{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b70993fd",
   "metadata": {},
   "source": [
    "# Example Jupyter notebook to work with the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c05b6a",
   "metadata": {},
   "source": [
    "# Read in and plot the Apollo 12 Grade A catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5aed08ff-db7b-48f2-807a-b734b5656ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from obspy import read\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad08d22d-bab8-4bcb-b317-f67c3a634a2c",
   "metadata": {},
   "source": [
    "Let's take a look at the training data for the lunar dataset. In addition to the data itself, we include a catalog that will tell you which events happen when in the data. The catalog includes the name of the file, the absolute time, the relative time in seconds (relative to the start of the file), the event ID (evid), and the type of moonquake. The types of moonquakes include impacts, deep moonquakes, and shallow moonquakes. You do not have to worry about predicting the type of moonquakes, that's just fun information for you to know! \n",
    "\n",
    "**Note**: For your prediction, feel free to include either the absolute time or relative time, just make sure to mark it using the same header in the CSV file so we can easily score it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f7b59be",
   "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>filename</th>\n",
       "      <th>time_abs(%Y-%m-%dT%H:%M:%S.%f)</th>\n",
       "      <th>time_rel(sec)</th>\n",
       "      <th>evid</th>\n",
       "      <th>mq_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xa.s12.00.mhz.1970-01-19HR00_evid00002</td>\n",
       "      <td>1970-01-19T20:25:00.000000</td>\n",
       "      <td>73500.0</td>\n",
       "      <td>evid00002</td>\n",
       "      <td>impact_mq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>xa.s12.00.mhz.1970-03-25HR00_evid00003</td>\n",
       "      <td>1970-03-25T03:32:00.000000</td>\n",
       "      <td>12720.0</td>\n",
       "      <td>evid00003</td>\n",
       "      <td>impact_mq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>xa.s12.00.mhz.1970-03-26HR00_evid00004</td>\n",
       "      <td>1970-03-26T20:17:00.000000</td>\n",
       "      <td>73020.0</td>\n",
       "      <td>evid00004</td>\n",
       "      <td>impact_mq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>xa.s12.00.mhz.1970-04-25HR00_evid00006</td>\n",
       "      <td>1970-04-25T01:14:00.000000</td>\n",
       "      <td>4440.0</td>\n",
       "      <td>evid00006</td>\n",
       "      <td>impact_mq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>xa.s12.00.mhz.1970-04-26HR00_evid00007</td>\n",
       "      <td>1970-04-26T14:29:00.000000</td>\n",
       "      <td>52140.0</td>\n",
       "      <td>evid00007</td>\n",
       "      <td>deep_mq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>xa.s12.00.mhz.1974-10-14HR00_evid00156</td>\n",
       "      <td>1974-10-14T17:43:00.000000</td>\n",
       "      <td>63780.0</td>\n",
       "      <td>evid00156</td>\n",
       "      <td>impact_mq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>xa.s12.00.mhz.1975-04-12HR00_evid00191</td>\n",
       "      <td>1975-04-12T18:15:00.000000</td>\n",
       "      <td>65700.0</td>\n",
       "      <td>evid00191</td>\n",
       "      <td>impact_mq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>xa.s12.00.mhz.1975-05-04HR00_evid00192</td>\n",
       "      <td>1975-05-04T10:05:00.000000</td>\n",
       "      <td>36300.0</td>\n",
       "      <td>evid00192</td>\n",
       "      <td>impact_mq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>xa.s12.00.mhz.1975-06-24HR00_evid00196</td>\n",
       "      <td>1975-06-24T16:03:00.000000</td>\n",
       "      <td>57780.0</td>\n",
       "      <td>evid00196</td>\n",
       "      <td>impact_mq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>xa.s12.00.mhz.1975-06-26HR00_evid00198</td>\n",
       "      <td>1975-06-26T03:24:00.000000</td>\n",
       "      <td>12240.0</td>\n",
       "      <td>evid00198</td>\n",
       "      <td>impact_mq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  filename time_abs(%Y-%m-%dT%H:%M:%S.%f)  \\\n",
       "0   xa.s12.00.mhz.1970-01-19HR00_evid00002     1970-01-19T20:25:00.000000   \n",
