mouthful

flexknot/__init__.py

@@ -8,8 +8,8 @@
 """
 
 from flexknot.core import AdaptiveKnot, FlexKnot
-from flexknot.likelihoods import FlexKnotLikelihood
-from flexknot.priors import AdaptiveKnotPrior, FlexKnotPrior
+from flexknot.likelihoods import Likelihood
+from flexknot.priors import AdaptivePrior, Prior
 
 __all__ = [
     "AdaptiveKnot", "FlexKnot",

flexknot/likelihoods.py

@@ -7,7 +7,7 @@
 from flexknot.core import AdaptiveKnot, FlexKnot
 
 
-class FlexKnotLikelihood:
+class Likelihood:
     """
     Likelihood for a flex-knot, with data described by xs, ys, and sigma.
 

flexknot/priors.py

@@ -15,7 +15,7 @@
 )
 
 
-class FlexKnotPrior(UniformPrior):
+class Prior(UniformPrior):
     """Interleaved uniform and sorted uniform priors for a flex-knot."""
 
     def __init__(self, x_min, x_max, y_min, y_max):
@@ -50,7 +50,7 @@ def __call__(self, hypercube):
         )
 
 
-class AdaptiveKnotPrior(FlexKnotPrior):
+class AdaptivePrior(Prior):
     """
     Interleaved uniform and sorted uniform priors appropriate for a flex-knot.
 

pyproject.toml

@@ -1,14 +1,13 @@
 [project]
 name = "flexknot"
-version = "1.2.1"
+version = "1.3.0"
 description = "Flex-Knot"
 authors = [{name="Adam Neil Ormondroyd", email="[email protected]"}]
 readme = "README.md"
 dependencies = ["numpy", "scipy", "pypolychord"]
 
 [project.urls]
-repository = "https://github.com/Ormorod/flexknot"
+repository = "https://github.com/adamormondroyd/flexknot"
 
 [project.optional-dependencies]
 dev = ["pytest", "flake8", "pydocstyle"]
-

tests/test_likelihood.py

@@ -3,7 +3,7 @@
 """
 import numpy as np
 from scipy.special import erf
-from flexknot import FlexKnotLikelihood
+from flexknot import Likelihood
 from flexknot.utils import create_theta
 
 
@@ -26,8 +26,7 @@ def test_likelihood():
     y_data = np.array([0, 1])
     sigma = 1
 
-    logl = FlexKnotLikelihood(x_min, x_max, x_data, y_data, sigma,
-                              adaptive=False)
+    logl = Likelihood(x_min, x_max, x_data, y_data, sigma, adaptive=False)
     assert logl(theta)[0] == -np.log(2 * np.pi)
 
 
@@ -42,8 +41,8 @@ def test_likelihood_sigma_x():
 
     The value for the likelihood in this case should be
     ln[(1/16π)(erf(1)-erf(0))(erf(0)-erf(-1))].
-    """
 
+    """
     x_min, x_max = 0, 1
     x_nodes = np.array([])
     y_nodes = np.array([0, 1])
@@ -53,8 +52,7 @@ def test_likelihood_sigma_x():
     y_data = np.array([0, 1])
     sigma = np.array([1, 1])
 
-    logl = FlexKnotLikelihood(x_min, x_max, x_data, y_data, sigma,
-                              adaptive=False)
+    logl = Likelihood(x_min, x_max, x_data, y_data, sigma, adaptive=False)
     assert np.isclose(
         logl(theta)[0], np.log((erf(1) - erf(0)) * (erf(0) - erf(-1))
                                / (16 * np.pi))

tests/test_prior.py

@@ -1,9 +1,9 @@
 """
-Test that FlexKnotPrior sorts the x nodes.
+Test that flexknot.Prior sorts the x nodes.
 """
 
 import numpy as np
-from flexknot import AdaptiveKnotPrior, FlexKnotPrior
+from flexknot import AdaptivePrior, Prior
 from flexknot.utils import get_x_nodes_from_theta
 
 rng = np.random.default_rng()
@@ -20,7 +20,7 @@ def test_flexknotprior_x_nodes_are_sorted():
     Test that the prior for the x_nodes is sorted.
     """
     hypercube = rng.random(2 * N_max - 2)
-    prior = FlexKnotPrior(x_min, x_max, y_min, y_max)(hypercube)
+    prior = Prior(x_min, x_max, y_min, y_max)(hypercube)
 
     assert np.all(np.diff(get_x_nodes_from_theta(prior, adaptive=False)) >= 0)
 
@@ -31,7 +31,7 @@ def test_adaptiveknotprior_x_nodes_are_sorted():
     """
 
     hypercube = rng.random(2 * N_max - 1)
-    prior = AdaptiveKnotPrior(
+    prior = AdaptivePrior(
         x_min, x_max, y_min, y_max, N_min, N_max
     )(hypercube)