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):
ModelPlot.__init__(self)
self.title = 'Background Model Contribution'
sinfo2 = f.calc_stat_info()
d.notice()
dump("sinfo1.numpoints")
dump("sinfo2.numpoints")
res = f.fit()
if res.succeeded: print("Fit succeeded")
if not res.succeeded: print("**** ERRRR, the fit failed folks")
report("res.format()")
report("res")
from sherpa.plot import DataPlot, ModelPlot
dplot = DataPlot()
dplot.prepare(f.data)
mplot = ModelPlot()
mplot.prepare(f.data, f.model)
dplot.plot()
mplot.overplot()
savefig("data_model_c0_c2.png")
dump("f.method.name")
original_method = f.method
from sherpa.optmethods import NelderMead
f.method = NelderMead()
resn = f.fit()
print("Change in statistic: {}".format(resn.dstatval))
fit2 = Fit(d, mdl, method=NelderMead())
hdul.close()
#normalize spliced intensities & invert
y = -1.0 * (y_raw - min(y_raw)) / norm_factor + 1.0
#Set data and model for fits
icorr = 0
G1 = Gauss1D('G1')
d = Data1D('He 1083', x, y, staterror=sd)
#guess parameters, this is important or sherpa won't know where to start looking
G1.fwhm = .05
G1.pos = 1083.03 + ref_value * 5
mdl = G1
mplot = ModelPlot()
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()
plt.plot(xmid, y2 / (xhi - xlo) / pha.exposure)
plt.xlabel('Energy (keV)')
plt.ylabel('Counts/sec/keV')
savefig('pha_eval_model_to_fit.png')
from sherpa.astro.plot import ModelHistogram
mplot = ModelHistogram()
mplot.prepare(pha, full)
mplot.plot()
savefig('pha_fullmodel_model.png')
from sherpa.fit import Fit
fit = Fit(pha, full)
res = fit.fit()
report('res.format()')
from sherpa.plot import ModelPlot
dplot.prepare(pha)
dplot.plot(xlog=True)
mplot2 = ModelPlot()
mplot2.prepare(pha, full)
mplot2.overplot()
savefig('pha_fullmodel_fit.png')
def prepare_spectra(group, nH, add_gal, redshift):
pha = read_pha("core_spectrum.pi")
pha.set_analysis("energy")
pha.notice(0.5, 7.0)
tabs = ~pha.mask
pha.group_counts(group, tabStops=tabs)
x = pha.get_x()
x = pha.apply_filter(x, pha._middle)
y = pha.get_y(filter=True)
pha.set_analysis("energy")
model = xsphabs.abs1 * powlaw1d.srcp1
print("Fitting the spectrum")
zFlag = False
if (nH is not None) and (nH > 0.0):
if add_gal == 1:
model = xsphabs.gal * xszphabs.abs1 * powlaw1d.srcp
gal.nH = nH
freeze(gal.nH)
zFlag = True
else:
model = xsphabs.abs1 * powlaw1d.srcp1
abs1.nH = nH
freeze(abs1.nH)
else:
model = xszphabs.abs1 * powlaw1d.srcp1
zFlag = True
if zFlag is True and add_gal == 1:
# print('REDSHIFT',redshift)
abs1.redshift = redshift
freeze(abs1.redshift)
full_model = RSPModelPHA(pha.get_arf(), pha.get_rmf(), pha, pha.exposure * model)
print(full_model)
fit = Fit(pha, full_model, method=MonCar(), stat=WStat())
res = fit.fit()
print(res.format())
print(fit.est_errors())
# calculate the p-value for wstat
mplot2 = ModelPlot()
mplot2.prepare(pha, full_model)
miu = mplot2.y * pha.exposure * 0.0146
obs = y * pha.exposure * 0.0146
c, ce, cv = gof_cstat(miu, obs)
print(f"C0={c},C_e={ce},C_v={cv}")
zval = (fit.calc_stat() - ce) / np.sqrt(cv)
if zval > 0:
pval = special.erfc(zval / np.sqrt(2))
else:
pval = special.erf(abs(zval) / np.sqrt(2))
print(f"p-value for wstat = {pval}")
set_data(pha)
set_model(model)
save_chart_spectrum("core_flux_chart.dat", elow=0.5, ehigh=7.0)
# save_chart_spectrum("core_flux_chart.rdb",format='text/tsv', elow=0.5, ehigh=7.0)
save_spectrum_rdb("core_flux_chart.dat")
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
def prepare_spectra(nH: float,
group: int = 1,
add_gal: bool = False,
redshift: Optional[float] = None,
**kwargs) -> float:
"""
Fit the spectra using an absorbed powerlaw model using the Wstat statistic. The function also returns a p-value for the gof.
