Multiple_Damping_Curves#

class PySeismoSoil.class_curves.Multiple_Damping_Curves(filename_or_list_of_curves: str | list[numpy.ndarray], *, sep: str = '\t')[source]#

Bases: Multiple_Curves

Class implementation of multiple damping curves.

Its behavior is similar to a list, but with a more stringent requirement: all elements are of the same data type, i.e., Damping_Curve.

The list-like behaviors available are:

  • indexing: foo[3]

  • slicing: foo[:4]

  • setting values: foo[2] = …

  • length: len(foo)

  • deleting item: del foo[2]

  • checking existance: bar in foo

  • appending: foo.append(bar)

Parameters:

  • filename_or_list_of_curves (str | list[np.ndarray]) – A file name of a validly formatted “curve file”, or a list of 2-column numpy arrays, which are in (strain [%], damping [%]) format.

  • sep (str) – Delimiter of the file to be imported. If filename_or_list_of_curves is a list, this parameter has no effect.

curves#

A list of Damping_Curve objects.

Type:

list[Damping_Curve]

n_layer#

The number of soil layers (i.e., the length of the list).

Type:

int

Raises:

TypeError – When filename_or_list_of_curves has unrecognizable type

Methods Summary

append(item)

Append another curve item to the curves.

get_all_H4_x_params([use_scipy, pop_size, ...])

Obtain the H4_x parameters from the damping curve data (for all the curves), using the genetic algorithm provided in DEAP.

get_all_HH_x_params([use_scipy, pop_size, ...])

Obtain the HH_x parameters from the damping curve data (for all the curves), using the genetic algorithm provided in DEAP.

get_curve_matrix([GGmax_filler_value, ...])

Produce a full "curve matrix" based on the damping data defined in objects of this class.

plot([plot_interpolated, fig, ax, title, ...])

Plot multiple curves together on one figure.

Methods Documentation

append(item) → None#

Append another curve item to the curves.

get_all_H4_x_params(use_scipy: bool = True, pop_size: int = 800, n_gen: int = 100, lower_bound_power: float = - 4, upper_bound_power: float = 6, eta: float = 0.1, seed: float = 0, show_fig: bool = False, verbose: bool = False, parallel: bool = False, n_cores: int | None = None, save_txt: bool = False, txt_filename: str | None = None, sep: str | None = None, save_fig: bool = False, fig_filename: str | None = None, dpi: float = 100) → MKZ_Param_Multi_Layer[source]#

Obtain the H4_x parameters from the damping curve data (for all the curves), using the genetic algorithm provided in DEAP.

Parameters:

  • use_scipy (bool) – Whether to use the “differential_evolution” algorithm implemented in scipy (https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html) to perform optimization. If False, use the algorithm in the DEAP package.

  • pop_size (int) – The number of individuals in a generation.

  • n_gen (int) – Number of generations that the evolution lasts.

  • lower_bound_power (float) – The 10-based power of the lower bound of all the 9 parameters. For example, if your desired lower bound is 0.26, then set this parameter to be numpy.log10(0.26).

  • upper_bound_power (float) – The 10-based power of the upper bound of all the 9 parameters.

  • eta (float) – Crowding degree of the mutation or crossover. A high eta will produce children resembling to their parents, while a low eta will produce solutions much more different.

  • seed (float) – Seed value for the random number generator.

  • show_fig (bool) – Whether to show the curve fitting results as a figure.

  • verbose (bool) – Whether to display information (statistics of the loss in each generation) on the console.

  • parallel (bool) – Whether to use parallel computing across layers, i.e., calculate multiple layers simultaneously.

  • n_cores (int | None) – Number of CPU cores to use. If None, all cores are used. No effects if the parallelization options are set to False.

  • save_txt (bool) – Whether to save the results as a “HH_x_STATION_NAME.txt” file.

  • txt_filename (str | None) – File name of the text file to save HH parameters. If the object is created via a “curve” text file, then txt_filename can be None and the output filename will be determined automatically.

  • sep (str | None) – Delimiter to separate columns of data in the output file.

  • save_fig (bool) – Whether to save damping fitting figures to hard drive.

  • fig_filename (str | None) – Full file name of the figure. If the object is created via a “curve” text file, then fig_filename can be None, and the output figure name will be determined automatically.

  • dpi (float) – Figure resolution

Returns:

H4_x_param – The best parameters for each soil layer found in the optimization.

