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.

Overview:
  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.

Out:

Processed 1 catalogs in 0.002107858657836914 seconds
Processed 2 catalogs in 0.003748655319213867 seconds
Processed 3 catalogs in 0.005095958709716797 seconds
Processed 4 catalogs in 0.006224155426025391 seconds
Processed 5 catalogs in 0.007375001907348633 seconds
Processed 6 catalogs in 0.008680343627929688 seconds
Processed 7 catalogs in 0.009808778762817383 seconds
Processed 8 catalogs in 0.011142969131469727 seconds
Processed 9 catalogs in 0.013647794723510742 seconds
Processed 10 catalogs in 0.014953851699829102 seconds
Processed 20 catalogs in 0.02821207046508789 seconds
Processed 30 catalogs in 0.04221844673156738 seconds
Processed 40 catalogs in 0.05575966835021973 seconds
Processed 50 catalogs in 0.07080197334289551 seconds
Processed 60 catalogs in 0.08435988426208496 seconds
Processed 70 catalogs in 0.0980987548828125 seconds
Processed 80 catalogs in 0.11175704002380371 seconds
Processed 90 catalogs in 0.1264650821685791 seconds
Processed 100 catalogs in 0.1394639015197754 seconds
Processed 200 catalogs in 0.2811751365661621 seconds
Processed 300 catalogs in 0.42132115364074707 seconds
Processed 400 catalogs in 0.5614380836486816 seconds
Processed 500 catalogs in 0.7749993801116943 seconds
Processed 600 catalogs in 0.9109189510345459 seconds
Processed 700 catalogs in 1.0494837760925293 seconds
Processed 800 catalogs in 1.2610962390899658 seconds
Processed 900 catalogs in 1.3981959819793701 seconds
Processed 1000 catalogs in 1.5381824970245361 seconds
Processed 2000 catalogs in 3.256136178970337 seconds
Processed 3000 catalogs in 4.879938364028931 seconds
Processed 4000 catalogs in 6.539683818817139 seconds
Processed 5000 catalogs in 8.164491653442383 seconds
Processed 6000 catalogs in 9.78456997871399 seconds
Processed 7000 catalogs in 11.477269172668457 seconds
Processed 8000 catalogs in 13.05143690109253 seconds
Processed 9000 catalogs in 14.802133083343506 seconds
Processed 10000 catalogs in 16.402840614318848 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]})
ucerf3-landers

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 18.456 seconds)

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