Working with catalog-based forecasts

This example shows some basic interactions with data-based forecasts. We will load in a forecast stored in the CSEP data format, and compute the expected rates on a 0.1° x 0.1° grid covering the state of California. We will plot the expected rates in the spatial cells.

  1. Define forecast properties (time horizon, spatial region, etc).

  2. Compute the expected rates in space and magnitude bins

  3. Plot expected rates in the spatial cells

Load required libraries

Most of the core functionality can be imported from the top-level csep package. Utilities are available from the csep.utils subpackage.

import numpy

import csep
from csep.core import regions
from csep.utils import datasets

Load data forecast

PyCSEP contains some basic forecasts that can be used to test of the functionality of the package. This forecast has already been filtered to the California RELM region.

Define spatial and magnitude regions

Before we can compute the bin-wise rates we need to define a spatial region and a set of magnitude bin edges. The magnitude bin edges # are the lower bound (inclusive) except for the last bin, which is treated as extending to infinity. We can bind these # to the forecast object. This can also be done by passing them as keyword arguments into csep.load_catalog_forecast().

# Magnitude bins properties
min_mw = 4.95
max_mw = 8.95
dmw = 0.1

# Create space and magnitude regions
magnitudes = regions.magnitude_bins(min_mw, max_mw, dmw)
region = regions.california_relm_region()

# Bind region information to the forecast (this will be used for binning of the catalogs)
forecast.region = regions.create_space_magnitude_region(region, magnitudes)

Compute spatial event counts

The csep.core.forecasts.CatalogForecast provides a method to compute the expected number of events in spatial cells. This requires a region with magnitude information.


Processed 1 catalogs in 0.0022003650665283203 seconds
Processed 2 catalogs in 0.003962993621826172 seconds
Processed 3 catalogs in 0.0052411556243896484 seconds
Processed 4 catalogs in 0.006463289260864258 seconds
Processed 5 catalogs in 0.007567167282104492 seconds
Processed 6 catalogs in 0.008912801742553711 seconds
Processed 7 catalogs in 0.010033845901489258 seconds
Processed 8 catalogs in 0.011408567428588867 seconds
Processed 9 catalogs in 0.013780593872070312 seconds
Processed 10 catalogs in 0.015349626541137695 seconds
Processed 20 catalogs in 0.027870655059814453 seconds
Processed 30 catalogs in 0.04236125946044922 seconds
Processed 40 catalogs in 0.05606484413146973 seconds
Processed 50 catalogs in 0.07095909118652344 seconds
Processed 60 catalogs in 0.08448553085327148 seconds
Processed 70 catalogs in 0.0992743968963623 seconds
Processed 80 catalogs in 0.11299419403076172 seconds
Processed 90 catalogs in 0.1283876895904541 seconds
Processed 100 catalogs in 0.14180541038513184 seconds
Processed 200 catalogs in 0.2786884307861328 seconds
Processed 300 catalogs in 0.42009949684143066 seconds
Processed 400 catalogs in 0.5562233924865723 seconds
Processed 500 catalogs in 0.7499270439147949 seconds
Processed 600 catalogs in 0.8826608657836914 seconds
Processed 700 catalogs in 1.0217328071594238 seconds
Processed 800 catalogs in 1.223193883895874 seconds
Processed 900 catalogs in 1.3565142154693604 seconds
Processed 1000 catalogs in 1.4999444484710693 seconds
Processed 2000 catalogs in 3.145129442214966 seconds
Processed 3000 catalogs in 4.723707675933838 seconds
Processed 4000 catalogs in 6.336601257324219 seconds
Processed 5000 catalogs in 7.920715808868408 seconds
Processed 6000 catalogs in 9.47014594078064 seconds
Processed 7000 catalogs in 11.093459606170654 seconds
Processed 8000 catalogs in 12.612549304962158 seconds
Processed 9000 catalogs in 14.256327867507935 seconds
Processed 10000 catalogs in 15.817620515823364 seconds

Plot expected event counts

We can plot the expected event counts the same way that we plot a csep.core.forecasts.GriddedForecast

ax = forecast.expected_rates.plot(plot_args={'clim': [-3.5, 0]})

The images holes in the image are due to under-sampling from the forecast.

Quick sanity check

The forecasts were filtered to the spatial region so all events should be binned. We loop through each data in the forecast and count the number of events and compare that with the expected rates. The expected rate is an average in each space-magnitude bin, so we have to multiply this value by the number of catalogs in the forecast.

Total running time of the script: ( 0 minutes 17.784 seconds)

Gallery generated by Sphinx-Gallery