isocompy 1.0.2

An open source library for environmental isotopic modelling

Isocompy

This library contains an open source Python library that focuses on user defined (such as meteorological, spatial, etc.) and isotopic composition variables analysis and generating the regression – statistical estimation models using machine-learning techniques

---

---

Package information:

Name: Isocompy

version: 1.0.0

Author: Ashkan Hassanzadeh

Email: [e-mail]

python: 3.*

License: agpl-3.0

---

---

Installation:

pip can be used for the installation:

pip install isocompy

Alternatively, download the isocompy folder and add it to lib folder in python path alongside other python libraries.

---

---

Jupyter Notebook:

There is a notebook that explains an example of implementing isocompy on spatial and isotopic data

Instruction manual:

class preprocess( ):

The class to preprocess the input variables of each model group and initiate the models properties such as cross-validation and brute-force searching.

---

---

Parameters:

(__init__ method of preprocess class)

tunedpars_rfr dic, default= {"min_weight_fraction_leaf":[0,0.02,0.04],"n_estimators":[50,100,150,2'00,250,300],"criterion": ["mse","mae"],"min_samples_split":[2,5] }

The dictionary that determines brute-force searching parameters of Random Forest regression. For more details on the regression method inputs, refer to sklearn library.

---

tunedpars_svr dic default={"kernel":[ "poly", "rbf", "sigmoid"],"C":np.logspace(-1, 1, 3),"gamma":np.logspace(-3, 1, 3) }

The dictionary that determines brute-force searching parameters of Support Vector regression. For more details on the regression method inputs, refer to sklearn library.

---

tunedpars_nusvr dic default={"kernel":["linear", "poly", "rbf", "sigmoid"] }

The dictionary that determines brute-force searching parameters of Nu Support Vector Regression. For more details on the regression method inputs, refer to sklearn library.

---

tunedpars_mlp dic default={"activation" : [ "logistic", "tanh"],"solver" : ["lbfgs", "sgd", "adam"],"alpha":[0.0001,0.0003],"hidden_layer_sizes":[(50,)*2,(50,)*3,(50,)*4,(100,)*2,(100,)*3,(100,)*4],"max_iter":[1000],"n_iter_no_change":[10]}

The dictionary that determines brute-force searching parameters of Multilayer Perceptron regression. For more details on the regression method inputs, refer to sklearn library.

---

tunedpars_lr dic default={}

The dictionary that determines brute-force searching parameters of Linear regression. For more details on the regression method inputs, refer to sklearn library.

---

tunedpars_br dic default={}

The dictionary that determines brute-force searching parameters of Bayesian Ridge regression. For more details on the regression method inputs, refer to sklearn library.

---

tunedpars_ard dic default={}

The dictionary that determines brute-force searching parameters of Bayesian ARD regression. For more details on the regression method inputs, refer to sklearn library.

---

tunedpars_omp dic default={}

The dictionary that determines brute-force searching parameters of Orthogonal Matching Pursuit regression. For more details on the regression method inputs, refer to sklearn library.

---

tunedpars_elnet dic default={"l1_ratio":[.1, .5, .7,.9,.99]}

The dictionary that determines brute-force searching parameters of ElasticNet (Linear regression with combined L1 and L2 priors as regularizer) regression. For more details on the regression method inputs, refer to sklearn library.

---

tunedpars_muelnet dic default= {"l1_ratio":[.1, .5, .7,.9,.99]}

The dictionary that determines brute-force searching parameters of Multi-task ElasticNet (trained with L1/L2 mixed-norm as regularizer) regression. For more details on the regression method inputs, refer to sklearn library.

---

which_regs dic default= {"muelnet":True, "rfr":True, "mlp":True, "elnet":True, "omp":True, "br":True, "ard":True, "svr":True, "nusvr":False}

The dictionary that determines which regression models have to be included in cross-validation and brute-force searching process

---

apply_on_log Boolean default=True

If True, Apart from the main values, fits the models to log(1 + x) (Natural logarithm) of data. If the scores of the regression on logarithm of the data is higher than the real values, The chosen method will always calculate the log(1 + x) of the data before fitting the models. Note that if this is the case, to have the real outputs, exp(x) - 1 (the inverse of log(1 + x)) will be calculated.

