Fix cases where floating point numbers are used as array indices.

examples/DSBP_demo.py

@@ -96,7 +96,7 @@
 
 plt.subplot(1,2,1)
 plt.title('Full Backprojection')
-imgTools.imshow(img_bp[177-N/2:177+N/2,202-N/2:202+N/2], dB_scale = [-25,0], extent = extent)
+imgTools.imshow(img_bp[177-N//2:177+N//2,202-N//2:202+N//2], dB_scale = [-25,0], extent = extent)
 plt.xlabel('meters'); plt.ylabel('meters')
 
 plt.subplot(1,2,2)

examples/dictionaries/SARplatform.py

@@ -62,7 +62,7 @@ def plat_dict(aux = []):
         R_c = pos[npulses/2]
     else:
         R_c = np.mean(
-                pos[npulses/2-1:npulses/2+1],
+                pos[npulses//2-1:npulses//2+1],
                 axis = 0)
 
     #Coherent integration angle

examples/dictionaries/SARplatformUHF.py

@@ -58,7 +58,7 @@ def plat_dict(aux = []):
         R_c = pos[npulses/2]
     else:
         R_c = np.mean(
-                pos[npulses/2-1:npulses/2+1],
+                pos[npulses//2-1:npulses//2+1],
                 axis = 0)
 
     #Coherent integration angle

ritsar/imgTools.py

@@ -161,7 +161,7 @@ def polar_format(phs, platform, img_plane, taylor = 20):
     #Interpolate in along track direction to obtain polar formatted data
     real_polar = np.zeros([nv,nu])
     imag_polar = np.zeros([nv,nu])
-    isSort = (ky_new[npulses/2, nu/2] < ky_new[npulses/2+1, nu/2])
+    isSort = (ky_new[npulses//2, nu//2] < ky_new[npulses//2+1, nu//2])
     if isSort:
         for i in range(nu):
             print('cross-range interpolating for sample %i'%(i+1))
@@ -291,7 +291,7 @@ def backprojection(phs, platform, img_plane, taylor = 20, upsample = 6, prnt = T
     #Derive parameters
     nu = u.size
     nv = v.size
-    k_c = k_r[nsamples/2]
+    k_c = k_r[nsamples//2]
 
     #Create window
     win_x = sig.taylor(nsamples,taylor)
@@ -313,7 +313,7 @@ def backprojection(phs, platform, img_plane, taylor = 20, upsample = 6, prnt = T
 
     #Filter phase history and perform FT w.r.t t
     Q = sig.ft(phs_pad)    
-    dr = np.linspace(-nsamples*delta_r/2, nsamples*delta_r/2, N_fft)
+    dr = np.linspace(-nsamples*delta_r//2, nsamples*delta_r//2, N_fft)
 
     #Perform backprojection for each pulse
     img = np.zeros(nu*nv)+0j
@@ -329,7 +329,7 @@ def backprojection(phs, platform, img_plane, taylor = 20, upsample = 6, prnt = T
         Q_hat = Q_real+1j*Q_imag        
         img += Q_hat*np.exp(-1j*k_c*dr_i)
 
-    r0 = np.array([pos[npulses/2]]).T
+    r0 = np.array([pos[npulses//2]]).T
     dr_i = norm(r0)-norm(r-r0, axis = 0)
     img = img*np.exp(1j*k_c*dr_i)   
     img = np.reshape(img, [nv, nu])[::-1,:]
@@ -394,8 +394,8 @@ def DSBP(phs, platform, img_plane, center=None, size=None, derate = 1.05, taylor
     #update platform
     platformDS['nsamples'] = freq.size
     platformDS['freq']     = freq
-    deltaF = freq[freq.size/2]-freq[freq.size/2-1] #Assume sample spacing can be determined by difference between last two values (first two are distorted by decimation filter)
-    freq   = freq[freq.size/2]+np.arange(-freq.size/2,freq.size/2)*deltaF
+    deltaF = freq[freq.size//2]-freq[freq.size//2-1] #Assume sample spacing can be determined by difference between last two values (first two are distorted by decimation filter)
+    freq   = freq[freq.size//2]+np.arange(-freq.size//2,freq.size//2)*deltaF
     platformDS['k_r'] = 4*pi*freq/c
 
     #interpolate phs and pos using uniform azimuth spacing
@@ -445,12 +445,12 @@ def DSBP(phs, platform, img_plane, center=None, size=None, derate = 1.05, taylor
         center_index = np.array(np.unravel_index(center_index, [v.size, u.size]))
 
         #Update u and v
-        img_planeDS['u']    = np.arange(-size[1]/2,size[1]/2)*du
-        img_planeDS['v']    = np.arange(-size[0]/2,size[0]/2)*dv
+        img_planeDS['u']    = np.arange(-size[1]//2,size[1]//2)*du
+        img_planeDS['v']    = np.arange(-size[0]//2,size[0]//2)*dv
 
