platform-demo-scripts/notebooks/Transforming mseeds from Bogdanka to Seisbench format.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a96a25b-b38a-4485-b7ff-2509236757cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import obspy\n",
    "from obspy.core.event import read_events\n",
    "\n",
    "import seisbench\n",
    "import seisbench.data as sbd\n",
    "import seisbench.util as sbu\n",
    "import numpy as np\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d8aeaf-56e7-4bb0-9e3d-a41f2197d0f4",
   "metadata": {},
   "source": [
    "# Creating a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "53e5a331-6b24-4da7-a0ba-7931a9d79dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_path = \"/net/pr2/projects/plgrid/plggeposai/datasets/bogdanka\"\n",
    "catalog_path = \"/net/pr2/projects/plgrid/plggeposai/datasets/bogdanka/BOIS_all.xml\"\n",
    "output_path = \"/net/pr2/projects/plgrid/plggeposai/kmilian/platform-demo-scripts/datasets/bogdanka/seisbench_format\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "426d4428-2bfa-492a-93d0-01c143d77ddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/net/pr2/projects/plgrid/plggeposai/mambaforge/envs/eposai/lib/python3.11/site-packages/obspy/io/quakeml/core.py:193: UserWarning: Setting attribute \"preferred_description\" failed. Value \"uncertainty elipse\" could not be converted to type \"Enum([\"horizontal uncertainty\", \"uncertainty ellipse\", \"confidence ellipsoid\"])\". The attribute \"preferred_description\" will not be set and will be missing in the resulting object.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "events = read_events(catalog_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "281a13f7-d8d9-4d6c-a97c-89b1044671ad",
   "metadata": {},
   "source": [
    "### Define train/dev/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28f666f4-16c8-446f-9428-d28d9ce6f8f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_event_params(event): \n",
    "\n",
    "    \n",
    "    origin = event.preferred_origin()\n",
    "    if origin is None:\n",
    "        return {}\n",
    "\n",
    "    mag = event.preferred_magnitude()\n",
    "\n",
    "    source_id = str(event.resource_id)\n",
    "\n",
    "    event_params = {\n",
    "        \"source_id\": source_id,\n",
    "        \"source_origin_uncertainty_sec\": origin.time_errors[\"uncertainty\"],\n",
    "        \"source_latitude_deg\": origin.latitude,\n",
    "        \"source_latitude_uncertainty_km\": origin.latitude_errors[\"uncertainty\"],\n",
    "        \"source_longitude_deg\": origin.longitude,\n",
    "        \"source_longitude_uncertainty_km\": origin.longitude_errors[\"uncertainty\"],\n",
    "        \"source_depth_km\": origin.depth / 1e3,\n",
    "        \"source_depth_uncertainty_km\": origin.depth_errors[\"uncertainty\"] / 1e3 if origin.depth_errors[\"uncertainty\"] is not None else None,\n",
    "    }\n",
    "\n",
    "    if mag is not None:\n",
    "        event_params[\"source_magnitude\"] = mag.mag\n",
    "        event_params[\"source_magnitude_uncertainty\"] = mag.mag_errors[\"uncertainty\"]\n",
    "        event_params[\"source_magnitude_type\"] = mag.magnitude_type\n",
    "        event_params[\"source_magnitude_author\"] = mag.creation_info.agency_id if mag.creation_info is not None else None\n",
    "        \n",
    "    return event_params\n",
    "\n",
    "\n",
    "def get_trace_params(pick):\n",
    "    net = pick.waveform_id.network_code\n",
    "    sta = pick.waveform_id.station_code\n",
    "\n",
    "    trace_params = {\n",
    "        \"station_network_code\": net,\n",
    "        \"station_code\": sta,\n",
    "        \"trace_channel\": pick.waveform_id.channel_code,\n",
    "        \"station_location_code\": pick.waveform_id.location_code,\n",
    "        \"time\": pick.time\n",
    "    }\n",
    "\n",
    "    return trace_params\n",
    "\n",
    "\n",
    "def get_trace_path(input_path, trace_params):\n",
    "\n",
    "    year = trace_params[\"time\"].year\n",
    "    day_of_year = pd.Timestamp(str(trace_params[\"time\"])).day_of_year\n",
    "    net = trace_params[\"station_network_code\"]\n",
    "    station = trace_params[\"station_code\"]\n",
    "    tr_channel = trace_params[\"trace_channel\"]\n",
    "\n",
    "    path = f\"{input_path}/{year}/{net}/{station}/{tr_channel}.D/{net}.{station}..{tr_channel}.D.{year}.{day_of_year}\"\n",
    "    return path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8415f6c1-0dfc-41e2-a06e-2e9bc94f0d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_events(events, input_path):\n",
    "\n",
