def test_present():
# Read in the image data.
data = np.arange(100000)
data = data.reshape((100,100,10))
full=present_3d(data)
assert full.shape == (400,300)
#plt.close()
#print("Initial plot with NO MASK (using old bh function) done.")
print("# ==== BEGIN varying the Q value = .005 (FDR) ==== #")
Q = .005
significant_pvals = bh_procedure(p_vals, Q)
# Reshape significant_pvals
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[...,7]
masked_data = make_mask(original_slice, reshaped_sig_p, fit=False)
plt.imshow(present_3d(masked_data))
plt.clim(0, 1600)
plt.colorbar()
plt.title('Slice with Significant p-values (Q = .005)')
plt.savefig(location_of_images+"significant_p_slice1.png")
plt.close()
print("# ==== END plot with Q = .005 done. ==== #")
print("# ==== BEGIN varying the Q value = .05 (FDR) ==== #")
Q = .05
significant_pvals = bh_procedure(p_vals, Q)
# Reshape significant_pvals
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[...,7]
final_bh = np.mean(bh_mean, axis = 3)
np.save("../data/bh_t_beta/bh_all.npy",bh_mean)
np.save("../data/bh_t_beta/final_bh_average.npy", final_bh)
final_t = np.mean(t_mean, axis = 3)
np.save("../data/bh_t_beta/final_t_average.npy", final_t)
np.save("../data/bh_t_beta/t_all.npy",t_mean)
final_beta = np.mean(beta_mean, axis = 3)
np.save("../data/bh_t_beta/final_beta_average.npy", final_beta)
np.save("../data/bh_t_beta/beta_all.npy",beta_mean)
# plot/save the result (BH)
plt.imshow(present_3d(final_bh), interpolation = 'nearest', cmap = 'seismic')
plt.title("Mean BH Value Across 25 Subjects with Q = .25")
plt.colorbar()
plt.savefig("../../images/bh_mean_final.png")
plt.close()
# plot/save the result (t)
plt.imshow(present_3d(final_t), interpolation = 'nearest', cmap = 'seismic')
plt.title("Mean t_grouping Value Across 25 Subjects with proportion = .1")
plt.colorbar()
plt.savefig("../../images/tgroup_mean_final.png")
plt.close()
# plot/save the result (beta)
plt.imshow(present_3d(final_beta), interpolation = 'nearest', cmap = 'seismic')
plt.title("Mean beta_grouping Value Across 25 Subjects with proportion = .2")
residuals = np.load(residual_data+i+"_residual.npy")
sw_pvals = check_sw(residuals)
print(i+" proportion of voxels with p-value above 0.05 (unmasked): "+str(np.mean(sw_pvals > 0.05)))
mask = nib.load(path_to_data+i+'/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
masked_pvals = make_mask(sw_pvals, mask_data, fit=True)
pvals_in_brain = sw_pvals.ravel()[masked_pvals.ravel() != 0]
print(i+" proportion of voxels with p-value above 0.05 (masked): "+str(np.mean(pvals_in_brain > 0.05)))
sys.stdout.write("-")
sys.stdout.flush()
# Save image plots of masked and unmasked p-values for a single subject.
plt.imshow(present_3d(sw_pvals), cmap=plt.get_cmap('gray'))
plt.savefig(location_of_images+i+'sw.png')
plt.close()
plt.imshow(present_3d(masked_pvals), cmap=plt.get_cmap('gray'))
plt.savefig(location_of_images+i+'swmasked.png')
plt.close()
sys.stdout.write("\n")
data = img.get_data()
data = data[...,6:] # Knock off the first 6 observations.
