{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6377d707-491b-4bd6-985c-72820709afb1",
   "metadata": {},
   "source": [
    "# Make an MTH5 from NIMS data\n",
    "\n",
    "This notebook provides an example of how to read in NIMS (.BIN) files into an MTH5. NIMS files represent a single run.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1208c257-8064-4ed3-a7db-eebe14b248d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-07 13:02:52,583 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\GitHub\\mth5\\logs\\mth5_debug.log\n"
     ]
    }
   ],
   "source": [
    "from mth5.mth5 import MTH5\n",
    "from mth5.io.nims import NIMSCollection\n",
    "from mth5 import read_file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0fe5fce-0345-4b25-b1bb-64268a7e5861",
   "metadata": {},
   "source": [
    "### NIMS Collection\n",
    "\n",
    "We will use the `NIMSCollection` to assemble the *.bin* files into a logical order by run. The output NIMS files include all data for each channel for a single run. Therefore the collection is relatively simple.\n",
    "\n",
    "*Metadata:* we need to input the `survey_id` to provide minimal metadata when making an MTH5 file. \n",
    "\n",
    "The `NIMSCollection.get_runs()` will return a two level ordered dictionary (`OrderedDict`).  The first level is keyed by station ID.  These objects are in turn ordered dictionaries by run ID.  Therefore you can loop over stations and runs.  \n",
    "\n",
    "**Note**: `n_samples` and `end` are estimates based on file size not the data.  To get an accurate number you should read in the full file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e0dc972-54eb-43d3-af4f-0abc2c4834e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-07 13:02:53,062 [line 123] mth5.io.nims.header.NIMS.read_header - INFO: Reading NIMS file c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\mnp300a.BIN\n",
      "2022-09-07 13:02:53,081 [line 242] mth5.io.nims.header.NIMS.end_time - WARNING: Estimating end time from n_samples\n",
      "2022-09-07 13:02:53,086 [line 123] mth5.io.nims.header.NIMS.read_header - INFO: Reading NIMS file c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\mnp300b.BIN\n",
      "2022-09-07 13:02:53,099 [line 242] mth5.io.nims.header.NIMS.end_time - WARNING: Estimating end time from n_samples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1 station with 2 runs\n"
     ]
    }
   ],
   "source": [
    "nc = NIMSCollection(r\"c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\")\n",
    "nc.survey_id = \"test\"\n",
    "runs = nc.get_runs(sample_rates=[8])\n",
    "print(f\"Found {len(runs)} station with {len(runs[list(runs.keys())[0]])} runs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "039570b6-e1c1-46ce-bdec-d92554e30497",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survey</th>\n",
       "      <th>station</th>\n",
       "      <th>run</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>channel_id</th>\n",
       "      <th>component</th>\n",
       "      <th>fn</th>\n",
       "      <th>sample_rate</th>\n",
       "      <th>file_size</th>\n",
       "      <th>n_samples</th>\n",
       "      <th>sequence_number</th>\n",
       "      <th>instrument_id</th>\n",
       "      <th>calibration_fn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300a</td>\n",
       "      <td>2019-09-26 18:29:29+00:00</td>\n",
       "      <td>2019-10-01 15:03:23+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>hx,hy,hz,ex,ey,temperature</td>\n",
       "      <td>c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\mnp30...</td>\n",
       "      <td>8</td>\n",
       "      <td>54972155</td>\n",
       "      <td>3357078</td>\n",
       "      <td>1</td>\n",
       "      <td>NIMS</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  survey station      run                     start                       end  \\\n",
       "0   test  mnp300  mnp300a 2019-09-26 18:29:29+00:00 2019-10-01 15:03:23+00:00   \n",
       "\n",
       "   channel_id                   component  \\\n",
       "0           1  hx,hy,hz,ex,ey,temperature   \n",
       "\n",
       "                                                  fn  sample_rate  file_size  \\\n",
       "0  c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\mnp30...            8   54972155   \n",
       "\n",
       "   n_samples  sequence_number instrument_id calibration_fn  \n",
       "0    3357078                1          NIMS           None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survey</th>\n",
       "      <th>station</th>\n",
       "      <th>run</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>channel_id</th>\n",
       "      <th>component</th>\n",
       "      <th>fn</th>\n",
       "      <th>sample_rate</th>\n",
       "      <th>file_size</th>\n",
       "      <th>n_samples</th>\n",
       "      <th>sequence_number</th>\n",
       "      <th>instrument_id</th>\n",
       "      <th>calibration_fn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300b</td>\n",
       "      <td>2019-10-01 16:16:42+00:00</td>\n",
       "      <td>2019-10-03 22:55:52+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>hx,hy,hz,ex,ey,temperature</td>\n",
       "      <td>c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\mnp30...</td>\n",
       "      <td>8</td>\n",
       "      <td>25774314</td>\n",
       "      <td>1574003</td>\n",
       "      <td>2</td>\n",
