SavGolSmooth-elapsedtime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Plotting Savgol smoothed, first, second and extrema using the second (newest) time step iteration where \n",
    "    # tstep = StepProcessTime (or ElapsedProcessTime, depending on the tool)\n",
    "    # Currently testing 22 STI, step 6, wavelength C516\n",
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "import numpy as np\n",
    "import dask.dataframe as dd\n",
    "from datetime import datetime,timedelta\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import find_peaks,savgol_filter\n",
    "from scipy import interpolate, stats\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm                       # For progress bar, yay!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "process = '22 STI INTEGRATED DRY ETCH' #'22 STI INTEGRATED DRY ETCH' # 'GH CONTACT OXIDE DRY ETCH' # 'SLIT INTEGRATED DRY ETCH'\n",
    "if process == 'GH CONTACT OXIDE DRY ETCH':\n",
    "    metro_cols = ['waferscribe','ext_mv']\n",
    "    txt = \"metro/GHContactOx\"\n",
    "    oes_cols = oes_cols = ['WAFER_SCRIBE','ProcessElapsedTime','StepID','OESIB1','OESIB2','OESIB3','OESIB4','OESIB5','OESIB6','OESIB7','OESIB8'] # GHContact\n",
    "    csv = \"oes/3500-GH CONTACT OXIDE DRY ETCH_\"\n",
    "elif process == '22 STI INTEGRATED DRY ETCH':\n",
    "    metro_cols = ['waferscribe','ext_mv']\n",
    "    txt = \"metro/22STIINT\"\n",
    "    oes_cols = ['WAFER_SCRIBE','StepProcessTime','StepID','OES_BCl272','OES_C516','OES_CN387','OES_CO483','OES_F703','OES_H656','OES_O777','OES_OH309','OES_Si251','OES_Si288'] # 22STI\n",
    "    csv = \"oes/5030-22 STI INTEGRATED DRY ETCH_\"\n",
    "elif process == 'SLIT INTEGRATED DRY ETCH':\n",
    "    metro_cols = ['WaferScribe','DEPTH_SLIT_BOX11'] # DEPTH_SLIT_BOX11    HEIGHT_OXIDE_TOP_BOX11    OCD_SLIT_TOP_BOX11\n",
    "    txt = \"metro/46slit\"\n",
    "    oes_cols = ['WAFER_SCRIBE','ProcessElapsedTime','StepID','OESIB1','OESIB2','OESIB3','OESIB4','OESIB5','OESIB6','OESIB7','OESIB8'] # GHContact\n",
    "    csv = \"oes/46SLITintegrated\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading Metro Data\n",
      "\tmetro\\22STIINT.txt Data Finished\n"
     ]
    }
   ],
   "source": [
    "# Retrieving Metro Data from .txt files\n",
    "mfile = []\n",
    "metro = {}\n",
    "# metro_cols = ['waferscribe','ext_mv'] # ext_mv is the column containing the metro parameter values\n",
    "print('Reading Metro Data')\n",
    "for filename in glob.glob(txt+\"*\"): # Reading txt files\n",
    "        # Only reading the previously defined columns with the filename as a key\n",
    "    metro[filename[6:-4]] = pd.read_csv(filename,\n",
    "                                        usecols=metro_cols)[metro_cols] # type(metro) = dict\n",
    "        # Create index of filename strings ...Maybe I don't need this...\n",
    "    mfile.append(filename[6:-4])\n",
    "        # Turning into DataFrame to help with Memeory Errors\n",
    "    met = pd.concat(metro.values(),ignore_index=True) # type(met) = pandas.core.frame.DataFrame\n",
    "    print(f'\\t{filename} Data Finished')\n",
    "\n",
    "    # Trim down 'met' to relevant metro parameter values (This is broken down due to previous error troubleshooting, can be condensed)\n",
    "paramVal = met[metro_cols[1]]     # type(paramVal)    = pandas.core.series.Series\n",
    "paramBool = met[paramVal%1==0]    # type(paramBool)   = pandas.core.frame.DataFrame\n",
    "dropParam = paramBool.index       # type(dropParam)   = pandas.core.indexes.numeric.Int64Index\n",
    "    # Removing values with decimal values = 0\n",
    "met.drop(dropParam, inplace=True) \n",
    "MetroWafers = met[metro_cols[0]]    # type(MetroWafers) = pandas.core.series.Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading OES Data\n",
      "\toes/5030-22 STI INTEGRATED DRY ETCH_2.csv Data Finished\n"
     ]
    }
   ],
   "source": [
