Use rst code-block over code::

docs/source/contributing/developer_guide.md

@@ -245,7 +245,7 @@ usually created in order to optimise performance. But getting a
 possible (see also in
 {ref}`doc <pymc_overview##Transformed-distributions-and-changes-of-variables>`):
 
-.. code:: python
+.. code-block:: python
 
 
     lognorm = Exp().apply(pm.Normal.dist(0., 1.))
@@ -262,15 +262,15 @@ Now, back to ``model.RV(...)`` - things returned from ``model.RV(...)``
 are PyTensor tensor variables, and it is clear from looking at
 ``TransformedRV``:
 
-.. code:: python
+.. code-block:: python
 
     class TransformedRV(TensorVariable):
         ...
 
 as for ``FreeRV`` and ``ObservedRV``, they are ``TensorVariable``\s with
 ``Factor`` as mixin:
 
-.. code:: python
+.. code-block:: python
 
     class FreeRV(Factor, TensorVariable):
         ...
@@ -283,7 +283,7 @@ distribution into a ``TransformedDistribution``, and then ``model.Var`` is
 called again to added the RV associated with the
 ``TransformedDistribution`` as a ``FreeRV``:
 
-.. code:: python
+.. code-block:: python
 
         ...
         self.transformed = model.Var(
@@ -295,13 +295,13 @@ only add one ``FreeRV``. In another word, you *cannot* do chain
 transformation by nested applying multiple transforms to a Distribution
 (however, you can use ``Chain`` transformation.
 
-.. code:: python
+.. code-block:: python
 
     z = pm.LogNormal.dist(mu=0., sigma=1., transform=tr.Log)
     z.transform           # ==> pymc.distributions.transforms.Log
 
 
-.. code:: python
+.. code-block:: python
 
     z2 = Exp().apply(z)
     z2.transform is None  # ==> True

pymc/distributions/discrete.py

@@ -1357,7 +1357,7 @@ class OrderedProbit:
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         # Generate data for a simple 1 dimensional example problem
         n1_c = 300; n2_c = 300; n3_c = 300

pymc/distributions/multivariate.py

@@ -1390,7 +1390,7 @@ class LKJCholeskyCov:
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         with pm.Model() as model:
             # Note that we access the distribution for the standard
@@ -1682,28 +1682,25 @@ class LKJCorr:
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         with pm.Model() as model:
-
             # Define the vector of fixed standard deviations
-            sds = 3*np.ones(10)
+            sds = 3 * np.ones(10)
 
-            corr = pm.LKJCorr(
-                'corr', eta=4, n=10, return_matrix=True
-            )
+            corr = pm.LKJCorr("corr", eta=4, n=10, return_matrix=True)
 
             # Define a new MvNormal with the given correlation matrix
-            vals = sds*pm.MvNormal('vals', mu=np.zeros(10), cov=corr, shape=10)
+            vals = sds * pm.MvNormal("vals", mu=np.zeros(10), cov=corr, shape=10)
 
             # Or transform an uncorrelated normal distribution:
-            vals_raw = pm.Normal('vals_raw', shape=10)
+            vals_raw = pm.Normal("vals_raw", shape=10)
             chol = pt.linalg.cholesky(corr)
-            vals = sds*pt.dot(chol,vals_raw)
+            vals = sds * pt.dot(chol, vals_raw)
 
             # The matrix is internally still sampled as a upper triangular vector
             # If you want access to it in matrix form in the trace, add
-            pm.Deterministic('corr_mat', corr)
+            pm.Deterministic("corr_mat", corr)
 
 
     References
@@ -1797,7 +1794,7 @@ class MatrixNormal(Continuous):
     Define a matrixvariate normal variable for given row and column covariance
     matrices.
 
-    .. code:: python
+    .. code-block:: python
 
         import pymc as pm
         import numpy as np
@@ -1820,16 +1817,20 @@ class MatrixNormal(Continuous):
     constant, both the covariance and scaling could be learned as follows
     (see the docstring of `LKJCholeskyCov` for more information about this)
 
