Skip to main content

nuPyProp

Propagate neutrinos through the earth.

A python package and command line utility, including fortran for performance with openMP.

Documentation (WIP): https://nupyprop.readthedocs.io/en/latest/

Note: While the documentation is currently WIP, users and developers should consult the nuPyProp tutorial repository for visualizing output from the code and creating user-defined models.

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 107 neutrinos injected 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:

  1. -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.

  2. -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.

  3. -i or --idepth: depth of ice/water in km. Default value is 4 km.

  4. -cl or --charged_lepton: flavor of charged lepton used to propagate. Can be either muon or tau. Default is tau.

  5. -n or --nu_type: type of neutrino matter. Can be either neutrino or anti-neutrino. Default is neutrino.

  6. -t or --energy_loss: energy loss type for lepton - can be stochastic or continuous. Default is stochastic.

  7. -x or --xc_model: neutrino/anti-neutrino cross-section model used. Can be from the pre-defined set of models (see xc-table) or custom. Default is ct18nlo.

  8. -p or --pn_model: photonuclear interaction energy loss model used. Can be from the pre-defined set of models (see pn-table) or custom. Default is allm.

  9. -f or --fac_nu: rescaling factor for BSM cross-sections. Default is 1.

  10. -s or --stats: statistics or no. of injected neutrinos. Default is 1e7 neutrinos.

  11. -htc or --htc_mode: High throughput computing mode. If set to yes, the code will be optimized to run in high throughput computing mode. Default is no.

Viewing output results: output_*.h5 will contain the results of the code after it has finished running. In the terminal, run vitables (optional dependency) 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 = Charged lepton photonuclear energy loss model.
F = Energy loss type: can be stochastic or continuous.
G = Statistics (ie. no. of neutrinos/anti-neutrinos injected).

Model Tables

Neutrino/Anti-Neutrino Cross-Section Model

Reference

Abramowicz, Levin, Levy, Maor (ALLM)

hep-ph/9712415, Phys. Rev. D 81, 114012

Block, Durand, Ha, McKay (BDHM)

Phys. Rev. D 89, 094027, Phys. Rev. D 81, 114012

CTEQ18-NLO

Phys. Rev. D 103, 014013, Phys. Rev. D 81, 114012

Connolly, Thorne, Waters (CTW)

Phys. Rev. D 83, 113009

nCTEQ15

Phys. Rev. D 93, 085037, Phys. Rev. D 81, 114012

User Defined

See nuPyProp tutorial repository

Charged Lepton Photonuclear Energy Loss Model

Reference

Abramowicz, Levin, Levy, Maor (ALLM)

hep-ph/9712415, Phys. Rev. D 63, 094020

Bezrukov-Bugaev (BB)

Yad. Fiz. 33, 1195, Phys. Rev. D 63, 094020

Block, Durand, Ha, McKay (BDHM)

Phys. Rev. D 89, 094027, Phys. Rev. D 63, 094020

Capella, Kaidalov, Merino, Tran (CKMT)

Eur. Phys. J. C 10, 153 Phys. Rev. D 63, 094020

User Defined

See nuPyProp tutorial repository

Code Execution Timing Tables

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:

  1. data.py: contains functions for reading/writing from/to hdf5 files.

  2. geometry.py: contains the Earth geometry modules (including PREM) for use with python/fortran.

  3. models.py: contains neutrino cross-section & charged lepton energy loss model templates.

  4. propagate.f90: heart of the code; contains fortran modules to interpolate between geometry variables, cross-sections, energy loss parameters & propagate neutrinos and charged leptons through the Earth.

  5. main.py: forms the main skeleton of the code; propagates the neutrinos and charged leptons, and calculates the p_exit and collects outgoing lepton energies.

  6. run.py: contains all the run parameters and variables needed for all the other .py files.

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).

  1. Install the non-pypi required dependencies as described for users above.

  2. Install a fortran compiler. ex: sudo apt-get install gfortran

  3. git clone the source code: git clone git@github.com:NuSpaceSim/nupyprop.git

  4. cd nupyprop

  5. 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.

