def link(self, par):
datasets = self.filter_datasets()
name, parname = par.split('.')
fullparname0 = '{0}.{1}'.format(datasets[0]['model_comps'][name]['model_name'], parname)
for dataset in datasets[1:]:
fullparname = '{0}.{1}'.format(dataset['model_comps'][name]['model_name'], parname)
if fullparname != fullparname0:
logger.info('Linking {0} => {1}'.format(fullparname, fullparname0))
ui.link(fullparname, fullparname0)
def link(self, par):
datasets = self.filter_datasets()
name, parname = par.split('.')
fullparname0 = '{0}.{1}'.format(datasets[0]['model_comps'][name]['model_name'], parname)
for dataset in datasets[1:]:
fullparname = '{0}.{1}'.format(dataset['model_comps'][name]['model_name'], parname)
if fullparname != fullparname0:
logger.info('Linking {0} => {1}'.format(fullparname, fullparname0))
ui.link(fullparname, fullparname0)
def tie_par(self, par, base, *others):
"""Tie parameters in one or more shells to the base shell.
This is a limited form of the Sherpa ability to link parameters,
since it sets the parameter in the other shells to the same
value as the parameter in the base shell. More complex
situations will require direct calls to `sherpa.astro.ui.link`.
Parameters
----------
par : str
The parameter specifier, as <model_type>.<par_name>.
base : int
The base shell number.
*others : scalar
The shell, or shells to link to the base shell.
See Also
--------
set_par, untie_par
Examples
--------
Tie the temperature and abundance parameters in shell 9 to that
in shell 8, so that any fits will set the shell 9 values to those
used in shell 8 (so reducing the number of free parameters in the
fit).
>>> dep.tie_par('xsapec.kt', 8, 9)
Tying xsapec_9.kT to xsapec_8.kT
>>> dep.tie_par('xsapec.abundanc', 8, 9)
Tying xsapec_9.Abundanc to xsapec_8.Abundanc
Tie three annuli together:
>>> dep.tie_par('xsapec.kt', 12, 13, 14)
Tying xsapec_13.kT to xsapec_12.kT
Tying xsapec_14.kT to xsapec_12.kT
"""
bpar = self._find_shell_par(par, base)
for other in others:
opar = self._find_shell_par(par, other)
print('Tying {} to {}'.format(opar.fullname, bpar.fullname))
ui.link(opar, bpar)
def fixBkgModel(id=1, freeNorm=True):
"""
Fix the background model
Paramaters:
freeNorm = True
If freeNorm is True, leave the overall background normalization free.
"""
bk = ui.get_bkg_model(id=id)
if freeNorm:
pars = bk.pars
c0 = pars[0]
for p in pars[1:]:
if p.val != 0 and p.fullname.startswith('pca'):
ui.link(p, p.val / c0.val * c0)
else:
ui.freeze(p)
else:
ui.freeze(bk)
def fit_lines(linelist, id = None, delta_lam = .2, plot = False, outfile = None):
mymodel = filili.multilinemanager.GaussLines('const1d', id = id, baseline = baseline)
linelist['fililiname'] = [''] * len(linelist['linename'])
for i in range(len(linelist['linename'])):
lname = linelist['linename'][i]
lwave = linelist['wave'][i]
previouslines = set(mymodel.line_name_list())
mymodel.add_line(linename = filter(lambda x: x.isalnum(), lname), pos = lwave)
linenamelist = mymodel.line_name_list()
newline = (set(linenamelist) - previouslines).pop()
linelist['fililiname'][i] = newline
if i ==0:
firstline = linenamelist[0]
ui.get_model_component(firstline).pos.max = lwave + delta_lam/10.
ui.get_model_component(firstline).pos.min = lwave - delta_lam/10.
else:
dl = lwave - linelist['wave'][0]
ui.link(ui.get_model_component(newline).pos, ui.get_model_component(firstline).pos + dl)
#ui.set_analysis("wave")
ui.ignore(None, None) #ignores all data
ui.notice(min(linelist['wave'])-delta_lam, max(linelist['wave']) + delta_lam)
ui.fit(id)
if plot:
ui.plot_fit(id)
if has_chips:
pychips.set_curve("crv1",["err.*","true"])
pychips.set_plot_title(linelist['name'])
if outfile is not None:
pychips.print_window(outfile, ['clobber','true'])
elif has_mpl:
plt.title(linelist['name'])
if outfile is not None:
plt.savefig(outfile)
else:
raise NoPlottingSystemError("Neither pychips nor matplotlib are found.")
def link(self, par):
"""Link the parameter across the data stacks.