    "# Retrieving OES Data from the .csv files, takes MUCH longer, retrieving specfic columns helps\n",
    "ofile = []\n",
    "oes = {}\n",
    "data = {}\n",
    "    # Less 22STI columns to prevent the MemoryError from popping up\n",
    "#oes_cols = ['WAFER_SCRIBE','StepProcessTime','StepID','OES_CO483','OES_C516','OES_CN387','OES_Si251','OES_Si288'] # 22STI\n",
    "    # Full 22STI columns that will yield a MemoryError UNLESS, only a single 22STI file is being read\n",
    "#oes_cols = ['WAFER_SCRIBE','StepProcessTime','StepID','OES_BCl272','OES_C516','OES_CN387','OES_CO483','OES_F703','OES_H656','OES_O777','OES_OH309','OES_Si251','OES_Si288'] # 22STI\n",
    "    # GHContact Columns\n",
    "#oes_cols = ['WAFER_SCRIBE','ProcessElapsedTime','StepID','OESIB1','OESIB2','OESIB3','OESIB4','OESIB5','OESIB6','OESIB7','OESIB8'] # GHContact\n",
    "    # Note: OES_* corresponds to an AMAT tool(22STI), OESIB* corresponds to a LAM tool(GHContact)\n",
    "print('Reading OES Data')\n",
    "    # 5030-22 STI INTEGRATED DRY ETCH_2 \n",
    "    # 3500-GH CONTACT OXIDE DRY ETCH_1\n",
    "    # 5030-46 SLIT INTEGRATED DRY ETCH_1\n",
    "for filename in glob.glob(csv+\"*.csv\"): \n",
    "        # Reading OES data with previously defined columns, parsing dates and infering format\n",
    "    oes[filename[4:-4]] = pd.read_csv(filename,                            # type(oes) = dict\n",
    "                                      usecols=oes_cols)[oes_cols] \n",
    "        # Create index of filename strings ...Maybe I don't need this...\n",
    "    ofile.append(filename[4:-4]) \n",
    "    print(f'\\t{filename} Data Finished')\n",
    "    # Turning into DataFrame to help with Memory Errors\n",
    "data = pd.concat(oes.values(),ignore_index=True)# type(data) = pandas.core.frame.DataFrame\n",
    "\n",
    "    # Array of unique wafers within an OES data set\n",
    "UniqueWaferArray = data.WAFER_SCRIBE.unique()                                # type(UniqueWafers) = numpy.ndarray\n",
    "    # An inverted bool array of whether those unique wafers match any wafers in the metro data \n",
    "nonWafers = np.isin(UniqueWaferArray,list(MetroWafers),invert=True)          # type(nonWafers) = numpy.ndarray\n",
    "i = np.where(nonWafers)[0]                                                   # type(i) = numpy.ndarray\n",
    "    # Identify the wafers that don't have a metro parameter, used in NEXT loop\n",
    "WaferScribe = (UniqueWaferArray[i])                                          # type(WaferScribe) = numpy.ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/235 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trimming Down Data to Only Metro-Matching Wafers\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 235/235 [00:31<00:00,  7.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tDone!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print('Trimming Down Data to Only Metro-Matching Wafers') \n",
    "    # Having just the data for the wafers that have available metro parameters removes the possibility of accidentally using \n",
    "    # data that has no associated metro parameter. Including that data may skew the model building and therefore the results.\n",
    "indexNames = []\n",
    "for x in tqdm(WaferScribe):                # For a wafer in the array of wafers that don't have a metro parameter,\n",
    "    WaferScribeData = data['WAFER_SCRIBE'] # Just the OES data column containing wafers that don't have a metro parameter\n",
    "    dataindex = data[WaferScribeData == x] # Entire OES data of the wafers that don't have a metro parameter\n",
    "    indexNames = dataindex.index           # An index of the OES data that doesn't have a metro parameter\n",
    "    data.drop(indexNames , inplace=True)   # Removing that data that doesn't have a metro parameter\n",
    "print('\\tDone!')\n",
    "    # Creating NEW unique wafer array for a loop turning date/time format to seconds and the matplotlib loop\n",
