{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "19bfcdef-6903-425c-905e-9a31065d1ec6",
   "metadata": {},
   "source": [
    "%matplotlib widget# Using observatory data as Remote Reference\n",
    "\n",
    "This is an example notebook for processing broadband data with an observatory as a remote reference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "752b5084-43dd-4ab5-b649-21975c144124",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715f5ec9-6e03-46aa-a298-59d53dfc5e68",
   "metadata": {},
   "source": [
    "## Download Geomagnetic Observatory Data\n",
    "\n",
    "Download data from the USGS Geomagnetic data center for the full time of the survey.  First make a request dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "952dd78d-26cc-417a-a915-3c3675b4fdc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-24 12:46:11,619 [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": [
    "import pandas as pd\n",
    "\n",
    "from mth5.clients import MakeMTH5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1dc97b9d-085f-4f80-8c05-875c2a026a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "request_df = pd.DataFrame(\n",
    "    {\n",
    "        \"observatory\": [\"frn\"],\n",
    "        \"type\": [\"adjusted\"],\n",
    "        \"elements\": [[\"x\", \"y\"]],\n",
    "        \"sampling_period\": [1],\n",
    "        \"start\": [\"2023-04-26T00:00:00\"],\n",
    "        \"end\": [\"2023-05-09T00:00:00\"],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe4bee23-6a07-445e-b45d-2acc87724ee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-24 12:46:23,563 [line 674] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023\\usgs_geomag_frn_xy.h5 in mode a\n",
      "2023-05-24 12:47:28,151 [line 678] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2023-05-09T00:00:06+00:00 does not match metadata end 2023-05-09T00:00:00+00:00 updating metatdata value to 2023-05-09T00:00:06+00:00\n",
      "2023-05-24 12:47:28,834 [line 755] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023\\usgs_geomag_frn_xy.h5\n",
      "2023-05-24 12:47:28,842 [line 325] mth5.mth5.MTH5.filename - WARNING: MTH5 file is not open or has not been created yet. Returning default name\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "make_mth5_object = MakeMTH5(\n",
    "    mth5_version=\"0.2.0\",\n",
    "    interact=False,\n",
    "    save_path=r\"c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023\",\n",
    ")\n",
    "mth5_filename = make_mth5_object.from_usgs_geomag(request_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fedbdedf-c379-4e50-919d-aa92a1c1f691",
   "metadata": {},
   "source": [
    "## Combine Runs with sample rate of 1 second\n",
    "\n",
    "Now we need to combine runs of various sample rates into a single run with the sample rate of 1 second.\n",
    "\n",
    "read into channels, create a run, use reindex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "107fa801-ab54-40c7-b3e6-627b64651589",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from mth5 import read_file\n",
    "from mth5.io.zen import Z3DCollection\n",
    "from mth5.mth5 import MTH5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f093947-1a60-414e-91ff-5e6cfb3c5f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "zc = Z3DCollection(r\"c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023\\gz317\")\n",
    "runs = zc.get_runs(sample_rates=[4096, 256])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15716586-e8f7-4f64-97b4-1d195c7cf294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>0</th>\n",
       "      <td></td>\n",
       "      <td>317</td>\n",
       "      <td>sr256_0001</td>\n",
       "      <td>2023-05-01 22:14:57+00:00</td>\n",
       "      <td>2023-05-02 01:10:09.867187500+00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>hy</td>\n",
       "      <td>c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023...</td>\n",
       "      <td>256.0</td>\n",
       "      <td>10761592</td>\n",
       "      <td>2691294</td>\n",
       "      <td>1</td>\n",
       "      <td>ZEN_026</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>317</td>\n",
       "      <td>sr256_0001</td>\n",
       "      <td>2023-05-01 22:14:57+00:00</td>\n",
       "      <td>2023-05-02 01:10:09.871094+00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>ey</td>\n",
       "      <td>c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023...</td>\n",
       "      <td>256.0</td>\n",
       "      <td>10759036</td>\n",
       "      <td>2691295</td>\n",
       "      <td>1</td>\n",
       "      <td>ZEN_026</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>317</td>\n",
