use trapezoid, update requirements (#678)

aloctavodia · web-flow · commit 3ad4cb44001b · 2025-07-17T10:52:28.000+03:00
* use trapezoid, update versions

* update bambi

* use generator

* fix test maxent
diff --git a/environment-dev.yml b/environment-dev.yml
@@ -5,10 +5,10 @@ channels:
 dependencies:
   - python >= 3.11
   - pip
-  - matplotlib>=3.7
-  - numba>=0.59
-  - numpy>=1.24
-  - scipy>=1.10, <1.15
+  - matplotlib>=3.8
+  - numba>=0.60
+  - numpy>=2.0
+  - scipy>=1.12
   - nbclient>=0.2
   - ipywidgets
   - ipympl
@@ -23,7 +23,7 @@ dependencies:
   - nbformat
   - ruff
   - ipykernel
-  - pymc>=5.16.0
+  - pymc>=5.24.0
   - bambi>=0.13.0
   - pip:
       - lxml
diff --git a/environment-docs.yml b/environment-docs.yml
@@ -4,10 +4,10 @@ channels:
   - conda-forge
 dependencies:
   - python >= 3.11
-  - matplotlib>=3.7
-  - numba>=0.59
-  - numpy>=1.24
-  - scipy>=1.10, <1.15
+  - matplotlib>=3.8
+  - numba>=0.60
+  - numpy>=2.0
+  - scipy>=1.12
   - nbclient>=0.2
   - ipywidgets
   - ipympl
@@ -20,5 +20,5 @@ dependencies:
   - sphinx-tabs
   - sphinxcontrib-bibtex
   - jupyter-sphinx
-  - pymc>=5.16.0
+  - pymc>=5.24.0
   - bambi>=0.13.0
diff --git a/environment.yml b/environment.yml
@@ -4,7 +4,7 @@ channels:
   - conda-forge
 dependencies:
   - python >= 3.11
-  - matplotlib>=3.7
-  - numba>=0.59
-  - numpy>=1.24
-  - scipy>=1.10, <1.15
+  - matplotlib>=3.8
+  - numba>=0.60
+  - numpy>=2.0
+  - scipy>=1.12
diff --git a/preliz/distributions/exgaussian.py b/preliz/distributions/exgaussian.py
@@ -99,7 +99,7 @@ def _neg_logpdf(self, x):
     def entropy(self):
         x_values = self.xvals("restricted")
         logpdf = self.logpdf(x_values)
-        return -np.trapz(np.exp(logpdf) * logpdf, x_values)
+        return -np.trapezoid(np.exp(logpdf) * logpdf, x_values)
 
     def mean(self):
         return self.mu + self.nu
diff --git a/preliz/distributions/hurdle.py b/preliz/distributions/hurdle.py
@@ -80,7 +80,7 @@ def mean(self):
         if self.kind == "discrete":
             return np.sum(x_values * pdf)
         else:
-            return np.trapz(x_values * pdf, x_values)
+            return np.trapezoid(x_values * pdf, x_values)
 
     def mode(self):
         if self.kind == "discrete":
@@ -101,7 +101,7 @@ def var(self):
         if self.kind == "discrete":
             return np.sum((x_values - self.mean()) ** 2 * pdf)
         else:
-            return np.trapz((x_values - self.mean()) ** 2 * pdf, x_values)
+            return np.trapezoid((x_values - self.mean()) ** 2 * pdf, x_values)
 
     def std(self):
         return self.var() ** 0.5
@@ -167,7 +167,7 @@ def entropy(self):
         if self.kind == "discrete":
             return -np.sum(np.exp(logpdf) * logpdf)
         else:
-            return -np.trapz(np.exp(logpdf) * logpdf, x_values)
+            return -np.trapezoid(np.exp(logpdf) * logpdf, x_values)
 