#######################
# a. (my) smoothing #
#######################
# Kind of arbitrary chosen time
time = 7
original_slice = data[..., 7]
# full width at half maximum (FWHM)
fwhm = 1.5
smoothed_slice = smoothvoxels(data, fwhm, time)
# visually compare original_slice to smoothed_slice
plt.imshow(present_3d(smoothed_slice))
plt.colorbar()
plt.title('Smoothed Slice')
plt.clim(0,1600)
plt.savefig(location_of_images+"smoothed_slice.png")
plt.close()
plt.imshow(present_3d(original_slice))
plt.colorbar()
plt.title('Original Slice')
plt.clim(0,1600)
plt.savefig(location_of_images+"original_slice.png")
plt.close()
]:
t_mean = np.zeros((64, 64, 34, 24))
count = 0
for i in sub_list:
t_stat = np.load(t_data + i + model)
mask = nib.load(path_to_data + i +
'/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
t_mean[..., count] = make_mask(t_stat, mask_data, fit=True)
count += 1
t_mean = np.mean(t_mean, axis=3)
final = present_3d(t_mean)
plt.imshow(final, interpolation='nearest', cmap='seismic')
plt.title("Mean T-Statistic Value Across 25 Subjects")
zero_out = max(abs(np.min(final)), np.max(final))
plt.clim(-zero_out, zero_out)
plt.colorbar()
plt.show()
#
# #####################################
# ########## Clustering##########
# #####################################
#
# data_new = t_mean[...,20:23]
# X = np.reshape(data_new, (-1, 1))
#
sys.stdout.write("\b" * (toolbar_width+1))
for i in desired_index:
name=sub_list[i]
# the mask for each subject
path_to_data = project_path + "data/ds009/" + name
brain = nib.load(path_to_data + '/anatomy/inplane001_brain.nii.gz')
brain=brain.get_data()
###########################
# Benjamini Hochberg Plot #
###########################
plt.imshow(present_3d(brain[::2,::2,:]),cmap="gray")
upper= np.percentile(np.ravel(brain[::2,::2,:]),95)
plt.colorbar()
if bRIGHT:
plt.clim(0,upper)
overlap=present_3d(bh_all[...,i])
overlap[overlap==0]=np.nan
overlap[-1,-1]=0 # to make the output correct
plt.imshow(overlap,cmap="bwr",alpha=.7)
plt.xticks([])
plt.yticks([])
plt.title(name+ ", Benjamini Hochberg")
plt.savefig("../../images/"+name+"_bh_overlay.png")
plt.close()
behav_suffix = "/behav/task001_run001/behavdata.txt"
smooth_data = final_data + 'smooth/'
hrf_data = final_data + 'hrf/'
sys.path.append(location_of_functions)
from Image_Visualizing import present_3d, make_mask, present_3d_options
i="sub001"
brain=nib.load(path_to_data+i+'/BOLD/task001_run001/bold.nii.gz')
data=brain.get_data()
brain_hi= nib.load(path_to_data + i+'/anatomy/inplane001_brain.nii.gz')
joy_hi=brain_hi.get_data()
joy=data[...,7]
plt.imshow(present_3d(data[...,7]),cmap="gray",interpolation="nearest")
plt.figure()
plt.imshow(present_3d_options(joy,2),cmap="gray",interpolation="nearest")
plt.title("2")
plt.figure()
plt.imshow(present_3d_options(joy,1),cmap="gray",interpolation="nearest")
plt.title("1")
plt.figure()
plt.imshow(present_3d_options(joy,0),cmap="gray",interpolation="nearest")
plt.title("0")
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]
for i in sub_list:
residuals = np.load(residual_data + i + "_residual.npy")
sw_pvals = check_sw(residuals)
unmasked_prop.append(np.mean(sw_pvals > 0.05))
mask = nib.load(path_to_data + i + '/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
masked_pvals = make_mask(sw_pvals, mask_data, fit=True)
masked_pvals[masked_pvals > 1] = 1
pvals_in_brain = sw_pvals.ravel()[masked_pvals.ravel() != 0]
masked_prop.append(np.mean(pvals_in_brain > 0.05))
if (i[-3:] == "010"):
# Save image plots of unmasked p-values for subject 10.
plt.imshow(present_3d(sw_pvals), cmap=plt.get_cmap('gray'))
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.title("p-values for " + i + " (Unmasked Data)")
plt.savefig(location_of_images + i + 'sw.png')
plt.close()