       "      <td>sr256_0001</td>\n",
       "      <td>2023-05-01 22:14:57+00:00</td>\n",
       "      <td>2023-05-02 01:10:09.871094+00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>ex</td>\n",
       "      <td>c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023...</td>\n",
       "      <td>256.0</td>\n",
       "      <td>10759036</td>\n",
       "      <td>2691295</td>\n",
       "      <td>1</td>\n",
       "      <td>ZEN_026</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>317</td>\n",
       "      <td>sr256_0001</td>\n",
       "      <td>2023-05-01 22:14:57+00:00</td>\n",
       "      <td>2023-05-02 01:10:09.871094+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>hx</td>\n",
       "      <td>c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023...</td>\n",
       "      <td>256.0</td>\n",
       "      <td>10761596</td>\n",
       "      <td>2691295</td>\n",
       "      <td>1</td>\n",
       "      <td>ZEN_026</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  survey station         run                     start  \\\n",
       "0            317  sr256_0001 2023-05-01 22:14:57+00:00   \n",
       "1            317  sr256_0001 2023-05-01 22:14:57+00:00   \n",
       "2            317  sr256_0001 2023-05-01 22:14:57+00:00   \n",
       "3            317  sr256_0001 2023-05-01 22:14:57+00:00   \n",
       "\n",
       "                                  end  channel_id component  \\\n",
       "0 2023-05-02 01:10:09.867187500+00:00           2        hy   \n",
       "1    2023-05-02 01:10:09.871094+00:00           5        ey   \n",
       "2    2023-05-02 01:10:09.871094+00:00           4        ex   \n",
       "3    2023-05-02 01:10:09.871094+00:00           1        hx   \n",
       "\n",
       "                                                  fn  sample_rate  file_size  \\\n",
       "0  c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023...        256.0   10761592   \n",
       "1  c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023...        256.0   10759036   \n",
       "2  c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023...        256.0   10759036   \n",
       "3  c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023...        256.0   10761596   \n",
       "\n",
       "   n_samples  sequence_number instrument_id calibration_fn  \n",
       "0    2691294                1       ZEN_026           None  \n",
       "1    2691295                1       ZEN_026           None  \n",
       "2    2691295                1       ZEN_026           None  \n",
       "3    2691295                1       ZEN_026           None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs[\"317\"][\"sr256_0001\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bdaf3c56-cc6c-4984-8646-7dfabe5dd1b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-24 12:50:06,591 [line 674] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2023\\gz317\\gz317_with_1s.h5 in mode a\n"
     ]
    }
   ],
   "source": [
    "calibrate = False\n",
    "m = MTH5()\n",
    "if calibrate:\n",
    "    m.data_level = 2\n",
    "m.open_mth5(zc.file_path.joinpath(\"gz317_with_1s.h5\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bfc71810-06e2-4aa7-972a-9c5c243b0949",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "read_z3d() got an unexpected keyword argument 'calibration_fn'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\mth5\\mth5\\io\\reader.py\u001b[0m in \u001b[0;36mread_file\u001b[1;34m(fn, file_type, **kwargs)\u001b[0m\n\u001b[0;32m    145\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m         \u001b[0mfile_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_reader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_ext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mfile_reader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: read_z3d() got an unexpected keyword argument 'calibration_fn'"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "survey_group = m.add_survey(\"geysers_03\")\n",
    "for station_id in runs.keys():\n",
    "    station_group = survey_group.stations_group.add_station(station_id)\n",
    "    station_group.metadata.update(zc.station_metadata_dict[station_id])\n",
    "    station_group.write_metadata()\n",
    "    run_list = []\n",
    "    for run_id, run_df in runs[station_id].items():\n",
    "        run_group = station_group.add_run(run_id)\n",
    "        for row in run_df.itertuples():\n",
    "            ch_ts = read_file(\n",
    "                row.fn,\n",
    "                calibration_fn=r\"c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\antenna_20190411.cal\",\n",