     def _neg_logpdf(self, x):
         return -self.logpdf(x).sum()
diff --git a/preliz/distributions/logitnormal.py b/preliz/distributions/logitnormal.py
@@ -118,20 +118,20 @@ def _neg_logpdf(self, x):
     def entropy(self):
         x_values = self.xvals("restricted")
         logpdf = self.logpdf(x_values)
-        return -np.trapz(np.exp(logpdf) * logpdf, x_values)
+        return -np.trapezoid(np.exp(logpdf) * logpdf, x_values)
 
     def mean(self):
         x_values = self.xvals("full")
         pdf = self.pdf(x_values)
-        return np.trapz(x_values * pdf, x_values)
+        return np.trapezoid(x_values * pdf, x_values)
 
     def median(self):
         return self.ppf(0.5)
 
     def var(self):
         x_values = self.xvals("full")
         pdf = self.pdf(x_values)
-        return np.trapz((x_values - self.mean()) ** 2 * pdf, x_values)
+        return np.trapezoid((x_values - self.mean()) ** 2 * pdf, x_values)
 
     def std(self):
         return self.var() ** 0.5
@@ -141,14 +141,14 @@ def skewness(self):
         std = self.std()
         x_values = self.xvals("full")
         pdf = self.pdf(x_values)
-        return np.trapz(((x_values - mean) / std) ** 3 * pdf, x_values)
+        return np.trapezoid(((x_values - mean) / std) ** 3 * pdf, x_values)
 
     def kurtosis(self):
         mean = self.mean()
         std = self.std()
         x_values = self.xvals("full")
         pdf = self.pdf(x_values)
-        return np.trapz(((x_values - mean) / std) ** 4 * pdf, x_values) - 3
+        return np.trapezoid(((x_values - mean) / std) ** 4 * pdf, x_values) - 3
 
     def mode(self):
         return find_mode_logitnormal(self)
diff --git a/preliz/distributions/loglogistic.py b/preliz/distributions/loglogistic.py
@@ -118,7 +118,7 @@ def skewness(self):
         x_values = self.xvals("full")
         pdf = self.pdf(x_values)
         return np.where(
-            self.beta > 3, np.trapz(((x_values - mean) / std) ** 3 * pdf, x_values), np.nan
+            self.beta > 3, np.trapezoid(((x_values - mean) / std) ** 3 * pdf, x_values), np.nan
         )
 
     def kurtosis(self):
@@ -127,7 +127,7 @@ def kurtosis(self):
         x_values = self.xvals("full")
         pdf = self.pdf(x_values)
         return np.where(
-            self.beta > 4, np.trapz(((x_values - mean) / std) ** 4 * pdf, x_values) - 3, np.nan
+            self.beta > 4, np.trapezoid(((x_values - mean) / std) ** 4 * pdf, x_values) - 3, np.nan
         )
 
     def rvs(self, size=None, random_state=None):
diff --git a/preliz/distributions/moyal.py b/preliz/distributions/moyal.py
@@ -92,7 +92,7 @@ def _neg_logpdf(self, x):
     def entropy(self):
         x_values = self.xvals("restricted")
         logpdf = self.logpdf(x_values)
-        return -np.trapz(np.exp(logpdf) * logpdf, x_values)
+        return -np.trapezoid(np.exp(logpdf) * logpdf, x_values)
 
     def mean(self):
         return self.mu + self.sigma * (np.euler_gamma + np.log(2))
diff --git a/preliz/distributions/rice.py b/preliz/distributions/rice.py
@@ -124,7 +124,7 @@ def _neg_logpdf(self, x):
     def entropy(self):
         x_values = self.xvals("restricted")
         logpdf = self.logpdf(x_values)
-        return -np.trapz(np.exp(logpdf) * logpdf, x_values)
+        return -np.trapezoid(np.exp(logpdf) * logpdf, x_values)
 
     def mean(self):
         return self.sigma * np.sqrt(np.pi / 2) * _l_half(-(self.nu**2) / (2 * self.sigma**2))
diff --git a/preliz/distributions/skew_studentt.py b/preliz/distributions/skew_studentt.py
@@ -120,7 +120,7 @@ def _neg_logpdf(self, x):
     def entropy(self):
         x_values = self.xvals("restricted")
         logpdf = self.logpdf(x_values)
-        return -np.trapz(np.exp(logpdf) * logpdf, x_values)
+        return -np.trapezoid(np.exp(logpdf) * logpdf, x_values)
 
