
data_dir = '/home/shared/2017/visual/OriColorMapper/preproc/'
#data_dir = '/home/barendregt/Projects/Attention/'
nifti_dir = os.path.join(data_dir, subid, 'psc/')
deriv_dir = os.path.join(data_dir, subid, 'deriv/')
ROI_dir = os.path.join(data_dir, subid, 'masks/dc/')
pickle_dir = os.path.join(data_dir, subid, 'beh/')
fig_dir = os.path.join(data_dir, subid, 'figures/')
# Load location results
location_betas = sio.loadmat(os.path.join(deriv_dir,'%s-location_betas.mat'%task))[ROI]
location_r_squared = sio.loadmat(os.path.join(deriv_dir,'%s-location_rsquareds.mat'%task))[ROI]
location_tvals = sio.loadmat(os.path.join(deriv_dir,'%s-location_tvals.mat'%task))[ROI]
all_tvals.append(location_tvals)
all_rs.append(location_r_squared)
# Get location pref distribution
location_count = np.array([np.sum(np.argmax(location_tvals, axis=1)==loc) for loc in np.unique(np.argmax(location_tvals, axis=1))]) / location_tvals.shape[0]
all_lc.append(location_count)
# Tuning prefs
feature_betas = sio.loadmat(os.path.join(deriv_dir,'%s-feature_betas.mat'%task))[ROI]
feature_r_squared = sio.loadmat(os.path.join(deriv_dir,'%s-feature_rsquareds.mat'%task))[ROI]
ori_pref = np.zeros((feature_betas.shape[1]))
col_pref = np.zeros((feature_betas.shape[1]))
for vii in range(feature_betas.shape[1]):
rs_betas = np.reshape(feature_betas[1:,vii],[8,8])
ori_pref[vii] = np.argmax(rs_betas.max(axis=1))
col_pref[vii] = np.argmax(rs_betas.max(axis=0))
# Locate nifti files
nifti_files = glob.glob('%s*_task-%s_*.nii.gz'%(nifti_dir, task))
trialinfo_files = glob.glob('%s*_task-%s_*_trialinfo.pickle'%(pickle_dir, task))
params_files = glob.glob('%s*_task-%s_*_params.pickle'%(pickle_dir, task))
nifti_files.sort()
trialinfo_files.sort()
params_files.sort()
# Load fMRI data if not previously saved
if not os.path.isfile(os.path.join(deriv_dir,'%s-roi_data.mat'%task)):
mri_data = {}
# for ROI in rois:
# Get all cortex data and task orders
lh_mask = np.array(nib.load(os.path.join(ROI_dir,'lh.%s_vol_dil.nii.gz'%ROI)).get_data(), dtype = bool)
rh_mask = np.array(nib.load(os.path.join(ROI_dir,'rh.%s_vol_dil.nii.gz'%ROI)).get_data(), dtype = bool)
mri_data[ROI] = np.array([np.vstack([nib.load(nf).get_data()[lh_mask,:], nib.load(nf).get_data()[rh_mask,:]]) for nf in nifti_files])
sio.savemat(file_name=os.path.join(deriv_dir,'%s-roi_data.mat'%task), mdict=mri_data)
else:
mri_data = sio.loadmat(os.path.join(deriv_dir,'%s-roi_data.mat'%task))
# Load trial data
task_data = {'trial_order': [],
'trial_stimuli': [],
'trial_params': []}
for ti,par in zip(trialinfo_files, params_files):
[trial_array, trial_indices, trial_params, per_trial_parameters, per_trial_phase_durations, staircase] = pickle.load(open(ti,'rb'))
task_data['trial_order'].append(trial_params[:,0])
task_data['trial_params'].append(trial_params)
task_data['trial_stimuli'].append(trial_array)
template_beta_mat = np.reshape(np.arange(64),[8,8])
concat_mri_data = np.hstack([(x-x.mean(axis=1)[:,np.newaxis])/x.std(axis=1)[:,np.newaxis] for x in mri_data[ROI]])#np.hstack(mri_data[ROI])#