       "1   xa.s12.00.mhz.1970-03-25HR00_evid00003     1970-03-25T03:32:00.000000   \n",
       "2   xa.s12.00.mhz.1970-03-26HR00_evid00004     1970-03-26T20:17:00.000000   \n",
       "3   xa.s12.00.mhz.1970-04-25HR00_evid00006     1970-04-25T01:14:00.000000   \n",
       "4   xa.s12.00.mhz.1970-04-26HR00_evid00007     1970-04-26T14:29:00.000000   \n",
       "..                                     ...                            ...   \n",
       "71  xa.s12.00.mhz.1974-10-14HR00_evid00156     1974-10-14T17:43:00.000000   \n",
       "72  xa.s12.00.mhz.1975-04-12HR00_evid00191     1975-04-12T18:15:00.000000   \n",
       "73  xa.s12.00.mhz.1975-05-04HR00_evid00192     1975-05-04T10:05:00.000000   \n",
       "74  xa.s12.00.mhz.1975-06-24HR00_evid00196     1975-06-24T16:03:00.000000   \n",
       "75  xa.s12.00.mhz.1975-06-26HR00_evid00198     1975-06-26T03:24:00.000000   \n",
       "\n",
       "    time_rel(sec)       evid    mq_type  \n",
       "0         73500.0  evid00002  impact_mq  \n",
       "1         12720.0  evid00003  impact_mq  \n",
       "2         73020.0  evid00004  impact_mq  \n",
       "3          4440.0  evid00006  impact_mq  \n",
       "4         52140.0  evid00007    deep_mq  \n",
       "..            ...        ...        ...  \n",
       "71        63780.0  evid00156  impact_mq  \n",
       "72        65700.0  evid00191  impact_mq  \n",
       "73        36300.0  evid00192  impact_mq  \n",
       "74        57780.0  evid00196  impact_mq  \n",
       "75        12240.0  evid00198  impact_mq  \n",
       "\n",
       "[76 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_directory = './data/lunar/training/catalogs/'\n",
    "cat_file = cat_directory + 'apollo12_catalog_GradeA_final.csv'\n",
    "cat = pd.read_csv(cat_file)\n",
    "cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49408b90",
   "metadata": {},
   "source": [
    "## Select a detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d1d115d-a75c-4d0b-a3e9-0ef66f2dc479",
   "metadata": {},
   "source": [
    "Let's pick the first seismic event in the catalog and let's take a look at the absolute time data. The way we show it here is by using pandas `.iloc` and datetime `.strptime`. We are going to keep the format shown in the absolute time header, which is `'%Y-%m-%dT%H:%M:%S.%f'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e23be5d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1970, 6, 26, 20, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = cat.iloc[6]\n",
    "arrival_time = datetime.strptime(row['time_abs(%Y-%m-%dT%H:%M:%S.%f)'],'%Y-%m-%dT%H:%M:%S.%f')\n",
    "arrival_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "81ba2b31-465d-4c50-95de-978b75de2878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72060.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If we want the value of relative time, we don't need to use datetime\n",
    "arrival_time_rel = row['time_rel(sec)']\n",
    "arrival_time_rel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01e9bf2a-c873-43b1-82dd-2d9109ae39b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'xa.s12.00.mhz.1970-06-26HR00_evid00009'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's also get the name of the file\n",
    "test_filename = row.filename\n",
    "test_filename"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e686ca29",
   "metadata": {},
   "source": [
    "## Read the CSV file corresponding to that detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a076b368-9982-4cad-ab53-06f462927aad",
   "metadata": {},
   "source": [
    "We will now find the csv data file corresponding to that time and plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e033b67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_abs(%Y-%m-%dT%H:%M:%S.%f)</th>\n",
       "      <th>time_rel(sec)</th>\n",
       "      <th>velocity(m/s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970-06-26T00:00:00.116000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.727977e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-06-26T00:00:00.266943</td>\n",
       "      <td>0.150943</td>\n",
       "      <td>-8.646711e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-06-26T00:00:00.417887</td>\n",
       "      <td>0.301887</td>\n",
       "      <td>-9.298738e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970-06-26T00:00:00.568830</td>\n",
       "      <td>0.452830</td>\n",
