# class plots ( ) The method to generate the model class plots and maps --- ## **Methods** * best_estimator_plots ( ) * partial_dep_plots ( ) * isotopes_meteoline_plot ( ) * map_generator ( ) --- ## plots.best_estimator_plots ( ) plots.best_estimator_plots( `cls, st1=True, st2=True` ) The method to plot the model class best estimators --- ### **Parameters** **st1** boolean default=`True` Generate plots for stage one regression models of the model class **st2** boolean default=`True` Generate plots for stage one regression models of the model class --- ## plots.partial_dep_plots ( ) plots. partial_dep_plots(`cls,st1=True,st2=True`) The method to plot the partial dependency of the features of the model class --- ### **Parameters** **st1** boolean default=`True` Generate plots for stage one regression models of the model class **st2** boolean default=`True` Generate plots for stage two regression models of the model class --- ## Method plots.isotopes_meteoline_plot ( ) plots.isotopes_meteoline_plot( `ev_class, iso_class, var_list, iso_18=None, iso_2h=None, a=8, b=10, obs_data=False, residplot=False` ) The method to plot the (meteorological) line between two features (isotopes) that are determined in var_list --- ### **Parameters** **ev_class** evaluation class evaluation class that contains the second stage models predictions --- **iso_class** model class model class that contains the second stage models --- **iso_18** none type or Pandas Dataframe default=`None` First feature (isotope) observed raw data. Ignored if obs_data=`False` --- **iso_2h** none type or Pandas Dataframe default=`None` Second feature (isotope) observed raw data. Ignored if obs_data=`False` --- **var_list** list of strings List of strings that identifies the names of two features in the evaluation and model class (in stage two) --- **a** float default=`8` Coefficient of the line --- **b** float default=`10` Intercept of the line --- **obs_data** boolean default=`False` `False` if iso_18 and iso_2h are not observed data. `True` if the predictions in evaluation class have an specified date, in `"month"` field. * *EXAMPLE:* ```python pred_inputs=model_class.all_preds[["CooX","CooY","CooZ","month","ID"]].reset_index() ev_class_obs=tools_copy.evaluation() ev_class_obs.predict(model_class,pred_inputs,direc=direc) tools_copy.plots.isotopes_meteoline_plot(ev_class_obs,model_class,var_list=['is1','is2'],obs_data=True) ``` --- **residplot** boolean default=`False` Ignored if obs_data=`False`. It create residual plots in each month for each ID. --- ## Method plots.map_generator ( ) plots.map_generator(`ev_class,feat_list,observed_class_list=None,month_list=None,unit_list=None,opt_title_list=None,x="CooX",y="CooY",shp_file=None,html=`True`,direc=None,minus_to_zero_list=None,max_to_hundred_list=None`) The method to generate the maps (.png and HTML) of the evaluation class --- ### **Parameters** **ev_class** evaluation class Evaluation class that contains the second stage models predictions --- **feat_list** list List of strings that identifies the desired features to map --- **observed_class_list** none type or list default=`None` List of the preprocess classes of the observed data. No observed data will be shown in the maps if observed_class_list=`None`, or an element of the list is none. --- **month_list** none type or list default=`None` List of the desired month to generate the maps. If `None`, the maps will be generated for all the months available in evaluation class --- **unit_list** list of strings default=`None` List of strings that identifies the units to be shown for every feature in the generated maps --- **opt_title_list** list of strings default=`None` List of strings that identifies the titles to be shown for every feature in the generated maps --- **x** string default="CooX" Identifies the name of the x (longitude) field in the evaluation class (same as defined in preprocess classess) --- **y** string default="CooY" Identifies the name of the y (latitude) field in the evaluation class (same as defined in preprocess classess) --- **shp_file** none type or string default=`None` Directory to the shape file to be used in .png maps. If `None`, no shape file will be included in the maps.If shapefile exists, it has to be in the same coordination system as the x & y. --- **html** boolean default=`True` If `True`, an HTML version of the maps will be created --- **direc** none type or string default=`None` The new directory to store the maps. If `None`, a new folder will be created in the directory that determined in the evaluation class --- **minus_to_zero_list** none type or list default=`None` If minus_to_zero_list is a list of booleans, when it is `True`, replace the minus values with zero for that feature. Usage in features such as relative humidity. --- **max_to_hundred_list** none type or list default=`None` If max_to_hundred_list is a list of booleans, when it is `True`, replace the values more that 100 with 100 for that feature. Usage in features such as relative humidity.