Link all occurences of the model parameter to the value in the
first dataset in the stack.
Parameters
----------
par : string
Parameter name, in the format "<model_type>.<par_name>".
"""
datasets = self.filter_datasets()
name, parname = par.split('.')
def _name(dset):
return dset['model_comps'][name]['model_name']
fullparname0 = '{0}.{1}'.format(_name(datasets[0]), parname)
for dataset in datasets[1:]:
fullparname = '{0}.{1}'.format(_name(dataset), parname)
if fullparname != fullparname0:
logger.info('Linking {0} => {1}'.format(fullparname,
fullparname0))
ui.link(fullparname, fullparname0)
def link(self, par):
"""Link the parameter across the data stacks.
Link all occurences of the model parameter to the value in the
first dataset in the stack.
Parameters
----------
par : string
Parameter name, in the format "<model_type>.<par_name>".
"""
datasets = self.filter_datasets()
name, parname = par.split('.')
def _name(dset):
return dset['model_comps'][name]['model_name']
fullparname0 = '{0}.{1}'.format(_name(datasets[0]), parname)
for dataset in datasets[1:]:
fullparname = '{0}.{1}'.format(_name(dataset), parname)
if fullparname != fullparname0:
logger.info('Linking {0} => {1}'.format(fullparname,
fullparname0))
ui.link(fullparname, fullparname0)
from astropy.io import fits
except:
import pyfits as fits
filename = 'skymap_ex.fits'
nomposstr = '05h34m31.94s 22d00m52.2s'
header = fits.getheader(filename)
proj = wcs.Projection(header)
xc, yc = float(header['NAXIS1']) / 2., float(header['NAXIS2']) / 2.
ui.load_image(filename)
ui.notice2d('circle({0}, {1}, {2})'.format(xc, yc, float(header['NAXIS2']) / 4.))
ui.set_source(ui.gauss2d.g1 + ui.gauss2d.g2)
g1.xpos = xc
g1.ypos = yc
g2.fwhm = g1.fwhm = 3.
ui.link(g2.xpos, g1.xpos)
ui.link(g2.ypos, g1.ypos)
g2.ampl = 50.
g1.ampl = 50.
ui.guess()
ui.fit()
ui.image_fit()
ui.covar()
conf = ui.get_covar_results()
conf_dict = dict([(n,(v, l, h)) for n,v,l,h in
zip(conf.parnames, conf.parvals, conf.parmins, conf.parmaxes)])
x, y = proj.toworld((conf_dict['g1.xpos'][0], conf_dict['g1.ypos'][0]))
xmin, ymin = proj.toworld((conf_dict['g1.xpos'][0] + conf_dict['g1.xpos'][1],
conf_dict['g1.ypos'][0] + conf_dict['g1.ypos'][1]))
xmax, ymax = proj.toworld((conf_dict['g1.xpos'][0] + conf_dict['g1.xpos'][2],
conf_dict['g1.ypos'][0] + conf_dict['g1.ypos'][2]))
except:
import pyfits as fits
filename = 'skymap_ex.fits'
nomposstr = '05h34m31.94s 22d00m52.2s'
header = fits.getheader(filename)
proj = wcs.Projection(header)
xc, yc = float(header['NAXIS1']) / 2., float(header['NAXIS2']) / 2.
ui.load_image(filename)
ui.notice2d('circle({0}, {1}, {2})'.format(xc, yc,
float(header['NAXIS2']) / 4.))
ui.set_source(ui.gauss2d.g1 + ui.gauss2d.g2)
g1.xpos = xc
g1.ypos = yc
g2.fwhm = g1.fwhm = 3.
ui.link(g2.xpos, g1.xpos)
ui.link(g2.ypos, g1.ypos)
g2.ampl = 50.
g1.ampl = 50.
ui.guess()
ui.fit()
ui.image_fit()
ui.covar()
conf = ui.get_covar_results()
conf_dict = dict([(n, (v, l, h)) for n, v, l, h in zip(
conf.parnames, conf.parvals, conf.parmins, conf.parmaxes)])
x, y = proj.toworld((conf_dict['g1.xpos'][0], conf_dict['g1.ypos'][0]))
xmin, ymin = proj.toworld((conf_dict['g1.xpos'][0] + conf_dict['g1.xpos'][1],
conf_dict['g1.ypos'][0] + conf_dict['g1.ypos'][1]))
xmax, ymax = proj.toworld((conf_dict['g1.xpos'][0] + conf_dict['g1.xpos'][2],
conf_dict['g1.ypos'][0] + conf_dict['g1.ypos'][2]))
