Applied Formatting using Black Command on 13 files

docs/source/conf.py

@@ -155,7 +155,7 @@
         ("Books + Videos", "learn"),
         ("API", "api"),
         ("Developer Guide", "developer_guide"),
-        ("About PyMC3", "about")
+        ("About PyMC3", "about"),
     ],
     #     "fixed_sidebar": "false",
     #     "description": "Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano"
@@ -264,9 +264,7 @@
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (master_doc, "pymc3.tex", "PyMC3 Documentation", "PyMC developers", "manual")
-]
+latex_documents = [(master_doc, "pymc3.tex", "PyMC3 Documentation", "PyMC developers", "manual")]
 
 # The name of an image file (relative to this directory) to place at the top of
 # the title page.
@@ -330,7 +328,5 @@
 
 
 def setup(app):
-    app.add_css_file(
-        "https://cdn.jsdelivr.net/npm/[email protected]/dist/semantic.min.css"
-    )
+    app.add_css_file("https://cdn.jsdelivr.net/npm/[email protected]/dist/semantic.min.css")
     app.add_css_file("default.css")

docs/source/sphinxext/gallery_generator.py

@@ -18,9 +18,7 @@
 from matplotlib import image
 
 DOC_SRC = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-DEFAULT_IMG_LOC = os.path.join(
-            os.path.dirname(DOC_SRC), "logos", "PyMC3.png"
-        )
+DEFAULT_IMG_LOC = os.path.join(os.path.dirname(DOC_SRC), "logos", "PyMC3.png")
 TABLE_OF_CONTENTS_FILENAME = "table_of_contents_{}.js"
 
 INDEX_TEMPLATE = """
@@ -69,13 +67,9 @@ class NotebookGenerator:
     def __init__(self, filename, target_dir):
         self.basename = os.path.basename(filename)
         self.stripped_name = os.path.splitext(self.basename)[0]
-        self.output_html = os.path.join(
-            "..", "notebooks", f"{self.stripped_name}.html"
-        )
+        self.output_html = os.path.join("..", "notebooks", f"{self.stripped_name}.html")
         self.image_dir = os.path.join(target_dir, "_images")
-        self.png_path = os.path.join(
-            self.image_dir, f"{self.stripped_name}.png"
-        )
+        self.png_path = os.path.join(self.image_dir, f"{self.stripped_name}.png")
         with open(filename) as fid:
             self.json_source = json.load(fid)
         self.pagetitle = self.extract_title()
@@ -141,8 +135,7 @@ def build_gallery(srcdir, gallery):
     source_dir = os.path.abspath(
         os.path.join(os.path.dirname(os.path.dirname(srcdir)), "notebooks")
     )
-    table_of_contents_file = os.path.join(
-        source_dir, TABLE_OF_CONTENTS_FILENAME.format(gallery))
+    table_of_contents_file = os.path.join(source_dir, TABLE_OF_CONTENTS_FILENAME.format(gallery))
     tocjs = TableOfContentsJS()
     tocjs.load(table_of_contents_file)
 
@@ -157,7 +150,7 @@ def build_gallery(srcdir, gallery):
 
     if not os.path.exists(source_dir):
         os.makedirs(source_dir)
-    
+
     # Create default image
     default_png_path = os.path.join(os.path.join(target_dir, "_images"), "default.png")
     shutil.copy(DEFAULT_IMG_LOC, default_png_path)
@@ -178,27 +171,25 @@ def build_gallery(srcdir, gallery):
             filename = basename.split(".")[0]
             data[basename] = {
                 "title": " ".join(filename.split("_")),
-                "url": os.path.join(os.sep, gallery, "../"+filename+".html"),
+                "url": os.path.join(os.sep, gallery, "../" + filename + ".html"),
                 "thumb": os.path.basename(default_png_path),
             }
 
     js_file = os.path.join(image_dir, f"gallery_{gallery}_contents.js")
     with open(table_of_contents_file) as toc:
         table_of_contents = toc.read()
 
