From 2850a08339e4c087401eac1e29cd5dea61ae09c2 Mon Sep 17 00:00:00 2001
From: Adam Nelson <nelsonag@umich.edu>
Date: Thu, 2 Mar 2017 21:17:45 -0500
Subject: [PATCH] took care of python3 floating point division issue with
 tile_factor in filter.py

---
 openmc/filter.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/openmc/filter.py b/openmc/filter.py
index 71b1c2313..bdb4c35bc 100644
--- a/openmc/filter.py
+++ b/openmc/filter.py
@@ -425,7 +425,7 @@ class Filter(object):
         df = pd.DataFrame()
 
         filter_bins = np.repeat(self.bins, self.stride)
-        tile_factor = data_size / len(filter_bins)
+        tile_factor = int(data_size / len(filter_bins))
         filter_bins = np.tile(filter_bins, tile_factor)
         df = pd.concat([df, pd.DataFrame(
             {self.short_name.lower(): filter_bins})])
@@ -613,7 +613,7 @@ class SurfaceFilter(IntegralFilter):
         df = pd.DataFrame()
 
         filter_bins = np.repeat(self.bins, self.stride)
-        tile_factor = data_size / len(filter_bins)
+        tile_factor = int(data_size / len(filter_bins))
         filter_bins = np.tile(filter_bins, tile_factor)
         filter_bins = [_CURRENT_NAMES[x] for x in filter_bins]
         df = pd.concat([df, pd.DataFrame(
@@ -781,26 +781,26 @@ class MeshFilter(Filter):
             nz = 1
 
         # Generate multi-index sub-column for x-axis
-        filter_bins = np.arange(1, nx+1)
+        filter_bins = np.arange(1, nx + 1)
         repeat_factor = ny * nz * self.stride
         filter_bins = np.repeat(filter_bins, repeat_factor)
-        tile_factor = data_size / len(filter_bins)
+        tile_factor = int(data_size / len(filter_bins))
         filter_bins = np.tile(filter_bins, tile_factor)
         filter_dict[(mesh_key, 'x')] = filter_bins
 
         # Generate multi-index sub-column for y-axis
-        filter_bins = np.arange(1, ny+1)
+        filter_bins = np.arange(1, ny + 1)
         repeat_factor = nz * self.stride
         filter_bins = np.repeat(filter_bins, repeat_factor)
-        tile_factor = data_size / len(filter_bins)
+        tile_factor = int(data_size / len(filter_bins))
         filter_bins = np.tile(filter_bins, tile_factor)
         filter_dict[(mesh_key, 'y')] = filter_bins
 
         # Generate multi-index sub-column for z-axis
-        filter_bins = np.arange(1, nz+1)
+        filter_bins = np.arange(1, nz + 1)
         repeat_factor = self.stride
         filter_bins = np.repeat(filter_bins, repeat_factor)
-        tile_factor = data_size / len(filter_bins)
+        tile_factor = int(data_size / len(filter_bins))
         filter_bins = np.tile(filter_bins, tile_factor)
         filter_dict[(mesh_key, 'z')] = filter_bins
 
@@ -1281,7 +1281,7 @@ class DistribcellFilter(Filter):
                 # Tile the Multi-index columns
                 for level_key, level_bins in level_dict.items():
                     level_bins = np.repeat(level_bins, self.stride)
-                    tile_factor = data_size / len(level_bins)
+                    tile_factor = int(data_size / len(level_bins))
                     level_bins = np.tile(level_bins, tile_factor)
                     level_dict[level_key] = level_bins
 
@@ -1297,7 +1297,7 @@ class DistribcellFilter(Filter):
         # requests Summary geometric information
         filter_bins = np.arange(self.num_bins)
         filter_bins = np.repeat(filter_bins, self.stride)
-        tile_factor = data_size / len(filter_bins)
+        tile_factor = int(data_size / len(filter_bins))
         filter_bins = np.tile(filter_bins, tile_factor)
         df = pd.DataFrame({self.short_name.lower() : filter_bins})
 
@@ -1402,7 +1402,7 @@ class MuFilter(RealFilter):
         # them as necessary to account for other filters.
         lo_bins = np.repeat(self.bins[:-1], self.stride)
         hi_bins = np.repeat(self.bins[1:], self.stride)
-        tile_factor = data_size / len(lo_bins)
+        tile_factor = int(data_size / len(lo_bins))
         lo_bins = np.tile(lo_bins, tile_factor)
         hi_bins = np.tile(hi_bins, tile_factor)
 
@@ -1505,7 +1505,7 @@ class PolarFilter(RealFilter):
         # them as necessary to account for other filters.
         lo_bins = np.repeat(self.bins[:-1], self.stride)
         hi_bins = np.repeat(self.bins[1:], self.stride)
-        tile_factor = data_size / len(lo_bins)
+        tile_factor = int(data_size / len(lo_bins))
         lo_bins = np.tile(lo_bins, tile_factor)
         hi_bins = np.tile(hi_bins, tile_factor)
 
@@ -1608,7 +1608,7 @@ class AzimuthalFilter(RealFilter):
         # them as necessary to account for other filters.
         lo_bins = np.repeat(self.bins[:-1], self.stride)
         hi_bins = np.repeat(self.bins[1:], self.stride)
-        tile_factor = data_size / len(lo_bins)
+        tile_factor = int(data_size / len(lo_bins))
         lo_bins = np.tile(lo_bins, tile_factor)
         hi_bins = np.tile(hi_bins, tile_factor)
 
@@ -1871,7 +1871,7 @@ class EnergyFunctionFilter(Filter):
         out = out[:14]
 
         filter_bins = np.repeat(out, self.stride)
-        tile_factor = data_size / len(filter_bins)
+        tile_factor = int(data_size / len(filter_bins))
         filter_bins = np.tile(filter_bins, tile_factor)
         df = pd.concat([df, pd.DataFrame(
             {self.short_name.lower(): filter_bins})])