def test_fit_plot_see_errorbar_warnings(caplog, statClass, flag):
"""Do we see the warning when expected - fit plot?
This looks for the 'The displayed errorbars have been supplied with
the data or calculated using chi2xspecvar; the errors are not used in
fits with <>' message. These are messages displayed to the Sherpa
logger at the warning level, rather than using the warnings module,
so the Sherpa capture_all_warnings test fixture does not come into
play.
Parameters
----------
stat : sherpa.stats.Stat instance
flag : bool
True if the warning should be created, False otherwise
"""
d = example_data()
m = example_model()
dplot = DataPlot()
mplot = ModelPlot()
fplot = FitPlot()
# Internal check: this test requires that either yerrorbars is set
# to True, or not included, in the plot preferences. So check this
# assumption.
#
# I am skipping model plot here, since it is assumed that there
# are no errors on the model.
#
prefname = 'yerrorbars'
for plot in [dplot, fplot]:
prefs = plot.plot_prefs
assert (prefname not in prefs) or prefs[prefname]
stat = statClass()
# Ensure that the logging is set to WARNING since there
# appears to be some test that changes it to ERROR.
#
with caplog.at_level(logging.INFO, logger='sherpa'):
dplot.prepare(d, stat)
mplot.prepare(d, m, stat)
fplot.prepare(dplot, mplot)
if flag:
nwarn = 1
else:
nwarn = 0
check_for_warning(caplog, nwarn, stat.name)
def test_fit_residstyle_plot_no_errors_no_errorbar_warnings(
caplog, plotClass, statClass):
"""Should not see warnings when no error bars are drawn (See #621).
This is a copy of test_fit_residstyle_plot_see_errorbar_warnings
except that the 'yerrorbars' preference setting for all plots is
'False'.
Parameters
----------
plotClass : {sherpa.plot.ResidPlot, sherpa.plot.RatioPlot}
The plot to test.
statClass : sherpa.stats.Stat instance
Notes
-----
Is this an accurate example of how 'plot_fit_resid' is created?
"""
d = example_data()
m = example_model()
dplot = DataPlot()
mplot = ModelPlot()
fplot = FitPlot()
rplot = plotClass()
jplot = JointPlot()
prefname = 'yerrorbars'
for plot in [dplot, rplot]:
prefs = plot.plot_prefs
prefs[prefname] = False
stat = statClass()
# Ensure that the logging is set to WARNING since there
# appears to be some test that changes it to ERROR.
#
with caplog.at_level(logging.INFO, logger='sherpa'):
dplot.prepare(d, stat)
mplot.prepare(d, m, stat)
fplot.prepare(dplot, mplot)
rplot.prepare(d, m, stat)
jplot.plottop(fplot)
jplot.plotbot(rplot)
check_for_warning(caplog, 0, stat.name)
def __init__(self):
FitPlot.__init__(self)
from sherpa.optmethods import NelderMead
f.method = NelderMead()
resn = f.fit()
print("Change in statistic: {}".format(resn.dstatval))
fit2 = Fit(d, mdl, method=NelderMead())
fit2.fit()
mdl.c1.thaw()
f.method = original_method
res2 = f.fit()
report("res2.format()")
from sherpa.plot import DelchiPlot, FitPlot, SplitPlot
fplot = FitPlot()
rplot = DelchiPlot()
splot = SplitPlot()
mplot.prepare(f.data, f.model)
fplot.prepare(dplot, mplot)
splot.addplot(fplot)
rplot.prepare(f.data, f.model, f.stat)
splot.addplot(rplot)
savefig("fit_delchi_c0_c1_c2.png")
# what does JointPlot do?
from sherpa.plot import JointPlot
jplot = JointPlot()
jplot.plottop(fplot)
jplot.plotbot(rplot)
mplot.prepare(d, mdl)
dplot = DataPlot()
dplot.prepare(d)
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)
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
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
report("print(f)")
res = f.fit()
dump("res.succeeded")
report("res.format()")
report("res")
report("mdl")
stat2 = f.calc_stat()
print("Statistic = {:.4f}".format(stat2))
from sherpa.plot import FitPlot, ResidPlot, SplitPlot
fplot = FitPlot()
mplot.prepare(d, mdl)
fplot.prepare(dplot, mplot)
splot = SplitPlot()
splot.addplot(fplot)
rplot = ResidPlot()
print("### should get a WARNING about missing errors")
rplot.prepare(d, mdl, stat=LeastSq())
rplot.plot_prefs['yerrorbars'] = False
splot.addplot(rplot)
savefig("data_model_resid.png")
report("mdl([2, 5, 10])")
