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.

start_time = time_utils.strptime_to_utc_datetime("1992-06-28 11:57:35.0")
end_time = time_utils.strptime_to_utc_datetime("1992-07-28 11:57:35.0")

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()

Fetched ComCat catalog in 0.3843650817871094 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


<GeoAxes: >

Perform number test

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

number_test_result = catalog_evaluations.number_test(forecast, comcat_catalog)

Processed 1 catalogs in 0.0011775493621826172 seconds
Processed 2 catalogs in 0.0016055107116699219 seconds
Processed 3 catalogs in 0.0019969940185546875 seconds
Processed 4 catalogs in 0.0022940635681152344 seconds
Processed 5 catalogs in 0.0025446414947509766 seconds
Processed 6 catalogs in 0.002915620803833008 seconds
Processed 7 catalogs in 0.0032036304473876953 seconds
Processed 8 catalogs in 0.003580808639526367 seconds
Processed 9 catalogs in 0.004534482955932617 seconds
Processed 10 catalogs in 0.0048749446868896484 seconds
Processed 20 catalogs in 0.008596420288085938 seconds
Processed 30 catalogs in 0.012471437454223633 seconds
Processed 40 catalogs in 0.016086101531982422 seconds
Processed 50 catalogs in 0.020208358764648438 seconds
Processed 60 catalogs in 0.02370429039001465 seconds
Processed 70 catalogs in 0.026987314224243164 seconds
Processed 80 catalogs in 0.03049778938293457 seconds
Processed 90 catalogs in 0.03418445587158203 seconds
Processed 100 catalogs in 0.03737139701843262 seconds
Processed 200 catalogs in 0.06969475746154785 seconds
Processed 300 catalogs in 0.1051025390625 seconds
Processed 400 catalogs in 0.13962149620056152 seconds
Processed 500 catalogs in 0.20219087600708008 seconds
Processed 600 catalogs in 0.23580455780029297 seconds
Processed 700 catalogs in 0.2702622413635254 seconds
Processed 800 catalogs in 0.33548426628112793 seconds
Processed 900 catalogs in 0.36832380294799805 seconds
Processed 1000 catalogs in 0.4022223949432373 seconds
Processed 2000 catalogs in 0.8769891262054443 seconds
Processed 3000 catalogs in 1.3070142269134521 seconds
Processed 4000 catalogs in 1.7738895416259766 seconds
Processed 5000 catalogs in 2.2142436504364014 seconds
Processed 6000 catalogs in 2.648728847503662 seconds
Processed 7000 catalogs in 3.1131176948547363 seconds
Processed 8000 catalogs in 3.519791841506958 seconds
Processed 9000 catalogs in 4.010651350021362 seconds
Processed 10000 catalogs in 4.443270444869995 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)