[skip ci] groupTable according to unique values in more than one column

This commit is contained in:
Philip Eisenlohr 2019-01-03 18:25:28 -05:00
parent b5d62c8e29
commit bcd4288f1e
1 changed files with 26 additions and 18 deletions

View File

@ -1,4 +1,4 @@
#!/usr/bin/env python2.7
#!/usr/bin/env python3
# -*- coding: UTF-8 no BOM -*-
import os,sys
@ -21,18 +21,19 @@ scriptID = ' '.join([scriptName,damask.version])
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Apply a user-specified function to condense all rows for which column 'label' has identical values into a single row.
Output table will contain as many rows as there are different (unique) values in the grouping column.
Apply a user-specified function to condense into a single row all those rows for which columns 'label' have identical values.
Output table will contain as many rows as there are different (unique) values in the grouping column(s).
Periodic domain averaging of coordinate values is supported.
Examples:
For grain averaged values, replace all rows of particular 'texture' with a single row containing their average.
""", version = scriptID)
{name} --label texture --function np.average data.txt
""".format(name = scriptName), version = scriptID)
parser.add_option('-l','--label',
dest = 'label',
type = 'string', metavar = 'string',
help = 'column label for grouping rows')
action = 'extend', metavar = '<string LIST>',
help = 'column label(s) for grouping rows')
parser.add_option('-f','--function',
dest = 'function',
type = 'string', metavar = 'string',
@ -40,7 +41,7 @@ parser.add_option('-f','--function',
parser.add_option('-a','--all',
dest = 'all',
action = 'store_true',
help = 'apply mapping function also to grouping column')
help = 'apply mapping function also to grouping column(s)')
group = OptionGroup(parser, "periodic averaging", "")
@ -57,6 +58,7 @@ parser.add_option_group(group)
parser.set_defaults(function = 'np.average',
all = False,
label = [],
boundary = [0.0, 1.0])
(options,filenames) = parser.parse_args()
@ -71,7 +73,7 @@ try:
except:
mapFunction = None
if options.label is None:
if options.label is []:
parser.error('no grouping column specified.')
if not hasattr(mapFunction,'__call__'):
parser.error('function "{}" is not callable.'.format(options.function))
@ -89,13 +91,20 @@ for name in filenames:
# ------------------------------------------ sanity checks ---------------------------------------
remarks = []
errors = []
table.head_read()
if table.label_dimension(options.label) != 1:
damask.util.croak('column {} is not of scalar dimension.'.format(options.label))
table.close(dismiss = True) # close ASCIItable and remove empty file
grpColumns = table.label_index(options.label)[::-1]
grpColumns = grpColumns[np.where(grpColumns>=0)]
if len(grpColumns) == 0: errors.append('no valid grouping column present.')
if remarks != []: damask.util.croak(remarks)
if errors != []:
damask.util.croak(errors)
table.close(dismiss=True)
continue
else:
grpColumn = table.label_index(options.label)
# ------------------------------------------ assemble info ---------------------------------------
@ -108,10 +117,9 @@ for name in filenames:
indexrange = table.label_indexrange(options.periodic) if options.periodic is not None else []
rows,cols = table.data.shape
table.data = table.data[np.lexsort([table.data[:,grpColumn]])] # sort data by grpColumn
values,index = np.unique(table.data[:,grpColumn], return_index = True) # unique grpColumn values and their positions
index = np.append(index,rows) # add termination position
table.data = table.data[np.lexsort(table.data[:,grpColumns].T)] # sort data by grpColumn(s)
values,index = np.unique(table.data[:,grpColumns], axis=0, return_index=True) # unique grpColumn values and their positions
index = sorted(np.append(index,rows)) # add termination position
grpTable = np.empty((len(values), cols)) # initialize output
for i in range(len(values)): # iterate over groups (unique values in grpColumn)
@ -119,7 +127,7 @@ for name in filenames:
grpTable[i,indexrange] = \
periodicAverage(table.data[index[i]:index[i+1],indexrange],options.boundary) # apply periodicAverage mapping function
if not options.all: grpTable[i,grpColumn] = table.data[index[i],grpColumn] # restore grouping column value
if not options.all: grpTable[i,grpColumns] = table.data[index[i],grpColumns] # restore grouping column value
table.data = grpTable