--- a
+++ b/utils/preprocessing/generate_patches.py
@@ -0,0 +1,123 @@
+import numpy as np 
+import SimpleITK as sitk 
+import random
+
+def generate_negative_patches(studies):
+
+    art_arr = sitk.GetArrayFromImage(studies[0])
+    ven_arr = sitk.GetArrayFromImage(studies[1])
+    noncon_arr = sitk.GetArrayFromImage(studies[2])
+    mask_arr = sitk.GetArrayFromImage(studies[3])
+
+    tot_num_neg = 1
+
+    neg_patch = 0
+    
+    #### Choose where to randomly select a negative patch using
+    #### Gaussian dist. with peak and std according to
+    #### blocks of 32 voxels. For example, 16 blocks in 512 voxels.
+    rand_xy = np.around(np.random.normal(4,2,size=(2,1000)))
+    rand_z = np.around(np.random.normal(10,4,1000))
+
+    rand_xyz = np.vstack((rand_xy, rand_z))
+
+    i = 0
+
+    chosen_xyz = list()
+    output_patches = list()
+
+    ##Create Random Negative Patches
+    while neg_patch<tot_num_neg:
+        bad_patch_flag=False
+        i = i+1
+        print (i)
+        
+        ## If no patch is found after 80 attempts, abort
+        if i == 80:
+            return
+        
+        ## Randomly selected blocks of 32 converted to voxels
+        x_start = int(rand_xyz[0][i]*32) 
+        y_start = int(rand_xyz[1][i]*32)
+        z_start = int(rand_xyz[2][i]*32)
+        
+        # Choose 256^3 patch
+        x_end = x_start+256
+        y_end = y_start+256
+        z_end = z_start+256
+
+        print ("Random patch: ")
+        print (x_start,x_end,y_start,y_end,z_start,z_end)
+
+        patch_noncon = noncon_arr[z_start:z_end,y_start:y_end,x_start:x_end]
+        patch_ven = ven_arr[z_start:z_end,y_start:y_end,x_start:x_end]
+        patch_art = art_arr[z_start:z_end,y_start:y_end,x_start:x_end]
+        patch_mask = mask_arr[z_start:z_end,y_start:y_end,x_start:x_end]
+
+        if [rand_xyz[0][i],rand_xyz[1][i],rand_xyz[2][i]] in chosen_xyz or rand_xyz[0][i]<0 \
+            or rand_xyz[0][i]>11 or rand_xyz[1][i]>11 or rand_xyz[1][i]<0 or rand_xyz[2][i]>15 or rand_xyz[2][i]<0:
+            print ("Exclude Random Patch --> Outside FOV")
+            continue
+        
+        ##Determine if patch has a low mean value, std or excessive 0 values
+        for arr_patch in [patch_noncon,patch_ven,patch_art]:
+            if len(np.where(arr_patch==0)[0]) > 0.3*(256**3) or arr_patch.mean()<-450:
+                print ("Try new patch, likely off target given stats")
+                print (len(np.where(arr_patch==0)[0])/(256**3))
+                print (arr_patch.mean())
+                print (arr_patch.std())
+                bad_patch_flag=True
+        
+        if bad_patch_flag == True or patch_mask.mean()>0:
+            print ("Rejected 'cause of flag or patch mask mean greater than 0")
+            print (patch_mask.mean()>0)
+            continue
+
+        print ([x_start,x_end,y_start,y_end,z_start,z_end])
+        output_patches.append([x_start,x_end,y_start,y_end,z_start,z_end])
+        chosen_xyz.append([rand_xyz[0][i],rand_xyz[1][i],rand_xyz[2][i]])
+        print ("Identified a neg_patch number : "+str(neg_patch))
+        neg_patch = neg_patch + 1
+
+        #sitk.WriteImage(sitk.GetImageFromArray(arr_patch),os.path.join(OUT_DIR,str(i)+".nii.gz"))
+                
+    return output_patches
+
+def generate_positive_patches(studies,patch_size,random_center_displacement):
+
+    q = random_center_displacement
+    
+    pos_patch = list()
+
+    mask_arr = sitk.GetArrayFromImage(studies[3])
+
+    where = np.where(mask_arr==1)
+    z_mid = int(where[0].mean())
+    y_mid = int(where[1].mean())
+    x_mid = int(where[2].mean())
+
+    print (z_mid,y_mid,x_mid)
+
+    z_start = random.randint(-(q),q)+z_mid-(patch_size[0]/2)
+    y_start = random.randint(-(q),q)+y_mid-(patch_size[1]/2)
+    x_start = random.randint(-(q),q)+x_mid-(patch_size[2]/2)
+
+    z_end = z_start+patch_size[0]
+    y_end = y_start+patch_size[1]
+    x_end = x_start+patch_size[2]
+
+    numbers = [ int(x) for x in [x_start,x_end,y_start,y_end,z_start,z_end] ]
+
+    pos_patch.append(numbers)
+
+    return pos_patch
+
+def test_onbody_patch(load_path):
+    try:
+        arr = sitk.GetArrayFromImage(sitk.ReadImage(load_path))
+        print (load_path)
+        print (np.mean(arr))
+        # print (np.max(arr))
+        # print (np.min(arr))
+    except Exception as e:
+        print ("Could not convert because "+str(e))