def clean(runpath='./',itmin=0,itmax=None,distance=8.0):
"""
Name: clean
Author: Kari A. Frank
Date: November 1, 2015
Purpose: Create a dataframe and associated saved file that includes
an iteration and emission measure column, and only includes
iterations after convergence
Usage:
import xmcinter.diagnostics as xd
xd.clean(runpath='./',itmin=0,itmax=None,distance=8.0)
Input:
runpath: string of path to the deconvolution files, or a dataframe
resulting from a previous call to xw.merge_output()
itmin: minimum iteration to keep
distance: distance to the object in kpc (default=8.0), used to
calculate the emission measure
Output:
Returns the dataframe of deconvolution parameters, filtered by iteration
add with the emission measure and iteration columns included, plus a
column with the blob sizes in arcsec (if blob shape = gaussian)
Usage Notes:
- typically this is run after xplt.chi2, to determine the minimum
iteration
- assumes that relevant column names begin with 'blob'. if not found,
will skip adding the new column.
Example:
"""
# -- import modules --
import astro_utilities as astro
# -- read deconvolution files --
if isinstance(runpath,str):
df = merge_output(runpath,save=False)
else:
df = runpath
# -- add blob size in arcsec column --
if 'blob_lnsigma' in df.columns:
df['blob_sigma'] = np.exp(df['blob_lnsigma'])
# -- add tau column, if used lvpshock --
if 'blob_logtau' in df.columns:
df['blob_tau'] = 10.0**(df['blob_tau'])
# -- add emission measure column --
if 'blob_norm' in df.columns:
df['blob_em'] = astro.norm_to_em(df['blob_norm'],
astro.convert_distance(distance,
'kpc',
'cm'))
# -- add hydrogen number densities of blobs in cm^-3, hydrogen mass --
if 'blob_sigma' in df.columns:
df['blob_volume'] = xw.gaussian_volume(astro.convert_arcsec(\
df['blob_sigma'],distance,'kpc','cm'))
df['blob_numberdensity'] = astro.em_to_density(df['blob_em'],\
df['blob_volume'],density_type='number')
df['blob_mass'] =astro.em_to_mass(df['blob_em'],df['blob_volume'],
tounit='sol')
# -- remove iterations before convergence --
if itmax == None:
itmax = np.max(df['iteration'])
df = xw.filterblobs(df,'iteration',minvals=itmin,maxvals=itmax)
# -- save as file --
outfile = ('deconvolution_merged_iter'
+str(int(itmin))+'-'+str(int(itmax))+'.txt')
df.to_csv(outfile,sep='\t')
# -- make traceplots --
# tracefigs = xplt.traceplots(df)
return df
def check(runpath='./',outpath='./',itmin=0,itmax=None,kTthresh=0.17):
"""
Name: clean
Author: Kari A. Frank
Date: November 23, 2015
Purpose: Make and display some diagnostic plots to check
ongoing xmc runs. Plots chi2, norm image, spectrum, and
histograms.
Usage:
import xmcinter.diagnostics as xd
xd.check(runpath='./',itmin=0,itmax=None)
Input:
runpath: string of path to the deconvolution files
outpath: string of path to store output files
itmin/itmax: minimum iteration to use in check
kTthresh: float to optionally specify a minimum kT. if set,
then all blobs with kT<kTthresh will not be included
in the map (but will be included in all other plots)
Output:
- Displays plots.
- Returns DataFrame of blob parameters
Usage Notes:
Example:
"""
# -- import modules --
import os
from file_utilities import ls_to_list
import xmcmap as xm
if kTthresh is None:
kTthresh = 0.0
# -- plot chi2 --
print "\nPlotting chi2 ...\n"
sf = xplt.chi2(runpath,itmax=itmax,
outfile=outpath+'/chi2_vs_iteration.html')
# -- calculate median chi2 --
if itmax is None:
itmax = np.max(sf.iteration)
sf = xw.filterblobs(sf,'iteration',minvals=itmin,maxvals=itmax)
medchi2 = xw.weighted_median(sf['redchi2'])
# -- read deconvolution files --
print "\nReading deconvolution files ...\n"
dfall=merge_output(runpath,save=False)
# -- add derivative columns --
dfall = clean(dfall,itmin=itmin,itmax=itmax)
print '\nIterations '+str(itmin)+' - '+str(itmax)+' : '
print "Total Number of Blobs = ",len(dfall.index)
print 'Median chi2/dof = '+str(medchi2)+'\n'
# -- plot model and data spectra --
print "\nPlotting spectrum ...\n"
smin = itmin/100
if itmax is None:
smax = None
else:
smax = itmax/100
sfig = xplt.spectrum(runpath=runpath,smin=smin,smax=smax,bins=0.03,
ylog=True,xlog=True,
outfile=outpath+'/spectrum.html',
lines=True,nlines=100,energy_range=(0.5,10.0))
# -- make median traceplots --
print "\nPlotting traces ...\n"
efig = xplt.trace(dfall,weights=None,
outfile=outpath+'/trace_plots.html')
# -- make histograms --
print "\nPlotting posteriors ...\n"
nbins = 75
w = 500
h = 200
hfigs = xplt.histogram_grid([dfall,dfall],weights=[None,'blob_em'],
bins=nbins,ncols=2,norm=True,
outfile=outpath+'/histogram_grid.html',
legends=['Unweighted','EM weighted'],
width=w,height=h,iterations='iteration')
print "\nPlotting posteriors with kT threshold ...\n"
hfigs = xplt.histogram_grid([xw.filterblobs(dfall,'blob_kT',
minvals=kTthresh),
xw.filterblobs(dfall,'blob_kT',
minvals=kTthresh)],
weights=[None,'blob_em'],
bins=nbins,ncols=2,norm=True,
outfile=outpath+'/histogram_grid_kTthresh.html',
legends=['Unweighted','EM weighted'],
width=w,height=h,iterations='iteration')
# -- scatter plots--
print "\nPlotting scatter plots ...\n"
blobcols = [c for c in dfall.columns if 'blob' in c]
sfigs2 = xplt.scatter_grid(dfall[blobcols],agg=None,sampling=2000)
# -- make norm map from most recent iteration --
print "\nMaking blob em map ...\n"
pixelsize = (dfall.phi.max()-dfall.phi.min())/50.0
# img1file = (outpath+'/bin'+str(int(pixelsize))+
# '_iter'+str(itmin)+'-'+str(itmax))
img1file = (outpath+'/bin'+str(int(pixelsize))+
'_iter'+str(itmax))
img = xm.make_map(xw.filterblobs(dfall,['blob_kT'],
minvals=[kTthresh,itmax],
maxvals=[None,itmax]),
paramname='blob_em',
paramweights=None,iteration_type='total',
binsize=pixelsize,nlayers=1,
withsignificance=True,nproc=2,
outfile=img1file,clobber=True)
return (dfall,sf)