       "      <td>-8.589095e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970-06-26T00:00:00.719774</td>\n",
       "      <td>0.603774</td>\n",
       "      <td>-7.139047e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572418</th>\n",
       "      <td>1970-06-27T00:00:02.832981</td>\n",
       "      <td>86402.716981</td>\n",
       "      <td>5.039820e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572419</th>\n",
       "      <td>1970-06-27T00:00:02.983925</td>\n",
       "      <td>86402.867925</td>\n",
       "      <td>-9.191068e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572420</th>\n",
       "      <td>1970-06-27T00:00:03.134868</td>\n",
       "      <td>86403.018868</td>\n",
       "      <td>-2.796955e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572421</th>\n",
       "      <td>1970-06-27T00:00:03.285811</td>\n",
       "      <td>86403.169811</td>\n",
       "      <td>-9.037156e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572422</th>\n",
       "      <td>1970-06-27T00:00:03.436755</td>\n",
       "      <td>86403.320755</td>\n",
       "      <td>-2.439395e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>572423 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time_abs(%Y-%m-%dT%H:%M:%S.%f)  time_rel(sec)  velocity(m/s)\n",
       "0          1970-06-26T00:00:00.116000       0.000000  -6.727977e-16\n",
       "1          1970-06-26T00:00:00.266943       0.150943  -8.646711e-16\n",
       "2          1970-06-26T00:00:00.417887       0.301887  -9.298738e-16\n",
       "3          1970-06-26T00:00:00.568830       0.452830  -8.589095e-16\n",
       "4          1970-06-26T00:00:00.719774       0.603774  -7.139047e-16\n",
       "...                               ...            ...            ...\n",
       "572418     1970-06-27T00:00:02.832981   86402.716981   5.039820e-17\n",
       "572419     1970-06-27T00:00:02.983925   86402.867925  -9.191068e-18\n",
       "572420     1970-06-27T00:00:03.134868   86403.018868  -2.796955e-17\n",
       "572421     1970-06-27T00:00:03.285811   86403.169811  -9.037156e-17\n",
       "572422     1970-06-27T00:00:03.436755   86403.320755  -2.439395e-16\n",
       "\n",
       "[572423 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_directory = './data/lunar/training/data/S12_GradeA/'\n",
    "csv_file = f'{data_directory}{test_filename}.csv'\n",
    "data_cat = pd.read_csv(csv_file)\n",
    "data_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "465d3473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27b41dbca30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in time steps and velocities\n",
    "csv_times = np.array(data_cat['time_rel(sec)'].tolist())\n",
    "csv_data = np.array(data_cat['velocity(m/s)'].tolist())\n",
    "\n",
    "# Plot the trace! \n",
    "fig,ax = plt.subplots(1,1,figsize=(10,3))\n",
    "ax.plot(csv_times,csv_data)\n",
    "\n",
    "# Make the plot pretty\n",
    "ax.set_xlim([min(csv_times),max(csv_times)])\n",
    "ax.set_ylabel('Velocity (m/s)')\n",
    "ax.set_xlabel('Time (s)')\n",
    "ax.set_title(f'{test_filename}', fontweight='bold')\n",
    "\n",
    "# Plot where the arrival time is\n",
    "arrival_line = ax.axvline(x=arrival_time_rel, c='red', label='Rel. Arrival')\n",
    "ax.legend(handles=[arrival_line])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68b6174e-ced7-4cad-8c09-9b63890aa12b",
   "metadata": {},
   "source": [
    "What if you wanted to plot in absolute time instead? The operations are very similar, just with a little extra datetime. It takes a bit longer, so we recommend working in relative time to start with!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80794f96-443e-43e9-8efa-3c86ee0101d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27b47983ee0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in time steps and velocities\n",
    "csv_times_dt = []\n",
    "for absval_str in data_cat['time_abs(%Y-%m-%dT%H:%M:%S.%f)'].values:\n",
    "    csv_times_dt.append(datetime.strptime(absval_str,'%Y-%m-%dT%H:%M:%S.%f'))\n",
    "\n",
    "csv_data = np.array(data_cat['velocity(m/s)'].tolist())\n",
    "\n",
    "# Plot the trace! \n",
    "fig,ax = plt.subplots(1,1,figsize=(10,3))\n",
    "ax.plot(csv_times_dt,csv_data)\n",
    "\n",
    "# Make the plot pretty\n",
    "ax.set_xlim((np.min(csv_times_dt),np.max(csv_times_dt)))\n",
