pep8

intsystems · Nov 26, 2024 · 7adc7c6 · 7adc7c6
1 parent f51e0b4
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Showing 36 changed files with 1,092 additions and 585 deletions.
diff --git a/basic/approx.ipynb b/basic/approx.ipynb
@@ -18,9 +18,13 @@
    "source": [
     "import torch\n",
     "import sys\n",
-    "sys.path.append('../src')\n",
+    "\n",
+    "sys.path.append(\"../src\")\n",
     "from relaxit.distributions.LogisticNormalSoftmax import LogisticNormalSoftmax\n",
-    "from relaxit.distributions.approx import lognorm_approximation_fn, dirichlet_approximation_fn\n",
+    "from relaxit.distributions.approx import (\n",
+    "    lognorm_approximation_fn,\n",
+    "    dirichlet_approximation_fn,\n",
+    ")\n",
     "from pyro.distributions import Dirichlet"
    ]
   },
@@ -44,29 +48,34 @@
     "# Compute midpoints of the triangle sides\n",
     "MIDPOINTS = [(CORNERS[(i + 1) % 3] + CORNERS[(i + 2) % 3]) / 2.0 for i in range(3)]\n",
     "\n",
-    "def xy2bc(xy, tol=1.e-3):\n",
+    "\n",
+    "def xy2bc(xy, tol=1.0e-3):\n",
     "    \"\"\"Converts 2D Cartesian coordinates to barycentric.\"\"\"\n",
     "    s = [(CORNERS[i] - MIDPOINTS[i]).dot(xy - MIDPOINTS[i]) / 0.75 for i in range(3)]\n",
     "    return np.clip(s, tol, 1.0 - tol)\n",
     "\n",
+    "\n",
     "def argmin_norm(sample, positions):\n",
     "    \"\"\"Finds the index of the closest point in positions to sample.\"\"\"\n",
     "    return np.argmin(np.sum(np.square(sample - positions), axis=1))\n",
     "\n",
+    "\n",
     "def refine_triangulation(subdiv=5):\n",
     "    \"\"\"Refines the triangulation and returns the refined mesh.\"\"\"\n",
     "    refiner = tri.UniformTriRefiner(TRIANGLE)\n",
     "    return refiner.refine_triangulation(subdiv=subdiv)\n",
     "\n",
+    "\n",
     "def plot_contours(trimesh, pvals, nlevels=200, **kwargs):\n",
     "    \"\"\"Plots the contours of the probability values on the triangulated mesh.\"\"\"\n",
     "    plt.tricontourf(trimesh, pvals, nlevels, **kwargs)\n",
-    "    plt.axis('equal')\n",
+    "    plt.axis(\"equal\")\n",
     "    plt.xlim(0, 1)\n",
     "    plt.ylim(0, 0.75**0.5)\n",
-    "    plt.axis('off')\n",
+    "    plt.axis(\"off\")\n",
     "    plt.show()\n",
     "\n",
+    "\n",
     "def sample_and_plot(distribution, nlevels=200, subdiv=5, num_samples=100000, **kwargs):\n",
     "    \"\"\"Samples from a given distribution and plots the contours.\"\"\"\n",
     "    trimesh = refine_triangulation(subdiv)\n",