first submission

day2_homework/anatomical.py

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+import nibabel as nib
+import numpy as np
+anatomical = np.loadtxt('anatomical.txt')
+K = 32
+P = anatomical.size/K
+TrueVals = []
+for i in range(120,200):
+	if P % i == 0:
+		TrueVals.append(i)
+I = TrueVals
+J = [P / x for x in TrueVals]

day2_homework/test_data_analy.py

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+import numpy as np
+import nibabel as nib
+import matplotlib.pyplot as plt
+task = np.loadtxt('ds114_sub009_t2r1_cond.txt')
+I = task.shape[0]
+J = task.shape[1]
+cond_array = np.array(range(I*J))
+cond_array = cond_array.reshape(I,J)
+TR_array = (task[:,0:2])/2.5
+img = nib.load('ds114_sub009_t2r1.nii')
+time_course = np.zeros(img.shape[3])
+nConds = TR_array.shape[0]
+for i in range(nConds):
+	temp_index1 = int(TR_array[i,0])
+	temp_index2 = int(TR_array[i,1])
+	time_course[(temp_index1):(temp_index1+temp_index2)] = 1
+is_task_tr = time_course==1
+is_rest_tr = time_course==0
+img_real = nib.load('ds114_sub009_t2r1.nii')
+data = img_real.get_data()
+data = np.asarray(data)
+data_at_task = data[:,:,:,(np.where(is_task_tr))]
+data_at_rest = data[:,:,:,(np.where(is_rest_tr))]
+data_at_task = np.squeeze(data_at_task)
+data_at_rest = np.squeeze(data_at_rest)
+mean_task = np.mean(data_at_task, axis=3)
+mean_rest = np.mean(data_at_rest, axis=3)
+subt_mean = mean_task-mean_rest
+plt.imshow(subt_mean[..., 15], cmap='gray')
+
+#Find the Standard Deviation (variance in this case)
+stdVals_rest = []
+for i in range(0,data_at_rest.shape[3]):
+	curr_vol = data_at_rest[...,i]
+	curr_stdVal = np.var(curr_vol)
+	stdVals_rest.append(curr_stdVal)
+plt.plot(stdVals_rest)
+plt.show()
+# The data won't plot correctly. Maybe try it as a float value???
+