Postprocessing Package

postprocessing.chart_singleday module

Charts for a single day.

class postprocessing.chart_singleday.ChartSingleDay(output: Any, component_name: str, units: Any, directory_path: str, time_correction_factor: float, output_description: str, data: Any, day: Any = 0, month: Any = 0, output2: Any | None = None, figure_format: Any | None = None, path_checker=None)

Bases: Chart, ChartFontsAndSize

For making visualisations for a single day.

static build_day_slice(data: Series, month: int, day: int, time_correction_factor: float, label_months_lowercase: List[str], title: str) → Tuple[Series, str]

Compute the slice of data for a single day and the matching plot title.

This is the pure, side-effect-free core of get_day_data(). It does neither touch self nor the filesystem, so it can be unit-tested with a plain pandas.Series and no Chart construction (whose parent Chart.__init__ would otherwise wire up directory paths and the result_path_provider singleton).

Args:

data: The full time-series to slice. month: Zero-based month index (0 = January, … 11 = December). day: Zero-based day-of-month index within that month (0 = 1st). time_correction_factor: seconds_per_timestep / 3600; its reciprocal

gives the number of timesteps per hour.

label_months_lowercase: The month labels indexed by month. title: The base plot title the date string is appended to.

Returns:

A (data_slice, plot_title) tuple, where data_slice is the sub-series covering the requested day (or the original data when the computed slice would exceed the available data) and plot_title is the assembled title string "{title} {month} {day}{ordinal}".

close(): Closes a chart and saves.

get_day_data(): Extracts data for a single day.

plot(close: Any) → ReportImageEntry: Plots a chart.

postprocessing.chartbase module

Contains the base class for the charts.

class postprocessing.chartbase.Chart(output, component_name, output_description, chart_type, units, directory_path, time_correction_factor, output2=None, figure_format=None, path_checker=None)

Bases: object

Parent class for plots to be exported.

ensure_output_dir() → None

Create the per-component output directory if it does not exist yet.

This used to be done unconditionally in __init__, which made every Chart (and subclass) construction touch the filesystem and depend on the global result_path_provider. It is now invoked lazily by the plot methods, right before they write into self.filepath2, so that constructing a chart is free of side effects and easy to test.

label_months_lowercase = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']

months_abbrev_uppercase = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', 'NOV', 'DEC']

rescale_y_axis(y_values: Any, units: Any) → Tuple[Any, Any]: Rescale y_values of plots.

class postprocessing.chartbase.ChartFontsAndSize

Bases: object

Give the font sizes and figure sizes of the figures.

dpi = 600

figsize = (6, 4)

fontsize_label = 12

fontsize_legend = 12

fontsize_ticks = 10

fontsize_title = 14

postprocessing.charts module

Contains all the chart classes.

class postprocessing.charts.BarChart(output: Any, component_name: str, units: Any, directory_path: str, time_correction_factor: float, output_description: str, figure_format: FigureFormat, path_checker=None)

Bases: Chart, ChartFontsAndSize

Makes Bar charts.

original_pv_sol = [385.66, 484.01, 981.05, 1096.7, 1157, 1299.9, 1415.3, 1266.1, 1075.8, 714.44, 422.51, 366.83]: Reference monthly PV production values (in kWh) used as a baseline for comparison.

plot(data: Any) → ReportImageEntry: Plots the bar chart.

class postprocessing.charts.Carpet(output: Any, component_name: str, units: Any, directory_path: str, time_correction_factor: float, output_description: str, figure_format: FigureFormat, path_checker=None)

Bases: Chart, ChartFontsAndSize

Class for carpet plots.

plot(xdims: int, data: Any) → ReportImageEntry | None

Makes a carpet plot.

Returns:: ReportImageEntry or None – None when data cannot be reshaped into entire days.

class postprocessing.charts.Line(output: Any, component_name: str, units: Any, directory_path: str, time_correction_factor: float, output_description: str, figure_format: FigureFormat, path_checker=None)

Bases: Chart, ChartFontsAndSize

Makes a line chart.

plot(data: Any) → ReportImageEntry: Makes a line plot.

postprocessing.compute_kpis module

postprocessing.generate_csv_for_housing_database module

postprocessing.postprocessing_datatransfer module

Data Transfer Object to get all the result data to the post processing.

class postprocessing.postprocessing_datatransfer.PostProcessingDataTransfer(results: DataFrame, all_outputs: List[ComponentOutput], simulation_parameters: SimulationParameters, wrapped_components: List[ComponentWrapper], mode: int, setup_function: str, module_filename: str, my_module_config: str | None, execution_time: float, results_monthly: DataFrame | None, results_hourly: DataFrame | None, results_cumulative: DataFrame | None, results_daily: DataFrame | None, kpi_collection_dict: Dict | None = None)