    "ax.set_ylabel('Velocity (m/s)')\n",
    "ax.set_xlabel('Time (month-day hour)')\n",
    "ax.set_title(f'{test_filename}', fontweight='bold')\n",
    "\n",
    "# Plot where the arrival time is\n",
    "arrival_line = ax.axvline(x=arrival_time, c='red', label='Abs. Arrival')\n",
    "ax.legend(handles=[arrival_line])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f32101",
   "metadata": {},
   "source": [
    "### Alternatively: read the miniseed file corresponding to that detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab68f39-5709-417f-b1e4-b4bc550f5753",
   "metadata": {},
   "source": [
    "Same procedure as above, just using the miniseed file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c25af2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 Trace(s) in Stream:\n",
       "XA.S12.00.MHZ | 1970-06-26T00:00:00.116000Z - 1970-06-27T00:00:03.436755Z | 6.6 Hz, 572423 samples"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_directory = './data/lunar/training/data/S12_GradeA/'\n",
    "mseed_file = f'{data_directory}{test_filename}.mseed'\n",
    "st = read(mseed_file)\n",
    "st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d2bf045-75bb-479a-abe1-48489190242a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         network: XA\n",
       "         station: S12\n",
       "        location: 00\n",
       "         channel: MHZ\n",
       "       starttime: 1970-06-26T00:00:00.116000Z\n",
       "         endtime: 1970-06-27T00:00:03.436755Z\n",
       "   sampling_rate: 6.625\n",
       "           delta: 0.1509433962264151\n",
       "            npts: 572423\n",
       "           calib: 1.0\n",
       "         _format: MSEED\n",
       "           mseed: AttribDict({'dataquality': 'D', 'number_of_records': 1136, 'encoding': 'FLOAT64', 'byteorder': '>', 'record_length': 4096, 'filesize': 4653056})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The stream file also contains some useful header information\n",
    "st[0].stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af3b24db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72059.884"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is how you get the data and the time, which is in seconds\n",
    "tr = st.traces[0].copy()\n",
    "tr_times = tr.times()\n",
    "tr_data = tr.data\n",
    "\n",
    "# Start time of trace (another way to get the relative arrival time using datetime)\n",
    "starttime = tr.stats.starttime.datetime\n",
    "arrival = (arrival_time - starttime).total_seconds()\n",
    "arrival"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591e4fbe",
   "metadata": {},
   "source": [
    "### Plot the trace and mark the arrival! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb8314a-c791-41b7-be49-e46455c2ab22",
   "metadata": {},
   "source": [
    "Use a similar method to plot the miniseed data and seismic arrival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ebcd8eee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'xa.s12.00.mhz.1970-06-26HR00_evid00009')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize figure\n",
    "fig,ax = plt.subplots(1,1,figsize=(10,3))\n",
    "\n",
    "# Plot trace\n",
    "ax.plot(tr_times,tr_data)\n",
    "\n",
    "# Mark detection\n",
    "ax.axvline(x = arrival, color='red',label='Rel. Arrival')\n",
    "ax.legend(loc='upper left')\n",
    "\n",
    "# Make the plot pretty\n",
    "ax.set_xlim([min(tr_times),max(tr_times)])\n",
    "ax.set_ylabel('Velocity (m/s)')\n",
    "ax.set_xlabel('Time (s)')\n",
    "ax.set_title(f'{test_filename}', fontweight='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa6219e-dea6-4c3f-9d28-5859c69f4c2d",
   "metadata": {},
   "source": [
    "There are multiple ways that we can do the absolute time using datetime, here is a simple way using the `.timedelta` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e28cb25b-5b3e-446a-8814-d84281eb1bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'xa.s12.00.mhz.1970-06-26HR00_evid00009')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a vector for the absolute time\n",
    "tr_times_dt = []\n",
    "for tr_val in tr_times:\n",
    "    tr_times_dt.append(starttime + timedelta(seconds=tr_val))\n",
    "\n",
    "# Plot the absolute result\n",
    "fig,ax = plt.subplots(1,1,figsize=(10,3))\n",
    "\n",
    "# Plot trace\n",
    "ax.plot(tr_times_dt,tr_data)\n",
