def test_2(): x = np.arange(100) x2 = 50 - x mask = np.zeros((5,5,4)) mask[:, 1:4, 1:3] = 1 x3 = x.reshape((5, 5, 4)) output3 = t_grouping_neighbor(x3, mask, 50, neighbors = 1, prop = False, abs_on = False, binary = True , off_value = 0,masked_value = .5) assert(np.sum(output3[0]) == 50) mask[:, 1:4, 1:3] = 1 x4 = x2.reshape((5, 5, 4)) output4 = t_grouping_neighbor(x4, mask, .5, neighbors = 2, prop = True, abs_on = True, binary = True , off_value = 0, masked_value = .5) assert(np.sum(output4[0]) == 50) # Test for when neighbors != None and binary == False output5 = t_grouping_neighbor(x4, mask, .5, neighbors = 2, prop = True, abs_on = True, binary = False, off_value = 0, masked_value = .5) assert(output5 == False) # Test for when binary == False output6 = t_grouping_neighbor(x4, mask, .5, neighbors = None, prop = True, abs_on = True, binary = False, off_value = 0, masked_value = .5) assert(np.sum(output6[0]) == 66)
def test_2(): x = np.arange(100) x2 = 50 - x mask = np.zeros((5, 5, 4)) mask[:, 1:4, 1:3] = 1 x3 = x.reshape((5, 5, 4)) output3 = t_grouping_neighbor(x3, mask, 50, neighbors=1, prop=False, abs_on=False, binary=True, off_value=0, masked_value=.5) assert (np.sum(output3[0]) == 50) mask[:, 1:4, 1:3] = 1 x4 = x2.reshape((5, 5, 4)) output4 = t_grouping_neighbor(x4, mask, .5, neighbors=2, prop=True, abs_on=True, binary=True, off_value=0, masked_value=.5) assert (np.sum(output4[0]) == 50) # Test for when neighbors != None and binary == False output5 = t_grouping_neighbor(x4, mask, .5, neighbors=2, prop=True, abs_on=True, binary=False, off_value=0, masked_value=.5) assert (output5 == False) # Test for when binary == False output6 = t_grouping_neighbor(x4, mask, .5, neighbors=None, prop=True, abs_on=True, binary=False, off_value=0, masked_value=.5) assert (np.sum(output6[0]) == 66)
bh_final = neighbor_smoothing_binary(bh_3d_1_good, neighbors) bh_mean[..., i] = bh_3d_1_good ##################################### # Run t_grouping for each subject # ##################################### t_3d = np.load("../data/t_stat/" + name + "_tstat.npy") #mask = fitted_mask t_group = t_grouping_neighbor(t_3d, mask, prop_t, neighbors=neighbors, prop=True, abs_on=True, binary=True, off_value=0, masked_value=.5)[0] t_mean[..., i] = t_group ###################################### # Run beta grouping for each subject # ###################################### beta_3d = np.load("../data/betas/" + name + "_beta.npy") beta_group = t_grouping_neighbor(beta_3d, mask, prop_beta, neighbors=neighbors,
########## Final Output ########## ##################################### #Create final output. total=np.zeros((3*64,3*64)) ward = label t_final = data_new prop_t = 0.15 rachels_ones = np.ones((64, 64, 34)) fitted_mask = make_mask(rachels_ones, mask_data, fit = True) fitted_mask[fitted_mask > 0] = 1 neighbors=1 t_cluster = t_grouping_neighbor(t_stat, fitted_mask, prop_t, neighbors = neighbors, prop = True, abs_on = True, binary = True, off_value = 0, masked_value = .5)[0] t_cluster = t_cluster[...,19:22] t_final_abs_max = np.max(abs(t_final)) for i in range(ward.shape[-1]): total[(i*64):((i+1)*64),0:64]=ward[...,i]*1/5 - 2/5 # centering around 0 for i in range(t_final.shape[-1]): total[(i*64):((i+1)*64),64:128]=t_final[...,i]*1/t_final_abs_max # centering around 0 for i in range(t_cluster.shape[-1]): total[(i*64):((i+1)*64),128:192]=t_cluster[...,i] -1/2 # centering around 0
############## toolbar_width=len(prod2) sys.stdout.write("T - Analysis: ") sys.stdout.write("[%s]" % (" " * toolbar_width)) sys.stdout.flush() sys.stdout.write("\b" * (toolbar_width+1)) # return to start of line, after '[' t_val=[] #values c*5 + d - 1 count_c=0 for c,d in itertools.product(range(5),range(5)): second=t_grouping_neighbor(t_3d, mask, prod2[c], neighbors= neighbors2[d], prop=True,abs_on=True, binary=True ,off_value=0,masked_value=.5)[0] t_val.append(second) if count_c==c and d==4: sys.stdout.write("-") sys.stdout.flush() count_c+=1 sys.stdout.write("\n") #------------------# # Image comparison # #------------------# present_t = np.ones((5*64,5*64))
