deploy: 42ee771

.buildinfo

@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: fcdc215d0bedfc57a14a8d537aa21449
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/about.md.txt

@@ -0,0 +1,3 @@
+# About this code
+
+This documentation is under construction

_sources/autoapi/aggregate_inputs/index.rst.txt

@@ -0,0 +1,47 @@
+aggregate_inputs
+================
+
+.. py:module:: aggregate_inputs
+
+.. autoapi-nested-parse::
+
+   Spatial Aggregation of Balmorel
+
+   Loads Balmorel input and aggregates them based on a gis file that includes cluster categories for each of the old region names 
+
+   Created on 01.10.2024
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   aggregate_inputs.get_symbols_to_aggregate
+   aggregate_inputs.merge_RRR_names
+   aggregate_inputs.merge_IRRRI_names
+   aggregate_inputs.loop_and_replace_names
+   aggregate_inputs.aggregate_parameter
+   aggregate_inputs.aggregate_sets
+   aggregate_inputs.main
+
+
+Module Contents
+---------------
+
+.. py:function:: get_symbols_to_aggregate(incfile_folder: str, exceptions: str, mean_aggfuncs: str, median_aggfuncs: str, zero_fillnas: str)
+
+.. py:function:: merge_RRR_names(df: pandas.DataFrame, clustering: geopandas.GeoDataFrame)
+
+.. py:function:: merge_IRRRI_names(df: pandas.DataFrame, clustering: geopandas.GeoDataFrame)
+
+.. py:function:: loop_and_replace_names(df: pandas.DataFrame, clustering: geopandas.GeoDataFrame, old_column: str)
+
+.. py:function:: aggregate_parameter(db: gams.GamsDatabase, symbol: str, clustering: geopandas.GeoDataFrame, aggfunc: str, unique_names: dict, fillna: Tuple[float, int, str] = 'EPS')
+
+.. py:function:: aggregate_sets(db: gams.GamsDatabase, symbol: str, clustering: geopandas.GeoDataFrame)
+
+.. py:function:: main(model_path: str, scenario: str, exceptions: str = '', mean_aggfuncs: str = '', median_aggfuncs: str = '', zero_fillnas: str = '', incfile_folder: str = 'Output')
+

_sources/autoapi/clustering/index.rst.txt

@@ -0,0 +1,66 @@
+clustering
+==========
+
+.. py:module:: clustering
+
+.. autoapi-nested-parse::
+
+   TITLE
+
+   Description
+
+   Created on 11.09.2024
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   clustering.cmap
+   clustering.columns
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   clustering.truncate_colormap
+   clustering.correct_VRE_data
+   clustering.convert_municipal_code_to_name
+   clustering.collect_clusterdata
+   clustering.apply_filters
+   clustering.gather_data
+   clustering.cluster
+   clustering.new_geofile
+   clustering.main
+
+
+Module Contents
+---------------
+
+.. py:data:: cmap
+
+.. py:function:: truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100)
+
+.. py:function:: correct_VRE_data(path_to_file, generation_name: str)
+
+.. py:function:: convert_municipal_code_to_name(to_be_converted: pandas.DataFrame, column_to_convert: Tuple[str, int], pivot_table: bool = False, exclude_regions: list = ['Herlev', 'Christiansø'], muni_geofile_path: str = 'C:\\Users\\mberos\\gitRepos\\balmorel-preprocessing\\src\\Data\\Shapefiles\\Denmark\\Adm\\gadm36_DNK_2.shp')
+
+.. py:function:: collect_clusterdata(plot_cf: bool = False)
+
+.. py:function:: apply_filters(df: pandas.DataFrame, value_name: str, aggfunc: str = 'sum')
+
+.. py:data:: columns
+
+.. py:function:: gather_data(db: gams.GamsDatabase, cluster_params: list, aggfuncs: list)
+
+.. py:function:: cluster(collected_data: pandas.DataFrame, n_clusters: int, use_connectivity: bool = True, manual_corrections: list = [['Bornholm', 'Christiansoe', 1], ['Bornholm', 'Dragoer', 1], ['Esbjerg', 'Fanoe', 1], ['Roedovre', 'Frederiksberg', 1], ['Slagelse', 'Nyborg', 0], ['Samsoe', 'Kalundborg', 0]], linkage: str = 'Ward', connection_remark: str = 'connec. included + artifical', data_remark: str = 'all combined + xy coords')
+
+.. py:function:: new_geofile(clustering: geopandas.GeoDataFrame, plot: bool = False)
+
+.. py:function:: main(model_path: str, scenario: str, cluster_params: str, aggregation_functions: str, cluster_size: int, plot_style: str = 'report')
+