    "\n",
    "# Mark detection\n",
    "arrival_line = ax.axvline(x=arrival_time, c='red', label='Abs. Arrival')\n",
    "ax.legend(handles=[arrival_line])\n",
    "\n",
    "# Make the plot pretty\n",
    "ax.set_xlim([min(tr_times_dt),max(tr_times_dt)])\n",
    "ax.set_ylabel('Velocity (m/s)')\n",
    "ax.set_xlabel('Time (s)')\n",
    "ax.set_title(f'{test_filename}', fontweight='bold')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d28df9-ecd2-42df-b0be-ee3fd87fad2f",
   "metadata": {},
   "source": [
    "It's completely up to you whether to work with the CSV file or the miniseed files. We recommend working with the miniseed file as it's a bit faster to run. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a970ebb9",
   "metadata": {},
   "source": [
    "## Let's filter the trace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d28e0d-5255-4fa9-b0ba-9cd8dbbed342",
   "metadata": {},
   "source": [
    "Sometimes, it's useful to filter the trace to bring out particular frequencies. This will change the shape of the data and make it easier to see certain parts of the signal. In this example, we will filter the data using a bandpass filter between 0.01 Hz to 0.5 Hz. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed7e1526",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the minimum frequency\n",
    "minfreq = 0.5\n",
    "maxfreq = 1.0\n",
    "\n",
    "# Going to create a separate trace for the filter data\n",
    "st_filt = st.copy()\n",
    "st_filt.filter('bandpass',freqmin=minfreq,freqmax=maxfreq)\n",
    "tr_filt = st_filt.traces[0].copy()\n",
    "tr_times_filt = tr_filt.times()\n",
    "tr_data_filt = tr_filt.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d0fe643-11cf-4d3e-9022-041350458d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To better see the patterns, we will create a spectrogram using the scipy function\n",
    "# It requires the sampling rate, which we can get from the miniseed header as shown a few cells above\n",
    "from scipy import signal\n",
    "from matplotlib import cm\n",
    "f, t, sxx = signal.spectrogram(tr_data_filt, tr_filt.stats.sampling_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32664fb0-d9e0-4e5a-bc01-7b9d32f0b36f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the time series and spectrogram\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = plt.subplot(2, 1, 1)\n",
    "# Plot trace\n",
    "ax.plot(tr_times_filt,tr_data_filt)\n",
    "\n",
    "# Mark detection\n",
    "ax.axvline(x = arrival, color='red',label='Detection')\n",
    "ax.legend(loc='upper left')\n",
    "\n",
    "# Make the plot pretty\n",
    "ax.set_xlim([min(tr_times_filt),max(tr_times_filt)])\n",
    "ax.set_ylabel('Velocity (m/s)')\n",
    "ax.set_xlabel('Time (s)')\n",
    "\n",
    "ax2 = plt.subplot(2, 1, 2)\n",
    "vals = ax2.pcolormesh(t, f, sxx, cmap=cm.jet, vmax=5e-17)\n",
    "ax2.set_xlim([min(tr_times_filt),max(tr_times_filt)])\n",
    "ax2.set_xlabel(f'Time (Day Hour:Minute)', fontweight='bold')\n",
    "ax2.set_ylabel('Frequency (Hz)', fontweight='bold')\n",
    "ax2.axvline(x=arrival, c='red')\n",
    "cbar = plt.colorbar(vals, orientation='horizontal')\n",
    "cbar.set_label('Power ((m/s)^2/sqrt(Hz))', fontweight='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b10d9ddf",
   "metadata": {},
   "source": [
    "# Sample short-term average / long-term average (STA/LTA) detection algorithm\n",
    "\n",
    "A STA/LTA algorithm moves two time windows of two lengths (one short, one long) across the seismic data. The algorithm calculates the average amplitude in both windows, and calculates the ratio between them. If the data contains an earthquake, then the short-term window containing the earthquake will be much larger than the long-term window -- resulting in a detection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "094d0348",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Characteristic function')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from obspy.signal.invsim import cosine_taper\n",
    "from obspy.signal.filter import highpass\n",
    "from obspy.signal.trigger import classic_sta_lta, plot_trigger, trigger_onset\n",
    "\n",
    "# Sampling frequency of our trace\n",
    "df = tr.stats.sampling_rate\n",
    "\n",
    "# How long should the short-term and long-term window be, in seconds?\n",