    "            )\n",
    "            # NOTE: this is where the calibration occurs\n",
    "            if calibrate:\n",
    "                ch_ts = ch_ts.remove_instrument_response()\n",
    "            run_group.from_channel_ts(ch_ts)\n",
    "        run_group.update_run_metadata()\n",
    "        run_list.append(run_group.to_runts())\n",
    "\n",
    "    combined_run = run_list[0].merge(run_list[1:], new_sample_rate=1)\n",
    "    combined_run.run_metadata.id = \"sr1_0001\"\n",
    "    combined_run_group = station_group.add_run(\"sr1_0001\")\n",
    "    combined_run_group.from_runts(combined_run)\n",
    "    combined_run_group.update_run_metadata()\n",
    "\n",
    "    station_group.update_station_metadata()\n",
    "\n",
    "survey_group.update_survey_metadata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c98b2e22-df25-4fa3-b3ba-9161772ae89a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 11:00:01,318 [line 753] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2021\\gz316\\gz316_with_1s.h5\n"
     ]
    }
   ],
   "source": [
    "m.close_mth5()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97216c29-07e6-4c01-ac4a-747cebfc7d51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RunTS Summary:\n",
       "\tSurvey:      geysers01\n",
       "\tStation:     gz316\n",
       "\tRun:         sr1_0001\n",
       "\tStart:       2021-04-09T23:24:59+00:00\n",
       "\tEnd:         2021-04-10T17:09:41+00:00\n",
       "\tSample Rate: 1.0\n",
       "\tComponents:  ['ex', 'ey', 'hx', 'hy']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5afe7d32-41d6-477d-a611-5544970c8e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_run.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22f0d66a-5612-49be-b759-5aac1dba24c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 11:00:04,015 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 11:00:04,439 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n"
     ]
    }
   ],
   "source": [
    "c = combined_run.calibrate(bandpass={\"low\": 1e-4, \"high\": 0.45, \"order\": 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c339ed9f-a61e-4e8a-a132-b86762812853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "013c8a9d-5ec6-4f7f-b92a-325791f55312",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_obs = MTH5()\n",
    "m_obs.open_mth5(r\"c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2021\\usgs_geomag_frn_xy.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5db301dc-9dbb-4fe7-b909-a708b9d263f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "r_obs = m_obs.get_run(\"Fresno\", \"sp1_001\", \"USGS-GEOMAG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2376b0ec-cfc7-4326-a7b4-401f29f57f56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 11:00:05,862 [line 664] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: start time of dataset 2021-04-09T23:24:59+00:00 does not match metadata start 2021-04-05T00:00:00+00:00 updating metatdata value to 2021-04-09T23:24:59+00:00\n",
      "2023-04-21 11:00:05,869 [line 678] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2021-04-10T17:09:41+00:00 does not match metadata end 2021-04-16T00:00:05+00:00 updating metatdata value to 2021-04-10T17:09:41+00:00\n"
     ]
    }
   ],
   "source": [
    "r_obs_slice = r_obs.to_runts(start=combined_run.start, end=combined_run.end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "469731e6-4e48-471b-87ec-25ac4ecac195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r_obs_slice.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c2f1e65-5afe-40e1-be67-4761c0540e8b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 11:00:06,382 [line 753] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2021\\usgs_geomag_frn_xy.h5\n"
     ]
    }
   ],
   "source": [
    "m_obs.close_mth5()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab8069d-aaf4-47ee-bdd5-87991b8796c6",
   "metadata": {},
   "source": [
    "## Process With Aurora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e868dbbb-4c1d-41d8-9945-708ad9e207a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Logging configuration file found, using defaults.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 16:55:44,347 [line 141] mth5.setup_logger - INFO: Logging file can be found C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\GitHub\\mth5\\logs\\mth5_debug.log\n"
     ]
    }
   ],
   "source": [
    "# Required imports for the program.\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "from aurora.config.config_creator import ConfigCreator\n",
    "from aurora.pipelines.process_mth5 import process_mth5\n",
