def test_simple():
sme = SME_Struct.load(filename)
sme2 = solve(sme, ["teff"])
assert sme2.synth is not None
assert sme2.fitresults is not None
assert sme2.fitresults.covar is not None
assert isinstance(sme2.fitresults.covar, np.ndarray)
assert np.all(sme2.fitresults.covar != 0)
assert isinstance(sme2.fitresults.punc, dict)
assert len(sme2.fitresults.punc) == 1
assert len(sme2.fitresults.punc.keys()) == 1
assert list(sme2.fitresults.punc.keys())[0] == "teff"
assert list(sme2.fitresults.punc.values())[0] != 0
assert np.array_equal(sme2.fitresults.covar.shape, [1, 1])
assert sme2.fitresults.covar.ndim == 2
assert sme2.fitresults.chisq != 0
def test_simple():
sme = SME_Struct.load(filename)
sme2 = solve(sme, ["teff"])
assert sme2.synth is not None
assert sme2.fitresults is not None
assert sme2.fitresults.covariance is not None
assert isinstance(sme2.fitresults.covariance, np.ndarray)
assert np.all(sme2.fitresults.covariance != 0)
assert isinstance(sme2.fitresults.uncertainties, np.ndarray)
assert len(sme2.fitresults.uncertainties) == 1
assert sme2.fitresults.parameters[0] == "teff"
assert sme2.fitresults.uncertainties[0] != 0
assert np.array_equal(sme2.fitresults.covariance.shape, [1, 1])
assert sme2.fitresults.covariance.ndim == 2
assert sme2.fitresults.chisq is not None
assert sme2.fitresults.chisq != 0
def main(fname_in, fname_out, fit_parameters, log_file=None):
if log_file is not None:
start_logging(log_file)
sme = SME_Structure.load(fname_in)
sme = solve(sme, fit_parameters)
sme.save(fname_out)
else:
param = [5000, 4.4, 0.4] # 5000.00, 4.00, 0.00
sme.teff = float(param[0])
sme.logg = float(param[1])
sme.monh = float(param[2])
sme.vmic = 1
sme.vmac = 3.4
sme.vsini = 0.5
# Like HARPS?
sme.iptype = "gauss"
sme.ipres = 100_500 * 2
# try:
fp = ["teff", "logg", "monh"]
sme = solve(sme, fp)
sme.save(
join(
examples_dir,
f"results/convergence_teff_{param[0]}_logg_{param[1]}_monh_{param[2]}.sme",
))
print(
f"{param[0]}, {param[1]}, {param[2]}, {sme.teff}, {sme.logg}, {sme.monh}"
)
plot_file = join(
examples_dir,
f"results/convergence_teff_{param[0]}_logg_{param[1]}_monh_{param[2]}.html",
)
fig = FinalPlot(sme)
fig.save(filename=plot_file)
# wmax *= 1 + rvel / 3e5
# sme.linelist = sme.linelist.trim(wmin, wmax)
# Start SME solver
# sme = synthesize_spectrum(sme, segments=np.arange(6, 31))
# sme.cscale_flag = "fix"
# sme.wave = sme.wave[6:31]
# sme.spec = sme.spec[6:31]
# sme.synth = sme.synth[6:31]
# sme.mask = sme.mask[6:31]
# sme.telluric = sme.telluric[6:31]
# save_as_idl(sme, "epseri.inp")
# sme.save(out_file)
for fp in fitparameters:
sme = solve(sme, fp, segments=np.arange(6, 31))
fname = f"{target}_mask_2_out_{'_'.join(fp)}"
out_file = os.path.join(examples_dir, "results", fname + ".sme")
sme.save(out_file)
plot_file = os.path.join(examples_dir, "results", fname + ".html")
fig = plot_plotly.FinalPlot(sme)
fig.save(filename=plot_file)
# print(sme.citation())
# Save results
# sme.save(out_file)
# Plot results
fig = plot_plotly.FinalPlot(sme)
vald_file = join(dirname(__file__), "data/sun_full.lin")
sme.linelist = ValdFile(vald_file)
# TODO: is this correct?
sme.linelist.medium = "air"
sme.cscale_flag = "none"
sme.vrad_flag = "whole"
sme.atmo.source = "marcs2012.sav"
sme.atmo.depth = "RHOX"
sme.atmo.interp = "TAU"
sme.mask = 1
idl_file = join(dirname(__file__), "sun.inp")
save_as_idl(sme, idl_file)
# sme = synthesize_spectrum(sme)
sme = solve(sme, ["teff", "logg", "monh"])
sme.save(save_file)
save_file = join(dirname(__file__), "results/sun_compare.sme")
sme = SME_Structure.load(save_file)
idl_file = join(dirname(__file__), "sun1.out")
idl = SME_Structure.load(idl_file)
# for i in range(30):
# plt.hist(
# sme.fitresults.residuals[i * 1024 : (i + 1) * 1024]
# / sme.fitresults.derivative[i * 1024 : (i + 1) * 1024, 0],
# bins="auto",
# histtype="step",
# )
# Load your existing SME structure or create your own
sme = SME.SME_Structure.load(in_file)
sme.abund = Abund(0, "asplund2009")
sme.linelist = ValdFile(os.path.join(examples_dir, "sun.lin"))
# Change parameters if your want
sme.vsini = 0
sme.vrad = 0.35
sme.vrad_flag = "each"
sme.cscale_flag = "linear"
sme.cscale_type = "mask"
# Define any fitparameters you want
# For abundances use: 'abund {El}', where El is the element (e.g. 'abund Fe')
# For linelist use: 'linelist {Nr} {p}', where Nr is the number in the
# linelist and p is the line parameter (e.g. 'linelist 17 gflog')
fitparameters = ["teff", "logg", "monh"]
# Start SME solver
# sme = synthesize_spectrum(sme)
sme = solve(sme, fitparameters)
print(sme.citation())
# Save results
sme.save(out_file)
# Plot results
fig = plot_plotly.FinalPlot(sme)
fig.save(filename=plot_file)