.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "tutorials/working_with_catalog_forecasts.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_tutorials_working_with_catalog_forecasts.py: 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: 1. Define forecast properties (time horizon, spatial region, etc). 2. Compute the expected rates in space and magnitude bins 3. Plot expected rates in the spatial cells .. GENERATED FROM PYTHON SOURCE LINES 16-21 Load required libraries ----------------------- Most of the core functionality can be imported from the top-level :mod:`csep` package. Utilities are available from the :mod:`csep.utils` subpackage. .. GENERATED FROM PYTHON SOURCE LINES 21-28 .. code-block:: Python import numpy import csep from csep.core import regions from csep.utils import datasets .. GENERATED FROM PYTHON SOURCE LINES 29-34 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. .. GENERATED FROM PYTHON SOURCE LINES 34-37 .. code-block:: Python forecast = csep.load_catalog_forecast(datasets.ucerf3_ascii_format_landers_fname) .. GENERATED FROM PYTHON SOURCE LINES 38-45 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 :func:`csep.load_catalog_forecast`. .. GENERATED FROM PYTHON SOURCE LINES 45-58 .. code-block:: Python # 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) .. GENERATED FROM PYTHON SOURCE LINES 59-64 Compute spatial event counts ---------------------------- The :class:`csep.core.forecasts.CatalogForecast` provides a method to compute the expected number of events in spatial cells. This requires a region with magnitude information. .. GENERATED FROM PYTHON SOURCE LINES 64-68 .. code-block:: Python _ = forecast.get_expected_rates(verbose=True) .. rst-class:: sphx-glr-script-out .. code-block:: none 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 0.003 seconds Processed 5 catalogs in 0.004 seconds Processed 6 catalogs in 0.005 seconds Processed 7 catalogs in 0.005 seconds Processed 8 catalogs in 0.006 seconds Processed 9 catalogs in 0.007 seconds Processed 10 catalogs in 0.008 seconds Processed 20 catalogs in 0.015 seconds Processed 30 catalogs in 0.022 seconds Processed 40 catalogs in 0.029 seconds Processed 50 catalogs in 0.037 seconds Processed 60 catalogs in 0.044 seconds Processed 70 catalogs in 0.050 seconds Processed 80 catalogs in 0.058 seconds Processed 90 catalogs in 0.065 seconds Processed 100 catalogs in 0.072 seconds Processed 200 catalogs in 0.140 seconds Processed 300 catalogs in 0.209 seconds Processed 400 catalogs in 0.280 seconds Processed 500 catalogs in 0.380 seconds Processed 600 catalogs in 0.448 seconds Processed 700 catalogs in 0.516 seconds Processed 800 catalogs in 0.618 seconds Processed 900 catalogs in 0.684 seconds Processed 1000 catalogs in 0.753 seconds Processed 2000 catalogs in 1.580 seconds Processed 3000 catalogs in 2.357 seconds Processed 4000 catalogs in 3.176 seconds Processed 5000 catalogs in 4.016 seconds Processed 6000 catalogs in 4.824 seconds Processed 7000 catalogs in 5.656 seconds Processed 8000 catalogs in 6.498 seconds Processed 9000 catalogs in 7.377 seconds Processed 10000 catalogs in 8.215 seconds .. GENERATED FROM PYTHON SOURCE LINES 69-73 Plot expected event counts -------------------------- We can plot the expected event counts the same way that we plot a :class:`csep.core.forecasts.GriddedForecast` .. GENERATED FROM PYTHON SOURCE LINES 73-76 .. code-block:: Python ax = forecast.expected_rates.plot(plot_args={'clim': [-3.5, 0]}, show=True) .. image-sg:: /tutorials/images/sphx_glr_working_with_catalog_forecasts_001.png :alt: ucerf3-landers :srcset: /tutorials/images/sphx_glr_working_with_catalog_forecasts_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 77-78 The images holes in the image are due to under-sampling from the forecast. .. GENERATED FROM PYTHON SOURCE LINES 80-86 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. .. GENERATED FROM PYTHON SOURCE LINES 86-91 .. code-block:: Python total_events = 0 for catalog in forecast: total_events += catalog.event_count numpy.testing.assert_allclose(total_events, forecast.expected_rates.sum() * forecast.n_cat) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 9.999 seconds) .. _sphx_glr_download_tutorials_working_with_catalog_forecasts.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: working_with_catalog_forecasts.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: working_with_catalog_forecasts.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_