    "UniqueWafers = data.WAFER_SCRIBE.unique()  # type(UniqueWafers) = numpy.ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 55%|████████████████████████████████████████████▏                                    | 12/22 [00:00<00:00, 113.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Dict of Wafers & Processing Dates into Seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100%|█████████████████████████████████████████████████████████████████████████████████| 22/22 [00:00<00:00, 112.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tDone!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print('Creating Dict of Wafers & Processing Dates into Seconds')\n",
    "    # This dict with enumerated wafers provides an easy way to reference and present a single wafer, or loop through many \n",
    "    # wafers. Creating this dict and translating the time stamps to seconds format is done in the same enumeration.\n",
    "t_sec = {}\n",
    "Wafer = {}\n",
    "    # Create a dict of empty DataFrames with keys of wafers\n",
    "DataFrameDict = {elem : pd.DataFrame for elem in UniqueWafers}\n",
    "for i,key in enumerate(tqdm(DataFrameDict.keys())):                      # For a number and waferscribe\n",
    "    Wafer[i] = pd.DataFrame.from_dict(data[:][data.WAFER_SCRIBE == key]) # Create a wafers DataFrame with numerical keys of the time, step ID and OES data\n",
    "    ProcessTime = [col for col in Wafer[i].columns if 'Process' in col]  # Identify the column name for elapsed time in a process step\n",
    "    t_sec[i] = Wafer[i][ProcessTime].dropna()                            # Remove rows containing NaN where a process is beginning/ending\n",
    "print('\\tDone!')\n",
    "OES_col = [col for col in Wafer[i].columns if 'OES' in col] # Create list of wavelengths present in wafer DataFrame\n",
    "step = 6                                                    # Change this to explore other etch steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__SGsmooth__: Filtered data of the wavelength intensities. This filtering is done to smooth the data in a way that increases the precision of the data without distorting the signal through a convolution where it fits a subset of data points, dictated by a `WinLen` of the number of coefficients, with a low degree polynomial, `POrder`. There's a cool visual on the wikipedia page. This technique was used instead of the interpolation because the the scipy.interpolation was generating plots with little to no features maintained, and when attempting to use a lower smoothing value, $s$, it'd break the function. I didn't explore that further, as the SavGol filter ended up working well, see Neale @ Work: 1/15. \n",
    "\n",
    "`array([ -2001.7248452 ,   8616.44195046,  -4515.35684091, -13334.40974041,\n",
    "        -3328.43522538,  29266.48953755,  80267.99995941, 140349.51529582,\n",
    "       191943.61345771, 232159.21793063, 254927.53841819, 259764.98462832,\n",
    "       251359.06114005, 244264.90376492, 238800.90887436, 231190.1153954 ,\n",
    "       225979.33230851, 227567.85251895, 238343.66017878, 255092.48522371,...`\n",
    "\n",
    "__first, second__: Differentiating with the Savitzky Golay filter with the same window length and polynomial order as the smoothing filter. \n",
    "\n",
    "`array([ 3.57954085e+04, -7.20324064e+03, -1.44299396e+04, -8.36153846e+02,\n",
    "        2.14287222e+04,  4.30173654e+04,  5.73845241e+04,  6.07870178e+04,\n",
    "        4.96078146e+04,  2.86599853e+04,  6.90237577e+03, -5.62715487e+03,\n",
    "       -6.11037656e+03, -4.49078188e+03, -5.15225739e+03, -5.27357549e+03,\n",
    "       -3.51985447e+03,  1.27673826e+03,  1.23016682e+04,  2.13269713e+04,...`\n",
    "       \n",
    "`array([-6.65689639e+04, -2.23872572e+04,  5.44194839e+03,  1.97207240e+04,\n",
    "        2.32511409e+04,  1.88352703e+04,  9.27518366e+03, -2.62704792e+03,\n",
