Add gitignore and precommit

.gitignore

@@ -0,0 +1 @@
+__pycache__

.pre-commit-config.yaml

@@ -0,0 +1,14 @@
+fail_fast: true
+
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.14.1
+    hooks:
+      - id: ruff
+        args: [ --fix, --exit-non-zero-on-fix ]
+      - id: ruff-format
+        types_or: [ python, pyi ]
+  - repo: https://github.com/MarcoGorelli/madforhooks
+    rev: 0.4.1
+    hooks:
+      - id: no-print-statements

distributions/__init__.py

@@ -1,5 +1,3 @@
-"""
-PyTensor powered distributions.
-"""
+"""PyTensor powered distributions."""
 
 __version__ = "0.1.0"

distributions/asymmetriclaplace.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import ppf_bounds_cont
+from distributions.helper import ppf_bounds_cont
 
 
 def mean(mu, b, kappa):

distributions/bernoulli.py

@@ -1,7 +1,7 @@
 import pytensor.tensor as pt
 from pytensor.tensor.xlogx import xlogx
 
-from .helper import ppf_bounds_disc, cdf_bounds
+from distributions.helper import cdf_bounds, ppf_bounds_disc
 
 
 def mean(p):

distributions/beta.py

@@ -3,7 +3,7 @@
 from pytensor.tensor.special import betaln
 from pytensor.tensor.xlogx import xlogy0
 
-from .helper import ppf_bounds_cont
+from distributions.helper import ppf_bounds_cont
 
 
 def mean(alpha, beta):

distributions/betabinomial.py

@@ -1,7 +1,7 @@
 import pytensor.tensor as pt
 
-from .helper import cdf_bounds, discrete_entropy, ppf_bounds_disc
-from .optimization import find_ppf_discrete
+from distributions.helper import cdf_bounds, discrete_entropy
+from distributions.optimization import find_ppf_discrete
 
 
 def mean(n, alpha, beta):

distributions/betascaled.py

@@ -1,9 +1,9 @@
 import pytensor.tensor as pt
+from pytensor.tensor.math import betaincinv
 from pytensor.tensor.special import betaln
 from pytensor.tensor.xlogx import xlogy0
-from pytensor.tensor.math import betaincinv
 
-from .helper import ppf_bounds_cont
+from distributions.helper import ppf_bounds_cont
 
 
 def mean(alpha, beta, lower, upper):

distributions/binomial.py

@@ -1,9 +1,8 @@
 import pytensor.tensor as pt
 from pytensor.tensor.xlogx import xlogy0
 
-from .helper import cdf_bounds, discrete_entropy, ppf_bounds_disc
-from .normal import entropy as normal_entropy
-from .optimization import find_ppf_discrete
+from distributions.helper import cdf_bounds, discrete_entropy
+from distributions.optimization import find_ppf_discrete
 
 
 def mean(n, p):

distributions/cauchy.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import ppf_bounds_cont
+from distributions.helper import ppf_bounds_cont
 
 
 def mean(alpha, beta):

distributions/chisquared.py

@@ -1,8 +1,8 @@
 import pytensor.tensor as pt
-
-from .helper import ppf_bounds_cont, cdf_bounds
 from pytensor.tensor.xlogx import xlogy0
 
+from distributions.helper import cdf_bounds, ppf_bounds_cont
+
 
 def mean(nu):
     return nu

distributions/discreteuniform.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import cdf_bounds, discrete_entropy, ppf_bounds_disc
+from distributions.helper import cdf_bounds, discrete_entropy, ppf_bounds_disc
 
 
 def mean(lower, upper):

distributions/exgaussian.py

@@ -1,9 +1,9 @@
 import pytensor.tensor as pt
 
-from .helper import logdiffexp, continuous_entropy
-from .optimization import find_ppf
-from .normal import logcdf as normal_logcdf
-from .normal import logpdf as normal_logpdf
+from distributions.helper import continuous_entropy, logdiffexp
+from distributions.normal import logcdf as normal_logcdf
+from distributions.normal import logpdf as normal_logpdf
+from distributions.optimization import find_ppf
 
 
 def mean(mu, sigma, nu):

distributions/exponential.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import ppf_bounds_cont, cdf_bounds
+from distributions.helper import cdf_bounds, ppf_bounds_cont
 
 
 def mean(lam):

distributions/gamma.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import ppf_bounds_cont
+from distributions.helper import ppf_bounds_cont
 
 
 def mean(alpha, beta):

distributions/geometric.py

@@ -1,7 +1,6 @@
 import pytensor.tensor as pt
-from pytensor.tensor.xlogx import xlogy0
 
