def _plot_shear(self, show=False):
import biggles
from biggles import plot
tab=biggles.Table(2,1)
tab[0,0]=plot(self.s1grid,
self.lnp,
xlabel='g1',
ylabel='log(p)',
visible=False)
p=numpy.exp(self.lnp-self.lnp.max())
tab[1,0]=plot(self.s1grid,
p,
xlabel='g1',
ylabel='p',
visible=False)
pngfile=files.get_psample_summed_url(self['run'],
self['is2n'],
itrial=self['itrial'],
ext='png')
print("writing:",pngfile)
tab.write_img(800,800,pngfile)
if show:
tab.show()
return tab
def add_scinv(self, show=False):
import fitsio
import zphot
from . import sigmacrit
import biggles
zsvals = self.get_zsvals()
cosmo=self['cosmo']
scalc = sigmacrit.ScinvCalculator(self['zlmin'],
self['zlmax'],
self['nzl'],
zsvals[0],
zsvals[-1],
H0=cosmo['H0'],
omega_m=cosmo['omega_m'])
n_zlens=scalc.nzl
data = self.read_original()
dt=data.dtype.descr
new_dtype=[]
for d in dt:
if d[0] != 'pofz':
new_dtype.append(d)
new_dtype += [('scinv','f8',n_zlens)]
out = numpy.zeros(data.size, dtype=new_dtype)
eu.numpy_util.ahelp(out)
print("copying common fields")
eu.numpy_util.copy_fields(data, out)
print('adding scinv to each')
for i in xrange(data.size):
if ((i+1) % 1000) == 0:
print("%s/%s %.1f%%" % (i+1,data.size,float(i+1)/data.size*100.))
out['scinv'][i,:] = scalc.calc_mean_scinv(zsvals, data['pofz'][i])
if show:
biggles.plot(scalc.zlvals, out['scinv'][i], type='solid')
key=raw_input('hit a key (q to quit): ')
if key=='q':
return
h=copy.deepcopy(cosmo)
h['zlvals'] = list(scalc.zlvals)
outf=self.original_file(with_scinv=True)
print("writing scinv file:",outf)
fitsio.write(outf, out, header=h, clobber=True)
def view_profile(image, **keys):
"""
View the image as a radial profile vs radius from the center.
If show=False just return the plot object.
Values below zero are clipped, so pre-subtract any background as
necessary.
parameters
----------
image or r,g,b: ndarray
The image(s) as a 2-d array or
cen: [c1,c2], optional
Optional center
show: bool
Set False to not show the image in an X window.
file: string
Write the image to the intput file name. .png will be written
as a png file, else an eps file.
width, height: integers
Size for output
**kw:
keywords for the FramedPlot and for the output image dimensions
width
"""
import biggles
plt=keys.pop('plt',None)
show=keys.pop('show',True)
cen=keys.pop('cen',None)
if cen is None:
cen=(numpy.array(image.shape)-1.0)/2.0
else:
assert len(cen)==2,"cen must have two elements"
rows,cols=numpy.mgrid[
0:image.shape[0],
0:image.shape[1],
]
rows = rows.astype('f8')-cen[0]
cols = cols.astype('f8')-cen[1]
r = numpy.sqrt(rows**2 + cols**2).ravel()
s = r.argsort()
r=r[s]
imravel = image.ravel()[s]
#pts = biggles.Points(r, imravel, size=size, type=type, **keys)
keys['visible']=False
plt = biggles.plot(r, imravel, plt=plt, **keys)
_writefile_maybe(plt, **keys)
_show_maybe(plt, show=show,**keys)
return plt
def _get_fracdev_guess(self, fitter):
tests=self.fracdev_tests
lnps=zeros(tests.size)
for i in xrange(tests.size):
lnps[i] = fitter.calc_lnprob(tests[i:i+1])
w,=where(isfinite(lnps))
if w.size == 0:
return None
if False:
from biggles import plot
plot(tests[w], lnps[w])
key=raw_input('hit a key: ')
ibest=lnps[w].argmax()
guess=tests[w[ibest]]
return guess
def view_profile(image, **keys):
"""
View the image as a radial profile vs radius from the center.
If show=False just return the plot object.
Values below zero are clipped, so pre-subtract any background as
necessary.
parameters
----------
image or r,g,b: ndarray
The image(s) as a 2-d array or
cen: [c1,c2], optional
Optional center
show: bool
Set False to not show the image in an X window.
file: string
Write the image to the intput file name. .png will be written
as a png file, else an eps file.
