deploy: c882b67

.buildinfo

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+# 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

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+# About this code
+
+This documentation is under construction

_sources/autoapi/clustering/index.rst.txt

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+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.style
+   clustering.fc
+   clustering.collected
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   clustering.truncate_colormap
+   clustering.correct_VRE_data
+   clustering.convert_municipal_code_to_name
+   clustering.collect_clusterdata
+   clustering.cluster
+
+
+Module Contents
+---------------
+
+.. py:data:: cmap
+
+.. py:function:: truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100)
+
+.. py:data:: style
+   :value: 'ppt'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. 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: (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:: cluster(con: Submodules.municipal_template.DataContainer, n_clusters: int, use_connectivity: bool = True, manual_corrections: list = [['Bornholm', 'Christiansø', 1], ['Bornholm', 'Dragør', 1], ['Esbjerg', 'Fanø', 1], ['Rødovre', 'Frederiksberg', 1], ['Slagelse', 'Nyborg', 0], ['Samsø', 'Kalundborg', 0]], connection_remark: str = 'connec. included + artifical', data_remark: str = 'all combined + xy coords')
+
+.. py:data:: collected
+

_sources/autoapi/createDH/index.rst.txt

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+createDH
+========
+
+.. py:module:: createDH
+
+.. autoapi-nested-parse::
+
+   Creating Danish heat demand for Balmorel 
+
+   Futuregas dataset default. Can be used to get temporal profiles
+
+   Aalborg dataset, can be used to get individual and district heat gross supply:
+   https://vbn.aau.dk/da/datasets/kommunepakker-varmeplan-danmark-2021
+   - Note that heat demand should be combined wrt. the s1-s5 scenarios (sums to 53.95 TWh without heat efficiency investments)
+
+   Python package requirements:
+   - xlrd 
+
+   Created on 11.03.2024
+
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   createDH.cmap
+   createDH.style
+   createDH.fc
+   createDH.X
+
+
+Classes
+-------
+
+.. autoapisummary::
+
+   createDH.DistrictHeat
+   createDH.DistrictHeatAAU
+
+
+Functions
+---------
+
+.. autoapisummary::
+
+   createDH.find_value
+
+
+Module Contents
+---------------
+
+.. py:data:: cmap
+
+.. py:data:: style
+   :value: 'report'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. py:class:: DistrictHeat(dataset: str = 'Denmark_Futuregas')
+
+   Class for district heating data
+
+   Existing datasets:
+       - Denmark_Futuregas
+
+   Args:
+       dataset (str, optional): _description_. Defaults to 'DK'.
+
+
+   .. py:method:: assign_DH(areas: geopandas.GeoDataFrame, df_intercepts: pandas.DataFrame, value_col: str = 'Value') -> None
+
+      DH data must have A and Y sets, where A matches the areas index
+
+      Ends up with format:
+
+          Y1  Y2      Y3      --      YN
+
+      A1
+
+      A2
+
+      |
+
+      AM
+
+
+
+   .. py:method:: assign_DHT(areas: geopandas.GeoDataFrame, df_intercepts: pandas.DataFrame, value_col: str = 'Value', agg_func: str = 'sum') -> None
+
+      DHT data must have S.T and A sets, where A matches the areas index
+
+      Ends up with format:
+
+                              A1      A2      A3      --      AN
+
+      S01     .       T001
+
+      S01     .       T002
+
+      |
+
+      S52     .       T168
+
+
+
+
+   .. py:method:: assign_DH_profile()
+
+
+   .. py:method:: join_geo_with(df: pandas.DataFrame, kwargs: dict = {'how': 'inner'}) -> None
+
+
+   .. py:method:: plot_original_data(year: str, areas: geopandas.GeoDataFrame, plot_density: bool = False, fc: str = 'white', area_fc: Union[str, list] = [0.85, 0.85, 0.85], ax: Union[matplotlib.axes._axes.Axes, str] = '') -> tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]
+
+
+   .. py:method:: plot_aggregated_data(year: str, areas: geopandas.GeoDataFrame, plot_density: bool = False) -> tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]
+
+
+.. py:function:: find_value(df: pandas.DataFrame, element: any, func: str = 'max', ind: int = 0) -> any
+
+.. py:class:: DistrictHeatAAU
+
+   .. py:attribute:: f1
+
+
+   .. py:attribute:: f2
+
+
+   .. py:method:: combine_data(plot: bool = False)
+
+
+.. py:data:: X
+