-    .. code:: python
+    .. code-block:: python
 
         # Setup data
-        true_colcov = np.array([[1.0, 0.5, 0.1],
-                                [0.5, 1.0, 0.2],
-                                [0.1, 0.2, 1.0]])
+        true_colcov = np.array(
+            [
+                [1.0, 0.5, 0.1],
+                [0.5, 1.0, 0.2],
+                [0.1, 0.2, 1.0],
+            ]
+        )
         m = 3
         n = true_colcov.shape[0]
         true_scale = 3
-        true_rowcov = np.diag([true_scale**(2*i) for i in range(m)])
+        true_rowcov = np.diag([true_scale ** (2 * i) for i in range(m)])
         mu = np.zeros((m, n))
         true_kron = np.kron(true_rowcov, true_colcov)
         data = np.random.multivariate_normal(mu.flatten(), true_kron)
@@ -1838,13 +1839,12 @@ class MatrixNormal(Continuous):
         with pm.Model() as model:
             # Setup right cholesky matrix
             sd_dist = pm.HalfCauchy.dist(beta=2.5, shape=3)
-            colchol,_,_ = pm.LKJCholeskyCov('colchol', n=3, eta=2,sd_dist=sd_dist)
+            colchol, _, _ = pm.LKJCholeskyCov("colchol", n=3, eta=2, sd_dist=sd_dist)
             # Setup left covariance matrix
-            scale = pm.LogNormal('scale', mu=np.log(true_scale), sigma=0.5)
-            rowcov = pt.diag([scale**(2*i) for i in range(m)])
+            scale = pm.LogNormal("scale", mu=np.log(true_scale), sigma=0.5)
+            rowcov = pt.diag([scale ** (2 * i) for i in range(m)])
 
-            vals = pm.MatrixNormal('vals', mu=mu, colchol=colchol, rowcov=rowcov,
-                                observed=data)
+            vals = pm.MatrixNormal("vals", mu=mu, colchol=colchol, rowcov=rowcov, observed=data)
     """
 
     rv_op = matrixnormal
@@ -2010,30 +2010,30 @@ class KroneckerNormal(Continuous):
     Define a multivariate normal variable with a covariance
     :math:`K = K_1 \otimes K_2`
 
-    .. code:: python
+    .. code-block:: python
 
-        K1 = np.array([[1., 0.5], [0.5, 2]])
-        K2 = np.array([[1., 0.4, 0.2], [0.4, 2, 0.3], [0.2, 0.3, 1]])
+        K1 = np.array([[1.0, 0.5], [0.5, 2]])
+        K2 = np.array([[1.0, 0.4, 0.2], [0.4, 2, 0.3], [0.2, 0.3, 1]])
         covs = [K1, K2]
         N = 6
         mu = np.zeros(N)
         with pm.Model() as model:
-            vals = pm.KroneckerNormal('vals', mu=mu, covs=covs, shape=N)
+            vals = pm.KroneckerNormal("vals", mu=mu, covs=covs, shape=N)
 
     Efficiency gains are made by cholesky decomposing :math:`K_1` and
     :math:`K_2` individually rather than the larger :math:`K` matrix. Although
     only two matrices :math:`K_1` and :math:`K_2` are shown here, an arbitrary
     number of submatrices can be combined in this way. Choleskys and
     eigendecompositions can be provided instead
 
-    .. code:: python
+    .. code-block:: python
 
         chols = [np.linalg.cholesky(Ki) for Ki in covs]
         evds = [np.linalg.eigh(Ki) for Ki in covs]
         with pm.Model() as model:
-            vals2 = pm.KroneckerNormal('vals2', mu=mu, chols=chols, shape=N)
+            vals2 = pm.KroneckerNormal("vals2", mu=mu, chols=chols, shape=N)
             # or
-            vals3 = pm.KroneckerNormal('vals3', mu=mu, evds=evds, shape=N)
+            vals3 = pm.KroneckerNormal("vals3", mu=mu, evds=evds, shape=N)
 
     neither of which will be converted. Diagonal noise can also be added to
     the covariance matrix, :math:`K = K_1 \otimes K_2 + \sigma^2 I_N`.
@@ -2042,13 +2042,13 @@ class KroneckerNormal(Continuous):
     utilizing eigendecompositons of the submatrices behind the scenes [1].
     Thus,
 
-    .. code:: python
+    .. code-block:: python
 
         sigma = 0.1
         with pm.Model() as noise_model:
-            vals = pm.KroneckerNormal('vals', mu=mu, covs=covs, sigma=sigma, shape=N)
-            vals2 = pm.KroneckerNormal('vals2', mu=mu, chols=chols, sigma=sigma, shape=N)
-            vals3 = pm.KroneckerNormal('vals3', mu=mu, evds=evds, sigma=sigma, shape=N)
+            vals = pm.KroneckerNormal("vals", mu=mu, covs=covs, sigma=sigma, shape=N)
+            vals2 = pm.KroneckerNormal("vals2", mu=mu, chols=chols, sigma=sigma, shape=N)
+            vals3 = pm.KroneckerNormal("vals3", mu=mu, evds=evds, sigma=sigma, shape=N)
 
     are identical, with `covs` and `chols` each converted to
     eigendecompositions.

