CP4.2.ipynb

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    "colab": {
      "name": "CP.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mswastik/optimization/blob/master/CP4.2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcrQOnfJgYxd"
      },
      "source": [
        "**Indices**  \n",
        "j - job  \n",
        "m - machine \n",
        "\n",
        "**Decision Variable**  \n",
        "$s_{j,m}$ - start time of job j at machine m  \n",
        "$e_{j,m}$ - end time of job j at machine m  \n",
        "$d_{j,m}$ - duration of job j at machine m  \n",
        "$b_{j,m}$ - machine m is selected for job j     \n",
        "$a_{j,i,m}$ - job i precedes job j at machine m \n",
        "\n",
        "**Parameters**  \n",
        "H - Horizon  \n",
        "$d_j$ - delivery date of job j  \n",
        "$q_j$ - order qty of job j  \n",
        "$o_{j,m}$ - output per hour of job j at machine m  \n",
        "$set_{i,j}$ - setup time between job i & j   \n",
        "$C_d $ - Order delay cost    \n",
        "$C_i$ - Inventory holding cost    \n",
        "$C_o$ - Changeover cost    \n",
        "\n",
        "\n",
        "\n",
        "**Objective** (Not implemented right now)  \n",
        "$ \\sum_{j} d_j - max(e_{j,m})  \\qquad \\forall m \\in mc$    \n",
        "\n",
        "\n",
        "**Constraints**  \n",
        "$ \\sum_m e_{j,m} < s_{i,m} or s_{j,m} > e_{i,m} \\qquad \\forall j,i \\in jobs$  \n",
        "$ \\sum_j o_{j,m} \\times d_{j,m} >= q_j \\qquad \\forall m \\in ma $    \n",
        "$ \\sum_m s_{i,m} >= e_{j,m} + set_{i,j} \\qquad if \\quad b_{j,m}=1, b_{i,m}=1, a_{j,i,m}=1 $  "
      ]
    },
    {
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          "height": 467
        },
        "outputId": "7849ca24-d9c4-451f-ce46-f2ba09a2a02e"
      },
      "source": [
        "!pip install ortools\n",
        "#!pip install dtale\n",
        "#!pip install -U plotly"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ortools\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/63/94/2832edee6f4fb4e77e8585b6034f9506be24361fe6ead4e76de38ab0a666/ortools-8.1.8487-cp36-cp36m-manylinux1_x86_64.whl (14.0MB)\n",
            "\u001b[K     |████████████████████████████████| 14.0MB 326kB/s \n",
            "\u001b[?25hCollecting protobuf>=3.14.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fe/fd/247ef25f5ec5f9acecfbc98ca3c6aaf66716cf52509aca9a93583d410493/protobuf-3.14.0-cp36-cp36m-manylinux1_x86_64.whl (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 19.7MB/s \n",
            "\u001b[?25hCollecting absl-py>=0.11\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bc/58/0aa6fb779dc69cfc811df3398fcbeaeefbf18561b6e36b185df0782781cc/absl_py-0.11.0-py3-none-any.whl (127kB)\n",
            "\u001b[K     |████████████████████████████████| 133kB 51.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.9 in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.14.0->ortools) (1.15.0)\n",
            "\u001b[31mERROR: tensorflow-metadata 0.26.0 has requirement absl-py<0.11,>=0.9, but you'll have absl-py 0.11.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: protobuf, absl-py, ortools\n",
            "  Found existing installation: protobuf 3.12.4\n",
            "    Uninstalling protobuf-3.12.4:\n",
            "      Successfully uninstalled protobuf-3.12.4\n",
            "  Found existing installation: absl-py 0.10.0\n",
            "    Uninstalling absl-py-0.10.0:\n",
            "      Successfully uninstalled absl-py-0.10.0\n",
            "Successfully installed absl-py-0.11.0 ortools-8.1.8487 protobuf-3.14.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "pip_warning": {
                "packages": [
                  "google"
                ]
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sG_LW15bCHKH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ad47178-b7a6-430f-f80d-8baebe307a79"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import collections\n",
        "from ortools.sat.python import cp_model\n",
        "# Clone the entire repo.\n",
        "!git clone -l -s https://github.com/mswastik/optimization.git cloned-repo\n",
        "%cd cloned-repo"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'cloned-repo'...\n",
            "warning: --local is ignored\n",
            "remote: Enumerating objects: 84, done.\u001b[K\n",
            "remote: Counting objects: 100% (84/84), done.\u001b[K\n",
            "remote: Compressing objects: 100% (84/84), done.\u001b[K\n",
            "remote: Total 454 (delta 54), reused 0 (delta 0), pack-reused 370\u001b[K\n",
            "Receiving objects: 100% (454/454), 2.59 MiB | 7.82 MiB/s, done.\n",
            "Resolving deltas: 100% (260/260), done.\n",
            "/content/cloned-repo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUhA6-OCCHKV"
      },
      "source": [
        "#Importing Data\n",
        "df=pd.read_excel('orders1.xlsx')\n",
        "ma=pd.read_excel('item master.xlsx')\n",
        "bu=pd.read_excel('item master.xlsx',sheet_name='Sheet2')\n",
        "pa=pd.read_excel('item master.xlsx',sheet_name='packing')\n",
        " \n",
        "# Formating Data\n",
        "df = df[df['Line']=='Tmmthpkhti Limi']\n",
        "df = df[df['Plant']==1024]\n",
        "del df['Line']\n",
        "df['due'] = df['Dispatch Date'] - df['SO Date'].min()\n",
        "df['due'] = df['due'].dt.days\n",
        "df['sodate'] = df['SO Date'] - df['SO Date'].min()\n",
        "df['sodate'] = df['sodate'].dt.days\n",
        "df.reset_index(inplace=True,drop=True)\n",
        "df['Key']=df['SO'].astype('str')+df['FG Code']\n",
        "df.drop(['Dispatch Date','SO Date','Description','Customer'],axis=1,inplace=True)\n",
        "df['Order Qty'] = df[df['Order Qty']!=0]['Order Qty']\n",
