bonus-combine-data.ipynb

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   "source": [
    "***\n",
    "\n",
    "# Geothermal Well Test Analysis with Python\n",
    "### Bonus material: Combine PTS and pump flow rate data\n",
    "#### Irene Wallis and Katie McLean \n",
    "#### Software Underground, Transform 2021\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from datetime import datetime\n",
    "import openpyxl\n",
    "\n",
    "\n",
    "#from utilities import* # functions in the utilities.py file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timedelta_seconds(dataframe_col, test_start):\n",
    "    '''\n",
    "    Make a float in seconds since the start of the test\n",
    "\n",
    "    args:   dataframe_col: dataframe column containing datetime objects\n",
    "            test_start: test start time formatted '2020-12-11 09:00:00'\n",
    "\n",
    "    returns: float in seconds since the start of the test\n",
    "    '''\n",
    "    test_start_datetime = pd.to_datetime(test_start)\n",
    "    list = []\n",
    "    for datetime in dataframe_col:\n",
    "        time_delta = datetime - test_start_datetime\n",
    "        seconds = time_delta.total_seconds()\n",
    "        list.append(seconds)\n",
    "    return list\n",
    "\n",
    "\n",
    "\n",
    "def read_pts(filename):\n",
    "    '''\n",
    "    Read PTS-2.xlsx in as a Pandas dataframe and munge for analysis\n",
    "\n",
    "    args: filename is r'PTS-2.xlsx'\n",
    "\n",
    "    returns: Pandas dataframe with datetime (local) and key coloumns of PTS data with the correct dtype\n",
    "    '''\n",
    "    df = pd.read_excel(filename)\n",
    "\n",
    "    dict = {\n",
    "        'DEPTH':'depth_m',\n",
    "        'SPEED': 'speed_mps',\n",
    "        'Cable Weight': 'cweight_kg',\n",
    "        'WHP': 'whp_barg',\n",
    "        'Temperature': 'temp_degC',\n",
    "        'Pressure': 'pressure_bara',\n",
    "        'Frequency': 'frequency_hz'\n",
    "    }\n",
    "    df.rename(columns=dict, inplace=True)\n",
    "\n",
    "    df.drop(0, inplace=True)\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "    list = []\n",
    "    for date in df.Timestamp:\n",
    "        newdate = openpyxl.utils.datetime.from_excel(date)\n",
    "        list.append(newdate)\n",
    "    df['datetime'] = list\n",
    "\n",
    "    df.drop(columns = ['Date', 'Time', 'Timestamp','Reed 0',\n",
    "       'Reed 1', 'Reed 2', 'Reed 3', 'Battery Voltage', \n",
    "       'PRT Ref Voltage','SGS Voltage', 'Internal Temp 1', \n",
    "       'Internal Temp 2', 'Internal Temp 3','Cal Temp', \n",
    "       'Error Code 1', 'Error Code 2', 'Error Code 3',\n",
    "       'Records Saved', 'Bad Pages',], inplace = True)\n",
    "    \n",
    "    df[\n",
    "        ['depth_m', 'speed_mps','cweight_kg','whp_barg','temp_degC','pressure_bara','frequency_hz']\n",
    "    ] = df[\n",
    "        ['depth_m','speed_mps','cweight_kg','whp_barg','temp_degC','pressure_bara','frequency_hz']\n",
    "        ].apply(pd.to_numeric)\n",
    "    \n",
    "    df['timedelta_sec'] = timedelta_seconds(df.datetime, '2020-12-11 09:26:44.448')\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "\n",
    "def read_flowrate(filename):\n",
    "    ''' \n",
    "    Read PTS-2-injection-rate.xlsx in as a pandas dataframe and munge for analysis\n",
    "\n",
    "    args: filename is r'PTS-2-injection-rate.xlsx'\n",
    "\n",
    "    returns: pandas dataframe with local NZ datetime and flowrate in t/hr\n",
    "    '''\n",
    "    df = pd.read_excel(filename, header=1) \n",
    "    df.columns = ['raw_datetime','flow_Lpm']\n",
    "\n",
    "    list = []\n",
    "    for date in df['raw_datetime']:\n",
    "        newdate = datetime.fromisoformat(date)\n",
    "        list.append(newdate)\n",
    "    df['ISO_datetime'] = list \n",
    "\n",
    "    list = []\n",
    "    for date in df.ISO_datetime:\n",
    "        newdate = pd.to_datetime(datetime.strftime(date,'%Y-%m-%d %H:%M:%S'))\n",
    "        list.append(newdate)\n",
    "    df['datetime'] = list\n",
    "\n",
    "    df['flow_tph'] = df.flow_Lpm * 0.060\n",
    "\n",
    "    df['timedelta_sec'] = timedelta_seconds(df.datetime, '2020-12-11 09:26:44.448')\n",
    "\n",
    "    df.drop(columns = ['raw_datetime', 'flow_Lpm', 'ISO_datetime'], inplace = True)\n",
    "\n",
    "    return df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