       "      <td>NIMS</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  survey station      run                     start                       end  \\\n",
       "1   test  mnp300  mnp300b 2019-10-01 16:16:42+00:00 2019-10-03 22:55:52+00:00   \n",
       "\n",
       "   channel_id                   component  \\\n",
       "1           1  hx,hy,hz,ex,ey,temperature   \n",
       "\n",
       "                                                  fn  sample_rate  file_size  \\\n",
       "1  c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\mnp30...            8   25774314   \n",
       "\n",
       "   n_samples  sequence_number instrument_id calibration_fn  \n",
       "1    1574003                2          NIMS           None  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for run_id, run_df in runs[\"mnp300\"].items():\n",
    "    display(run_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea10bfb-89ca-466b-895e-47840ada4569",
   "metadata": {},
   "source": [
    "## Build MTH5\n",
    "\n",
    "Now that we have a logical collection of files, lets load them into an MTH5.  We will simply loop of the stations, runs, and channels in the ordered dictionary.\n",
    "\n",
    "There are a few things that to keep in mind:  \n",
    "\n",
    "- The LEMI raw files come with very little metadata, so as a user you will have to manually input most of it. \n",
    "- The output files from a LEMI are already calibrated into units of nT and mV/km (I think), therefore there are no filter to apply to calibrate the data. \n",
    "- Since this is a MTH5 file version 0.2.0 the filters are in the `survey_group` so add them there.\n",
    "\n",
    "**TODO**:\n",
    "\n",
    "    - make sure filters get propagated throught to mth5\n",
    "    - think about run names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "77321550-53cc-4849-965f-e0a25fc579e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-07 13:02:53,674 [line 663] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\from_nims.h5 in mode a\n"
     ]
    }
   ],
   "source": [
    "calibrate = True\n",
    "m = MTH5()\n",
    "if calibrate:\n",
    "    m.data_level = 2\n",
    "m.open_mth5(nc.file_path.joinpath(\"from_nims.h5\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2957f307-ee67-4c61-90a3-cfdbc6e961f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_group = m.add_survey(nc.survey_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b8c1f73-45d2-448f-a2af-a7d3ff77b438",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-07 13:02:54,234 [line 123] mth5.io.nims.header.NIMS.read_header - INFO: Reading NIMS file c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\mnp300a.BIN\n",
      "2022-09-07 13:02:54,649 [line 927] mth5.io.nims.header.NIMS.read_nims - WARNING: odd number of bytes 54971209, not even blocks cutting down the data by 72 bits\n",
      "2022-09-07 13:02:55,667 [line 1019] mth5.io.nims.header.NIMS.read_nims - INFO: Reading took 1.43 seconds\n",
      "2022-09-07 13:02:56,835 [line 288] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2019-10-01T15:07:08+00:00 does not match metadata end 2019-10-01T15:07:07.875000+00:00 updating metatdata value to 2019-10-01T15:07:08+00:00\n",
      "2022-09-07 13:02:57,909 [line 288] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2019-10-01T15:07:08+00:00 does not match metadata end 2019-10-01T15:07:07.875000+00:00 updating metatdata value to 2019-10-01T15:07:08+00:00\n",
      "2022-09-07 13:03:07,154 [line 123] mth5.io.nims.header.NIMS.read_header - INFO: Reading NIMS file c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\mnp300b.BIN\n",
      "2022-09-07 13:03:07,769 [line 1019] mth5.io.nims.header.NIMS.read_nims - INFO: Reading took 0.61 seconds\n",
      "2022-09-07 13:03:08,620 [line 288] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2019-10-03T23:01:04+00:00 does not match metadata end 2019-10-03T23:01:03.875000+00:00 updating metatdata value to 2019-10-03T23:01:04+00:00\n",
      "2022-09-07 13:03:09,250 [line 288] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2019-10-03T23:01:04+00:00 does not match metadata end 2019-10-03T23:01:03.875000+00:00 updating metatdata value to 2019-10-03T23:01:04+00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 26.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for station_id in runs.keys():\n",
    "    station_group = survey_group.stations_group.add_station(station_id)\n",
    "    for run_id, run_df in runs[station_id].items():\n",
    "        run_group = station_group.add_run(run_id)\n",
    "        run_ts = read_file(run_df.fn.unique()[0])\n",
    "        if calibrate:\n",
    "            run_ts = run_ts.calibrate()\n",
    "        run_group.from_runts(run_ts)\n",
    "    station_group.metadata.update(run_ts.station_metadata)\n",
    "    station_group.write_metadata()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9654c7b8-a25f-4769-98db-6cdbf6f5dab6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 7.68 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "station_group.validate_station_metadata()\n",
    "station_group.write_metadata()\n",
    "\n",
    "survey_group.update_survey_metadata()\n",
    "survey_group.write_metadata()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c715640-c4bf-4dc9-99c9-f3d2d133b164",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### MTH5 Structure\n",
    "\n",
