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Seismic-2d
==========
This is a simplification of a model originally used for global-scale
seismology.
This is a simplification of a model originally used for global-scale seismology. The full model can be read here

NET-VISA: Network Processing Vertically Integrated Seismic
Analysis. Nimar S. Arora, Stuart Russell, Erik Sudderth. Bulletin of the
Seismological Society of America (BSSA) April 2013, vol. 103 no. 2A
pp709-729.
**NET-VISA: Network Processing Vertically Integrated Seismic Analysis. Nimar S. Arora, Stuart Russell, Erik Sudderth. Bulletin of the Seismological Society of America (BSSA) April 2013, vol. 103 no. 2A pp709-729.**

Please cite the above paper in scientific research using this model or
data.
Please cite the above paper in scientific research using this model or the included data.

Files
=====

* description.tex -- a description of the model
* generate.py -- generates the physics of a 2-D world and some episodes
* util.py -- some geophysical utility functions
* solve.py -- a sample solver that learns the physics and solves
the episodes
* pysolve.py -- a python-based solver
* csolve.c -- a C-based solver, identical to pysolve.py but much faster
* evaluate.py -- evaluates a solution versus a reference
* mwmatching.py -- utility script for max-weight max cardinality matching
* short_data/ or large_data/
-- data directory where the following files
need to be downloaded
* physics.data -- physics for the training and test episodes
* training.data -- 100 (or 10K) episodes for training
* test.data -- another 100 (or 10K) episodes for testing
* test.blind -- the test data with the event-to-detection
mapping omitted
* test.solution -- a sample solution on the test data
* `description.tex` -- a description of the model, also see the downloadable file `description.pdf`.
* `generate.py` -- generates the physics of a 2-D world and some episodes
* `util.py` -- some geophysical utility functions
* `solve.py` -- a sample solver that learns the physics and solves the episodes
* `pysolve.py` -- a python-based solver
* `csolve.c` -- a C-based solver, identical to pysolve.py but much faster
* `evaluate.py` -- evaluates a solution versus a reference
* `mwmatching.py` -- utility script for max-weight max cardinality matching
* `short_data/` or `large_data/` -- data directories created by uncompressing the downloadable file `data.tar.gz`. These directories contain the following files:
* `physics.data` -- physics for the training and test episodes
* `training.data` -- 100 (or 10K) episodes for training
* `test.data` -- another 100 (or 10K) episodes for testing
* `test.blind` -- the test data with the event-to-detection mapping omitted
* `test.solution` -- a sample solution on the test data


Overview
========

The model is completely described in ```description.tex``` and this
should be translated in the Probabilistic Programming Language of your
choosing. The unlabeled test data in ```test.blind``` and the labeled
training data in ```training.data``` comprises the observations to the
model. The query of interest is the seismic bulletin for each of the
observed episodes in the test data.
The simplified model is completely described in [`description.tex`](https://bitbucket.org/nimar/seismic-2d/downloads/description.pdf) and this should be translated in the Probabilistic Programming Language of your choosing. Your model can be tested on the included data files in the *Downloads* section of this repository. The unlabeled test data in `test.blind` and the labeled training data in `training.data` comprises the observations to the model. The query of interest is the seismic bulletin for each of the observed episodes in the test data.

Once all the bulletins have been produced, the script ```evaluate.py```
can be used to produce the reports on the accuracy versus the reference
script ```test.data```. One can also compare the results versus the
baseline in ```test.solution```.
Once all the bulletins have been produced, the script `evaluate.py` can be used to produce the reports on the accuracy versus the reference script `test.data`. One can also compare the results versus the baseline in `test.solution`. For example to check the accuracy of the sample solution:

The files ```generate.py``` and ```solve.py``` have only been provided
for convenience. These files shouldn't normally be used. However, if you
want to check the performance of your model on more than just the
provided data you may generate more as needed. The sample solver is
based loosely on the published greedy algorithm, and may be used as a
very simple baseline.
$> python evaluate.py short_data/test.data short_data/test.solution
188 matchable events, 243 guess events, and 140 matched
Precision 57.6 % , Recall 74.5 % , F1 65.0
Time Errors mean 8.4 std 8.0
Dist Errors mean 1.6 std 1.3
Mag Errors mean 0.3 std 0.2

The sample solver first tries to run the C-based solver, and if that is
not found it attempts the python version. Both versions of the solver
are identical except that the C one is a lot faster. Note that the
output of the solvers is already included with the provided data, so it
is only necessary to run the solver for additional data that you might
generate.

The files `generate.py` and `solve.py` have only been provided for convenience. These files shouldn't normally be used. However, if you want to check the performance of your model on more than just the provided data you may generate more as needed. The sample solver is based loosely on the published greedy algorithm, and may be used as a very simple baseline.

The sample solver first tries to run the C-based solver, and if that is not found it attempts the python version. Both versions of the solver are identical except that the C one is a lot faster. Note that the output of the solvers is already included with the provided data, so it is only necessary to run the solver for additional data that you might generate.

Note that the sample solver cheats a little bit by directly looking at the underlying physics rather than learning it from the training data. Your solver should *not* be reading any of the following files -- `physics.data`, `test.data`, or `test.solution`.

Compilation Instructions For C-Based Solver
===========================================
After building the C-based solver please run the script ```solve.py```
as usual, and it will automatically use the newly built extension ```csolve```.
After building the C-based solver please run the script `solve.py` as usual, and it will automatically use the newly built extension `csolve`.

In order to build the C-based solver the following command must be executed:

python setup.py build_ext --inplace

This will generate a file csolve.so (or csolve.pyd on
Windows). Compiling Python C extensions on Windows usually involves
additional steps. We strongly recommend using a 32-bit Python even on
64-bit Windows, as the steps are a lot easier.

This will generate a file csolve.so (or csolve.pyd on Windows). Compiling Python C extensions on Windows usually involves additional steps. We strongly recommend using a 32-bit Python even on 64-bit Windows, as the steps are a lot easier.
* Compiling on 32-bit Python using MinGW:
* Install MinGW
* Compile using the --compiler flag:

python setup.py build_ext --inplace --compiler=mingw32
* Compiling on 64-bit Python:
* Install Visual Studio 10.0 Express:
http://www.microsoft.com/visualstudio/eng/downloads#d-2010-express
* Install Microsoft Windows SDK for Windows 7 and .NET Framework 4:
http://www.microsoft.com/en-us/download/details.aspx?id=8279
* [Install Visual Studio 10.0 Express](http://www.microsoft.com/visualstudio/eng/downloads#d-2010-express)
* [Install Microsoft Windows SDK for Windows 7 and .NET Framework 4](http://www.microsoft.com/en-us/download/details.aspx?id=8279)
* Create the following file:
C:\Program Files (x86)\Microsoft Visual Studio 10.0\vc\bin\amd64\vcvars64.bat

C:\Program Files (x86)\Microsoft Visual Studio 10.0\vc\bin\amd64\vcvars64.bat
with the single-line content:
CALL "C:\Program Files\Microsoft SDKs\Windows\v7.1\Bin\SetEnv.cmd" /x64

CALL "C:\Program Files\Microsoft SDKs\Windows\v7.1\Bin\SetEnv.cmd" /x64
* Compile as usual
python setup.py build_ext --inplace


python setup.py build_ext --inplace
Authors
=======
* Nimar S. Arora, Bayesian Logic Inc., nimar.arora@gmail.com
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