concat_trial_order = np.array(np.hstack(task_data['trial_order']),dtype=int)
trial_locations = np.vstack(task_data['trial_params'])[:,[1,2]]
resampled_mri_data = resample(concat_mri_data, int(concat_mri_data.shape[1]/TR), axis=1)
ori_betas = np.zeros((9,resampled_mri_data.shape[0]))
col_betas = np.zeros((9,resampled_mri_data.shape[0]))
#alphas = np.zeros((resampled_dm.shape[1],resampled_mri_data.shape[0]))
for vii in range(resampled_mri_data.shape[0]):
a=locations[np.argmax(location_tvals,axis=1)[vii]][0]
b=locations[np.argmax(location_tvals,axis=1)[vii]][1]
ori_beta_mat = np.roll(np.repeat(np.arange(1,9)[:,np.newaxis],8,axis=1),-int(ori_pref[vii]), axis=0).flatten()
col_beta_mat = np.roll(np.repeat(np.arange(1,9)[:,np.newaxis],8,axis=1),-int(col_pref[vii]), axis=1).T.flatten()
recoded_trials = np.zeros((concat_trial_order.shape[0]))
recoded_trials[concat_trial_order<64] = ori_beta_mat[concat_trial_order[concat_trial_order<64]]
design_matrix = np.vstack([np.array((trial_locations[:,0]==a) * (trial_locations[:,1]==b) * (recoded_trials==stim), dtype=int) for stim in range(1,9)]).T
# resample signals to 1s resolution
resampled_dm = resample(design_matrix, int(concat_mri_data.shape[1]/TR), axis=0)
resampled_dm = np.hstack([np.ones((resampled_dm.shape[0],1)), fftconvolve(resampled_dm, hrf(np.arange(0,30,1/US_FACTOR)[::US_FACTOR,np.newaxis]))[:resampled_mri_data.shape[1],:]])
if np.sum(np.isnan(resampled_mri_data[vii,:]))==0:
mdl = RidgeCV(alphas=[1.0,10.0,100.0,1000.0])
mdl.fit(resampled_dm, resampled_mri_data[vii,:])
ori_betas[:,vii] = mdl.coef_
# ori_betas[:,vii] = la.lstsq(resampled_dm, resampled_mri_data[vii,:])[0]
#alphas[:,vii] = mdl.alpha_
recoded_trials = np.zeros((concat_trial_order.shape[0]))
recoded_trials[concat_trial_order<64] = col_beta_mat[concat_trial_order[concat_trial_order<64]]
design_matrix = np.vstack([np.array((trial_locations[:,0]==a) * (trial_locations[:,1]==b) * (recoded_trials==stim), dtype=int) for stim in range(1,9)]).T
# resample signals to 1s resolution
resampled_dm = resample(design_matrix, int(concat_mri_data.shape[1]/TR), axis=0)
resampled_dm = np.hstack([np.ones((resampled_dm.shape[0],1)), fftconvolve(resampled_dm, hrf(np.arange(0,30,1/US_FACTOR)[::US_FACTOR,np.newaxis]))[:resampled_mri_data.shape[1],:]])
if np.sum(np.isnan(resampled_mri_data[vii,:]))==0:
mdl = RidgeCV(alphas=[1.0,10.0,100.0,1000.0])
mdl.fit(resampled_dm, resampled_mri_data[vii,:])
col_betas[:,vii] = mdl.coef_
# col_betas[:,vii] = la.lstsq(resampled_dm, resampled_mri_data[vii,:])[0]
plt.figure()
plt.plot(ori_betas[1:,:].mean(axis=1),color='r')
plt.plot(col_betas[1:,:].mean(axis=1),color='b')
sn.despine(offset=5)
plt.savefig(os.path.join(deriv_dir,'avg_feature_tuning.pdf'))
plt.figure()
m_lc = np.mean(all_lc,axis=0)
s_lc = np.std(all_lc,axis=0)/len(subs)
plt.bar([0.1,1.1,2.1,3.1],m_lc)
plt.errorbar([0.5,1.5,2.5,3.5],m_lc,s_lc,fmt='.',color='k')
plt.axis([0,4,0,.5])
plt.ylabel('Proportion voxels/location')
sn.despine()
# plt.set('xticks',[.5,1.5,2.5,3.5])
plt.savefig('location_count.pdf')
plt.close()