width, height: integers
Size for output
**kw:
keywords for the FramedPlot and for the output image dimensions
width
"""
import biggles
plt=keys.pop('plt',None)
show=keys.pop('show',True)
cen=keys.pop('cen',None)
keys['xlabel']=keys.get('xlabel','radius')
r, pim = get_profile(image, cen=cen)
keys['visible']=False
plt = biggles.plot(r, pim, plt=plt, **keys)
_writefile_maybe(plt, **keys)
_show_maybe(plt, show=show, **keys)
return plt
def plot_fits(self):
means=self.means
if means.size == 1:
return
else:
import biggles
biggles.configure('default','fontsize_min',1.5)
fits=self.fits
args=self.args
#Q=calc_q(fits)
if args.yrange is not None:
yrange=[float(r) for r in args.yrange.split(',')]
else:
yrange=[-0.01,0.01]
xrng=args.xrange
if xrng is not None:
xrng=[float(r) for r in args.xrange.split(',')]
tab=biggles.Table(1,2)
tab.aspect_ratio=0.5
diff = means['shear'] - means['shear_true']
plts=[]
for i in [0,1]:
x = means['shear_true'][:,i]
plt =biggles.plot(
x,
diff[:,i],
xlabel=r'$\gamma_{%d}$ true' % (i+1,),
ylabel=r'$\Delta \gamma_{%d}$' % (i+1,),
yrange=yrange,
xrange=xrng,
visible=False,
)
yfit=fits['m'][0,i]*x + fits['c'][0,i]
z=biggles.Curve(x, x*0, color='black')
c=biggles.Curve(x, yfit, color='red')
plt.add(z,c)
'''
mstr='m%d: %.2g +/- %.2g' % (i+1,fits['m'][0,i],fits['merr'][0,i])
cstr='c%d: %.2g +/- %.2g' % (i+1,fits['c'][0,i],fits['cerr'][0,i])
mlab=biggles.PlotLabel(0.1,0.9,
mstr,
halign='left')
clab=biggles.PlotLabel(0.1,0.85,
cstr,
halign='left')
plt.add(mlab,clab)
'''
if False and i==0:
Qstr='Q: %d' % (int(Q),)
Qlab=biggles.PlotLabel(0.1,0.8,
Qstr,
halign='left')
plt.add(Qlab)
tab[0,i] = plt
fname=self._get_fit_plot_file()
eu.ostools.makedirs_fromfile(fname)
print("writing:",fname)
tab.write_eps(fname)
if args.show:
tab.show(width=1000, height=1000)
def plot_results(trials, **keys):
"""
Plot the points and histograms of trials from an MCMC chain.
"""
import biggles
import esutil
from esutil.numpy_util import where1, between
npars = trials.shape[1]
fontsize_min = keys.get("fontsize_min", 1)
biggles.configure("default", "fontsize_min", fontsize_min)
weights = keys.get("weights", None)
nbin = keys.get("nbin", 35)
names = keys.get("names", None)
show = keys.get("show", True)
nsigma = keys.get("nsigma", None)
means, cov = extract_stats(trials, weights=weights)
errs = sqrt(diag(cov))
plt = biggles.Table(npars, 2)
ind = numpy.arange(trials.shape[0])
for i in xrange(npars):
if names is not None:
name = names[i]
else:
name = r"$p_{%d}$" % i
use_trials = trials[:, i]
use_ind = ind
use_wts = weights
if nsigma is not None:
w = where1(between(trials[:, i], means[i] - nsigma * errs[i], means[i] + nsigma * errs[i]))
use_trials = trials[w, i]
use_ind = ind[w]
if weights is not None:
use_wts = weights[w]
burn_plot_i = biggles.plot(use_ind, use_trials, type="solid", xlabel="step", ylabel=name, visible=False)
plt[i, 0] = burn_plot_i
hcurve, bin_edges, harray = biggles.make_histc(use_trials, nbin=nbin, weights=use_wts, get_hdata=True)
plti = biggles.FramedPlot()
plti.xlabel = name
hmax = harray.max()
plti.yrange = [-0.05 * hmax, 1.2 * hmax]
plti.add(hcurve)
lab = r"$<%s> = %0.4g \pm %0.4g$" % (name, means[i], errs[i])
plab = biggles.PlotLabel(0.1, 0.8, lab, halign="left", color="blue")
plti.add(plab)
plt[i, 1] = plti
plt.title = keys.get("title", None)
if show:
plt.show()
return plt
def doplot(self,args):
import biggles
means=self.means
fits=self.fits
Q=calc_q(fits)
if args.yrange is not None:
yrange=[float(r) for r in args.yrange.split(',')]
else:
yrange=[-0.005,0.005]
xrng=args.xrange
if xrng is not None:
xrng=[float(r) for r in args.xrange.split(',')]
tab=biggles.Table(1,2)
tab.aspect_ratio=0.5
diff = means['shear'] - means['shear_true']
differr = means['shear_err']
plts=[]
for i in [0,1]:
x = means['shear_true'][:,i]
plt =biggles.plot(
x,
diff[:,i],
