def __init__(self,
function,
abscissa,
ordinate,
bounds=None,
ordinate_error=None):
"""
Initialisation function for a :py:class:`~uravu.relationship.Relationship` object.
"""
self.function = function
self.abscissa = abscissa
potential_y = []
for i, y in enumerate(ordinate):
if not isinstance(y, Distribution):
if not isinstance(y, stats._distn_infrastructure.rv_frozen):
if ordinate_error is not None:
potential_y.append(
Distribution(
stats.norm.rvs(loc=y,
scale=ordinate_error[i],
size=5000)))
else:
raise ValueError(
"uravu ordinate should be a list of uravu.distribution.Distribution objects or an ordinate_error should be given."
)
else:
potential_y.append(Distribution(y.rvs(size=5000)))
self.ordinate = Axis(potential_y)
else:
self.ordinate = Axis(ordinate)
if abscissa.shape[0] != len(ordinate):
raise ValueError(
"The number of data points in the abscissa does not match that for the ordinate."
)
self.bounds = bounds
self.variables = []
if bounds is not None:
if len(self.bounds) != self.len_parameters or not isinstance(
bounds[0], tuple):
raise ValueError(
"The number of bounds does not match the number of parameters"
)
for i, b in enumerate(self.bounds):
self.variables.append(
Distribution(
stats.uniform.rvs(loc=b[0],
scale=b[1] - b[0],
size=500)))
else:
for i in range(self.len_parameters):
self.variables.append(Distribution(1))
self.ln_evidence = None
self.mcmc_results = None
self.nested_sampling_results = None
def test_init_kde_size_change(self):
distro2 = Distribution(
norm.rvs(loc=1,
scale=1,
size=1000,
random_state=np.random.RandomState(2)))
AX = Axis([DISTRO1, distro2])
assert_equal(AX.values[1].samples, distro2.samples)
def test_shape_array(self):
ax = Axis(np.ones((3, 3)))
assert_equal(ax.shape, (3, 3))
import unittest
import numpy as np
from numpy.testing import assert_almost_equal, assert_equal
from uravu.distribution import Distribution
import scipy.stats
from uravu.axis import Axis
from scipy.stats import norm, uniform, gaussian_kde
DISTRO1 = Distribution(
norm.rvs(loc=0, scale=1, size=10000,
random_state=np.random.RandomState(1)))
DISTRO2 = Distribution(
norm.rvs(loc=1, scale=1, size=10000,
random_state=np.random.RandomState(2)))
AX = Axis([DISTRO1, DISTRO2])
AX_ARRAY = Axis([0, 1])
class TestDistribution(unittest.TestCase):
"""
Testing the Axis class.
"""
def test_init_values(self):
assert_equal(AX.values[0].samples, DISTRO1.samples)
assert_equal(AX.values[1].samples, DISTRO2.samples)
def test_init_kde(self):
assert_equal(isinstance(AX.kde, gaussian_kde), True)