    "sta_len = 120\n",
    "lta_len = 600\n",
    "\n",
    "# Run Obspy's STA/LTA to obtain a characteristic function\n",
    "# This function basically calculates the ratio of amplitude between the short-term \n",
    "# and long-term windows, moving consecutively in time across the data\n",
    "cft = classic_sta_lta(tr_data, int(sta_len * df), int(lta_len * df))\n",
    "\n",
    "# Plot characteristic function\n",
    "fig,ax = plt.subplots(1,1,figsize=(12,3))\n",
    "ax.plot(tr_times,cft)\n",
    "ax.set_xlim([min(tr_times),max(tr_times)])\n",
    "ax.set_xlabel('Time (s)')\n",
    "ax.set_ylabel('Characteristic function')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa5e1992",
   "metadata": {},
   "source": [
    "Next, we define the values of the characteristic function (i.e. amplitude ratio between short-term and long-term windows) where we flag a seismic detection. These values are called triggers. There are two types of triggers -- \"on\" and \"off\", defined as follows:\n",
    "\n",
    "1. \"on\" : If the characteristic function is above this value, then a seismic event begins. \n",
    "2. \"off\" : If the characteristic function falls below this value (after an \"on\" trigger), than a seismic event ends. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "315915f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27b01c16a60>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Play around with the on and off triggers, based on values in the characteristic function\n",
    "thr_on = 4\n",
    "thr_off = 1.5\n",
    "on_off = np.array(trigger_onset(cft, thr_on, thr_off))\n",
    "# The first column contains the indices where the trigger is turned \"on\". \n",
    "# The second column contains the indices where the trigger is turned \"off\".\n",
    "\n",
    "# Plot on and off triggers\n",
    "fig,ax = plt.subplots(1,1,figsize=(12,3))\n",
    "for i in np.arange(0,len(on_off)):\n",
    "    triggers = on_off[i]\n",
    "    ax.axvline(x = tr_times[triggers[0]], color='red', label='Trig. On')\n",
    "    ax.axvline(x = tr_times[triggers[1]], color='purple', label='Trig. Off')\n",
    "\n",
    "# Plot seismogram\n",
    "ax.plot(tr_times,tr_data)\n",
    "ax.set_xlim([min(tr_times),max(tr_times)])\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776b281b-911b-424a-8c2d-8ad2e4834da4",
   "metadata": {},
   "source": [
    "**Note**: You do not have to worry about marking the end of the seismic trace (as you can see, even for us it's not very accurate!). For this challenge, all we care about is the start of the seismic waveform."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9241cc8",
   "metadata": {},
   "source": [
    "## Sample detection export into a catalog! \n",
    "There are many ways to do this, but we'll show a way to do it using pandas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "490d0952",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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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>filename</th>\n",
       "      <th>time_abs(%Y-%m-%dT%H:%M:%S.%f)</th>\n",
       "      <th>time_rel(sec)</th>\n",
       "    </tr>\n",
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       "      <td>xa.s12.00.mhz.1970-06-26HR00_evid00009</td>\n",
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       "      <td>72201.207547</td>\n",
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       "                                 filename time_abs(%Y-%m-%dT%H:%M:%S.%f)  \\\n",
       "0  xa.s12.00.mhz.1970-06-26HR00_evid00009     1970-06-26T20:03:21.323547   \n",
       "\n",
       "   time_rel(sec)  \n",
       "0   72201.207547  "
      ]
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     "execution_count": 19,
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   "source": [
    "# File name and start time of trace\n",
    "fname = row.filename\n",
    "starttime = tr.stats.starttime.datetime\n",
    "\n",
    "# Iterate through detection times and compile them\n",
    "detection_times = []\n",
    "fnames = []\n",
    "for i in np.arange(0,len(on_off)):\n",
    "    triggers = on_off[i]\n",
    "    on_time = starttime + timedelta(seconds = tr_times[triggers[0]])\n",
    "    on_time_str = datetime.strftime(on_time,'%Y-%m-%dT%H:%M:%S.%f')\n",
    "    detection_times.append(on_time_str)\n",