Bases: object

Data class for transferring the result data to this class.

postprocessing.postprocessing_main module

Main postprocessing module that starts all other modules.

class postprocessing.postprocessing_main.PostProcessor

Bases: object

Core Post processor class.

compute_and_write_capex_costs_to_report(ppdt: PostProcessingDataTransfer, report: reportgenerator.ReportGenerator | None, building_objects_in_district_list: list) → None: Computes CAPEX costs and CO2-emissions for production of devices and writes them to report and csv.

compute_and_write_opex_costs_to_report(ppdt: PostProcessingDataTransfer, report: reportgenerator.ReportGenerator | None, building_objects_in_district_list: list) → None: Computes OPEX costs and operational CO2-emissions and writes them to report and csv.

compute_kpis_and_write_to_report_and_to_ppdt(ppdt: PostProcessingDataTransfer, report: reportgenerator.ReportGenerator | None, building_objects_in_district_list: list) → PostProcessingDataTransfer: Computes KPI’s and writes them to report and to ppdt kpi collection.

export_results_to_csv(ppdt: PostProcessingDataTransfer) → None: Exports the results to a CSV file.

export_results_to_pickle(ppdt: PostProcessingDataTransfer) → None: Exports the results to a Pickle file.

export_sankeys()

Exports Sankeys plots.

ToDo: implement

get_building_object_in_district(ppdt: PostProcessingDataTransfer) → list[str]: Get building names in district.

get_dict_from_opex_capex_lists(value_list: List[str]) → Dict[str, Any]: Get dict with values for webtool from opex capex lists.

get_variable_name_and_unit_from_ppdt_results_column(column: str) → Tuple[str, str]: Get variable name and unit for pyam dictionary.

iterate_over_results_and_add_values_to_dict(results_df: DataFrame, timeseries: Any) → DataFrame: Iterate over results and add values to dict, write to dataframe and save as csv.

make_carpet_plots(ppdt: PostProcessingDataTransfer, report_image_entries: List[ReportImageEntry]) → None: Make carpet plots.

make_csv_export(ppdt: PostProcessingDataTransfer) → None: Exports all data to CSV.

make_line_plots(ppdt: PostProcessingDataTransfer, report_image_entries: List[ReportImageEntry]) → None: Makes the line plots.

make_monthly_bar_charts(ppdt: PostProcessingDataTransfer, report_image_entries: List[ReportImageEntry]) → None: Make bar charts.

make_network_charts(ppdt: PostProcessingDataTransfer) → List[SystemChartEntry]: Generates the network charts that show the connection of the elements.

make_pkl_export(ppdt: PostProcessingDataTransfer) → None: Exports all data to Pickle.

make_single_day_plots(days: Dict[str, int], ppdt: PostProcessingDataTransfer, report_image_entries: List[ReportImageEntry]) → None: Makes plots for selected days.

make_special_one_day_debugging_plots(ppdt: PostProcessingDataTransfer, report_image_entries: List[ReportImageEntry]) → None: Makes special plots for debugging if only a single day was calculated.

open_dir_in_file_explorer(ppdt: PostProcessingDataTransfer) → None

Opens files in given path.

The keyword darwin is used for supporting macOS, xdg-open will be available on any unix client running X.

prepare_results_for_scenario_evaluation(ppdt: PostProcessingDataTransfer, my_sim: Simulator) → None: Prepare the results for the scenario evaluation.

run(ppdt: PostProcessingDataTransfer, my_sim: Simulator) → None: Runs the main post processing.

set_dir_results(dirname: str | None = None) → None: Sets the results directory.

shorten_path(path, max_length=250): Shorten path if its longer than 250.

write_all_outputs_to_report(ppdt: PostProcessingDataTransfer, report: reportgenerator.ReportGenerator) → None: Write all outputs to report.

write_component_configurations_to_json(ppdt: PostProcessingDataTransfer, my_sim: Simulator) → None: Collect all component configurations and write into JSON file in result directory.

write_components_to_report(ppdt: PostProcessingDataTransfer, report: reportgenerator.ReportGenerator, report_image_entries: List[ReportImageEntry]) → None: Writes information about the components used in the simulation to the simulation report.

write_config_data_for_scenario_evaluation(ppdt: PostProcessingDataTransfer, my_sim: Simulator) → None: Prepare the results for the scenario evaluation.