    "       -1.06064409e+04, -1.52783328e+04, -1.49973296e+04, -1.03321905e+04,\n",
    "       -3.52199790e+03, -5.09671224e+02,  5.21503821e+02,  2.96995340e+03,\n",
    "        5.64924951e+03,  7.09735292e+03,  5.81520012e+03,  2.44681309e+03,...`\n",
    "        \n",
    "__peaks__: Takes in a 1D array (second derivative values), and identify local maxima (extrema of the smoothed interpolation function). Output is the indices of the maxima with respect to an $x$ (time step) array. (Another option for another day, `find_peaks_cwt`, that identifies peaks in noisy data, but maybe I won't need it)\n",
    "\n",
    "`array([ 4, 17, 27, 31, 52], dtype=int32)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib notebook\n",
    "# Temporary, for my own understanding\n",
    "peakTime = []\n",
    "waferScribe = []\n",
    "for step in range(6,13):\n",
    "    fig = plt.figure()\n",
    "    for x in range (1): #len(UniqueWafers)):                            # Loop through all wafers\n",
    "        #fig = plt.figure() # Comment out for single plot of all wafers OR don't comment for multiple plots with single wafer on it\n",
    "        Tsteps = t_sec[x].loc[Wafer[x]['StepID']==step]            # Series of time in seconds for a given step\n",
    "        x_int = np.linspace(Tsteps.iloc[0], Tsteps.iloc[-1], 100)  # Create linearly spaced array of 100 time steps\n",
    "        for y in range(len(OES_col)):                                                                       # Loop through OES wavelengths\n",
    "            plt.title(f\"Wafer {UniqueWafers[x]}, Step {step}, Wavelength {OES_col[y]}\")            # Plot Title\n",
    "            WinLen = 15  # Window length:\n",
    "            POrder = 5   # Polynomial order:\n",
    "            Intensity = Wafer[x][OES_col[y]].loc[Wafer[x]['StepID']==step]                    # Intensities for a wavelength\n",
    "            if Tsteps.values[-1] == 0.:                                                       # Check if final time step is zero \n",
    "                Tsteps.drop(Tsteps.tail(1).index,inplace=True)                                # Remove it, then\n",
    "                Intensity.drop(Intensity.tail(len(Intensity)-len(Tsteps)).index,inplace=True) # Keep same length for plotting\n",
    "            if len(Tsteps)!= len(Intensity):                                                  # For unequal arrays\n",
    "                Intensity.drop(Intensity.tail(len(Intensity)-len(Tsteps)).index,inplace=True) # Remove the ending values\n",
    "            plt.plot(Tsteps,Intensity, label=f\"{OES_col[y]}\")                                       # OG Data\n",
    "\n",
    "        plt.ylabel(\"Intensity\")\n",
    "        plt.xlabel(\"Time in seconds\")\n",
    "        #plt.legend()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "# Temporary, for my own understanding\n",
    "step=6\n",
    "peakTime = []\n",
    "waferScribe = []\n",
    "for x in range (len(UniqueWafers)):                            # Loop through all wafers\n",
    "    #fig = plt.figure() # Comment out for single plot of all wafers OR don't comment for multiple plots with single wafer on it\n",
    "    Tsteps = t_sec[x].loc[Wafer[x]['StepID']==step]            # Series of time in seconds for a given step\n",
    "    x_int = np.linspace(Tsteps.iloc[0], Tsteps.iloc[-1], 100)  # Create linearly spaced array of 100 time steps\n",
    "    for y in range(1,2):                                                                       # Loop through OES wavelengths\n",
    "        plt.title(f\"Wafer {UniqueWafers[x]}, Step {step}, Wavelength {OES_col[y]}\")            # Plot Title\n",
    "        WinLen = 15  # Window length:\n",
    "        POrder = 5   # Polynomial order:\n",
    "        Intensity = Wafer[x][OES_col[y]].loc[Wafer[x]['StepID']==step]                    # Intensities for a wavelength\n",
    "        if Tsteps.values[-1] == 0.:                                                       # Check if final time step is zero \n",
