#!/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', nargs=2, type='string', metavar='string string',
help='column labels containing x and y %default')
parser.add_option('-w','--weight', dest='weight', metavar='string', type='string',
help='column label containing weight of (x,y) point [%default]')
parser.add_option('-b','--bins', dest='bins', nargs=2, type='int', metavar='int int',
help='number of bins in x and y direction %default')
parser.add_option('-t','--type', dest='type', nargs=3, metavar='string string string',
help='type (linear/log) of x, y, and z axis [linear]')
parser.add_option('-x','--xrange', dest='xrange', nargs=2, type='float', metavar='float float',
help='value minmax in x direction [auto]')
parser.add_option('-y','--yrange', dest='yrange', nargs=2, type='float', metavar='float float',
help='value minmax in y direction [auto]')
parser.add_option('-z','--zrange', dest='zrange', nargs=2, type='float', metavar='float float',
help='value minmax in z direction [auto]')
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(data = None)
parser.set_defaults(weight = None)
parser.set_defaults(bins = (10,10))
parser.set_defaults(type = ('linear','linear','linear'))
parser.set_defaults(xrange = (0.0,0.0))
parser.set_defaults(yrange = (0.0,0.0))
parser.set_defaults(zrange = (0.0,0.0))
parser.set_defaults(invert = False)
parser.set_defaults(normRow = False)
parser.set_defaults(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,'i')
result = np.zeros((options.bins[0],options.bins[1],3),'f')
datainfo = { # list of requested labels per datatype
'scalar': {'len':1,
'label':[]},
}
if options.data != None: datainfo['scalar']['label'] += options.data
if options.weight != None: datainfo['scalar']['label'] += options.weight
if len(datainfo['scalar']['label']) < 2:
parser.error('missing column labels')
# --- loop over input files -------------------------------------------------------------------------
if filenames == []:
filenames = ['STDIN']
for name in filenames:
if name == 'STDIN':
file = {'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr}
file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
else:
if not os.path.exists(name): continue
file = {'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr}
file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
table = damask.ASCIItable(file['input'],file['output'],buffered = False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
# --------------- figure out columns to process ---------------------------------------------------
active = []
column = {}
for label in datainfo['scalar']['label']:
if label in table.labels:
active.append(label)
column[label] = table.labels.index(label) # remember columns of requested data
else:
file['croak'].write('column %s not found...\n'%label)
# ------------------------------------------ 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()
# ------------------------------------------ process data ------------------------------------------
table.data_readArray([column[label] for label in active])
for i in (0,1): # check data minmax for x and y
if (minmax[i] == 0.0).all(): minmax[i] = [table.data[:,i].min(),table.data[:,i].max()]
if options.type[i].lower() == 'log': # if log scale
table.data[:,i] = np.log(table.data[:,i]) # change x,y coordinates to log
minmax[i] = np.log(minmax[i]) # 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 (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?
file['croak'].write('no data found on grid...\n')
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]))]
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):
if options.type[i].lower() == 'log': result[:,:,i] = np.exp(result[:,:,i])
if options.invert: result[:,:,2] = 1.0-result[:,:,2]
# ------------------------------------------ output result -----------------------------------------
prefix = 'binned%s-%s_'%(options.data[0],options.data[1])+ \
('weighted%s_'%(options.weight) if options.weight != None else '')
np.savetxt(file['output'],result.reshape(options.bins[0]*options.bins[1],3))
file['output'].close() # close output ASCII table
if file['name'] != 'STDIN':
os.rename(file['name']+'_tmp',\
os.path.join(os.path.dirname(file['name']),prefix+os.path.basename(file['name'])))