Skip to main content

A simulator for neutrino propagation through the earth.

Project description

Propagate neutrinos through the earth.

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

Documentation: https://nupyprop.readthedocs.io/en/latest/

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. -l or –lepton: flavor of 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 SM cross-sections. Default is 1.

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

  11. -c or –cdf_only: If set to yes, the output file will not contain outgoing lepton energies, and will only contain exit probabilities and binned outgoing energy CDF values. Default is no.

  12. -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 models/models.py

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 models/models.py

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 .

https://hitcounter.pythonanywhere.com/count/tag.svg?url=https%3A%2F%2Fgithub.com%2FNuSpaceSim%2Fnupyprop

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

nupyprop-0.1.7.post48-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

nupyprop-0.1.7.post48-cp39-cp39-macosx_10_9_x86_64.whl (5.0 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

nupyprop-0.1.7.post48-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

nupyprop-0.1.7.post48-cp38-cp38-macosx_10_9_x86_64.whl (5.0 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

nupyprop-0.1.7.post48-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

nupyprop-0.1.7.post48-cp37-cp37m-macosx_10_9_x86_64.whl (5.0 MB view details)

Uploaded CPython 3.7mmacOS 10.9+ x86-64

File details

Details for the file nupyprop-0.1.7.post48-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post48-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 dac926a5945f2afe7a14ad887cdf89506226ff241c915e06fda0742307866370
MD5 0deb94cb323420075b78c421e331d7d9
BLAKE2b-256 8df3a46792092f15b95478fea4a52dd1629ceaa5ea13a39e5dfef83454f617ca

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post48-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post48-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.3 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.9.6

File hashes

Hashes for nupyprop-0.1.7.post48-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 100a6ad7a91e8865ae80ff7b52747a774f44fe8e0b3310c36afcdbb7bf5b12e7
MD5 8208d8f2b1dc34a693bba8050a08b8a2
BLAKE2b-256 bb556b82a56467046939fc65c28a4c1076486232fcf7bdded5db1805045d0513

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post48-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post48-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9aae824a95830556db82b6b9ef5321003e7e021c031cf8685562bd9341a343b1
MD5 510465a287ae4a6d07b339eccd148b20
BLAKE2b-256 be8a94f3148c81a5128dfa17eac767ddabf1eea8372962bda4190367606c63d5

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post48-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post48-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.3 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.9.6

File hashes

Hashes for nupyprop-0.1.7.post48-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1869e31cb963db065263ac3794ac703db8f2c268706466893533cf9f9d270c62
MD5 5c28eb17932a1b6b73f47006be9982ab
BLAKE2b-256 69651bdeef058b761156733f716dd886817fd90f910ebceecd6f6c370094802a

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post48-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nupyprop-0.1.7.post48-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ab663a6fa3b197081e7debd87677f833a94ddcaca358e9afe70fe2e39b30cb3e
MD5 c11d5c1c6d751c1c6fe9a552d9a540e9
BLAKE2b-256 a01751168461c4bc0856bad6f3b80883b491f2df615458bebd531a213c526c94

See more details on using hashes here.

File details

Details for the file nupyprop-0.1.7.post48-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nupyprop-0.1.7.post48-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.3 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.9.6

File hashes

Hashes for nupyprop-0.1.7.post48-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 71d85fae086fcc6e5677be5a516c29b3d3f6964f9e38fc739f68edff85e68538
MD5 ea4acbf72d641f9c166a6459b9b11219
BLAKE2b-256 60aa79adf691353514c45ae941ea3c64297f0ec95cba79445b961f2860cee690

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page