     def mean(self):
         return (
diff --git a/preliz/distributions/skewnormal.py b/preliz/distributions/skewnormal.py
@@ -122,7 +122,7 @@ def _neg_logpdf(self, x):
     def entropy(self):
         x_values = self.xvals("restricted")
         logpdf = self.logpdf(x_values)
-        return -np.trapz(np.exp(logpdf) * logpdf, x_values)
+        return -np.trapezoid(np.exp(logpdf) * logpdf, x_values)
 
     def mean(self):
         return self.mu + self.sigma * np.sqrt(2 / np.pi) * self.alpha / np.sqrt(1 + self.alpha**2)
diff --git a/preliz/distributions/truncated.py b/preliz/distributions/truncated.py
@@ -85,7 +85,7 @@ def mean(self):
         if self.kind == "discrete":
             return np.sum(x_values * pdf)
         else:
-            return np.trapz(x_values * pdf, x_values)
+            return np.trapezoid(x_values * pdf, x_values)
 
     def mode(self):
         if self.kind == "discrete":
@@ -106,7 +106,7 @@ def var(self):
         if self.kind == "discrete":
             return np.sum((x_values - self.mean()) ** 2 * pdf)
         else:
-            return np.trapz((x_values - self.mean()) ** 2 * pdf, x_values)
+            return np.trapezoid((x_values - self.mean()) ** 2 * pdf, x_values)
 
     def std(self):
         return self.var() ** 0.5
@@ -150,7 +150,7 @@ def entropy(self):
         if self.kind == "discrete":
             return -np.sum(np.exp(logpdf) * logpdf)
         else:
-            return -np.trapz(np.exp(logpdf) * logpdf, x_values)
+            return -np.trapezoid(np.exp(logpdf) * logpdf, x_values)
 
     def _neg_logpdf(self, x):
         return -self.logpdf(x).sum()
diff --git a/preliz/internal/distribution_helper.py b/preliz/internal/distribution_helper.py
@@ -83,7 +83,7 @@ def num_skewness(dist):
     if dist.kind == "discrete":
         return np.sum(((x_values - mean) / std) ** 3 * pdf)
     else:
-        return np.trapz(((x_values - mean) / std) ** 3 * pdf, x_values)
+        return np.trapezoid(((x_values - mean) / std) ** 3 * pdf, x_values)
 
 
 def num_kurtosis(dist):
@@ -94,7 +94,7 @@ def num_kurtosis(dist):
     if dist.kind == "discrete":
         return np.sum(((x_values - mean) / std) ** 4 * pdf) - 3
     else:
-        return np.trapz(((x_values - mean) / std) ** 4 * pdf, x_values) - 3
+        return np.trapezoid(((x_values - mean) / std) ** 4 * pdf, x_values) - 3
 
 
 init_vals = {
diff --git a/preliz/internal/optimization.py b/preliz/internal/optimization.py
@@ -612,7 +612,7 @@ def func(x, dist, q):
 def get_weighted_rvs(target, size, rng):
     targets = [t[0] for t in target]
     weights = [t[1] for t in target]
-    target_rnd_choices = np.random.choice(len(targets), size=size, p=weights)
+    target_rnd_choices = rng.choice(len(targets), size=size, p=weights)
     samples = [target.rvs(size, random_state=rng) for target in targets]
     return np.choose(target_rnd_choices, samples)
 