    "Have a look at the MTH5 structure and make sure it looks correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a52dacb5-4b0b-4f5e-bcdf-e058c6f29094",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/:\n",
       "====================\n",
       "    |- Group: Experiment\n",
       "    --------------------\n",
       "        |- Group: Reports\n",
       "        -----------------\n",
       "        |- Group: Standards\n",
       "        -------------------\n",
       "            --> Dataset: summary\n",
       "            ......................\n",
       "        |- Group: Surveys\n",
       "        -----------------\n",
       "            |- Group: test\n",
       "            --------------\n",
       "                |- Group: Filters\n",
       "                -----------------\n",
       "                    |- Group: coefficient\n",
       "                    ---------------------\n",
       "                        |- Group: dipole_101.00\n",
       "                        -----------------------\n",
       "                        |- Group: dipole_106.00\n",
       "                        -----------------------\n",
       "                        |- Group: dipole_109.00\n",
       "                        -----------------------\n",
       "                        |- Group: e_analog_to_digital\n",
       "                        -----------------------------\n",
       "                        |- Group: h_analog_to_digital\n",
       "                        -----------------------------\n",
       "                        |- Group: to_mt_units\n",
       "                        ---------------------\n",
       "                    |- Group: fap\n",
       "                    -------------\n",
       "                    |- Group: fir\n",
       "                    -------------\n",
       "                    |- Group: time_delay\n",
       "                    --------------------\n",
       "                        |- Group: ex_time_offset\n",
       "                        ------------------------\n",
       "                        |- Group: ey_time_offset\n",
       "                        ------------------------\n",
       "                        |- Group: hx_time_offset\n",
       "                        ------------------------\n",
       "                        |- Group: hy_time_offset\n",
       "                        ------------------------\n",
       "                        |- Group: hz_time_offset\n",
       "                        ------------------------\n",
       "                    |- Group: zpk\n",
       "                    -------------\n",
       "                        |- Group: nims_1_pole_butterworth\n",
       "                        ---------------------------------\n",
       "                            --> Dataset: poles\n",
       "                            ....................\n",
       "                            --> Dataset: zeros\n",
       "                            ....................\n",
       "                        |- Group: nims_3_pole_butterworth\n",
       "                        ---------------------------------\n",
       "                            --> Dataset: poles\n",
       "                            ....................\n",
       "                            --> Dataset: zeros\n",
       "                            ....................\n",
       "                        |- Group: nims_5_pole_butterworth\n",
       "                        ---------------------------------\n",
       "                            --> Dataset: poles\n",
       "                            ....................\n",
       "                            --> Dataset: zeros\n",
       "                            ....................\n",
       "                |- Group: Reports\n",
       "                -----------------\n",
       "                |- Group: Standards\n",
       "                -------------------\n",
       "                    --> Dataset: summary\n",
       "                    ......................\n",
       "                |- Group: Stations\n",
       "                ------------------\n",
       "                    |- Group: mnp300\n",
       "                    ----------------\n",
       "                        |- Group: Transfer_Functions\n",
       "                        ----------------------------\n",
       "                        |- Group: mnp300a\n",
       "                        -----------------\n",
       "                            --> Dataset: ex\n",
       "                            .................\n",
       "                            --> Dataset: ey\n",
       "                            .................\n",
       "                            --> Dataset: hx\n",
       "                            .................\n",
       "                            --> Dataset: hy\n",
       "                            .................\n",
       "                            --> Dataset: hz\n",
       "                            .................\n",
       "                            --> Dataset: temperature\n",
       "                            ..........................\n",
       "                        |- Group: mnp300b\n",
       "                        -----------------\n",
       "                            --> Dataset: ex\n",
       "                            .................\n",
       "                            --> Dataset: ey\n",
