BlueFors Plot log.ipynb

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    "# Run this first."
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   "source": [
    "import matplotlib\n",
    "matplotlib.use(\"QtAgg\") # set matplotlib backend\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from datetime import datetime, date, timedelta\n",
    "import textwrap\n",
    "\n",
    "logFolder = r'C:\\Users\\Jaseung\\Downloads\\logfiles'\n",
    "# logFolder = r'/home/jaseung/Downloads/logfiles'\n",
    "\n",
    "def load_temperature_oneday(date:date):\n",
    "    \"\"\"\n",
    "        Read temperature log files and return two pandas dataframe of datetime and temperatures.\n",
    "    \"\"\"\n",
    "    \n",
    "    date_str = date.strftime(\"%y-%m-%d\")\n",
    "    base_path = os.path.join(logFolder, date_str)    \n",
    "    file_names = ['CH1 T ' + date_str + '.log', \n",
    "                  'CH2 T ' + date_str + '.log', \n",
    "                  'CH5 T ' + date_str + '.log', \n",
    "                  'CH6 T ' + date_str + '.log' ]\n",
    "\n",
    "    full_file_names = [ os.path.join(base_path, file_name) for file_name in file_names ]\n",
    "\n",
    "    df_datetimes, df_temperatures = pd.DataFrame(), pd.DataFrame()\n",
    "    \n",
    "    for file_name in full_file_names:\n",
    "        with open(file_name, 'r') as f: \n",
    "            df = pd.read_csv(f, names=['date', 'time', 'temperature'], header=0)\n",
    "            df_datetime  = pd.to_datetime(df['date'] + df['time'], format=' %d-%m-%y%H:%M:%S')\n",
    "            df_datetimes = pd.concat([df_datetimes, df_datetime], axis=1)             \n",
    "            df_temperatures = pd.concat([df_temperatures, df['temperature']], axis=1)\n",
    "           \n",
    "#     print(df_datetimes.shape)\n",
    "#     print(df_temperatures.shape)   \n",
    "    \n",
    "    return df_datetimes, df_temperatures\n",
    "\n",
    "def load_pressure_oneday(date:date):\n",
    "    \"\"\"\n",
    "        Read pressure log file and return two pandas dataframe of datetime and pressure.\n",
    "    \"\"\"\n",
    "    date_str = date.strftime(\"%y-%m-%d\")\n",
    "    base_path = os.path.join(logFolder, date_str)  \n",
    "    file_name = 'maxigauge ' + date_str + '.log'\n",
    "    full_file_name = os.path.join(base_path, file_name)\n",
    "\n",
    "    df_datetimes, df_pressures = pd.DataFrame(), pd.DataFrame()\n",
    "    with open(full_file_name, 'r') as f:\n",
    "        df = pd.read_csv(f, header=None)\n",
    "        df_datetimes  = pd.to_datetime(df.iloc[:,0] + df.iloc[:,1], format='%d-%m-%y%H:%M:%S')\n",
    "        df_pressures = df.iloc[:, [5,11,17,23,29,35]]\n",
    "       \n",
    "    # print(df_datetimes.shape)\n",
    "    # print(df_pressures.shape)   \n",
    "\n",
    "    return df_datetimes, df_pressures\n",
    "\n",
    "def load_flowmeter_oneday(date: date):\n",
    "    \"\"\"\n",
    "        Read flowmeter log file and return two pandas dataframe of datetime and flowrate.\n",
    "    \"\"\"\n",
    "    date_str = date.strftime(\"%y-%m-%d\")\n",
    "    base_path = os.path.join(logFolder, date_str)  \n",
    "    file_name = 'Flowmeter ' + date_str + '.log'\n",
    "    full_file_name = os.path.join(base_path, file_name)\n",
    "\n",
    "    df_datetimes, df_flowmeter = pd.DataFrame(), pd.DataFrame()\n",
    "    with open(full_file_name, 'r') as f:\n",
    "        df = pd.read_csv(f, header=None)\n",
    "        df_datetimes  = pd.to_datetime(df.iloc[:,0] + df.iloc[:,1], format=' %d-%m-%y%H:%M:%S')\n",
    "        df_flowmeter = df.iloc[:,2]\n",
    "       \n",
