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DAMASK_EICMD
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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 2015-05-21 00:08:32 +00:00
parent 99adf42f0f
commit acc2cb656b
1 changed files with 31 additions and 36 deletions
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67
processing/post/binXY.py
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@ -38,6 +38,7 @@ 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(type = ('linear','linear','linear'))
parser.set_defaults(xrange = (0.0,0.0))
@ -49,10 +50,10 @@ parser.set_defaults(normCol = False)
(options,filenames) = parser.parse_args()
minmax = np.array([np.array(options.xrange),
np.array(options.yrange),
np.array(options.zrange)])
grid = np.zeros(options.bins,'f')
minmax = np.array([np.array(options.xrange),
np.array(options.yrange),
np.array(options.zrange)])
grid = np.zeros(options.bins,'f')
result = np.zeros((options.bins[0],options.bins[1],3),'f')
datainfo = { # list of requested labels per datatype
@ -63,10 +64,8 @@ datainfo = {
if options.data != None: datainfo['scalar']['label'] += options.data
if options.weight != None: datainfo['scalar']['label'] += [options.weight] # prevent character splitting of single string value
if len(datainfo['scalar']['label']) < 2:
parser.error('missing column labels')
# --- loop over input files ------------------------------------------------------------------------
# --- loop over input files -------------------------------------------------------------------------
if filenames == []:
filenames = ['STDIN']
@ -82,34 +81,28 @@ for name in filenames:
table = damask.ASCIItable(file['input'],file['output'],buffered = False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
# --------------- figure out columns to process and read ------------------------------------------
active = []
for label in datainfo['scalar']['label']:
if label in table.labels:
active.append(label)
else:
file['croak'].write('column %s not found...\n'%label)
table.data_readArray([label for label in active])
# --- process data ---------------------------------------------------------------------------------
# ------------------------------------------ process data ------------------------------------------
for j in (0,1): # check data minmax for x and y
i = table.labels.index(options.data[j])
if (minmax[i] == 0.0).all(): minmax[i] = [table.data[:,i].min(),table.data[:,i].max()]
if options.type[i].lower() == 'log': # if log scale
table.data[:,i] = np.log(table.data[:,i]) # change x,y coordinates to log
minmax[i] = np.log(minmax[i]) # change minmax to log, too
missing_labels = table.data_readArray(datainfo['scalar']['label'])
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.close(dismiss = True)
continue
for c in (0,1): # check data minmax for x and y (i = 0 and 1)
if (minmax[c] == 0.0).all(): minmax[c] = [table.data[:,c].min(),table.data[:,c].max()]
if options.type[c].lower() == 'log': # if log scale
table.data[:,c] = np.log(table.data[:,c]) # change x,y coordinates to log
minmax[c] = np.log(minmax[c]) # change minmax to log, too
delta = minmax[:,1]-minmax[:,0]
xCol = table.labels.index(options.data[0])
yCol = table.labels.index(options.data[1])
if options.weight != None: wCol = table.labels.index(options.weight)
for i in xrange(len(table.data)):
x = int(options.bins[0]*(table.data[i,xCol]-minmax[0,0])/delta[0])
y = int(options.bins[1]*(table.data[i,yCol]-minmax[1,0])/delta[1])
x = int(options.bins[0]*(table.data[i,0]-minmax[0,0])/delta[0])
y = int(options.bins[1]*(table.data[i,1]-minmax[1,0])/delta[1])
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 table.data[i,wCol] # count (weighted) occurrences
grid[x,y] += 1. if options.weight == None else table.data[i,2] # count (weighted) occurrences
if options.normCol:
for x in xrange(options.bins[0]):
@ -141,22 +134,24 @@ for name in filenames:
minmax[1,0]+delta[1]/options.bins[1]*(y+0.5),
min(1.0,max(0.0,(grid[x,y]-minmax[2,0])/delta[2]))]
for i in xrange(2):
if options.type[i].lower() == 'log': result[:,:,i] = np.exp(result[:,:,i])
for c in (0,1):
if options.type[c].lower() == 'log': result[:,:,c] = np.exp(result[:,:,c])
if options.invert: result[:,:,2] = 1.0 - result[:,:,2]
# ------------------------------------------ assemble header ---------------------------------------
# --- assemble header -------------------------------------------------------------------------------
table.info_clear()
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
table.labels = ['bin_%s'%options.data[0],'bin_%s'%options.data[1],'z']
table.head_write()
# ------------------------------------------ output result -----------------------------------------
prefix = 'binned%s-%s_'%(options.data[0],options.data[1])+ \
('weighted%s_'%(options.weight) if options.weight != None else '')
# --- output result ---------------------------------------------------------------------------------
prefix = 'binned-%s-%s_'%(options.data[0],options.data[1])+ \
('weighted-%s_'%(options.weight) if options.weight != None else '')
np.savetxt(file['output'],result.reshape(options.bins[0]*options.bins[1],3))
file['output'].close() # close output ASCII table
table.output_close() # close output ASCII table
if file['name'] != 'STDIN':
os.rename(file['name']+'_tmp',\
os.path.join(os.path.dirname(file['name']),prefix+os.path.basename(file['name'])))