    "from aurora.pipelines.run_summary import RunSummary\n",
    "from aurora.transfer_function.kernel_dataset import KernelDataset\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05f66856-cd70-4086-9bd6-136305b5a062",
   "metadata": {},
   "outputs": [],
   "source": [
    "station_path = Path(\n",
    "    r\"c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2021\\gz316\\gz316_with_1s.h5\"\n",
    ")\n",
    "obs_path = Path(r\"c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2021\\usgs_geomag_frn_xy.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "263d12f8-f98a-40cf-9d64-25fe9857dcaf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 16:56:00,649 [line 753] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2021\\gz316\\gz316_with_1s.h5\n",
      "2023-04-21 16:56:00,754 [line 753] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2021\\usgs_geomag_frn_xy.h5\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survey</th>\n",
       "      <th>station_id</th>\n",
       "      <th>run_id</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>geysers01</td>\n",
       "      <td>gz316</td>\n",
       "      <td>sr4096_0002</td>\n",
       "      <td>2021-04-10 00:59:59+00:00</td>\n",
       "      <td>2021-04-10 01:09:43.997314453+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>geysers01</td>\n",
       "      <td>gz316</td>\n",
       "      <td>sr4096_0004</td>\n",
       "      <td>2021-04-10 06:59:59+00:00</td>\n",
       "      <td>2021-04-10 07:09:43.998535156+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>geysers01</td>\n",
       "      <td>gz316</td>\n",
       "      <td>sr4096_0006</td>\n",
       "      <td>2021-04-10 12:59:59+00:00</td>\n",
       "      <td>2021-04-10 13:09:43.997314453+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      survey station_id       run_id                     start  \\\n",
       "2  geysers01      gz316  sr4096_0002 2021-04-10 00:59:59+00:00   \n",
       "4  geysers01      gz316  sr4096_0004 2021-04-10 06:59:59+00:00   \n",
       "6  geysers01      gz316  sr4096_0006 2021-04-10 12:59:59+00:00   \n",
       "\n",
       "                                  end  \n",
       "2 2021-04-10 01:09:43.997314453+00:00  \n",
       "4 2021-04-10 07:09:43.998535156+00:00  \n",
       "6 2021-04-10 13:09:43.997314453+00:00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mth5_run_summary = RunSummary()\n",
    "mth5_run_summary.from_mth5s([station_path, obs_path])\n",
    "run_summary = mth5_run_summary.clone()\n",
    "run_summary.add_duration()\n",
    "run_summary.df = run_summary.df[run_summary.df.sample_rate == 4096]\n",
    "run_summary.mini_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df8fd266-d216-4768-9421-fad10ad0dfda",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>survey</th>\n",
       "      <th>station_id</th>\n",
       "      <th>run_id</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>geysers01</td>\n",
       "      <td>gz316</td>\n",
       "      <td>sr4096_0002</td>\n",
       "      <td>2021-04-10 00:59:59+00:00</td>\n",
       "      <td>2021-04-10 01:09:43.997314453+00:00</td>\n",
       "      <td>584.997314</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>geysers01</td>\n",
       "      <td>gz316</td>\n",
       "      <td>sr4096_0004</td>\n",
       "      <td>2021-04-10 06:59:59+00:00</td>\n",
       "      <td>2021-04-10 07:09:43.998535156+00:00</td>\n",
       "      <td>584.998535</td>\n",
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       "      <td>2021-04-10 13:09:43.997314453+00:00</td>\n",
       "      <td>584.997314</td>\n",
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      "text/plain": [
       "      survey station_id       run_id                     start  \\\n",
       "0  geysers01      gz316  sr4096_0002 2021-04-10 00:59:59+00:00   \n",
       "1  geysers01      gz316  sr4096_0004 2021-04-10 06:59:59+00:00   \n",
       "2  geysers01      gz316  sr4096_0006 2021-04-10 12:59:59+00:00   \n",
       "\n",
       "                                  end    duration  \n",
       "0 2021-04-10 01:09:43.997314453+00:00  584.997314  \n",
       "1 2021-04-10 07:09:43.998535156+00:00  584.998535  \n",
       "2 2021-04-10 13:09:43.997314453+00:00  584.997314  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_dataset = KernelDataset()\n",
    "kernel_dataset.from_run_summary(run_summary, \"gz316\")#, \"Fresno\")\n",
    "mimimum_run_duration = 100  # seconds\n",
    "kernel_dataset.drop_runs_shorter_than(mimimum_run_duration)\n",
