simeasren.plotting

Submodules

Functions

`generate_LCOF_diff_plot`(LCOF_diff_results, ...[, ...])	Generate Levelized Cost of Fuel (LCOF) difference plots for a given location and year.
`generate_PV_timeseries_plots`(data_sim_meas, ...[, ...])	Generate photovoltaic (PV) time-series comparison plots for measured and simulated data.
`generate_high_res_PV_plots`(clear_sky_df, ...[, ...])	Generate high-resolution photovoltaic (PV) plots for clear and cloudy sky conditions.
`generate_PV_plots`(data_sim_meas, clear_sky_df, ...[, ...])	Generate all photovoltaic (PV) plots for a given location and year.
`prepare_pv_data_for_plots`(location_name, year[, ...])	Load, clean, and prepare all measured and simulated PV datasets

Package Contents

simeasren.plotting.generate_LCOF_diff_plot(LCOF_diff_results, location_name: str, year: str, H2_end_user_min_load: float, output_dir_technoeco: str = None)

Generate Levelized Cost of Fuel (LCOF) difference plots for a given location and year.

This function visualizes the percentage difference in LCOF values between measured and simulated datasets for different modeling tools or simulation sources. It uses the results from the techno-economic assessment (e.g., obtained via calculate_all_LCOF_diff) and produces a bar plot comparing performance across tools.

Parameters:

LCOF_diff_results (list of dict) –
List of dictionaries containing LCOF comparison data for each simulation tool.
Each dictionary must include:
- ”Location” : str — the location name
- ”Tool” : str — the name of the simulation tool
- ”LCOF Difference (%)” : float — percentage difference vs. measured data
location_name (str) – Name of the analyzed location (e.g., “Turin”, “Almeria”, “Utrecht”).
year (str) – Year corresponding to the techno-economic assessment (used for labeling).
H2_end_user_min_load (float) – Minimum hydrogen end-user load used in the techno-economic calculations (e.g., 0.2 for 20% of full load).
output_root (str, optional) – Root directory where all plots and assessment results are saved. Defaults to “results”.

Returns:

The function does not return any objects. It generates and saves plot files to the output directory.

Return type:

None

Raises:

ValueError – If LCOF_diff_results is empty or not properly formatted.

Notes

Creates or ensures the existence of the following directory structure:

{output_root}/{location_name}/Techno-eco assessments results/
    End-user flex[{H2_end_user_min_load}-1]/
        System size and costs/
        Hourly profiles/

Additional details: - Relies on the helper plotting function plot_LCOF_diff(). - Uses the color palette defined in style_config.PLOT_PALETTE.

Examples

>>> from simeasren import generate_LCOF_diff_plot, prepare_pv_data_for_plots, calculate_all_LCOF_diff
>>> data_sim_meas, clear_sky_df, cloudy_sky_df = prepare_pv_data_for_plots("Utrecht", "2017")
>>> results = calculate_all_LCOF_diff(data_sim_meas, "Utrecht", 0, "GUROBI_CMD")
Fuel cost: 1518.0033140603043 EUR/t
Fuel cost: 1422.2281035151118 EUR/t
Fuel cost: 1315.902904806376 EUR/t
Fuel cost: 1441.4028983580704 EUR/t
Fuel cost: 1320.3851528711755 EUR/t
>>> generate_LCOF_diff_plot(
...     LCOF_diff_results=results,
...     location_name="Utrecht",
...     year="2017",
...     H2_end_user_min_load=0
... )
LCOF difference figure successfully generated in the 'results/Utrecht/Techno-eco assessments results" folder for Utrecht'

simeasren.plotting.generate_PV_timeseries_plots(data_sim_meas, location_name: str, year: str, output_dir_timeseries: str = None)

Generate photovoltaic (PV) time-series comparison plots for measured and simulated data.

This function produces and saves multiple visual analyses for a given location and year:

Capacity factor time-series plot comparing measured vs. simulated data.
Scatter comparison plot between measured and simulated PV outputs.
Error metrics bar charts (mean difference, MAE, RMSE).

Results are saved under a structured directory within the specified output root.

