%g':ddlmZmZmZddlZddlZdZdZ dZ y))absolute_importdivisionprint_functionNc|tj|j|dk(|jjdS)aAGiven the name of a regressor, return its (column) index in the `beta` matrix. Parameters ---------- m: Prophet model object, after fitting. name: Name of the regressor, as passed into the `add_regressor` function. Returns ------- The column index of the regressor in the `beta` matrix. r)npextracttrain_component_colsindex)mnames N/var/www/dash_apps/app1/venv/lib/python3.12/site-packages/prophet/utilities.pyregressor_indexr s> :: t$)1+A+A+G+G   c,t|jdkDsJdg}|jjD]\}}|jdddt ||f}|ddk(r||j z|dz }n||dz }d|j z d z dd|j z d z z g}tj|| }||d|d |dtj||dd }|j|tj|S) a1Summarise the coefficients of the extra regressors used in the model. For additive regressors, the coefficient represents the incremental impact on `y` of a unit increase in the regressor. For multiplicative regressors, the incremental impact is equal to `trend(t)` multiplied by the coefficient. Coefficients are measured on the original scale of the training data. Parameters ---------- m: Prophet model object, after fitting. Returns ------- pd.DataFrame containing: - `regressor`: Name of the regressor - `regressor_mode`: Whether the regressor has an additive or multiplicative effect on `y`. - `center`: The mean of the regressor if it was standardized. Otherwise 0. - `coef_lower`: Lower bound for the coefficient, estimated from the MCMC samples. Only different to `coef` if `mcmc_samples > 0`. - `coef`: Expected value of the coefficient. - `coef_upper`: Upper bound for the coefficient, estimated from MCMC samples. Only to different to `coef` if `mcmc_samples > 0`. rzNo extra regressors found.betaNmodeadditivestdr)qmu) regressorregressor_modecenter coef_lowercoef coef_upper) lenextra_regressorsitemsparamsry_scaleinterval_widthrquantilemeanappendpd DataFrame) r coefsrr"rr percentiles coef_boundsrecords rregressor_coefficientsr.s/4 q!! "Q &D(DD & E//557 6xx?1i#@ @A &>Z '!))#fUm3D&-'D !! !Q & Q%%%* * kk$+6 "$VnTl%a.GGDM%a.    V'* << rcXi}dD]O}|jdk(r|j|dd||<+tj|j|||<QdD]N}|jdk(r|j|d||<(tj|j|d||<P|S)a Retrieve parameters from a trained model in the format used to initialize a new Stan model. Note that the new Stan model must have these same settings: n_changepoints, seasonality features, mcmc sampling for the retrieved parameters to be valid for the new model. Parameters ---------- m: A trained model of the Prophet class. Returns ------- A Dictionary containing retrieved parameters of m. )kr sigma_obsr)deltar)axis) mcmc_samplesr"rr&)r respnames rwarm_start_paramsr7Os C(2 >>Q %+A.CJ%1CJ 2 #: >>Q %+CJ%q9CJ : Jr) __future__rrrnumpyrpandasr(rr.r7rrr<s$A@ 1fr