    "    # print(df_datetimes.shape)\n",
    "    # print(df_flowmeter.shape)   \n",
    "\n",
    "    return df_datetimes, df_flowmeter\n",
    "\n",
    "def load_status_oneday(date:date):\n",
    "    \"\"\"\n",
    "    Read status log file and return two pandas dataframe of datetime and status.\n",
    "    \"\"\"\n",
    "    date_str = date.strftime(\"%y-%m-%d\")\n",
    "    base_path = os.path.join(logFolder, date_str)  \n",
    "    file_name = 'Status_' + date_str + '.log'\n",
    "    full_file_name = os.path.join(base_path, file_name)\n",
    "\n",
    "    df_datetimes, df_status = pd.DataFrame(), pd.DataFrame()\n",
    "    with open(full_file_name, 'r') as f:\n",
    "        df = pd.read_csv(f, header=None)\n",
    "        df_datetimes  = pd.to_datetime(df.iloc[:,0] + df.iloc[:,1], format='%d-%m-%y%H:%M:%S')\n",
    "        df_status = df.iloc[:, list(np.arange(3,51,2))]\n",
    "        \n",
    "    # print(df_datetimes.shape)\n",
    "    # print(df_pressures.shape)   \n",
    "\n",
    "    return df_datetimes, df_status\n",
    "\n",
    "\n",
    "def load_temperature(start_date:date, end_date:date):\n",
    "    \"\"\"\n",
    "    Return time and temperature dataframes between start_date and end_date\n",
    "    \"\"\"\n",
    "    df_datetimes_all, df_temperatures_all = pd.DataFrame(), pd.DataFrame()\n",
    "    \n",
    "    while start_date <= end_date:\n",
    "        \n",
    "        try:\n",
    "            df_datetimes, df_temperatures = load_temperature_oneday(start_date)\n",
    "        except FileNotFoundError:\n",
    "            df_datetimes, df_temperatures = pd.DataFrame(), pd.DataFrame()\n",
    "            print(f\"FileNotFound: {start_date}, temperature\")\n",
    "\n",
    "        df_datetimes_all = pd.concat([df_datetimes_all, df_datetimes], axis=0)\n",
    "        df_temperatures_all = pd.concat([df_temperatures_all, df_temperatures], axis=0)\n",
    "        \n",
    "        start_date += timedelta(days=1)\n",
    "    \n",
    "    return df_datetimes_all, df_temperatures_all\n",
    "        \n",
    "def load_pressure(start_date:date, end_date:date):\n",
    "    \"\"\"\n",
    "    Return time and pressure dataframes between start_date and end_date\n",
    "    \"\"\"\n",
    "    df_datetimes_all, df_pressures_all = pd.DataFrame(), pd.DataFrame()\n",
    "    while start_date <= end_date:\n",
    "        try:\n",
    "            df_datetimes, df_pressures = load_pressure_oneday(start_date)\n",
    "        except FileNotFoundError:\n",
    "            df_datetimes, df_pressures = pd.DataFrame(), pd.DataFrame()\n",
    "            print(f\"FileNotFound: {start_date}, pressure\")\n",
    "\n",
    "        df_datetimes_all = pd.concat([df_datetimes_all, df_datetimes], axis=0)\n",
    "        df_pressures_all = pd.concat([df_pressures_all, df_pressures], axis=0)\n",
    "        \n",
    "        start_date += timedelta(days=1)\n",
    "    \n",
    "    return df_datetimes_all, df_pressures_all\n",
    "\n",
    "def load_flowmeter(start_date: date, end_date: date):\n",
    "    \"\"\"\n",
    "    Return time and flowmeter dataframes between start_date and end_date\n",
    "    \"\"\"\n",
    "    df_datetimes_all, df_flowmeters_all = pd.DataFrame(), pd.DataFrame()\n",
    "    while start_date <= end_date:\n",
    "        try:\n",
    "            df_datetimes, df_flowmeters = load_flowmeter_oneday(start_date)\n",
    "        except FileNotFoundError:\n",
    "            df_datetimes, df_flowmeters = pd.DataFrame(), pd.DataFrame()\n",
    "            print(f\"FileNotFound: {start_date}, flowmeter\")\n",
    "    \n",