# Save image plots of masked p-values for a single subject.
plt.imshow(present_3d(masked_pvals), cmap=plt.get_cmap('gray'))
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.title("p-values for " + i + " (Masked Data)")
plt.savefig(location_of_images + i + 'swmasked.png')
plt.close()
location_of_functions = project_path + "code/utils/functions/"
sys.path.append(location_of_functions)
from Image_Visualizing import present_3d
# mean_across for all the process outputs for each subject
final_bh = np.load("../data/bh_t_beta/final_bh_average.npy")
final_t = np.load("../data/bh_t_beta/final_t_average.npy")
final_beta = np.load("../data/bh_t_beta/final_beta_average.npy")
#####################################
# Benjamini Hochberg Plots Q = 0.25 #
#####################################
desired_image = present_3d(final_bh)
desired_image[320:, 256:] = .5
plt.imshow(desired_image, interpolation='nearest', cmap='gray')
plt.title("Mean BH Value Across 24 Subjects with Q = .25")
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.savefig("../../images/bh_mean_final.png")
plt.close()
######################################
# T-statistic Plots Proportion = 0.1 #
######################################
desired_image = present_3d(final_t)
#plt.savefig(location_of_images+"OLD_significant_p_slice_NOMASK.png")
#plt.close()
#print("Initial plot with NO MASK (using old bh function) done.")
print("# ==== BEGIN varying the Q value = .005 (FDR) ==== #")
Q = .005
significant_pvals = bh_procedure(p_vals, Q)
# Reshape significant_pvals
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[..., 7]
masked_data = make_mask(original_slice, reshaped_sig_p, fit=False)
plt.imshow(present_3d(masked_data))
plt.clim(0, 1600)
plt.colorbar()
plt.title('Slice with Significant p-values (Q = .005)')
plt.savefig(location_of_images + "significant_p_slice1.png")
plt.close()
print("# ==== END plot with Q = .005 done. ==== #")
print("# ==== BEGIN varying the Q value = .05 (FDR) ==== #")
Q = .05
significant_pvals = bh_procedure(p_vals, Q)
# Reshape significant_pvals
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[..., 7]
TR, n_vols)
convolved = np.convolve(neural_prediction, hrf_at_trs) # hrf_at_trs sample
N = len(neural_prediction) # N == n_vols == 173
M = len(hrf_at_trs) # M == 12
np_hrf = convolved[:N]
B, t, df, p = t_stat(data, np_hrf, np.array([0, 1]))
#Simple mask function
mask = nib.load(pathtodata + i + '/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
t_mean[..., int(i[-1])] = make_mask(np.reshape(t, (64, 64, 34)),
mask_data,
fit=True)
final = present_3d(np.mean(t_mean, axis=3))
#######################
# Plot the results #
#######################
plt.imshow(final, interpolation='nearest', cmap='seismic')
plt.title("Mean T-Statistic Value Across 25 Subjects")
zero_out = max(abs(np.min(final)), np.max(final))
plt.clim(-zero_out, zero_out)
plt.colorbar()
plt.savefig("../../../images/hypothesis_testing.png")
plt.close()
data = img.get_data()
data = data[..., 6:] # Knock off the first 6 observations.
