Catalog-based Forecast Evaluation

This example shows how to evaluate a catalog-based forecasting using the Number test. This test is the simplest of the evaluations.

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

  2. Access catalog from ComCat

  3. Filter catalog to be consistent with the forecast properties

  4. Apply catalog-based number test to catalog

  5. Visualize results for catalog-based forecast

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 csep
from csep.core import regions, catalog_evaluations
from csep.utils import datasets, time_utils

Define start and end times of forecast

Forecasts should define a time horizon in which they are valid. The choice is flexible for catalog-based forecasts, because the catalogs can be filtered to accommodate multiple end-times. Conceptually, these should be separate forecasts.

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. The forecast is already filtered in space and magnitude
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)
space_magnitude_region = regions.create_space_magnitude_region(region, magnitudes)

Load catalog forecast

To reduce the file size of this example, we’ve already filtered the catalogs to the appropriate magnitudes and spatial locations. The original forecast was computed for 1 year following the start date, so we still need to filter the catalog in time. We can do this by passing a list of filtering arguments to the forecast or updating the class.

By default, the forecast loads catalogs on-demand, so the filters are applied as the catalog loads. On-demand means that until we loop over the forecast in some capacity, none of the catalogs are actually loaded.

More fine-grain control and optimizations can be achieved by creating a csep.core.forecasts.CatalogForecast directly.

forecast = csep.load_catalog_forecast(datasets.ucerf3_ascii_format_landers_fname,
                                      start_time = start_time, end_time = end_time,
                                      region = space_magnitude_region,
                                      apply_filters = True)

# Assign filters to forecast
forecast.filters = [f'origin_time >= {forecast.start_epoch}', f'origin_time < {forecast.end_epoch}']

Obtain evaluation catalog from ComCat

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.

We need to filter the ComCat catalog to be consistent with the forecast. This can be done either through the ComCat API or using catalog filtering strings. Here we’ll use the ComCat API to make the data access quicker for this example. We still need to filter the observed catalog in space though.

# Obtain Comcat catalog and filter to region.
comcat_catalog = csep.query_comcat(start_time, end_time, min_magnitude=forecast.min_magnitude)

# Filter observed catalog using the same region as the forecast
comcat_catalog = comcat_catalog.filter_spatial(forecast.region)
print(comcat_catalog)

# Plot the catalog
comcat_catalog.plot()
catalog forecast evaluation

Out:

Fetched ComCat catalog in 0.49035072326660156 seconds.

Downloaded catalog from ComCat with following parameters
Start Date: 1992-06-28 12:00:45+00:00
End Date: 1992-07-24 18:14:36.250000+00:00
Min Latitude: 33.901 and Max Latitude: 36.705
Min Longitude: -118.067 and Max Longitude: -116.285
Min Magnitude: 4.95
Found 19 events in the ComCat catalog.

        Name: None

        Start Date: 1992-06-28 12:00:45+00:00
        End Date: 1992-07-24 18:14:36.250000+00:00

        Latitude: (33.901, 36.705)
        Longitude: (-118.067, -116.285)

        Min Mw: 4.95
        Max Mw: 6.3

        Event Count: 19


<GeoAxesSubplot:>

Perform number test

We can perform the Number test on the catalog based forecast using the observed catalog we obtained from Comcat.

Out:

Processed 1 catalogs in 0.0017671585083007812 seconds
Processed 2 catalogs in 0.002437114715576172 seconds
Processed 3 catalogs in 0.003017902374267578 seconds
Processed 4 catalogs in 0.0034117698669433594 seconds
Processed 5 catalogs in 0.003760814666748047 seconds
Processed 6 catalogs in 0.0042798519134521484 seconds
Processed 7 catalogs in 0.004662513732910156 seconds
Processed 8 catalogs in 0.005202054977416992 seconds
Processed 9 catalogs in 0.006605863571166992 seconds
Processed 10 catalogs in 0.007108449935913086 seconds
Processed 20 catalogs in 0.012439489364624023 seconds
Processed 30 catalogs in 0.018009185791015625 seconds
Processed 40 catalogs in 0.023232698440551758 seconds
Processed 50 catalogs in 0.02914285659790039 seconds
Processed 60 catalogs in 0.03407931327819824 seconds
Processed 70 catalogs in 0.03882956504821777 seconds
Processed 80 catalogs in 0.04395627975463867 seconds
Processed 90 catalogs in 0.04943203926086426 seconds
Processed 100 catalogs in 0.054168701171875 seconds
Processed 200 catalogs in 0.10164284706115723 seconds
Processed 300 catalogs in 0.15456175804138184 seconds
Processed 400 catalogs in 0.20607566833496094 seconds
Processed 500 catalogs in 0.30308985710144043 seconds
Processed 600 catalogs in 0.35331034660339355 seconds
Processed 700 catalogs in 0.4047842025756836 seconds
Processed 800 catalogs in 0.5068275928497314 seconds
Processed 900 catalogs in 0.5562684535980225 seconds
Processed 1000 catalogs in 0.6073720455169678 seconds
Processed 2000 catalogs in 1.343996286392212 seconds
Processed 3000 catalogs in 2.008786201477051 seconds
Processed 4000 catalogs in 2.7166647911071777 seconds
Processed 5000 catalogs in 3.3935704231262207 seconds
Processed 6000 catalogs in 4.065521001815796 seconds
Processed 7000 catalogs in 4.779319524765015 seconds
Processed 8000 catalogs in 5.406042814254761 seconds
Processed 9000 catalogs in 6.166693449020386 seconds
Processed 10000 catalogs in 6.829816818237305 seconds

Plot number test result

We can create a simple visualization of the number test from the evaluation result class.

ax = number_test_result.plot(show=True)
Number Test

Total running time of the script: ( 0 minutes 12.754 seconds)

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