177 lines
7.5 KiB
Python
Executable File
177 lines
7.5 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',
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dest = 'data',
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type='string', nargs = 2, metavar = 'string string',
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help = 'column labels containing x and y ')
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parser.add_option('-w','--weight',
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dest = 'weight',
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type = 'string', metavar = 'string',
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help = 'column label containing weight of (x,y) point')
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parser.add_option('-b','--bins',
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dest = 'bins',
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type = 'int', nargs = 2, 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',
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dest = 'type',
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type = 'string', 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',
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dest = 'xrange',
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type = 'float', nargs = 2, metavar = 'float float',
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help = 'min max value in x direction [autodetect]')
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parser.add_option('-y','--yrange',
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dest = 'yrange',
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type = 'float', nargs = 2, metavar = 'float float',
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help = 'min max value in y direction [autodetect]')
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parser.add_option('-z','--zrange',
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dest = 'zrange',
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type = 'float', nargs = 2, metavar = 'float float',
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help = 'min max value in z direction [autodetect]')
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parser.add_option('-i','--invert',
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dest = 'invert',
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action = 'store_true',
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help = 'invert probability density [%default]')
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parser.add_option('-r','--rownormalize',
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dest = 'normRow',
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action = 'store_true',
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help = 'normalize probability density in each row [%default]')
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parser.add_option('-c','--colnormalize',
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dest = 'normCol',
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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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type = ('linear','linear','linear'),
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xrange = (0.0,0.0),
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yrange = (0.0,0.0),
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zrange = (0.0,0.0),
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invert = False,
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normRow = False,
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normCol = False,
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)
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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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if options.data == None: parser.error('no data columns specified.')
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labels = options.data
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if options.weight != None: labels += [options.weight] # prevent character splitting of single string value
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name,
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outname = os.path.join(os.path.dirname(name),
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'binned-{}-{}_'.format(*options.data)+ \
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('weighted-{}_'.format(options.weight) if options.weight else '') + \
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os.path.basename(name)) if name else name,
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buffered = False)
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except:
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continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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missing_labels = table.data_readArray(labels)
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if len(missing_labels) > 0:
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damask.util.croak('column{} {} not found.'.format('s' if len(missing_labels) > 1 else '',', '.join(missing_labels)))
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table.close(dismiss = True)
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continue
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for c in (0,1): # check data minmax for x and y (i = 0 and 1)
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if (minmax[c] == 0.0).all(): minmax[c] = [table.data[:,c].min(),table.data[:,c].max()]
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if options.type[c].lower() == 'log': # if log scale
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table.data[:,c] = np.log(table.data[:,c]) # change x,y coordinates to log
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minmax[c] = np.log(minmax[c]) # change minmax to log, too
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delta = minmax[:,1]-minmax[:,0]
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for i in xrange(len(table.data)):
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x = int(options.bins[0]*(table.data[i,0]-minmax[0,0])/delta[0])
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y = int(options.bins[1]*(table.data[i,1]-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,2] # 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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damask.util.croak('no data found on grid...')
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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 c in (0,1):
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if options.type[c].lower() == 'log': result[:,:,c] = np.exp(result[:,:,c])
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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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table.data = result.reshape(options.bins[0]*options.bins[1],3)
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table.data_writeArray()
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table.close()
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