---

cv int or Boolean default="auto"

If cv="auto", the cross-validation number of folds will be calculated automatically. It is beneficial when there is few data available (max=10, min=2). If cv is an integer values, it determines number of folds of the cross-validation.

---

---

Methods:

• __init__ (self)

• fit()

• model_pars()

---

---

Attributes:

model_pars_name_dic dic

A dictionary that stores brute-force searching parameters of all models. The keys and values are dictionary names and parameters dictionaries respectively

EXAMPLE:

key: "tunedpars_rfr"

Value: tunedpars_rfr= {"min_weight_fraction_leaf":[0,0.04],"n_estimators":[150,200],"criterion": ["mse"] }

IMPORTANT NOTE: 'model_pars' method have to be used to change the brute-force searching parameters

---

db_input_args_dics dic

A dictionary that stores input parameters of the fit method

---

direc str

The directory of the data preprocessing class output

---

month_grouped_inp_var list

A list of monthly grouped data. Each element of the list is a dataframe containing the grouped data of an specific month

---

---

---

preprocess.fit( )

preprocess.fit(self, inp_var, var_name, fields, direc, remove_outliers=True,write_outliers_input=True, year_type="all", inc_zeros_inp_var=False, write_integrated_data=True, q1=0.05, q3=0.95, IQR_inp_var=True, IQR_rat_inp_var=3, mean_mode_inp_var="arithmetic", elnino=None, lanina=None)

The method to preprocess the input data of the models

---

---

Parameters:

inp_var Pandas dataframe

Input pandas dataframe containing the dependent value, unique IDs for each sample, and date. (The columns "Value", "ID" and "Date" have to be found in the dataframe)

---

var_name str

Name of the dependent variable.

---

fields list of strings

List of independent variable names that have to be existed in the inp_var dataframe

---

direc str

The directory of the data preprocessing class output

---

remove_outliers Boolean default=True

To remove outliers based on the introduced variables. If False, the variables related to removing outliers will be ignored.

---

write_outliers_input Boolean default=True

Effective if remove_outliers=True. To generate .xls file in directory folders of the class after removing outliers

---

year_type str default="all"

"all", "elnino" or "lanina". In case elnino or lanina years have to be selected from the database

---

inc_zeros_inp_var Boolean default=False

Effective if remove_outliers=True. Removing outliers could be done not considering the zero values in the database. (The zero values will be seperated, outliers will be removed, then zero values will be added to database)

---

write_integrated_data Boolean default=True

Generate two .xls outputs consisting of integrated data, and the quantity of data in each month

---

q1 float default=0.05

Effective if remove_outliers=True and IQR_inp_var=False. Lower percentile limit to determine the outliers.

---

q3 float default=0.95

Effective if remove_outliers=True and IQR_inp_var=False. Upper percentile limit to determine the outliers

---

IQR_inp_var Boolean default=True

Effective if remove_outliers=True. Determining the upper limit of the outliers using this formula: $X*q_{0.75} + IQR_{rat}abs(X q{0.25} - X * q_{0.75})$

Lower limit = q1

---

IQR_rat_inp_var float default=3

Effective if remove_outliers=True and IQR_inp_var=True. This parameter used in Xq_0.75 + IQR_ratabs(Xq_0.25 - Xq_0.75) to determine upper boundary limit.

---

mean_mode_inp_var str default="arithmetic"

Data averaging method. available options are "arithmetic" or "geometric"

---

elnino None type or list of integers default=None

List of elnino years

---

lanina None type or list of integers default=None

List of lanina years

---

---

Attributes:

db_input_args_dics dic

A dictionary that stores input parameters of the fit method

---

direc str

The directory of the data preprocessing class output

---

month_grouped_inp_var list

A list of monthly grouped data. Each element of the list is a dataframe containing the grouped data of an specific month

---

---

---

preprocess.model_pars( )

preprocess.model_pars(self,**kwargs):

To change the brute-force searching parameters that is already determined in a class.

