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.

  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)

# Plot the catalog
catalog forecast evaluation
Fetched ComCat catalog in 0.2278444766998291 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.

Processed 1 catalogs in 0.001497507095336914 seconds
Processed 2 catalogs in 0.0020096302032470703 seconds
Processed 3 catalogs in 0.002482891082763672 seconds
Processed 4 catalogs in 0.0028061866760253906 seconds
Processed 5 catalogs in 0.0030999183654785156 seconds
Processed 6 catalogs in 0.0035276412963867188 seconds
Processed 7 catalogs in 0.0038444995880126953 seconds
Processed 8 catalogs in 0.004296302795410156 seconds
Processed 9 catalogs in 0.005551815032958984 seconds
Processed 10 catalogs in 0.005974531173706055 seconds
Processed 20 catalogs in 0.010535478591918945 seconds
Processed 30 catalogs in 0.015411138534545898 seconds
Processed 40 catalogs in 0.01989293098449707 seconds
Processed 50 catalogs in 0.025135040283203125 seconds
Processed 60 catalogs in 0.029491662979125977 seconds
Processed 70 catalogs in 0.03358888626098633 seconds
Processed 80 catalogs in 0.03802895545959473 seconds
Processed 90 catalogs in 0.042795419692993164 seconds
Processed 100 catalogs in 0.04706931114196777 seconds
Processed 200 catalogs in 0.08764243125915527 seconds
Processed 300 catalogs in 0.13276433944702148 seconds
Processed 400 catalogs in 0.17674493789672852 seconds
Processed 500 catalogs in 0.26114749908447266 seconds
Processed 600 catalogs in 0.30416440963745117 seconds
Processed 700 catalogs in 0.35005950927734375 seconds
Processed 800 catalogs in 0.43798112869262695 seconds
Processed 900 catalogs in 0.4804103374481201 seconds
Processed 1000 catalogs in 0.5239396095275879 seconds
Processed 2000 catalogs in 1.1534292697906494 seconds
Processed 3000 catalogs in 1.722480297088623 seconds
Processed 4000 catalogs in 2.3237133026123047 seconds
Processed 5000 catalogs in 2.907395839691162 seconds
Processed 6000 catalogs in 3.4851255416870117 seconds
Processed 7000 catalogs in 4.104825019836426 seconds
Processed 8000 catalogs in 4.643313646316528 seconds
Processed 9000 catalogs in 5.298011541366577 seconds
Processed 10000 catalogs in 5.866347551345825 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 11.999 seconds)

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