From ee322be870726c89889e77a49ef0d500e23db79f Mon Sep 17 00:00:00 2001
From: chen <chenzhang8722@gamil.com>
Date: Fri, 9 Sep 2016 16:17:00 -0400
Subject: [PATCH] use np.histogram2d, fixed list.append bug when using weight
 column

---
 processing/post/binXY.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/processing/post/binXY.py b/processing/post/binXY.py
index 4fd635120..2c02b8dcb 100755
--- a/processing/post/binXY.py
+++ b/processing/post/binXY.py
@@ -20,7 +20,7 @@ Produces a binned grid of two columns from an ASCIItable, i.e. a two-dimensional
 
 parser.add_option('-d','--data',
                   dest = 'data',
-                  type='string', nargs = 2, metavar = 'string string',
+                  type = 'string', nargs = 2, metavar = 'string string',
                   help = 'column labels containing x and y ')
 parser.add_option('-w','--weight',
                   dest = 'weight',
@@ -49,15 +49,15 @@ parser.add_option('-z','--zrange',
 parser.add_option('-i','--invert',
                   dest = 'invert',
                   action = 'store_true',
-                  help = 'invert probability density [%default]')
+                  help = 'invert probability density')
 parser.add_option('-r','--rownormalize',
                   dest = 'normRow',
                   action = 'store_true',
-                  help = 'normalize probability density in each row [%default]')
+                  help = 'normalize probability density in each row')
 parser.add_option('-c','--colnormalize',
                   dest = 'normCol',
                   action = 'store_true',
-                  help = 'normalize probability density in each column [%default]')
+                  help = 'normalize probability density in each column')
 
 parser.set_defaults(bins = (10,10),
                     type = ('linear','linear','linear'),
@@ -79,7 +79,8 @@ result = np.zeros((options.bins[0],options.bins[1],3),'f')
 
 if options.data is None: parser.error('no data columns specified.')
 
-labels = options.data
+labels = list(options.data)
+
 
 if options.weight is not None: labels += [options.weight]                                               # prevent character splitting of single string value
 
@@ -106,7 +107,7 @@ for name in filenames:
 # ------------------------------------------ sanity checks ----------------------------------------
 
   missing_labels = table.data_readArray(labels)
- 
+
   if len(missing_labels) > 0:
     damask.util.croak('column{} {} not found.'.format('s' if len(missing_labels) > 1 else '',', '.join(missing_labels)))
     table.close(dismiss = True)
@@ -119,12 +120,11 @@ for name in filenames:
       minmax[c] = np.log(minmax[c])                                                                 # change minmax to log, too
 
   delta = minmax[:,1]-minmax[:,0]
-  
-  for i in xrange(len(table.data)):
-    x = int(options.bins[0]*(table.data[i,0]-minmax[0,0])/delta[0])
-    y = int(options.bins[1]*(table.data[i,1]-minmax[1,0])/delta[1])
-    if x >= 0 and x < options.bins[0] and y >= 0 and y < options.bins[1]:
-      grid[x,y] += 1. if options.weight is None else table.data[i,2]                                # count (weighted) occurrences
+
+  (grid,xedges,yedges) = np.histogram2d(table.data[:,0],table.data[:,1],
+                                        bins=options.bins,
+                                        range=minmax,
+                                        weights=None if options.weight is None else table.data[:,2])
 
   if options.normCol:
     for x in xrange(options.bins[0]):
@@ -136,7 +136,7 @@ for name in filenames:
       sum = np.sum(grid[:,y])
       if sum > 0.0:
         grid[:,y] /= sum
-  
+
   if (minmax[2] == 0.0).all(): minmax[2] = [grid.min(),grid.max()]                                   # auto scale from data
   if minmax[2,0] == minmax[2,1]:
     minmax[2,0] -= 1.
@@ -147,7 +147,7 @@ for name in filenames:
   if options.type[2].lower() == 'log':
     grid = np.log(grid)
     minmax[2] = np.log(minmax[2])
-    
+
   delta[2] = minmax[2,1]-minmax[2,0]
 
   for x in xrange(options.bins[0]):