added options for x and y normalization
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Tias Maiti
parent
d8260b12df
commit
5257a2161f
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@ -18,24 +18,28 @@ Produces a binned grid of two columns from an ASCIItable, i.e. a two-dimensional
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""", version = scriptID)
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parser.add_option('-d','--data', dest='data', nargs=2, type='int', metavar='int int',
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help='columns containing x and y %default')
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parser.add_option('-w','--weight', dest='weight', metavar='int', type='int',
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help='column containing weight of (x,y) point [%default]')
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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 %default')
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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 [%default]')
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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 [linear]')
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parser.add_option('-x','--xrange', dest='xrange', nargs=2, type='float', metavar='float float',
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help='value range in x direction [auto]')
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help='value minmax in x direction [auto]')
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parser.add_option('-y','--yrange', dest='yrange', nargs=2, type='float', metavar='float float',
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help='value range in y direction [auto]')
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help='value minmax in y direction [auto]')
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parser.add_option('-z','--zrange', dest='zrange', nargs=2, type='float', metavar='float float',
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help='value range in z direction [auto]')
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help='value minmax in z direction [auto]')
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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(data = (1,2))
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parser.set_defaults(data = None)
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parser.set_defaults(weight = None)
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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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@ -43,76 +47,113 @@ 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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range = 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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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,'i')
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result = np.zeros((options.bins[0]*options.bins[1],3),'f')
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result = np.zeros((options.bins[0],options.bins[1],3),'f')
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prefix='binned%i-%i_'%(options.data[0],options.data[1])+ \
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('weighted%i_'%(options.weight) if options.weight != None else '')
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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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# ------------------------------------------ setup file handles ------------------------------------
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files = []
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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
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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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files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr})
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else:
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for name in filenames:
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if os.path.exists(name):
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files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
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filenames = ['STDIN']
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# ------------------------------------------ loop over input files ---------------------------------
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for file in files:
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if file['name'] != 'STDIN': file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
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else: file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
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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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skip = int(file['input'].readline().split()[0])
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for i in xrange(skip): headers = file['input'].readline().split()
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data = np.loadtxt(file['input'],usecols=np.array(options.data+((options.weight,) if options.weight != None else ()))-1)
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file['input'].close() # close input ASCII table
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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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for i in (0,1): # check data range for x and y
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if (range[i] == 0.0).all(): range[i] = [data[:,i].min(),data[:,i].max()]
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# --------------- figure out columns to process ---------------------------------------------------
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active = []
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column = {}
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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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column[label] = table.labels.index(label) # remember columns of requested data
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else:
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file['croak'].write('column %s not found...\n'%label)
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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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# ------------------------------------------ process data ------------------------------------------
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table.data_readArray([column[label] for label in active])
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for i in (0,1): # check data minmax for x and y
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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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data[:,i] = np.log(data[:,i]) # change x,y coordinates to log
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range[i] = np.log(range[i]) # change range to log, too
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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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delta = range[:,1]-range[:,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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for i in xrange(len(data)):
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x = int(options.bins[0]*(data[i,0]-range[0,0])/delta[0])
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y = int(options.bins[1]*(data[i,1]-range[1,0])/delta[1])
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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 data[i,2]
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if (range[2] == 0.0).all(): range[2] = [grid.min(),grid.max()]
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if (range[2] == 0.0).all(): # no data in grid?
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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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range[2,:] = np.array([0.0,1.0]) # making up arbitrary z range
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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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range[2] = np.log(range[2])
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minmax[2] = np.log(minmax[2])
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delta[2] = range[2,1]-range[2,0]
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delta[2] = minmax[2,1]-minmax[2,0]
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i = 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[i,:] = [range[0,0]+delta[0]/options.bins[0]*(x+0.5),
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range[1,0]+delta[1]/options.bins[1]*(y+0.5),
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min(1.0,max(0.0,(grid[x,y]-range[2,0])/delta[2]))]
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if options.type[0].lower() == 'log': result[i,0] = np.exp(result[i,0])
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if options.type[1].lower() == 'log': result[i,1] = np.exp(result[i,1])
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if options.invert: result[i,2] = 1.0-result[i,2]
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i += 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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if options.normCol:
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for x in xrange(options.bins[0]):
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result[x,:,2] /= np.sum(result[x,:,2])
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if options.normRow:
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for y in xrange(options.bins[1]):
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result[:,y,2] /= np.sum(result[:,y,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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# ------------------------------------------ output result -----------------------------------------
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file['output'].write('1\thead\n')
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file['output'].write('bin_%s\tbin_%s\tz\n'%(headers[options.data[0]-1],headers[options.data[1]-1]))
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np.savetxt(file['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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