Source code for openquake.hmtk.plotting.seismicity.completeness.plot_stepp_1972

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4

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# D. Monelli.
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"""
Module :mod: 'openquake.hmtk.plotting.seismicity.completeness.plot_stepp_1971'
creates plot to illustrate outcome of Stepp (1972) method for completeness
analysis
"""
import os.path
import itertools

import numpy as np
import matplotlib.pyplot as plt

# markers which can be filled or empty
VALID_MARKERS = [
    "s",
    "o",
    "^",
    "D",
    "p",
    "h",
    "8",
    "*",
    "d",
    "v",
    "<",
    ">",
    "H",
]


[docs]def create_stepp_plot( model, figure_size=(8, 6), filename=None, filetype="png", dpi=300, ax=None ): """ Creates the classic Stepp (1972) plots for a completed Stepp analysis, and exports the figure to a file. :param model: Completed Stepp (1972) analysis as instance of :class: `openquake.hmtk.seismicity.completeness.comp_stepp_1971.Stepp1971` :param string filename: Name of output file :param string filetype: Type of file (from list supported by matplotlib) :param int dpi: Resolution (dots per inch) of output file """ if ax is None: fig, ax = plt.subplots(figsize=figure_size) else: fig = ax.get_figure() if filename and os.path.exists(filename): raise IOError("File already exists!") # get colours from current axes: thus user can set up before calling prop_cycler = ax._get_lines.prop_cycler prop_cyclers = itertools.tee(itertools.cycle(prop_cycler), 3) marker_cyclers = itertools.tee(itertools.cycle(VALID_MARKERS), 3) # plot observed Sigma lambda for i, (min_mag, max_mag) in enumerate( zip(model.magnitude_bin[:-1], model.magnitude_bin[1:]) ): label = "(%g, %g]: %d" % ( min_mag, max_mag, model.completeness_table[i, 0], ) colour = next(prop_cyclers[0])["color"] ax.loglog( model.time_values, model.sigma[:, i], linestyle="none", marker=next(marker_cyclers[0]), markersize=3, markerfacecolor=colour, markeredgecolor=colour, label=label, ) # plot expected Poisson rate for i in range(0, len(model.magnitude_bin) - 1): ax.loglog( model.time_values, model.model_line[:, i], color=next(prop_cyclers[1])["color"], linewidth=0.5, ) # mark breaks from expected rate for i in range(0, len(model.magnitude_bin) - 1): colour = next(prop_cyclers[2])["color"] if np.any(np.isnan(model.model_line[:, i])): continue xmarker = model.end_year - model.completeness_table[i, 0] knee = model.model_line[:, i] > 0.0 ymarker = 10.0 ** np.interp( np.log10(xmarker), np.log10(model.time_values[knee]), np.log10(model.model_line[knee, i]), ) ax.loglog( xmarker, ymarker, marker=next(marker_cyclers[2]), markerfacecolor="white", markeredgecolor=colour, ) ax.legend(loc="lower left", frameon=False, fontsize="small") ax.set_xlabel("Time (years)") ax.set_ylabel("$\\sigma_{\\lambda} = \\sqrt{\\lambda} / \\sqrt{T}$") ax.autoscale(enable=True, axis="both", tight=True) # save figure to file if filename is not None: fig.savefig(filename, dpi=dpi, format=filetype)