        "df['Order Qty'] = df['Order Qty']*1000\n",
        "df=df[df['Key']!='17023287gd39000800iX']\n",
        " \n",
        " \n",
        "# Creating Parameter Data\n",
        "jobs =df['Key'].copy()\n",
        "op =[1,2]  # Ignore as of now\n",
        "mc = [1,2,3,4,5,6]\n",
        "#due = df.set_index('Key')\n",
        "df1=df.copy()\n",
        "#df1 = df1.set_index('Key')\n",
        "#dur=df[['Key','FG Code','Order Qty']]\n",
        "df1=df1.merge(pa[[\"FG Code\",\"Line\",\"Output\"]], on=\"FG Code\")\n",
        "df1['Output']=df1['Output']*100\n",
        "df1['duration']=df1['Order Qty']/df1['Output']\n",
        "df1['duration'] = df1['duration'].astype('float64').round(decimals = 2)\n",
        "df1['operation'] = 2\n",
        "budf=df.merge(ma[[\"FG Code\",\"Bulk Code\",\"Case Size\",\"Pack Wt(g)\"]],on='FG Code',how='left')\n",
        "budf['Qty']=budf['Order Qty']*budf['Case Size']*budf['Pack Wt(g)']/1000\n",
        " \n",
        "pasp= pa.groupby('FG Code').mean()\n",
        "budf=budf.merge(pasp,on='FG Code')\n",
        "budf['cons'] = budf['Speed']*budf['Pack Wt(g)']*60/1000\n",
        "budf.drop(['Speed','Line'],axis=1,inplace=True)\n",
        "budf=budf.merge(bu[[\"Bulk Code\", \"Line\",\"Output\"]],on=\"Bulk Code\",how=\"left\")\n",
        " \n",
        "budf['Output'] = budf['Output']*100\n",
        " \n",
        "budf['Output'] = budf['Output'].replace(0,1)\n",
        "budf['duration'] = budf['Qty']/budf['Output']\n",
        "budf['duration'] = budf['duration'].fillna(0).astype('int')\n",
        "budf['operation'] = 1\n",
        "budf['Order Qty'] = budf['Qty']\n",
        "#budf.drop(['Plant','due','sodate','Case Size','Pack Wt(g)','SO','Qty','Bulk Code'],axis=1,inplace=True)\n",
        "budf.drop('Qty',axis=1,inplace=True)\n",
        " \n",
        "df1=df1.append(budf)\n",
        "df1.set_index(['Key','Line','operation'],inplace=True)\n",
        "#del df1['FG Code']\n",
        "df1['Output'].replace(0,1,inplace=True)\n",
        "df1['Order Qty']=df1['Order Qty'].fillna(0).astype('int')\n",
        "df1['duration']=df1['duration'].fillna(0).astype('int')\n",
        "df1['Output']=df1['Output'].fillna(0).astype('int')\n",
        "#horizon = sum(df1['duration'])\n",
        "horizon = max(df[\"due\"])+300\n",
        " \n",
        "#Setup\n",
        "setup = pd.DataFrame(columns=('Key1','Key2','setup'))\n",
        "for i in df['Key']:\n",
        "  for k in df['Key']:\n",
        "    if df[df['Key']==i][\"FG Code\"].values[0]==df[df['Key']==k][\"FG Code\"].values[0]:\n",
        "      f = {'Key1':[i],\"Key2\":[k],\"setup\":[0]}\n",
        "    else:\n",
        "      f = {'Key1':[i],\"Key2\":[k],\"setup\":[40]}\n",
        "    f = pd.DataFrame(data=f)\n",
        "    setup = pd.concat([setup,f])\n",
        "setup.set_index(['Key1','Key2'],inplace=True)\n",
        " \n",
        "Cd = 125\n",
        "Ci = 110\n",
        "Cc = 12000"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kluFz3V77yw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76b8b21d-dd39-460d-e1cc-6fc481efa62e"
      },
      "source": [
        "# Initialise model\n",
        "model = cp_model.CpModel()\n",
        " \n",
        "# Creating variables to store data\n",
        "task_type = collections.namedtuple('task_type', 'start end dur run1')\n",
        "mtj = collections.defaultdict(list)\n",
        "all_tasks = {}\n",
        "a={}\n",
        "b={}\n",
        "d={}\n",
        "for j in jobs:\n",
        "  for o in op:\n",
        "    for m in mc:\n",
        "      suffix = '_%s_%i_%i' % (j,m,o)\n",
        "      #h=df1['due'].loc[j].values[0]+190\n",
        "      start = model.NewIntVar(0, horizon, \"start\"+suffix)\n",
        "      end = model.NewIntVar(0,horizon, \"end\"+suffix)\n",
        "      run1 = model.NewIntVar(0, df1['duration'].loc[(j,m,o)].item(), \"run\"+suffix)\n",
        "      b[j,m,o] = model.NewBoolVar('selection'+suffix)\n",
        "      duration = model.NewOptionalIntervalVar(start, run1, end, b[j,m,o],\"duration\"+suffix)\n",
        "      all_tasks[j, m,o] = task_type(start=start, end=end, dur= duration, run1=run1)\n",
        "      mtj[o,m].append(duration)\n",
        "\n",
        "      # Selection variable to store whether machine is selected to process job\n",
        "      model.Add(all_tasks[j, m,o].run1>0).OnlyEnforceIf(b[j,m,o])\n",
        "      model.Add(all_tasks[j, m,o].run1==0).OnlyEnforceIf(b[j,m,o].Not())\n",
        "\n",
        "      # (New) Small run\n",
        "      if df1['duration'].loc[(j,m,o)].item()<160:\n",
        "        model.Add(all_tasks[j, m,o].run1==df1['duration'].loc[(j,m,o)].item()).OnlyEnforceIf(b[j,m,o])\n",
        "        model.Add(all_tasks[j, m,o].run1==0).OnlyEnforceIf(b[j,m,o].Not())\n",
        "\n",
        "      # Variable to store whether order is delayed or not\n",
        "      d[j,m,o] = model.NewBoolVar('delay')\n",
        "      model.Add(all_tasks[j,m,o].end <= df1['due'].loc[j].values[0]).OnlyEnforceIf(d[j,m,o])\n",
        "      model.Add(all_tasks[j,m,o].end > df1['due'].loc[j].values[0]).OnlyEnforceIf(d[j,m,o].Not())\n",
        "\n",
        "  # Only 4 mixers available\n",
        "  model.Add(all_tasks[j, 5,1].run1==0)\n",
        "  model.Add(all_tasks[j, 6,1].run1==0)\n",
        "  \n",
        "  # Paste Transfer Constraint\n",
        "  model.Add(all_tasks[j, 5,2].run1==0).OnlyEnforceIf(b[j,1,1])\n",
        "  model.Add(all_tasks[j, 6,2].run1==0).OnlyEnforceIf(b[j,1,1])\n",
        "  model.Add(all_tasks[j, 6,2].run1==0).OnlyEnforceIf(b[j,2,1])\n",
        "  model.Add(all_tasks[j, 5,2].run1==0).OnlyEnforceIf(b[j,2,1])\n",
        "  model.Add(all_tasks[j, 1,2].run1==0).OnlyEnforceIf(b[j,3,1])\n",
