zen_garden.preprocess.extract_input_data¶

Functions to extract the input data from the provided input files.

Classes

DataInput(element, system, analysis, solver, ...)

Class to extract input data.

class zen_garden.preprocess.extract_input_data.DataInput(element, system, analysis, solver, energy_system, unit_handling, optimization_setup=None)¶

Class to extract input data.

__init__(element, system, analysis, solver, energy_system, unit_handling, optimization_setup=None)¶

Data input object to extract input data.

Parameters:

element – element for which data is extracted
system – dictionary defining the system
analysis – dictionary defining the analysis framework
solver – dictionary defining the solver
energy_system – instance of class <EnergySystem> to define energy_system
unit_handling – instance of class <UnitHandling> to convert units

construct_index_list(index_sets, time_steps)¶

Constructs index list from index sets and returns list of indices and list of index names.

Parameters:

index_sets – index sets of attribute. Creates (multi)index. Corresponds to order in pe.Set/pe.Param
time_steps – specific time_steps of element

Returns:

list of indices index_name_list: list of name of indices

Return type:

index_list

convert_real_to_generic_time_indices(df_input, time_steps, file_name, index_name_list)¶

Convert yearly time indices to generic time indices.

Parameters:

df_input – raw input dataframe
time_steps – specific time_steps of element
file_name – name of selected file
index_name_list – list of name of indices

Returns:

df_input: input dataframe with generic time indices

create_default_output(index_sets, unit_category, file_name=None, time_steps=None, manual_default_value=None, subelement=None)¶

Creates default output dataframe.

Parameters:

index_sets – index sets of attribute. Creates (multi)index. Corresponds to order in pe.Set/pe.Param
unit_category – dict defining the dimensions of the parameter’s unit
file_name – name of selected file.
time_steps – specific time_steps of subelement
manual_default_value – if given, use manual_default_value instead of searching for default value in attributes.json
subelement – dependent element for which data is extracted

extract_attribute(attribute_name, unit_category, return_unit=False, subelement=None)¶

Reads input data and restructures the dataframe to return (multi)indexed dict.

Parameters:

attribute_name – name of selected attribute
unit_category – dict defining the dimensions of the parameter’s unit
return_unit – only returns unit
subelement – dependent element for which data is extracted

Returns:

attribute value and multiplier

Returns:

unit of attribute

extract_carriers(carrier_type)¶

Reads input data and extracts conversion carriers.

Returns:: list with input, output or reference carriers of technology
Return type:: list

static extract_from_input_for_capacities_existing(df_input, df_output, index_name_list, column, missing_index)¶

Extracts the demanded values from input dataframe if extracting existing capacities.

Parameters:

df_input – raw input dataframe
df_output – default output dataframe
index_name_list – list of name of indices
column – select specific column
missing_index – missing index in df_input

Returns:

df_output: filled output dataframe

static extract_from_input_with_missing_index(df_input, df_output, index_name_list, file_name, missing_index)¶

Extracts the demanded values from Input dataframe and reformulates dataframe if the index is missing. Either, the missing index is the column of df_input, or it is actually missing in df_input. Then, the values in df_input are extended to all missing index values.

Parameters:

df_input – raw input dataframe
df_output – default output dataframe
index_name_list – list of name of indices
file_name – name of selected file
missing_index – missing index in df_input

Returns:

reformulated input dataframe

Return type:

pandas.DataFrame

static extract_from_input_without_missing_index(df_input, index_name_list, file_name)¶

Extracts the demanded values from Input dataframe and reformulates dataframe.

Parameters:

df_input – raw input dataframe
index_name_list – list of name of indices
file_name – name of selected file

Returns:

df_input: reformulated input dataframe

extract_general_input_data(df_input, df_output, file_name, index_name_list, default_value, time_steps)¶

Fills df_output with data from df_input.

Parameters:

df_input – raw input dataframe
df_output – empty output dataframe, only filled with default_value
file_name – name of selected file
index_name_list – list of name of indices
default_value – default for dataframe
time_steps – specific time_steps of element

Returns:

df_output: filled output dataframe

extract_input_data(file_name, index_sets, unit_category, time_steps=None, subelement=None)¶

Reads input data and restructures the dataframe to return (multi)indexed dict.

Parameters:

file_name – name of selected file.
index_sets – index sets of attribute. Creates (multi)index. Corresponds to order in pe.Set/pe.Param
unit_category – dict defining the dimensions of the parameter’s unit
time_steps – string specifying time_steps
subelement – string specifying dependent element

Returns:

dictionary with attribute values

extract_lifetime_existing(file_name, index_sets)¶

Reads input data and restructures the dataframe to return (multi)indexed dict.

Parameters:

file_name – name of selected file
index_sets – index sets of attribute. Creates (multi)index. Corresponds to order in pe.Set/pe.Param

Returns:

return existing capacity and existing lifetime

Return type:

df_output

extract_locations(extract_nodes=True, extract_coordinates=False)¶

Reads input data to extract nodes or edges.

Args

extract_nodes: boolean to switch between nodes and edges

extract_coordinates: boolean to switch between nodes and nodes + coordinates

extract_pwa_capex()¶

Reads input data and restructures the dataframe to return (multi)indexed dict.

Returns:: pwa_dict: dictionary with pwa parameters

extract_retrofit_base_technology()¶

Extract base technologies for retrofitting technology.

Returns:: return base technology of retrofit technology

extract_set_technologies_existing(storage_energy=False)¶

Reads input data and creates setExistingCapacity for each technology.

Parameters:: storage_energy – boolean if existing energy capacity of storage technology (instead of power)
Returns:: return set existing technologies
Return type:: set_technologies_existing

extract_year_specific_ts(file_name, index_name_list, time_steps, subelement, default_value, df_output_generic)¶

Reads and saves the year specific time series data. The year specific time series are saved in the dictionary self.optimization_setup.year_specific_ts.

Parameters:

file_name – name of selected file
index_name_list – list of name of indices
default_value – default for dataframe
time_steps – specific time_steps of element
subelement – string specifying dependent element
df_output_generic – original/generic time series data (base case)

extract_yearly_variation(file_name, index_sets)¶

Reads the yearly variation of a time dependent quantity.

Parameters:

file_name – name of selected file.
index_sets – index sets of attribute. Creates (multi)index. Corresponds to order in pe.Set/pe.Param

get_attribute_dict(attribute_name)¶

Get attribute dict and factor for attribute.

Parameters:: attribute_name – name of selected attribute
Returns:: attribute_dict: attribute dict
Returns:: factor: factor for attribute

load_attribute_file(filename='attributes')¶: Loads attribute file. Either as csv (old version) or json (new version) :param filename: name of attributes file, default is ‘attributes’ :return: attribute_dict.

read_input_csv(input_file_name)¶

Reads input data and returns raw input dataframe.

Parameters:: input_file_name – name of selected file
Returns:: df_input: pd.DataFrame with input data

read_input_json(input_file_name)¶

Reads json input data and returns a dict.

Parameters:: input_file_name – name of selected file
Returns:: data: dict with input data

read_pwa_capex_files()¶

Reads pwa files.

Returns:: df_input: raw input file