def plot_points(self, axes, **kwargs):
""" Plot the SED using matpotlib. """
data_kwargs = dict(color='black')
data_kwargs.update(kwargs)
edict = self.results['Energy']
fdict = self.results['dNdE']
file_energy_units = units.fromstring(edict['Units'])
file_flux_units = units.fromstring(fdict['Units'])
ce = lambda x: units.convert(np.asarray(x), file_energy_units, axes.
energy_units_obj)
# get energy part
energy = ce(edict['Value'])
if 'Lower' in edict and 'Upper' in edict:
lower_energy = ce(edict['Lower'])
upper_energy = ce(edict['Upper'])
has_energy_errors = True
else:
has_energy_errors = False
# get spectral part
cf = lambda y: units.convert(
energy**2 * np.asarray(y), axes.energy_units_obj**2 *
file_flux_units, axes.flux_units_obj / units.cm**2 / units.s)
dnde = cf(fdict['Value'])
if 'Lower_Error' in fdict and 'Upper_Error' in fdict:
# assymetric errors
dnde_lower_err = cf(fdict['Lower_Error'])
dnde_upper_err = cf(fdict['Upper_Error'])
has_assymetric_errors = True
else:
has_assymetric_errors = False
dnde_err = cf(fdict['Average_Error'])
# get limits, otherwise assume all significant
if 'Upper_Limit' in fdict and 'Significant' in self.results:
dnde_ul = cf(fdict['Upper_Limit'])
significant = np.asarray(self.results['Significant'])
has_upper_limits = True
else:
has_upper_limits = False
plot_points(
x=energy,
xlo=lower_energy if has_energy_errors else None,
xhi=upper_energy if has_energy_errors else None,
y=dnde,
y_lower_err=dnde_lower_err if has_assymetric_errors else dnde_err,
y_upper_err=dnde_upper_err if has_assymetric_errors else dnde_err,
y_ul=dnde_ul if has_upper_limits else None,
significant=significant
if has_upper_limits else np.ones(len(energy), dtype=bool),
axes=axes,
**data_kwargs)
def plot_points(self, axes, **kwargs):
""" Plot the SED using matpotlib. """
data_kwargs=dict(color='black')
data_kwargs.update(kwargs)
edict = self.results['Energy']
fdict = self.results['dNdE']
file_energy_units = units.fromstring(edict['Units'])
file_flux_units = units.fromstring(fdict['Units'])
ce = lambda x: units.convert(np.asarray(x),file_energy_units, axes.energy_units_obj)
# get energy part
energy = ce(edict['Value'])
if 'Lower' in edict and 'Upper' in edict:
lower_energy = ce(edict['Lower'])
upper_energy = ce(edict['Upper'])
has_energy_errors = True
else:
has_energy_errors = False
# get spectral part
cf = lambda y: units.convert(energy**2*np.asarray(y),
axes.energy_units_obj**2*file_flux_units,
axes.flux_units_obj/units.cm**2/units.s)
dnde = cf(fdict['Value'])
if 'Lower_Error' in fdict and 'Upper_Error' in fdict:
# assymetric errors
dnde_lower_err = cf(fdict['Lower_Error'])
dnde_upper_err = cf(fdict['Upper_Error'])
has_assymetric_errors = True
else:
has_assymetric_errors = False
dnde_err = cf(fdict['Average_Error'])
# get limits, otherwise assume all significant
if 'Upper_Limit' in fdict and 'Significant' in self.results:
dnde_ul = cf(fdict['Upper_Limit'])
significant = np.asarray(self.results['Significant'])
has_upper_limits=True
else:
has_upper_limits=False
plot_points(
x=energy,
xlo=lower_energy if has_energy_errors else None,
xhi=upper_energy if has_energy_errors else None,
y=dnde,
y_lower_err=dnde_lower_err if has_assymetric_errors else dnde_err,
y_upper_err=dnde_upper_err if has_assymetric_errors else dnde_err,
y_ul=dnde_ul if has_upper_limits else None,
significant=significant if has_upper_limits else np.ones(len(energy),dtype=bool),
axes=axes, **data_kwargs)
def __init__(self, sed_file, energy_units_str='MeV', flux_units_str='erg'):
self.sed_file = sed_file
self.energy_units_str = energy_units_str
self.flux_units_str = flux_units_str
self.energy_units = units.fromstring(self.energy_units_str)
self.flux_units = units.fromstring(self.flux_units_str)
self.__load__(sed_file)
def __init__(self, sed_file, energy_units_str='MeV', flux_units_str='erg'):
self.sed_file = sed_file
self.energy_units_str = energy_units_str
self.flux_units_str = flux_units_str
self.energy_units = units.fromstring(self.energy_units_str)
self.flux_units = units.fromstring(self.flux_units_str)
self.__load__(sed_file)
def plot(self, filename=None, axes=None, title=None,
sed_results=None,
fignum=None, figsize=(4,4),
model_0_kwargs=dict(color='red', zorder=0),
model_1_kwargs=dict(color='blue', zorder=0),
sed_kwargs=dict(),
plot_kwargs=dict(),):
""" Plots the cutoff test performed of a spectrum using the function
gtlike_test_cutoff.