        "  model.Add(all_tasks[j, 2,2].run1==0).OnlyEnforceIf(b[j,3,1])\n",
        "  model.Add(all_tasks[j, 3,2].run1==0).OnlyEnforceIf(b[j,3,1])\n",
        "  model.Add(all_tasks[j, 1,2].run1==0).OnlyEnforceIf(b[j,4,1])\n",
        "  model.Add(all_tasks[j, 2,2].run1==0).OnlyEnforceIf(b[j,4,1])\n",
        "  model.Add(all_tasks[j, 3,2].run1==0).OnlyEnforceIf(b[j,4,1])\n",
        "  model.Add(all_tasks[j, 4,2].run1==0).OnlyEnforceIf(b[j,4,1])\n",
        "\n",
        "\n",
        "# Variable to store sequence of jobs\n",
        "a={}\n",
        "for o in op:\n",
        "  for j in jobs:\n",
        "    for m in mc:\n",
        "      for i in jobs:\n",
        "        a[j,i,m,o] = model.NewBoolVar('sequence')\n",
        "        if j==i:\n",
        "          model.Add(all_tasks[i, m,o].start==all_tasks[j, m,o].start)\n",
        "        if j != i and b[j,m,o] and b[i,m,o]:\n",
        "          model.Add(all_tasks[i, m,o].start>all_tasks[j, m,o].start).OnlyEnforceIf(a[j,i,m,o])\n",
        "          model.Add(all_tasks[i, m,o].start<all_tasks[j, m,o].start).OnlyEnforceIf(a[j,i,m,o].Not())\n",
        "        else:\n",
        "          model.Add(a[j,i,m,o]==0).OnlyEnforceIf(b[j,m,o].Not())\n",
        "          model.Add(a[j,i,m,o]==0).OnlyEnforceIf(b[i,m,o].Not())\n",
        "        \n",
        "        # Constraint of Setup Time\n",
        "        model.Add(all_tasks[i, m,o].start >= all_tasks[j, m,o].end + setup.loc[(j,i)].values[0]).OnlyEnforceIf(a[j,i,m,o]).OnlyEnforceIf(b[j,m,o]).OnlyEnforceIf(b[i,m,o])\n",
        "\n",
        "# Constraint: A machine can process only 1 job at a time \n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    model.AddNoOverlap(mtj[o,m])\n",
        " \n",
        "# Constraint: Complete production of full order quantity\n",
        "com = {}\n",
        "for o in op:\n",
        "  for j in jobs:\n",
        "    tt= 0\n",
        "    for m in mc:\n",
        "      comp = all_tasks[j,m,o].run1*df1[\"Output\"].loc[(j,m,o)]\n",
        "      tt = tt + comp\n",
        "    com[j] = tt\n",
        "    model.Add(com[j]*10 >= df1.loc[(j,slice(None),o)]['Order Qty'].values[0])\n",
        "\n",
        "'''       \n",
        "# Delay between jobs due to storage tank full\n",
        "for j in jobs:\n",
        "  for m in mc:\n",
        "    for i in jobs:\n",
        "      if df1.loc[(j,m,1)]['Order Qty']>=3000:\n",
        "        model.Add(all_tasks[i, m,1].start >= all_tasks[j, m,1].end+int((df1.loc[(j,m,1)]['Order Qty']-3000)/df1.loc[(j,m,1)]['cons'])).OnlyEnforceIf(a[j,i,m,o])\n",
        "        #model.AddReservoirConstraint()\n",
        "'''\n",
        "mixst={}\n",
        "minstart={}\n",
        "for j in jobs:\n",
        "  for m in mc:\n",
        "    for k in mc:\n",
        "      su2 = '_%s_%i' % (j,k)\n",
        "      mixst[j,k]=model.NewIntVar(0, horizon, \"mixst\"+su2)\n",
        "      model.Add(mixst[j,k]==all_tasks[j,k,1].start).OnlyEnforceIf(b[j,k,1])\n",
        "      model.Add(mixst[j,k]==horizon).OnlyEnforceIf(b[j,k,1].Not())\n",
        "    su3 = '_%s_%i' % (j,m)\n",
        "    minstart[j,m]=model.NewIntVar(0, horizon, \"minstart\"+su3)\n",
        "    model.AddMinEquality(minstart[j,m],[mixst[j,k] for k in mc])\n",
        "    model.Add(all_tasks[j, m,2].start >= minstart[j,m]+4).OnlyEnforceIf(b[j,m,2])\n",
        "    model.Add(all_tasks[j, m,2].start <= minstart[j,m]+24).OnlyEnforceIf(b[j,m,2])\n",
        "\n",
        "\n",
        "tc=0\n",
        "tt1={}\n",
        "s1={}\n",
        "s3={}\n",
        "s4={}\n",
        "expr={}\n",
        "for i in jobs:\n",
        "  for m in mc:\n",
        "    for o in op: \n",
        "      for j in jobs:\n",
        "        suf = '_%s_%i_%i' % (i,m,o)\n",
        "        s4[i,m,o] = model.NewIntVar(0, Cc*setup.loc[(j,i)].values[0], 's4'+suf)\n",
        "        model.Add(s4[i,m,o]==Cc*setup.loc[(j,i)].values[0]).OnlyEnforceIf(a[j,i,m,o])\n",
        "    suffix = '_%s_%i' % (i,m)\n",
        "    s1[i,m] = model.NewIntVar(-Cd*horizon, Cd*horizon, 's1'+suffix)\n",
        "    model.Add(s1[i,m] == Ci * (df1['due'].loc[i].values[0]-48 - all_tasks[i,m,2].end))\n",
        "    s3[i,m] = model.NewIntVar(-Cd*horizon, Cd*horizon, 's3'+suffix)\n",
        "    model.Add(s3[i,m] == Cd * (all_tasks[i,m,2].end - df1['due'].loc[i].values[0]-48))\n",
        "    expr[i,m] = model.NewIntVar(-Cd *horizon, Cd*horizon, 'expr'+suffix)\n",
        "    model.AddMaxEquality(expr[i,m], [s1[i,m], 0, s3[i,m]])\n",
        "'''\n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    for j in jobs:\n",
        "      #model.AddDecisionStrategy([all_tasks[j,m,o].start],cp_model.CHOOSE_FIRST,cp_model.SELECT_MIN_VALUE)\n",
        "      model.AddDecisionStrategy([all_tasks[j,m,o].run1],cp_model.CHOOSE_FIRST,cp_model.SELECT_MAX_VALUE)\n",
        "'''\n",
        "model.Minimize(sum(expr.values())+sum(s4.values()))\n",
        "solver = cp_model.CpSolver()\n",
        "#solver.parameters.search_branching = cp_model.FIXED_SEARCH\n",
        "solver.parameters.num_search_workers = 16\n",
        "solver.Solve(model)\n",
        "print(solver.StatusName(),solver.ObjectiveValue())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FEASIBLE 138175.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niO4NG7l8_7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "outputId": "804a17b7-de62-4a0f-f52a-ec69e3017c38"
      },
      "source": [
        "ff=pd.DataFrame()\n",
        "for o in op:\n",
        "  for m in mc:\n",
        "    for j in jobs:\n",
        "      if solver.Value(all_tasks[j,m,o].run1)>0:\n",
        "        kk={'Job':j,'line':m,'operation':o,'start':solver.Value(all_tasks[j,m,o].start),\n",