    "kernel_dataset.mini_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "897aef38-234b-4e50-8a5c-0fc6e84517a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bands not defined; setting to EMTF BANDS_DEFAULT_FILE\n",
      "C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\GitHub\\aurora\\aurora\\config\\emtf_band_setup\\bs_test.cfg\n"
     ]
    }
   ],
   "source": [
    "cc = ConfigCreator()\n",
    "config = cc.create_from_kernel_dataset(kernel_dataset)\n",
    "for decimation in config.decimations:\n",
    "    decimation.estimator.engine = \"RME\"\n",
    "    decimation.window.type = \"hamming\"\n",
    "    decimation.output_channels = [\"ex\", \"ey\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7e0210e-089f-47ef-ba5b-d8975674b231",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-04-21T16:56:20 [line 242] aurora.pipelines.transfer_function_kernel.TransferFunctionKernel.memory_warning - INFO: Total memory: 15.88 GB\n",
      "2023-04-21T16:56:20 [line 246] aurora.pipelines.transfer_function_kernel.TransferFunctionKernel.memory_warning - INFO: Total Bytes of Raw Data: 0.054 GB\n",
      "2023-04-21T16:56:20 [line 251] aurora.pipelines.transfer_function_kernel.TransferFunctionKernel.memory_warning - INFO: Raw Data will use: 0.337 % of memory\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 16:56:22,963 [line 678] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2021-04-10T01:09:43.997070312+00:00 does not match metadata end 2021-04-10T01:09:43.998779296+00:00 updating metatdata value to 2021-04-10T01:09:43.997070312+00:00\n",
      "2023-04-21 16:56:23,791 [line 2800] mth5.groups.master_station_run_channel.Magnetic.time_slice - WARNING: Requested slice is larger than data.  Slice length = 2396154, data length = (2396144,). Setting end_index to (2396144,)\n",
      "2023-04-21 16:56:24,083 [line 2800] mth5.groups.master_station_run_channel.Magnetic.time_slice - WARNING: Requested slice is larger than data.  Slice length = 2396154, data length = (2396150,). Setting end_index to (2396150,)\n",
      "2023-04-21 16:56:24,891 [line 358] mth5.timeseries.run_ts.RunTS._align_channels - INFO: Channels do not have a common end, using latest: 2021-04-10T07:09:43.998291015\n",
      "2023-04-21 16:56:28,122 [line 678] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2021-04-10T07:09:43.998047+00:00 does not match metadata end 2021-04-10T07:09:43.998535156+00:00 updating metatdata value to 2021-04-10T07:09:43.998047+00:00\n",
      "2023-04-21 16:56:30,167 [line 678] mth5.timeseries.run_ts.RunTS.validate_metadata - WARNING: end time of dataset 2021-04-10T13:09:43.997070312+00:00 does not match metadata end 2021-04-10T13:09:43.999268+00:00 updating metatdata value to 2021-04-10T13:09:43.997070312+00:00\n",
      "2023-04-21 16:56:30,257 [line 312] aurora.pipelines.process_mth5.process_mth5 - INFO: Processing config indicates 4 decimation levels \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATASET DF POPULATED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 16:56:34,782 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 16:56:34,899 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 16:56:39,785 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 16:56:39,947 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 16:56:44,598 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 16:56:44,702 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-04-21T16:56:44 [line 133] aurora.transfer_function_helpers.get_band_for_tf_estimate - INFO: Processing band 0.006281s\n",
      "2023-04-21T16:56:51 [line 133] aurora.transfer_function_helpers.get_band_for_tf_estimate - INFO: Processing band 0.004866s\n",
      "2023-04-21T16:57:03 [line 133] aurora.transfer_function_helpers.get_band_for_tf_estimate - INFO: Processing band 0.003702s\n",
      "2023-04-21T16:57:16 [line 133] aurora.transfer_function_helpers.get_band_for_tf_estimate - INFO: Processing band 0.002868s\n",
      "2023-04-21T16:57:45 [line 133] aurora.transfer_function_helpers.get_band_for_tf_estimate - INFO: Processing band 0.002245s\n",
      "2023-04-21T16:58:09 [line 133] aurora.transfer_function_helpers.get_band_for_tf_estimate - INFO: Processing band 0.001797s\n",
      "2023-04-21T16:58:30 [line 133] aurora.transfer_function_helpers.get_band_for_tf_estimate - INFO: Processing band 0.001430s\n",
      "2023-04-21T16:58:53 [line 133] aurora.transfer_function_helpers.get_band_for_tf_estimate - INFO: Processing band 0.001143s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 16:59:20,231 [line 237] aurora.pipelines.process_mth5.update_dataset_df - INFO: DECIMATION LEVEL 1\n",