Input:
cutoff_dict: created by gtlike_test_cutoff
sed_dict: created by GtlikeSED.todict(). Can also be a yaml
file created by GtlikeSED.save().
model_0_kwargs: kwargs for model_0's plot
model_1_kwargs: kwargs for model_0's plot
sed_kwargs: kwargs to pass into SED
E.G. flux_units, flux_units, figsize, ...
"""
if axes is None:
fig = P.figure(fignum,figsize)
axes = SpectralAxes(fig=fig, rect=(0.22,0.15,0.75,0.8),
flux_units=self.flux_units,
energy_units=self.energy_units)
fig.add_axes(axes)
energy = self.results['energy']
axes.set_xlim_units(energy['emin']*units.fromstring(energy['energy_units']),
energy['emax']*units.fromstring(energy['energy_units']))
if sed_results is not None:
sed = SED(sed_results)
sed.plot_points(axes=axes, **sed_kwargs)
sp = SpectrumPlotter(axes=axes)
sp.plot(self.results['hypothesis_0']['spectrum'],
autoscale=False, **model_0_kwargs)
sp.plot_error(self.results['hypothesis_0']['spectrum'],
self.results['hypothesis_0']['spectrum']['covariance_matrix'],
alpha=0.5,
autoscale=False, **model_0_kwargs)
sp.plot(self.results['hypothesis_1']['spectrum'],
autoscale=False, **model_1_kwargs)
sp.plot_error(self.results['hypothesis_1']['spectrum'],
self.results['hypothesis_1']['spectrum']['covariance_matrix'],
alpha=0.5,
autoscale=False, **model_1_kwargs)
if title is not None: axes.set_title(title)
if filename is not None:
P.savefig(expandvars(filename))
return axes
def __init__(self, *args, **kwargs):
self.energy_units = kwargs.pop('energy_units', 'MeV')
self.flux_units = kwargs.pop('flux_units', 'erg')
self.energy_units_obj = units.fromstring(self.energy_units)
self.flux_units_obj = units.fromstring(self.flux_units)
super(SpectralAxes,self).__init__(*args, **kwargs)
self.set_xscale('log')
self.set_yscale('log')
self.set_xlabel('Energy (%s)' % self.energy_units)
self.set_ylabel('E$^2$ dN/dE (%s cm$^{-2}$ s$^{-1}$)' % self.flux_units)
def __init__(self, *args, **kwargs):
self.energy_units = kwargs.pop('energy_units', 'MeV')
self.flux_units = kwargs.pop('flux_units', 'erg')
self.energy_units_obj = units.fromstring(self.energy_units)
self.flux_units_obj = units.fromstring(self.flux_units)
super(SpectralAxes, self).__init__(*args, **kwargs)
self.set_xscale('log')
self.set_yscale('log')
self.set_xlabel('Energy (%s)' % self.energy_units)
self.set_ylabel('E$^2$ dN/dE (%s cm$^{-2}$ s$^{-1}$)' %
self.flux_units)
def plot(self, filename=None, axes=None, title=None,
fignum=None, figsize=(4,4),
spectral_kwargs=dict(),
spectral_error_kwargs=dict(),
):
pass_spectral_kwargs=dict(color='red',zorder=1.9)
pass_spectral_kwargs.update(spectral_kwargs)
pass_spectral_error_kwargs=dict(color='red',alpha=0.5,zorder=1.8)
pass_spectral_error_kwargs.update(spectral_error_kwargs)
if axes is None:
fig = P.figure(fignum,figsize)
axes = SpectralAxes(fig=fig, rect=(0.22,0.15,0.75,0.8),
flux_units=self.flux_units,
energy_units=self.energy_units)
fig.add_axes(axes)
axes.set_xlim_units(
self.results['bands'][0]['energy']['emin']*units.fromstring(self.results['bands'][0]['energy']['energy_units']),