:param nH: The galactic absorption column density in units of 10^22 /cm3
:param group: The number of counts per energy bin
:param add_gal: Setting this to True would add an intrinsic abrosption column density along side the galactic one
:param redshift: The redshift to use in the fit. Only takes effect if add_gal is set to True
...
:return: Returns the p-value of the gof. The null hypothesis states that the model and the observation differ while alternate says that the model explains the data
"""
pha = read_pha("core_spectrum.pi")
pha.set_analysis("energy")
pha.notice(0.5, 7.0)
tabs = ~pha.mask
pha.group_counts(group, tabStops=tabs)
x = pha.get_x()
x = pha.apply_filter(x, pha._middle)
y = pha.get_y(filter=True)
pha.set_analysis("energy")
model = xsphabs.abs1 * powlaw1d.srcp1
print("Fitting the spectrum")
zFlag = False
if (nH is not None) and (nH > 0.0):
if add_gal == 1:
model = xsphabs.gal * xszphabs.abs1 * powlaw1d.srcp
gal.nH = nH
freeze(gal.nH)
zFlag = True
else:
model = xsphabs.abs1 * powlaw1d.srcp1
abs1.nH = nH
freeze(abs1.nH)
else:
model = xszphabs.abs1 * powlaw1d.srcp1
zFlag = True
if zFlag is True and add_gal == 1:
# print('REDSHIFT',redshift)
abs1.redshift = redshift
freeze(abs1.redshift)
full_model = RSPModelPHA(pha.get_arf(), pha.get_rmf(), pha,
pha.exposure * model)
print(full_model)
fit = Fit(pha, full_model, method=MonCar(), stat=WStat())
res = fit.fit()
print(res.format())
print(fit.est_errors())
# calculate the p-value for wstat
mplot2 = ModelPlot()
mplot2.prepare(pha, full_model)
miu = mplot2.y * pha.exposure * 0.0146
obs = y * pha.exposure * 0.0146
c, ce, cv = SpecUtils.estimate_gof_cstat(miu, obs)
#print(f"C0={c},C_e={ce},C_v={cv}")
zval = (fit.calc_stat() - ce) / np.sqrt(cv)
if zval > 0:
pval = special.erfc(zval / np.sqrt(2))
else:
pval = special.erf(abs(zval) / np.sqrt(2))
print(f"p-value for wstat = {pval}")
set_data(pha)
set_model(model)
save_chart_spectrum("core_flux_chart.dat", elow=0.5, ehigh=7.0)
# save_chart_spectrum("core_flux_chart.rdb",format='text/tsv', elow=0.5, ehigh=7.0)
SAOTraceUtils.save_spectrum_rdb("core_flux_chart.dat")
return pval
from sherpa.plot import Histogram
hplot = Histogram()
hplot.overplot(d.xlo, d.xhi, d.y)
savefig('dataplot_histogram_overplot.png')
from sherpa.models.basic import Const1D, Gauss1D
mdl = Const1D('base') - Gauss1D('line')
mdl.pars[0].val = 10
mdl.pars[1].val = 25
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
dplot.prepare(d)
dplot.plot()
savefig('data.png')
# Note: can not print(dplot) as there is a problem with the fact
# the input to the data object is a list, not ndarray
# Sherpa 4.10.0
from sherpa.models.basic import Polynom1D
mdl = Polynom1D()
report("print(mdl)")
mdl.c2.thaw()
from sherpa.plot import ModelPlot
mplot = ModelPlot()
mplot.prepare(d, mdl)
dplot.plot()
mplot.overplot()
savefig("data_model_initial.png")
from sherpa.stats import LeastSq
from sherpa.optmethods import NelderMead
from sherpa.fit import Fit
f = Fit(d, mdl, stat=LeastSq(), method=NelderMead())
report("print(f)")
res = f.fit()
dump("res.succeeded")
report("res.format()")
dump("d.get_filter(format='%d')")
dplot.prepare(d)
from sherpa.models.basic import Const1D, Exp
plateau = Const1D('plateau')
rise = Exp('rise')
mdl = plateau / (1 + rise)
report("mdl")
rise.ampl.freeze()
report("mdl")
from sherpa.plot import ModelPlot
mplot = ModelPlot()
mplot.prepare(d, mdl)
plt.subplot(2, 1, 1)
mplot.plot(clearwindow=False)
plt.subplot(2, 1, 2)
dplot.plot(clearwindow=False)
plt.title('')
savefig("model_data_before_fit.png")
from sherpa.stats import Chi2
from sherpa.fit import Fit
f = Fit(d, mdl, stat=Chi2())
report("f")
print("Starting statistic: {}".format(f.calc_stat()))