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   "source": [
    "flowrate = read_flowrate(r'Data-FlowRate.xlsx')\n",
    "pts = read_pts(r'Data-PTS.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime', 'flow_tph', 'timedelta_sec'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flowrate.columns"
   ]
  },
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   "execution_count": 17,
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   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of ['timestamp'] are in the columns\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-b343d4fdc0e9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mflowrate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mflowrate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'timestamp'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mflowrate\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mset_index\u001b[1;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[0;32m   4725\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4726\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4727\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of {missing} are in the columns\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4729\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of ['timestamp'] are in the columns\""
     ]
    }
   ],
   "source": [
    "flowrate = flowrate.set_index('timestamp')\n",
    "flowrate.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "pts = pts.set_index('timestamp')\n",
    "pts.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "combined = pts.join(flowrate, how = 'outer',  lsuffix = '_pts', rsuffix = '_fr', )"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "combined.columns"
   ]
  },
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   "outputs": [],
   "source": [
    "combined.head(3)"
   ]
  },
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   },
   "outputs": [],
   "source": [
    "combined.drop(columns = ['datetime_fr'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "combined.columns"
   ]
  },
  {
   "cell_type": "code",
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   },
   "outputs": [],
   "source": [
    "combined.columns = ['depth_m', 'speed_mps', 'cweight_kg', 'whp_barg', 'temp_degC',\n",
    "       'pressure_bara', 'frequency_hz', 'datetime', 'flow_tph']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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      "block": "XcTnMkWP7RlshjC5g4TP",
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   "source": [
    "combined.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "combined.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "combined['flow_tph'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "combined['new_flow_tph'] = combined['flow_tph'].interpolate(method='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "combined['new_flow_tph'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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      "block": "aHqKKTZBprw7DlZju790",
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   },
   "outputs": [],
   "source": [
    "combined.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "fig, (ax) = plt.subplots(1, 1, figsize=(24,8))\n",
    "\n",
    "ax.scatter(combined.datetime, combined.new_flow_tph, \n",
    "    c='k', s = 5, linewidths = 0)\n",
    "\n",
    "ax.scatter(combined.datetime, combined.flow_tph, \n",
    "    c='r', s = 30, linewidths = 0)\n",
    "\n",
    "ax.scatter(pts.datetime, pts.cweight_kg, \n",
    "    c='b', s = 5, linewidths = 0, label = 'Raw cabel weight')\n",
    "\n",
    "ax.scatter(remaining_nan.datetime, remaining_nan.cweight_kg, \n",
    "    c='g', s = 30, linewidths = 0, label = 'NAN cabel weight')\n",
    "\n",
    "ax.set_ylim(0,150)\n",
    "\n",
    "ax.set_xlabel('Time [hh:mm]')\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
    "\n",
    "\n",
    "start_time = pd.to_datetime('2020-12-11 09:25:00')\n",
    "end_time = pd.to_datetime('2020-12-11 09:30:00')\n",
    "\n",
    "#ax.set_xlim(start_time,end_time);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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      "block": "jfNGHUo2E9esn0S3c9C1",
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     }
    }
   },
   "source": [
