Interactive 2D plots -> Hierarchical clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "\n",
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# needed imports# neede \n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some setting for this notebook to actually show the graphs inline\n",
    "# you probably won't need this\n",
    "%matplotlib inline\n",
    "np.set_printoptions(precision=5, suppress=True)  # suppress scientific float notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "reducedX = pd.read_csv('datalabel1.csv', index_col = [0])\n",
    "reducedX = reducedX.loc[:, (reducedX != 0).any(axis=0)]\n",
    "#reducedX = reducedX.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-91-d38e349892d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mreducedX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mreducedX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'filename'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"f\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1571\u001b[0m         raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[1;32m   1572\u001b[0m                          \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1573\u001b[0;31m                          .format(self.__class__.__name__))\n\u001b[0m\u001b[1;32m   1574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1575\u001b[0m     \u001b[0m__bool__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "mamlist = ['./20131209_P96solsRun/monopeaklists/20131209_P96solsRun_N7.csv', './20131218_PH92solRun/monopeaklists/20131218_PH92solRun_G17.csv', './20140116_PH60SOLrun/monopeaklists/20140116_PH60SOLrun_C21.csv', './20131112_P140sols/monopeaklists/20131112_P140sols_H10.csv', './20131112_P140sols/monopeaklists/20131112_P140sols_I24.csv', './20131112_P140sols/monopeaklists/20131112_P140sols_L9.csv']\n",
    "\n",
    "for i in mamlist:\n",
    "    reducedX.replace(i, 1)\n",
    "\n",
    "if reducedX['filename'] == 1:\n",
    "    print(\"f\")\n",
    "    \n",
    "print(reducedX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               filename  (699, 700)  \\\n",
      "0     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "1     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "2     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "3     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "4     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "5     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "6     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "7     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "8     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "9     ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "10    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "11    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "12    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "13    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "14    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "15    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "16    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "17    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "18    ./20131112_P136sols/monopeaklists/20131112_P13...           1   \n",
      "19    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "20    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "21    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "22    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "23    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "24    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "25    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "26    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "27    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "28    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "29    ./20131112_P136sols/monopeaklists/20131112_P13...           0   \n",
      "...                                                 ...         ...   \n",
      "5212  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5213  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           1   \n",
      "5214  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5215  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5216  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5217  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5218  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5219  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5220  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5221  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5222  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5223  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5224  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5225  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5226  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5227  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5228  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5229  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5230  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5231  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5232  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5233  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5234  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5235  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5236  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5237  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5238  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5239  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5240  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "5241  ./20140115_PH72SOLrun/monopeaklists/20140115_P...           0   \n",
      "\n",
      "      (700, 701)  (701, 702)  (702, 703)  (703, 704)  (704, 705)  (705, 706)  \\\n",
      "0              0           0           0           0           0           0   \n",
      "1              0           0           0           0           0           0   \n",
      "2              0           0           0           0           0           0   \n",
      "3              0           0           0           0           0           0   \n",
      "4              0           0           0           0           0           0   \n",
      "5              0           0           0           0           0           0   \n",
      "6              0           0           0           0           0           0   \n",
      "7              0           0           0           0           0           0   \n",
