simplifications due to better functionality available through asciitable.py
output filename format has slightly changed: binned-X-Y_weighted-W_
This commit is contained in:
Philip Eisenlohr
parent
99adf42f0f
commit
acc2cb656b
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@ -38,6 +38,7 @@ 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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@ -63,10 +64,8 @@ datainfo = {
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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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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []:
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filenames = ['STDIN']
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@ -82,34 +81,28 @@ for name in filenames:
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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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# --- process data ---------------------------------------------------------------------------------
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table.data_readArray([label for label in active])
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missing_labels = table.data_readArray(datainfo['scalar']['label'])
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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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if len(missing_labels) > 0:
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file['croak'].write('column%s %s not found...\n'%('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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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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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,wCol] # count (weighted) occurrences
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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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@ -141,22 +134,24 @@ for name in filenames:
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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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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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# --- 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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# --- 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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table.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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