import os
import arviz as az
from numpy import array
from ..utils import PACKAGE_DIR
from netCDF4 import Dataset
example_star = {
"n": array([13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]),
"nu": array(
[
1601.25483295,
1712.37568989,
1822.86668365,
1932.24319991,
2042.29649891,
2153.48031271,
2265.19596148,
2377.14428885,
2488.87477862,
2601.02112942,
2713.51145217,
2826.39969194,
2939.55800865,
3052.67058431,
]
),
"nu_obs": array(
[
1601.42919156,
1711.94260502,
1823.08134191,
1932.41344902,
2042.10242007,
2153.42600828,
2265.19705524,
2377.14416919,
2488.87448949,
2600.9706218,
2713.62279006,
2826.57056208,
2939.58036448,
3053.21503524,
]
),
"nu_err": array(
[
5.72198344e-01,
4.16434127e-01,
2.86038937e-01,
1.81007332e-01,
9.94748044e-02,
4.17027255e-02,
8.55565859e-03,
3.78361795e-04,
1.72087208e-02,
5.92086931e-02,
1.26606938e-01,
2.19686184e-01,
3.38567162e-01,
4.82994170e-01,
]
),
"nu_max": (2357.692764609278, 23.57692764609278),
"delta_nu": (111.8411243661503, 0.1),
}
"""Example input data for a star."""
tau_prior = {
"cov": array(
[
[
[
4.74107689e-02,
2.10374753e01,
-4.50673626e-02,
-4.29660982e-02,
],
[2.10374753e01, 8.67476626e04, -3.09374296e01, -4.05217581e01],
[
-4.50673626e-02,
-3.09374296e01,
4.47311492e-02,
4.40438337e-02,
],
[
-4.29660982e-02,
-4.05217581e01,
4.40438337e-02,
4.57177839e-02,
],
],
[
[
6.67087500e-02,
1.70161389e00,
-6.39337291e-02,
-5.62773436e-02,
],
[1.70161389e00, 8.45548647e04, -6.47441494e00, -1.00756761e01],
[
-6.39337291e-02,
-6.47441494e00,
6.16228667e-02,
5.45286708e-02,
],
[
-5.62773436e-02,
-1.00756761e01,
5.45286708e-02,
4.95863600e-02,
],
],
[
[
5.07927599e-02,
4.05138534e01,
-5.23410991e-02,
-5.44883625e-02,
],
[4.05138534e01, 2.67804989e05, -5.16274407e01, -6.53399774e01],
[
-5.23410991e-02,
-5.16274407e01,
5.43879067e-02,
5.70692375e-02,
],
[
-5.44883625e-02,
-6.53399774e01,
5.70692375e-02,
6.06935544e-02,
],
],
[
[
2.08871762e-01,
9.16630930e01,
-1.94785240e-01,
-1.62530199e-01,
],
[9.16630930e01, 9.03280803e04, -8.75301499e01, -7.32076331e01],
[
-1.94785240e-01,
-8.75301499e01,
1.81892066e-01,
1.51661243e-01,
],
[
-1.62530199e-01,
-7.32076331e01,
1.51661243e-01,
1.27039502e-01,
],
],
]
),
"loc": array(
[
[3.21707973e00, 6.06545680e03, 3.07287855e00, 3.48599734e00],
[2.54691234e00, 4.83659081e03, 3.82659388e00, 4.29677828e00],
[3.37467968e00, 4.99321780e03, 3.00907652e00, 3.52206736e00],
[1.50187411e00, 4.61453211e03, 4.80374637e00, 5.11142826e00],
]
),
"weights": array([0.2191733, 0.2555788, 0.21523927, 0.31000863]),
}
"""Gaussian mixture parameters for the glitch acoustic depths with nu_max
and effective temperature."""
[docs]def get_example_results() -> az.InferenceData:
"""Get example inference results data.
Returns:
arviz.InferenceData: Inference data object.
"""
return az.from_netcdf(
os.path.join(PACKAGE_DIR, "data", "example_results.nc")
)
[docs]def get_tau_prior_data():
"""Returns tuple of tau prior data. The first is an (N, 2) array of
nu_max, effective temperature, and the second is a (N, 2) array of
tau_he and tau_cz estimated from stellar evolutionary models.
"""
filename = os.path.join(PACKAGE_DIR, "data", "tau_prior.nc")
with Dataset(filename, "r") as root:
x = array(root["training/x"][()])
y = array(root["training/y"][()])
return x, y