bfs = 12
sfs = bfs - 2
params = { 'axes.labelsize': bfs,
'text.fontsize': bfs,
'legend.fontsize': bfs,
'xtick.labelsize': sfs,
'ytick.labelsize': sfs}
pylab.rcParams.update(params)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 1st data file defines labels and parameter ranges:
if k == 0:
alllabels,alllimits,usedylimits = pappy.read_header(datafiles[k])
if (usedylimits and vb): print 'No axis limits found, using 5-sigma ranges'
# Now pull out just the labels and limits we need:
limits = numpy.zeros([npars,2])
ii = 0
for i in index:
limits[ii,:] = alllimits[i,:]
ii = ii + 1
labels = alllabels[:]
# Set up dynamic axis limits, and smoothing scales:
dylimits = numpy.zeros([npars,2])
smooth = numpy.zeros(npars)
for i in range(npars):
col = index[i]
bfs = 10
sfs = bfs - 2
params = { 'axes.labelsize': bfs,
'text.fontsize': bfs,
'legend.fontsize': bfs,
'xtick.labelsize': sfs,
'ytick.labelsize': sfs}
pylab.rcParams.update(params)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 1st data file defines labels and parameter ranges:
if k == 0:
alllabels,alllimits,usedylimits = pappy.read_header(datafiles[k])
if vb: print 'No axis limits found, using 5-sigma ranges'
# Now pull out just the labels and limits we need:
limits = numpy.zeros([npars,2])
ii = 0
for i in index:
limits[ii,:] = alllimits[i,:]
ii = ii + 1
labels = alllabels[:]
# Set up dynamic axis limits, and smoothing scales:
dylimits = numpy.zeros([npars,2])
smooth = numpy.zeros(npars)
for i in range(npars):
col = index[i]
else:
Lhood = 0.0*data[:,0].copy() + 1.0
# Having done all that, optionally overwrite index with specified list
# of column numbers. Note conversion to zero-indexed python:
if columns != 'All':
pieces = columns.split(',')
index = []
for piece in pieces:
index.append(int(piece) - 1)
npars = len(index)
if vb: print "Only using data in",npars,"columns (",index,"): "
# Now parameter list is in index - which is fixed for other datasets
labels,limits,dummy = pappy.read_header(datafile)
# --------------------------------------------------------------------
# Loop over parameters, doing calculations:
for i in range(npars):
col = index[i]
d = data[:,col].copy()
mean,stdev,Neff,N95 = pappy.meansd(d,wht=wht)
if histogram:
dylimits = numpy.zeros([2])
dylimits[0] = mean - 10*stdev