-from .helper import cdf_bounds, ppf_bounds_disc
+from distributions.helper import cdf_bounds, ppf_bounds_disc
 
 
 def mean(p):

distributions/gumbel.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import cdf_bounds, ppf_bounds_cont
+from distributions.helper import cdf_bounds, ppf_bounds_cont
 
 
 def mean(mu, beta):

distributions/halfcauchy.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import ppf_bounds_cont, cdf_bounds
+from distributions.helper import ppf_bounds_cont
 
 
 def mean(beta):

distributions/halfnormal.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import ppf_bounds_cont
+from distributions.helper import ppf_bounds_cont
 
 
 def mean(sigma):

distributions/halfstudentt.py

@@ -1,11 +1,11 @@
 import pytensor.tensor as pt
-from pytensor.tensor.special import betaln
 from pytensor.tensor.math import betaincinv
+from pytensor.tensor.special import betaln
 
-from .helper import cdf_bounds, ppf_bounds_cont
-from .halfnormal import entropy as halfnormal_entropy
-from .halfnormal import cdf as halfnormal_cdf
-from .halfnormal import logpdf as halfnormal_logpdf
+from distributions.halfnormal import cdf as halfnormal_cdf
+from distributions.halfnormal import entropy as halfnormal_entropy
+from distributions.halfnormal import logpdf as halfnormal_logpdf
+from distributions.helper import cdf_bounds, ppf_bounds_cont
 
 
 def mean(nu, sigma):

distributions/helper.py

@@ -169,16 +169,12 @@ def discrete_entropy(min_x, max_x, logpdf, *params):
 
 
 def from_tau(tau):
-    """
-    Convert precision (tau) to standard deviation (sigma).
-    """
+    """Convert precision (tau) to standard deviation (sigma)."""
     sigma = 1 / pt.sqrt(tau)
     return sigma
 
 
 def to_tau(sigma):
-    """
-    Convert standard deviation (sigma) to precision (tau).
-    """
+    """Convert standard deviation (sigma) to precision (tau)."""
     tau = pt.power(sigma, -2)
     return tau

distributions/inversegamma.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import cdf_bounds, ppf_bounds_cont
+from distributions.helper import cdf_bounds, ppf_bounds_cont
 
 
 def mean(alpha, beta):

distributions/kumaraswamy.py

@@ -1,7 +1,7 @@
 import pytensor.tensor as pt
 from pytensor.tensor.xlogx import xlogy0
 
-from .helper import cdf_bounds, ppf_bounds_cont, sf_bounds
+from distributions.helper import cdf_bounds, ppf_bounds_cont, sf_bounds
 
 
 def mean(a, b):

distributions/laplace.py

@@ -1,5 +1,6 @@
 import pytensor.tensor as pt
-from .helper import ppf_bounds_cont, cdf_bounds
+
+from distributions.helper import cdf_bounds, ppf_bounds_cont
 
 
 def mean(mu, b):

distributions/logistic.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import ppf_bounds_cont
+from distributions.helper import ppf_bounds_cont
 
 
 def mean(mu, s):

distributions/lognormal.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import cdf_bounds, ppf_bounds_cont
+from distributions.helper import cdf_bounds, ppf_bounds_cont
 
 
 def mean(mu, sigma):

distributions/negativebinomial.py

@@ -1,8 +1,8 @@
 import pytensor.tensor as pt
 from pytensor.tensor.xlogx import xlogy0
 
-from .helper import cdf_bounds, discrete_entropy, sf_bounds
-from .optimization import find_ppf_discrete
+from distributions.helper import cdf_bounds, discrete_entropy, sf_bounds
+from distributions.optimization import find_ppf_discrete
 
 
 def mean(n, p):

distributions/normal.py

@@ -1,6 +1,6 @@
 import pytensor.tensor as pt
 
-from .helper import ppf_bounds_cont
+from distributions.helper import ppf_bounds_cont
 
 
 def mean(mu, sigma):

distributions/optimization.py

@@ -1,12 +1,12 @@
 import pytensor.tensor as pt
 
-from .helper import ppf_bounds_cont, ppf_bounds_disc
-from .normal import ppf as normal_ppf
+from distributions.helper import ppf_bounds_cont, ppf_bounds_disc
 
 
 def find_ppf(q, lower, upper, cdf, *params):
     """
     Compute the inverse CDF using the bisection method.
+
     Uses iterative expansion for infinite bounds.
 