#######################
# a. (my) smoothing #
#######################
# Kind of arbitrary chosen time
time = 7
original_slice = data[..., 7]
# full width at half maximum (FWHM)
fwhm = 1.5
smoothed_slice = smoothvoxels(data, fwhm, time)
# visually compare original_slice to smoothed_slice
plt.imshow(present_3d(smoothed_slice))
plt.colorbar()
plt.title('Smoothed Slice')
plt.clim(0, 1600)
plt.savefig(location_of_images + "smoothed_slice.png")
plt.close()
plt.imshow(present_3d(original_slice))
plt.colorbar()
plt.title('Original Slice')
plt.clim(0, 1600)
plt.savefig(location_of_images + "original_slice.png")
plt.close()
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])
############################################ # a. Pick a good voxel to compare against # ############################################ from glm import glm from Image_Visualizing import present_3d beta_np, X_np = glm(data, conv_np) # beta_2,X_2=glm(data,conv_2) not correct shape beta_3, X_3 = glm(data, conv_3) beta_4, X_4 = glm(data, conv_4_30) #beta_5,X_5=glm(data,conv_5) # non-np are stronger/more clear plt.imshow(present_3d(beta_np[..., 1]), cmap="gray", interpolation="nearest") plt.imshow(present_3d(beta_3[..., 1]), cmap="gray", interpolation="nearest") plt.imshow(present_3d(beta_4[..., 1]), cmap="gray", interpolation="nearest") #plt.imshow(present_3d(beta_5[...,1]),cmap="gray",interpolation="nearest") plt.imshow(beta_4[..., 2, 1], cmap="gray", interpolation="nearest") plt.colorbar() plt.close() # From visual analysis # In the regression has a really high beta_1 value at: # beta_my[41,47,2,1] (voxel data[41,47,2] ) # lets use the comparisons (I know that is not good practice to check created # X based on betas based on X) ###########################################
from Image_Visualizing import present_3d
# mean_across for all the process outputs for each subject
final_bh = np.load("../data/bh_t_beta/final_bh_average.npy")
final_t = np.load("../data/bh_t_beta/final_t_average.npy")
final_beta = np.load("../data/bh_t_beta/final_beta_average.npy")
#####################################
# Benjamini Hochberg Plots Q = 0.25 #
#####################################
desired_image = present_3d(final_bh)
desired_image[320:, 256:] = 0.5
plt.imshow(desired_image, interpolation="nearest", cmap="gray")
plt.title("Mean BH Value Across 24 Subjects with Q = .25")
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.savefig("../../images/bh_mean_final.png")
plt.close()
######################################
# T-statistic Plots Proportion = 0.1 #
######################################
plt.savefig(location_of_images + "NOMASK_significant_p_slice.png")
plt.close()
print("# ==== END No Mask, bh_procedure ==== #")
print("# ==== BEGIN varying the Q value = .005 (FDR) ==== #")
Q = .005
significant_pvals = bh_procedure(p_vals, Q)
# Reshape significant_pvals
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[..., 7]
masked_data = make_mask(original_slice, reshaped_sig_p, fit=False)
plt.imshow(present_3d(masked_data))
plt.clim(0, 1600)
plt.colorbar()
plt.title('Slice with Significant p-values (Q = .005)')
plt.savefig(location_of_images + "significant_p_slice1.png")
plt.close()
print("# ==== END plot with Q = .005 done. ==== #")
print("# ==== BEGIN varying the Q value = .05 (FDR) ==== #")
Q = .05
significant_pvals = bh_procedure(p_vals, Q)
# Reshape significant_pvals
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[..., 7]
#plt.close()
#print("Initial plot with NO MASK (using old bh function) done.")