      "8              0           0           0           0           0           0   \n",
      "9              0           0           0           0           0           0   \n",
      "10             0           0           0           0           0           0   \n",
      "11             0           0           0           0           0           0   \n",
      "12             0           0           0           0           0           0   \n",
      "13             0           0           0           0           0           0   \n",
      "14             0           0           0           0           0           0   \n",
      "15             0           0           0           0           0           0   \n",
      "16             0           0           0           0           0           0   \n",
      "17             0           0           0           0           0           0   \n",
      "18             0           0           0           0           0           0   \n",
      "19             0           0           0           0           0           0   \n",
      "20             0           0           0           0           0           0   \n",
      "21             0           0           0           0           0           0   \n",
      "22             0           0           0           0           0           0   \n",
      "23             0           0           0           0           0           0   \n",
      "24             0           0           0           0           0           0   \n",
      "25             0           0           0           0           0           0   \n",
      "26             0           0           0           0           0           0   \n",
      "27             0           0           0           0           0           0   \n",
      "28             0           0           0           0           0           0   \n",
      "29             0           0           0           0           0           0   \n",
      "...          ...         ...         ...         ...         ...         ...   \n",
      "5212           0           0           0           0           0           0   \n",
      "5213           0           0           0           0           0           0   \n",
      "5214           0           0           0           0           0           0   \n",
      "5215           0           0           0           0           0           0   \n",
      "5216           0           0           0           0           0           0   \n",
      "5217           0           0           0           0           0           0   \n",
      "5218           0           0           0           0           0           0   \n",
      "5219           0           0           0           0           0           0   \n",
      "5220           0           0           0           0           0           0   \n",
      "5221           1           0           0           0           0           0   \n",
      "5222           0           0           0           0           0           0   \n",
      "5223           0           0           0           0           0           0   \n",
      "5224           0           0           0           0           0           0   \n",
      "5225           0           0           0           0           0           0   \n",
      "5226           0           0           0           0           0           0   \n",
      "5227           0           0           0           0           0           0   \n",
      "5228           0           0           0           0           0           0   \n",
      "5229           0           0           0           0           0           0   \n",
      "5230           0           0           0           0           0           0   \n",
      "5231           1           0           0           0           1           0   \n",
      "5232           0           0           0           0           0           0   \n",
      "5233           0           0           0           0           0           0   \n",
      "5234           0           0           0           0           0           0   \n",
      "5235           0           0           0           0           0           0   \n",
      "5236           0           0           1           0           0           0   \n",
      "5237           0           0           0           1           0           0   \n",
      "5238           0           0           0           0           0           0   \n",
      "5239           0           0           0           0           0           0   \n",
      "5240           0           0           0           0           0           0   \n",
      "5241           0           0           0           0           0           0   \n",
      "\n",
      "      (706, 707)  (707, 708)      ...       (3690, 3691)  (3691, 3692)  \\\n",
      "0              0           0      ...                  0             0   \n",
      "1              0           0      ...                  0             0   \n",
      "2              0           0      ...                  0             0   \n",
      "3              0           0      ...                  0             0   \n",
      "4              0           0      ...                  0             0   \n",
      "5              0           0      ...                  0             0   \n",
      "6              0           0      ...                  0             0   \n",
      "7              0           0      ...                  0             0   \n",
      "8              0           0      ...                  0             0   \n",
      "9              0           0      ...                  0             0   \n",
      "10             0           0      ...                  0             0   \n",
      "11             0           0      ...                  0             0   \n",
      "12             1           0      ...                  0             0   \n",
      "13             0           0      ...                  0             0   \n",
      "14             0           0      ...                  0             0   \n",
      "15             0           0      ...                  0             0   \n",
      "16             0           0      ...                  0             0   \n",
      "17             0           0      ...                  0             0   \n",
      "18             0           0      ...                  0             0   \n",
      "19             0           0      ...                  0             0   \n",
