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@@ -7,13 +7,13 @@ from matplotlib.ticker import MultipleLocator
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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import numpy as np
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import numpy as np
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global ncp_choice, tcp_max, torder_min, torder_max, AOI_lat, AOI_lon
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global ncp_choice, tcp_max, torder_min, torder_max
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ncp_choice = 'default'
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ncp_choice = 'default'
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tcp_max = 5
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tcp_max = 5
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torder_min = 0
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torder_min = 0
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torder_max = 1
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torder_max = 1
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AOI_lat = np.array([51.48, 51.54])
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#AOI_lat = np.array([51.48, 51.54])
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AOI_lon = np.array([16.15, 16.24])
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#AOI_lon = np.array([16.15, 16.24])
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#AOI_lat = np.array([None, None])
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#AOI_lat = np.array([None, None])
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#AOI_lon = np.array([None, None])
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#AOI_lon = np.array([None, None])
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@@ -253,8 +253,8 @@ def bins_and_beast(dates, unit, bin_dur, multiplicator):
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def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max,
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def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max,
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m_kde_method, xy_select, grid_dim, xy_win_method, rate_select, time_win_duration,
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m_kde_method, xy_select, grid_dim, xy_win_method, rate_select, time_win_duration,
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forecast_select, custom_rate, forecast_len, time_unit, model, products_string, verbose):
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# forecast_select, custom_rate, forecast_len, time_unit, model, products_string, verbose):
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# forecast_select, custom_rate, forecast_len, time_unit, AOI_extent, model, products_string, verbose):
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forecast_select, custom_rate, forecast_len, time_unit, AOI_extent, model, products_string, verbose):
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"""
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"""
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Python application that reads an earthquake catalog and performs seismic hazard forecasting.
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Python application that reads an earthquake catalog and performs seismic hazard forecasting.
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Arguments:
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Arguments:
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@@ -344,8 +344,8 @@ def main(catalog_file, mc_file, pdf_file, m_file, m_select, mag_label, mc, m_max
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exclude_low_fxy = False # skip low probability areas of the map
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exclude_low_fxy = False # skip low probability areas of the map
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thresh_fxy = 1e-3 # minimum fxy value (location PDF) needed to do PGA estimation (to skip low probability areas); also should scale according to number of grid points
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thresh_fxy = 1e-3 # minimum fxy value (location PDF) needed to do PGA estimation (to skip low probability areas); also should scale according to number of grid points
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# AOI_lat = np.array(AOI_extent[:2])
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AOI_lat = np.array(AOI_extent[:2])
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# AOI_lon = np.array(AOI_extent[2:])
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AOI_lon = np.array(AOI_extent[2:])
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# log user selections
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# log user selections
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logger.debug(f"User input files\n Catalog: {catalog_file}\n Mc: {mc_file}\n Mag_PDF: {pdf_file}\n Mag: {m_file}")
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logger.debug(f"User input files\n Catalog: {catalog_file}\n Mc: {mc_file}\n Mag_PDF: {pdf_file}\n Mag: {m_file}")
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