AIModels package¶
Submodules¶
- AIModels.AIClasses module
- AIModels.AIutil module
- Utility Treatment Routines for ML Predicting models
func_name()copy_dict()get_arealat_arealon()select_field()select_field_key()select_field_eof()select_area()make_matrix()make_eof()make_field()make_field_V5()make_field_HAD()normalize_training_data()make_data()make_features()make_data_base()init_weights()count_parameters()epoch_time()create_time_features()rescale()matrix_rank_light()CPRSS()transform_strings()make_fcst_array()eof_to_grid()advance_months()project_dyn()make_dyn_verification_new()compute_increments()cumsum_with_init()select_fcst()variance_features()create_subdirectory()eof_to_grid_new()get_common_dates()
- AIModels.AutoEncoder module
- AIModels.ClimFormer module
- AIModels.ClimFormerAttn module
- AIModels.ClimFormerAttn2 module
- AIModels.ClimLSTM module
- AIModels.LocalInformer module
- AIModels.ModelTraining module
- AIModels.UtilPlot module
- Auxiliary Plotting routines
Single_Forecast_plots()many_plots()Forecast_plots()Three_Forecast_plots()select_months()plot_skill()extract_and_merge_csv()write_skill_to_csv()plot_csv()boxplot()write_var_excel()Two_Forecast_plots()write_var_table()define_defaults_values()get_common_dates()calculate_significance()
Module contents¶
AIModels is a Python package for the designing ML networks of atmospheric and ocean data. It is based on the xarray package.
It uses xarray as a basic data structure.
AIModels contains several modules with classes and utility routines The plotting modules are based on matplotlib and cartopy. The computation modules are based on numpy and scipy.
- ClimFormer :
Class for ClimFormer network
- ClimLSTM :
Class for ClimLSTM network
- LocalInformer :
Class for LocalInformer network, a modified version of Informer for time series from HuggingFace
- ModelTraining :
Class for training, validate and inference the models
- UtilPlot :
Routines for plotting and visualization of the data
- AIutil :
Utilities for the rest of the modules
Version 2.1