diff --git a/FastCCD.py b/FastCCD.py
index 1b8ca489f6e82dd39aafa73f7a9e236d54133cb1..6d4b2a1bde2552ecb3509e026c004415074ac197 100644
--- a/FastCCD.py
+++ b/FastCCD.py
@@ -496,7 +496,7 @@ class FastCCD:
         im_pumped_mean = im_pumped_arranged.mean(axis=0)
         im_unpumped_mean = im_unpumped_arranged.mean(axis=0)
 
-        ai = tb.azimuthal_integrator(im_pumped_mean.shape, center, angle_range, dr=dr)
+        ai = tb.azimuthal_integrator(im_pumped_mean.shape, center, angle_range, dr=dr, aspect=1)
         norm = ai(~np.isnan(im_pumped_mean))
 
         az_pump = []
diff --git a/azimuthal_integrator.py b/azimuthal_integrator.py
index e064a392c2aaf497a365b17a25b3df5461c1f753..b84df6c347c9445e713d53234562857f29098135 100644
--- a/azimuthal_integrator.py
+++ b/azimuthal_integrator.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 class azimuthal_integrator(object):
-    def __init__(self, imageshape, center, polar_range, dr=2):
+    def __init__(self, imageshape, center, polar_range, dr=2, aspect=204/236):
         '''
         Create a reusable integrator for repeated azimuthal integration of similar
         images. Calculates array indices for a given parameter set that allows
@@ -21,6 +21,9 @@ class azimuthal_integrator(object):
         dr : int, default 2
             radial width of the integration slices. Takes non-square DSSC pixels into account.
         
+        aspect: float, default 204/236 for DSSC
+            aspect ratio of the pixel pitch
+        
         Returns
         =======
         ai : azimuthal_integrator instance
@@ -39,7 +42,7 @@ class azimuthal_integrator(object):
         ycoord -= cy
 
         # distance from center, hexagonal pixel shape taken into account
-        dist_array = np.hypot(xcoord * 204 / 236, ycoord)
+        dist_array = np.hypot(xcoord * aspect, ycoord)
 
         # array of polar angles
         if np.abs(polar_range[1]-polar_range[0]) > 180:
@@ -53,7 +56,7 @@ class azimuthal_integrator(object):
         polar_array = np.mod(polar_array, np.pi)
         self.polar_mask = (polar_array > tmin) * (polar_array < tmax)
 
-        self.maxdist = min(sx  - cx, sy  - cy)
+        self.maxdist = max(sx  - cx, sy  - cy)
 
         ix, iy = np.indices(dimensions=(sx, sy))
         self.index_array = np.ravel_multi_index((ix, iy), (sx, sy))