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 plot_stat(cut, **kwargs):
# plot PWN
print 'kwargs',kwargs
plot_points(Edot[cut], luminosity[cut],
xlo=None, xhi=None,
y_lower_err=luminosity_error_statistical[cut],
y_upper_err=luminosity_error_statistical[cut],
y_ul=luminosity_ul[cut], significant=luminosity_significant[cut],
axes=axes, **kwargs)
def plot_sys(cut, **kwargs):
# plot PWN
print 'sys: kwargs',kwargs
print luminosity[cut],luminosity_lower_error_systematic[cut],luminosity_upper_error_systematic[cut],
plot_points(Edot[cut], luminosity[cut],
xlo=None, xhi=None,
y_lower_err=luminosity_lower_error_systematic[cut],
y_upper_err=luminosity_upper_error_systematic[cut],
y_ul=luminosity_ul[cut], significant=luminosity_significant[cut],
axes=axes, **kwargs)
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,
figsize=(7,4),
plot_gtlike=True,
plot_pointlike=True,
gtlike_color='black',
pointlike_color='red',
**kwargs):
""" Create a plot from a dictionary. """
bands = self.results['bands']
all_time = self.results['all_time']
min_ts = self.results['min_ts']
if axes is None:
fig = P.figure(None,figsize)
axes = fig.add_subplot(111)
# astype(float) converts None to nan
a=lambda x: np.asarray(x).astype(float)
starts=a(self.results['time']['starts'])
stops=a(self.results['time']['stops'])
time=(starts+stops)/2.0
loop = [ ]
if plot_gtlike:
loop.append(['gtlike',gtlike_color],)
if plot_pointlike:
loop.append(['pointlike',pointlike_color])
for t,color in loop:
f0 = all_time[t]['flux']['flux']
ts = a([b[t]['TS'] for b in bands])
significant = (ts >= min_ts)
f = a([b[t]['flux']['flux'] for b in bands])
ferr=a([b[t]['flux']['flux_err'] for b in bands])
ferr=a([b[t]['flux']['flux_err'] for b in bands])
fup=a([b[t]['upper_limit']['flux'] if b[t]['upper_limit'] is not None else None for b in bands])
plot_points(
axes=axes,
x=time,
xlo=starts,
xhi=stops,
y=f,
y_lower_err=ferr,
y_upper_err=ferr,
y_ul=fup,
significant=significant,
color=color,
label=t,
**kwargs)
axes.axhline(f0, color=color, dashes=[5,2])
axes.set_xlabel('MET (s)')
axes.set_ylabel('Flux (ph$\,$cm$^{-2}$s$^{-1}$)')
axes.legend()
if filename is not None:
P.savefig(expandvars(filename))
return axes
def plot(self,
filename=None,
axes=None,
figsize=(7, 4),
plot_gtlike=True,
plot_pointlike=True,
gtlike_color='black',
pointlike_color='red',
**kwargs):
""" Create a plot from a dictionary. """
bands = self.results['bands']
all_time = self.results['all_time']
min_ts = self.results['min_ts']
if axes is None:
fig = P.figure(None, figsize)
axes = fig.add_subplot(111)
# astype(float) converts None to nan
a = lambda x: np.asarray(x).astype(float)
starts = a(self.results['time']['starts'])
stops = a(self.results['time']['stops'])
time = (starts + stops) / 2.0
loop = []
if plot_gtlike:
loop.append(['gtlike', gtlike_color], )
if plot_pointlike:
loop.append(['pointlike', pointlike_color])
for t, color in loop:
f0 = all_time[t]['flux']['flux']
ts = a([b[t]['TS'] for b in bands])
significant = (ts >= min_ts)
f = a([b[t]['flux']['flux'] for b in bands])
ferr = a([b[t]['flux']['flux_err'] for b in bands])
ferr = a([b[t]['flux']['flux_err'] for b in bands])
fup = a([
b[t]['upper_limit']['flux']
if b[t]['upper_limit'] is not None else None for b in bands
])
plot_points(axes=axes,
x=time,
xlo=starts,
xhi=stops,
y=f,
y_lower_err=ferr,
y_upper_err=ferr,
y_ul=fup,
significant=significant,
color=color,
label=t,
**kwargs)
axes.axhline(f0, color=color, dashes=[5, 2])
axes.set_xlabel('MET (s)')
axes.set_ylabel('Flux (ph$\,$cm$^{-2}$s$^{-1}$)')
axes.legend()
if filename is not None:
P.savefig(expandvars(filename))
return axes