        "            'run':solver.Value(all_tasks[j,m,o].run1),'end':solver.Value(all_tasks[j,m,o].end),'due':df1['due'].loc[j].values[0]}\n",
        "        ff=ff.append(kk,ignore_index=True)\n",
        "\n",
        "!pip install -U plotly\n",
        "import plotly.express as px\n",
        "import datetime\n",
        "#fig=px.timeline(data_frame=ff, x_start=ff['start'].astype('datetime64[h]'),x_end=ff['end'].astype('datetime64[h]'),\n",
        "#                facet_row='line',facet_col='operation',y='Job',height=1200,width=1500,color='Job')\n",
        "fig=px.timeline(data_frame=ff, x_start=ff['start'].astype('datetime64[h]'),x_end=ff['end'].astype('datetime64[h]'),y=ff['line'].astype('str'),facet_col='operation',color=ff[\"Job\"])\n",
        "fig.update_xaxes(dtick=14400000,tickformat=\"%H-%d\")\n",
        "fig.update_yaxes(autorange=\"reversed\")\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting plotly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c9/09/315462259ab7b60a3d4b7159233ed700733c87d889755bdc00a9fb46d692/plotly-4.14.1-py2.py3-none-any.whl (13.2MB)\n",
            "\u001b[K     |████████████████████████████████| 13.2MB 303kB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from plotly) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly) (1.3.3)\n",
            "Installing collected packages: plotly\n",
            "  Found existing installation: plotly 4.4.1\n",
            "    Uninstalling plotly-4.4.1:\n",
            "      Successfully uninstalled plotly-4.4.1\n",
            "Successfully installed plotly-4.14.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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\"showarrow\": false, \"text\": \"operation=2.0\", \"x\": 0.755, \"xanchor\": \"center\", \"xref\": \"paper\", \"y\": 1.0, \"yanchor\": \"bottom\", \"yref\": \"paper\"}], \"barmode\": \"overlay\", \"legend\": {\"title\": {\"text\": \"Job\"}, \"tracegroupgap\": 0}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.49], \"dtick\": 14400000, \"tickformat\": \"%H-%d\", \"type\": \"date\"}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.51, 1.0], \"dtick\": 14400000, \"matches\": \"x\", \"tickformat\": \"%H-%d\", \"type\": \"date\"}, \"yaxis\": {\"anchor\": \"x\", \"autorange\": \"reversed\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"y\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"autorange\": \"reversed\", \"domain\": [0.0, 1.0], \"matches\": \"y\", \"showticklabels\": false}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('e0febe7a-aba3-4309-a52a-dd33c947a4c5');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "197VvxgElnE8",
        "outputId": "7c300f92-6eaf-42e9-9220-98946b312a4c"
      },
      "source": [
        "from bokeh.io import output_notebook, show\r\n",
        "import bokeh.plotting\r\n",
        "from bokeh.models import ColumnDataSource, FactorRange\r\n",
        "from bokeh.palettes import Spectral6,Category20\r\n",
        "from bokeh.plotting import figure\r\n",
        "from bokeh.layouts import gridplot\r\n",
        "from bokeh.transform import factor_cmap\r\n",
        "from bokeh.palettes import Category20 as palette\r\n",
        "\r\n",
        "output_notebook()\r\n",
        "tools = [\"ybox_select\",\"box_select\", \"hover\", \"reset\"]\r\n",
        "#x = [ (fruit, year) for fruit in fruits for year in years ]\r\n",
        "#sprint.Year = sprint.Year.astype(str)\r\n",
        "#group = ff.groupby(['line']).sum()\r\n",
        "length = len(ff)\r\n",
        "#colors = palette[length]\r\n",
        "#source = ColumnDataSource(ff)\r\n",
        "#while len(colors)>length:\r\n",
        "#    colors.pop()\r\n",
        "ff['line'] = ff['line'].astype('str')\r\n",
        "ff['operation'] = ff['operation'].astype('str')\r\n",
        "kkk=ff['line'].unique()\r\n",
        "ff1=ff.set_index(['operation','Job','line'])\r\n",
        "group = ff.groupby(by=['Job','operation','line'])\r\n",
        "source = ColumnDataSource(ff1)\r\n",
        "y_range_list = list(ff1.index)\r\n",
        "y_range_list = [x for x in y_range_list[::-1]]\r\n",
        "\r\n",
        "p = figure(y_range=FactorRange(*y_range_list),title=\"Schedule\", tools=tools,tooltips=[(\"Operation:\",\"@operation\"), (\"Start:\",\"@start\"), (\"End:\",\"@end\")])\r\n",
        "#p.hbar(y='operation_Job_line', left='start_min', right='end_min', height=0.4,source=source,color=factor_cmap('line', Spectral6, sorted(kkk)))\r\n",
        "p.hbar(y='operation_Job_line', left='start', right='end',height=.9,source=source)\r\n",
        "p.yaxis.group_label_orientation=\"horizontal\"\r\n",
        "p.yaxis.subgroup_label_orientation=\"horizontal\"\r\n",
        "#p.legend.click_policy=\"hide\"\r\n",
        "r = figure(y_range=ff['line'].unique(),title=\"Schedule1\", tools=tools,tooltips=[(\"line:\",\"@line\"), (\"Start:\",\"@start\"), (\"End:\",\"@end\")])\r\n",
        "r.hbar(y='line', left='start', right='end',height=.9,source=source,legend_field='Job',color=factor_cmap('Job', Spectral6, sorted(ff['Job'].unique())))\r\n",
        "r.legend.click_policy=\"hide\"\r\n",
        "pg = gridplot([[p,r]])\r\n",
        "show(p)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/bokeh/models/mappers.py:147: UserWarning:\n",
            "\n",
            "Palette length does not match number of factors. ['17023335gd30212200iX', '17023439gd28311300iX', '17023440gd23010000gR', '17023572gd43208500iX', '17023636gd28001701iX', '17023636gd280043C0iX', '17023637gd28001701iX', '17023637gd28002400iX', '4501763335gC25020000UC', '4501763335gd23010000Ud', '4501763335gd251100C0Ud'] will be assigned to `nan_color` gray\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
              "  }\n",
              "\n",
