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# california-inlabru-forecasts | ||
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This folder contains all neccessary data and code to reproduce the forecasts and figures in "Pseudo-prospective testing of 5-year earthquake forecasts for California using inlabru", Bayliss, Naylor, Kamranzad and Main (submitted 2021). | ||
UCERF3 data downloaded from https://pubs.usgs.gov/of/2013/1165/ in June 2019. | ||
The strain rate model used in this work is available at https://platform.openquake.org/maps/82 and was downloaded in April 2021. | ||
The exact versions are included in the 'Data' folder to ensure future reproducibility. | ||
The Comcat_catalogues folder contains the catalogues used to test the forecasts. The included code downloads these directly, but they are saved here in case of any changes to the catalogue. | ||
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California_forecast_inlabru_testing.Rmd is an R-markdown file that runs all steps for model generation and then tests the models with the `pyCSEP` python package using the R package `reticulate`. | ||
inlabru_import_functions.R contains functions to generate the two types of forecast as well as process some of the input data. | ||
pycsep_testing.py runs only the pyCSEP testing using the inlabru forecasts, which are included in the 'Forecasts' folder. | ||
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System information: | ||
R version 4.1.0 (2021-05-18) | ||
Platform: x86_64-w64-mingw32/x64 (64-bit) | ||
Running under: Windows 10 x64 (build 19041) | ||
AMD Ryzen 5 3600 processor, 64GB RAM | ||
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R packages (forecast model assembly, fitting and simulation): | ||
reticulate_1.20 | ||
RColorBrewer_1.1-2 | ||
maptools_1.1-1 | ||
dplyr_1.0.6 | ||
raster_3.4-13 | ||
future.apply_1.8.1 | ||
future_1.22.1 | ||
sf_1.0-2 | ||
INLA_21.06.11 | ||
foreach_1.5.1 | ||
Matrix_1.3-3 | ||
fields_12.5 | ||
viridis_0.6.1 | ||
viridisLite_0.4.0 | ||
spam_2.7-0 | ||
dotCall64_1.0-1 | ||
rgeos_0.5-5 | ||
rgdal_1.5-23 | ||
inlabru_2.3.1 | ||
sp_1.4-5 | ||
ggplot2_3.3.5 | ||
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Most packages can be downloaded directly from CRAN, with the exception of INLA which is using the current (as of December 2021) testing version, | ||
downloaded with `install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE)` | ||
The latest version of inlabru should also be installed using `remotes::install_github("inlabru-org/inlabru", ref="devel")` | ||
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The forecast models take approx 2 hours each to run fully, including the simulation step and grid-based projections. | ||
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------------------------------------------------------------------------------ | ||
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Python packages (pycsep testing + plotting): | ||
Python 3.8.5 | ||
pycsep 0.5.1 | ||
numpy 1.20.3 | ||
matplotlib 3.4.3 | ||
cartopy 0.18.0 | ||
pandas 1.3.4 | ||
seaborn 0.11.1 | ||
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All python packages installed with conda, pyCSEP installed with `conda install --channel conda-forge pycsep` | ||
File pycsep_testing.py runs these steps only. | ||
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For further details on installation of the INLA package see https://www.r-inla.org/download-install | ||
For further details on installation of inlabru see https://github.com/inlabru-org/inlabru | ||
For further details on installation of pyCSEP, see https://docs.cseptesting.org/getting_started/installing.html | ||
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All websites last accessed 16/12/2021 |