def plot_model_components(self, legend = True):
'''Plot contribution of individual model components
If matplotlib can be imported, it is used directly, otherwise
if falls back to Sherpa plotting (e.g. with chips).
Returns
-------
fig : matplotlib figure instance
'''
fig = plt.figure()
if has_mpl:
ax = fig.add_subplot(111)
pl = get_data_plot(self.id)
ax.errorbar(pl.x, pl.y, yerr=pl.yerr, fmt='+', label = 'data')
for part in smh.get_model_parts():
pl = get_model_component_plot(self.id, model = part)
ax.plot(pl.x, pl.y, label = part)
if legend:
ax.legend(ncol = 2)
ax.set_title('Individual model components')
else:
plot_fit(self.id)
for part in smh.get_model_parts():
plot_model_component(self.id, part, overplot = True)
return fig
def line_name_list(self):
'''get list of all model components that are lines
'''
parts_list = smh.get_model_parts(self.id)
return list([part for part in parts_list if part.startswith(self.linemodname)])
def mainloop(mymodel, fwhm, id = None, maxiter = 5, mindist = 0., do_plots = 0):
if id is None:
id = ui.get_default_id()
data = ui.get_data(id)
wave = data.get_indep()[0]
error = data.get_error()[0]
# model could habe been initalized with arbitrary values
ui.fit(id)
for i in range(maxiter):
oldmodel = smh.get_model_parts(id)
res_flux = ui.get_resid_plot(id).y
if smoothwindow is not None:
fwhminpix = int(fwhm / np.diff(wave).mean())
y = smooth(res_flux/error, window_len = 3*fwhminpix, window = smoothwindow)
else:
y = res_flux/error
peaks = findlines(wave, y, fwhm, smoothwindow = None, sigma_threshold = sigma_threshold)
if has_mpl and (do_plots > 2):
plt.figure()
plt.plot(wave, res_flux/error, 's')
for pos in mymodel.line_value_list('pos'):
plt.plot([pos, pos], plt.ylim(),'k:')
for peak in peaks:
plt.plot([wave[peak], wave[peak]], plt.ylim())
plt.plot(wave, y)
plt.draw()
for peak in peaks:
if (len(mymodel.line_value_list('pos')) == 0) or (min(np.abs(mymodel.line_value_list('pos') - wave[peak])) >= mindist):
mymodel.add_line(**mymodel.guess(wave, smooth(res_flux, window_len = 3*fwhminpix, window = smoothwindow), peak, fwhm = fwhm))
newmodel = smh.get_model_parts(id)
print 'Iteration {0:3n}: {1:3n} lines added'.format(i, len(newmodel) - len(oldmodel))
if set(newmodel) == set(oldmodel):
print 'No new lines added this step - fitting finished'
break
# Now do the fitting in Sherpa
#ui.set_method('simplex')
ui.fit(id)
#ui.set_method('moncar')
#ui.fit(id)
if has_mpl and (do_plots > 0):
if do_plots > 1:
plt.figure()
else:
plt.clf()
ui.plot_fit(id)
for pos in mymodel.line_value_list('pos'):
plt.plot([pos, pos], plt.ylim(),'k:')
for peak in peaks:
plt.plot([wave[peak], wave[peak]], plt.ylim())
plt.plot(wave, res_flux)
plt.draw()
else:
print 'Max number of iterations reached'
#model.cleanup() #remove lines running to 0 etc.
return mymodel