              "  var force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "  var JS_MIME_TYPE = 'application/javascript';\n",
              "  var HTML_MIME_TYPE = 'text/html';\n",
              "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
              "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
              "\n",
              "  /**\n",
              "   * Render data to the DOM node\n",
              "   */\n",
              "  function render(props, node) {\n",
              "    var script = document.createElement(\"script\");\n",
              "    node.appendChild(script);\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when an output is cleared or removed\n",
              "   */\n",
              "  function handleClearOutput(event, handle) {\n",
              "    var cell = handle.cell;\n",
              "\n",
              "    var id = cell.output_area._bokeh_element_id;\n",
              "    var server_id = cell.output_area._bokeh_server_id;\n",
              "    // Clean up Bokeh references\n",
              "    if (id != null && id in Bokeh.index) {\n",
              "      Bokeh.index[id].model.document.clear();\n",
              "      delete Bokeh.index[id];\n",
              "    }\n",
              "\n",
              "    if (server_id !== undefined) {\n",
              "      // Clean up Bokeh references\n",
              "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
              "      cell.notebook.kernel.execute(cmd, {\n",
              "        iopub: {\n",
              "          output: function(msg) {\n",
              "            var id = msg.content.text.trim();\n",
              "            if (id in Bokeh.index) {\n",
              "              Bokeh.index[id].model.document.clear();\n",
              "              delete Bokeh.index[id];\n",
              "            }\n",
              "          }\n",
              "        }\n",
              "      });\n",
              "      // Destroy server and session\n",
              "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
              "      cell.notebook.kernel.execute(cmd);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  /**\n",
              "   * Handle when a new output is added\n",
              "   */\n",
              "  function handleAddOutput(event, handle) {\n",
              "    var output_area = handle.output_area;\n",
              "    var output = handle.output;\n",
              "\n",
              "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
              "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
              "      return\n",
              "    }\n",
              "\n",
              "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
              "\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
              "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
              "      // store reference to embed id on output_area\n",
              "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
              "    }\n",
              "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
              "      var bk_div = document.createElement(\"div\");\n",
              "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
              "      var script_attrs = bk_div.children[0].attributes;\n",
              "      for (var i = 0; i < script_attrs.length; i++) {\n",
              "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
              "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
              "      }\n",
              "      // store reference to server id on output_area\n",
              "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
              "    }\n",
              "  }\n",
              "\n",
              "  function register_renderer(events, OutputArea) {\n",
              "\n",
              "    function append_mime(data, metadata, element) {\n",
              "      // create a DOM node to render to\n",
              "      var toinsert = this.create_output_subarea(\n",
              "        metadata,\n",
              "        CLASS_NAME,\n",
              "        EXEC_MIME_TYPE\n",
              "      );\n",
              "      this.keyboard_manager.register_events(toinsert);\n",
              "      // Render to node\n",
              "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
              "      render(props, toinsert[toinsert.length - 1]);\n",
              "      element.append(toinsert);\n",
              "      return toinsert\n",
              "    }\n",
              "\n",
              "    /* Handle when an output is cleared or removed */\n",
              "    events.on('clear_output.CodeCell', handleClearOutput);\n",
              "    events.on('delete.Cell', handleClearOutput);\n",
              "\n",
              "    /* Handle when a new output is added */\n",
              "    events.on('output_added.OutputArea', handleAddOutput);\n",
              "\n",
              "    /**\n",
              "     * Register the mime type and append_mime function with output_area\n",
              "     */\n",
              "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
              "      /* Is output safe? */\n",
              "      safe: true,\n",
              "      /* Index of renderer in `output_area.display_order` */\n",
              "      index: 0\n",
              "    });\n",
              "  }\n",
              "\n",
              "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
              "  if (root.Jupyter !== undefined) {\n",
              "    var events = require('base/js/events');\n",
              "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
              "\n",