    "            Tsteps.drop(Tsteps.tail(1).index,inplace=True)                                # Remove it, then\n",
    "            Intensity.drop(Intensity.tail(len(Intensity)-len(Tsteps)).index,inplace=True) # Keep same length for plotting\n",
    "        if len(Tsteps)!= len(Intensity):                                                  # For unequal arrays\n",
    "            Intensity.drop(Intensity.tail(len(Intensity)-len(Tsteps)).index,inplace=True) # Remove the ending values\n",
    "        #plt.plot(Tsteps,Intensity, label=f\"{OES_col[y]}\")                                       # OG Data\n",
    "        SGsmooth = savgol_filter(Intensity,window_length=WinLen, polyorder = POrder, deriv = 0) # See above    \n",
    "        plt.plot(Tsteps,SGsmooth, label=f\"{UniqueWafers[x]}\")                                   # Smoothed Data\n",
    "        first = savgol_filter(Intensity,window_length=WinLen, polyorder = POrder, deriv = 1)    # See above  \n",
    "        #plt.plot(Tsteps,first, label=f\"1st {OES_col[y]}\",linestyle = '--',color=\"tab:red\")      # First Derivative Plot\n",
    "        second = savgol_filter(Intensity,window_length=WinLen, polyorder = POrder, deriv = 2)   # See above  \n",
    "        #plt.plot(Tsteps,second, label=f\"2nd {OES_col[y]}\",linestyle = ':',color=\"tab:green\")    # Second Derivative Plot\n",
    "        peaks, _ = find_peaks(Intensity) #, height=5e2)                                               # See above  \n",
    "        #peaks = peaks[np.concatenate(Tsteps.values[peaks], axis=None)>17.5]    # Remove peaks less than 4 seconds\n",
    "        #peaks = peaks[np.concatenate(Tsteps.values[peaks], axis=None)<3.5]    # Remove peaks greater than 8 seconds\n",
    "        #plt.plot(Tsteps.values[peaks], first[peaks], \"x\",color=\"tab:purple\")                    # First Derivative Peaks\n",
    "        #plt.plot(Tsteps.values[peaks], second[peaks], \"x\",color=\"tab:purple\")                   # Second Derivative Peaks\n",
    "        #plt.plot(Tsteps.values[peaks], SGsmooth[peaks], \"x\",color=\"tab:purple\")                 # Smoothed Peaks\n",
    "        \n",
    "        try:    # Here to remedy the case of an empty array\n",
    "            peakTime.append(np.concatenate(Tsteps.values[peaks[0]].astype('float'), axis=None)) # List containing etch time\n",
    "            waferScribe.append(UniqueWafers[x])                                 # List with the wafer associated with that etch time\n",
    "        except IndexError:\n",
    "            pass\n",
    "        \n",
    "    plt.ylabel(\"Intensity\")\n",
    "    plt.xlabel(\"Time in seconds\")\n",
    "    #plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of Determination: 0.012680290462052995\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "# Perform and present linear regression, matching etch time to metrology parameters for a wafer\n",
    "etch_time = []\n",
    "    # DataFrame of wafers and their associated metro parameter\n",
    "wafer_param_time = met[['waferscribe','ext_mv']] \n",
    "    # Turning the two lists of wafers and etch times to a DataFrame\n",
    "etch_time = pd.DataFrame({'waferscribe':waferScribe,'peaks':peakTime}) \n",
    "    # Joining the etch time and wafer parameter into a single DataFrame, matching up the etch and parameter to the correct wafer\n",
    "result = pd.merge(etch_time, wafer_param_time, on = ['waferscribe'])\n",
    "    # Performing a linear regression and calculating the slope, intercept, correlation coefficient, probability value, and standard error\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(x=result.peaks.astype('float'), y=result.ext_mv)\n",
    "print(f\"Coefficient of Determination: {r_value**2}\")\n",
    "\n",
    "    # Plotting Linear Regression\n",
    "fig = plt.figure()\n",