diff --git a/preliz/tests/predictive_explorer.ipynb b/preliz/tests/predictive_explorer.ipynb
@@ -97,10 +97,12 @@
    "source": [
     "%%ipytest\n",
     "\n",
+    "rng = np.random.default_rng(1241)\n",
+    "\n",
     "data = pd.DataFrame(\n",
     "    {\n",
-    "        \"y\": np.random.normal(size=117),\n",
-    "        \"x\": np.random.normal(size=117),\n",
+    "        \"y\": rng.normal(size=117),\n",
+    "        \"x\": rng.normal(size=117),\n",
     "    }\n",
     ")\n",
     "data.head()\n",
diff --git a/preliz/tests/test_maxent.py b/preliz/tests/test_maxent.py
@@ -101,7 +101,7 @@
         (Normal(), -1, 1, 0.683, (-np.inf, np.inf), (0, 1)),
         (Normal(), 10, 12, 0.99, (-np.inf, np.inf), (11, 0.388)),
         (Normal(mu=0.5), -1, 1, 0.8, (-np.inf, np.inf), (0.581)),
-        (Pareto(), 1, 4, 0.9, (1, np.inf), (1.660, 1)),
+        (Pareto(), 1, 4, 0.9, (1, np.inf), (1.694, 0.997)),
         (Pareto(m=2), 1, 4, 0.9, (2, np.inf), (3.321)),
         (Rice(), 0, 4, 0.7, (0, np.inf), (0, 2.577)),
         (Rice(), 1, 10, 0.9, (0, np.inf), (3.454, 3.734)),
diff --git a/preliz/tests/test_ppe.py b/preliz/tests/test_ppe.py
@@ -9,7 +9,7 @@
 
 import preliz as pz
 
-np.random.seed(42)
+rng = np.random.default_rng(1241)
 
 
 @pytest.mark.parametrize(
@@ -20,7 +20,7 @@
             "sigma_x": 10,
             "sigma_z": 10,
             "target": pz.Normal(mu=174, sigma=20),
-            "X": np.random.normal(0, 10, 120),
+            "X": rng.normal(0, 10, 120),
             "new_x": 174,
             "new_z": 2.9,
         },
@@ -29,7 +29,7 @@
             "sigma_x": [10, 10],
             "sigma_z": 10,
             "target": pz.Normal(mu=174, sigma=20),
-            "X": np.random.normal(0, 10, 120),
+            "X": rng.normal(0, 10, 120),
             "new_x": [174, 174],
             "new_z": 2.9,
         },
@@ -38,7 +38,7 @@
             "sigma_x": 10,
             "sigma_z": 10,
             "target": [(pz.Normal(mu=174, sigma=20), 0.5), (pz.Normal(mu=176, sigma=19.5), 0.5)],
-            "X": np.random.normal(0, 10, 120),
+            "X": rng.normal(0, 10, 120),
             "new_x": 175,
             "new_z": 2.9,
         },
@@ -51,7 +51,7 @@
                 (pz.Normal(mu=176, sigma=19.5), 0.4),
                 (pz.StudentT(mu=174, sigma=20, nu=3), 0.1),
             ],
-            "X": np.random.normal(0, 10, 120),
+            "X": rng.normal(0, 10, 120),
             "new_x": [175, 175],
             "new_z": 3,
         },
diff --git a/pyproject.toml b/pyproject.toml
@@ -26,10 +26,10 @@ dynamic = ["version"]
 description = "Exploring and eliciting probability distributions."
 dependencies = [
   "arviz_stats>=0.6.0",  
-  "matplotlib>=3.7",
+  "matplotlib>=3.8",
   "numba>=0.59",
-  "numpy>=1.24",
-  "scipy>=1.10, <1.16"
+  "numpy>=2.0",
+  "scipy>=1.12"
 ]
 
 [tool.flit.module]
@@ -89,8 +89,6 @@ ignore = [
     "PLR2004",  # magic value comparison
     "PLR0915",  # too many statements
     "PLC0415",  # import outside of top level
-    "NPY002",   # Replace legacy `np.random.randn` call with `np.random.Generator`
-    "NPY201",   # `np.trapz` will be removed in NumPy 2.0. Use `numpy.trapezoid` on NumPy 2.0, or ignore this warning on earlier versions.
     "D1"    # Missing docstring
 ]
 
diff --git a/requirements-dev.txt b/requirements-dev.txt
@@ -9,6 +9,6 @@ nbformat
 lxml
 ipykernel
 pandas
-pymc>=5.16.0
+pymc>=5.24.0
 bambi>=0.13.0
 ruff
