Propagate neutrinos through the earth.
A python package and command line utility, including fortran for performance with openMP.
Installation
with pip
python3 -m pip install nupyprop
with conda
We recommend installing nupyprop into a conda environment like so. In this example the name of the environment is “nupyprop”
conda create -n nupyprop -c conda-forge -c nuspacesim nupyprop conda activate nupyprop
Usage
nupyprop --help
Example for running tau propagation for 107 GeV neutrinos at 10 degrees with a statistics of 107 particles with stochastic energy loss & with all other parameters as defaults:
nupyprop -e 7 -a 10 -t stochastic -s 1e7
Run parameters are defined in run.py. Different switches are described as follows:
-e or –energy: incoming neutrino energy in log_10(GeV). Works for single energy or multiple energies. For multiple energies, separate energies with commas eg. 7,8,9,10,11. Default energies are 107,107.25,107.5,…1011 GeV.
-a or –angle: slant Earth angles in degrees. Works for single angle or multiple angles. For multiple angles, separate angles with commas eg. 1,3,5,7,10. Default angles are 1->35 degrees, in steps of 1 degree.
-i or –idepth: depth of ice/water in km. Default value is 4 km.
-l or –lepton: flavor of lepton used to propagate. Can be either muon or tau. Default is tau.
-n or –nu_type: type of neutrino matter. Can be either neutrino or anti-neutrino. Default is Neutrino.
-t or –energy_loss: energy loss type for lepton - can be stochastic or continuous. Default is stochastic.
-x or –xc_model: neutrino/anti-neutrino cross-section model used. For a list of model names, see lookup_tables.h5/Neutrino_Cross_Sections. Default is ct18nlo.
-p or –pn_model: photonuclear interaction energy loss model used. For now, can be either bb (Bezrukov-Bugaev), allm (Abramowicz, Levin, Levy, Maor), bdhm (Block, Durand, Ha, McKay) or ckmt (Capella, Kaidalov, Merino, Tran). Default is allm.
-f or –fac_nu: rescaling factor for SM cross-sections. Default is 1.
-s or –stat: statistics. Default is 1e7 particles.
[STRIKEOUT:WARNING: Running the code will replace the results of the output.h5 file. Backing up previous output files is recommended (or just ask me for a pre-populated output file if need be, for now, since the pre-populated output file is >25 MB). Future fixes include overwrite warnings for the user.] Fixed!
Viewing output results: output_*.h5 will contain the results of the code after it has finished running. In the terminal, run vitables and open the output_*.h5 file to view the output results.
output_*.h5 naming convention is as follows: output_A_B_C_D_E_F_G, where
A = Neutrino type: nu is for neutrino & anu is for anti-neutrino. B = Lepton_type: tau is for tau leptons & muon is for muons. C = idepth: depth of water layer in km. D = Neutrino (or anti-neutrino) cross-section model. E = Lepton photonuclear energy loss model. F = Energy loss type: can be stochastic or continuous. G = Statistics (ie. no. of neutrinos/anti-neutrinos injected).
Charged Lepton |
Energy Loss Type |
E|nu| [GeV] |
Angles |
N|nu|;;in |
Time (hrs) |
|---|---|---|---|---|---|
τ |
Stochastic |
107 |
1-35 |
108 |
1.07*, 0.26*** |
τ |
Continuous |
107 |
1-35 |
108 |
0.88* |
τ |
Stochastic |
108 |
1-35 |
108 |
6.18*, 1.53*** |
τ |
Continuous |
108 |
1-35 |
108 |
5.51* |
τ |
Stochastic |
109 |
1-35 |
108 |
27.96*, 5.08*** |
τ |
Continuous |
109 |
1-35 |
108 |
19.11* |
τ |
Stochastic |
1010 |
1-35 |
108 |
49.80*, 12.43*** |
τ |
Continuous |
1010 |
1-35 |
108 |
35.59* |
τ |
Stochastic |
1011 |
1-35 |
108 |
12.73*** |
τ |
Continuous |
1011 |
1-35 |
108 |
Charged Lepton |
Energy Loss Type |
E|nu| [GeV] |
Angles |
N|nu|;;in |
Time (hrs) |
|---|---|---|---|---|---|
μ |
Stochastic |
106 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
|
μ |
Continuous |
106 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
0.95* |
μ |
Stochastic |
107 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
|
μ |
Continuous |
107 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
3.19* |
μ |
Stochastic |
108 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
|
μ |
Continuous |
108 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
5.17* |
μ |
Stochastic |
109 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
111.77** |
μ |
Continuous |
109 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
7.42* |
μ |
Stochastic |
1010 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
98.17* |
μ |
Continuous |
1010 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
9.76* |
μ |
Stochastic |
1011 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
|
μ |
Continuous |
1011 |
1,2,3,5,7,10,12,15,17,20,25,30,35 |
108 |
* - Intel Core i7-8750H; 6 cores & 12 threads. ** - Intel Core i5-10210; 4 cores & 8 threads. *** - UIowa Argon cluster; 56 cores.
For debugging/development: The correct order to look at the code is in the following order:
data.py: contains the reading/writing modules from/to the hdf5 files.
geometry_py.py: contains the Earth geometry modules (including PREM) for use with python.
cross_section.py: contains neutrino/anti-neutrino cross_section models.
energy_loss.py: contains lepton energy loss models.
propagate.f90: heart of the code; contains fortran modules to interpolate between geometry variables, cross-sections, energy loss parameters & propagate neutrinos and leptons through the Earth.
main.py: forms the main skeleton of the code; propagates the neutrinos and leptons, and calculates the p_exit and collects outgoing lepton energies.
run.py: contains all the run parameters and variables needed for all the other .py files.
