163 lines
8.0 KiB
Python
Executable File
163 lines
8.0 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,string
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import numpy as np
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from optparse import OptionParser
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import damask
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = os.path.splitext(scriptID.split()[1])[0]
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
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Produces a binned grid of two columns from an ASCIItable, i.e. a two-dimensional probability density map.
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""", version = scriptID)
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parser.add_option('-d','--data', dest='data', nargs=2, type='string', metavar='string string',
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help='column labels containing x and y ')
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parser.add_option('-w','--weight', dest='weight', metavar='string', type='string',
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help='column label containing weight of (x,y) point')
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parser.add_option('-b','--bins', dest='bins', nargs=2, type='int', metavar='int int',
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help='number of bins in x and y direction [%default]')
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parser.add_option('-t','--type', dest='type', nargs=3, metavar='string string string',
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help='type (linear/log) of x, y, and z axis [%default]')
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parser.add_option('-x','--xrange', dest='xrange', nargs=2, type='float', metavar='float float',
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help='min max value in x direction [autodetect]')
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parser.add_option('-y','--yrange', dest='yrange', nargs=2, type='float', metavar='float float',
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help='min max value in y direction [autodetect]')
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parser.add_option('-z','--zrange', dest='zrange', nargs=2, type='float', metavar='float float',
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help='min max value in z direction [autodetect]')
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parser.add_option('-i','--invert', dest='invert', action='store_true',
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help='invert probability density [%default]')
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parser.add_option('-r','--rownormalize', dest='normRow', action='store_true',
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help='normalize probability density in each row [%default]')
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parser.add_option('-c','--colnormalize', dest='normCol', action='store_true',
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help='normalize probability density in each column [%default]')
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parser.set_defaults(bins = (10,10))
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parser.set_defaults(type = ('linear','linear','linear'))
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parser.set_defaults(xrange = (0.0,0.0))
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parser.set_defaults(yrange = (0.0,0.0))
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parser.set_defaults(zrange = (0.0,0.0))
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parser.set_defaults(invert = False)
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parser.set_defaults(normRow = False)
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parser.set_defaults(normCol = False)
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(options,filenames) = parser.parse_args()
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minmax = np.array([np.array(options.xrange),
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np.array(options.yrange),
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np.array(options.zrange)])
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grid = np.zeros(options.bins,'f')
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result = np.zeros((options.bins[0],options.bins[1],3),'f')
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datainfo = { # list of requested labels per datatype
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'scalar': {'len':1,
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'label':[]},
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}
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if options.data != None: datainfo['scalar']['label'] += options.data
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if options.weight != None: datainfo['scalar']['label'] += [options.weight] # prevent character splitting of single string value
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if len(datainfo['scalar']['label']) < 2:
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parser.error('missing column labels')
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []:
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filenames = ['STDIN']
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for name in filenames:
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if name == 'STDIN':
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file = {'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr}
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file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
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else:
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if not os.path.exists(name): continue
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file = {'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr}
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file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
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table = damask.ASCIItable(file['input'],file['output'],buffered = False) # make unbuffered ASCII_table
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table.head_read() # read ASCII header info
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# --------------- figure out columns to process and read ------------------------------------------
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active = []
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for label in datainfo['scalar']['label']:
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if label in table.labels:
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active.append(label)
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else:
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file['croak'].write('column %s not found...\n'%label)
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table.data_readArray([label for label in active])
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# ------------------------------------------ process data ------------------------------------------
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for j in (0,1): # check data minmax for x and y
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i = table.labels.index(options.data[j])
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if (minmax[i] == 0.0).all(): minmax[i] = [table.data[:,i].min(),table.data[:,i].max()]
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if options.type[i].lower() == 'log': # if log scale
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table.data[:,i] = np.log(table.data[:,i]) # change x,y coordinates to log
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minmax[i] = np.log(minmax[i]) # change minmax to log, too
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delta = minmax[:,1]-minmax[:,0]
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xCol = table.labels.index(options.data[0])
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yCol = table.labels.index(options.data[1])
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if options.weight != None: wCol = table.labels.index(options.weight)
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for i in xrange(len(table.data)):
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x = int(options.bins[0]*(table.data[i,xCol]-minmax[0,0])/delta[0])
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y = int(options.bins[1]*(table.data[i,yCol]-minmax[1,0])/delta[1])
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if x >= 0 and x < options.bins[0] and y >= 0 and y < options.bins[1]:
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grid[x,y] += 1. if options.weight == None else table.data[i,wCol] # count (weighted) occurrences
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if options.normCol:
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for x in xrange(options.bins[0]):
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sum = np.sum(grid[x,:])
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if sum > 0.0:
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grid[x,:] /= sum
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if options.normRow:
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for y in xrange(options.bins[1]):
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sum = np.sum(grid[:,y])
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if sum > 0.0:
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grid[:,y] /= sum
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if (minmax[2] == 0.0).all(): minmax[2] = [grid.min(),grid.max()] # auto scale from data
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if minmax[2,0] == minmax[2,1]:
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minmax[2,0] -= 1.
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minmax[2,1] += 1.
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if (minmax[2] == 0.0).all(): # no data in grid?
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file['croak'].write('no data found on grid...\n')
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minmax[2,:] = np.array([0.0,1.0]) # making up arbitrary z minmax
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if options.type[2].lower() == 'log':
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grid = np.log(grid)
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minmax[2] = np.log(minmax[2])
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delta[2] = minmax[2,1]-minmax[2,0]
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for x in xrange(options.bins[0]):
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for y in xrange(options.bins[1]):
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result[x,y,:] = [minmax[0,0]+delta[0]/options.bins[0]*(x+0.5),
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minmax[1,0]+delta[1]/options.bins[1]*(y+0.5),
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min(1.0,max(0.0,(grid[x,y]-minmax[2,0])/delta[2]))]
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for i in xrange(2):
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if options.type[i].lower() == 'log': result[:,:,i] = np.exp(result[:,:,i])
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if options.invert: result[:,:,2] = 1.0 - result[:,:,2]
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# ------------------------------------------ assemble header ---------------------------------------
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table.info_clear()
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.labels = ['bin_%s'%options.data[0],'bin_%s'%options.data[1],'z']
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table.head_write()
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# ------------------------------------------ output result -----------------------------------------
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prefix = 'binned%s-%s_'%(options.data[0],options.data[1])+ \
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('weighted%s_'%(options.weight) if options.weight != None else '')
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np.savetxt(file['output'],result.reshape(options.bins[0]*options.bins[1],3))
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file['output'].close() # close output ASCII table
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if file['name'] != 'STDIN':
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os.rename(file['name']+'_tmp',\
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os.path.join(os.path.dirname(file['name']),prefix+os.path.basename(file['name'])))
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