Processing Core

class pymoskito.processing_core.MetaProcessingModule[source]

Base Class for Meta-Processing Modules

plot_family(family, x_path, y_path, mode, x_index=-1, y_index=-1)[source]

plots y over x for all members that can be found in family sources :param family: :param x_path: :param y_path: :param mode: :param x_index: :param y_index: :return:

set_plot_labeling(title='', grid=True, x_label='', y_label='', line_type='line')[source]

helper to quickly set axis labeling with the good font sizes :param title: :param grid: :param x_label: :param y_label: :param line_type: :return:

class pymoskito.processing_core.PostProcessingModule[source]

Base Class for Postprocessing Modules

static calc_l1_norm_abs(meas_values, desired_values, step_width)[source]

Calculate the L1-Norm of the absolute error.

Parameters:

  • step_width (float) – Time difference between measurements.

  • desired_values (array-like) – Desired values.

  • meas_values (array-like) – Measured values.

static calc_l1_norm_itae(meas_values, desired_values, step_width)[source]

Calculate the L1-Norm of the ITAE (Integral of Time-multiplied Absolute value of Error).

Parameters:

  • step_width (float) – Time difference between measurements.

  • desired_values (array-like) – Desired values.

  • meas_values (array-like) – Measured values.

process(files)[source]

worker-wrapper function that processes an array of result files This is an convenience wrapper for simple processor implementation. Overload for more sophisticated implementations :param files:

abstractmethod run(data)[source]

Run this postprocessor.

This function will be called from process() with the simulation results from one simulation result file.

Overwrite this function to implement your own PostProcessor.

Args; param data: simulation results from a pymoskito simulation result file.

Returns:

With a figure Canvas an a name.

Return type:

Dict