total = np.zeros((3 * 64, 3 * 64)) ward = label t_final = data_new prop_t = 0.15 rachels_ones = np.ones((64, 64, 34)) fitted_mask = make_mask(rachels_ones, mask_data, fit=True) fitted_mask[fitted_mask > 0] = 1 neighbors = 1 t_cluster = t_grouping_neighbor(t_stat, fitted_mask, prop_t, neighbors=neighbors, prop=True, abs_on=True, binary=True, off_value=0, masked_value=.5)[0] t_cluster = t_cluster[..., 19:22] t_final_abs_max = np.max(abs(t_final)) for i in range(ward.shape[-1]): total[(i * 64):((i + 1) * 64), 0:64] = ward[..., i] * 1 / 5 - 2 / 5 # centering around 0 for i in range(t_final.shape[-1]): total[(i * 64):((i + 1) * 64), 64:128] = t_final[..., i] * 1 / t_final_abs_max # centering around 0
mask = make_mask(inner_ones, mask, True) mask[mask > 0] = 1 t_vals = t t_vals_3d = t_vals.reshape(data.shape[:-1]) pro = [.25, .1, .1, .05, .025] folks = [1, 1, 5, 5, 10] plt.close() for i in np.arange(5): start, cutoff = t_grouping_neighbor(t_vals_3d, mask, pro[i], prop=True, neighbors=folks[i], abs_on=True) plt.imshow(present_3d(2 * start - 1), interpolation='nearest', cmap="seismic") plt.title("T statistics " + str(pro[i]) + " proportion \n (cutoff=" + str(cutoff) + ") , neighbors: " + str(folks[i])) plt.colorbar() plt.savefig(location_of_images + str(pro[i]) + "_" + str(folks[i]) + "_t.png") plt.close() ################## # Beta # ##################
mask= make_mask(inner_ones,mask,True) mask[mask>0]=1 t_vals=t t_vals_3d=t_vals.reshape(data.shape[:-1]) pro=[.25,.1,.1,.05,.025] folks=[1,1,5,5,10] plt.close() for i in np.arange(5): start,cutoff=t_grouping_neighbor(t_vals_3d,mask,pro[i],prop=True,neighbors= folks[i],abs_on=True) plt.imshow(present_3d(2*start-1),interpolation='nearest',cmap="seismic") plt.title("T statistics " +str(pro[i])+" proportion \n (cutoff=" + str(cutoff)+") , neighbors: " + str(folks[i])) plt.colorbar() plt.savefig(location_of_images+str(pro[i])+"_" + str(folks[i])+"_t.png") plt.close() ################## # Beta # ################## b1 = B[1] #cutoff = .6 b1_vals_3d=b1.reshape(data.shape[:-1]) pro=[.25,.1,.1,.05,.025] folks=[1,1,5,5,10]
toolbar_width = len(prod2) sys.stdout.write("T - Analysis: ") sys.stdout.write("[%s]" % (" " * toolbar_width)) sys.stdout.flush() sys.stdout.write("\b" * (toolbar_width + 1)) # return to start of line, after '[' t_val = [] #values c*5 + d - 1 count_c = 0 for c, d in itertools.product(range(5), range(5)): second = t_grouping_neighbor(t_3d, mask, prod2[c], neighbors=neighbors2[d], prop=True, abs_on=True, binary=True, off_value=0, masked_value=.5)[0] t_val.append(second) if count_c == c and d == 4: sys.stdout.write("-") sys.stdout.flush() count_c += 1 sys.stdout.write("\n") #------------------# # Image comparison # #------------------#
bh_first = bh_procedure(p_bh, q) bh_3d = masking_reshape_end(bh_first, mask, off_value = .5) bh_3d[bh_3d < .5] = 0 bh_3d_1_good = 1 - bh_3d bh_final = neighbor_smoothing_binary(bh_3d_1_good, neighbors) bh_mean[..., i] = bh_3d_1_good ##################################### # Run t_grouping for each subject # ##################################### t_3d = np.load("../data/t_stat/" + name + "_tstat.npy") #mask = fitted_mask t_group = t_grouping_neighbor(t_3d, mask, prop_t, neighbors = neighbors, prop = True, abs_on = True, binary = True, off_value = 0, masked_value = .5)[0] t_mean[..., i] = t_group ###################################### # Run beta grouping for each subject # ###################################### beta_3d = np.load("../data/betas/" + name + "_beta.npy") beta_group = t_grouping_neighbor(beta_3d, mask, prop_beta, neighbors = neighbors, prop = True, abs_on = True, binary = True, off_value = 0, masked_value = .5)[0] beta_mean[..., i] = beta_group sys.stdout.write("-") sys.stdout.flush()