Parameters:

data_sim_meas (pandas.DataFrame) – DataFrame containing both measured and simulated PV time-series data. Each column represents a dataset (e.g., different simulation tools) and must include one column containing “PV-MEAS” for measured data.
location_name (str) – Name of the analyzed location (e.g., “Turin”, “Almeria”).
year (str) – Year corresponding to the PV data (used for labeling and output directory naming).
output_root (str, optional) – Root directory where all plots and results will be saved. Defaults to “results”.

Returns:

The function does not return any objects. It generates and saves plot images to disk.

Return type:

None

Raises:

FileNotFoundError – If required plotting functions or style configuration files are missing.
ValueError – If the input DataFrame does not contain measured PV data (“PV-MEAS”).

Notes

Creates or overwrites output files in:

{output_root}/{location_name}/Time series analysis results/

Uses the following helper functions: - capacity_factor_formatting() - plot_capacity_factors() - plot_scatter_comparison() - calculate_error_metrics() - plot_error_metrics()

Plot aesthetics (colors, linestyles, and colormaps) are managed through the style_config module.

Examples

>>> from simeasren import generate_PV_timeseries_plots, prepare_pv_data_for_plots
>>> df, _, _ = prepare_pv_data_for_plots("Turin", "2019")
>>> generate_PV_timeseries_plots(
...     data_sim_meas=df,
...     location_name="Turin",
...     year="2019"
... )
Capacity factors figure successfully saved at 'results\Turin\Time series analysis results\Turin2019_Capacity_Factors.png'
Scatter plot figure successfully saved at 'results\Turin\Time series analysis results\Turin2019_scatterplot.png'
Combined error analysis figure successfully saved at 'results\Turin\Time series analysis results\Turin2019_Errors_Analysis.png'

By default, the following plots will be saved in:

results/Turin/Time series analysis results/

- Capacity factor comparison (*_capacity_factor.png)
- Scatter plot comparison (*_scatter.png)
- Error metrics summary (*_error_metrics.png)

simeasren.plotting.generate_high_res_PV_plots(clear_sky_df, cloudy_sky_df, location_name: str, year: str, output_dir_timeseries: str = None)

Generate high-resolution photovoltaic (PV) plots for clear and cloudy sky conditions.

This function visualizes high-frequency PV time-series data for both clear and cloudy days to assess model performance and temporal dynamics under different weather conditions.

Parameters:

clear_sky_df (pandas.DataFrame) – High-resolution PV data corresponding to clear-sky conditions. Each column should represent a different dataset (e.g., measured and simulated values).
cloudy_sky_df (pandas.DataFrame) – High-resolution PV data corresponding to cloudy-sky conditions. Must have the same column naming convention as clear_sky_df.
location_name (str) – Name of the analyzed location (e.g., “Turin”, “Almeria”).
year (str) – Year corresponding to the PV data (used for labeling and output directory naming).
output_root (str, optional) – Root directory where all plots and results will be saved. Defaults to “results”.

Returns:

This function does not return any objects. It generates and saves plot images to the specified results directory.

Return type:

None

Raises:

ValueError – If input DataFrames are empty or do not contain the expected columns.

Notes

By default creates or overwrites output files in: ` {output_root}/{location_name}/Time series analysis results/ `
Uses helper functions for formatting and plotting: - highres_plot_formatting() - plot_high_res_days()
Column names are expected to follow the pattern: “Location Source” (e.g., “Almeria PV-MEAS”).
Plot appearance (colors, linestyles, widths) is controlled through style_config for visual consistency.

Examples

>>> from simeasren import generate_high_res_PV_plots, prepare_pv_data_for_plots
>>> data_sim_meas, clear_sky_df, cloudy_sky_df = prepare_pv_data_for_plots("Almeria", "2023")
>>> generate_high_res_PV_plots(
...     clear_sky_df=clear_sky_df,
...     cloudy_sky_df=cloudy_sky_df,
...     location_name="Almeria",
...     year="2023"
... )
High-resolution PV plot saved at 'results\Almeria\Time series analysis results\Almeria_highres_clear_vs_cloudy.png'

simeasren.plotting.generate_PV_plots(data_sim_meas, clear_sky_df, cloudy_sky_df, location_name: str, year: str, output_dir_timeseries: str = None)

Generate all photovoltaic (PV) plots for a given location and year.

This wrapper function coordinates the generation of both:

Time-series PV plots — including capacity factors, scatter comparisons, and error metrics.
High-resolution PV plots — for clear-sky and cloudy-sky conditions.