    "        df_datetimes_all = pd.concat([df_datetimes_all, df_datetimes], axis=0)\n",
    "        df_flowmeters_all = pd.concat([df_flowmeters_all, df_flowmeters], axis=0)\n",
    "        \n",
    "        start_date += timedelta(days=1)\n",
    "    \n",
    "    return df_datetimes_all, df_flowmeters_all\n",
    "\n",
    "def load_status(start_date:date, end_date:date):\n",
    "    \"\"\"\n",
    "    Return time and status dataframes between start_date and end_date\n",
    "    \"\"\"\n",
    "    df_datetimes_all, df_status_all = pd.DataFrame(), pd.DataFrame()\n",
    "    while start_date <= end_date:\n",
    "        try:\n",
    "            df_datetimes, df_status = load_status_oneday(start_date)\n",
    "        except FileNotFoundError:\n",
    "            df_datetimes, df_status = pd.DataFrame(), pd.DataFrame()\n",
    "            print(f\"FileNotFound: {start_date}, status\")\n",
    "\n",
    "        df_datetimes_all = pd.concat([df_datetimes_all, df_datetimes], axis=0)\n",
    "        df_status_all = pd.concat([df_status_all, df_status], axis=0)\n",
    "        \n",
    "        start_date += timedelta(days=1)\n",
    "    \n",
    "    return df_datetimes_all, df_status_all\n",
    "\n",
    "\n",
    "def plot_temperature(start_date: date, end_date: date):\n",
    "    \"\"\"\n",
    "    Plot temperature.\n",
    "    \"\"\"\n",
    "    df_df_datetimes_all, df_temperatures_all = load_temperature(start_date, end_date)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    text = \"Temperature from \" + start_date.strftime(\"%y-%m-%d\") + \" to \" + end_date.strftime(\"%y-%m-%d\")\n",
    "    title = \"Click on legend line to toggle line on/off\" + \"\\n\" + \"\\n\".join(textwrap.wrap(text, 60))\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel('Datetime')\n",
    "    ax.set_ylabel('Temperature(K)')\n",
    "    ax.grid()\n",
    "    \n",
    "    line1, = ax.plot(df_df_datetimes_all.iloc[:,0], df_temperatures_all.iloc[:,0], label=\"50 K\")\n",
    "    line2, = ax.plot(df_df_datetimes_all.iloc[:,1], df_temperatures_all.iloc[:,1], label=\"4 K\")\n",
    "    line3, = ax.plot(df_df_datetimes_all.iloc[:,2], df_temperatures_all.iloc[:,2], label=\"Still\")\n",
    "    line4, = ax.plot(df_df_datetimes_all.iloc[:,3], df_temperatures_all.iloc[:,3], label=\"MCX\")\n",
    "    leg = ax.legend(fancybox=True, shadow=True)\n",
    "    \n",
    "    lines = [line1, line2, line3, line4]\n",
    "    lined = {}  # Will map legend lines to original lines.\n",
    "    for legline, origline in zip(leg.get_lines(), lines):\n",
    "        legline.set_picker(True)  # Enable picking on the legend line.\n",
    "        lined[legline] = origline\n",
    "    \n",
    "    def on_pick(event):\n",
    "        # On the pick event, find the original line corresponding to the legend\n",
    "        # proxy line, and toggle its visibility.\n",
    "        legline = event.artist\n",
    "        origline = lined[legline]\n",
    "        visible = not origline.get_visible()\n",
    "        origline.set_visible(visible)\n",
    "        # Change the alpha on the line in the legend so we can see what lines\n",
    "        # have been toggled.\n",
    "        legline.set_alpha(1.0 if visible else 0.2)\n",
    "        fig.canvas.draw()\n",
    "\n",
    "    fig.canvas.mpl_connect('pick_event', on_pick)\n",
    "    plt.show(block=False)\n",
    "\n",
    "    return fig, ax   \n",
    "\n",
    "def plot_pressure(start_date: date, end_date: date):\n",
    "    \"\"\"\n",
    "    Plot pressure\n",
    "    \"\"\"\n",
    "    df_datetimes_all, df_pressures_all = load_pressure(start_date, end_date)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    text = \"Pressures from \" + start_date.strftime(\"%y-%m-%d\") + \" to \" + end_date.strftime(\"%y-%m-%d\")\n",