    "    print(\"Splitting available events into train, dev and test sets ...\")\n",
    "    events_stats = pd.DataFrame()\n",
    "    events_stats.index.name = \"event\"\n",
    "    missing_mseeds = []\n",
    "\n",
    "    for i, event in enumerate(events):\n",
    "        #check if mseed exists\n",
    "        actual_picks = 0\n",
    "        for pick in event.picks:\n",
    "            trace_params = get_trace_params(pick)\n",
    "            trace_path = get_trace_path(input_path, trace_params)\n",
    "            if os.path.isfile(trace_path):\n",
    "                actual_picks += 1\n",
    "            else: \n",
    "                missing_mseeds.append(trace_path.split('/'))\n",
    "                \n",
    "\n",
    "        events_stats.loc[i, \"pick_count\"] = len(event.picks)\n",
    "        events_stats.loc[i, \"available_picks\"] = actual_picks\n",
    "        if len(event.picks):\n",
    "            events_stats.loc[i, \"first_peak_time\"] = trace_params[\"time\"]\n",
    "\n",
    "    events_stats['pick_count_cumsum'] = events_stats.pick_count.cumsum()\n",
    "\n",
    "    train_th = 0.7 * events_stats.pick_count_cumsum.values[-1]\n",
    "    dev_th = 0.85 * events_stats.pick_count_cumsum.values[-1]\n",
    "\n",
    "    events_stats['split'] = 'test'\n",
    "    for i, event in events_stats.iterrows():\n",
    "        if event['pick_count_cumsum'] < train_th:\n",
    "            events_stats.loc[i, 'split'] = 'train'\n",
    "        elif event['pick_count_cumsum'] < dev_th:\n",
    "            events_stats.loc[i, 'split'] = 'dev'\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    missing_mseeds = pd.DataFrame(np.array(missing_mseeds)[:, -5:], columns=['y', 'n','s','tr','f'])\n",
    "    missing_mseeds['last_dot'] = missing_mseeds['f'].apply(lambda x: x.rindex('.'))\n",
    "\n",
    "    for i, row in missing_mseeds.iterrows(): \n",
    "        missing_mseeds.loc[i, 'd'] = int(row['f'][row['last_dot']+1:])\n",
    "\n",
    "\n",
    "    return events_stats, missing_mseeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96b8945a-06d6-498c-81d9-ba76788fdf37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Splitting available events into train, dev and test sets ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/net/ascratch/people/plgkmilian/slurm_jobdir/5092284/tmp/ipykernel_248521/649615512.py:23: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '2018-09-24T14:20:07.521000Z' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  events_stats.loc[i, \"first_peak_time\"] = trace_params[\"time\"]\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pick_count</th>\n",
       "      <th>available_picks</th>\n",
       "      <th>first_peak_time</th>\n",
       "      <th>pick_count_cumsum</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2018-09-24T14:20:07.521000Z</td>\n",
       "      <td>10.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2018-10-04T21:44:08.474000Z</td>\n",
       "      <td>20.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2018-10-05T23:25:45.835000Z</td>\n",
       "      <td>32.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2018-10-07T22:02:11.773000Z</td>\n",
       "      <td>42.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pick_count  available_picks              first_peak_time  \\\n",
       "event                                                             \n",
       "0             0.0              0.0                          NaN   \n",
       "1            10.0              8.0  2018-09-24T14:20:07.521000Z   \n",
       "2            10.0             10.0  2018-10-04T21:44:08.474000Z   \n",
       "3            12.0             10.0  2018-10-05T23:25:45.835000Z   \n",
       "4            10.0             10.0  2018-10-07T22:02:11.773000Z   \n",
       "\n",
       "       pick_count_cumsum  split  \n",
       "event                            \n",
       "0                    0.0  train  \n",
       "1                   10.0  train  \n",
       "2                   20.0  train  \n",
       "3                   32.0  train  \n",
       "4                   42.0  train  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_stats, missing_mseeds = split_events(events, input_path)\n",
    "events_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f2e3d49-03da-4086-8d42-a77c2aa29c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>n</th>\n",
       "      <th>s</th>\n",
       "      <th>tr</th>\n",
       "      <th>f</th>\n",
       "      <th>last_dot</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>PL</td>\n",
       "      <td>BOG5</td>\n",