_sources/autoapi/createHYDROGEN/index.rst.txt

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+createHYDROGEN
+==============
+
+.. py:module:: createHYDROGEN
+
+.. autoapi-nested-parse::
+
+   Created on 17.03.2024
+
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   createHYDROGEN.style
+   createHYDROGEN.fc
+   createHYDROGEN.XH2LOSS
+   createHYDROGEN.XH2COST
+   createHYDROGEN.XH2INVCOST
+
+
+Classes
+-------
+
+.. autoapisummary::
+
+   createHYDROGEN.Hydrogen
+
+
+Module Contents
+---------------
+
+.. py:data:: style
+   :value: 'report'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. py:data:: XH2LOSS
+   :value: 2.5e-08
+
+
+.. py:data:: XH2COST
+   :value: 1e-06
+
+
+.. py:data:: XH2INVCOST
+   :value: 0.55
+
+
+.. py:class:: Hydrogen(choice: str)
+
+   .. py:attribute:: grid
+
+
+   .. py:attribute:: gasstorage
+
+
+   .. py:method:: create_hydrogen_data(areas: geopandas.GeoDataFrame, the_index: str) -> dict
+
+

_sources/autoapi/createINDUSTRY/index.rst.txt

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+createINDUSTRY
+==============
+
+.. py:module:: createINDUSTRY
+
+.. autoapi-nested-parse::
+
+   Created on 14.03.2024
+
+   @author: Mathias Berg Rosendal, PhD Student at DTU Management (Energy Economics & Modelling)
+
+   Script for creating data for industry sector
+   Sources:
+       - Balmorel Data 2024
+       - Manz, Pia, Tobias Fleiter, and Wolfgang Eichhammer. “The Effect of Low-Carbon Processes on Industrial Excess Heat Potentials for District Heating in the EU: A GIS-Based Analysis.” Smart Energy 10 (May 1, 2023): 100103. https://doi.org/10.1016/j.segy.2023.100103.
+
+
+
+Attributes
+----------
+
+.. autoapisummary::
+
+   createINDUSTRY.cmap
+   createINDUSTRY.style
+   createINDUSTRY.fc
+   createINDUSTRY.XHLOSS
+   createINDUSTRY.XHCOST
+   createINDUSTRY.XHINVCOST
+
+
+Classes
+-------
+
+.. autoapisummary::
+
+   createINDUSTRY.Industry
+
+
+Module Contents
+---------------
+
+.. py:data:: cmap
+
+.. py:data:: style
+   :value: 'report'
+
+
+.. py:data:: fc
+   :value: 'white'
+
+
+.. py:data:: XHLOSS
+   :value: 0.1
+
+
+.. py:data:: XHCOST
+   :value: 0.001
+
+
+.. py:data:: XHINVCOST
+   :value: 396000
+
+
+.. py:class:: Industry(dataset: str = 'EUBalmorel')
+
+   Class for industry addon data
+
+   Existing datasets:
+       - Denmark
+
+   Args:
+       dataset (str, optional): _description_. Defaults to 'DK'.
+
+
+   .. py:attribute:: PS
+
+
+   .. py:attribute:: index
+
+
+   .. py:attribute:: crs
+      :value: 'EPSG:4326'
+
+
+
+   .. py:attribute:: geoindex
+
+
+   .. py:method:: assign_original_region()
+
+
+   .. py:method:: assign_emission_fractions()
+
+
+   .. py:method:: apply_ef_to_original_data(incfiles: dict, original_area: str, new_area: str, frac_sum: str, include_new_area: bool = False) -> None
+
+      Assign industry heat demand from original area to new area
+
+
+
+   .. py:method:: create_industry_data(areas: geopandas.GeoDataFrame, save_new_area_to_df: bool = False, plot_point_sources: bool = False, ax: matplotlib.axes.Axes = '') -> dict
+
+      Prepares industry data .inc files  
+
+
+
+
+   .. py:method:: plot_original_data(emission_scale: float = 500000.0, bounds: list[float, float, float, float] = [-12, 30, 33, 73], fc: str = 'white', area_fc: Union[str, list] = [0.85, 0.85, 0.85], ax: Union[matplotlib.axes._axes.Axes, str] = '') -> tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]
+
+
+   .. py:method:: plot_aggregated_data(incfiles: dict, areas: geopandas.GeoDataFrame, indicator: str = 'DH') -> tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]
+
+