    "As the plot and the remaining_nan.describe() show that I have filled all the PTS values with flow rates, I will now drop the flow values that have no pts value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "nan_trimmed = combined.dropna()\n",
    "\n",
    "nan_trimmed = combined[combined['depth_m'].notna()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "nan_trimmed.shape "
   ]
  },
  {
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   },
   "outputs": [],
   "source": [
    "combined.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "nan_trimmed.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "nan_trimmed.head(2)"
   ]
  },
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  },
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   "outputs": [],
   "source": [
    "fig, (ax) = plt.subplots(1, 1, figsize=(24,8))\n",
    "\n",
    "ax.scatter(combined.datetime, combined.new_flow_tph, \n",
    "    c='k', s = 5, linewidths = 0)\n",
    "\n",
    "ax.scatter(combined.datetime, combined.flow_tph, \n",
    "    c='r', s = 30, linewidths = 0)\n",
    "\n",
    "ax.scatter(pts.datetime, pts.cweight_kg, \n",
    "    c='b', s = 5, linewidths = 0, label = 'Raw cabel weight')\n",
    "\n",
    "ax.scatter(nan_trimmed.datetime, nan_trimmed.cweight_kg, \n",
    "    c='g', s = 30, linewidths = 0, label = 'NAN cabel weight')\n",
    "\n",
    "ax.set_ylim(0,150)\n",
    "\n",
    "ax.set_xlabel('Time [hh:mm]')\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))\n",
    "\n",
    "\n",
    "start_time = pd.to_datetime('2020-12-11 09:25:00')\n",
    "end_time = pd.to_datetime('2020-12-11 09:30:00')\n",
    "\n",
    "#ax.set_xlim(start_time,end_time);"
   ]
  },
  {
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   "source": [
    "# turning the method into a function\n",
    "\n",
    "flowrate_f = read_flowrate(r'Data-FlowRate.xlsx')\n",
    "pts_f = read_pts(r'Data-PTS.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "def append_flowrate_to_pts(flowrate_df, pts_df):\n",
    "    '''\n",
    "    Add surface flowrate to pts data\n",
    "\n",
    "    Note that the flowrate data is recorded at a courser time resolution than the pts data\n",
    "    The function makes a linear interpolation to fill the data gaps\n",
    "    Refer to bonus-combine-data.ipynb to review this method and adapt it for your own data\n",
    "\n",
    "    Args:   flowrate and pts dataframes generated by the read_flowrate and read_pts functions\n",
    "\n",
    "    Returns: pts dataframe with flowrate tph added\n",
    "    '''\n",
    "    flowrate_df = flowrate_df.set_index('timestamp')\n",
    "    pts_df = pts_df.set_index('timestamp')\n",
    "    combined_df = pts_df.join(flowrate_df, how = 'outer',  lsuffix = '_pts', rsuffix = '_fr')\n",
    "    combined_df.drop(columns = ['datetime_fr'], inplace = True)\n",
    "    combined_df.columns = ['depth_m', 'speed_mps', 'cweight_kg', 'whp_barg', 'temp_degC',\n",
    "       'pressure_bara', 'frequency_hz', 'datetime', 'flow_tph']\n",
    "    combined_df['interpolated_flow_tph'] = combined_df['flow_tph'].interpolate(method='linear')\n",
    "    trimmed_df = combined_df[combined_df['depth_m'].notna()]\n",
    "    trimmed_df.reset_index(inplace=True)\n",
    "    return trimmed_df\n",
    "\n",
    "mudged_df = append_flowrate_to_pts(flowrate_f, pts_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "iooxa": {
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      "block": "iH3CuxDafoGsyheGJPd7",
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     "outputId": null
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   },
   "outputs": [],
   "source": [
    "mudged_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "H5CbXVTkXJbLcJUjOG6Q",
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      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "mudged_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "6TqbXoJJOfXv1oeGbJIQ",
      "project": "mmReuqVTAa9JzPpNr22I",
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    }
   },
   "source": [
    "***\n",
    "\n",
    "<p><center>© 2021 <a href=\"https://www.cubicearth.nz/\">Irene Wallis</a> and <a href=\"https://www.linkedin.com/in/katie-mclean-25994315/\">Katie McLean</a> <a href=\"https://creativecommons.org/licenses/by/4.0/\"</a></center></p>\n",
    "\n",
    "<p><center>Licensed under the Apache License, Version 2.0</center></p>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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