    "sns.regplot(x=result.peaks.astype('float'), y=result.ext_mv) # Includes a confidence interval display that I like\n",
    "    # But won't calculate the R-squared value\n",
    "plt.plot(result.peaks, result.peaks*slope+intercept, label = f\"R-squared: {r_value**2}\")  # Plots the linear regression again\n",
    "plt.title(\"Linear Regression Attempt\")\n",
    "plt.ylabel(\"Metrology Parameter\")\n",
    "plt.xlabel(\"Etch Time, in seconds\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3rd party imports\n",
    "import pandas as pd\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "result = pd.read_csv('result.csv')\n",
    "\n",
    "# Performing a linear regression and calculating the slope, intercept, correlation coefficient, probability value, and standard error\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here's where I'm playing around and getting familiar with correlation functions:\n",
    "__pandas.Series.corr__: Computes a correlation with *other* Series. Takes in a Series and outputs a float. A method can be called, either Pearson, Kendall, Spearman or a callable function. \n",
    " - Pearson: Within [-1:+1] measuring the linear correlation between two variables, where 0 is no correlation, -1 is a negative linear correlation +1 is a positive linear correlation. The Pearson correlation coefficient is the covariance(positive or negative depending on the tendency of the linear relationship, like a non-normalized version of the correlation coefficient) of the two variables divided by the product of their standard deviations.\n",
    "\n",
    "|           |OES_CO483 | OES_C516 | OES_CN387 | OES_Si251 | OES_Si288\n",
    "|-----------|----------|----------|-----------|-----------|----------\n",
    "|OES_CO483  | 1.000000 | 0.167694 |  0.853307 |  0.917994 |  0.813568\n",
    "|OES_C516   | 0.167694 | 1.000000 | -0.086855 | -0.138526 |  0.346425\n",
    "|OES_CN387  | 0.853307 |-0.086855 |  1.000000 |  0.945755 |  0.663284\n",
    "|OES_Si251  | 0.917994 |-0.138526 |  0.945755 |  1.000000 |  0.644521\n",
    "|OES_Si288  | 0.813568 | 0.346425 |  0.663284 |  0.644521 |  1.000000\n",
    "\n",
    "  - Kendall: Measures the ordinal association(relationship of rankings of ordered categories) between two measured quantities\n",
    "  \n",
    "|           |OES_CO483 | OES_C516 | OES_CN387 | OES_Si251 | OES_Si288\n",
    "|-----------|----------|----------|-----------|-----------|----------\n",
    "|OES_CO483  | 1.000000 |-0.129874 |  0.372038 |  0.225554 |  0.097983\n",
    "|OES_C516   |-0.129874 | 1.000000 |  0.007445 | -0.705770 |  0.364580\n",
    "|OES_CN387  | 0.372038 | 0.007445 |  1.000000 |  0.248132 |  0.130097\n",
    "|OES_Si251  | 0.225554 |-0.705770 |  0.248132 |  1.000000 | -0.240706\n",
    "|OES_Si288  | 0.097983 | 0.364580 |  0.130097 | -0.240706 |  1.000000\n",
    "\n",
    " - Spearman: Equal to the Pearson correlation between the rank values, assess monotonic relationships, less sensitive than Pearson due to the outliers being limited to its rank, rather than its value\n",
    "\n",
    "|           |OES_CO483 | OES_C516 | OES_CN387 | OES_Si251 | OES_Si288\n",
    "|-----------|----------|----------|-----------|-----------|----------\n",
    "|OES_CO483  | 1.000000 |-0.281470 |  0.413025 |  0.432723 |  0.065206\n",
    "|OES_C516   |-0.281470 | 1.000000 | -0.098767 | -0.740971 |  0.503524\n",
    "|OES_CN387  | 0.413025 |-0.098767 |  1.000000 |  0.493433 |  0.128761\n",
    "|OES_Si251  | 0.432723 |-0.740971 |  0.493433 |  1.000000 | -0.279335\n",
    "|OES_Si288  | 0.065206 | 0.503524 |  0.128761 | -0.279335 |  1.000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "wavelengths = Wafer[x][OES_col[:]].loc[Wafer[x]['StepID']==step]\n",
    "Pearson = wavelengths.corr(method = 'pearson')\n",
    "Kendall = wavelengths.corr(method = 'kendall')\n",
    "Spearman = wavelengths.corr(method = 'spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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