print("# ==== BEGIN varying the Q value = .005 (FDR) ==== #")
Q = .005
significant_pvals = bh_procedure(p_vals, Q)
# Reshape significant_pvals
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[...,7]
masked_data = make_mask(original_slice, reshaped_sig_p, fit=False)
plt.imshow(present_3d(masked_data))
plt.clim(0, 1600)
plt.colorbar()
plt.title('Slice with Significant p-values (Q = .005)')
plt.savefig(location_of_images+"significant_p_slice1.png")
plt.close()
print("# ==== END plot with Q = .005 done. ==== #")
print("# ==== BEGIN varying the Q value = .05 (FDR) ==== #")
Q = .05
significant_pvals = bh_procedure(p_vals, Q)
# Reshape significant_pvals
reshaped_sig_p = np.reshape(significant_pvals, data.shape[:-1])
slice_reshaped_sig_p = reshaped_sig_p[...,7]
from glm import glm from Image_Visualizing import present_3d beta_np,X_np=glm(data,conv_np) # beta_2,X_2=glm(data,conv_2) not correct shape beta_3,X_3=glm(data,conv_3) beta_4,X_4=glm(data,conv_4_30) #beta_5,X_5=glm(data,conv_5) # non-np are stronger/more clear plt.imshow(present_3d(beta_np[...,1]),cmap="gray",interpolation="nearest") plt.imshow(present_3d(beta_3[...,1]),cmap="gray",interpolation="nearest") plt.imshow(present_3d(beta_4[...,1]),cmap="gray",interpolation="nearest") #plt.imshow(present_3d(beta_5[...,1]),cmap="gray",interpolation="nearest") plt.imshow(beta_4[...,2,1],cmap="gray",interpolation="nearest") plt.colorbar() plt.close() # From visual analysis # In the regression has a really high beta_1 value at: # beta_my[41,47,2,1] (voxel data[41,47,2] ) # lets use the comparisons (I know that is not good practice to check created # X based on betas based on X)
############################
# Condition 2 vs Time Plot #
############################
plt.plot(X_np[:,2])
plt.title("Condition 2 (pop) time predictions")
plt.xlabel("Time")
plt.ylabel("Hemoglobin response")
plt.savefig(location_of_images+'cond2_time.png')
plt.close()
############################
# Condition 2 brain image #
############################
plt.imshow(present_3d(B_np[...,2]),interpolation='nearest', cmap='seismic')
# instead of cmap="gray"
plt.title("Condition 2 (pop) beta Brain Image")
plt.colorbar()
zero_out=max(abs(np.min(present_3d(B_np[...,2]))),np.max(present_3d(B_np[...,2])))
plt.clim(-zero_out,zero_out)
plt.savefig(location_of_images+'mr_cond2_beta_brain.png')
plt.close()
############################
# Condition 3 vs Time Plot #
############################
plt.plot(X_np[:,3])
plt.title("Condition 3 (save) time predictions")
plt.xlabel("Time")
N = len(neural_prediction) # N == n_vols == 173
M = len(hrf_at_trs) # M == 12
np_hrf=convolved[:N]
B,t,df,p = t_stat(data, np_hrf, np.array([0,1]))
#Simple mask function
mask = nib.load(pathtodata+i+'/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
t_mean[...,int(i[-1])] = make_mask(np.reshape(t,(64,64,34)), mask_data, fit=True)
final = present_3d(np.mean(t_mean,axis=3))
#######################
# Plot the results #
#######################
plt.imshow(final,interpolation='nearest', cmap='seismic')
plt.title("Mean T-Statistic Value Across 25 Subjects")
zero_out=max(abs(np.min(final)),np.max(final))
plt.clim(-zero_out,zero_out)
plt.colorbar()
plt.savefig("../../../images/hypothesis_testing.png")
plt.close()
behav_suffix = "/behav/task001_run001/behavdata.txt"
smooth_data = final_data + 'smooth/'
hrf_data = final_data + 'hrf/'
sys.path.append(location_of_functions)
from Image_Visualizing import present_3d, make_mask, present_3d_options
i = "sub001"
brain = nib.load(path_to_data + i + '/BOLD/task001_run001/bold.nii.gz')
data = brain.get_data()
brain_hi = nib.load(path_to_data + i + '/anatomy/inplane001_brain.nii.gz')
joy_hi = brain_hi.get_data()
joy = data[..., 7]