    "    title = \"Click on legend line to toggle line on/off\" + \"\\n\" + \"\\n\".join(textwrap.wrap(text, 60))\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel('Datetime')\n",
    "    ax.set_ylabel('Pressure (mBar)')\n",
    "    ax.grid()\n",
    "    \n",
    "    line1, = ax.plot(df_datetimes_all, df_pressures_all.iloc[:,0], label=\"P1\")\n",
    "    line2, = ax.plot(df_datetimes_all, df_pressures_all.iloc[:,1], label=\"P2\")\n",
    "    line3, = ax.plot(df_datetimes_all, df_pressures_all.iloc[:,2], label=\"P3\")\n",
    "    line4, = ax.plot(df_datetimes_all, df_pressures_all.iloc[:,3], label=\"P4\")\n",
    "    line5, = ax.plot(df_datetimes_all, df_pressures_all.iloc[:,4], label=\"P5\")\n",
    "    line6, = ax.plot(df_datetimes_all, df_pressures_all.iloc[:,5], label=\"P6\")\n",
    "    leg = ax.legend(fancybox=True, shadow=True)\n",
    "    \n",
    "    lines = [line1, line2, line3, line4, line5, line6]\n",
    "    lined = {}  # Will map legend lines to original lines.\n",
    "    for legline, origline in zip(leg.get_lines(), lines):\n",
    "        legline.set_picker(True)  # Enable picking on the legend line.\n",
    "        lined[legline] = origline\n",
    "    \n",
    "    def on_pick(event):\n",
    "        # On the pick event, find the original line corresponding to the legend\n",
    "        # proxy line, and toggle its visibility.\n",
    "        legline = event.artist\n",
    "        origline = lined[legline]\n",
    "        visible = not origline.get_visible()\n",
    "        origline.set_visible(visible)\n",
    "        # Change the alpha on the line in the legend so we can see what lines\n",
    "        # have been toggled.\n",
    "        legline.set_alpha(1.0 if visible else 0.2)\n",
    "        fig.canvas.draw()\n",
    "\n",
    "    fig.canvas.mpl_connect('pick_event', on_pick)\n",
    "    plt.show(block=False)\n",
    "\n",
    "def plot_flowmeter(start_date: date, end_date: date):\n",
    "    \"\"\"\n",
    "    Plot flowmeter.\n",
    "    \"\"\"\n",
    "    df_datetimes_all, df_flowmeters_all = load_flowmeter(start_date, end_date)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    text = \"Flowmeter from \" + start_date.strftime(\"%y-%m-%d\") + \" to \" + end_date.strftime(\"%y-%m-%d\")\n",
    "    title = \"Click on legend line to toggle line on/off\" + \"\\n\" + \"\\n\".join(textwrap.wrap(text, 60))\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel('Datetime')\n",
    "    ax.set_ylabel('Flowrate (mmol/s)')\n",
    "    ax.grid()\n",
    "    \n",
    "    line1, = ax.plot(df_datetimes_all, df_flowmeters_all.iloc[:,0], label=\"Flowmeter\")\n",
    "    leg = ax.legend(fancybox=True, shadow=True)\n",
    "    plt.show(block=False)\n",
    "\n",
    "def plot_status(start_date: date, end_date: date):\n",
    "    \"\"\"\n",
    "    Plot status.\n",
    "    \"\"\"\n",
    "    df_datetimes_all, df_status_all = load_status(start_date, end_date)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    text = \"Status from \" + start_date.strftime(\"%y-%m-%d\") + \" to \" + end_date.strftime(\"%y-%m-%d\")\n",
    "    title = \"Click on legend line to toggle line on/off\" + \"\\n\" + \"\\n\".join(textwrap.wrap(text, 60))\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel('Datetime')\n",
    "    ax.set_ylabel('Status')\n",
    "    ax.grid()\n",
    "    \n",
    "\n",