self.results['bands'][-1]['energy']['emax']*units.fromstring(self.results['bands'][-1]['energy']['energy_units'])
)
for i,r in enumerate(self.results['bands']):
emin=r['energy']['emin']*units.fromstring(r['energy']['energy_units'])
emax=r['energy']['emax']*units.fromstring(r['energy']['energy_units'])
spectrum = r['spectrum']
if i > 0 and 'label' in spectral_kwargs:
# only one label
spectral_kwargs.pop('label')
if r['significant']:
sp = SpectrumPlotter(axes=axes)
sp.plot(spectrum, emin=emin, emax=emax, **pass_spectral_kwargs)
sp.plot_error(spectrum, emin=emin, emax=emax, **pass_spectral_error_kwargs)
else:
ul = UpperLimit(r['upper_limit'])
ul.plot(axes=axes, **pass_spectral_kwargs)
if title is not None: axes.set_title(title)
if filename is not None:
P.savefig(expandvars(filename))
return axes
def plot(
self,
filename=None,
axes=None,
title=None,
fignum=None,
figsize=(4, 4),
rect=(0.22, 0.15, 0.75, 0.8),
plot_spectral_fit=True,
plot_spectral_error=True,
data_kwargs=dict(),
spectral_kwargs=dict(),
spectral_error_kwargs=dict(),
):
if axes is None:
fig = P.figure(fignum, figsize)
axes = SpectralAxes(fig=fig,
rect=rect,
flux_units=self.flux_units,
energy_units=self.energy_units)
fig.add_axes(axes)
edict = self.results['Energy']
file_energy_units = units.fromstring(edict['Units'])
if 'Lower' in edict and 'Upper' in edict:
axes.set_xlim_units(edict['Lower'][0] * file_energy_units,
edict['Upper'][-1] * file_energy_units)
else:
axes.set_xlim_units(edict['Energy'][0] * file_energy_units,
edict['Energy'][-1] * file_energy_units)
self.plot_points(axes=axes, **data_kwargs)
if plot_spectral_fit:
self.plot_spectral_fit(axes=axes, **spectral_kwargs)
if plot_spectral_error:
self.plot_spectral_error(axes=axes, **spectral_error_kwargs)
if title is not None: axes.set_title(title)
if filename is not None:
P.savefig(expandvars(filename))
return axes
def plot(self, filename=None, axes=None, title=None,
fignum=None, figsize=(4,4), **kwargs):
""" Plot the upper limit. """
spectral_kwargs=dict(color='red',zorder=1.9)
spectral_kwargs.update(kwargs)
file_energy_units = units.fromstring(self.results['energy_units'])
emin = self.results['emin']*file_energy_units
emax = self.results['emax']*file_energy_units
if axes is None:
fig = P.figure(fignum,figsize)
axes = SpectralAxes(fig=fig,
rect=(0.22,0.15,0.75,0.8),
flux_units=self.flux_units,
energy_units=self.energy_units)
fig.add_axes(axes)
axes.set_xlim_units(emin,emax)
# plot the spectral model
spectrum = self.results['spectrum']
sp=SpectrumPlotter(axes=axes)
sp.plot(spectrum, emin=emin, emax=emax, **spectral_kwargs)
# plot in the middle an arrow pointing down
e_middle = units.tosympy([np.sqrt(self.results['emin']*self.results['emax'])],file_energy_units)
dnde = sp.get_dnde(spectrum, e_middle)
energies, e2_dnde = axes.convert_points(e_middle, dnde)
if 'autoscale' in spectral_kwargs: spectral_kwargs.pop('autoscale')
if 'label' in spectral_kwargs: spectral_kwargs.pop('label')
plot_points(x=energies, y=e2_dnde,