     Note: We need to improve this method!!!

distributions/poisson.py

@@ -1,8 +1,8 @@
 import pytensor.tensor as pt
-
 from pytensor.tensor.xlogx import xlogy0
-from .helper import cdf_bounds, discrete_entropy, sf_bounds
-from .optimization import find_ppf_discrete
+
+from distributions.helper import cdf_bounds, discrete_entropy, sf_bounds
+from distributions.optimization import find_ppf_discrete
 
 
 def mean(mu):

distributions/skewnormal.py

@@ -1,8 +1,8 @@
 import pytensor.tensor as pt
 
-from .optimization import find_ppf
-from .halfnormal import entropy as halfnormal_entropy
-from .normal import entropy as normal_entropy
+from distributions.halfnormal import entropy as halfnormal_entropy
+from distributions.normal import entropy as normal_entropy
+from distributions.optimization import find_ppf
 
 
 def mean(mu, sigma, alpha):

distributions/studentt.py

@@ -1,11 +1,11 @@
 import pytensor.tensor as pt
-from pytensor.tensor.special import betaln
 from pytensor.tensor.math import betaincinv
+from pytensor.tensor.special import betaln
 
-from .helper import ppf_bounds_cont
-from .normal import logcdf as normal_logpdf
-from .normal import cdf as normal_logcdf
-from .normal import entropy as normal_entropy
+from distributions.helper import ppf_bounds_cont
+from distributions.normal import cdf as normal_logcdf
+from distributions.normal import entropy as normal_entropy
+from distributions.normal import logcdf as normal_logpdf
 
 
 def mean(nu, mu, sigma):

distributions/wald.py

@@ -1,7 +1,7 @@
 import pytensor.tensor as pt
 
-from .helper import cdf_bounds
-from .optimization import find_ppf
+from distributions.helper import cdf_bounds
+from distributions.optimization import find_ppf
 
 
 def mean(mu, lam):

tests/helper_empirical.py

@@ -1,13 +1,10 @@
-"""
-Common utilities for testing distributions against empirical samples.
-For distributions not available in scipy.
-"""
+"""Common utilities for testing distributions not available in scipy."""
 
 import numpy as np
 import pytensor.tensor as pt
 from numpy.testing import assert_allclose
-from scipy.stats import skew, kurtosis
 from scipy.integrate import quad
+from scipy.stats import kurtosis, skew
 
 
 def run_empirical_tests(
@@ -24,9 +21,7 @@ def run_empirical_tests(
     quantiles_rtol=1e-4,
     cdf_rtol=1e-4,
 ):
-    """
-    Test a distribution against empirical samples for distributions not in scipy.
-    """
+    """Test a distribution against empirical samples for distributions not in scipy."""
     rng_p = pt.random.default_rng(1)
     rvs = p_dist.rvs(*p_params, size=sample_size, random_state=rng_p).eval()
     sample_x = rvs[:20]
@@ -178,9 +173,9 @@ def run_empirical_tests(
     mask = np.abs(pdf_vals) > 1e-4
     if np.any(mask):
         rel_error = np.abs(numerical_pdf[mask] - pdf_vals[mask]) / (np.abs(pdf_vals[mask]) + 1e-10)
-        assert np.all(
-            rel_error < 1e-3
-        ), f"PDF doesn't match CDF derivative. Max rel error: {np.max(rel_error)}"
+        assert np.all(rel_error < 1e-3), (
+            f"PDF doesn't match CDF derivative. Max rel error: {np.max(rel_error)}"
+        )
 
     # PPF-CDF inverse
     x_vals = p_dist.ppf(q, *p_params).eval()