def test_warning_dataplot_linecolor(caplog):
"""We get a warning when using linecolor: DataPlot"""
data = Data1D('tst', np.asarray([1, 2, 3]), np.asarray([10, 12, 10.5]))
plot = DataPlot()
plot.prepare(data, stat=None)
with caplog.at_level(logging.INFO, logger='sherpa'):
plot.plot(linecolor='mousey')
assert len(caplog.records) == 1
lname, lvl, msg = caplog.record_tuples[0]
assert lname == 'sherpa.plot.pylab_backend'
assert lvl == logging.WARNING
assert msg == 'The linecolor attribute, set to mousey, is unused.'
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())
fit2.fit()
dump("pha.get_arf()")
dump("pha.get_rmf()")
dump("pha.header['INSTRUME']")
dump("pha.header['DETNAM']")
dump("pha.channel.size")
pha.set_analysis('energy')
pha.notice(0.3, 7)
tabs = ~pha.mask
pha.group_counts(20, tabStops=tabs)
from sherpa.plot import DataPlot
dplot = DataPlot()
dplot.prepare(pha)
dplot.plot(xlog=True, ylog=True)
savefig('pha_data.png')
chans, = pha.get_indep(filter=True)
counts = pha.get_dep(filter=True)
dump("chans.size, counts.size")
gchans = pha.apply_filter(chans, pha._middle)
dump("gchans.size")
plt.clf()
plt.plot(gchans, counts, 'o')
plt.xlabel('Channel')
plt.ylabel('Counts')
savefig('pha_data_manual.png')
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
leftrange = len(mplot2.y) / (2)
left = int(round(leftrange))
rightrange = len(mplot2.y) / (2)
right = int(round(rightrange))
### test to see if eq calc knows only to do filled in area
y_cont = np.linspace(max(mplot2.y[:left]),
max(mplot2.y[right:]),
num=len(mplot2.x))
y_model = mplot2.y
y_cn = y_model / y_cont
dcontplot = DataPlot()
dcont = Data1D("normalized", mplot.x, y_cn)
dcontplot.prepare(dcont)
dcontplot.plot()
os.chdir(r"/home/dtyler/Desktop/DocumentsDT/outputs/standard_" +
fits_image_filename)
plt.savefig(fits_image_filename[:-4] + "norm_continuum.png")
os.chdir(r"/home/dtyler/Desktop/DocumentsDT/Programs")
###################################
### calculate equivalent width
spectrum = Spectrum1D(flux=y_cn * u.dimensionless_unscaled,
spectral_axis=mplot.x * u.AA)
eqw = equivalent_width(spectrum)
"""print("EW", eqw.value)
print('norm factor', norm_factor)
for par in bmdl.pars:
print(par.fullname, par.val)"""
print("# dump")
print("{}".format(name))
print(repr(eval(name)))
print("----------------------------------------")
edges = np.asarray([-10, -5, 5, 12, 17, 20, 30, 56, 60])
y = np.asarray([28, 62, 17, 4, 2, 4, 125, 55])
from sherpa.data import Data1DInt
d = Data1DInt('example histogram', edges[:-1], edges[1:], y)
from sherpa.plot import DataPlot
dplot = DataPlot()
dplot.prepare(d)
dplot.plot()
savefig('dataplot_histogram.png')
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
print("# dump")
print(name)
print(repr(eval(name)))
from sherpa.data import Data1D
x = [1, 1.5, 2, 4, 8, 17]
y = [1, 1.5, 1.75, 3.25, 6, 16]
d = Data1D('interpolation', x, y)
report("print(d)")
from sherpa.plot import DataPlot
dplot = DataPlot()
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)
from openpyxl import load_workbook
wb = load_workbook('pone.0171996.s001.xlsx')
fig4 = wb['Fig4data']
t = []; y = []; dy = []
for r in list(fig4.values)[2:]:
t.append(r[0])
y.append(r[3])
dy.append(r[4])
from sherpa.data import Data1D
d = Data1D('NaNO_3', t, y, dy)
from sherpa.plot import DataPlot
dplot = DataPlot()
dplot.prepare(d)
dplot.plot()
savefig("data.png")
report("d")
dump("d.get_filter()")
d.ignore(None, 1)
dump("d.get_filter()")
dump("d.get_filter(format='%d')")
dplot.prepare(d)
from sherpa.models.basic import Const1D, Exp
plateau = Const1D('plateau')
rise = Exp('rise')