      "20             0           0      ...                  0             0   \n",
      "21             0           0      ...                  0             0   \n",
      "22             0           1      ...                  0             0   \n",
      "23             0           0      ...                  0             0   \n",
      "24             0           0      ...                  0             0   \n",
      "25             0           0      ...                  0             0   \n",
      "26             0           0      ...                  0             0   \n",
      "27             0           0      ...                  0             0   \n",
      "28             0           1      ...                  0             0   \n",
      "29             0           0      ...                  0             0   \n",
      "...          ...         ...      ...                ...           ...   \n",
      "5212           0           1      ...                  0             0   \n",
      "5213           0           0      ...                  0             0   \n",
      "5214           0           0      ...                  0             0   \n",
      "5215           0           0      ...                  0             0   \n",
      "5216           0           0      ...                  0             0   \n",
      "5217           0           1      ...                  0             0   \n",
      "5218           0           0      ...                  0             0   \n",
      "5219           0           0      ...                  0             0   \n",
      "5220           0           0      ...                  0             0   \n",
      "5221           0           0      ...                  0             0   \n",
      "5222           0           0      ...                  0             0   \n",
      "5223           0           0      ...                  0             0   \n",
      "5224           0           0      ...                  0             0   \n",
      "5225           0           0      ...                  0             0   \n",
      "5226           0           1      ...                  0             0   \n",
      "5227           0           0      ...                  0             0   \n",
      "5228           0           0      ...                  0             0   \n",
      "5229           0           0      ...                  0             0   \n",
      "5230           0           0      ...                  0             0   \n",
      "5231           0           0      ...                  0             1   \n",
      "5232           0           0      ...                  0             0   \n",
      "5233           0           0      ...                  0             0   \n",
      "5234           0           0      ...                  0             0   \n",
      "5235           0           0      ...                  0             0   \n",
      "5236           0           0      ...                  0             0   \n",
      "5237           0           0      ...                  0             0   \n",
      "5238           0           0      ...                  0             0   \n",
      "5239           0           0      ...                  0             0   \n",
      "5240           0           1      ...                  0             0   \n",
      "5241           0           1      ...                  0             0   \n",
      "\n",
      "      (3692, 3693)  (3693, 3694)  (3694, 3695)  (3695, 3696)  (3696, 3697)  \\\n",
      "0                0             0             0             0             0   \n",
      "1                0             0             0             0             0   \n",
      "2                0             0             0             0             0   \n",
      "3                0             0             0             0             0   \n",
      "4                1             0             0             0             0   \n",
      "5                0             0             0             0             0   \n",
      "6                0             0             0             0             0   \n",
      "7                0             0             0             0             0   \n",
      "8                0             0             1             0             0   \n",
      "9                0             0             0             0             0   \n",
      "10               0             0             0             0             0   \n",
      "11               0             0             0             0             0   \n",
      "12               0             0             0             0             0   \n",
      "13               0             0             0             0             0   \n",
      "14               0             0             0             0             0   \n",
      "15               0             0             0             0             0   \n",
      "16               0             0             0             0             0   \n",
      "17               0             0             0             0             0   \n",
      "18               0             0             0             0             0   \n",
      "19               0             0             0             0             0   \n",
      "20               0             0             0             0             0   \n",
      "21               0             0             0             0             0   \n",
      "22               0             0             0             0             0   \n",
      "23               1             0             0             0             0   \n",
      "24               0             0             0             0             0   \n",
      "25               0             0             0             0             0   \n",
      "26               0             0             0             0             0   \n",
      "27               0             0             0             0             0   \n",
      "28               0             0             0             0             1   \n",
      "29               0             0             0             0             0   \n",
      "...            ...           ...           ...           ...           ...   \n",
      "5212             0             0             0             0             0   \n",
      "5213             0             0             0             0             0   \n",
      "5214             0             0             0             0             0   \n",
      "5215             0             0             0             0             0   \n",
