Note

Click here to download the full example code

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

Compute the expected rates in space and magnitude bins

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.

```
_ = forecast.get_expected_rates(verbose=True)
```

```
Processed 1 catalogs in 0.0019121170043945312 seconds
Processed 2 catalogs in 0.0038230419158935547 seconds
Processed 3 catalogs in 0.005231380462646484 seconds
Processed 4 catalogs in 0.00641632080078125 seconds
Processed 5 catalogs in 0.0075931549072265625 seconds
Processed 6 catalogs in 0.008911371231079102 seconds
Processed 7 catalogs in 0.010149955749511719 seconds
Processed 8 catalogs in 0.011470317840576172 seconds
Processed 9 catalogs in 0.013839960098266602 seconds
Processed 10 catalogs in 0.015150785446166992 seconds
Processed 20 catalogs in 0.027729272842407227 seconds
Processed 30 catalogs in 0.040936946868896484 seconds
Processed 40 catalogs in 0.054509878158569336 seconds
Processed 50 catalogs in 0.07319974899291992 seconds
Processed 60 catalogs in 0.08653879165649414 seconds
Processed 70 catalogs in 0.09967160224914551 seconds
Processed 80 catalogs in 0.11327266693115234 seconds
Processed 90 catalogs in 0.12743520736694336 seconds
Processed 100 catalogs in 0.140059232711792 seconds
Processed 200 catalogs in 0.2699129581451416 seconds
Processed 300 catalogs in 0.40221619606018066 seconds
Processed 400 catalogs in 0.535344123840332 seconds
Processed 500 catalogs in 0.7156691551208496 seconds
Processed 600 catalogs in 0.8480005264282227 seconds
Processed 700 catalogs in 0.9821999073028564 seconds
Processed 800 catalogs in 1.162113904953003 seconds
Processed 900 catalogs in 1.294830083847046 seconds
Processed 1000 catalogs in 1.4286472797393799 seconds
Processed 2000 catalogs in 2.983614444732666 seconds
Processed 3000 catalogs in 4.455923795700073 seconds
Processed 4000 catalogs in 5.9920408725738525 seconds
Processed 5000 catalogs in 7.458043575286865 seconds
Processed 6000 catalogs in 8.937757968902588 seconds
Processed 7000 catalogs in 10.458558320999146 seconds
Processed 8000 catalogs in 11.86892294883728 seconds
Processed 9000 catalogs in 13.398468255996704 seconds
Processed 10000 catalogs in 14.844976425170898 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]}, show=True)
```

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 17.555 seconds)