Prevent phantom code from running during test

Author: etpeterson

phantoms/brain/sim_brain_phantom.py

@@ -5,57 +5,59 @@
 import nibabel as nib
 from scipy.ndimage import zoom
 
-DIFFUSIVE_REGIME = 'diffusive'
-BALLISTIC_REGIME = 'ballistic'
-
-folder = os.path.dirname(__file__)
-
-###########################
-#  Simulation parameters  #
-regime = DIFFUSIVE_REGIME
-snr = 200
-resolution = [3,3,3]
-#                         #
-###########################
-
-
-# Ground truth
-nii = nib.load(os.path.join(folder,'ground_truth','hrgt_icbm_2009a_nls_3t.nii.gz'))
-segmentation = np.squeeze(nii.get_fdata()[...,-1])
-
-with open(os.path.join(folder,'ground_truth',regime+'_groundtruth.json'), 'r') as f:
-       ivim_pars = json.load(f)
-S0 = 1
-
-# Sequence parameters
-bval_file = os.path.join(folder,'ground_truth',regime+'.bval')
-b = np.loadtxt(bval_file)
-if regime == BALLISTIC_REGIME:
-    cval_file = bval_file.replace('bval','cval')
-    c = np.loadtxt(cval_file)
-
-# Calculate signal
-S = np.zeros(list(np.shape(segmentation))+[b.size])
-
-if regime == BALLISTIC_REGIME:
-    Db = ivim_pars["Db"]
-    for i,(D,f,vd) in enumerate(zip(ivim_pars["D"],ivim_pars["f"],ivim_pars["vd"])):
-        S[segmentation==i+1,:] = S0*((1-f)*np.exp(-b*D)+f*np.exp(-b*Db-c**2*vd**2))
-else:
-    for i,(D,f,Dstar) in enumerate(zip(ivim_pars["D"],ivim_pars["f"],ivim_pars["Dstar"])):
-        S[segmentation==i+1,:] = S0*((1-f)*np.exp(-b*D)+f*np.exp(-b*Dstar))
-
-# Resample to suitable resolution
-im = zoom(S,np.append(np.diag(nii.affine)[:3]/np.array(resolution),1),order=1)
-sz = im.shape
-
-# Add noise
-im_noise = np.abs(im + S0/snr*(np.random.randn(sz[0],sz[1],sz[2],sz[3])+1j*np.random.randn(sz[0],sz[1],sz[2],sz[3])))
-
-# Save as image and sequence parameters 
-nii_out = nib.Nifti1Image(im_noise,np.eye(4))
-base_name = os.path.join(folder,'data','{}_snr{}'.format(regime,snr))
-nib.save(nii_out,base_name+'.nii.gz')
-shutil.copyfile(bval_file,base_name+'.bval')
-if regime == BALLISTIC_REGIME:
-    shutil.copyfile(cval_file,base_name+'.cval')
+if __name__ == "__main__":
+
+    DIFFUSIVE_REGIME = 'diffusive'
+    BALLISTIC_REGIME = 'ballistic'
+
+    folder = os.path.dirname(__file__)
+
+    ###########################
+    #  Simulation parameters  #
+    regime = DIFFUSIVE_REGIME
+    snr = 200
+    resolution = [3,3,3]
+    #                         #
+    ###########################
+
+
+    # Ground truth
+    nii = nib.load(os.path.join(folder,'ground_truth','hrgt_icbm_2009a_nls_3t.nii.gz'))
+    segmentation = np.squeeze(nii.get_fdata()[...,-1])
+
+    with open(os.path.join(folder,'ground_truth',regime+'_groundtruth.json'), 'r') as f:
+        ivim_pars = json.load(f)
+    S0 = 1
+
+    # Sequence parameters
+    bval_file = os.path.join(folder,'ground_truth',regime+'.bval')
+    b = np.loadtxt(bval_file)
+    if regime == BALLISTIC_REGIME:
+        cval_file = bval_file.replace('bval','cval')
+        c = np.loadtxt(cval_file)
+
+    # Calculate signal
+    S = np.zeros(list(np.shape(segmentation))+[b.size])
+
+    if regime == BALLISTIC_REGIME:
+        Db = ivim_pars["Db"]
+        for i,(D,f,vd) in enumerate(zip(ivim_pars["D"],ivim_pars["f"],ivim_pars["vd"])):
+            S[segmentation==i+1,:] = S0*((1-f)*np.exp(-b*D)+f*np.exp(-b*Db-c**2*vd**2))
+    else:
+        for i,(D,f,Dstar) in enumerate(zip(ivim_pars["D"],ivim_pars["f"],ivim_pars["Dstar"])):
+            S[segmentation==i+1,:] = S0*((1-f)*np.exp(-b*D)+f*np.exp(-b*Dstar))
+
+    # Resample to suitable resolution
+    im = zoom(S,np.append(np.diag(nii.affine)[:3]/np.array(resolution),1),order=1)
+    sz = im.shape
+
+    # Add noise
+    im_noise = np.abs(im + S0/snr*(np.random.randn(sz[0],sz[1],sz[2],sz[3])+1j*np.random.randn(sz[0],sz[1],sz[2],sz[3])))
+
+    # Save as image and sequence parameters 
+    nii_out = nib.Nifti1Image(im_noise,np.eye(4))
+    base_name = os.path.join(folder,'data','{}_snr{}'.format(regime,snr))
+    nib.save(nii_out,base_name+'.nii.gz')
+    shutil.copyfile(bval_file,base_name+'.bval')
+    if regime == BALLISTIC_REGIME:
+        shutil.copyfile(cval_file,base_name+'.cval')