Merge pull request #161 from berkeley-stat159/Tests

merging with master

code/utils/organize_behavior_data.py

@@ -99,45 +99,6 @@ def load_in_dataframe(subject_number):
 
 	return run_total
 
-def load_model_one(subject_number, run_number):
-	""" Return the model (conditions) of subject's single run
-
-    Parameters
-    ----------
-    subject_number : int
-    	Subject Number
-    run_number : int
-    	Run Number	
-    
-    Returns
-    -------
-    task : np.array
-    	the model (conditions) of subject's single run 
-    gain : np.array
-    	the model (conditions) of subject's single run
-    loss : np.array
-    	the model (conditions) of subject's single run
-    dist : np.array
-    	the model (conditions) of subject's single run
-        
-    """
-
-	task=np.loadtxt(data_location+'sub00%s/model/model001/onsets/task001_run00%s/cond001.txt'%(subject_number,run_number),
-		skiprows=1)
-	gain=np.loadtxt(data_location+'sub00%s/model/model001/onsets/task001_run00%s/cond002.txt'%(subject_number,run_number),
-		skiprows=1)
-	loss=np.loadtxt(data_location+'sub00%s/model/model001/onsets/task001_run00%s/cond003.txt'%(subject_number,run_number),
-		skiprows=1)
-	dist=np.loadtxt(data_location+'sub00%s/model/model001/onsets/task001_run00%s/cond004.txt'%(subject_number,run_number),
-		skiprows=1)
-
-	#delete the rows that contain -1 in respcat (these are errors in experiment so we should take them out
-
-	return task, gain, loss, dist
-
-
-
-
 
 
 

code/utils/scripts/convolution_high_res_script.py

@@ -9,15 +9,18 @@
 
 Steps:
 -----------------------------------------------------------------------------------
-1. Extract 4 conditions of subject 1's first run
+1. Extract 4 conditions of subject __'s all run
 2. Gain higher time resolutions
 3. Convolve with hrf
 4. Plot sampled HRFs with the high resolution neural time course
 5. Save to txt files
 """
 
-import sys
-sys.path.append(".././utils")
+
+from __future__ import absolute_import, division, print_function
+import sys, os
+#TODO : i'm gonna fix the location when i finish the test for stimuli.py
+sys.path.append(os.path.join(os.path.dirname(__file__), "../"))
 import numpy as np
 import matplotlib.pyplot as plt
 import nibabel as nib
@@ -26,90 +29,54 @@
 from organize_behavior_data import *
 from load_BOLD import *
 
