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DAMASK_EICMD
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changed grid to float from (wrong) integer type. 

row and column probability density normalization now takes place BEFORE limiting data range.
This commit is contained in:
Philip Eisenlohr 2015-04-13 19:55:28 +00:00
parent 0c7e7c66f8
commit 4bcc80911e
1 changed files with 14 additions and 10 deletions
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24
processing/post/binXY.py
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@ -55,7 +55,7 @@ parser.set_defaults(normCol = False)
minmax = np.array([np.array(options.xrange), minmax = np.array([np.array(options.xrange),
np.array(options.yrange), np.array(options.yrange),
np.array(options.zrange)]) np.array(options.zrange)])
grid = np.zeros(options.bins,'i') grid = np.zeros(options.bins,'f')
result = np.zeros((options.bins[0],options.bins[1],3),'f') result = np.zeros((options.bins[0],options.bins[1],3),'f')
datainfo = { # list of requested labels per datatype datainfo = { # list of requested labels per datatype
@ -117,7 +117,18 @@ for name in filenames:
x = int(options.bins[0]*(table.data[i,0]-minmax[0,0])/delta[0]) 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]) 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]: if x >= 0 and x < options.bins[0] and y >= 0 and y < options.bins[1]:
grid[x,y] += 1 if options.weight == None else table.data[i,2] # count (weighted) occurrences grid[x,y] += 1. if options.weight == None else table.data[i,2] # count (weighted) occurrences
if options.normCol:
for x in xrange(options.bins[0]):
sum = np.sum(grid[x,:])
if sum > 0.0:
grid[x,:] /= sum
if options.normRow:
for y in xrange(options.bins[1]):
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] == 0.0).all(): minmax[2] = [grid.min(),grid.max()] # auto scale from data
if minmax[2,0] == minmax[2,1]: if minmax[2,0] == minmax[2,1]:
@ -138,17 +149,10 @@ for name in filenames:
minmax[1,0]+delta[1]/options.bins[1]*(y+0.5), minmax[1,0]+delta[1]/options.bins[1]*(y+0.5),
min(1.0,max(0.0,(grid[x,y]-minmax[2,0])/delta[2]))] min(1.0,max(0.0,(grid[x,y]-minmax[2,0])/delta[2]))]
if options.normCol:
for x in xrange(options.bins[0]):
result[x,:,2] /= np.sum(result[x,:,2])
if options.normRow:
for y in xrange(options.bins[1]):
result[:,y,2] /= np.sum(result[:,y,2])
for i in xrange(2): for i in xrange(2):
if options.type[i].lower() == 'log': result[:,:,i] = np.exp(result[:,:,i]) if options.type[i].lower() == 'log': result[:,:,i] = np.exp(result[:,:,i])
if options.invert: result[:,:,2] = 1.0-result[:,:,2] if options.invert: result[:,:,2] = 1.0 - result[:,:,2]
# ------------------------------------------ output result ----------------------------------------- # ------------------------------------------ output result -----------------------------------------
prefix = 'binned%s-%s_'%(options.data[0],options.data[1])+ \ prefix = 'binned%s-%s_'%(options.data[0],options.data[1])+ \