UML Diagram
Developing the code on Ubuntu
These notes should help developers of this code build and install the package locally using a pep518 compliant build system (pip).
Install the non-pypi required dependencies as described for users above.
Install a fortran compiler. ex: sudo apt-get install gfortran
git clone the source code: git clone git@github.com:NuSpaceSim/nupyprop.git
cd nupyprop
build and install the package in ‘editable’ mode python3 -m pip install -e .
Developing the code on MacOS
These notes should help developers of this code build and install the package locally using a pep518 compliant build system (pip). Currently we do not support the default system python3 on MacOS which is out of date and missing critical functionality. Use the homebrew python instead, or a virtualenv, or a conda environment.
Install the non-pypi required dependencies as described for users above.
Install a fortran compiler. ex: brew install gcc
git clone the source code: git clone git@github.com:NuSpaceSim/nupyprop.git
cd nupyprop
build and install the package in ‘editable’ mode python3 -m pip install -e .
1 maintainer
Metadata
1 maintainer
Release files for nupyprop 0.1.7.post31
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| nupyprop-0.1.7.post31-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | CPython 3.9 | Linux glibc 2.17+ x86-64 | Details |
| nupyprop-0.1.7.post31-cp39-cp39-macosx_10_9_x86_64.whl | CPython 3.9 | CPython 3.9 | macOS 10.9+ x86-64 | Details |
| nupyprop-0.1.7.post31-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.8 | CPython 3.8 | Linux glibc 2.17+ x86-64 | Details |
| nupyprop-0.1.7.post31-cp38-cp38-macosx_10_9_x86_64.whl | CPython 3.8 | CPython 3.8 | macOS 10.9+ x86-64 | Details |
| nupyprop-0.1.7.post31-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.7 | CPython 3.7 pymalloc | Linux glibc 2.17+ x86-64 | Details |
| nupyprop-0.1.7.post31-cp37-cp37m-macosx_10_9_x86_64.whl | CPython 3.7 | CPython 3.7 pymalloc | macOS 10.9+ x86-64 | Details |
Total release size: 28.8 MB
Release files / nupyprop-0.1.7.post31-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | nupyprop-0.1.7.post31-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 4.8 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
25d1cb4ab89b8216b92f76cb881a424cb68d07a023f53416b33fe451a1de8b07
|
|
BLAKE2b-256 checksum How to use checksums |
513bb355ef7da698318b2e3de77281d7c4695769f6187bf2683a21dff81d416e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
|
Release files / nupyprop-0.1.7.post31-cp39-cp39-macosx_10_9_x86_64.whl
| Download URL | nupyprop-0.1.7.post31-cp39-cp39-macosx_10_9_x86_64.whl |
|---|---|
| Size | 4.8 MB |
| Tags | CPython 3.9 macOS 10.9+ x86-64 |
|
SHA-256 checksum How to use checksums |
e5c423ff93fdd9a5f983a1b127f87760297bd7f0138e64ca130f5aeb88caa570
|
|
BLAKE2b-256 checksum How to use checksums |
96f8934fe54d123584a336155f8cc29a74ea35e9bf23dfdb5921fdcaab5bb364
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
|
Release files / nupyprop-0.1.7.post31-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | nupyprop-0.1.7.post31-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 4.8 MB |
| Tags | CPython 3.8 Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
c57bf7e89341311f5b4b9372f16c4e84df43efe30f6a3ea5a7968a2e251b5847
|
|
BLAKE2b-256 checksum How to use checksums |
716a792d30cadb2e88dbdfeb4cd62f75f59dc00af84f9d73a044adb998d8c1e9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
|
Release files / nupyprop-0.1.7.post31-cp38-cp38-macosx_10_9_x86_64.whl
| Download URL | nupyprop-0.1.7.post31-cp38-cp38-macosx_10_9_x86_64.whl |
|---|---|
| Size | 4.8 MB |
| Tags | CPython 3.8 macOS 10.9+ x86-64 |
|
SHA-256 checksum How to use checksums |
851b36d3926eb33b429b956ba53bf33936a0a354a7a2dd36d44926aeddf4a63f
|
|
BLAKE2b-256 checksum How to use checksums |
867e9a669f8c2b5d4da14631e2647cda7fa914b515c62e1cb26f6a0b74370713
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
|
Release files / nupyprop-0.1.7.post31-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | nupyprop-0.1.7.post31-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 4.8 MB |
| Tags | CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
8d92882665718fffea7b02376bc5aa4fe41e3a1ef40b99d05030b826cc55b37b
|
|
BLAKE2b-256 checksum How to use checksums |
6fe78223b51dab0684b938715cc5de458d20bc300d5ca1325e9ef0e1448d33d6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
|
Release files / nupyprop-0.1.7.post31-cp37-cp37m-macosx_10_9_x86_64.whl
| Download URL | nupyprop-0.1.7.post31-cp37-cp37m-macosx_10_9_x86_64.whl |
|---|---|
| Size | 4.8 MB |
| Tags | CPython 3.7 CPython 3.7 pymalloc macOS 10.9+ x86-64 |
|
SHA-256 checksum How to use checksums |
05deedbddc5d788386ce6abdc5b575b0cef26478d69a46d8153ca3641e328273
|
|
BLAKE2b-256 checksum How to use checksums |
cf67baf1ed94aab7762e15733a890d107022f3ec19d697a7490f89b9b21d396b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
|
Security for nupyprop 0.1.7.post31
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
