test_for_ruptures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "from nptdms import TdmsFile\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib.mlab import PCA\n",
    "import npfeintool as npF\n",
    "from npfeintool import filterVar\n",
    "import matplotlib.pyplot as plt\n",
    "import ruptures as rpt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/nptdms/tdms.py:772: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n",
      "  for ch in self.tdms_file.group_channels(self.group)])\n"
     ]
    }
   ],
   "source": [
    "# load in data, take the TDMS data type as example\n",
    "tdms_file = TdmsFile(\"AKF-FW1-1-H1021-1500.tdms\")\n",
    "#preprocessing the data type 'DINT' \n",
    "df = tdms_file.object('DINT').as_dataframe()\n",
    "df_power = df['SupplyUnit 50U4 (Unidrive M701 Regen).Transmit PDO Mapping 8.Leistung']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change point detection-----model l1\n",
    "n_bkps=5  # the number of checkpoint\n",
    "model = \"l1\"  # \"l2\", \"rbf\"\n",
    "a = 3500   # get one unit to do the segmentation\n",
    "b = 5000\n",
    "data = df_power[a:b].values\n",
    "algo = rpt.Dynp(model=model, min_size=15, jump=15).fit(data)\n",
    "my_bkps = algo.predict(n_bkps)\n",
    "\n",
    "# show results\n",
    "#rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))\n",
    "#plt.show()\n",
    "i=0\n",
    "st=a\n",
    "while i<= n_bkps:\n",
    "    df_power[st:my_bkps[i]+a].plot()\n",
    "    st = my_bkps[i]+a\n",
    "    i = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change point detection-----model l2\n",
    "n_bkps=5  # the number of checkpoint\n",
    "model = \"l2\"  # \"l2\", \"rbf\"\n",
    "a = 3500   # get one unit to do the segmentation\n",
    "b = 5000\n",
    "data = df_power[a:b].values\n",
    "algo = rpt.Dynp(model=model, min_size=15, jump=15).fit(data)\n",
    "my_bkps = algo.predict(n_bkps)\n",
    "\n",
    "# show results\n",
    "#rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))\n",
    "#plt.show()\n",
    "i=0\n",
    "st=a\n",
    "while i<= n_bkps:\n",
    "    df_power[st:my_bkps[i]+a].plot()\n",
    "    st = my_bkps[i]+a\n",
    "    i = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change point detection-----model rbf\n",
    "n_bkps=5  # the number of checkpoint\n",
    "model = \"rbf\"  # \"l2\", \"rbf\"\n",
    "a = 3500   # get one unit to do the segmentation\n",
    "b = 5000\n",
    "data = df_power[a:b].values\n",
    "algo = rpt.Dynp(model=model, min_size=15, jump=15).fit(data)\n",
    "my_bkps = algo.predict(n_bkps)\n",
    "\n",
    "# show results\n",
    "#rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))\n",
    "#plt.show()\n",
    "i=0\n",
    "st=a\n",
    "while i<= n_bkps:\n",
    "    df_power[st:my_bkps[i]+a].plot()\n",
    "    st = my_bkps[i]+a\n",
    "    i = i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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