plt.imshow(present_3d(data[..., 7]), cmap="gray", interpolation="nearest")
plt.figure()
plt.imshow(present_3d_options(joy, 2), cmap="gray", interpolation="nearest")
plt.title("2")
plt.figure()
plt.imshow(present_3d_options(joy, 1), cmap="gray", interpolation="nearest")
plt.title("1")
plt.figure()
plt.imshow(present_3d_options(joy, 0), cmap="gray", interpolation="nearest")
plt.title("0")
plt.close()
plt.imshow(present_3d_options(joy_hi, 2), cmap="gray", interpolation="nearest")
plt.title("2")
plt.savefig(location_of_images + "kent_brain_2.png")
t_mean = np.zeros((64, 64, 34,24))
#loop through each person's T-statistic
count=0
for i in sub_list:
t_stat = np.load(t_data+i+"_tstat.npy")
mask = nib.load(path_to_data+i+'/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
t_mean[...,count] = make_mask(t_stat, mask_data, fit=True)
count+=1
t_mean = np.mean(t_mean,axis=3)
final = present_3d(t_mean)
plt.imshow(final,interpolation='nearest', cmap='seismic')
plt.title("Mean T-Statistic Value Across 25 Subjects")
zero_out=max(abs(np.min(final)),np.max(final))
plt.clim(-zero_out,zero_out)
plt.colorbar()
plt.show()
#Cluster
data_new = t_mean[...,10:15]
X = np.reshape(data_new, (-1, 1))
connectivity = grid_to_graph(n_x= data_new.shape[0], n_y = data_new.shape[1], n_z = data_new.shape[2])
bh_all[bh_all!=1]=np.nan
t_all[t_all!=1]=np.nan
beta_all[beta_all!=1]=np.nan
for i, name in enumerate(sub_list):
# the mask for each subject
path_to_data = project_path + "data/ds009/" + name
brain = nib.load(path_to_data + '/anatomy/inplane001_brain.nii.gz')
brain=brain.get_data()
# bh
plt.imshow(present_3d(brain[::2,::2,:]),cmap="gray")
upper= np.percentile(np.ravel(brain[::2,::2,:]),95)
plt.colorbar()
#plt.clim(0,upper)
overlap=present_3d(bh_all[...,i])
overlap[overlap==0]=np.nan
overlap[-1,-1]=0 # to make the output correct
plt.imshow(overlap,cmap="Blues",alpha=.5)
plt.savefig("../../images/"+name+"_bh_overlay.png")
plt.close()
# t
plt.imshow(present_3d(brain[::2,::2,:]),cmap="gray")
############################
# Condition 2 vs Time Plot #
############################
plt.plot(X_np[:,2])
plt.title("Condition 2 (pop) time predictions")
plt.xlabel("Time")
plt.ylabel("Hemoglobin response")
plt.savefig(location_of_images+'cond2_time.png')
plt.close()
############################
# Condition 2 brain image #
############################
plt.imshow(present_3d(B_np[...,2]),interpolation='nearest', cmap='seismic')
# instead of cmap="gray"
plt.title("Condition 2 (pop) beta Brain Image")
plt.colorbar()
zero_out=max(abs(np.min(present_3d(B_np[...,2]))),np.max(present_3d(B_np[...,2])))
plt.clim(-zero_out,zero_out)
plt.savefig(location_of_images+'mr_cond2_beta_brain.png')
plt.close()
############################
# Condition 3 vs Time Plot #
############################
plt.plot(X_np[:,3])
plt.title("Condition 3 (save) time predictions")
plt.xlabel("Time")
p_3d = np.load("../data/p-values/"+name+"_pvalue_fourier.npy")
t_3d = np.load("../data/t_stat/"+name+"_tstat_fourier.npy")
beta_3d = np.load("../data/betas/"+name+"_beta_fourier.npy")
X = np.load("../data/X/"+name+"_covX.npy")
mask = nib.load(path_to_data + '/anatomy/inplane001_brain_mask.nii.gz')
mask_data = mask.get_data()
rachels_ones = np.ones((64, 64, 34))
fitted_mask = make_mask(rachels_ones, mask_data, fit = True)
fitted_mask[fitted_mask>0]=1
plt.imshow(present_3d(beta_3d*fitted_mask),cmap="seismic")
plt.colorbar()
plt.clim(-np.max(abs(beta_3d)),np.max(abs(beta_3d)))
plt.title(name+" beta values")
plt.yticks([])
plt.xticks([])
plt.savefig(location_of_images+name+"_"+"beta_fourier.png")
plt.close()
plt.imshow(present_3d(t_3d*fitted_mask),cmap="seismic")
plt.colorbar()
plt.clim(-np.max(abs(t_3d)),np.max(abs(t_3d)))
plt.title(name+" t values")
plt.yticks([])
plt.xticks([])