       "      <td>EHN.D</td>\n",
       "      <td>PL.BOG5..EHN.D.2018.267</td>\n",
       "      <td>19</td>\n",
       "      <td>267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>PL</td>\n",
       "      <td>BOG5</td>\n",
       "      <td>EHZ.D</td>\n",
       "      <td>PL.BOG5..EHZ.D.2018.267</td>\n",
       "      <td>19</td>\n",
       "      <td>267.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>PL</td>\n",
       "      <td>BOG5</td>\n",
       "      <td>EHE.D</td>\n",
       "      <td>PL.BOG5..EHE.D.2018.278</td>\n",
       "      <td>19</td>\n",
       "      <td>278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>PL</td>\n",
       "      <td>BOG5</td>\n",
       "      <td>EHZ.D</td>\n",
       "      <td>PL.BOG5..EHZ.D.2018.278</td>\n",
       "      <td>19</td>\n",
       "      <td>278.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>PL</td>\n",
       "      <td>BOG5</td>\n",
       "      <td>EHN.D</td>\n",
       "      <td>PL.BOG5..EHN.D.2018.281</td>\n",
       "      <td>19</td>\n",
       "      <td>281.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      y   n     s     tr                        f  last_dot      d\n",
       "0  2018  PL  BOG5  EHN.D  PL.BOG5..EHN.D.2018.267        19  267.0\n",
       "1  2018  PL  BOG5  EHZ.D  PL.BOG5..EHZ.D.2018.267        19  267.0\n",
       "2  2018  PL  BOG5  EHE.D  PL.BOG5..EHE.D.2018.278        19  278.0\n",
       "3  2018  PL  BOG5  EHZ.D  PL.BOG5..EHZ.D.2018.278        19  278.0\n",
       "4  2018  PL  BOG5  EHN.D  PL.BOG5..EHN.D.2018.281        19  281.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_mseeds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fadeaa9e-b7d7-48c7-a09c-872a545f000f",
   "metadata": {},
   "source": [
    "#### Check missing files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ffd246b8-118b-406a-bb87-4cd504970711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Missing mseeds grouped by year and station'}, xlabel='y,s'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_mseeds.groupby(['y','s']).size().plot(kind='bar', figsize=(20,4), title='Missing mseeds grouped by year and station')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24ee2524-6d1a-41a2-bc29-bb64905e8201",
   "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></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th>s</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <th>BOG5</th>\n",
       "      <td>267.0</td>\n",
       "      <td>281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2019</th>\n",
       "      <th>ALBE</th>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BARK</th>\n",
       "      <td>11.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYCO</th>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRAT</th>\n",
       "      <td>2.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2023</th>\n",
       "      <th>OSTR</th>\n",
       "      <td>10.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PIAS</th>\n",
       "      <td>12.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUCH</th>\n",
       "      <td>10.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROGO</th>\n",
       "      <td>12.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WESO</th>\n",
       "      <td>10.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             min    max\n",
       "y    s                 \n",
       "2018 BOG5  267.0  281.0\n",
       "2019 ALBE    2.0   99.0\n",
       "     BARK   11.0   99.0\n",
       "     CYCO    2.0   99.0\n",
       "     DRAT    2.0   99.0\n",
       "...          ...    ...\n",
       "2023 OSTR   10.0  149.0\n",
       "     PIAS   12.0  124.0\n",
       "     PUCH   10.0  149.0\n",
       "     ROGO   12.0  149.0\n",
       "     WESO   10.0  149.0\n",
       "\n",
       "[62 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_mseeds.groupby(['y', 's'])['d'].agg(['min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "211c37be-e5eb-40ed-bbe8-0d7a5e92ae77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "s\n",
       "ALBE    Axes(0.125,0.11;0.775x0.77)\n",
       "BARK    Axes(0.125,0.11;0.775x0.77)\n",
       "CYCO    Axes(0.125,0.11;0.775x0.77)\n",
       "DRAT    Axes(0.125,0.11;0.775x0.77)\n",
       "GARB    Axes(0.125,0.11;0.775x0.77)\n",
       "KANI    Axes(0.125,0.11;0.775x0.77)\n",
       "KOPI    Axes(0.125,0.11;0.775x0.77)\n",
       "OSTR    Axes(0.125,0.11;0.775x0.77)\n",
       "PIAS    Axes(0.125,0.11;0.775x0.77)\n",
       "PUCH    Axes(0.125,0.11;0.775x0.77)\n",
       "ROGO    Axes(0.125,0.11;0.775x0.77)\n",
       "WESO    Axes(0.125,0.11;0.775x0.77)\n",