---

EXAMPLE:

#Define the new brute-force searching parameters dictionaries:

#brute-force searching parameters for random forest
brutesearch_ran_for_dic={"min_weight_fraction_leaf":[0,0.04],"n_estimators":[150,200],"criterion": ["mse"] }

#brute-force searching parameters for elastic net
brutesearch_elasticnet_dic={"l1_ratio":[ .5, .9]}

#change the brute-force searching parameters
prep_class.model_pars( "tunedpars_rfr" = brutesearch_ran_for_dic, "tunedpars_elnet" = brutesearch_elasticnet_dic )

---

---

---

class model ( ):

The class to fit the regression models in stage one, predict the stage one results and fit stage two regresison models

---

---

Methods:

• __init__(self)

• st1_fit()

• st1_predict()

• st2_fit()

• choose_estimator_by_meteo_line()

• stage2_output_report()

---

---

Attributes:

(__init__ method of model class)

direc str

Directory of the class

---

st1_model_results_dic dict

A dictionary consist of st1 model results

---

st1_varname_list list

List of the names of independent variables in st1

---

st1_model_month_list list

List of desired months to model in st1

---

used_feature_list list

List of all used features (strings) in st1

---

cls_list list

A list of preprocess class objects that we wish to model in st2

---

all_pred Pandas Dataframe

A dataframe of all st1 predictions

---

predictions_monthly_list list

Dataframes of predictions of stage one, seperated monthly as list elements

---

st2_model_month_list list

List of desired months to model in st1. Indicated months have to exist in st1_model_month_list

---

dic_second_stage_names dict

Helps in generating model_var_dict in st2_fit

---

st2_model_results_dic dict

A dictionary consist of st2 model results

---

dependent_model_selection boolean

To select the best model based on meteorological line. only useful if there is a linear refrence line (EX:Isotopes)

---

meteo_coef float

If dependent_model_selection=True,global_line, coefficient of the line

---

meteo_intercept float

If dependent_model_selection=True,global_line, intercept of the line

---

selection_method str

If dependent_model_selection=True, selection_method: independent,local_line,global_line, point_to_point

---

thresh_meteoline_high_scores None type or float

A threshold to just consider models with scores higher than that value. if none, equal to mean of scores+std of scores/3

---

model_selection_report boolean

True or False, to determine if there is a need to model selection method report

---

---

---

model.st1_fit ( )

model.st1_fit (self,var_cls_list,direc,st1_model_month_list="all",args_dic= { "feature_selection" : "auto" , "vif_threshold" : 5, "vif_selection_pairs":[],"correlation_threshold":0.87,"vif_corr":True,"p_val":0.05})

The method to fit regression models to identified preprocess class objects in stage one

---

---

Parameters:

var_cls_list list

A list of preprocess class objects to to fit regression models. Regression models will be fitted to each elemnt of the list (a preprocess class object).

---

direc str

Directory of the class

---

st1_model_month_list str or list of integers default="all"

List of desired months to model in st1

---

args_dic dict default={"feature_selection":"auto","vif_threshold":5, "vif_selection_pairs":[],"correlation_threshold":0.87,"vif_corr":True,"p_val":0.05}

A dictionary of parameters that identifies the behaviour of feature selection prior to regressions:

• args_dic["feature_selection"] ="manual": Statistical information will be shown to the user, and the desired features will be chosen by the user

• args_dic["feature_selection"] ="auto": Feature selection will be done automatically

• args_dic["vif_threshold"] =None: VIF (Variation Inflation Factor) will not be considered as a factor in feature selection

• args_dic["vif_threshold"] = float type: A threshold to identify high VIF values

• args_dic["vif_corr"] = True: If True, use correlation coefficient values to identify multicolinearity in features with high vif value

• args_dic["correlation_threshold"] = 0.87 A threshold to identify high correlation coefficient values