-
-location_of_data="../../data/ds005/"
-location_of_model="ds005/sub002/model/model001/onsets/task001_run001/"
-location_of_plot="../../plots/"
-location_of_txt="../txt_files/"
-
-
-# Extract 4 conditions of subject 1's first run
-<<<<<<< HEAD
-task, gain, loss, dist = load_model_one(3,1)
-
-# load data (subject 1 run 1 for now) (you can change it if you want)
-data = load_img(3,1)
-=======
-task, gain, loss, dist = load_model_one(2,1)
-
-# load data (subject 1 run 1 for now) ( you can change it if you want)
-data = load_img(2,1)
->>>>>>> d722bc9dddc177505a29e418d2d00c3ab33db388
-
-# Gain higher time resolutions
-high_res_times, high_task = events2neural_high(task)
-_, high_gain = events2neural_high(gain)
-_, high_loss = events2neural_high(loss)
-_, high_dist = events2neural_high(dist)
-
-
-# Convolve with hrf
-hrf_times = np.arange(0,30,1.0/100)
+# Create the necessary directories if they do not exist
+dirs = ['../../../txt_output', '../../../txt_output/conv_high_res',\
+        '../../../fig','../../../fig/conv_high_res']
+for d in dirs:
+    if not os.path.exists(d):
+            os.makedirs(d)
+
+# Locate the different paths
+#TODO: the current location for this file project-epsilon/code/scripts
+project_path = '../../../'
+# TODO: change it to relevant path
+data_path = project_path+'data/ds005/'
+
+#change here to get your subject !
+subject_list = ['11', '5', '1']
+#change here to get your run number !
+run_list = [str(i) for i in range(1,4)]
+cond_list = [str(i) for i in range(1,5)]
+
+condition_paths = [('ds005_sub' + s.zfill(3) + '_t1r' + r +'_conv_'+ c.zfill(3), \
+	data_path + 'sub' + s.zfill(3) + '/model/model001/onsets/task001_run' \
+	+ r.zfill(3) + '/cond'+ c.zfill(3) + '.txt') for c in cond_list \
+	for r in run_list \
+	for s in subject_list]
+
+condition = ['task','gain','loss','dist']
+hrf_times = np.arange(0,24,1.0/100)
 hrf_at_hr = hrf(hrf_times)
-high_res_hemo_task = np.convolve(high_task, hrf_at_hr)[:len(high_task)]
-high_res_hemo_gain = np.convolve(high_gain, hrf_at_hr)[:len(high_gain)]
-high_res_hemo_loss = np.convolve(high_loss, hrf_at_hr)[:len(high_loss)]
-high_res_hemo_dist = np.convolve(high_dist, hrf_at_hr)[:len(high_dist)]
-
-
-tr_indices = np.arange(240)
-tr_times = tr_indices * 2
-hr_tr_indices = np.round(tr_indices * 100).astype(int)
-
-# Plot sampled HRFs with the high resolution neural time course
-tr_hemo_task = high_res_hemo_task[hr_tr_indices]
-plt.plot(tr_times, tr_hemo_task)
-plt.xlabel('Time (seconds)')
-plt.ylabel('Convolved values at TR onsets (condition: task)')
-plt.savefig(location_of_plot+'task_high_res_convolution')
-plt.clf()
-
-tr_hemo_gain = high_res_hemo_gain[hr_tr_indices]
-plt.plot(tr_times, tr_hemo_gain)
-plt.xlabel('Time (seconds)')
-plt.ylabel('Convolved values at TR onsets (condition: gain)')
-plt.savefig(location_of_plot+'gain_high_res_convolution')
-plt.clf()
-
-tr_hemo_loss = high_res_hemo_loss[hr_tr_indices]
-plt.plot(tr_times, tr_hemo_loss)
-plt.xlabel('Time (seconds)')
-plt.ylabel('Convolved values at TR onsets (condition: loss)')
-plt.savefig(location_of_plot+'loss_high_res_convolution')
-plt.clf()
-
-tr_hemo_dist = high_res_hemo_dist[hr_tr_indices]
-plt.plot(tr_times, tr_hemo_dist)
-plt.xlabel('Time (seconds)')
-plt.ylabel('Convolved values at TR onsets (condition: dist)')
-plt.savefig(location_of_plot+'dist_high_res_convolution')
-plt.clf()
-
-
-# Save convolved information into txt files
-np.savetxt(location_of_txt+'ds005_sub001_t1r1_conv1_high_res.txt', tr_hemo_task)
-np.savetxt(location_of_txt+'ds005_sub001_t1r1_conv2_high_res.txt', tr_hemo_gain)
-np.savetxt(location_of_txt+'ds005_sub001_t1r1_conv3_high_res.txt', tr_hemo_loss)
-np.savetxt(location_of_txt+'ds005_sub001_t1r1_conv4_high_res.txt', tr_hemo_dist)
-
-
-# create the matrix using np.convolve and plot them
-n_vols = data.shape[-1]
-X_matrix_high_res = np.ones((n_vols,5)) #design matrix (1 at the 0th column)
-condition = [tr_hemo_task, tr_hemo_gain, tr_hemo_loss, tr_hemo_dist]
-for i,name in enumerate(condition):
-	X_matrix_high_res[:,i+1] = condition[i]
-
-
-
 
+for cond_path in condition_paths:
+	name = cond_path[0]
+	path = cond_path[1]
+	cond = np.loadtxt(path, skiprows = 1)
+	# Gain higher time resolutions
+	high_res_times, high_cond = events2neural_high(cond)
+	# Convolve with hrf
+	high_res_hemo = np.convolve(high_cond, hrf_at_hr)[:len(high_cond)]
+	tr_indices = np.arange(240)
+	tr_times = tr_indices * 2
+	# Plot sampled HRFs with the high resolution neural time course
+	hr_tr_indices = np.round(tr_indices * 100).astype(int)
+	tr_hemo = high_res_hemo[hr_tr_indices]
+	plt.plot(tr_times, tr_hemo, label="convolved")
+	plt.title(name+'_%s'%(condition[int(name[25])-1]))
+	plt.xlabel('Time (seconds)')
+	plt.ylabel('Convolved values at TR onsets (condition: %s)'%(condition[int(name[25])-1]))
+	plt.legend(loc='lower right')
+	plt.savefig(dirs[3]+'/'+ name +'_high_res_.png')
+	plt.clf()
+	#save the txt file
+	np.savetxt(dirs[1] +'/'+ name +'_high_res.txt', tr_hemo)