| Name | nupyosc JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | |
| Summary | NuPy: A new way to numerically compute neutrino oscillations in matter |
| upload_time | 2023-05-22 03:24:16 |
| maintainer | |
| docs_url | None |
| author | Baalateja Kataru |
| requires_python | |
| license | |
| keywords |
probability
neutrino
oscillations
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# NuPy
We use the recently discovered Eigenvalue-Eigenvector and Adjugate Identities to compute oscillation probabilities with minimal algorithmic steps.
# Usage
## Imports
```Python
import numpy as np
np.set_printoptions(precision=3)
from NuPy.oscprobs import OscProbIdentities
```
## Define your oscillation parameters
```Python
a = 0.2
theta12 = np.radians(33.2)
theta13 = np.radians(4.4)
theta23 = np.radians(46.1)
alpha = 0.026
delta = 50
deltacp = np.pi/6
```
## Compute probabilities
```Python
prob = OscProbIdentities(alpha, a, delta, deltacp, theta12, theta13, theta23)
print(prob.mat_angles_phase()) # prints mixing angles and phase in matter
# (29.988882340423384, 84.48628984963665, 3.5930259417910695, 44.188561816129464)
print(prob.PMNS()) # prints values of PMNS matrix elements
"""
[[ 0.849+0.000e+00j -0.297+0.000e+00j -0.062-4.318e-01j]
[-0.335+8.346e-02j -0.92 -2.921e-02j -0.184+1.342e-18j]
[ 0.01 +3.989e-01j 0.214-1.396e-01j -0.881-7.870e-19j]]
"""
print(prob.PMNS() @ prob.PMNS().H) # unitarity check
"""
[[1.000e+00-1.696e-22j 0.000e+00+1.561e-17j 6.939e-18+5.551e-17j]
[0.000e+00-1.561e-17j 1.000e+00-4.276e-19j 5.551e-17-1.327e-18j]
[1.388e-17-4.857e-17j 5.551e-17+0.000e+00j 1.000e+00-8.097e-19j]]
"""
print(prob.probabilities()) # prints all 9 probabilities
"""
[[0.97 0.015 0.014]
[0.014 0.236 0.75 ]
[0.015 0.749 0.236]]
"""
```
Raw data
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"description": "# NuPy\n\nWe use the recently discovered Eigenvalue-Eigenvector and Adjugate Identities to compute oscillation probabilities with minimal algorithmic steps.\n\n# Usage\n\n\n## Imports\n```Python\nimport numpy as np\nnp.set_printoptions(precision=3)\n\nfrom NuPy.oscprobs import OscProbIdentities\n```\n\n## Define your oscillation parameters\n\n```Python\na = 0.2\ntheta12 = np.radians(33.2)\ntheta13 = np.radians(4.4)\ntheta23 = np.radians(46.1)\nalpha = 0.026\ndelta = 50\ndeltacp = np.pi/6\n```\n\n## Compute probabilities\n```Python\nprob = OscProbIdentities(alpha, a, delta, deltacp, theta12, theta13, theta23)\n\nprint(prob.mat_angles_phase()) # prints mixing angles and phase in matter\n# (29.988882340423384, 84.48628984963665, 3.5930259417910695, 44.188561816129464)\n\nprint(prob.PMNS()) # prints values of PMNS matrix elements\n\"\"\"\n[[ 0.849+0.000e+00j -0.297+0.000e+00j -0.062-4.318e-01j]\n [-0.335+8.346e-02j -0.92 -2.921e-02j -0.184+1.342e-18j]\n [ 0.01 +3.989e-01j 0.214-1.396e-01j -0.881-7.870e-19j]]\n\"\"\"\n\nprint(prob.PMNS() @ prob.PMNS().H) # unitarity check\n\"\"\"\n[[1.000e+00-1.696e-22j 0.000e+00+1.561e-17j 6.939e-18+5.551e-17j]\n [0.000e+00-1.561e-17j 1.000e+00-4.276e-19j 5.551e-17-1.327e-18j]\n [1.388e-17-4.857e-17j 5.551e-17+0.000e+00j 1.000e+00-8.097e-19j]]\n\"\"\"\n\nprint(prob.probabilities()) # prints all 9 probabilities\n\"\"\"\n[[0.97 0.015 0.014]\n [0.014 0.236 0.75 ]\n [0.015 0.749 0.236]]\n\"\"\"\n```\n",
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