       "Name: d, dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_mseeds[missing_mseeds.y=='2019'].groupby(['s'])['d'].plot(kind='hist', legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1ead7261-da65-4832-af20-f841a798febc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "2018       6\n",
       "2019    2540\n",
       "2020    4486\n",
       "2021    6507\n",
       "2022    6111\n",
       "2023    1379\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_mseeds.groupby('y').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "327f21de-64bc-49b1-849b-4d2d79b505ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2099, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_stats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a7407b0a-2efc-4c82-bf6a-8509dbdbc07c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='event'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "events_stats.pick_count_cumsum.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12dbaa70-a178-4867-a710-0d8147ce4ecb",
   "metadata": {},
   "source": [
    "### Transform mseeds to Seisbench format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80fc0198-6a1a-48e1-ada9-4faa048c6e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_trace(pick_time, traces): \n",
    "    for tr in traces: \n",
    "        if pick_time>tr.stats.endtime: \n",
    "            continue\n",
    "        if pick_time>=tr.stats.starttime: \n",
    "            # print(pick_time, \" - selected trace: \", tr)\n",
    "            return tr\n",
    "\n",
    "    print(pick_time, \" no matching trace \")\n",
    "    return None\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "60ee1d41-d3db-4661-ade7-882bd2371f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_trace(input_path, trace_params):\n",
    "    trace_path = get_trace_path(input_path, trace_params)\n",
    "    trace, stream = None, None\n",
    "\n",
    "    if not os.path.isfile(trace_path):\n",
    "        print(trace_path + \" not found\")\n",
    "    else:\n",
    "        stream = obspy.read(trace_path)\n",
    "        if len(stream.traces) > 1:\n",
    "            trace = find_trace(trace_params[\"time\"], stream.traces)\n",
    "        elif len(stream.traces) == 0:\n",
    "            print(\"no data in:\", trace_path)\n",
    "        else:\n",
    "            trace = stream.traces[0]\n",
    "\n",
    "    return trace, stream\n",
    "\n",
    "\n",
    "def load_stream(input_path, trace_params, time_before=60, time_after=60):\n",
    "    trace_path = get_trace_path(input_path, trace_params)\n",
    "    sampling_rate, stream = None, None\n",
    "    pick_time = trace_params[\"time\"]\n",
    "\n",
    "    if not os.path.isfile(trace_path):\n",
    "        print(trace_path + \" not found\")\n",
    "    else:\n",
    "        stream = obspy.read(trace_path)\n",
    "        stream = stream.slice(pick_time - time_before, pick_time + time_after)\n",
    "        if len(stream.traces) == 0:\n",
    "            print(f\"no data in: {trace_path}\")\n",
    "        else:\n",
    "            sampling_rate = stream.traces[0].stats.sampling_rate\n",
    "\n",
    "    return sampling_rate, stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7426f90a-4779-4c87-b445-4c41635a04f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_path = output_path + \"/metadata.csv\"\n",
    "waveforms_path = output_path + \"/waveforms.hdf5\"\n",
    "train = 0.7\n",
    "dev = 0.15\n",
    "test = 0.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc393ac1-791e-414a-892f-e8cf0d8e2a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "with sbd.WaveformDataWriter(metadata_path, waveforms_path) as writer:\n",
    "    writer.data_format = {\n",
    "        \"dimension_order\": \"CW\",\n",
    "        \"component_order\": \"ZNE\",\n",
    "    }\n",
    "    for i, event in enumerate(events):\n",
    "        logger.debug(f\"Converting {i} event\")\n",
    "        event_params = get_event_params(event)\n",
    "        event_params[\"split\"] = events_stats.loc[i, \"split\"]\n",
    "        #             b = False\n",
    "\n",
    "        for pick in event.picks:\n",
    "            trace_params = get_trace_params(pick)\n",
    "            sampling_rate, stream = load_stream(input_path, trace_params)\n",
    "            if stream is None:\n",
    "                continue\n",
    "\n",
    "            actual_t_start, data, _ = sbu.stream_to_array(\n",
    "                stream,\n",
    "                component_order=writer.data_format[\"component_order\"],\n",
    "            )\n",
    "\n",
