Restructured installation

pyproject.toml

@@ -0,0 +1,6 @@
+[build-system]
+requires = [
+    "setuptools>=42",
+    "wheel"
+]
+build-backend = "setuptools.build_meta"

setup.py

@@ -13,8 +13,10 @@
     long_description_content_type="text/markdown",
     url="https://github.com/larsgeb/simpleSVGD",
     project_urls={
-        "Bug Tracker": "https://github.com/larsgeb/simpleSVGD/issues", },
-    packages=setuptools.find_packages(),
+        "Bug Tracker": "https://github.com/larsgeb/simpleSVGD/issues",
+    },
+    package_dir={"": "src"},
+    packages=setuptools.find_packages(where="src", exclude=["test"]),
     classifiers=[
         "Development Status :: 4 - Beta",
         "Programming Language :: Python :: 3.7",
@@ -23,5 +25,10 @@
     ],
     python_requires=">=3.7",
     install_requires=["numpy", "tqdm", "scipy", "matplotlib"],
-    extras_require={"dev": ["black", "pytest", ]},
+    extras_require={
+        "dev": [
+            "black",
+            "pytest",
+        ]
+    },
 )

simpleSVGD/__init__.py → src/simpleSVGD/__init__.py

@@ -23,9 +23,7 @@ def _rbf_kernel(theta, h=-1):
     return (Kxy, dxkxy)
 
 
-def update(
-    x0, gradient_fn, n_iter=1000, stepsize=1e-3, bandwidth=-1, alpha=0.9, h=-1
-):
+def update(x0, gradient_fn, n_iter=1000, stepsize=1e-3, bandwidth=-1, alpha=0.9, h=-1):
     # Check input
     if x0 is None or gradient_fn is None:
         raise ValueError("x0 or gradient_fn cannot be None!")
@@ -47,10 +45,10 @@ def update(
             if iter == 0:
                 historical_grad = historical_grad + grad_theta ** 2
             else:
-                historical_grad = alpha * historical_grad + \
-                    (1 - alpha) * (grad_theta ** 2)
-            adj_grad = _np.divide(grad_theta, fudge_factor +
-                                  _np.sqrt(historical_grad))
+                historical_grad = alpha * historical_grad + (1 - alpha) * (
+                    grad_theta ** 2
+                )
+            adj_grad = _np.divide(grad_theta, fudge_factor + _np.sqrt(historical_grad))
             theta = theta + stepsize * adj_grad
     except KeyboardInterrupt:
         pass
@@ -59,11 +57,19 @@ def update(
 
 
 def update_visual(
-    x0, lnprob, n_iter=1000, stepsize=1e-3, bandwidth=-1, alpha=0.9, figure=None, dimensions_to_plot=[0, 1]
+    x0,
+    lnprob,
+    n_iter=1000,
+    stepsize=1e-3,
+    bandwidth=-1,
+    alpha=0.9,
+    figure=None,
+    dimensions_to_plot=[0, 1],
+    background=None,
 ):
 
     if figure is None:
-        figure = _plt.figure(figsize=(4, 4))
+        figure = _plt.figure(figsize=(8, 8))
 
     axis = _plt.gca()
 
@@ -77,9 +83,26 @@ def update_visual(
     fudge_factor = 1e-6
     historical_grad = 0
 
+    if background is not None:
+        x1s, x2s, background_image = background
+
+        axis.contour(
+            x1s,
+            x2s,
+            _np.exp(-background_image),
+            levels=20,
+            alpha=0.5,
+            zorder=0,
+        )
+
     scatter = axis.scatter(
         theta[:, dimensions_to_plot[0]], theta[:, dimensions_to_plot[1]]
     )
+
+    if background is not None:
+        _plt.xlim([x1s.min(), x1s.max()])
+        _plt.ylim([x2s.min(), x2s.max()])
+
     axis.set_aspect(1)
 
     figure.canvas.draw()
@@ -97,15 +120,19 @@ def update_visual(
             if iter == 0:
                 historical_grad = historical_grad + grad_theta ** 2
             else:
-                historical_grad = alpha * historical_grad + \
-                    (1 - alpha) * (grad_theta ** 2)
-            adj_grad = _np.divide(grad_theta, fudge_factor +
-                                  _np.sqrt(historical_grad))
+                historical_grad = alpha * historical_grad + (1 - alpha) * (
+                    grad_theta ** 2
+                )
+            adj_grad = _np.divide(grad_theta, fudge_factor + _np.sqrt(historical_grad))
             theta = theta + stepsize * adj_grad
 
             scatter.set_offsets(
-                _np.hstack((theta[:, dimensions_to_plot[0], None],
-                           theta[:, dimensions_to_plot[1], None]))
+                _np.hstack(
+                    (
+                        theta[:, dimensions_to_plot[0], None],
+                        theta[:, dimensions_to_plot[1], None],
+                    )
+                )
             )
             figure.canvas.draw()
             _plt.pause(0.00001)