-    js_contents = "Gallery.examples = {}\n{}".format(
-        json.dumps(data), table_of_contents
-    )
+    js_contents = "Gallery.examples = {}\n{}".format(json.dumps(data), table_of_contents)
 
     with open(js_file, "w") as js:
         js.write(js_contents)
 
     with open(os.path.join(target_dir, "index.rst"), "w") as index:
-        index.write(INDEX_TEMPLATE.format(
-            sphinx_tag="notebook_gallery",
-            gallery=gallery,
-            Gallery=gallery.title().rstrip("s")
-        ))
+        index.write(
+            INDEX_TEMPLATE.format(
+                sphinx_tag="notebook_gallery", gallery=gallery, Gallery=gallery.title().rstrip("s")
+            )
+        )
 
     os.chdir(working_dir)
 

pymc3/gp/cov.py

@@ -87,10 +87,13 @@ def full(self, X, Xs):
 
     def _slice(self, X, Xs):
         if self.input_dim != X.shape[-1]:
-            warnings.warn(f"Only {self.input_dim} column(s) out of {X.shape[-1]} are"
-                          " being used to compute the covariance function. If this"
-                          " is not intended, increase 'input_dim' parameter to"
-                          " the number of columns to use. Ignore otherwise.", UserWarning)
+            warnings.warn(
+                f"Only {self.input_dim} column(s) out of {X.shape[-1]} are"
+                " being used to compute the covariance function. If this"
+                " is not intended, increase 'input_dim' parameter to"
+                " the number of columns to use. Ignore otherwise.",
+                UserWarning,
+            )
         X = tt.as_tensor_variable(X[:, self.active_dims])
         if Xs is not None:
             Xs = tt.as_tensor_variable(Xs[:, self.active_dims])
@@ -109,9 +112,9 @@ def __rmul__(self, other):
         return self.__mul__(other)
 
     def __pow__(self, other):
-        if(
-            isinstance(other, theano.compile.SharedVariable) and
-            other.get_value().squeeze().shape == ()
+        if (
+            isinstance(other, theano.compile.SharedVariable)
+            and other.get_value().squeeze().shape == ()
         ):
             other = tt.squeeze(other)
             return Exponentiated(self, other)
@@ -123,7 +126,6 @@ def __pow__(self, other):
 
         raise ValueError("A covariance function can only be exponentiated by a scalar value")
 
-
     def __array_wrap__(self, result):
         """
         Required to allow radd/rmul by numpy arrays.
@@ -132,7 +134,9 @@ def __array_wrap__(self, result):
         if len(result.shape) <= 1:
             result = result.reshape(1, 1)
         elif len(result.shape) > 2:
-            raise ValueError(f"cannot combine a covariance function with array of shape {result.shape}")
+            raise ValueError(
+                f"cannot combine a covariance function with array of shape {result.shape}"
+            )
         r, c = result.shape
         A = np.zeros((r, c))
         for i in range(r):
@@ -149,11 +153,7 @@ def __array_wrap__(self, result):
 class Combination(Covariance):
     def __init__(self, factor_list):
         input_dim = max(
-            [
-                factor.input_dim
-                for factor in factor_list
-                if isinstance(factor, Covariance)
-            ]
+            [factor.input_dim for factor in factor_list if isinstance(factor, Covariance)]
         )
         super().__init__(input_dim=input_dim)
         self.factor_list = []
@@ -205,10 +205,7 @@ class Exponentiated(Covariance):
     def __init__(self, kernel, power):
         self.kernel = kernel
         self.power = power
-        super().__init__(
-            input_dim=self.kernel.input_dim,
-            active_dims=self.kernel.active_dims
-        )
+        super().__init__(input_dim=self.kernel.input_dim, active_dims=self.kernel.active_dims)
 
     def __call__(self, X, Xs=None, diag=False):
         return self.kernel(X, Xs, diag=diag) ** self.power
@@ -247,9 +244,7 @@ def _split(self, X, Xs):
 
     def __call__(self, X, Xs=None, diag=False):
         X_split, Xs_split = self._split(X, Xs)
-        covs = [
-            cov(x, xs, diag) for cov, x, xs in zip(self.factor_list, X_split, Xs_split)
-        ]
+        covs = [cov(x, xs, diag) for cov, x, xs in zip(self.factor_list, X_split, Xs_split)]
         return reduce(mul, covs)
 