• args_dic["vif_selection_pairs"] = empty list or list of list(s): If empty: feature elimination based on vif will be automatic

if args_dic["vif_selection_pairs"] =[ ["a","b"] ], in case both "a" and "b" have high vif values and high correlations, the b values will be eliminated

---

---

Attributes:

direc str

Directory of the class

---

st1_model_results_dic dict

A dictionary consist of st1 model results

---

st1_varname_list list

List of the names of independent variables in st1

---

st1_model_month_list list

List of desired months to model in st1

---

---

---

model.st1_predict()

model.st1_predict(self, cls_list, st2_model_month_list=None, trajectories=False, daily_rain_data_for_trajs=None)

The method to estimate the independent features that modeled in st1 using the new observations that are in a new list (cls_list) which each element is a preprocess class objects

---

---

Parameters:

cls_list list

A list of preprocess class objects that we wish to model in st2

---

st2_model_month_list list or None type default=None

List of desired months to model in st1. Indicated months have to exist in st1_model_month_list. If None, it will be equal to st1_model_month_list

---

---

Attributes:

used_feature_list list

List of all used features (strings) in st1

---

cls_list list

A list of preprocess class objects that we wish to model in st2

---

all_pred Pandas Dataframe

A dataframe of all st1 predictions in observed samples

---

predictions_monthly_list list

Dataframes of predictions of stage one, seperated monthly as list elements

---

st2_model_month_list list

List of desired months to model in st1. Indicated months have to exist in st1_model_month_list

---

---

---

model.st2_fit ( )

model.st2_fit (self,model_var_dict=None, output_report=True, dependent_model_selection=False, dependent_model_selection_list=None, meteo_coef=8, meteo_intercept=10, selection_method="point_to_point", thresh_meteoline_high_scores=None, model_selection_report=True, args_dic={"feature_selection":"auto", "vif_threshold":5, "vif_selection_pairs":[], "correlation_threshold":0.87, "vif_corr":True,"p_val":0.05}):

The method to fit regression models to identified preprocess class objects in stage one

---

---

Parameters:

model_var_dict None type or dict default=None

A dictionary that determines dependent (key - string) and independent (value) features of the second stage regression models. Independent features (value) have to be a list of feature names (string).

If None, all features (independent st1 features and dependent st1 features) will be considered as independent features of second stage models.

• EXAMPLE:

model_var_dict = {"is1":["CooZ","hmd"],"is2":["prc","hmd"],}

---

output_report boolean default=True

To generate output reports

---

Parameters used in choose_estimator_by_meteo_line

dependent_model_selection boolean default=False To select the best model based on a (meteorological) line. only useful if there is a linear refrence line (EX:Isotopes)

---

dependent_model_selection_list default=None

Used if dependent_model_selection=True. List of two features that have to be used in dependent_model_selection

---

meteo_coef default=8

Used if dependent_model_selection=True and selection_method="global_line". Coefficient of the line

---

meteo_intercept default=10

Used if dependent_model_selection=True and selection_method="global_line". Intercept of the line

---

selection_method default="point_to_point"

Used if dependent_model_selection=True. selection_method could be:

• independent
• local_line: coef and intercept derived from a linear regression of observed data
• global_line
• point_to_point: find the models pair with shortest average distance between observed and predicted data

---

thresh_meteoline_high_scores None type or float default=None

A threshold to just consider models with scores higher than that value. if None, equal to mean of scores+std of scores/3

---

model_selection_report boolean default =True

To determine if there is a need to model selection method report

---

args_dic dict default={"feature_selection":"auto","vif_threshold":5, "vif_selection_pairs":[], "correlation_threshold": 0.87, "vif_corr": True, "p_val":0.05}

A dictionary of parameters that identifies the behaviour of feature selection prior to regressions:

• args_dic["feature_selection"] ="manual": Statistical information will be shown to the user, and the desired features will be chosen by the user

• args_dic["feature_selection"] ="auto": Feature selection will be done automatically