       "                            .................\n",
       "                            --> Dataset: hx\n",
       "                            .................\n",
       "                            --> Dataset: hy\n",
       "                            .................\n",
       "                            --> Dataset: hz\n",
       "                            .................\n",
       "                            --> Dataset: temperature\n",
       "                            ..........................\n",
       "        --> Dataset: channel_summary\n",
       "        ..............................\n",
       "        --> Dataset: tf_summary\n",
       "        ........................."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0c040c-560d-4208-932a-a9e1f261f371",
   "metadata": {},
   "source": [
    "### Channel Summary\n",
    "\n",
    "Have a look at the channel summary and make sure everything looks good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "413436b3-2986-4dda-a3d9-3dc921adbce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survey</th>\n",
       "      <th>station</th>\n",
       "      <th>run</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elevation</th>\n",
       "      <th>component</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>n_samples</th>\n",
       "      <th>sample_rate</th>\n",
       "      <th>measurement_type</th>\n",
       "      <th>azimuth</th>\n",
       "      <th>tilt</th>\n",
       "      <th>units</th>\n",
       "      <th>hdf5_reference</th>\n",
       "      <th>run_hdf5_reference</th>\n",
       "      <th>station_hdf5_reference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300a</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>ex</td>\n",
       "      <td>2019-09-26 18:33:21+00:00</td>\n",
       "      <td>2019-10-01 15:07:08+00:00</td>\n",
       "      <td>3357016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>electric</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300a</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>ey</td>\n",
       "      <td>2019-09-26 18:33:21+00:00</td>\n",
       "      <td>2019-10-01 15:07:08+00:00</td>\n",
       "      <td>3357016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>electric</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300a</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>hx</td>\n",
       "      <td>2019-09-26 18:33:21+00:00</td>\n",
       "      <td>2019-10-01 15:07:08+00:00</td>\n",
       "      <td>3357016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>magnetic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300a</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>hy</td>\n",
       "      <td>2019-09-26 18:33:21+00:00</td>\n",
       "      <td>2019-10-01 15:07:08+00:00</td>\n",
       "      <td>3357016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>magnetic</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300a</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>hz</td>\n",
       "      <td>2019-09-26 18:33:21+00:00</td>\n",
       "      <td>2019-10-01 15:07:08+00:00</td>\n",
       "      <td>3357016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>magnetic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300a</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>temperature</td>\n",
       "      <td>2019-09-26 18:33:21+00:00</td>\n",
       "      <td>2019-10-01 15:07:08+00:00</td>\n",
       "      <td>3357016</td>\n",
       "      <td>8.0</td>\n",
       "      <td>auxiliary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>none</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300b</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>ex</td>\n",
       "      <td>2019-10-01 16:22:01+00:00</td>\n",
       "      <td>2019-10-03 23:01:04+00:00</td>\n",
       "      <td>1573944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>electric</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300b</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>ey</td>\n",
       "      <td>2019-10-01 16:22:01+00:00</td>\n",
       "      <td>2019-10-03 23:01:04+00:00</td>\n",
       "      <td>1573944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>electric</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300b</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>hx</td>\n",
       "      <td>2019-10-01 16:22:01+00:00</td>\n",
       "      <td>2019-10-03 23:01:04+00:00</td>\n",
       "      <td>1573944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>magnetic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300b</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>hy</td>\n",
       "      <td>2019-10-01 16:22:01+00:00</td>\n",
       "      <td>2019-10-03 23:01:04+00:00</td>\n",
       "      <td>1573944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>magnetic</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300b</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>hz</td>\n",
       "      <td>2019-10-01 16:22:01+00:00</td>\n",
       "      <td>2019-10-03 23:01:04+00:00</td>\n",
       "      <td>1573944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>magnetic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>count</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>test</td>\n",
       "      <td>mnp300</td>\n",
       "      <td>mnp300b</td>\n",
       "      <td>34.726823</td>\n",
