mplot.overplot()
#set error methods, ChiSq() or LeastSq()
#Chi square is a way to compare which profile best describes data, ie: is it more gaussian or lorentzian
#Least Square says how good the data fits the particular model instance
#opt - optimizers improve the fit. Monte Carlo is what I used, it is slow but it is most robust. Many options on sherpas site
ustat = LeastSq()
opt = MonCar() #LevMar() #NelderMead() #
#apply actual Fit
f = Fit(d, mdl, stat=ustat, method=opt)
res = f.fit()
fplot = FitPlot()
mplot.prepare(d, mdl)
fplot.prepare(dplot, mplot)
fplot.plot()
#param_errors = f.est_errors()
#plotting routine
plt.plot(d.x, d.y, "c.", label="Data")
plt.plot(d.x, mdl(d.x), linewidth=2, label="Gaussian")
plt.legend(loc=2)
plt.title(fits_image_filename)
plt.xlabel("Wavelength nm")
plt.ylabel("Normalized Intensity")
os.chdir(r"/home/dtyler/Desktop/DocumentsDT/outputs")
os.mkdir("images_" + fits_image_filename)
os.chdir(r"/home/dtyler/Desktop/DocumentsDT/outputs/images_" +
fits_image_filename)
plt.savefig(fits_image_filename[:-4] + "_model" + str(icorr + 1) +
def fit(star_name, data, model, silent=False, breakdown=False):
"""A function that will fit a given multi-part model to a given spectrum.
:param star_name: Name of the target star
:type star_name: str
:param data: Spectrum data in the form (wave, flux)
:type data: tuple
:param model: An unfit spectrum model
:type model: object
:param silent: If true, no plots will generate, defaults to False
:type silent: bool
:return: model that is fit to the data
:rtype: object
"""
wave, flux = data
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
d = Data1D(star_name, wave, flux)
# ==========================================
# Initial guesses
# Dataset 1
dplot = DataPlot()
dplot.prepare(d)
if silent is False:
dplot.plot()
mplot = ModelPlot()
mplot.prepare(d, model)
if silent is False:
dplot.plot()
mplot.overplot()
plt.show()
# =========================================
# Fitting happens here - don't break please
start = time.time()
stat = LeastSq()
opt = LevMar()
opt.verbose = 0
opt.ftol = 1e-15
opt.xtol = 1e-15
opt.gtol = 1e-15
opt.epsfcn = 1e-15
if silent is False:
print(opt)
vfit = Fit(d, model, stat=stat, method=opt)
if silent is False:
print(vfit)
vres = vfit.fit()
if silent is False:
print()
print()
print(vres.format())
# =========================================
# Plotting after fit
# Dataset 1
if silent is False:
fplot = FitPlot()
mplot.prepare(d, model)
fplot.prepare(dplot, mplot)
fplot.plot()
# residual
plt.title(star_name)
plt.plot(wave, flux - model(wave))
# plt.xaxis(fontsize = )
plt.xlabel("Wavelength (AA)", fontsize=12)
plt.ylabel("Flux", fontsize=12)
plt.tick_params(axis="both", labelsize=12)
if silent is False:
duration = time.time() - start
print()
print("Time taken: " + str(duration))
print()
plt.show()
if breakdown is True:
params = []
cont = model[0]
if silent is False:
plt.scatter(wave, flux, marker=".", c="black")
plt.plot(wave, model(wave), c="C1")
for line in model:
if line.name[0] != "(":
if line.name == "Cont_flux":
if silent is False:
print(line)
plt.plot(wave, line(wave), linestyle="--")
else:
params.append(line)
if silent is False:
print()
print(line)
plt.plot(wave, line(wave) * cont(wave), linestyle="--")
plt.show()
return model, params
return model
mdl.pars[2].val = 22
mdl.pars[3].val = 10
report("mdl")
from sherpa.plot import ModelPlot
mplot = ModelPlot()
mplot.prepare(d, mdl)
mplot.plot()
dplot.overplot()
savefig('modelplot_histogram_overplot.png')
from sherpa.plot import FitPlot
fplot = FitPlot()
fplot.prepare(dplot, mplot)
fplot.plot()
savefig('fitplot_histogram.png')
# settings
report("dplot.plot_prefs")
dplot.plot_prefs['ylog'] = True
dplot.plot(marker='s', linestyle='dashed')
savefig('settings_dataplot_combined.png')
dplot.plot()
savefig('settings_dataplot_ylog.png')
def multifit(star_name, data_list, model_list, silent=False):
"""A function that will fit 2 models to 2 spectra simultaneously.