_sources/autoapi/create_addon_files/index.rst.txt

@@ -0,0 +1,37 @@
+create_addon_files
+==================
+
+.. py:module:: create_addon_files
+
+.. autoapi-nested-parse::
+
+   Create Empty Sets for Aggregated Model
+
+   The clustered model will include exogenous heat and electricity demands from addons in DE, DE_VAR_T, DH, DH_VAR_T etc.
+   Thus, this script is needed to 1) create empty files for INDUSTRY_DH, INDIVUSERS_DH etc., 
+   and 2) to create the INDUSTRY_INDUSTRY_AAA and INDIVUSERS_INDIVUSERS_AAA etc. files required for categorising sets.
+
+   Created on 04.10.2024
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   create_addon_files.create_empty_set_files
+   create_addon_files.create_category_files
+   create_addon_files.main
+
+
+Module Contents
+---------------
+
+.. py:function:: create_empty_set_files(addons: list, empty_files: list)
+
+.. py:function:: create_category_files(addons: list, clustering: geopandas.GeoDataFrame, category_files: list, **kwargs)
+
+.. py:function:: main(addons: Tuple[list, str] = ['INDUSTRY', 'HYDROGEN', 'INDIVUSERS'], empty_files: Tuple[list, str] = ['DE', 'DE_VAR_T', 'DH', 'DH_VAR_T', 'CCCRRRAAA', 'RRRAAA', 'AAA', 'AGKN', 'DISLOSS_E_AG'])
+

_sources/autoapi/create_conversion_dictionaries/index.rst.txt

@@ -0,0 +1,30 @@
+create_conversion_dictionaries
+==============================
+
+.. py:module:: create_conversion_dictionaries
+
+.. autoapi-nested-parse::
+
+   Is used to convert the easy-to-read-for-non-balmorel-user sets and elements to Balmorel input
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   create_conversion_dictionaries.exo_elec_dem_conversion_dictionaries
+   create_conversion_dictionaries.exo_heat_dem_conversion_dictionaries
+   create_conversion_dictionaries.exo_grid_conversion_dictionaries
+
+
+Module Contents
+---------------
+
+.. py:data:: exo_elec_dem_conversion_dictionaries
+
+.. py:data:: exo_heat_dem_conversion_dictionaries
+
+.. py:data:: exo_grid_conversion_dictionaries
+

_sources/autoapi/exo_electricity_demand/index.rst.txt

@@ -0,0 +1,65 @@
+exo_electricity_demand
+======================
+
+.. py:module:: exo_electricity_demand
+
+.. autoapi-nested-parse::
+
+   Create electricity demand profiles for Denmark
+   Use Energinet data for municipalities:
+   https://www.energidataservice.dk/tso-electricity/consumptionindustry
+   Convert LAU code to municipality name with: 
+   LAU – NUTS 2021, EU-27 and EFTA / available candidate countries
+   https://ec.europa.eu/eurostat/web/nuts/local-administrative-units
+   Note: The dataset above also contains population
+
+   Created on 22.08.2024
+
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   exo_electricity_demand.cmap
+   exo_electricity_demand.style
+   exo_electricity_demand.fc
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   exo_electricity_demand.main
+
+
+Module Contents
+---------------
+
+.. py:data:: cmap
+
+.. py:data:: style
+   :value: 'report'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. py:function:: main(conversion_file: str, el_dataset: str, show_difference: bool = False)
+
+       Main function to process and convert dataset names, and create .inc files.
+   Args:
+       conversion_file (str): Path to the file containing conversion mappings.
+       el_dataset (str): Path to the dataset file to be processed.
+       show_difference (bool, optional): Flag to indicate whether to print the 
+                                           dataset before and after conversion. 
+                                           Defaults to False.
+   Returns:
+       None
+
+

_sources/autoapi/exo_heat_demand/index.rst.txt

@@ -0,0 +1,95 @@
+exo_heat_demand
+===============
+
+.. py:module:: exo_heat_demand
+
+.. autoapi-nested-parse::
+
+   Exogenous Heat Demands
+
+   Create exogenous heat demands using data from various places
+
+   Created on 20.09.2024
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   exo_heat_demand.cmap
+   exo_heat_demand.style
+   exo_heat_demand.fc
+
+
+Classes
+-------
+
+.. autoapisummary::
+
+   exo_heat_demand.DistrictHeatAAU
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   exo_heat_demand.create_incfile
+   exo_heat_demand.create_DH
+   exo_heat_demand.create_INDUSTRY_DH
+   exo_heat_demand.create_INDIVUSERS_DH
+   exo_heat_demand.create_INDUSTRY_DH_VAR_T
+   exo_heat_demand.main
+
+
+Module Contents
+---------------
+
+.. py:data:: cmap
+
+.. py:data:: style
+   :value: 'report'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. py:class:: DistrictHeatAAU
+
+   .. py:attribute:: district_heat
+
+
+   .. py:attribute:: industry
+
+
+   .. py:attribute:: annual_industry_demand
+
+
+   .. py:method:: combine_data(plot: bool = False)
+
+
+.. py:function:: create_incfile(unique_processing)
+
+   The general wrapper for creating and saving .inc files, 
+   because the creating of the IncFile class and saving it is the same every time.
+   The unique processing of the .body content differs, however. 
+
+   Args:
+       unique_processing (func): The unique processing per case
+       **incfile_kwargs: Keyword arguments to pass to IncFile
+
+
+.. py:function:: create_DH(incfile, new_dataset: xarray.Dataset)
+
+.. py:function:: create_INDUSTRY_DH(incfile, new_dataset: xarray.Dataset)
+
+.. py:function:: create_INDIVUSERS_DH(incfile, new_dataset: xarray.Dataset)
+
+.. py:function:: create_INDUSTRY_DH_VAR_T(incfile, el_new_dataset: xarray.Dataset, A_suffix: str)
+
+.. py:function:: main(show_difference: bool = False)
+