• args_dic["vif_threshold"] =None: VIF (Variation Inflation Factor) will not be considered as a factor in feature selection

• args_dic["vif_threshold"] = float type: A threshold to identify high VIF values

• args_dic["vif_corr"] = True: If True, use correlation coefficient values to identify multicolinearity in features with high vif value

• args_dic["correlation_threshold"] = 0.87 A threshold to identify high correlation coefficient values

• args_dic["vif_selection_pairs"] = empty list or list of list(s): If empty: feature elimination based on vif will be automatic

if args_dic["vif_selection_pairs"] =[ ["a","b"] ], in case both "a" and "b" have high vif values and high correlations, the b values will be eliminated

---

---

Attributes:

st2_model_results_dic dict

A dictionary consist of st2 model results

---

Attributes used in choose_estimator_by_meteo_line

dependent_model_selection boolean

To select the best model based on meteorological line. only useful if there is a linear refrence line (EX:Isotopes)

---

meteo_coef float

If dependent_model_selection=True, global_line, coefficient of the line

---

meteo_intercept float

If dependent_model_selection=True, global_line, intercept of the line

---

selection_method str

If dependent_model_selection=True, selection_method: "independent", "local_line", "global_line", "point_to_point"

---

**thresh_meteoline_high_scores None type or float

A threshold to just consider models with scores higher than that value. if None, equal to mean of scores+std of scores/3

---

model_selection_report boolean

To determine if there is a need to model selection method report

---

---

---

model.choose_estimator_by_meteo_line ( )

model.choose_estimator_by_meteo_line( self, dependent_model_selection_list, selection_method="point_to_point", model_selection_report=True, thresh_meteoline_high_scores=None, meteo_coef=8, meteo_intercept=10 ):

The method to select the best model based on a (meteorological) line. only useful if there is a linear refrence line (EX:Isotopes). This method could be called automatically in st2_fit if dependent_model_selection=True. or it can be called after st2_fit execution to see the changes in best regression models based on different criterias.

IMPORTANT NOTE: Executing this method will update the st2_model_results_dic to match the latest chosen selection_method. st2_model_results_dic stores the second stage results.

---

---

Parameters:

dependent_model_selection_list default=None

Used if dependent_model_selection=True. List of two features that have to be used in dependent_model_selection

---

meteo_coef default=8

Used if dependent_model_selection=True and selection_method="global_line". Coefficient of the line

---

meteo_intercept default=10

Used if dependent_model_selection=True and selection_method="global_line". Intercept of the line

---

selection_method default="point_to_point"

Used if dependent_model_selection=True. Selection_method could be:

• "independent"
• "local_line": coef and intercept derived from a linear regression of observed data
• "global_line"
• "point_to_point": find the models pair with shortest average distance between observed and predicted data

---

thresh_meteoline_high_scores None type or float default=None

A threshold to just consider models with scores higher than that value. if none, equal to mean of scores+std of scores/3

---

model_selection_report boolean default =True

True or False, to determine if there is a need to model selection method report

---

---

Attributes:

st2_model_results_dic dict

Updated dictionary of st2 model results

---

dependent_model_selection boolean

To select the best model based on meteorological line. only useful if there is a linear refrence line (EX:Isotopes)

---

meteo_coef float

If dependent_model_selection=True,global_line, coefficient of the line

---

meteo_intercept float

If dependent_model_selection=True,global_line, intercept of the line

---

selection_method str

If dependent_model_selection=True, selection_method: "independent","local_line","global_line", "point_to_point"

---

thresh_meteoline_high_scores None type or float

A threshold to just consider models with scores higher than that value. if none, equal to mean of scores+std of scores/3

---

model_selection_report boolean

True or False, to determine if there is a need to model selection method report

---

---

---

model.stage2_output_report ( )

model.stage2_output_report(self,direc=None):

This method is useful to update st2_fit output files results in case they are changed. (Normally the change can happen if choose_estimator_by_meteo_line method is executed)

---

---

Parameters:

direc str default=None

Directory of the output

---

---

---

class session ( ):

The class to save and load the objects and sessions

---

---

Methods:

• save()

• load()

• save_session()

• load_session()

---

---

---

session.save ( )

session.save(self,name="isocompy_saved_object")

The method to save an object

---

---

Parameters:

name str default="isocompy_saved_object"

The output name string

---

---

Returns:

filename string

Directory of the saved object

---

---

---

session.load ( )

session.load(direc)

The method to load a pkl object. direc is the directory of the object to be loaded.