xlo=None, xhi=None,
y_lower_err=None, y_upper_err=None,
y_ul=e2_dnde,
significant=False,
axes=axes, **spectral_kwargs)
if title is not None: axes.set_title(title)
if filename is not None:
P.savefig(expandvars(filename))
return axes
def plot(self, filename=None, axes=None, title=None,
fignum=None, figsize=(4,4),
rect=(0.22,0.15,0.75,0.8),
plot_spectral_fit=True,
plot_spectral_error=True,
data_kwargs=dict(),
spectral_kwargs=dict(),
spectral_error_kwargs=dict(),
):
if axes is None:
fig = P.figure(fignum,figsize)
axes = SpectralAxes(fig=fig,
rect=rect,
flux_units=self.flux_units,
energy_units=self.energy_units)
fig.add_axes(axes)
edict = self.results['Energy']
file_energy_units = units.fromstring(edict['Units'])
if 'Lower' in edict and 'Upper' in edict:
axes.set_xlim_units(edict['Lower'][0]*file_energy_units, edict['Upper'][-1]*file_energy_units)
else:
axes.set_xlim_units(edict['Energy'][0]*file_energy_units, edict['Energy'][-1]*file_energy_units)
self.plot_points(axes=axes, **data_kwargs)
if plot_spectral_fit:
self.plot_spectral_fit(axes=axes, **spectral_kwargs)
if plot_spectral_error:
self.plot_spectral_error(axes=axes, **spectral_error_kwargs)
if title is not None: axes.set_title(title)
if filename is not None:
P.savefig(expandvars(filename))
return axes
def plot(
self,
filename=None,
axes=None,
title=None,
sed_results=None,
fignum=None,
figsize=(4, 4),
model_0_kwargs=dict(color='red', zorder=0),
model_1_kwargs=dict(color='blue', zorder=0),
sed_kwargs=dict(),
plot_kwargs=dict(),
):
""" Plots the cutoff test performed of a spectrum using the function
gtlike_test_cutoff.
Input:
cutoff_dict: created by gtlike_test_cutoff
sed_dict: created by GtlikeSED.todict(). Can also be a yaml
file created by GtlikeSED.save().
model_0_kwargs: kwargs for model_0's plot
model_1_kwargs: kwargs for model_0's plot
sed_kwargs: kwargs to pass into SED
E.G. flux_units, flux_units, figsize, ...
"""
if axes is None:
fig = P.figure(fignum, figsize)
axes = SpectralAxes(fig=fig,
rect=(0.22, 0.15, 0.75, 0.8),
flux_units=self.flux_units,
energy_units=self.energy_units)
fig.add_axes(axes)
energy = self.results['energy']
axes.set_xlim_units(
energy['emin'] * units.fromstring(energy['energy_units']),
energy['emax'] * units.fromstring(energy['energy_units']))
if sed_results is not None:
sed = SED(sed_results)
sed.plot_points(axes=axes, **sed_kwargs)
sp = SpectrumPlotter(axes=axes)
sp.plot(self.results['hypothesis_0']['spectrum'],
autoscale=False,
**model_0_kwargs)
sp.plot_error(
self.results['hypothesis_0']['spectrum'],
self.results['hypothesis_0']['spectrum']['covariance_matrix'],
alpha=0.5,
autoscale=False,
**model_0_kwargs)
sp.plot(self.results['hypothesis_1']['spectrum'],
autoscale=False,
**model_1_kwargs)
sp.plot_error(
self.results['hypothesis_1']['spectrum'],
self.results['hypothesis_1']['spectrum']['covariance_matrix'],
alpha=0.5,
autoscale=False,
**model_1_kwargs)
if title is not None: axes.set_title(title)
if filename is not None:
P.savefig(expandvars(filename))
return axes