    "    fnames.append(fname)\n",
    "    \n",
    "# Compile dataframe of detections\n",
    "detect_df = pd.DataFrame(data = {'filename':fnames, 'time_abs(%Y-%m-%dT%H:%M:%S.%f)':detection_times, 'time_rel(sec)':tr_times[triggers[0]]})\n",
    "detect_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b25a395-0a93-4804-8d88-0cd0a1d78681",
   "metadata": {},
   "source": [
    "This can then be exported to a csv using:\n",
    "\n",
    "`detect_df.to_csv('output/path/catalog.csv', index=False)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38478a52-6e57-453b-9885-71d05679a841",
   "metadata": {},
   "source": [
    "# Download additional data from Earth-based stations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1cf964b-f01b-4317-bb9b-cc4150b24582",
   "metadata": {},
   "source": [
    "You may find that you need to download additional data from Earth stations to supplement your models and algorithms. We recommend that you download any events from IRIS (Incorporated Research Institutations for Seismology).\n",
    "\n",
    "https://www.iris.edu/hq/\n",
    "\n",
    "**Note**: The organization has been recently renamed to SAGE (Seismological Facility for the Advancement of Geoscience), but all the previous links should still work. \n",
    "\n",
    "They maintain and curate data from seismic stations all around the world. There are many different ways to get data from them, but I recommend using the utility *PyWeed*:\n",
    "\n",
    "https://ds.iris.edu/ds/nodes/dmc/software/downloads/pyweed/\n",
    "\n",
    "We can use the utility to select seismic stations and the earthquake data (or **events**) recorded at those stations.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/civilinifr/fc_deeplearning/master/eq_detector_images/station_data_location.png\" alt=\"Drawing\" style=\"width: 1200px;\">\n",
    "\n",
    "For this test case, let's download all of the earthquakes magnitude 3 and above that are within 1 degree distance (approximately 110 km) from a site called PFO (Pinon Flat Observatory) in California. **Location** is a number designating the instrument at a particular site (sites may have multiple instruments), and **channel** is an IRIS code that specifies instrument information. \n",
    "\n",
    "In short, the first latter refers to the samplerate of the instrument (how many data points it records per second), the second to the type of instrument (certain types of seismometers are better at recording nearby earthquakes while others are more suited for distant earthquakes), and the last to the directional component being recored (most seismometers will record motion across two horizontal directions and the vertical). We will pick the channel HHZ, which refers to a (H) high-samplerate (100 samples per second) (H) strong-motion accelerometer (best resolution for nearby strong earthquakes) recording in the (Z) vertical direction. Once you've selected all the earthquakes, you can download the traces. \n",
    "\n",
    "An earthquake is composed of the following types of waves (in order): pressure (P-wave), shear (S-wave), and surface (Rayleigh and Love). For our challenge, we are only interested in identifying the start of the earthquake. The IRIS dataset contains P-wave arrivals (onset of the P-wave at the seismometer) for each earthquake. In order to get noise prior to the earthquake arrival, we pick our data traces to span 101 seconds before to 60 seconds past the P-wave arrival:\n",
    "\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/civilinifr/fc_deeplearning/master/eq_detector_images/selected_seismograms.png\" alt=\"Drawing\" style=\"width: 1200px;\">\n",
    "\n",
    "\n",
    "As you can see from the output list, some of the earthquakes don't record any earthquake data (3.4 Ml 2005-08-31) and others have an incorrect P-wave arrival time (4.0 Ml 2005-08-31). Make sure to go through the earthquakes and remove those types of events from the waveform preview prior to download. For output file type, choose miniseed to match the planetary data (SAC is probably fine too, but the file sizes tend to be a bit bigger). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ead268-58bb-43b2-96db-b8236cc7af05",
   "metadata": {},
   "source": [
    "## Thank you very much for being a part of this challenge! Good luck!!!"
   ]
  }
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