#!/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,'f') 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] # prevent character splitting of single string value 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 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? 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]))] 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'])))