Return type:

MKZ_Param_Multi_Layer

get_all_HH_x_params(use_scipy: bool = True, pop_size: int = 800, n_gen: int = 100, lower_bound_power: float = - 4, upper_bound_power: float = 6, eta: float = 0.1, seed: float = 0, show_fig: bool = False, verbose: bool = False, parallel: bool = False, n_cores: int | None = None, save_txt: bool = False, txt_filename: str | None = None, sep: str | None = None, save_fig: bool = False, fig_filename: str | None = None, dpi: float = 100) → HH_Param_Multi_Layer[source]#

Obtain the HH_x parameters from the damping curve data (for all the curves), using the genetic algorithm provided in DEAP.

Parameters:

  • use_scipy (bool) – Whether to use the “differential_evolution” algorithm implemented in scipy (https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html) to perform optimization. If False, use the algorithm in the DEAP package.

  • pop_size (int) – The number of individuals in a generation.

  • n_gen (int) – Number of generations that the evolution lasts.

  • lower_bound_power (float) – The 10-based power of the lower bound of all the 9 parameters. For example, if your desired lower bound is 0.26, then set this parameter to be numpy.log10(0.26).

  • upper_bound_power (float) – The 10-based power of the upper bound of all the 9 parameters.

  • eta (float) – Crowding degree of the mutation or crossover. A high eta will produce children resembling to their parents, while a low eta will produce solutions much more different.

  • seed (float) – Seed value for the random number generator.

  • show_fig (bool) – Whether to show the curve fitting results as a figure.

  • verbose (bool) – Whether to display information (statistics of the loss in each generation) on the console.

  • parallel (bool) – Whether to use parallel computing across layers, i.e., calculate multiple layers simultaneously.

  • n_cores (int | None) – Number of CPU cores to use. If None, all cores are used. No effects if the parallelization options are set to False.

  • save_txt (bool) – Whether to save the results as a “HH_x_STATION_NAME.txt” file.

  • txt_filename (str | None) – File name of the text file to save HH parameters. If the object is created via a “curve” text file, then txt_filename can be None and the output filename will be determined automatically.

  • sep (str | None) – Delimiter to separate columns of data in the output file.

  • save_fig (bool) – Whether to save damping fitting figures to hard drive.

  • fig_filename (str | None) – Full file name of the figure. If the object is created via a “curve” text file, then fig_filename can be None, and the output figure name will be determined automatically.

  • dpi (float) – Figure resolution.

Returns:

HH_x_param – The best parameters for each soil layer found in the optimization.

Return type:

HH_Param_Multi_Layer

get_curve_matrix(GGmax_filler_value: float = 1.0, save_to_file: bool = False, full_file_name: str | None = None) → ndarray[source]#

Produce a full “curve matrix” based on the damping data defined in objects of this class.

The “curve matrix” will be in the following format:

strain [%]

G/Gmax

strain [%]

damping [%]

strain [%]

G/Gmax

…

…

…

…

…

…

…

…

Since this class only defines damping curves, not G/Gmax curves, G/Gmax will be filled with some dummy values.

Parameters:

  • GGmax_filler_value (float) – A dummy value to fill all the G/Gmax curves.

  • save_to_file (bool) – Whether to save the “curve matrix” as a text file.

  • full_file_name (str | None) – Full file name to save to the hard drive. It can be None if save_to_file is set to False.

Returns:

curve_matrix – A matrix containing damping curves in the above-mentioned format.

Return type:

np.ndarray

plot(plot_interpolated: bool = True, fig: matplotlib.figure.Figure | None = None, ax: matplotlib.axes._axes.Axes | None = None, title: str | None = None, xlabel: str | None = 'Strain [%]', ylabel: str | None = 'Damping [%]', figsize: tuple[float, float] = (3, 3), dpi: float = 100, **kwargs_to_matplotlib: dict[Any, Any]) → tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes][source]#

Plot multiple curves together on one figure.

Parameters:

  • plot_interpolated (bool) – Whether to plot the interpolated curve or the raw data.

  • fig (Figure | None) – Figure object. If None, a new figure will be created.

  • ax (Axes | None) – Axes object. If None, a new axes will be created.

  • title (str | None) – Title of plot.

  • xlabel (str | None) – X label of plot.

  • ylabel (str | None) – Y label of plot.

  • figsize (tuple[float, float]) – Figure size in inches, as a tuple of two numbers. The figure size of fig (if not None) will override this parameter.

  • dpi (float) – Figure resolution. The dpi of fig (if not None) will override this parameter.

  • **kwargs_to_matplotlib (dict[Any, Any]) – Keyword arguments to be passed to matplotlib.pyplot.plot().

Returns:

  • fig (Figure) – The figure object being created or being passed into this function.

  • ax (Axes) – The axes object being created or being passed into this function.