yerr=differr[:,i],
xlabel='shear%d true' % (i+1,),
ylabel='shear%d diff' % (i+1,),
yrange=yrange,
xrange=xrng,
visible=False,
)
yfit=fits['m'][0,i]*x + fits['c'][0,i]
c=biggles.Curve(x, yfit, color='red')
z=biggles.Curve(x, x*0, color='black')
plt.add(c,z)
mstr='m%d: %.2g +/- %.2g' % (i+1,fits['m'][0,i],fits['merr'][0,i])
cstr='c%d: %.2g +/- %.2g' % (i+1,fits['c'][0,i],fits['cerr'][0,i])
mlab=biggles.PlotLabel(0.1,0.9,
mstr,
halign='left')
clab=biggles.PlotLabel(0.1,0.85,
cstr,
halign='left')
plt.add(mlab,clab)
if i==0:
Qstr='Q: %d' % (int(Q),)
Qlab=biggles.PlotLabel(0.1,0.8,
Qstr,
halign='left')
plt.add(Qlab)
tab[0,i] = plt
fname=files.get_fit_file(self['run'],
extra='fit-m-c',
ext='eps')
eu.ostools.makedirs_fromfile(fname)
print("writing:",fname)
tab.write_eps(fname)
if args.show:
tab.show(width=1000, height=1000)
def plot_fits(self):
means = self.means
if means.size == 1:
return
else:
import biggles
biggles.configure('default', 'fontsize_min', 1.5)
fits = self.fits
args = self.args
#Q=calc_q(fits)
if args.yrange is not None:
yrange = [float(r) for r in args.yrange.split(',')]
else:
yrange = [-0.01, 0.01]
xrng = args.xrange
if xrng is not None:
xrng = [float(r) for r in args.xrange.split(',')]
tab = biggles.Table(1, 2)
tab.aspect_ratio = 0.5
diff = means['shear'] - means['shear_true']
plts = []
for i in [0, 1]:
x = means['shear_true'][:, i]
plt = biggles.plot(
x,
diff[:, i],
xlabel=r'$\gamma_{%d}$ true' % (i + 1, ),
ylabel=r'$\Delta \gamma_{%d}$' % (i + 1, ),
yrange=yrange,
xrange=xrng,
visible=False,
)
yfit = fits['m'][0, i] * x + fits['c'][0, i]
z = biggles.Curve(x, x * 0, color='black')
c = biggles.Curve(x, yfit, color='red')
plt.add(z, c)
'''
mstr='m%d: %.2g +/- %.2g' % (i+1,fits['m'][0,i],fits['merr'][0,i])
cstr='c%d: %.2g +/- %.2g' % (i+1,fits['c'][0,i],fits['cerr'][0,i])
mlab=biggles.PlotLabel(0.1,0.9,
mstr,
halign='left')
clab=biggles.PlotLabel(0.1,0.85,
cstr,
halign='left')
plt.add(mlab,clab)
'''
if False and i == 0:
Qstr = 'Q: %d' % (int(Q), )
Qlab = biggles.PlotLabel(0.1, 0.8, Qstr, halign='left')
plt.add(Qlab)
tab[0, i] = plt
fname = self._get_fit_plot_file()
eu.ostools.makedirs_fromfile(fname)
print("writing:", fname)
tab.write_eps(fname)
if args.show:
tab.show(width=1000, height=1000)
def make_summed_pofz(scat_vers, tilenames=None):
"""
match the dg scat to scinv using a Matcher
parameters
----------
scat_vers: string
name of a source catalog version e.g. scat-006
tilenames: list, optional
subset to process, for testing
"""
import fitsio
pz_sum_colname = get_pz_sum_colname(scat_vers)
print("will write to column: '%s'" % pz_sum_colname)
xi = XShearInput(scat_vers)
cols = pz.open_pz_columns(xi["pz_vers"], xi["pz_type"])
zvals = cols["zgrid"][:]
pzsum = zeros(zvals.size)
if tilenames is None:
tilenames = get_tilenames(xi["scat_table"])
ntile = len(tilenames)
ntile_use = 0
nuse = 0
first = True
for i, tilename in enumerate(tilenames):
print("-" * 70)
print("processing tile %d/%d: %s" % (i + 1, ntile, tilename))
data = xi.read_input_data(tilename)
if data is None:
# not all tiles are present
continue
w = xi.select(data)
indices = data["coadd_objects_id"][w]
matches = cols["index"].match(indices)
nmatches = matches.size
print(" matches: %d/%d" % (nmatches, indices.size))
pofz = cols["pofz"][matches]
good = zeros(nmatches, dtype="i8")
for mi in xrange(nmatches):
wnan, = where(isnan(pofz[mi, :]))
if wnan.size == 0:
good[mi] = 1
wgood, = where(good)
if wgood.size > 0:
print(" not NaN: %d/%d" % (wgood.size, nmatches))
pofz = pofz[wgood, :]
pzsum += pofz.sum(axis=0)
ntile_use += 1
nuse += wgood.size
if False and i > 5:
from biggles import plot
plot(zvals, pzsum)
break
print("finally used: %d/%d tiles" % (ntile_use, ntile))
print("summed %d p(z)" % nuse)
print("writing to column: '%s'" % pz_sum_colname)
cols.write_column(pz_sum_colname, pzsum, create=True)
