def get_actual_temperature(fitter, Tmeas, Tmeas_err, cache=None, ret_cache=None, summarize=True):
"""
Get the actual temperature from the measured temperature
:param fitter: a Bayesian_TLS instance which has already been fit
:param Tmeas: the measured temperature. Either a float or a numpy array with independent temperatures
:param Tmeas_err: uncertainty on the measured temperature. Same shape as Tmeas.
:return: posterior samples for the actual temperature
"""
# First, build up a cache of the MCMC predicted measured temperatures for lots of actual temperatures
if cache is None:
logging.info('Generating cache...')
Ta_arr = np.arange(2000, 10000, 1.0)
Tmeas_pred = fitter.predict(Ta_arr, N=10000)
cache = pd.DataFrame(Tmeas_pred, columns=Ta_arr)
ret_cache = True if ret_cache is None else False
del Tmeas_pred
# Get the probability of each value in the cache
Tmeas = np.atleast_1d(Tmeas).astype(np.float)
Tmeas_err = np.atleast_1d(Tmeas_err).astype(np.float)
def get_prob(Tm_pred, Tm, Tm_err):
return np.exp(-((Tm_pred - Tm) / Tm_err)**2)
probs = np.array([get_prob(cache.values, Tm, Tme) for Tm, Tme in zip(Tmeas, Tmeas_err)])
# Get the posterior probability distribution
tmp = np.mean(probs, axis=1)
tmp /= np.sum(tmp, axis=1)[:, np.newaxis]
P = np.prod(tmp, axis=0)
# Find the maximum and FWHM of the probabilities
#best_T = cache.columns.values[np.argmax(P)]
#roots = fwhm(cache.columns.values, P, k=0, ret_roots=True)
#h, l = max(roots), min(roots)
l, best_T, h = integral(cache.columns.values, P, [0.16, 0.5, 0.84], k=0)
print('$T = {}^{{+{}}}_{{-{}}}$'.format(best_T, h-best_T, best_T-l))
# Return the requested things.
if ret_cache:
if summarize:
return best_T, h - best_T, best_T - l, cache
return cache.columns.values, P, cache
if summarize:
return best_T, h - best_T, best_T - l
return cache.columns.values, P
def _correct(self, df, cache=True):
"""
This function is called by convert_measured_to_actual and is NOT meant to be called directly!
It takes a pandas dataframe that all have the same star
"""
# Group by instrument and addmode, and get the PDF for the actual temperature for each
df = df.dropna(subset=['Tmeas'])
if len(df) == 0:
df['Corrected_Temperature'] = np.nan
df['T_uperr'] = np.nan
df['T_lowerr'] = np.nan
return df[['Star', 'Corrected_Temperature', 'T_uperr', 'T_lowerr']].copy()
groups = df.groupby(('Instrument', 'addmode'))
Probabilities = []
for (inst, addmode), group in groups:
# Make a fitter instance
d = {'instrument': inst,
'directory': self._caldir[inst],
'addmode': addmode}
key = (inst, addmode)
fitter = self._fitters[inst]()
# get/set the cache
if key in self._chainCache:
chain, probs = self._chainCache[key]
Tpredictions = self._predictionCache[key]
fitter.spoof_sampler(chain, probs)
else:
chain = np.loadtxt(self._flatchain_format.format(**d))
probs = np.loadtxt(self._flatlnprob_format.format(**d))
fitter.spoof_sampler(chain, probs)
Ta_arr = np.arange(2000, 12000, 2.0)
Tmeas_pred = fitter.predict(Ta_arr, N=10000)
Tpredictions = pd.DataFrame(Tmeas_pred, columns=Ta_arr)
if cache:
self._chainCache[key] = (chain, probs)
self._predictionCache[key] = Tpredictions
# Get the PDF (probability distribution function)
Tmeas = group['Tmeas'].values
Tmeas_err = group['Tmeas_err'].values
for Tm, Tm_err in zip(Tmeas, Tmeas_err):
temperature, probability = CCF_Systematics.get_actual_temperature(fitter, Tm, Tm_err,
cache=Tpredictions,
summarize=False)
Probabilities.append(probability / probability.sum())
# Multiply all the PDFs
Prob = np.array(Probabilities).prod(axis=0)
# Summarize the PDF (similar interface to np.percentile)
l, m, h = integral(temperature, Prob, [0.16, 0.5, 0.84], k=0)
# Save in a Pandas DataFrame and return
return pd.DataFrame(data={'Star': df['Star'].values[0], 'Corrected_Temperature': m,
'T_uperr': h - m, 'T_lowerr': m - l}, index=[0])
