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def test_modelfit():
indparams = np.linspace(0.0, 1.0, nsamples)
data = np.random.normal(1.00, 1.0, nsamples)
uncert = np.random.normal(1.0, 0.1, nsamples)
model = np.tile(1.0, nsamples)
axes = mp.modelfit(data, uncert, indparams, model)
mp.histogram(posterior, pnames=texnames[ifree], bestp=best_freepars,
savefile=fname+"_posterior.png",
quantile=0.683, pdf=pdf, xpdf=xpdf)
log.msg("'{:s}'".format(fname+"_posterior.png"), indent=2)
# RMS vs bin size:
if rms:
RMS, RMSlo, RMShi, stderr, bs = ms.time_avg(output['best_model']-data)
mp.rms(bs, RMS, stderr, RMSlo, RMShi, binstep=len(bs)//500+1,
savefile=fname+"_RMS.png")
log.msg("'{:s}'".format(fname+"_RMS.png"), indent=2)
# Sort of guessing that indparams[0] is the X array for data as in y=y(x):
if (indparams != []
and isinstance(indparams[0], (list, tuple, np.ndarray))
and np.size(indparams[0]) == ndata):
try:
mp.modelfit(data, uncert, indparams[0], output['best_model'],
savefile=fname+"_model.png")
log.msg("'{:s}'".format(fname+"_model.png"), indent=2)
except:
pass
# Close the log file if necessary:
if closelog:
log.msg("'{:s}'".format(log.logname), indent=2)
log.close()
return output