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 0.0018303394317626953 seconds
Processed 2 catalogs in 0.003473997116088867 seconds
Processed 3 catalogs in 0.004858493804931641 seconds
Processed 4 catalogs in 0.005986690521240234 seconds
Processed 5 catalogs in 0.007094860076904297 seconds
Processed 6 catalogs in 0.008314847946166992 seconds
Processed 7 catalogs in 0.009415388107299805 seconds
Processed 8 catalogs in 0.010674715042114258 seconds
Processed 9 catalogs in 0.012680292129516602 seconds
Processed 10 catalogs in 0.013915061950683594 seconds
Processed 20 catalogs in 0.02548837661743164 seconds
Processed 30 catalogs in 0.0379176139831543 seconds
Processed 40 catalogs in 0.05014514923095703 seconds
Processed 50 catalogs in 0.06300973892211914 seconds
Processed 60 catalogs in 0.0749809741973877 seconds
Processed 70 catalogs in 0.08691906929016113 seconds
Processed 80 catalogs in 0.09904932975769043 seconds
Processed 90 catalogs in 0.11171984672546387 seconds
Processed 100 catalogs in 0.12388253211975098 seconds
Processed 200 catalogs in 0.24247193336486816 seconds
Processed 300 catalogs in 0.36585307121276855 seconds
Processed 400 catalogs in 0.4890410900115967 seconds
Processed 500 catalogs in 0.6546247005462646 seconds
Processed 600 catalogs in 0.7763700485229492 seconds
Processed 700 catalogs in 0.8996851444244385 seconds
Processed 800 catalogs in 1.0697822570800781 seconds
Processed 900 catalogs in 1.1910791397094727 seconds
Processed 1000 catalogs in 1.3133690357208252 seconds
Processed 2000 catalogs in 2.7362942695617676 seconds
Processed 3000 catalogs in 4.095929861068726 seconds
Processed 4000 catalogs in 5.533249378204346 seconds
Processed 5000 catalogs in 7.012770891189575 seconds
Processed 6000 catalogs in 8.481545448303223 seconds
Processed 7000 catalogs in 9.988016605377197 seconds
Processed 8000 catalogs in 11.40559720993042 seconds
Processed 9000 catalogs in 12.904781579971313 seconds
Processed 10000 catalogs in 14.287434339523315 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]}, show=True)

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 15.986 seconds)

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