    "    s = \"01-01-22,00:14:17,cptempwi,1.450000E+1,cptempwo,2.480000E+1,cptemph,7.050000E+1,cptempo,3.090000E+1,cpttime,2.548976E+6,cperrcode,2.800000E+1,cpavgl,8.840000E+1,cpavgh,2.748000E+2,nxdsf,3.000000E+1,nxdsct,4.100000E+1,nxdst,4.910500E+4,nxdsbs,4.050300E+4,nxdstrs,2.185700E+4,tc400remoteprio,1.000000E+0,tc400spdswptatt,1.000000E+0,tc400errorcode,0.000000E+0,tc400ovtempelec,0.000000E+0,tc400ovtemppump,0.000000E+0,tc400setspdatt,1.000000E+0,tc400pumpaccel,0.000000E+0,tc400heating,0.000000E+0,tc400standby,0.000000E+0,tc400pumpstatn,1.000000E+0,tc400commerr,0.000000E+0\"\n",
    "    legend_labels = s.split(',')[2::2]\n",
    "    \n",
    "    lines = []\n",
    "    for i in range(24):\n",
    "        line, = ax.plot(df_datetimes_all, df_status_all.iloc[:,i], label=legend_labels[i])\n",
    "        lines.append(line)\n",
    "\n",
    "    leg = ax.legend(fancybox=True, shadow=True)\n",
    "    \n",
    "    lined = {}  # Will map legend lines to original lines.\n",
    "    for legline, origline in zip(leg.get_lines(), lines):\n",
    "        legline.set_picker(True)  # Enable picking on the legend line.\n",
    "        lined[legline] = origline\n",
    "    \n",
    "    def on_pick(event):\n",
    "        # On the pick event, find the original line corresponding to the legend\n",
    "        # proxy line, and toggle its visibility.\n",
    "        legline = event.artist\n",
    "        origline = lined[legline]\n",
    "        visible = not origline.get_visible()\n",
    "        origline.set_visible(visible)\n",
    "        # Change the alpha on the line in the legend so we can see what lines\n",
    "        # have been toggled.\n",
    "        legline.set_alpha(1.0 if visible else 0.2)\n",
    "        fig.canvas.draw()\n",
    "\n",
    "    fig.canvas.mpl_connect('pick_event', on_pick)\n",
    "    plt.show(block=False)\n",
    "\n",
    "def plot_temperature_pressure_flowmeter(start_date:date, end_date:date):\n",
    "\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(3,1, sharex=False)\n",
    "    \n",
    "    # temperature\n",
    "    df_df_datetimes_all, df_temperatures_all = load_temperature(start_date, end_date)\n",
    "    \n",
    "    title = \"Click on legend line to toggle line on/off\" \n",
    "    ax1.set_title(title)\n",
    "    ax1.set_xlabel('Datetime')\n",
    "    ax1.set_ylabel('Temperature(K)')\n",
    "    ax1.grid()\n",
    "    \n",
    "    line1, = ax1.plot(df_df_datetimes_all.iloc[:,0], df_temperatures_all.iloc[:,0], label=\"50 K\")\n",
    "    line2, = ax1.plot(df_df_datetimes_all.iloc[:,1], df_temperatures_all.iloc[:,1], label=\"4 K\")\n",
    "    line3, = ax1.plot(df_df_datetimes_all.iloc[:,2], df_temperatures_all.iloc[:,2], label=\"Still\")\n",
    "    line4, = ax1.plot(df_df_datetimes_all.iloc[:,3], df_temperatures_all.iloc[:,3], label=\"MCX\")\n",
    "    leg1 = ax1.legend(fancybox=True, shadow=True)\n",
    "    \n",
    "    # pressure\n",
    "    df_datetimes_all, df_pressures_all = load_pressure(start_date, end_date)\n",
    "   \n",
    "    ax2.set_xlabel('Datetime')\n",
    "    ax2.set_ylabel('Pressure (mBar)')\n",
    "    ax2.grid()\n",
    "    \n",
    "    line11, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,0], label=\"P1\")\n",
    "    line12, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,1], label=\"P2\")\n",
    "    line13, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,2], label=\"P3\")\n",
    "    line14, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,3], label=\"P4\")\n",
    "    line15, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,4], label=\"P5\")\n",
    "    line16, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,5], label=\"P6\")\n",
    "    leg2 = ax2.legend(fancybox=True, shadow=True)\n",
    "\n",
    "    # flowmeter\n",
