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])