It ensures all visualization outputs for the specified site and year are consistently formatted, saved in the correct directory structure, and produced with a single function call.

Parameters:

data_sim_meas (pandas.DataFrame) – DataFrame containing measured and simulated PV time-series data. Must include a “PV-MEAS” column representing measured data.
clear_sky_df (pandas.DataFrame) – High-resolution PV data under clear-sky conditions.
cloudy_sky_df (pandas.DataFrame) – High-resolution PV data under cloudy-sky conditions.
location_name (str) – Name of the analyzed location (e.g., “Turin”, “Utrecht”).
year (str) – Year corresponding to the PV data (used for labeling and output directories).
output_root (str, optional) – Root directory where all plots and results will be saved. Defaults to “results”.

Returns:

This function does not return any objects. It calls subfunctions that generate and save plots to disk.

Return type:

None

Raises:

ValueError – If data_sim_meas is empty or lacks required columns.

Notes

Output files are saved to: ` {output_root}/{location_name}/Time series analysis results/ `
If data_sim_meas is empty, the function exits without generating plots.

Examples

>>> from simeasren import generate_PV_plots, prepare_pv_data_for_plots
>>> data_sim_meas, clear_sky_df, cloudy_sky_df = prepare_pv_data_for_plots("Almeria", "2023")
>>> generate_PV_plots(
...     data_sim_meas=data_sim_meas,
...     clear_sky_df=clear_sky_df,
...     cloudy_sky_df=cloudy_sky_df,
...     location_name="Almeria",
...     year="2023"
... )
Capacity factors figure successfully saved at 'results\Almeria\Time series analysis results\Almeria2023_Capacity_Factors.png'
Scatter plot figure successfully saved at 'results\Almeria\Time series analysis results\Almeria2023_scatterplot.png'
Combined error analysis figure successfully saved at 'results\Almeria\Time series analysis results\Almeria2023_Errors_Analysis.png'
High-resolution PV plot saved at 'results\Almeria\Time series analysis results\Almeria_highres_clear_vs_cloudy.png'

The generated plots will include:

Time-series comparisons (capacity factor, scatter, error metrics)
High-resolution plots for clear and cloudy conditions

simeasren.plotting.prepare_pv_data_for_plots(location_name: str, year: str, file_path_sim_meas: str = None, file_path_pvdata: str = None)

Load, clean, and prepare all measured and simulated PV datasets for subsequent time series and high-resolution plotting.

This function consolidates and preprocesses photovoltaic (PV) performance data by loading measured Excel files and corresponding simulated CSV outputs for a given location and year. It also merges high-resolution daily data (clear and cloudy sky) with hourly simulation results to ensure consistent plotting inputs.

Parameters:

location_name (str) – Name of the PV site or measurement location (e.g., “Almeria”, “Turin”, “Utrecht”). Must match the filename in the data/measured_PV/ directory
year (str) – Specific year to filter the simulation data (e.g., “2023”). The year should exist in the measured data files

Returns:

data_sim_meas (pd.DataFrame) – Hourly simulated and measured PV data (limited to 8760 hours for a full year). Columns include simulated and measured normalized power outputs per model/tool.
clear_sky_df (pd.DataFrame) – Processed and merged clear-sky day data, combining high-resolution measured values with corresponding simulation results.
cloudy_sky_df (pd.DataFrame) – Processed and merged cloudy-sky day data, combining high-resolution measured values with corresponding simulation results.

Raises:

FileNotFoundError – If the measured PV Excel file for the specified location does not exist.
UserWarning – If the specified year is not found in the dataset, resulting in empty outputs.

Notes

Measured PV data are expected in: ` data/measured_PV/{location_name}.xlsx ` with sheet names “Clear sky day” and “Cloudy sky day”.
By default simulated PV results are expected in: ` results/{location_name}/simulated_pv/{location_name}_meas_sim.csv `
Helper functions used: - convert_comma_to_dot() for numeric conversion. - extract_year_selected() for extracting year tags from column names.

Examples

>>> from simeasren import prepare_pv_data_for_plots
>>> data_sim_meas, clear_sky_df, cloudy_sky_df = prepare_pv_data_for_plots(
...     location_name="Almeria",
...     year="2023"
... )
>>> data_sim_meas.shape
(8760, 3)