    "    df_datetimes_all, df_flowmeters_all = load_flowmeter(start_date, end_date)\n",
    "    \n",
    "    ax3.set_xlabel('Datetime')\n",
    "    ax3.set_ylabel('Flowrate (mmol/s)')\n",
    "    ax3.grid()\n",
    "    \n",
    "    line21, = ax3.plot(df_datetimes_all, df_flowmeters_all.iloc[:,0], label=\"Flowmeter\")\n",
    "    leg3 = ax3.legend(fancybox=True, shadow=True)\n",
    "\n",
    "    plt.show(block=False)\n",
    "    \n",
    "def plot_temperature_pressure_flowmeter_status(start_date:date, end_date:date):\n",
    "\n",
    "    fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,1)\n",
    "    \n",
    "    # temperature\n",
    "    df_df_datetimes_all, df_temperatures_all = load_temperature(start_date, end_date)\n",
    "    \n",
    "    title = \"Click on legend line to toggle line on/off\" \n",
    "    ax1.set_title(title)\n",
    "    ax1.set_xlabel('Datetime')\n",
    "    ax1.set_ylabel('Temperature(K)')\n",
    "    ax1.grid()\n",
    "    \n",
    "    line1, = ax1.plot(df_df_datetimes_all.iloc[:,0], df_temperatures_all.iloc[:,0], label=\"50 K\")\n",
    "    line2, = ax1.plot(df_df_datetimes_all.iloc[:,1], df_temperatures_all.iloc[:,1], label=\"4 K\")\n",
    "    line3, = ax1.plot(df_df_datetimes_all.iloc[:,2], df_temperatures_all.iloc[:,2], label=\"Still\")\n",
    "    line4, = ax1.plot(df_df_datetimes_all.iloc[:,3], df_temperatures_all.iloc[:,3], label=\"MCX\")\n",
    "    # leg1 = ax1.legend(fancybox=True, shadow=True)\n",
    "    \n",
    "    # pressure\n",
    "    df_datetimes_all, df_pressures_all = load_pressure(start_date, end_date)\n",
    "\n",
    "    ax2.set_xlabel('Datetime')\n",
    "    ax2.set_ylabel('Pressure (mBar)')\n",
    "    ax2.grid()\n",
    "    \n",
    "    line11, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,0], label=\"P1\")\n",
    "    line12, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,1], label=\"P2\")\n",
    "    line13, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,2], label=\"P3\")\n",
    "    line14, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,3], label=\"P4\")\n",
    "    line15, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,4], label=\"P5\")\n",
    "    line16, = ax2.plot(df_datetimes_all, df_pressures_all.iloc[:,5], label=\"P6\")\n",
    "    # leg2 = ax2.legend(fancybox=True, shadow=True)\n",
    "\n",
    "    # flowmeter\n",
    "    df_datetimes_all, df_flowmeters_all = load_flowmeter(start_date, end_date)\n",
    "    \n",
    "    ax3.set_xlabel('Datetime')\n",
    "    ax3.set_ylabel('Flowrate (mmol/s)')\n",
    "    ax3.grid()\n",
    "    \n",
    "    line21, = ax3.plot(df_datetimes_all, df_flowmeters_all.iloc[:,0], label=\"Flowmeter\")\n",
    "    # leg3 = ax3.legend(fancybox=True, shadow=True)\n",
    "\n",
    "    # status\n",
    "    df_datetimes_all, df_status_all = load_status(start_date, end_date)\n",
    "\n",
    "    ax4.set_xlabel('Datetime')\n",
    "    ax4.set_ylabel('Stauts')\n",
    "    ax4.grid()\n",
    "\n",
    "    s = \"01-01-22,00:14:17,cptempwi,1.450000E+1,cptempwo,2.480000E+1,cptemph,7.050000E+1,cptempo,3.090000E+1,cpttime,2.548976E+6,cperrcode,2.800000E+1,cpavgl,8.840000E+1,cpavgh,2.748000E+2,nxdsf,3.000000E+1,nxdsct,4.100000E+1,nxdst,4.910500E+4,nxdsbs,4.050300E+4,nxdstrs,2.185700E+4,tc400remoteprio,1.000000E+0,tc400spdswptatt,1.000000E+0,tc400errorcode,0.000000E+0,tc400ovtempelec,0.000000E+0,tc400ovtemppump,0.000000E+0,tc400setspdatt,1.000000E+0,tc400pumpaccel,0.000000E+0,tc400heating,0.000000E+0,tc400standby,0.000000E+0,tc400pumpstatn,1.000000E+0,tc400commerr,0.000000E+0\"\n",
    "    legend_labels = s.split(',')[2::2]\n",
    "\n",
