refactoring

src/HowBadIsMyBatch.ipynb

@@ -664,10 +664,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html\n",
+    "from SymptomsCausedByVaccines.MultiLineFitting.MultiLineFitter import MultiLineFitter\n",
     "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
-    "from sklearn import linear_model\n"
+    "from skspatial.objects import Line\n"
    ]
   },
   {
@@ -707,9 +707,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from SymptomsCausedByVaccines.MultiLineFitting.MultiLineFitter import MultiLineFitter\n",
+    "def draw(points, clusters, lines, symptomX, symptomY, minClusterSize):\n",
+    "    _, ax = plt.subplots()\n",
+    "    plt.scatter(_getXs(points), _getYs(points), color = \"blue\", marker = \".\", s = 100)\n",
+    "    for cluster, line in zip(clusters, lines):\n",
+    "        if len(cluster) >= minClusterSize:\n",
+    "            _drawLine(line, cluster, ax)\n",
+    "            plt.scatter(_getXs(cluster), _getYs(cluster), marker = \".\", s = 100)\n",
+    "    plt.xlabel(symptomX)\n",
+    "    plt.ylabel(symptomY)\n",
+    "    plt.show()\n",
     "\n",
-    "clusters, lines = MultiLineFitter.fitPointsByAscendingLines(points, consensusThreshold = 0.001)"
+    "def _drawLine(line, cluster, ax):\n",
+    "    coords = line.transform_points(cluster)\n",
+    "    magnitude = line.direction.norm()\n",
+    "    line.plot_2d(ax, t_1 = min(coords) / magnitude, t_2 = max(coords) / magnitude)\n",
+    "\n",
+    "def _getXs(xys):\n",
+    "    return [x for (x, _) in xys]\n",
+    "\n",
+    "def _getYs(xys):\n",
+    "    return [y for (_, y) in xys]"
    ]
   },
   {
@@ -718,30 +736,34 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
+    "clustersAscending, linesAscending = MultiLineFitter.fitPointsByAscendingLines(points, consensusThreshold = 0.001)"
-    "from skspatial.objects import Line\n",
+   ]
-    "\n",
+  },
-    "_, ax = plt.subplots()\n",
+  {
-    "plt.scatter(\n",
+   "cell_type": "code",
-    "    [x for (x, _) in points],\n",
+   "execution_count": null,
-    "    [y for (_, y) in points],\n",
+   "metadata": {},
-    "    color = \"blue\",\n",
+   "outputs": [],
-    "    marker = \".\",\n",
+   "source": [
-    "    s = 100,\n",
+    "draw(points, clustersAscending, linesAscending, symptomX, symptomY, minClusterSize = 3)"
-    "    label = \"Dots\")\n",
+   ]
-    "for cluster, line in zip(clusters, lines):\n",
+  },
-    "    if len(cluster) >= 3:\n",
+  {
-    "        coords = line.transform_points(cluster)\n",
+   "cell_type": "code",
-    "        magnitude = line.direction.norm()\n",
+   "execution_count": null,
-    "        line.plot_2d(ax, t_1 = min(coords) / magnitude, t_2 = max(coords) / magnitude)\n",
+   "metadata": {},
-    "        plt.scatter(\n",
+   "outputs": [],
-    "            [x for (x, _) in cluster],\n",
+   "source": [
-    "            [y for (_, y) in cluster],\n",
+    "clusters, lines = MultiLineFitter.fitPointsByLines(points, consensusThreshold = 0.001)"
-    "            marker = \".\",\n",
+   ]
-    "            s = 100)\n",
+  },
-    "plt.xlabel(symptomX)\n",
+  {
-    "plt.ylabel(symptomY)\n",
+   "cell_type": "code",
-    "plt.show()"
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "draw(points, clusters, lines, symptomX, symptomY,  minClusterSize = 3)"
    ]
   },
   {