              "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
              "      register_renderer(events, OutputArea);\n",
              "    }\n",
              "  }\n",
              "\n",
              "  \n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
              "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
              "     \"<div style='background-color: #fdd'>\\n\"+\n",
              "     \"<p>\\n\"+\n",
              "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
              "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
              "     \"</p>\\n\"+\n",
              "     \"<ul>\\n\"+\n",
              "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
              "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
              "     \"</ul>\\n\"+\n",
              "     \"<code>\\n\"+\n",
              "     \"from bokeh.resources import INLINE\\n\"+\n",
              "     \"output_notebook(resources=INLINE)\\n\"+\n",
              "     \"</code>\\n\"+\n",
              "     \"</div>\"}};\n",
              "\n",
              "  function display_loaded() {\n",
              "    var el = document.getElementById(null);\n",
              "    if (el != null) {\n",
              "      el.textContent = \"BokehJS is loading...\";\n",
              "    }\n",
              "    if (root.Bokeh !== undefined) {\n",
              "      if (el != null) {\n",
              "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
              "      }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(display_loaded, 100)\n",
              "    }\n",
              "  }\n",
              "\n",
              "\n",
              "  function run_callbacks() {\n",
              "    try {\n",
              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
              "      });\n",
              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls == null || js_urls.length === 0) {\n",
              "      run_callbacks();\n",
              "      return null;\n",
              "    }\n",
              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
              "        run_callbacks()\n",
              "      }\n",
              "    }\n",
              "\n",
              "    function on_error() {\n",
              "      console.error(\"failed to load \" + url);\n",
              "    }\n",
              "\n",
              "    for (var i = 0; i < css_urls.length; i++) {\n",
              "      var url = css_urls[i];\n",
              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n",
              "\n",
              "    for (var i = 0; i < js_urls.length; i++) {\n",
              "      var url = js_urls[i];\n",
              "      var element = document.createElement('script');\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      if (url in hashes) {\n",
              "        element.crossOrigin = \"anonymous\";\n",
              "        element.integrity = \"sha384-\" + hashes[url];\n",
              "      }\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
              "\n",
              "  \n",
              "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n",
              "  var css_urls = [];\n",
              "  \n",
              "\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "    \n",
              "    \n",
              "    }\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    \n",
              "    if (root.Bokeh !== undefined || force === true) {\n",
              "      \n",
              "    for (var i = 0; i < inline_js.length; i++) {\n",
              "      inline_js[i].call(root, root.Bokeh);\n",
              "    }\n",
              "    } else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    } else if (force !== true) {\n",
              "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
              "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
              "    }\n",
              "\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\": \"kLr4fYcqcSpbuI95brIH3vnnYCquzzSxHPU6XGQCIkQRGJwhg0StNbj1eegrHs12\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\": \"xIGPmVtaOm+z0BqfSOMn4lOR6ciex448GIKG4eE61LsAvmGj48XcMQZtKcE/UXZe\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\": \"Dc9u1wF/0zApGIWoBbH77iWEHtdmkuYWG839Uzmv8y8yBLXebjO9ZnERsde5Ln/P\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\": \"cT9JaBz7GiRXdENrJLZNSC6eMNF3nh3fa5fTF51Svp+ukxPdwcU5kGXGPBgDCa2j\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "  <div class=\"bk-root\" id=\"eaab09b7-495d-4847-bcf3-0947fabad30f\" data-root-id=\"17559\"></div>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(function(root) {\n",
              "  function embed_document(root) {\n",
              "    \n",
              "  var docs_json = {\"9890f528-729e-4947-b1c3-2bb558efec9e\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"17569\"}],\"center\":[{\"id\":\"17572\"},{\"id\":\"17575\"}],\"left\":[{\"id\":\"17573\"}],\"renderers\":[{\"id\":\"17590\"}],\"title\":{\"id\":\"17560\"},\"toolbar\":{\"id\":\"17582\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"17562\"},\"x_scale\":{\"id\":\"17565\"},\"y_range\":{\"id\":\"17558\"},\"y_scale\":{\"id\":\"17567\"}},\"id\":\"17559\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"axis\":{\"id\":\"17569\"},\"ticker\":null},\"id\":\"17572\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"17642\"},\"group_label_orientation\":\"horizontal\",\"subgroup_label_orientation\":\"horizontal\",\"ticker\":{\"id\":\"17574\"}},\"id\":\"17573\",\"type\":\"CategoricalAxis\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"height\":{\"value\":0.9},\"left\":{\"field\":\"start\"},\"line_color\":{\"value\":\"#1f77b4\"},\"right\":{\"field