This was created to fit the NaI doublets at ~3300 and ~5890 Angstroms.
:param star_name: Name of the target star
:type star_name: str
:param data_list: List of spectrum data in the form [(wave, flux), (wave, flux),...]
:type data_list: tuple
:param model_list: A list of unfit spectrum models
:type model_list: list
:param silent: If true, no plots will generate, defaults to False
:type silent: bool
:return: models that are fit to the data
:rtype: list
"""
wave1, flux1 = data_list[0]
wave2, flux2 = data_list[1]
model1 = model_list[0]
model2 = model_list[1]
name_1 = star_name + " 1"
name_2 = star_name + " 2"
d1 = Data1D(name_1, wave1, flux1)
d2 = Data1D(name_2, wave2, flux2)
dall = DataSimulFit("combined", (d1, d2))
mall = SimulFitModel("combined", (model1, model2))
# # ==========================================
# # Initial guesses
# Dataset 1
dplot1 = DataPlot()
dplot1.prepare(d1)
if silent is False:
dplot1.plot()
mplot1 = ModelPlot()
mplot1.prepare(d1, model1)
if silent is False:
dplot1.plot()
mplot1.overplot()
plt.show()
# Dataset 2
dplot2 = DataPlot()
dplot2.prepare(d2)
if silent is False:
dplot2.plot()
mplot2 = ModelPlot()
mplot2.prepare(d2, model2)
if silent is False:
dplot2.plot()
mplot2.overplot()
plt.show()
# # =========================================
# # Fitting happens here - don't break please
stat = LeastSq()
opt = LevMar()
opt.verbose = 0
opt.ftol = 1e-15
opt.xtol = 1e-15
opt.gtol = 1e-15
opt.epsfcn = 1e-15
print(opt)
vfit = Fit(dall, mall, stat=stat, method=opt)
print(vfit)
vres = vfit.fit()
print()
print()
print("Did the fit succeed? [bool]")
print(vres.succeeded)
print()
print()
print(vres.format())
# # =========================================
# # Plotting after fit
if silent is False:
# Dataset 1
fplot1 = FitPlot()
mplot1.prepare(d1, model1)
fplot1.prepare(dplot1, mplot1)
fplot1.plot()
# residual
title = "Data 1"
plt.title(title)
plt.plot(wave1, flux1 - model1(wave1))
plt.show()
# Dataset 2
fplot2 = FitPlot()
mplot2.prepare(d2, model2)
fplot2.prepare(dplot2, mplot2)
fplot2.plot()
# residual
title = "Data 2"
plt.title(title)
plt.plot(wave2, flux2 - model2(wave2))
plt.show()
# both datasets - no residuals
splot = SplitPlot()
splot.addplot(fplot1)
splot.addplot(fplot2)
plt.tight_layout()
plt.show()
return model_list
from sherpa.plot import DelchiPlot
residplot = DelchiPlot()
residplot.prepare(d, mdl, f.stat)
residplot.plot()
savefig("model_data_delchi.png")
d.notice()
dump("d.get_filter(format='%d')")
from sherpa.plot import FitPlot
fitplot = FitPlot()
dplot.prepare(d)
mplot.prepare(d, mdl)
fitplot.prepare(dplot, mplot)
fitplot.plot()
savefig("model_data_fit_all.png")
# do we get an error? Actually, it looks to not be the divide-by-zero
# being the problem but list/list instead:
#
"""
residplot.prepare(d, mdl, f.stat)
/home/djburke/miniconda2/envs/sherpa410-py35/lib/python3.5/site-packages/sherpa-4.10.0-py3.5-linux-x86_64.egg/sherpa/plot/__init__.py:1128: RuntimeWarning: divide by zero encountered in true_divide
return (ylist[0] - ylist[1]) / staterr
Traceback (most recent call last):
File "example.py", line 125, in <module>
residplot.prepare(d, mdl, f.stat)
File "/home/djburke/miniconda2/envs/sherpa410-py35/lib/python3.5/site-packages/sherpa-4.10.0-py3.5-linux-x86_64.egg/sherpa/plot/__init__.py", line 1140, in prepare
self.yerr = staterr / staterr