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:

-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. 6,7,8,9,10,11. Default energies are 10⁶,10^6.25,10^6.5,…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->42 degrees, in steps of 1 degree.
-i or --idepth: depth of ice/water in km. Default value is 4 km.
-cl or --charged_lepton: flavor of charged 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. Can be from the pre-defined set of models (see xc-table) or custom. Default is ct18nlo.
-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.
-f or --fac_nu: rescaling factor for BSM cross-sections. Default is 1.
-s or --stats: statistics or no. of injected neutrinos. Default is 1e7 neutrinos.
-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.

Note: This program uses OpenMP for propagating the huge number of neutrinos injected. For this purpose, the code will use all the threads available in your processor by default. To control the number of threads used for running the code, use export OMP_NUM_THREADS=x, where x is the number of threads you want the code to run with.

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

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

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)
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

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 functions for reading/writing from/to hdf5 files.
geometry.py: contains the Earth geometry modules (including PREM) for use with python/fortran.
models.py: contains neutrino cross-section & charged lepton energy loss model templates.
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.
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.
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).

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 .

Metadata

Release files for nupyprop 0.1.7.post122

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.post122
File	Interpreter	ABI	Platform	Reset
nupyprop-0.1.7.post122-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 16.4 MB Oct 14, 2021 Without provenance	CPython 3.9	CPython 3.9	Linux glibc 2.17+ x86-64	Details
nupyprop-0.1.7.post122-cp39-cp39-macosx_10_9_x86_64.whl 16.5 MB Oct 14, 2021 Without provenance	CPython 3.9	CPython 3.9	macOS 10.9+ x86-64	Details
nupyprop-0.1.7.post122-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 16.4 MB Oct 14, 2021 Without provenance	CPython 3.8	CPython 3.8	Linux glibc 2.17+ x86-64	Details
nupyprop-0.1.7.post122-cp38-cp38-macosx_10_9_x86_64.whl 16.5 MB Oct 14, 2021 Without provenance	CPython 3.8	CPython 3.8	macOS 10.9+ x86-64	Details
nupyprop-0.1.7.post122-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 16.4 MB Oct 14, 2021 Without provenance	CPython 3.7	CPython 3.7 pymalloc	Linux glibc 2.17+ x86-64	Details
nupyprop-0.1.7.post122-cp37-cp37m-macosx_10_9_x86_64.whl 16.5 MB Oct 14, 2021 Without provenance	CPython 3.7	CPython 3.7 pymalloc	macOS 10.9+ x86-64	Details

Total release size: 98.6 MB

Release files / nupyprop-0.1.7.post122-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	nupyprop-0.1.7.post122-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size	16.4 MB
Tags	CPython 3.9 Linux glibc 2.17+ x86-64
SHA-256 checksum How to use checksums	`03bb047c229f420c274870381267a0f96418564e4675213b0a3daa4975b172fa`
BLAKE2b-256 checksum How to use checksums	`c1d0dc42b3796403717cf4699cb45a6c348d960e1745fceb4e43e0eaa1bf09ee`
Upload date	Oct 14, 2021
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/4.62.3 CPython/3.9.7`

Release files / nupyprop-0.1.7.post122-cp39-cp39-macosx_10_9_x86_64.whl

Download URL	nupyprop-0.1.7.post122-cp39-cp39-macosx_10_9_x86_64.whl
Size	16.5 MB
Tags	CPython 3.9 macOS 10.9+ x86-64
SHA-256 checksum How to use checksums	`78156bd3663376c1b566986854c29cf4f92b191df171ace2c99d2bab17959228`
BLAKE2b-256 checksum How to use checksums	`da6acd85ccd44dc95b5a307d262884b11fb0deb7e32eac3ef00a0bbb444ccad6`
Upload date	Oct 14, 2021
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/4.62.3 CPython/3.9.7`

Release files / nupyprop-0.1.7.post122-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	nupyprop-0.1.7.post122-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size	16.4 MB
Tags	CPython 3.8 Linux glibc 2.17+ x86-64
SHA-256 checksum How to use checksums	`807f1a55a92b44e00419855bec799487338699fba304e8c210cf8edc165c94c2`
BLAKE2b-256 checksum How to use checksums	`2b099f41bba63a12c6a7dcbcf2660f1f25b6d83727021d8e3564ddffd03d7fa0`
Upload date	Oct 14, 2021
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/4.62.3 CPython/3.9.7`

Release files / nupyprop-0.1.7.post122-cp38-cp38-macosx_10_9_x86_64.whl

Download URL	nupyprop-0.1.7.post122-cp38-cp38-macosx_10_9_x86_64.whl
Size	16.5 MB
Tags	CPython 3.8 macOS 10.9+ x86-64
SHA-256 checksum How to use checksums	`5081e211505a5ec537fae55aae891d2a4fbc053edec647fbdf260e409ceb8ae7`
BLAKE2b-256 checksum How to use checksums	`490a4851e214cc1829133d4a936ddcfebe740f0b771c208ca03549e2af0b1805`
Upload date	Oct 14, 2021
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/4.62.3 CPython/3.9.7`

Release files / nupyprop-0.1.7.post122-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL	nupyprop-0.1.7.post122-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size	16.4 MB
Tags	CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ x86-64
SHA-256 checksum How to use checksums	`ebab1b5ba6484e219166aef7a6537fcb61aa654069dc9b3f47638a2378a423d8`
BLAKE2b-256 checksum How to use checksums	`49a5c77fd2b51498bcbcfb39d4cb09d95b70954301caa429191239d2a49ed4d3`
Upload date	Oct 14, 2021
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/4.62.3 CPython/3.9.7`

Release files / nupyprop-0.1.7.post122-cp37-cp37m-macosx_10_9_x86_64.whl

Download URL	nupyprop-0.1.7.post122-cp37-cp37m-macosx_10_9_x86_64.whl
Size	16.5 MB
Tags	CPython 3.7 CPython 3.7 pymalloc macOS 10.9+ x86-64
SHA-256 checksum How to use checksums	`be2a70497d2e98266d06f568fb39bb6d95d6b56df1354831d7de18c25f7b2dbf`
BLAKE2b-256 checksum How to use checksums	`c4b03b9ef90eea2e84aa93f46bf8940da5b5e99b0d9079cd05579994516a41dc`
Upload date	Oct 14, 2021
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/4.62.3 CPython/3.9.7`

Security for nupyprop 0.1.7.post122

Report project as malware

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.