      "2023-04-21 16:59:23,316 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 16:59:23,425 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not detrend hx in time range 2021-04-10T06:59:59 to 2021-04-10T07:09:43.812500124. NO DATA\n",
      "Could not detrend hy in time range 2021-04-10T06:59:59 to 2021-04-10T07:09:43.812500124. NO DATA\n",
      "Could not detrend hx in time range 2021-04-10T06:59:59 to 2021-04-10T07:09:43.812500124. NO DATA\n",
      "Could not detrend hy in time range 2021-04-10T06:59:59 to 2021-04-10T07:09:43.812500124. NO DATA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 16:59:24,753 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 16:59:24,857 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 16:59:26,277 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n",
      "2023-04-21 16:59:26,410 [line 222] mt_metadata.base.metadata.frequency_response_table_filter.complex_response - WARNING: Extrapolating, use values outside calibration frequencies with caution\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-04-21T16:59:26 [line 133] aurora.transfer_function_helpers.get_band_for_tf_estimate - INFO: Processing band 0.025126s\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "indexes along dimension 'observation' are not equal",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\aurora\\aurora\\pipelines\\process_mth5.py\u001b[0m in \u001b[0;36mprocess_mth5\u001b[1;34m(config, tfk_dataset, units, show_plot, z_file_path, return_collection)\u001b[0m\n\u001b[0;32m    363\u001b[0m         \u001b[1;31m# Could downweight bad FCs here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 365\u001b[1;33m         tf_obj = process_tf_decimation_level(\n\u001b[0m\u001b[0;32m    366\u001b[0m             \u001b[0mtfk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    367\u001b[0m             \u001b[0mi_dec_level\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\aurora\\aurora\\pipelines\\process_mth5.py\u001b[0m in \u001b[0;36mprocess_tf_decimation_level\u001b[1;34m(config, i_dec_level, local_stft_obj, remote_stft_obj, units)\u001b[0m\n\u001b[0;32m    140\u001b[0m     )\n\u001b[0;32m    141\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m     transfer_function_obj = process_transfer_functions(\n\u001b[0m\u001b[0;32m    143\u001b[0m         \u001b[0mconfig\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m         \u001b[0mi_dec_level\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\aurora\\aurora\\pipelines\\transfer_function_helpers.py\u001b[0m in \u001b[0;36mprocess_transfer_functions\u001b[1;34m(config, i_dec_level, local_stft_obj, remote_stft_obj, transfer_function_obj, segment_weights, channel_weights)\u001b[0m\n\u001b[0;32m    246\u001b[0m                 ].to_dataset()  # keep as a dataset, maybe not needed\n\u001b[0;32m    247\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m                 \u001b[0mX_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRR_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhandle_nan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_ch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRR\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdrop_dim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"observation\"\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    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m                 \u001b[1;31m# W = effective_degrees_of_freedom_weights(X_, RR_, edf_obj=None)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\aurora\\aurora\\time_series\\xarray_helpers.py\u001b[0m in \u001b[0;36mhandle_nan\u001b[1;34m(X, Y, RR, drop_dim)\u001b[0m\n\u001b[0;32m     54\u001b[0m     \u001b[0mRR\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRR\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_var_add_label_mapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m     \u001b[0mmerged_xr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"exact\"\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     57\u001b[0m     \u001b[1;31m# Workaround for issue #228\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[1;31m# merged_xr = merged_xr.merge(RR, join=\"exact\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\em\\lib\\site-packages\\xarray\\core\\dataset.py\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(self, other, overwrite_vars, compat, join, fill_value, combine_attrs)\u001b[0m\n\u001b[0;32m   4329\u001b[0m         \"\"\"\n\u001b[0;32m   4330\u001b[0m         \u001b[0mother\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataArray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4331\u001b[1;33m         merge_result = dataset_merge_method(\n\u001b[0m\u001b[0;32m   4332\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4333\u001b[0m             \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\em\\lib\\site-packages\\xarray\\core\\merge.py\u001b[0m in \u001b[0;36mdataset_merge_method\u001b[1;34m(dataset, other, overwrite_vars, compat, join, fill_value, combine_attrs)\u001b[0m\n\u001b[0;32m    944\u001b[0m         \u001b[0mpriority_arg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    945\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 946\u001b[1;33m     return merge_core(\n\u001b[0m\u001b[0;32m    947\u001b[0m         \u001b[0mobjs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    948\u001b[0m         \u001b[0mcompat\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\em\\lib\\site-packages\\xarray\\core\\merge.py\u001b[0m in \u001b[0;36mmerge_core\u001b[1;34m(objects, compat, join, combine_attrs, priority_arg, explicit_coords, indexes, fill_value)\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    628\u001b[0m     \u001b[0mcoerced\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcoerce_pandas_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 629\u001b[1;33m     aligned = deep_align(\n\u001b[0m\u001b[0;32m    630\u001b[0m         \u001b[0mcoerced\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    631\u001b[0m     )\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\em\\lib\\site-packages\\xarray\\core\\alignment.py\u001b[0m in \u001b[0;36mdeep_align\u001b[1;34m(objects, join, copy, indexes, exclude, raise_on_invalid, fill_value)\u001b[0m\n\u001b[0;32m    434\u001b[0m             \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m     aligned = align(\n\u001b[0m\u001b[0;32m    437\u001b[0m         \u001b[1;33m*\u001b[0m\u001b[0mtargets\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m         \u001b[0mjoin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\em\\lib\\site-packages\\xarray\\core\\alignment.py\u001b[0m in \u001b[0;36malign\u001b[1;34m(join, copy, indexes, exclude, fill_value, *objects)\u001b[0m\n\u001b[0;32m    322\u001b[0m             ):\n\u001b[0;32m    323\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mjoin\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"exact\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 324\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"indexes along dimension {dim!r} are not equal\"\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    325\u001b[0m                 \u001b[0mjoiner\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_joiner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatching_indexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m                 \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjoiner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatching_indexes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: indexes along dimension 'observation' are not equal"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "tf_cls = process_mth5(\n",
    "    config,\n",
    "    kernel_dataset,\n",
    "    units=\"MT\",\n",
    "    show_plot=False,\n",
    "    z_file_path=None,\n",
    ")\n",
    "print(\"=== FINISHED ===\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0aeb8b81-3375-466d-9dd1-ffddc9c5843d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 11:01:13,464 [line 330] mt_metadata.base.metadata.define_measurement.write_measurement - INFO: No XMEAS information.\n",
      "2023-04-21 11:01:13,464 [line 215] mt_metadata.transfer_functions.io.readwrite.write_file - INFO: Wrote c:\\Users\\jpeacock\\OneDrive - DOI\\MTData\\GZ2021\\gz316\\gz316_1s_obs_rr.edi\n"
     ]
    }
   ],
   "source": [
    "edi = tf_cls.write_tf_file(station_path.parent.joinpath(\"gz316_4096.edi\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ece9af-8ff2-4be2-917c-c52f6bfc6753",
   "metadata": {},
   "source": [
    "## Use MTpy to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2299e732-b567-404f-94d4-aa918773a851",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-21 11:01:19,409 [line 113] matplotlib.get_mtpy_logger - INFO: Logging file can be found C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\GitHub\\mtpy\\logs\\matplotlib_warn.log\n"
     ]
    }
   ],
   "source": [
    "from mtpy import MT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "46bf2ead-17ef-4370-b7b6-5f2a68690cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "mt_obj = MT()\n",
    "mt_obj.read_tf_file(edi.fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e1d05311-9d74-4cdc-9520-7e84c1c1c5a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 = mt_obj.plot_mt_response(fig_num=5, plot_num=2)"
   ]
  }
 ],
 "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
}