    "    lines = []\n",
    "    for i in range(24):\n",
    "        line, = ax4.plot(df_datetimes_all, df_status_all.iloc[:,i], label=legend_labels[i])\n",
    "        lines.append(line)\n",
    "\n",
    "    leg = ax4.legend(fancybox=True, shadow=True)\n",
    "\n",
    "\n",
    "    lined = {}  # Will map legend lines to original lines.\n",
    "    for legline, origline in zip(leg.get_lines(), lines):\n",
    "        legline.set_picker(True)  # Enable picking on the legend line.\n",
    "        lined[legline] = origline\n",
    "    \n",
    "    def on_pick(event):\n",
    "        # On the pick event, find the original line corresponding to the legend\n",
    "        # proxy line, and toggle its visibility.\n",
    "        legline = event.artist\n",
    "        origline = lined[legline]\n",
    "        visible = not origline.get_visible()\n",
    "        origline.set_visible(visible)\n",
    "        # Change the alpha on the line in the legend so we can see what lines\n",
    "        # have been toggled.\n",
    "        legline.set_alpha(1.0 if visible else 0.2)\n",
    "        fig.canvas.draw()\n",
    "\n",
    "        fig.canvas.mpl_connect('pick_event', on_pick)\n",
    "\n",
    "\n",
    "    plt.show(block=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot BlueFors log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FileNotFound: 2022-01-22, temperature\n",
      "FileNotFound: 2022-01-23, temperature\n",
      "FileNotFound: 2022-02-26, temperature\n",
      "FileNotFound: 2022-02-27, temperature\n",
      "FileNotFound: 2022-03-01, temperature\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(                       0\n",
       " 0    2022-01-01 00:00:48\n",
       " 1    2022-01-01 00:01:50\n",
       " 2    2022-01-01 00:02:50\n",
       " 3    2022-01-01 00:03:52\n",
       " 4    2022-01-01 00:04:52\n",
       " ...                  ...\n",
       " 1425 2022-03-01 23:55:11\n",
       " 1426 2022-03-01 23:56:11\n",
       " 1427 2022-03-01 23:57:12\n",
       " 1428 2022-03-01 23:58:12\n",
       " 1429 2022-03-01 23:59:12\n",
       " \n",
       " [84979 rows x 1 columns],\n",
       "             5       11     17     23      29     35\n",
       " 0     0.000003  0.0337  402.0  424.0    8.71  1.220\n",
       " 1     0.000003  0.0338  408.0  424.0    8.77  1.230\n",
       " 2     0.000003  0.0338  405.0  424.0    8.76  1.230\n",
       " 3     0.000003  0.0338  405.0  424.0    8.76  1.230\n",
       " 4     0.000003  0.0338  408.0  424.0    8.71  1.230\n",
       " ...        ...     ...    ...    ...     ...    ...\n",
       " 1425  0.000004  0.0233   19.0  169.0  728.00  0.509\n",
       " 1426  0.000004  0.0233   19.0  168.0  727.00  0.509\n",
       " 1427  0.000004  0.0233   19.0  169.0  728.00  0.510\n",
       " 1428  0.000004  0.0233   19.0  168.0  728.00  0.509\n",
       " 1429  0.000004  0.0233   19.0  168.0  728.00  0.510\n",
       " \n",
       " [84979 rows x 6 columns])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = date(2022,1,1)\n",
    "end_date   = date(2022,3,1)\n",
    "\n",
    "plot_temperature(start_date, end_date)\n",
    "# plot_pressure(start_date, end_date)\n",
    "# plot_flowmeter(start_date, end_date)\n",
    "# plot_status(start_date, end_date)\n",
    "# plot_temperature_pressure_flowmeter(start_date, end_date)\n",
    "# plot_temperature_pressure_flowmeter_status(start_date, end_date)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'module://matplotlib_inline.backend_inline'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.get_backend() # show current backend"
   ]
  }
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