\":\"end\"},\"y\":{\"field\":\"operation_Job_line\"}},\"id\":\"17588\",\"type\":\"HBar\"},{\"attributes\":{},\"id\":\"17574\",\"type\":\"CategoricalTicker\"},{\"attributes\":{\"axis\":{\"id\":\"17573\"},\"dimension\":1,\"ticker\":null},\"id\":\"17575\",\"type\":\"Grid\"},{\"attributes\":{\"dimensions\":\"height\",\"overlay\":{\"id\":\"17580\"}},\"id\":\"17576\",\"type\":\"BoxSelectTool\"},{\"attributes\":{\"overlay\":{\"id\":\"17581\"}},\"id\":\"17577\",\"type\":\"BoxSelectTool\"},{\"attributes\":{\"callback\":null,\"tooltips\":[[\"Operation:\",\"@operation\"],[\"Start:\",\"@start\"],[\"End:\",\"@end\"]]},\"id\":\"17578\",\"type\":\"HoverTool\"},{\"attributes\":{},\"id\":\"17579\",\"type\":\"ResetTool\"},{\"attributes\":{\"factors\":[[\"2.0\",\"4501763335gd251100C0Ud\",\"6.0\"],[\"2.0\",\"17023636gd280043C0iX\",\"6.0\"],[\"2.0\",\"17023440gd23010000gR\",\"5.0\"],[\"2.0\",\"17023287gd55102400iX\",\"5.0\"],[\"2.0\",\"17023287gd29907800iX\",\"5.0\"],[\"2.0\",\"17023637gd28001701iX\",\"4.0\"],[\"2.0\",\"17023636gd28001701iX\",\"4.0\"],[\"2.0\",\"17023287gd29904300iX\",\"4.0\"],[\"2.0\",\"4501763335gd23010000Ud\",\"3.0\"],[\"2.0\",\"17023439gd28311300iX\",\"3.0\"],[\"2.0\",\"17023335gd28001700iX\",\"3.0\"],[\"2.0\",\"17023572gd43208500iX\",\"3.0\"],[\"2.0\",\"4501763335gC25020000UC\",\"2.0\"],[\"2.0\",\"17023335gd30212200iX\",\"2.0\"],[\"2.0\",\"17023637gd28002400iX\",\"1.0\"],[\"2.0\",\"17023335gd28018100iX\",\"1.0\"],[\"2.0\",\"17023335gd28013000iX\",\"1.0\"],[\"1.0\",\"4501763335gd251100C0Ud\",\"4.0\"],[\"1.0\",\"17023287gd55102400iX\",\"4.0\"],[\"1.0\",\"17023287gd29907800iX\",\"4.0\"],[\"1.0\",\"17023440gd23010000gR\",\"3.0\"],[\"1.0\",\"17023637gd28001701iX\",\"3.0\"],[\"1.0\",\"17023636gd280043C0iX\",\"3.0\"],[\"1.0\",\"4501763335gd23010000Ud\",\"2.0\"],[\"1.0\",\"17023335gd28001700iX\",\"2.0\"],[\"1.0\",\"17023335gd28018100iX\",\"2.0\"],[\"1.0\",\"17023636gd28001701iX\",\"2.0\"],[\"1.0\",\"17023572gd43208500iX\",\"2.0\"],[\"1.0\",\"17023287gd29904300iX\",\"2.0\"],[\"1.0\",\"4501763335gC25020000UC\",\"1.0\"],[\"1.0\",\"17023439gd28311300iX\",\"1.0\"],[\"1.0\",\"17023637gd28002400iX\",\"1.0\"],[\"1.0\",\"17023335gd30212200iX\",\"1.0\"],[\"1.0\",\"17023335gd28013000iX\",\"1.0\"]]},\"id\":\"17558\",\"type\":\"FactorRange\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"17576\"},{\"id\":\"17577\"},{\"id\":\"17578\"},{\"id\":\"17579\"}]},\"id\":\"17582\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"17565\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"17640\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"17567\",\"type\":\"CategoricalScale\"},{\"attributes\":{\"data\":{\"due\":{\"__ndarray__\":\"AAAAAADAVUAAAAAAAMBVQAAAAAAAwFVAAAAAAAAAVkAAAAAAAEBXQAAAAAAAAFRAAAAAAACAVEAAAAAAAIBUQAAAAAAAwFVAAAAAAADAVUAAAAAAAEBXQAAAAAAAgFRAAAAAAADAVUAAAAAAAABWQAAAAAAAAFRAAAAAAAAAVEAAAAAAAEBXQAAAAAAAwFVAAAAAAADAVUAAAAAAAMBVQAAAAAAAwFVAAAAAAABAV0AAAAAAAIBUQAAAAAAAwFVAAAAAAAAAVkAAAAAAAEBXQAAAAAAAAFRAAAAAAACAVEAAAAAAAMBVQAAAAAAAAFRAAAAAAAAAVEAAAAAAAABWQAAAAAAAgFRAAAAAAABAV0A=\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[34]},\"end\":{\"__ndarray__\":\"AAAAAABgYUAAAAAAAMB3QAAAAAAAgEpAAAAAAABwcUAAAAAAAEBpQAAAAAAAAFdAAAAAAADwc0AAAAAAAIBiQAAAAAAAoGlAAAAAAABgcEAAAAAAAHB3QAAAAAAAAD9AAAAAAACgdUAAAAAAAEBWQAAAAAAAkHZAAAAAAAAQcUAAAAAAAOBiQAAAAAAAoGlAAAAAAACAVkAAAAAAAABCQAAAAAAAQFhAAAAAAAAQdUAAAAAAAMBkQAAAAAAAwHJAAAAAAACASEAAAAAAAEBbQAAAAAAAADVAAAAAAAAAW0AAAAAAAABqQAAAAAAAYHBAAAAAAACAXEAAAAAAAAA9QAAAAAAAwFtAAAAAAABgZ0A=\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[34]},\"operation_Job_line\":[[\"1.0\",\"17023335gd28013000iX\",\"1.0\"],[\"1.0\",\"17023335gd30212200iX\",\"1.0\"],[\"1.0\",\"17023637gd28002400iX\",\"1.0\"],[\"1.0\",\"17023439gd28311300iX\",\"1.0\"],[\"1.0\",\"4501763335gC25020000UC\",\"1.0\"],[\"1.0\",\"17023287gd29904300iX\",\"2.0\"],[\"1.0\",\"17023572gd43208500iX\",\"2.0\"],[\"1.0\",\"1702363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Application\",\"version\":\"2.1.1\"}};\n",
              "  var render_items = [{\"docid\":\"9890f528-729e-4947-b1c3-2bb558efec9e\",\"root_ids\":[\"17559\"],\"roots\":{\"17559\":\"eaab09b7-495d-4847-bcf3-0947fabad30f\"}}];\n",
              "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "\n",
              "  }\n",
              "  if (root.Bokeh !== undefined) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    var attempts = 0;\n",
              "    var timer = setInterval(function(root) {\n",
              "      if (root.Bokeh !== undefined) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else {\n",
              "        attempts++;\n",
              "        if (attempts > 100) {\n",
              "          clearInterval(timer);\n",
              "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
              "        }\n",
              "      }\n",
              "    }, 10, root)\n",
              "  }\n",
              "})(window);"
            ],
            "application/vnd.bokehjs_exec.v0+json": ""
          },
          "metadata": {
            "tags": [],
            "application/vnd.bokehjs_exec.v0+json": {
              "id": "17559"
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZfYbn5EeTsY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02ca3203-e7d7-4ad2-d216-ba5d2714be28"
      },
      "source": [
        "source.data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Job': array(['17023335gd28013000iX', '17023335gd30212200iX',\n",
              "        '17023637gd28002400iX', '17023439gd28311300iX',\n",
              "        '4501763335gC25020000UC', '17023287gd29904300iX',\n",
              "        '17023572gd43208500iX', '17023636gd28001701iX',\n",
              "        '17023335gd28018100iX', '17023335gd28001700iX',\n",
              "        '4501763335gd23010000Ud', '17023636gd280043C0iX',\n",