       "      <td>-115.735015</td>\n",
       "      <td>940.0</td>\n",
       "      <td>temperature</td>\n",
       "      <td>2019-10-01 16:22:01+00:00</td>\n",
       "      <td>2019-10-03 23:01:04+00:00</td>\n",
       "      <td>1573944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>auxiliary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>none</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "      <td>&lt;HDF5 object reference&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survey station      run   latitude   longitude  elevation    component  \\\n",
       "0    test  mnp300  mnp300a  34.726823 -115.735015      940.0           ex   \n",
       "1    test  mnp300  mnp300a  34.726823 -115.735015      940.0           ey   \n",
       "2    test  mnp300  mnp300a  34.726823 -115.735015      940.0           hx   \n",
       "3    test  mnp300  mnp300a  34.726823 -115.735015      940.0           hy   \n",
       "4    test  mnp300  mnp300a  34.726823 -115.735015      940.0           hz   \n",
       "5    test  mnp300  mnp300a  34.726823 -115.735015      940.0  temperature   \n",
       "6    test  mnp300  mnp300b  34.726823 -115.735015      940.0           ex   \n",
       "7    test  mnp300  mnp300b  34.726823 -115.735015      940.0           ey   \n",
       "8    test  mnp300  mnp300b  34.726823 -115.735015      940.0           hx   \n",
       "9    test  mnp300  mnp300b  34.726823 -115.735015      940.0           hy   \n",
       "10   test  mnp300  mnp300b  34.726823 -115.735015      940.0           hz   \n",
       "11   test  mnp300  mnp300b  34.726823 -115.735015      940.0  temperature   \n",
       "\n",
       "                       start                       end  n_samples  \\\n",
       "0  2019-09-26 18:33:21+00:00 2019-10-01 15:07:08+00:00    3357016   \n",
       "1  2019-09-26 18:33:21+00:00 2019-10-01 15:07:08+00:00    3357016   \n",
       "2  2019-09-26 18:33:21+00:00 2019-10-01 15:07:08+00:00    3357016   \n",
       "3  2019-09-26 18:33:21+00:00 2019-10-01 15:07:08+00:00    3357016   \n",
       "4  2019-09-26 18:33:21+00:00 2019-10-01 15:07:08+00:00    3357016   \n",
       "5  2019-09-26 18:33:21+00:00 2019-10-01 15:07:08+00:00    3357016   \n",
       "6  2019-10-01 16:22:01+00:00 2019-10-03 23:01:04+00:00    1573944   \n",
       "7  2019-10-01 16:22:01+00:00 2019-10-03 23:01:04+00:00    1573944   \n",
       "8  2019-10-01 16:22:01+00:00 2019-10-03 23:01:04+00:00    1573944   \n",
       "9  2019-10-01 16:22:01+00:00 2019-10-03 23:01:04+00:00    1573944   \n",
       "10 2019-10-01 16:22:01+00:00 2019-10-03 23:01:04+00:00    1573944   \n",
       "11 2019-10-01 16:22:01+00:00 2019-10-03 23:01:04+00:00    1573944   \n",
       "\n",
       "    sample_rate measurement_type  azimuth  tilt  units  \\\n",
       "0           8.0         electric      0.0   0.0  count   \n",
       "1           8.0         electric     90.0   0.0  count   \n",
       "2           8.0         magnetic      0.0   0.0  count   \n",
       "3           8.0         magnetic     90.0   0.0  count   \n",
       "4           8.0         magnetic      0.0  90.0  count   \n",
       "5           8.0        auxiliary      0.0   0.0   none   \n",
       "6           8.0         electric      0.0   0.0  count   \n",
       "7           8.0         electric     90.0   0.0  count   \n",
       "8           8.0         magnetic      0.0   0.0  count   \n",
       "9           8.0         magnetic     90.0   0.0  count   \n",
       "10          8.0         magnetic      0.0  90.0  count   \n",
       "11          8.0        auxiliary      0.0   0.0   none   \n",
       "\n",
       "             hdf5_reference       run_hdf5_reference   station_hdf5_reference  \n",
       "0   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "1   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "2   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "3   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "4   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "5   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "6   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "7   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "8   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "9   <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "10  <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  \n",
       "11  <HDF5 object reference>  <HDF5 object reference>  <HDF5 object reference>  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.channel_summary.summarize()\n",
    "m.channel_summary.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0960c550-fd15-4b0c-89e9-dc7c11f95b05",
   "metadata": {},
   "source": [
    "## Close the MTH5\n",
    "\n",
    "This is important, you should close the file after you are done using it.  Otherwise bad things can happen if you try to open it with another program or Python interpreter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e99299cb-0165-4b10-9c5a-1ee5dcbc82bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-09-07 13:03:29,078 [line 744] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\nims\\from_nims.h5\n"
     ]
    }
   ],
   "source": [
    "m.close_mth5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae318855-b40c-411f-ab60-1510d722bf1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "115c9adb-2cee-4c66-b3c9-ba1eeda34e63",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}