  1. Install the non-pypi required dependencies as described for users above.

  2. Install a fortran compiler. ex: brew install gcc

  3. git clone the source code: git clone git@github.com:NuSpaceSim/nupyprop.git

  4. cd nupyprop

  5. build and install the package in ‘editable’ mode python3 -m pip install -e .

Metadata

Release files for nupyprop 0.1.7.post93

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for nupyprop 0.1.7.post93
File
nupyprop-0.1.7.post93-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.post93-cp39-cp39-macosx_10_9_x86_64.whl CPython 3.9 CPython 3.9 macOS 10.9+ x86-64 Details
nupyprop-0.1.7.post93-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.post93-cp38-cp38-macosx_10_9_x86_64.whl CPython 3.8 CPython 3.8 macOS 10.9+ x86-64 Details
nupyprop-0.1.7.post93-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.post93-cp37-cp37m-macosx_10_9_x86_64.whl CPython 3.7 CPython 3.7 pymalloc macOS 10.9+ x86-64 Details

Total release size: 29.8 MB

Release files / nupyprop-0.1.7.post93-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL nupyprop-0.1.7.post93-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 4.9 MB
Tags CPython 3.9 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
512be2bfca47db896b97870aa834017d4fa0eee16ea0a710b2de9291d13c3315
BLAKE2b-256 checksum
How to use checksums
134e3f88fab7e98135632c12c3b2a0c7a4c412fa5fd43f1098064bcca119bf37
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7

Release files / nupyprop-0.1.7.post93-cp39-cp39-macosx_10_9_x86_64.whl

Download URL nupyprop-0.1.7.post93-cp39-cp39-macosx_10_9_x86_64.whl
Size 5.0 MB
Tags CPython 3.9 macOS 10.9+ x86-64
SHA-256 checksum
How to use checksums
d2c5a9ca823b0942fc88ffd6ee218e89055d438bc39cf45d17319459aefda045
BLAKE2b-256 checksum
How to use checksums
cfaf9a6eeabe8a91732fbaa32fecb625405018cdb853050ebfb58961c997d9d6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7

Release files / nupyprop-0.1.7.post93-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL nupyprop-0.1.7.post93-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 4.9 MB
Tags CPython 3.8 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
63e1c4994e060fbf20280ddbefabd191324accfc905ea38a34ec149911122f19
BLAKE2b-256 checksum
How to use checksums
8e2a048c659693ff15babe4ba9c15f193b26c82d67c633b1c5df1f29c970bb71
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7

Release files / nupyprop-0.1.7.post93-cp38-cp38-macosx_10_9_x86_64.whl

Download URL nupyprop-0.1.7.post93-cp38-cp38-macosx_10_9_x86_64.whl
Size 5.0 MB
Tags CPython 3.8 macOS 10.9+ x86-64
SHA-256 checksum
How to use checksums
b303931a2ac1b39219fff56edbdad4378887ef9affbb58ec5a825a172ebff3a3
BLAKE2b-256 checksum
How to use checksums
f4f467ae11a035df053a7f25c6b9e11a590bc61f5aefcdd03327c142a8a8c11b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7

Release files / nupyprop-0.1.7.post93-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL nupyprop-0.1.7.post93-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 4.9 MB
Tags CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
292fb290e33a3127f200727a14f9b6599759109eeeb6335eec701d67953ee85e
BLAKE2b-256 checksum
How to use checksums
d25d6809d4314d7d22965a539bfc560db9021685c5244ec590a48c4ed5b73018
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7

Release files / nupyprop-0.1.7.post93-cp37-cp37m-macosx_10_9_x86_64.whl

Download URL nupyprop-0.1.7.post93-cp37-cp37m-macosx_10_9_x86_64.whl
Size 5.0 MB
Tags CPython 3.7 CPython 3.7 pymalloc macOS 10.9+ x86-64
SHA-256 checksum
How to use checksums
6b04d0fd0e7222b3e023de5b8811a2de7996402ae886c89a7dfa9099157e8ba7
BLAKE2b-256 checksum
How to use checksums
35950754f45a038bb94825a05f931c37960f00b3affa6a18ebb497df3985df9c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.7

Security for nupyprop 0.1.7.post93

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.

Release history Release notifications | RSS feed

This release

0.1.7.post93 This release

6 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page