      "5216             0             0             0             0             0   \n",
      "5217             0             0             0             0             0   \n",
      "5218             0             0             0             0             0   \n",
      "5219             0             0             0             0             0   \n",
      "5220             0             0             0             0             0   \n",
      "5221             0             0             0             0             0   \n",
      "5222             0             0             0             0             0   \n",
      "5223             0             0             0             0             0   \n",
      "5224             0             0             0             0             0   \n",
      "5225             0             0             0             0             0   \n",
      "5226             0             0             0             0             0   \n",
      "5227             0             0             0             0             0   \n",
      "5228             0             0             0             0             0   \n",
      "5229             0             0             0             0             0   \n",
      "5230             0             0             0             0             0   \n",
      "5231             0             0             0             1             0   \n",
      "5232             0             0             0             0             0   \n",
      "5233             0             0             0             0             0   \n",
      "5234             0             0             0             0             0   \n",
      "5235             0             1             0             0             0   \n",
      "5236             0             0             0             0             0   \n",
      "5237             0             0             0             0             0   \n",
      "5238             0             0             0             0             0   \n",
      "5239             0             0             0             0             0   \n",
      "5240             0             0             0             0             0   \n",
      "5241             0             0             0             0             0   \n",
      "\n",
      "      (3697, 3698)  (3698, 3699)  (3699, 3700)  \n",
      "0                0             0             0  \n",
      "1                0             0             0  \n",
      "2                0             0             0  \n",
      "3                0             0             0  \n",
      "4                0             0             0  \n",
      "5                0             0             0  \n",
      "6                0             0             0  \n",
      "7                0             0             0  \n",
      "8                0             0             0  \n",
      "9                0             0             0  \n",
      "10               0             0             0  \n",
      "11               0             0             0  \n",
      "12               0             0             0  \n",
      "13               0             0             0  \n",
      "14               0             0             0  \n",
      "15               0             0             0  \n",
      "16               0             0             0  \n",
      "17               0             0             0  \n",
      "18               1             0             0  \n",
      "19               0             0             0  \n",
      "20               0             0             0  \n",
      "21               0             0             0  \n",
      "22               0             0             0  \n",
      "23               0             0             0  \n",
      "24               0             0             0  \n",
      "25               0             0             0  \n",
      "26               0             0             0  \n",
      "27               0             0             0  \n",
      "28               0             0             0  \n",
      "29               0             1             0  \n",
      "...            ...           ...           ...  \n",
      "5212             0             0             0  \n",
      "5213             0             0             0  \n",
      "5214             0             0             0  \n",
      "5215             0             0             0  \n",
      "5216             0             0             0  \n",
      "5217             0             0             0  \n",
      "5218             0             0             0  \n",
      "5219             0             0             0  \n",
      "5220             0             0             0  \n",
      "5221             0             1             0  \n",
      "5222             0             0             0  \n",
      "5223             0             1             0  \n",
      "5224             0             0             0  \n",
      "5225             0             0             0  \n",
      "5226             0             0             0  \n",
      "5227             0             0             0  \n",
      "5228             0             0             0  \n",
      "5229             0             0             0  \n",
      "5230             0             0             0  \n",
      "5231             0             0             0  \n",
      "5232             0             0             0  \n",
      "5233             0             0             0  \n",
      "5234             0             0             0  \n",
      "5235             0             0             0  \n",
      "5236             0             0             0  \n",
      "5237             0             0             0  \n",
      "5238             0             1             0  \n",
      "5239             0             0             0  \n",
      "5240             0             0             0  \n",
      "5241             0             0             0  \n",
      "\n",
      "[5242 rows x 3002 columns]\n"
     ]
    }
   ],
   "source": [
    "X = pd.read_csv('data.csv', index_col = [0])\n",
    "\n",
    "labels = reducedX.iloc[:,0]\n",
    "str1 = labels.tolist()\n",
    "X = X.loc[:, (X != 0).any(axis=0)]\n",
    "\n",
    "#\n",
    "X.head()\n",
    "\n",
    "reducedX.set_index('filename')\n",
    "print(reducedX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5242"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mamlist = ['./20131209_P96solsRun/monopeaklists/20131209_P96solsRun_N7.csv', './20131218_PH92solRun/monopeaklists/20131218_PH92solRun_G17.csv', './20140116_PH60SOLrun/monopeaklists/20140116_PH60SOLrun_C21.csv', './20131112_P140sols/monopeaklists/20131112_P140sols_H10.csv', './20131112_P140sols/monopeaklists/20131112_P140sols_I24.csv', './20131112_P140sols/monopeaklists/20131112_P140sols_L9.csv']\n",
