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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#!/usr/bin/env python

'''
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 numpy as np
import matplotlib.pyplot as plt

valid_markers = ['*', '+', '1', '2', '3', '4', '8', '<', '>', 'D', 'H', '^',
                 '_', 'd', 'h', 'o', 'p', 's', 'v', 'x', '|']

DEFAULT_SIZE = (8., 6.)
DEFAULT_OFFSET = (1.3, 1.0)


[docs]def create_stepp_plot(model, filename, filetype='png', filedpi=300): ''' 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 filedpi: Resolution (dots per inch) of output file ''' plt.figure(figsize=DEFAULT_SIZE) if os.path.exists(filename): raise IOError('File already exists!') legend_list = [(str(model.magnitude_bin[iloc] + 0.01) + ' - ' + str(model.magnitude_bin[iloc + 1])) for iloc in range(0, len(model.magnitude_bin) - 1)] rgb_list = [] marker_vals = [] # Get marker from valid list while len(valid_markers) < len(model.magnitude_bin): valid_markers.append(valid_markers) marker_sampler = np.arange(0, len(valid_markers), 1) np.random.shuffle(marker_sampler) # Get colour for each bin for value in range(0, len(model.magnitude_bin) - 1): rgb_samp = np.random.uniform(0., 1., 3) rgb_list.append((rgb_samp[0], rgb_samp[1], rgb_samp[2])) marker_vals.append(valid_markers[marker_sampler[value]]) # Plot observed Sigma lambda for iloc in range(0, len(model.magnitude_bin) - 1): plt.loglog(model.time_values, model.sigma[:, iloc], linestyle='None', marker=marker_vals[iloc], color=rgb_list[iloc]) lgd = plt.legend(legend_list, bbox_to_anchor=DEFAULT_OFFSET) plt.grid(True) # Plot expected Poisson rate for iloc in range(0, len(model.magnitude_bin) - 1): plt.loglog(model.time_values, model.model_line[:, iloc], linestyle='-', marker='None', color=rgb_list[iloc]) plt.xlim(model.time_values[0] / 2., 2. * model.time_values[-1]) xmarker = model.end_year - model.completeness_table[iloc, 0] id0 = model.model_line[:, iloc] > 0. ymarker = 10.0 ** np.interp(np.log10(xmarker), np.log10(model.time_values[id0]), np.log10(model.model_line[id0, iloc])) plt.loglog(xmarker, ymarker, 'ks') plt.xlabel('Time (years)', fontsize=15) plt.ylabel("$\\sigma_{\\lambda} = \\sqrt{\\lambda} / \\sqrt{T}$", fontsize=15) # Save figure to file plt.tight_layout() plt.savefig(filename, dpi=filedpi, format=filetype, bbox_extra_artists=(lgd,), bbox_inches="tight")