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 5.521312475204468 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 0.0017209053039550781 seconds
Processed 2 catalogs in 0.0023674964904785156 seconds
Processed 3 catalogs in 0.002956867218017578 seconds
Processed 4 catalogs in 0.0033762454986572266 seconds
Processed 5 catalogs in 0.0038039684295654297 seconds
Processed 6 catalogs in 0.004372596740722656 seconds
Processed 7 catalogs in 0.00478816032409668 seconds
Processed 8 catalogs in 0.005365610122680664 seconds
Processed 9 catalogs in 0.006761074066162109 seconds
Processed 10 catalogs in 0.007306814193725586 seconds
Processed 20 catalogs in 0.01274871826171875 seconds
Processed 30 catalogs in 0.018480777740478516 seconds
Processed 40 catalogs in 0.023876428604125977 seconds
Processed 50 catalogs in 0.029891490936279297 seconds
Processed 60 catalogs in 0.03498959541320801 seconds
Processed 70 catalogs in 0.0398867130279541 seconds
Processed 80 catalogs in 0.045206546783447266 seconds
Processed 90 catalogs in 0.05069541931152344 seconds
Processed 100 catalogs in 0.05542731285095215 seconds
Processed 200 catalogs in 0.10318732261657715 seconds
Processed 300 catalogs in 0.1557304859161377 seconds
Processed 400 catalogs in 0.2069072723388672 seconds
Processed 500 catalogs in 0.30017614364624023 seconds
Processed 600 catalogs in 0.349865198135376 seconds
Processed 700 catalogs in 0.4005591869354248 seconds
Processed 800 catalogs in 0.4985778331756592 seconds
Processed 900 catalogs in 0.5474035739898682 seconds
Processed 1000 catalogs in 0.5981652736663818 seconds
Processed 2000 catalogs in 1.3034062385559082 seconds
Processed 3000 catalogs in 1.9456160068511963 seconds
Processed 4000 catalogs in 2.620499849319458 seconds
Processed 5000 catalogs in 3.272523880004883 seconds
Processed 6000 catalogs in 3.920696973800659 seconds
Processed 7000 catalogs in 4.606456279754639 seconds
Processed 8000 catalogs in 5.210495471954346 seconds
Processed 9000 catalogs in 5.93693995475769 seconds
Processed 10000 catalogs in 6.573297500610352 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 18.599 seconds)

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