    "            trace_params[\"trace_sampling_rate_hz\"] = sampling_rate\n",
    "            trace_params[\"trace_start_time\"] = str(actual_t_start)\n",
    "\n",
    "            pick_time = obspy.core.utcdatetime.UTCDateTime(trace_params[\"time\"])\n",
    "            pick_idx = (pick_time - actual_t_start) * sampling_rate\n",
    "\n",
    "            trace_params[f\"trace_{pick.phase_hint}_arrival_sample\"] = int(pick_idx)\n",
    "\n",
    "            writer.add_trace({**event_params, **trace_params}, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf46368-2e92-4aea-8fe8-298f3200f509",
   "metadata": {},
   "source": [
    "### Load created dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bcbefdd8-91b0-461b-8677-5b10325822d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sbd.WaveformDataset(output_path, sampling_rate=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77f8060d-0703-41ce-ade8-c738e7a8e540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seisbench.data.base.WaveformDataset at 0x14b58e696850>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc4f1ea-5e33-458d-82eb-ab6e5982ef63",
   "metadata": {},
   "source": [
    "#### Check train/dev/test proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "98f2ea18-18d8-4d7c-8e09-8c242842bda4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All samples:  6743\n",
      "Training examples: 4720 70.0%\n",
      "Development examples: 1009 15.0%\n",
      "Test examples: 1014 15.0 %\n"
     ]
    }
   ],
   "source": [
    "all_samples = len(data.train()) + len(data.dev()) + len(data.test())\n",
    "print(\"All samples: \", all_samples)\n",
    "print(f\"Training examples: {len(data.train())} {len(data.train())/all_samples * 100:.1f}%\" )\n",
    "print(f\"Development examples: {len(data.dev())} {len(data.dev())/all_samples * 100:.1f}%\")\n",
    "print(f\"Test examples: {len(data.test())} {len(data.test())/all_samples * 100:.1f} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ffd0f88-5a5c-4f21-8688-b34d8540ad30",
   "metadata": {},
   "source": [
    "#### Plot converted sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3af6da7-864f-490b-b260-733e49bd6d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "idx = 0\n",
    "ax.plot(data.get_waveforms(idx).T)\n",
    "ax.axvline(data.metadata[\"trace_Pg_arrival_sample\"].iloc[idx], color=\"green\", lw=1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "12d829c7-1478-4fc4-9b8f-565d4fdae824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "idx = 1\n",
    "ax.plot(data.get_waveforms(idx).T)\n",
    "# ax.axvline(data.metadata[\"trace_Pg_arrival_sample\"].iloc[idx], color=\"green\", lw=1)\n",
    "ax.axvline(data.metadata[\"trace_Sg_arrival_sample\"].iloc[idx], color=\"black\", lw=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3534d4e-13a4-4780-8f7d-7490ae3608c6",
   "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>trace_Pg_arrival_sample</th>\n",
       "      <th>trace_Sg_arrival_sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5999.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5999.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6738</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6739</th>\n",
       "      <td>5999.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6740</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6741</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6742</th>\n",
       "      <td>5999.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6743 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trace_Pg_arrival_sample  trace_Sg_arrival_sample\n",
       "0                      5999.0                      NaN\n",
       "1                         NaN                   5999.0\n",
       "2                         NaN                   6000.0\n",
       "3                      5999.0                      NaN\n",
       "4                      6000.0                      NaN\n",
       "...                       ...                      ...\n",
       "6738                      NaN                   5999.0\n",
       "6739                   5999.0                      NaN\n",
       "6740                   6000.0                      NaN\n",
       "6741                      NaN                   5999.0\n",
       "6742                   5999.0                      NaN\n",
       "\n",
       "[6743 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.metadata[[\"trace_Pg_arrival_sample\", \"trace_Sg_arrival_sample\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d023f5f-120e-46ea-bfed-ead098fb247f",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}