              "        '17023637gd28001701iX', '17023440gd23010000gR',\n",
              "        '17023287gd29907800iX', '17023287gd55102400iX',\n",
              "        '4501763335gd251100C0Ud', '17023335gd28013000iX',\n",
              "        '17023335gd28018100iX', '17023637gd28002400iX',\n",
              "        '17023335gd30212200iX', '4501763335gC25020000UC',\n",
              "        '17023572gd43208500iX', '17023335gd28001700iX',\n",
              "        '17023439gd28311300iX', '4501763335gd23010000Ud',\n",
              "        '17023287gd29904300iX', '17023636gd28001701iX',\n",
              "        '17023637gd28001701iX', '17023287gd29907800iX',\n",
              "        '17023287gd55102400iX', '17023440gd23010000gR',\n",
              "        '17023636gd280043C0iX', '4501763335gd251100C0Ud'], dtype=object),\n",
              " 'due': array([87., 87., 87., 88., 93., 80., 82., 82., 87., 87., 93., 82., 87.,\n",
              "        88., 80., 80., 93., 87., 87., 87., 87., 93., 82., 87., 88., 93.,\n",
              "        80., 82., 87., 80., 80., 88., 82., 93.]),\n",
              " 'end': array([139., 380.,  53., 279., 202.,  92., 319., 148., 205., 262., 375.,\n",
              "         31., 346.,  89., 361., 273., 151., 205.,  90.,  36.,  97., 337.,\n",
              "        166., 300.,  49., 109.,  21., 108., 208., 262., 114.,  29., 111.,\n",
              "        187.]),\n",
              " 'index': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]),\n",
              " 'line': array(['1.0', '1.0', '1.0', '1.0', '1.0', '2.0', '2.0', '2.0', '2.0',\n",
              "        '2.0', '2.0', '3.0', '3.0', '3.0', '4.0', '4.0', '4.0', '1.0',\n",
              "        '1.0', '1.0', '2.0', '2.0', '3.0', '3.0', '3.0', '3.0', '4.0',\n",
              "        '4.0', '4.0', '5.0', '5.0', '5.0', '6.0', '6.0'], dtype=object),\n",
              " 'operation': array(['1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0',\n",
              "        '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '1.0', '2.0',\n",
              "        '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0',\n",
              "        '2.0', '2.0', '2.0', '2.0', '2.0', '2.0', '2.0'], dtype=object),\n",
              " 'run': array([ 33.,  61.,  53.,  37.,  23.,  92.,  17.,  16.,  17.,  17.,  16.,\n",
              "         31.,  34.,  17.,  48.,  82.,  24.,  75.,  14.,  32.,  93., 200.,\n",
              "         17.,  75.,  45.,  15.,  17.,  47., 100., 108.,  45.,  24., 107.,\n",
              "         36.]),\n",
              " 'start': array([106., 319.,   0., 242., 179.,   0., 302., 132., 188., 245., 359.,\n",
              "          0., 312.,  72., 313., 191., 127., 130.,  76.,   4.,   4., 137.,\n",
              "        149., 225.,   4.,  94.,   4.,  61., 108., 154.,  69.,   5.,   4.,\n",
              "        151.])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "moZgDcX1apWh"
      },
      "source": [
        "!pip install dtale\n",
        "from dtale import show\n",
        "show(df1,ignore_duplicate=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTrYTZe2u6FW"
      },
      "source": [
        "#Exploring Result (click on last link to view result)\n",
        "#!pip install dtale\n",
        "from dtale import show\n",
        "import dtale.app as dtale_app\n",
        "dtale_app.USE_COLAB = True\n",
        "#show(dur,ignore_duplicate=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOCR23A5N-l1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5adf6f06-0d03-4731-85ec-8169230026d4"
      },
      "source": [
        "show(df,ignore_duplicate=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "https://toi3k07s9do-496ff2e9c6d22116-40000-colab.googleusercontent.com/dtale/main/2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2dwvTF7GebQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "7d1bfcaf-de05-4de6-dc98-31ae3c306e7b"
      },
      "source": [
        "print(model.ModelStats())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimization model '':\n",
            "#Variables: 2040 (17 in objective)\n",
            " - 1734 in [0,1]\n",
            " - 5 in [0,15]\n",
            " - 5 in [0,36]\n",
            " - 1 in [0,66]\n",
            " - 5 in [0,75]\n",
            " - 5 in [0,93]\n",
            " - 5 in [0,108]\n",
            " - 5 in [0,133]\n",
            " - 1 in [0,162]\n",
            " - 2 in [0,168]\n",
            " - 3 in [0,189]\n",
            " - 1 in [0,213]\n",
            " - 2 in [0,216]\n",
            " - 5 in [0,233]\n",
            " - 3 in [0,243]\n",
            " - 1 in [0,300]\n",
            " - 2 in [0,320]\n",
            " - 2 in [0,400]\n",
            " - 8 in [0,450]\n",
            " - 3 in [0,451]\n",
            " - 1 in [0,462]\n",
            " - 1 in [0,497]\n",
            " - 2 in [0,693]\n",
            " - 2 in [0,746]\n",
            " - 1 in [0,940]\n",
            " - 3 in [0,978]\n",
            " - 1 in [0,995]\n",
            " - 3 in [0,1054]\n",
            " - 6 in [0,1066]\n",
            " - 2 in [0,1493]\n",
            " - 5 in [0,2000]\n",
            " - 3 in [0,2108]\n",
            " - 1 in [0,2160]\n",
            " - 1 in [0,4500]\n",
            " - 1 in [0,5600]\n",
            " - 1 in [0,8000]\n",
            " - 1 in [0,8640]\n",
            " - 1 in [0,14000]\n",
            " - 1 in [0,15120]\n",
            " - 1 in [0,36000]\n",
            " - 204 in [0,1442570]\n",
            "#kInterval: 102\n",
            "#kLinear1: 204 (#enforced: 204)\n",
            "#kLinear2: 4896 (#enforced: 4896)\n",
            "#kLinearN: 17\n",
            "#kNoOverlap: 6\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}