DAMASK_EICMD/processing/post/binXY.py

177 lines
7.5 KiB
Python
Executable File

#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,sys,string
import numpy as np
from optparse import OptionParser
import damask
scriptID = string.replace('$Id$','\n','\\n')
scriptName = os.path.splitext(scriptID.split()[1])[0]
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Produces a binned grid of two columns from an ASCIItable, i.e. a two-dimensional probability density map.
""", version = scriptID)
parser.add_option('-d','--data',
dest = 'data',
type='string', nargs = 2, metavar = 'string string',
help = 'column labels containing x and y ')
parser.add_option('-w','--weight',
dest = 'weight',
type = 'string', metavar = 'string',
help = 'column label containing weight of (x,y) point')
parser.add_option('-b','--bins',
dest = 'bins',
type = 'int', nargs = 2, metavar = 'int int',
help = 'number of bins in x and y direction [%default]')
parser.add_option('-t','--type',
dest = 'type',
type = 'string', nargs = 3, metavar = 'string string string',
help = 'type (linear/log) of x, y, and z axis [%default]')
parser.add_option('-x','--xrange',
dest = 'xrange',
type = 'float', nargs = 2, metavar = 'float float',
help = 'min max value in x direction [autodetect]')
parser.add_option('-y','--yrange',
dest = 'yrange',
type = 'float', nargs = 2, metavar = 'float float',
help = 'min max value in y direction [autodetect]')
parser.add_option('-z','--zrange',
dest = 'zrange',
type = 'float', nargs = 2, metavar = 'float float',
help = 'min max value in z direction [autodetect]')
parser.add_option('-i','--invert',
dest = 'invert',
action = 'store_true',
help = 'invert probability density [%default]')
parser.add_option('-r','--rownormalize',
dest = 'normRow',
action = 'store_true',
help = 'normalize probability density in each row [%default]')
parser.add_option('-c','--colnormalize',
dest = 'normCol',
action = 'store_true',
help = 'normalize probability density in each column [%default]')
parser.set_defaults(bins = (10,10),
type = ('linear','linear','linear'),
xrange = (0.0,0.0),
yrange = (0.0,0.0),
zrange = (0.0,0.0),
invert = False,
normRow = False,
normCol = False,
)
(options,filenames) = parser.parse_args()
minmax = np.array([np.array(options.xrange),
np.array(options.yrange),
np.array(options.zrange)])
grid = np.zeros(options.bins,'f')
result = np.zeros((options.bins[0],options.bins[1],3),'f')
if options.data == None: parser.error('no data columns specified.')
labels = options.data
if options.weight != None: labels += [options.weight] # prevent character splitting of single string value
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
try:
table = damask.ASCIItable(name = name,
outname = os.path.join(os.path.dirname(name),
'binned-{}-{}_'.format(*options.data)+ \
('weighted-{}_'.format(options.weight) if options.weight else '') + \
os.path.basename(name)) if name else name,
buffered = False)
except:
continue
damask.util.report(scriptName,name)
# ------------------------------------------ read header ------------------------------------------
table.head_read()
# ------------------------------------------ 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)
continue
for c in (0,1): # check data minmax for x and y (i = 0 and 1)
if (minmax[c] == 0.0).all(): minmax[c] = [table.data[:,c].min(),table.data[:,c].max()]
if options.type[c].lower() == 'log': # if log scale
table.data[:,c] = np.log(table.data[:,c]) # change x,y coordinates to log
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 == 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] == minmax[2,1]:
minmax[2,0] -= 1.
minmax[2,1] += 1.
if (minmax[2] == 0.0).all(): # no data in grid?
damask.util.croak('no data found on grid...')
minmax[2,:] = np.array([0.0,1.0]) # making up arbitrary z minmax
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]):
for y in xrange(options.bins[1]):
result[x,y,:] = [minmax[0,0]+delta[0]/options.bins[0]*(x+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]))]
for c in (0,1):
if options.type[c].lower() == 'log': result[:,:,c] = np.exp(result[:,:,c])
if options.invert: result[:,:,2] = 1.0 - result[:,:,2]
# --- assemble header -------------------------------------------------------------------------------
table.info_clear()
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
table.labels = ['bin_%s'%options.data[0],'bin_%s'%options.data[1],'z']
table.head_write()
# --- output result ---------------------------------------------------------------------------------
table.data = result.reshape(options.bins[0]*options.bins[1],3)
table.data_writeArray()
table.close()