         #get pixel locs for sub_image
-        u_index = np.arange(center_index[1]-size[1]/2,center_index[1]+size[1]/2)
-        v_index = np.arange(center_index[0]-size[0]/2,center_index[0]+size[0]/2)
+        u_index = np.arange(center_index[1]-size[1]//2,center_index[1]+size[1]//2)
+        v_index = np.arange(center_index[0]-size[0]//2,center_index[0]+size[0]//2)
         uu,vv = np.meshgrid(u_index,v_index)
         locs_index = np.ravel_multi_index((vv.flatten(),uu.flatten()),(v.size,u.size))
         img_planeDS['pixel_locs']     = img_plane['pixel_locs'][:,locs_index]-np.array([center]).T
@@ -521,8 +521,8 @@ def DS(phs, platform, img_plane, center=None, size=None, derate = 1.05, taylor =
     #update platform
     platformDS['nsamples'] = freq.size
     platformDS['freq']     = freq
-    deltaF = freq[freq.size/2]-freq[freq.size/2-1] #Assume sample spacing can be determined by difference between last two values (first two are distorted by decimation filter)
-    freq   = freq[freq.size/2]+np.arange(-freq.size/2,freq.size/2)*deltaF
+    deltaF = freq[freq.size//2]-freq[freq.size//2-1] #Assume sample spacing can be determined by difference between last two values (first two are distorted by decimation filter)
+    freq   = freq[freq.size//2]+np.arange(-freq.size//2,freq.size//2)*deltaF
     platformDS['k_r'] = 4*pi*freq/c
 
     #interpolate phs and pos using uniform azimuth spacing
@@ -1065,7 +1065,7 @@ def autoFocus2(img, win = 'auto', win_params = [100,0.5]):
         #Circularly shift image so max values line up   
         f = np.zeros(img.shape)+0j
         for i in range(nsamples):
-            f[:,i] = np.roll(img_af[:,i], npulses/2-index[i])
+            f[:,i] = np.roll(img_af[:,i], npulses//2-index[i])
 
         if win == 'auto':
             #Compute window width    
@@ -1076,15 +1076,15 @@ def autoFocus2(img, win = 'auto', win_params = [100,0.5]):
             if iii == 0:
                 width = npulses
             elif iii == 1:
-                width = npulses/2
+                width = npulses//2
             #For all other iterations, use twice the 30 dB threshold
             else:
                 width = np.sum(s>-30)
-            window = np.arange(npulses/2-width/2,npulses/2+width/2)
+            window = np.arange(npulses//2-width//2,npulses//2+width//2)
         else:
             #Compute window width using win_params if win not set to 'auto'    
             width = int(win_params[0]*win_params[1]**iii)
-            window = np.arange(npulses/2-width/2,npulses/2+width/2)
+            window = np.arange(npulses//2-width//2,npulses//2+width//2)
             if width<5:
                 break
 

ritsar/phsRead.py

@@ -73,10 +73,10 @@ def AFRL(directory, pol, start_az, n_az=3):
 
         #Vector to scene center at synthetic aperture center
         if np.mod(npulses,2)>0:
-            R_c = pos[npulses/2]
+            R_c = pos[npulses//2]
         else:
             R_c = np.mean(
-                    pos[npulses/2-1:npulses/2+1],
+                    pos[npulses//2-1:npulses//2+1],
                     axis = 0)
 
         #Save values to dictionary for export
@@ -105,10 +105,10 @@ def AFRL(directory, pol, start_az, n_az=3):
         pos = np.vstack((pos, platform[i]['pos']))
 
     if np.mod(npulses,2)>0:
-        R_c = pos[npulses/2]
+        R_c = pos[npulses//2]
     else:
         R_c = np.mean(
-                pos[npulses/2-1:npulses/2+1],
+                pos[npulses//2-1:npulses//2+1],
                 axis = 0)
 
     #Replace Dictionary values
@@ -255,10 +255,10 @@ def Sandia(directory):
     k_r = 2*omega/c
 
     if np.mod(npulses,2)>0:
-        R_c = pos[npulses/2]
+        R_c = pos[npulses//2]
     else:
         R_c = np.mean(
-                pos[npulses/2-1:npulses/2+1],
+                pos[npulses//2-1:npulses//2+1],
                 axis = 0)
 
     platform = \
@@ -317,7 +317,7 @@ def DIRSIG(directory):
         pos_dirs.append(float(children[0].text))
         pos_dirs.append(float(children[1].text))
         pos_dirs.append(float(children[2].text))
-    pos_dirs = np.asarray(pos_dirs).reshape([len(pos_dirs)/3,3])
+    pos_dirs = np.asarray(pos_dirs).reshape([len(pos_dirs)//3,3])
 
     t_dirs=[]
     for children in root.iter('datetime'):
@@ -371,7 +371,7 @@ def DIRSIG(directory):
         R_c = pos[npulses/2]
     else:
         R_c = np.mean(
-                pos[npulses/2-1:npulses/2+1],
+                pos[npulses//2-1:npulses//2+1],
                 axis = 0)
 
     #Derived Parameters

ritsar/signal.py

@@ -170,7 +170,7 @@ def decimate(x, q, n=None, axis=-1, beta = None, cutoff = 'nyq'):
     if beta == None:
         beta = 1.*n/8
 
-    padlen = n/2
+    padlen = n//2
 
     if cutoff == 'nyq':
         eps = np.finfo(np.float).eps