    "str2 = str1\n",
    "        \n",
    "mamlist = ['./20131209_P96solsRun/monopeaklists/20131209_P96solsRun_N7.csv', './20131218_PH92solRun/monopeaklists/20131218_PH92solRun_G17.csv', './20140116_PH60SOLrun/monopeaklists/20140116_PH60SOLrun_C21.csv', './20131112_P140sols/monopeaklists/20131112_P140sols_H10.csv', './20131112_P140sols/monopeaklists/20131112_P140sols_I24.csv', './20131112_P140sols/monopeaklists/20131112_P140sols_L9.csv']\n",
    "for index, item in enumerate(str2):\n",
    "    if not (item in mamlist):\n",
    "        str2[index] = None\n",
    "\n",
    "for index, item in enumerate(str2):\n",
    "    if not (item == None):\n",
    "        str2[index] = 1\n",
    "        \n",
    "#print(str2)\n",
    "len(str2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: './20140115_PH72SOLrun/monopeaklists/20140115_PH72SOLrun_E15.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-77-2fa504142c11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# generate the linkage matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinkage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreducedX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ward'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/scipy/cluster/hierarchy.py\u001b[0m in \u001b[0;36mlinkage\u001b[0;34m(y, method, metric, optimal_ordering)\u001b[0m\n\u001b[1;32m    692\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid method: {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 694\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_to_double\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    696\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/scipy/cluster/hierarchy.py\u001b[0m in \u001b[0;36m_convert_to_double\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m   1214\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_convert_to_double\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdouble\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdouble\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1217\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: './20140115_PH72SOLrun/monopeaklists/20140115_PH72SOLrun_E15.csv'"
     ]
    }
   ],
   "source": [
    "\n",
    "# generate the linkage matrix\n",
    "Z = linkage(reducedX, 'ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25564767328839194"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import cophenet\n",
    "from scipy.spatial.distance import pdist\n",
    "\n",
    "c, coph_dists = cophenet(Z, pdist(X))\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2300.     , 2462.     ,    7.07107,    2.     ])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z[100]\n",
    "#len(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate full dendrogram\n",
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('sample index')\n",
    "plt.ylabel('distance')\n",
    "dendrogram(\n",
    "    Z,\n",
    "    leaf_rotation=90.,  # rotates the x axis labels\n",
    "    leaf_font_size=8.1, # font size for the x axis labels\n",
    "    \n",
    "    \n",
    "    \n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Hierarchical Clustering Dendrogram (truncated)')\n",
    "plt.xlabel('sample index or (cluster size)')\n",
    "plt.ylabel('distance')\n",
    "dendrogram(\n",
    "    Z,\n",
    "    truncate_mode='lastp',  # show only the last p merged clusters\n",
    "    p=12,  # show only the last p merged clusters\n",
    "    leaf_rotation=90.,\n",
    "    leaf_font_size=12.,\n",
    "    show_contracted=True,  # to get a distribution impression in truncated branches\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fancy_dendrogram(*args, **kwargs):\n",
    "    max_d = kwargs.pop('max_d', None)\n",
    "    if max_d and 'color_threshold' not in kwargs:\n",
    "        kwargs['color_threshold'] = max_d\n",
    "    annotate_above = kwargs.pop('annotate_above', 0)\n",
    "\n",
    "    ddata = dendrogram(*args, **kwargs)\n",
    "\n",
    "    if not kwargs.get('no_plot', False):\n",
    "        plt.title('Hierarchical Clustering Dendrogram (truncated)')\n",
    "        plt.xlabel('sample index or (cluster size)')\n",
    "        plt.ylabel('distance')\n",
    "        for i, d, c in zip(ddata['icoord'], ddata['dcoord'], ddata['color_list']):\n",
    "            x = 0.5 * sum(i[1:3])\n",
    "            y = d[1]\n",
    "            if y > annotate_above:\n",
    "                plt.plot(x, y, 'o', c=c)\n",
    "                plt.annotate(\"%.3g\" % y, (x, y), xytext=(0, -5),\n",
    "                             textcoords='offset points',\n",
    "                             va='top', ha='center')\n",
    "        if max_d:\n",
    "            plt.axhline(y=max_d, c='k')\n",
    "    return ddata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fancy_dendrogram(\n",
    "    Z,\n",
    "    truncate_mode='lastp',\n",
    "    p=12,\n",
    "    leaf_rotation=90.,\n",
    "    leaf_font_size=12.,\n",
    "    show_contracted=True,\n",
    "    annotate_above=10,  # useful in small plots so annotations don't overlap\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set cut-off to 50\n",
    "max_d = 139  # max_d as in max_distance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fancy_dendrogram(\n",
    "    Z,\n",
    "    truncate_mode='lastp',\n",
    "    p=22,\n",
    "    leaf_rotation=90.,\n",
    "    leaf_font_size=12.,\n",
    "    show_contracted=True,\n",
    "    annotate_above=10,\n",
    "    max_d=max_d,  # plot a horizontal cut-off line\n",
    ")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clusters: 2\n"
     ]
    }
   ],
   "source": [
    "last = Z[-10:, 2]\n",
    "last_rev = last[::-1]\n",
    "idxs = np.arange(1, len(last) + 1)\n",
    "plt.plot(idxs, last_rev)\n",
    "\n",
    "acceleration = np.diff(last, 2)  # 2nd derivative of the distances\n",
    "acceleration_rev = acceleration[::-1]\n",
    "plt.plot(idxs[:-2] + 1, acceleration_rev)\n",
    "plt.show()\n",
    "k = acceleration_rev.argmax() + 2  # if idx 0 is the max of this we want 2 clusters\n",
    "print(\"clusters:\", k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17,  5, 17, ...,  9,  5,  9], dtype=int32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "max_d = 50\n",
    "clusters = fcluster(Z, max_d, criterion='distance')\n",
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}