Transmutagen

Transmutation generates CRAM approximations for solving transmutation problems

Usage

Transmutagen depends on

• mpmath
• sympy >= 1.1
• matplotlib
• numpy
• scipy
• pyne
• jinja2
• gmpy2
• Cython
• scikit-umfpack

To build transmutagen from source requires GCC. Clang support can be added, although the py_solve solver generated from Clang is slower than the one from GCC, so presently GCC is supported only.

Generating the CRAM approximation to exp(-x) on [0, oo)

Run

python -m transmutagen.cram D N

where D is the degree of the approximation and N is the number of digits. A typical run would be something like

python -m transmutagen.cram 14 200

Note that the digits returned are not necessarily all accurate. To compute N correct digits one generally needs to many more working digits in the computation. Furthermore, when roots are taken, the precision may decrease even further. So it is recommend to always compute the CRAM expression with a very high number of digits.

There are many options, but it is not recommending changing them unless you know what you are doing. See python -m transmutagen --help. To increase/reduce the verbosity of the output, use the --log-level flag.

If you use iTerm2, install iterm2-tools (from conda-forge) to get plots in the terminal.

Note: all output and plots are logged to the logs and plots directories.

Generating solver code

To generate solver code, use

python -m transmutagen.gensolve

This will generate solve.c and solve.h. Use

python -m transmutagen.gensolve --help

to see various options, such as how to change the degrees that are generated, and the namespace of the generated functions.

This will use a default list of nuclides and sparsity pattern. To add or remove nuclides, modify the JSON file, and pass it in with

python -m transmutagen.gensolve --json-file gensolve.json

The format of the JSON file is

{
    "nucs": ["H1", "H2", ...],
    "fromto": [["H1", "H1"], ["H1", "H2"], ...]
}

Where "nucs" is a list of nuclides and "fromto" is a list of lists of every possible reaction product pair. Every nuclide should be listed as a reaction with itself.

To generate a JSON file from ORIGEN libraries, run

python -m transmutagen.generate_json /path/to/origen/libs/ --outfile gensolve.json

This will save the JSON to gensolve.json. Note that transmutagen comes with a a JSON file generated from ORIGEN in transmutagen/data/gensolve_origen.json as well as one generated from PyNE in transmutagen/data/gensolve.json.

The resulting solve.c will have functions {namespace}_expm_multiply{N}(double* A, double* b, double* x), where {namespace} is the namespace specified by the --namespace flag to python -m transmutagen.gensolve (the default is transmutagen), and {N} is the degree of the approximation used in the solve, specified by the --degree flag (the default is 14). The function computes exp(A)*b and stores the result in x. A should be in a flattened format, according to the sparsity pattern the solver was generated from.

Converting ORIGEN libraries to a sparse matrix representation

If you'd like to convert an origen file to a matrix representation, please use something like:

python -m transmutagen.tape9sparse ~/origen22/libs/pwru50.lib --decay ~/origen22/libs/decay.lib

See --help and the transmutagen.tape9utils docs for more details.

Running tests against ORIGEN

Put ORIGEN.zip in the docker/origen_all directory. Then run

./docker/origen_all/build_and_run.sh

This requires the docker daemon to be running, and may require sudo. There are various options, which you can see with

./docker/origen_all/build_and_run.sh --help

This will run both ORIGEN and transmutagen (CRAM) on a suite of ORIGEN libraries, starting nuclides, and times, writing the results to data/results.hdf5. The output will also be logged to logs/origen_all.log. Be warned total suite takes over 24 hours to run.