Note

Go to the end 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 0.001 seconds
Processed 2 catalogs in 0.002 seconds
Processed 3 catalogs in 0.003 seconds
Processed 4 catalogs in 0.004 seconds
Processed 5 catalogs in 0.004 seconds
Processed 6 catalogs in 0.005 seconds
Processed 7 catalogs in 0.006 seconds
Processed 8 catalogs in 0.006 seconds
Processed 9 catalogs in 0.008 seconds
Processed 10 catalogs in 0.009 seconds
Processed 20 catalogs in 0.016 seconds
Processed 30 catalogs in 0.024 seconds
Processed 40 catalogs in 0.031 seconds
Processed 50 catalogs in 0.039 seconds
Processed 60 catalogs in 0.047 seconds
Processed 70 catalogs in 0.054 seconds
Processed 80 catalogs in 0.062 seconds
Processed 90 catalogs in 0.069 seconds
Processed 100 catalogs in 0.077 seconds
Processed 200 catalogs in 0.152 seconds
Processed 300 catalogs in 0.229 seconds
Processed 400 catalogs in 0.307 seconds
Processed 500 catalogs in 0.413 seconds
Processed 600 catalogs in 0.487 seconds
Processed 700 catalogs in 0.562 seconds
Processed 800 catalogs in 0.669 seconds
Processed 900 catalogs in 0.744 seconds
Processed 1000 catalogs in 0.819 seconds
Processed 2000 catalogs in 1.717 seconds
Processed 3000 catalogs in 2.558 seconds
Processed 4000 catalogs in 3.426 seconds
Processed 5000 catalogs in 4.217 seconds
Processed 6000 catalogs in 4.997 seconds
Processed 7000 catalogs in 5.826 seconds
Processed 8000 catalogs in 6.587 seconds
Processed 9000 catalogs in 7.419 seconds
Processed 10000 catalogs in 8.232 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 9.983 seconds)