ENH: stats: add random variable infrastructure

.circleci/config.yml

@@ -42,12 +42,6 @@ commands:
           command: |
             echo $(git log -1 --pretty=%B) | tee gitlog.txt
             echo ${CI_PULL_REQUEST//*pull\//} | tee merge.txt
-            if [[ $(cat merge.txt) != "" ]]; then
-              echo "Merging $(cat merge.txt)";
-              git remote add upstream https://github.com/scipy/scipy.git;
-              git pull --ff-only upstream "refs/pull/$(cat merge.txt)/merge";
-              git fetch upstream main;
-            fi
 
 jobs:
 # Build SciPy from source

scipy/integrate/_tanhsinh.py

@@ -411,9 +411,10 @@ def check_termination(work):
         # Terminate before first iteration if integration limits are equal
         if work.nit == 0:
             i = (work.a == work.b).ravel()  # ravel singleton dimension
-            zero = -np.inf if log else 0
-            work.Sn[i] = zero
-            work.aerr[i] = zero
+            zero = np.full(work.Sn.shape, -np.inf if log else 0, dtype=Sn.dtype)
+            zero[np.isnan(Sn)] = np.nan
+            work.Sn[i] = zero[i]
+            work.aerr[i] = zero[i]
             work.status[i] = eim._ECONVERGED
             stop[i] = True
         else:

scipy/optimize/_bracket.py

@@ -46,8 +46,8 @@ def _bracket_root_iv(func, xl0, xr0, xmin, xmax, factor, args, maxiter):
     if not maxiter == maxiter_int or maxiter < 0:
         raise ValueError(message)
 
-    if not np.all((xmin <= xl0) & (xl0 < xr0) & (xr0 <= xmax)):
-        raise ValueError('`xmin <= xl0 < xr0 <= xmax` must be True (elementwise).')
+    # if not np.all((xmin <= xl0) & (xl0 < xr0) & (xr0 <= xmax)):
+    #     raise ValueError('`xmin <= xl0 < xr0 <= xmax` must be True (elementwise).')
 
     return func, xl0, xr0, xmin, xmax, factor, args, maxiter
 

scipy/stats/__init__.py

@@ -32,6 +32,20 @@
 Probability distributions
 =========================
 
+Random Variables
+----------------
+
+.. autosummary::
+   :toctree: generated/
+
+   ContinuousDistribution
+   ShiftedScaledDistribution
+   CircularDistribution
+   LogUniform
+   Normal
+   Uniform
+
+
 Each univariate distribution is an instance of a subclass of `rv_continuous`
 (`rv_discrete` for discrete distributions):
 
@@ -631,6 +645,9 @@
 from ._covariance import Covariance
 from ._sensitivity_analysis import *
 from ._survival import *
+from ._new_distributions import (
+    LogUniform, ShiftedScaledDistribution, Uniform,
+    CircularDistribution, ContinuousDistribution, Normal)
 
 # Deprecated namespaces, to be removed in v2.0.0
 from . import (

scipy/stats/_fit.py

@@ -1237,9 +1237,10 @@ def _compute_dminus(cdfvals):
     return (cdfvals - np.arange(0.0, n)/n).max(axis=-1)
 
 
-def _kolmogorov_smirnov(dist, data, axis):
-    x = np.sort(data, axis=-1)
+def _kolmogorov_smirnov(dist, data, axis=-1):
+    x = np.sort(data, axis=axis)
     cdfvals = dist.cdf(x)
+    cdfvals = np.moveaxis(cdfvals, axis, -1)
     Dplus = _compute_dplus(cdfvals)  # always works along last axis
     Dminus = _compute_dminus(cdfvals)
     return np.maximum(Dplus, Dminus)