---

---

Returns:

obj object

The loaded object

---

---

---

session.save_session( )

save_session(direc,name="isocompy_saved_session", *argv):

The method to save a session

---

---

Parameters:

name: str default="isocompy_saved_object"

The output name string

---

*argv

The objects that wanted to be stored in the session

---

---

Returns:

filename string

Directory of the saved session

---

---

---

session.load_session ( )

session.load_session(dir)

The method to load a session

---

---

Parameters:

*argv

The objects that wanted to be stored in the session

---

---

Returns:

Loads the session

---

---

---

class evaluation ( )

The class to predict the second stage regression models

---

---

Methods:

• __init__ (self)

• predict ( )

---

---

Attributes:

direc str

directory of the class

---

monthly_st2_output_list_all_vars list

list of stage two models outputs, seperated by month

---

monthly_st2_output_dic_all_vars_df dict

dictionary of stage two models outputs, seperated by month. key is the month, and value is the output df of that specific month

---

pred_inputs Pandas Dataframe

A Dataframe, that have to be contain of features that is used in stage one, that is going to be used to estimate the stage one and two models

---

st2_predicted_month_list list

List of the months that have stage two regression models

---

---

---

evaluation.predict( )

evaluation.predict(self, cls, pred_inputs, stage2_vars_to_predict=None, direc=None, write_to_file=True )

The method to predict the second stage regression models

---

---

Parameters:

**cls ** model class

The model class that contains st1 and st2 models

---

**pred_inputs ** Pandas dataframe

A Dataframe, that have to be contain of features that is used in stage one, that is going to be used to estimate the stage one and two models.

It can contain "month" field which could be used in evaluating the stage two predictions in observed data.

• EXAMPLE:

  pred_inputs=model_class.all_preds[["CooX","CooY","CooZ","month","ID"]].reset_index()
  

---

**stage2_vars_to_predict ** None type or list of strs default=None

List of stage two dependent features to predict the outputs. If None, The results will be predicted for all two dependent features

---

**direc ** None type or str default=None

Directory of the class. If None, it is the same directory as the model class.

---

**write_to_file ** boolean default=True

To write the outputs in .xls files, seperated by the month

---

---

Attributes:

direc str

directory of the class

---

monthly_st2_output_list_all_vars list

list of stage two models outputs, seperated by month

---

monthly_st2_output_dic_all_vars_df dict

dictionary of stage two models outputs, seperated by month. key is the month, and value is the output df of that specific month

---

pred_inputs Pandas Dataframe

A Dataframe, that have to be contain of features that is used in stage one, that is going to be used to estimate the stage one and two models

---

st2_predicted_month_list list

List of the months that have stage two regression models

---

---

---

class stats( )

The class to calculate and generate statistical reports for the second stage models

---

---

Methods:

• annual_stats( )

• mensual_stats( )

---

---

---

stats.seasonal_stats( )

stats.seasonal_stats(model_cls_obj)

The method to generate statistical reports for the second stage models based on all specified month in second stage data

---

---

Parameters:

model_cls_obj

Input model class object

---

---

---

stats.mensual_stats()

stats.mensual_stats(model_cls_obj)

The method to generate statistical reports for the second stage models based on each specified month in second stage data

---

---

Parameters:

model_cls_obj

Input model class object

---

---

---

class plots ( )

The method to generate the model class plots

---

---

Methods:

• best_estimator_plots ( )

• partial_dep_plots ( )

• isotopes_meteoline_plot ( )

---

---

---

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

---

---

---

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:

  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.

#------------------