pymc/gp/cov.py

@@ -956,7 +956,7 @@ class WrappedPeriodic(Covariance):
     In order to construct a kernel equivalent to the `Periodic` kernel you
     can do the following (though using `Periodic` will likely be a bit faster):
 
-    .. code:: python
+    .. code-block:: python
 
         exp_quad = pm.gp.cov.ExpQuad(1, ls=0.5)
         cov = pm.gp.cov.WrappedPeriodic(exp_quad, period=5)

pymc/gp/gp.py

@@ -117,7 +117,7 @@ class Latent(Base):
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         # A one dimensional column vector of inputs.
         X = np.linspace(0, 1, 10)[:, None]
@@ -439,7 +439,7 @@ class Marginal(Base):
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         # A one dimensional column vector of inputs.
         X = np.linspace(0, 1, 10)[:, None]
@@ -719,7 +719,7 @@ class MarginalApprox(Marginal):
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         # A one dimensional column vector of inputs.
         X = np.linspace(0, 1, 10)[:, None]
@@ -963,7 +963,7 @@ class LatentKron(Base):
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         # One dimensional column vectors of inputs
         X1 = np.linspace(0, 1, 10)[:, None]
@@ -1066,7 +1066,7 @@ def conditional(self, name, Xnew, jitter=JITTER_DEFAULT, **kwargs):
         1, and 4, respectively, then `Xnew` must have 7 columns and a
         covariance between the prediction points
 
-        .. code:: python
+        .. code-block:: python
 
             cov_func(Xnew) = cov_func1(Xnew[:, :2]) * cov_func1(Xnew[:, 2:3]) * cov_func1(Xnew[:, 3:])
 
@@ -1118,13 +1118,13 @@ class MarginalKron(Base):
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         # One dimensional column vectors of inputs
         X1 = np.linspace(0, 1, 10)[:, None]
         X2 = np.linspace(0, 2, 5)[:, None]
         Xs = [X1, X2]
-        y = np.random.randn(len(X1)*len(X2))  # toy data
+        y = np.random.randn(len(X1) * len(X2))  # toy data
         with pm.Model() as model:
             # Specify the covariance functions for each Xi
             cov_func1 = pm.gp.cov.ExpQuad(1, ls=0.1)  # Must accept X1 without error
@@ -1266,7 +1266,7 @@ def conditional(self, name, Xnew, pred_noise=False, diag=False, **kwargs):
         1, and 4, respectively, then `Xnew` must have 7 columns and a
         covariance between the prediction points
 
-        .. code:: python
+        .. code-block:: python
 
             cov_func(Xnew) = cov_func1(Xnew[:, :2]) * cov_func1(Xnew[:, 2:3]) * cov_func1(Xnew[:, 3:])
 

pymc/gp/hsgp_approx.py

@@ -215,7 +215,7 @@ class HSGP(Base):
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         # A three dimensional column vector of inputs.
         X = np.random.rand(100, 3)
@@ -357,7 +357,7 @@ def prior_linearized(self, X: TensorLike):
 
         Examples
         --------
-        .. code:: python
+        .. code-block:: python
 
             # A one dimensional column vector of inputs.
             X = np.linspace(0, 10, 100)[:, None]
@@ -544,7 +544,7 @@ class HSGPPeriodic(Base):
 
     Examples
     --------
-    .. code:: python
+    .. code-block:: python
 
         # A three dimensional column vector of inputs.
         X = np.random.rand(100, 3)
@@ -640,7 +640,7 @@ def prior_linearized(self, X: TensorLike):
 
         Examples
         --------
-        .. code:: python
+        .. code-block:: python
 
             # A one dimensional column vector of inputs.
             X = np.linspace(0, 10, 100)[:, None]
@@ -665,8 +665,7 @@ def prior_linearized(self, X: TensorLike):
 
                 # The (non-centered) GP approximation is given by
                 f = pm.Deterministic(
-                    "f",
-                    phi_cos @ (psd * beta[:m]) + phi_sin[..., 1:] @ (psd[1:] * beta[m:])
+                    "f", phi_cos @ (psd * beta[:m]) + phi_sin[..., 1:] @ (psd[1:] * beta[m:])
                 )
                 ...