cleaning for release
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Martin Diehl
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0e11d6f049
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PRIVATE
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Subproject commit a6109acd264c683fd335b1d1f69934fc4a4078e3
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Subproject commit 8318aa6b9b1ef2c59c1f6fa946ede92640baf93c
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#!/usr/bin/env python3
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import os
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import sys
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from io import StringIO
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from optparse import OptionParser
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import numpy as np
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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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 [ASCIItable(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 limits in x direction (optional)')
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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 limits in y direction (optional)')
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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 limits in z direction (optional)')
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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')
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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')
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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')
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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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)
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(options,filenames) = parser.parse_args()
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if filenames == []: filenames = [None]
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minmax = np.array([options.xrange,options.yrange,options.zrange])
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result = np.empty((options.bins[0],options.bins[1],3),'f')
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if options.data is None: parser.error('no data columns specified.')
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for name in filenames:
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damask.util.report(scriptName,name)
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table = damask.Table.from_ASCII(StringIO(''.join(sys.stdin.read())) if name is None else name)
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data = np.hstack((table.get(options.data[0]),table.get(options.data[1])))
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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] = [data[:,c].min(),data[:,c].max()]
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if options.type[c].lower() == 'log': # if log scale
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data[:,c] = np.log(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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(grid,xedges,yedges) = np.histogram2d(data[:,0],data[:,1],
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bins=options.bins,
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range=minmax[:2],
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weights=table.get(options.weight) if options.weight else None)
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if options.normCol:
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for x in range(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 range(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 range(options.bins[0]):
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for y in range(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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np.clip((grid[x,y]-minmax[2,0])/delta[2],0.0,1.0)]
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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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comments = scriptID + '\t' + ' '.join(sys.argv[1:])
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shapes = {'bin_%s'%options.data[0]:(1,),'bin_%s'%options.data[1]:(1,),'z':(1,)}
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table = damask.Table(result.reshape(options.bins[0]*options.bins[1],3),shapes,[comments])
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if name:
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outname = os.path.join(os.path.dirname(name),'binned-{}-{}_'.format(*options.data) +
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('weighted-{}_'.format(options.weight) if options.weight else '') +
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os.path.basename(name))
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table.to_ASCII(outname)
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else:
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table.to_ASCII(sys.stdout)
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