Esempio n. 1
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input_file = 'data_input/hmtk_bsb2013.csv'

parser = CsvCatalogueParser(input_file)
catalogue = parser.read_file()
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year, catalogue.end_year)

# Sort catalogue chronologically
catalogue.sort_catalogue_chronologically()
print 'Catalogue sorted chronologically!'

# Plot magnitude time density
from hmtk.plotting.seismicity.catalogue_plots import plot_magnitude_time_density
magnitude_bin = 0.2
time_bin = 10.0
plot_magnitude_time_density(catalogue, magnitude_bin, time_bin)


# In[ ]:

mmax_config = {'b-value': 1.0,
               'input_mmin': 4.5,
               'input_mmax': None,
               'input_mmax_uncertainty': 0.5}

mmax_ks = KijkoSellevolFixedb()

mmax, mmax_sigma = mmax_ks.get_mmax(catalogue, mmax_config)

print 'Mmax = %8.3f +/- %8.3f' %(mmax, mmax_sigma)
Esempio n. 2
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        plot_rate(catalogue, cumulative=True, color='#22475E', new_figure=False, overlay=True, linewidth=1.5, label="Original", alpha=0.8)
        plt.legend(loc="upper left", fontsize='small')
        plt.show()

    # seismic_rate plot
    #plot_rate(catalogue, color='#5fbdce', filename=output_base+"_rate.png", linewidth=2)
    # seismic_cumulative rate plot
    #plot_rate(catalogue, cumulative=True, color='#5fbdce', filename=output_base+"_cum.png", linewidth=2)

    #catalogue = 

# completeness catalog
    if plot_density:
        # magnitude density plot
        plot_magnitude_time_density(catalogue, mag_int=0.5, time_int=1.0, 
                                    filename=output_base+"_time_density.png", figsize=(18,6))


    if plot_stepp:
        # completeness analysis
        stepp = Stepp1971()
        
        completeness_config = {'magnitude_bin': 0.5,
                               'time_bin': 2.5,
                               'increment_lock': True}
        print completeness_config
        
        # Run analysis
        print 'Running Stepp (1971) completeness analysis:'
        print np.min(catalogue.data['magnitude'])
        completeness_table = stepp.completeness(catalogue, completeness_config)
Esempio n. 3
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    'max_lat': 14.0,
    'resolution': 'l'
}

# Create a hmtk basemap
basemap1 = HMTKBaseMap(map_config, 'Earthquake Catalogue')
# Add a catalogue
basemap1.add_catalogue(catalogue)

# In[ ]:

# Limit the catalogue to the time period 1960 - 2012
valid_time = np.logical_and(catalogue.data['year'] >= 1900,
                            catalogue.data['year'] <= 2014)
catalogue.select_catalogue_events(valid_time)
plot_magnitude_time_density(catalogue, 0.2, 2.0)
print 'Catalogue now contains %s events' % catalogue.get_number_events()

# In[ ]:

# Show distribution of magnitudes with time
plot_magnitude_time_scatter(catalogue, fmt_string='.')

# In[ ]:

# Plot the magnitude-time density
magnitude_bin = 0.2
time_bin = 5.0  # in Decimal Years
plot_magnitude_time_density(catalogue, magnitude_bin, time_bin)

# In[ ]:
Esempio n. 4
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    parser = nrmlSourceModelParser(source_model_file)
    source_model = parser.read_file(2.0)

    # add source model
    #basemap1.add_source_model(source_model, area_border, border_width, point_marker, point_size, overlay)
    #basemap1.add_source_model(source_model, overlay=True)

if plot_mag_time_count:
    filename = "/Users/pirchiner/Desktop/tmp_plot.png"
    # Limit the catalogue to the time period 1960 - 2012
    valid_time = np.logical_and(catalogue.data['year'] >= 1960,
                                catalogue.data['year'] <= 2014)
    catalogue.select_catalogue_events(valid_time)
    plot_magnitude_time_density(catalogue,
                                0.5,
                                1.0,
                                filename=filename,
                                figsize=(18, 6))
    print 'Catalogue now contains %s events' % catalogue.get_number_events()

# Show distribution of magnitudes with time
#plot_magnitude_time_scatter(catalogue, fmt_string='o', alpha=0.3, linewidth=0.0)

# Depth histogram
# plot_depth_histogram(catalogue, 10.)

filename = "/Users/pirchiner/Desktop/tmp_plot.png"

# Time-varying completeness
completeness = np.array([[1980., 3.0], [1985., 4.0], [1964., 5.0],
                         [1910., 6.5], [1900., 9.0]])
Esempio n. 5
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selector1 = CatalogueSelector(catalogue_depth_clean, create_copy=True)
for source in source_model.sources:
    source.select_catalogue(selector1)

    llon, ulon, llat, ulat = source.catalogue.get_bounding_box()
    print llon, ulon, llat, ulat
    # Map the Source
    src_basemap = HMTKBaseMap(map_config, "Source: {:s}".format(source.name))
    print "Source ID: %s  Source Name: %s   Number of Events: %g" % (
        source.id, source.name, source.catalogue.get_number_events())
    # Add on the catalogue
    src_basemap.add_catalogue(source.catalogue, overlay=False)

completeness_table_a = np.array([[1990., 3.0], [1980., 3.5], [1965., 4.0]])
plot_magnitude_time_density(source.catalogue,
                            0.1,
                            1.0,
                            completeness=completeness_table_a)

grid_lims = [110., 160.0, 0.1, -45.0, -5.0, 0.1, 0., 20., 20.]

try:
    os.remove("Aus1_tmp.hdf5")
except OSError:
    pass
config = {
    "bandwidth": 50,
    "r_min": 1.0E-7,
    "bvalue": 1.0,
    "mmin": 3.0,
    "learning_start": 1965,
    "learning_end": 2003,
Esempio n. 6
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    source_model_file = "/Users/pirchiner/dev/pshab/dourado_reproduction/source_model.xml"

    # read source model file
    parser = nrmlSourceModelParser(source_model_file)
    source_model = parser.read_file(2.0)

    # add source model
    # basemap1.add_source_model(source_model, area_border, border_width, point_marker, point_size, overlay)
    # basemap1.add_source_model(source_model, overlay=True)

if plot_mag_time_count:
    filename = "/Users/pirchiner/Desktop/tmp_plot.png"
    # Limit the catalogue to the time period 1960 - 2012
    valid_time = np.logical_and(catalogue.data["year"] >= 1960, catalogue.data["year"] <= 2014)
    catalogue.select_catalogue_events(valid_time)
    plot_magnitude_time_density(catalogue, 0.5, 1.0, filename=filename, figsize=(18, 6))
    print "Catalogue now contains %s events" % catalogue.get_number_events()


# Show distribution of magnitudes with time
# plot_magnitude_time_scatter(catalogue, fmt_string='o', alpha=0.3, linewidth=0.0)


# Depth histogram
# plot_depth_histogram(catalogue, 10.)

filename = "/Users/pirchiner/Desktop/tmp_plot.png"

# Time-varying completeness
completeness = np.array([[1980.0, 3.0], [1985.0, 4.0], [1964.0, 5.0], [1910.0, 6.5], [1900.0, 9.0]])
plot_observed_recurrence(