simeasren.plotting.plots

Functions

plot_capacity_factors(data_sim_meas, location_name, ...)	Plot the capacity factor figure for a given location and dataset.
capacity_factor_formatting(legend_names[, highlight_label])	Generate colors, linestyles, and line widths for capacity factor plots.
plot_scatter_comparison(data_sim_meas, location_name, ...)	Plot scatter plots comparing measured vs simulated data for multiple simulation columns.
plot_error_metrics(location_name, year, ...)	Plot error metrics (Mean Difference, MAE, RMSE) for a location.
plot_high_res_days(df_clear, df_cloudy, location_name, ...)	Plot high-resolution PV data for Clear Sky and Cloudy Sky days side by side.
highres_plot_formatting(legend_names_high_res[, ...])	Generate legend names, colors, linestyles, and line widths for
plot_LCOF_diff(LCOF_diff_results, plot_palette, ...)	Function to plot LCOF difference for error analysis and save the figure.

Module Contents

simeasren.plotting.plots.plot_capacity_factors(data_sim_meas, location_name, year, legend_names, colors_CF, linestyles_CF, line_widths_CF, output_dir_timeseries, x_limit=5000)

Plot the capacity factor figure for a given location and dataset.

Parameters:

data_sim_meas (pd.DataFrame): DataFrame with capacity factor time series for the location. location_name (str): Name of the location. legend_names (list): List of time series/legend names corresponding to the columns. colors_CF (list): List of colors for each time series. linestyles_CF (list): List of line styles for each time series. line_widths_CF (list): List of line widths for each time series. output_dir_timeseries (str): Path to save the figure. x_limit (int, optional): Maximum x-axis value (default is 5000).

simeasren.plotting.plots.capacity_factor_formatting(legend_names, highlight_label='PV-MEAS')

Generate colors, linestyles, and line widths for capacity factor plots.

Parameters:

data_sim_meas (pd.DataFrame): DataFrame containing all simulation and measurement columns. user_color_mapping (dict, optional): Custom color mapping to override defaults. user_linestyle_mapping (dict, optional): Custom linestyle mapping to override defaults. highlight_label (str, optional): Label that should be highlighted with a thicker line width.

Returns:

tuple: (legend_names, colors_CF, linestyles_CF, line_widths_CF)

simeasren.plotting.plots.plot_scatter_comparison(data_sim_meas, location_name, year, custom_cmap, output_dir_timeseries)

Plot scatter plots comparing measured vs simulated data for multiple simulation columns.

Parameters:

filtered_data_scat (pd.DataFrame): DataFrame with filtered simulation data (rows where measured data != 0). measured_data_scat (pd.Series): Series with measured data. sim_columns (list): List of simulation column names to plot. location_name (str): Name of the location. custom_cmap: Colormap for KDE plot. output_dir_timeseries (str): Directory to save the scatter plot figure.

simeasren.plotting.plots.plot_error_metrics(location_name, year, mean_diff_results, mae_results, rmse_results, plot_palette, legend_names, output_dir_timeseries)

Plot error metrics (Mean Difference, MAE, RMSE) for a location.

Parameters:

location_name (str): Name of the location. year (str): Year of the simulation. mean_diff_results (list of dicts): List of mean difference results with keys “Location”, “Tool”, “Mean Difference (%)”. mae_results (list of dicts): List of MAE results with keys “Location”, “Tool”, “MAE (%)”. rmse_results (list of dicts): List of RMSE results with keys “Location”, “Tool”, “RMSE (%)”. plot_palette (dict): Dictionary mapping tools to colors for the bar plots. legend_names (list): List of tools to include in the legend. output_dir_timeseries (str): Directory to save the combined metrics figure.

simeasren.plotting.plots.plot_high_res_days(df_clear, df_cloudy, location_name, legend_names_high_res, colors_high_res, linestyles_high_res, line_widths_high_res, output_dir_timeseries)

Plot high-resolution PV data for Clear Sky and Cloudy Sky days side by side.

Parameters:

df_clear (pd.DataFrame): DataFrame with clear sky day data. df_cloudy (pd.DataFrame): DataFrame with cloudy sky day data. location_name (str): Name of the location. legend_names_high_res (list): List of time series/legend names. colors_high_res (list): List of colors corresponding to the legends. linestyles_high_res (list): List of line styles for the legends. line_widths_high_res (list): List of line widths for the legends. output_dir_timeseries (str): Directory to save the figure.

simeasren.plotting.plots.highres_plot_formatting(legend_names_high_res, highlight_label='PV-MEAS')

Generate legend names, colors, linestyles, and line widths for high-resolution (clear sky and cloudy sky) PV plots.

Parameters:

clear_sky_df (pd.DataFrame): DataFrame for the clear-sky high-res data. cloudy_sky_df (pd.DataFrame): DataFrame for the cloudy-sky high-res data. highlight_label (str, optional): Label that should be highlighted with a thicker line width.

Returns:

tuple: (legend_names_high_res, colors_high_res, linestyles_high_res, line_widths_high_res)

simeasren.plotting.plots.plot_LCOF_diff(LCOF_diff_results, plot_palette, location_name, year, H2_end_user_min_load, output_dir_technoeco, legend_names)

Function to plot LCOF difference for error analysis and save the figure.

Args: LCOF_diff_results (list): List of dictionaries containing LCOF differences for each location and tool. plot_palette (dict): Dictionary containing the simulation tools for plotting. location_name (str): The location name used for saving the plot. H2_end_user_min_load (float): Minimum load for H2 end user, included in the figure filename. output_dir_technoeco (str): Directory to save the plot. legend_names (list): List of legend names to filter the plot legend

Returns: None