 
@@ -431,9 +426,7 @@ class Matern52(Stationary):
     def full(self, X, Xs=None):
         X, Xs = self._slice(X, Xs)
         r = self.euclidean_dist(X, Xs)
-        return (1.0 + np.sqrt(5.0) * r + 5.0 / 3.0 * tt.square(r)) * tt.exp(
-            -1.0 * np.sqrt(5.0) * r
-        )
+        return (1.0 + np.sqrt(5.0) * r + 5.0 / 3.0 * tt.square(r)) * tt.exp(-1.0 * np.sqrt(5.0) * r)
 
 
 class Matern32(Stationary):
@@ -605,14 +598,10 @@ def __init__(self, input_dim, lengthscale_func, args=None, active_dims=None):
         super().__init__(input_dim, active_dims)
         if active_dims is not None:
             if len(active_dims) > 1:
-                raise NotImplementedError(
-                    ("Higher dimensional inputs ", "are untested")
-                )
+                raise NotImplementedError(("Higher dimensional inputs ", "are untested"))
         else:
             if input_dim != 1:
-                raise NotImplementedError(
-                    ("Higher dimensional inputs ", "are untested")
-                )
+                raise NotImplementedError(("Higher dimensional inputs ", "are untested"))
         if not callable(lengthscale_func):
             raise TypeError("lengthscale_func must be callable")
         self.lfunc = handle_args(lengthscale_func, args)
@@ -642,9 +631,7 @@ def full(self, X, Xs=None):
             r2 = self.square_dist(X, Xs)
         rx2 = tt.reshape(tt.square(rx), (-1, 1))
         rz2 = tt.reshape(tt.square(rz), (1, -1))
-        return tt.sqrt((2.0 * tt.outer(rx, rz)) / (rx2 + rz2)) * tt.exp(
-            -1.0 * r2 / (rx2 + rz2)
-        )
+        return tt.sqrt((2.0 * tt.outer(rx, rz)) / (rx2 + rz2)) * tt.exp(-1.0 * r2 / (rx2 + rz2))
 
     def diag(self, X):
         return tt.alloc(1.0, X.shape[0])
@@ -734,19 +721,15 @@ def __init__(self, input_dim, W=None, kappa=None, B=None, active_dims=None):
             raise ValueError("Coregion requires exactly one dimension to be active")
         make_B = W is not None or kappa is not None
         if make_B and B is not None:
-            raise ValueError(
-                "Exactly one of (W, kappa) and B must be provided to Coregion"
-            )
+            raise ValueError("Exactly one of (W, kappa) and B must be provided to Coregion")
         if make_B:
             self.W = tt.as_tensor_variable(W)
             self.kappa = tt.as_tensor_variable(kappa)
             self.B = tt.dot(self.W, self.W.T) + tt.diag(self.kappa)
         elif B is not None:
             self.B = tt.as_tensor_variable(B)
         else:
-            raise ValueError(
-                "Exactly one of (W, kappa) and B must be provided to Coregion"
-            )
+            raise ValueError("Exactly one of (W, kappa) and B must be provided to Coregion")
 
     def full(self, X, Xs=None):
         X, Xs = self._slice(X, Xs)

pymc3/gp/mean.py

@@ -14,7 +14,7 @@
 
 import theano.tensor as tt
 
-__all__ = ['Zero', 'Constant', 'Linear']
+__all__ = ["Zero", "Constant", "Linear"]
 
 
 class Mean:
@@ -48,6 +48,7 @@ class Zero(Mean):
     def __call__(self, X):
         return tt.alloc(0.0, X.shape[0])
 
+
 class Constant(Mean):
     R"""
     Constant mean function for Gaussian process.