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DAMASK_EICMD
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adopted new ASCIItable API. some polishing.

This commit is contained in:
Philip Eisenlohr 2015-08-07 22:08:54 +00:00
parent 4ed56a8ba6
commit a4772343b0
1 changed files with 74 additions and 57 deletions
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131
processing/post/binXY.py
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@ -18,35 +18,56 @@ Produces a binned grid of two columns from an ASCIItable, i.e. a two-dimensional
""", version = scriptID) """, version = scriptID)
parser.add_option('-d','--data', dest='data', nargs=2, type='string', metavar='string string', parser.add_option('-d','--data',
help='column labels containing x and y ') dest = 'data',
parser.add_option('-w','--weight', dest='weight', metavar='string', type='string', type='string', nargs = 2, metavar = 'string string',
help='column label containing weight of (x,y) point') help = 'column labels containing x and y ')
parser.add_option('-b','--bins', dest='bins', nargs=2, type='int', metavar='int int', parser.add_option('-w','--weight',
help='number of bins in x and y direction [%default]') dest = 'weight',
parser.add_option('-t','--type', dest='type', nargs=3, metavar='string string string', type = 'string', metavar = 'string',
help='type (linear/log) of x, y, and z axis [%default]') help = 'column label containing weight of (x,y) point')
parser.add_option('-x','--xrange', dest='xrange', nargs=2, type='float', metavar='float float', parser.add_option('-b','--bins',
help='min max value in x direction [autodetect]') dest = 'bins',
parser.add_option('-y','--yrange', dest='yrange', nargs=2, type='float', metavar='float float', type = 'int', nargs = 2, metavar = 'int int',
help='min max value in y direction [autodetect]') help = 'number of bins in x and y direction [%default]')
parser.add_option('-z','--zrange', dest='zrange', nargs=2, type='float', metavar='float float', parser.add_option('-t','--type',
help='min max value in z direction [autodetect]') dest = 'type',
parser.add_option('-i','--invert', dest='invert', action='store_true', type = 'string', nargs = 3, metavar = 'string string string',
help='invert probability density [%default]') help = 'type (linear/log) of x, y, and z axis [%default]')
parser.add_option('-r','--rownormalize', dest='normRow', action='store_true', parser.add_option('-x','--xrange',
help='normalize probability density in each row [%default]') dest = 'xrange',
parser.add_option('-c','--colnormalize', dest='normCol', action='store_true', type = 'float', nargs = 2, metavar = 'float float',
help='normalize probability density in each column [%default]') help = 'min max value in x direction [autodetect]')
parser.add_option('-y','--yrange',
dest = 'yrange',
type = 'float', nargs = 2, metavar = 'float float',
help = 'min max value in y direction [autodetect]')
parser.add_option('-z','--zrange',
dest = 'zrange',
type = 'float', nargs = 2, metavar = 'float float',
help = 'min max value in z direction [autodetect]')
parser.add_option('-i','--invert',
dest = 'invert',
action = 'store_true',
help = 'invert probability density [%default]')
parser.add_option('-r','--rownormalize',
dest = 'normRow',
action = 'store_true',
help = 'normalize probability density in each row [%default]')
parser.add_option('-c','--colnormalize',
dest = 'normCol',
action = 'store_true',
help = 'normalize probability density in each column [%default]')
parser.set_defaults(bins = (10,10)) parser.set_defaults(bins = (10,10),
parser.set_defaults(type = ('linear','linear','linear')) type = ('linear','linear','linear'),
parser.set_defaults(xrange = (0.0,0.0)) xrange = (0.0,0.0),
parser.set_defaults(yrange = (0.0,0.0)) yrange = (0.0,0.0),
parser.set_defaults(zrange = (0.0,0.0)) zrange = (0.0,0.0),
parser.set_defaults(invert = False) invert = False,
parser.set_defaults(normRow = False) normRow = False,
parser.set_defaults(normCol = False) normCol = False,
)
(options,filenames) = parser.parse_args() (options,filenames) = parser.parse_args()
@ -56,37 +77,36 @@ minmax = np.array([np.array(options.xrange),
grid = np.zeros(options.bins,'f') grid = np.zeros(options.bins,'f')
result = np.zeros((options.bins[0],options.bins[1],3),'f') result = np.zeros((options.bins[0],options.bins[1],3),'f')
datainfo = { # list of requested labels per datatype if options.data == None: parser.error('no data columns specified.')
