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-rw-r--r--Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb59
1 files changed, 56 insertions, 3 deletions
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 @@
27 "import readCSV as reader\n", 27 "import readCSV as reader\n",
28 "import ipywidgets as widgets\n", 28 "import ipywidgets as widgets\n",
29 "import matplotlib.pyplot as plt\n", 29 "import matplotlib.pyplot as plt\n",
30 "import random\n" 30 "import random\n",
31 "import numpy as np\n"
31 ] 32 ]
32 }, 33 },
33 { 34 {
@@ -87,11 +88,51 @@
87 " distance += value\n", 88 " distance += value\n",
88 " \n", 89 " \n",
89 " distance = distance / len(targets)\n", 90 " distance = distance / len(targets)\n",
90 " return distance" 91 " return distance\n"
91 ] 92 ]
92 }, 93 },
93 { 94 {
94 "cell_type": "markdown", 95 "cell_type": "markdown",
96 "source": [
97 "* Find the median ks distance of the same number of nodes"
98 ],
99 "metadata": {
100 "collapsed": false,
101 "pycharm": {
102 "name": "#%% md\n"
103 }
104 }
105 },
106 {
107 "cell_type": "code",
108 "execution_count": null,
109 "outputs": [],
110 "source": [
111 "def find_median(x, metric_distances):\n",
112 " distance_dic = {}\n",
113 " for index, num_of_nodes in enumerate(x):\n",
114 " if num_of_nodes[0] not in distance_dic:\n",
115 " distance_dic[num_of_nodes[0]] = []\n",
116 " distance_dic[num_of_nodes[0]].append(metric_distances[index])\n",
117 " median_x = []\n",
118 " y = []\n",
119 " for num_of_nodes, distances in distance_dic.items():\n",
120 " median_x.append(num_of_nodes)\n",
121 " y.append(np.median(distances))\n",
122 " order = np.argsort(median_x)\n",
123 " median_x = np.array(median_x)[order]\n",
124 " median_y = np.array(y)[order]\n",
125 " return median_x, median_y\n"
126 ],
127 "metadata": {
128 "collapsed": false,
129 "pycharm": {
130 "name": "#%%\n"
131 }
132 }
133 },
134 {
135 "cell_type": "markdown",
95 "metadata": {}, 136 "metadata": {},
96 "source": [ 137 "source": [
97 "* Plot Diagram" 138 "* Plot Diagram"
@@ -110,6 +151,9 @@
110 " graph = plt.figure(id,figsize=(18, 10))\n", 151 " graph = plt.figure(id,figsize=(18, 10))\n",
111 " plt.title(title)\n", 152 " plt.title(title)\n",
112 " plt.plot(x, metric_distances, color='red', linestyle='', marker='o',alpha=0.7)\n", 153 " plt.plot(x, metric_distances, color='red', linestyle='', marker='o',alpha=0.7)\n",
154 " #plot ks distance median\n",
155 " median_x, median_y = find_median(x, metric_distances)\n",
156 " plt.plot(median_x, median_y, color='black',marker='o')\n",
113 " for i in range(0, len(lines)):\n", 157 " for i in range(0, len(lines)):\n",
114 " line_infos = retrive_info_from_list(lambda a: infos[a], lines[i])\n", 158 " line_infos = retrive_info_from_list(lambda a: infos[a], lines[i])\n",
115 " line_y = retrive_info_from_list(metric_selector, line_infos)\n", 159 " line_y = retrive_info_from_list(metric_selector, line_infos)\n",
@@ -434,8 +478,17 @@
434 "nbconvert_exporter": "python", 478 "nbconvert_exporter": "python",
435 "pygments_lexer": "ipython3", 479 "pygments_lexer": "ipython3",
436 "version": "3.7.3" 480 "version": "3.7.3"
481 },
482 "pycharm": {
483 "stem_cell": {
484 "cell_type": "raw",
485 "source": [],
486 "metadata": {
487 "collapsed": false
488 }
489 }
437 } 490 }
438 }, 491 },
439 "nbformat": 4, 492 "nbformat": 4,
440 "nbformat_minor": 2 493 "nbformat_minor": 2
441} 494} \ No newline at end of file