From d1f199a161e2441d428f8db196de98a28ed41fa9 Mon Sep 17 00:00:00 2001
From: chuningli <lilylcn13@gmail.com>
Date: Mon, 27 May 2019 23:30:42 -0400
Subject: added plot median ks distance line

---
 .../metrics_plot/src/metrics_distance.ipynb        | 59 ++++++++++++++++++++--
 1 file changed, 56 insertions(+), 3 deletions(-)

(limited to 'Metrics')

diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb
index 9fad79d7..c7bf9817 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb
@@ -27,7 +27,8 @@
     "import readCSV as reader\n",
     "import ipywidgets as widgets\n",
     "import matplotlib.pyplot as plt\n",
-    "import random\n"
+    "import random\n",
+    "import numpy as np\n"
    ]
   },
   {
@@ -87,9 +88,49 @@
     "        distance += value\n",
     "    \n",
     "    distance = distance / len(targets)\n",
-    "    return distance"
+    "    return distance\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "* Find the median ks distance of the same number of nodes"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "def find_median(x, metric_distances):\n",
+    "    distance_dic = {}\n",
+    "    for index, num_of_nodes in enumerate(x):\n",
+    "        if num_of_nodes[0] not in distance_dic:\n",
+    "            distance_dic[num_of_nodes[0]] = []\n",
+    "        distance_dic[num_of_nodes[0]].append(metric_distances[index])\n",
+    "    median_x = []\n",
+    "    y = []\n",
+    "    for num_of_nodes, distances in distance_dic.items():\n",
+    "        median_x.append(num_of_nodes)\n",
+    "        y.append(np.median(distances))\n",
+    "    order = np.argsort(median_x)\n",
+    "    median_x = np.array(median_x)[order]\n",
+    "    median_y = np.array(y)[order]\n",
+    "    return median_x, median_y\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -110,6 +151,9 @@
     "    graph = plt.figure(id,figsize=(18, 10))\n",
     "    plt.title(title)\n",
     "    plt.plot(x, metric_distances, color='red', linestyle='', marker='o',alpha=0.7)\n",
+    "    #plot ks distance median\n",
+    "    median_x, median_y = find_median(x, metric_distances)\n",
+    "    plt.plot(median_x, median_y, color='black',marker='o')\n",
     "    for i in range(0, len(lines)):\n",
     "        line_infos = retrive_info_from_list(lambda a: infos[a], lines[i])\n",
     "        line_y = retrive_info_from_list(metric_selector, line_infos)\n",
@@ -434,8 +478,17 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.3"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
-- 
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