'scalar': {'len':1,
'label':[]},
}
if options.data != None: datainfo['scalar']['label'] += options.data labels = options.data
if options.weight != None: datainfo['scalar']['label'] += [options.weight] # prevent character splitting of single string value
# --- loop over input files ------------------------------------------------------------------------ if options.weight != None: labels += [options.weight] # prevent character splitting of single string value
if filenames == []: # --- loop over input files -------------------------------------------------------------------------
filenames = ['STDIN']
if filenames == []: filenames = ['STDIN']
for name in filenames: for name in filenames:
if name == 'STDIN': if not (name == 'STDIN' or os.path.exists(name)): continue
file = {'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr} table = damask.ASCIItable(name = name,
file['croak'].write('\033[1m'+scriptName+'\033[0m\n') outname = os.path.join(os.path.dirname(name),
else: 'binned-%s-%s_'%(options.data[0],options.data[1])+ \
if not os.path.exists(name): continue ('weighted-%s_'%(options.weight) if options.weight != None else '') + \
file = {'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr} os.path.basename(name))
file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n') buffered = False)
table.croak('\033[1m'+scriptName+'\033[0m'+(': '+name if name != 'STDIN' else ''))
table = damask.ASCIItable(file['input'],file['output'],buffered = False) # make unbuffered ASCII_table # ------------------------------------------ read header ------------------------------------------
table.head_read() # read ASCII header info
# --- process data --------------------------------------------------------------------------------- table.head_read()
missing_labels = table.data_readArray(datainfo['scalar']['label']) # ------------------------------------------ sanity checks ----------------------------------------
missing_labels = table.data_readArray(labels)
if len(missing_labels) > 0: if len(missing_labels) > 0:
file['croak'].write('column%s %s not found...\n'%('s' if len(missing_labels) > 1 else '',', '.join(missing_labels))) table.croak('column{} {} not found.'.format('s' if len(missing_labels) > 1 else '',', '.join(missing_labels)))
table.close(dismiss = True) table.close(dismiss = True)
continue continue
@ -120,7 +140,7 @@ for name in filenames:
minmax[2,0] -= 1. minmax[2,0] -= 1.
minmax[2,1] += 1. minmax[2,1] += 1.
if (minmax[2] == 0.0).all(): # no data in grid? if (minmax[2] == 0.0).all(): # no data in grid?
file['croak'].write('no data found on grid...\n') table.croak('no data found on grid...')
minmax[2,:] = np.array([0.0,1.0]) # making up arbitrary z minmax minmax[2,:] = np.array([0.0,1.0]) # making up arbitrary z minmax
if options.type[2].lower() == 'log': if options.type[2].lower() == 'log':
grid = np.log(grid) grid = np.log(grid)
@ -148,10 +168,7 @@ for name in filenames:
# --- output result --------------------------------------------------------------------------------- # --- output result ---------------------------------------------------------------------------------
prefix = 'binned-%s-%s_'%(options.data[0],options.data[1])+ \ table.data = result.reshape(options.bins[0]*options.bins[1],3)
('weighted-%s_'%(options.weight) if options.weight != None else '') table.data_writeArray()
np.savetxt(file['output'],result.reshape(options.bins[0]*options.bins[1],3))
table.output_close() # close output ASCII table table.close()
if file['name'] != 'STDIN':
os.rename(file['name']+'_tmp',\
os.path.join(os.path.dirname(file['name']),prefix+os.path.basename(file['name'])))