write_filename_and_save_to_csv(dataframe: DataFrame, folder: str, time_resolution_of_data: str, simulation_duration: int) → None: Write file to csv.

write_kpis_in_dict(ppdt: PostProcessingDataTransfer, simple_dict_cumulative_data: Dict[str, Any]) → Dict: Write kpis in dictionary.

write_kpis_to_json_file(ppdt: PostProcessingDataTransfer) → None: Write all KPIs o json file.

write_kpis_to_json_for_building_sizer(ppdt: PostProcessingDataTransfer, building_objects_in_district_list: list) → None: Write KPIs to json file for building sizer.

write_network_charts_to_report(ppdt: PostProcessingDataTransfer, report: reportgenerator.ReportGenerator, system_chart_entries: List[SystemChartEntry]) → None: Write network charts to report.

write_new_chapter_with_table_to_report(report: reportgenerator.ReportGenerator, table_as_list_of_list: List, headline: str, comment: List) → None: Write new chapter with headline and a table to report.

write_new_chapter_with_text_content_to_report(report: reportgenerator.ReportGenerator, lines: List, headline: str) → None: Write new chapter with headline and some general information e.g. KPIs to report.

write_operation_data_for_webtool(ppdt: PostProcessingDataTransfer) → None: Collect daily operation results and write into json for webtool.

write_results_for_webtool_to_json_file(ppdt: PostProcessingDataTransfer, building_objects_in_district_list: list) → None: Collect results and write into json for webtool.

write_simulation_parameters_to_report(ppdt: PostProcessingDataTransfer, report: reportgenerator.ReportGenerator) → None: Write simulation parameters to report.

postprocessing.reportgenerator module

Module for generating reports.

class postprocessing.reportgenerator.MyDocTemplate(filename, **kw)

Bases: BaseDocTemplate

MyDocTemplate class.

afterFlowable(flowable): Registers TOC entries.

class postprocessing.reportgenerator.ReportGenerator(dirpath: str)

Bases: object

Class for generating reports.

add_page_number(canvas, doc): Add page number to report.

add_spacer(): Add spacer.

close(): Close the report.

copy_story(): Replace the story with a deep copy of itself.

get_story(**kwargs): Core function.

open(): Open a file.

page_break(): Make a page break.

write_figures_to_report(file_path: str) → None: Add figure to the report.

write_figures_to_report_with_size_four_six(file_path: str) → None: Add figure to the report with certain size.

write_figures_to_report_with_size_seven_four(file_path: str) → None: Add figure to the report with certain size.

write_heading_with_style_heading_one(text: List[str]) → None: Write text as heading.

write_heading_with_style_heading_two(text: List[str]) → None: Write text as heading.

write_preamble(): Write the preamble.

write_table_of_content(**kwargs): Core function.

write_table_of_contents(): Write the table of contents.

write_tables_to_report(table_as_list_of_lists: List) → None: Add table to the report.

write_with_center_alignment(text: List[str]) → None: Write a paragraph.

write_with_normal_alignment(text: List[str] | List[str | None]) → None: Write a paragraph.

postprocessing.system_chart module

Module for visualizing the entire system as a flow chart.

class postprocessing.system_chart.SystemChart(ppdt: PostProcessingDataTransfer)

Bases: object

Class for generating charts that show all the components.

make_chart() → List[SystemChartEntry]

Generate four variants of the system flow chart and return their entries.

Produces four Graphviz-rendered PNG charts of the simulated system: without edge labels (with and without class names), with edge labels, and with edge labels plus cumulative result values. Each successfully generated chart is appended to the returned list; failed charts are skipped.

Returns:: A list of SystemChartEntry objects, one per successfully generated chart variant.

make_graphviz_chart(with_labels: bool, with_class_names: bool, filename: str, caption: str, with_results: bool = False) → SystemChartEntry | None

Render a single system flow chart to a PNG file using Graphviz.

Builds a directed graph (via _build_graph()) where each wrapped component is a node and each input connection is an edge, optionally annotating edges with labels and cumulative result values, then writes the rendered PNG to the simulation result directory.

Args:

with_labels: If True, edges are annotated with input/field/unit: (and optionally result) text.
with_class_names: If True, appends the component’s Python class: name to each node label.
filename: Base filename (including extension) for the output PNG,: written under the simulation result directory.

caption: Human-readable caption stored in the returned entry. with_results: If True, appends the cumulative result value to each

edge label (requires with_labels for visible effect).

Returns:

A SystemChartEntry for the generated chart, or None if chart generation failed (e.g., Graphviz is not installed on the system). Failures are logged and swallowed rather than raised.