pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9948e21a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import hashlib\n",
    "import io\n",
    "import json\n",
    "import numpy as np\n",
    "import os\n",
    "import requests\n",
    "from datetime import datetime, timedelta\n",
    "from pathlib import Path\n",
    "\n",
    "import dateparser\n",
    "import keyring\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "class MovebankAPI:\n",
    "    def __init__(self, username, password, study_id=None):\n",
    "        self.username = username\n",
    "        self.password = password\n",
    "        self.study_id = study_id\n",
    "\n",
    "    def callMovebankAPI(self, params):\n",
    "        \"\"\"\"\n",
    "        params: Requests Movebank API with ((param1, value1), (param2, value2),).\n",
    "        Return the API response as plain text.\n",
    "        \"\"\"\n",
    "        response = requests.get(\n",
    "            'https://www.movebank.org/movebank/service/direct-read',\n",
    "            params=params,\n",
    "            auth=(self.username, self.password))\n",
    "        print(\"Request \" + response.url)\n",
    "        if response.status_code == 200:  # successful request\n",
    "            if 'License Terms:' in str(response.content):\n",
    "                # only the license terms are returned, hash and append them in a\n",
    "                # subsequent request.\n",
    "                # See also\n",
    "                # https://github.com/movebank/movebank-api-doc/blob/master/movebank\n",
    "                # api.md#read-and-accept-license-terms-using-curl\n",
    "                print(\"Has license terms\")\n",
    "                hash = hashlib.md5(response.content).hexdigest()\n",
    "                params = params + (('license-md5', hash), )\n",
    "                # also attach previous cookie:\n",
    "                response = requests.get(\n",
    "                    'https://www.movebank.org/movebank/service/direct-read',\n",
    "                    params=params,\n",
    "                    cookies=response.cookies,\n",
    "                    auth=(self.username, self.password))\n",
    "                if response.status_code == 403:  # incorrect hash\n",
    "                    print(\"Incorrect hash\")\n",
    "                    return ''\n",
    "            return response.content.decode('utf-8')\n",
    "        print(str(response.content))\n",
    "        return str(response.content)\n",
    "\n",
    "    def getStudies(self):\n",
    "        studies = self.callMovebankAPI(\n",
    "            (('entity_type', 'study'), ('i_can_see_data', 'true'),\n",
    "             ('there_are_data_which_i_cannot_see', 'false')))\n",
    "        if len(studies) > 0:\n",
    "            # parse raw text to dicts\n",
    "            studies = csv.DictReader(io.StringIO(studies), delimiter=',')\n",
    "            all_studies = [\n",
    "                s for s in studies if s['i_can_see_data'] == 'true'\n",
    "                and s['there_are_data_which_i_cannot_see'] == 'false'\n",
    "            ]\n",
    "            return all_studies\n",
    "\n",
    "    @staticmethod\n",
    "    def getStudiesBySensor(studies, sensorname='GPS'):\n",
    "        return [s for s in studies if sensorname in s['sensor_type_ids']]\n",
    "\n",
    "    def getIndividualsByStudy(self):\n",
    "        individuals = self.callMovebankAPI(\n",
    "            (('entity_type', 'individual'), ('study_id', self.study_id)))\n",
    "        if len(individuals) > 0:\n",
    "            return list(csv.DictReader(io.StringIO(individuals),\n",
    "                                       delimiter=','))\n",
    "\n",
    "    def getIndividualEvents(self,\n",
    "                            individual_id,\n",
    "                            sensor_type_id=653,\n",
    "                            transform=False):\n",
    "        \"\"\"\n",
    "        SENSORS\n",
    "        ===============================================================================\n",
    "        description,external_id,id,is_location_sensor,name\n",
    "        \"\",\"bird-ring\",397,true,\"Bird Ring\"\n",
    "        \"\",\"gps\",653,true,\"GPS\"\n",
    "        \"\",\"radio-transmitter\",673,true,\"Radio Transmitter\"\n",
    "        \"\",\"argos-doppler-shift\",82798,true,\"Argos Doppler Shift\"\n",
    "        \"\",\"natural-mark\",2365682,true,\"Natural Mark\"\n",
    "        \"\",\"acceleration\",2365683,false,\"Acceleration\"\n",
    "        \"\",\"solar-geolocator\",3886361,true,\"Solar Geolocator\"\n",
    "        \"\",\"accessory-measurements\",7842954,false,\"Accessory Measurements\"\n",
    "        \"\",\"solar-geolocator-raw\",9301403,false,\"Solar Geolocator Raw\"\n",
    "        \"\",\"barometer\",77740391,false,\"Barometer\"\n",
    "        \"\",\"magnetometer\",77740402,false,\"Magnetometer\"\n",
    "        \"\",\"orientation\",819073350,false,\"Orientation\"\n",
    "        \"\",\"solar-geolocator-twilight\",914097241,false,\"Solar Geolocator Twilight\"\n",
    "        \"\"\"\n",
    "        params = (('entity_type', 'event'), ('study_id', self.study_id),\n",
    "                  ('individual_id', individual_id),\n",
    "                  ('sensor_type_id', sensor_type_id), ('attributes', 'all'))\n",
    "        events_ = self.callMovebankAPI(params)\n",
    "        if events_:\n",
    "            events = list(csv.DictReader(io.StringIO(events_), delimiter=','))\n",
    "            if sensor_type_id == 653 and transform:\n",
    "                return self.transformRawGPS(events)\n",
    "            elif sensor_type_id == 2365683 and transform:\n",
    "                return self.transformRawACC(events)\n",
    "            else:\n",
    "                return events\n",
    "\n",
    "    @staticmethod\n",
    "    def transformRawGPS(gpsevents):\n",
    "        # Returns a list of (ts, deployment_id, lat, long) tuples\n",
    "\n",
    "        def transform(e):  # dimension reduction and data type conversion\n",
    "            try:\n",
    "                if len(e['location_lat']) > 0:\n",
    "                    e['location_lat'] = float(e['location_lat'])\n",
    "                if len(e['location_long']) > 0:\n",
    "                    e['location_long'] = float(e['location_long'])\n",
    "            except:\n",
    "                print(\"Could not parse long/lat.\")\n",
    "            return e['timestamp'], e['deployment_id'], e['location_lat'], e[\n",
    "                'location_long']\n",
    "\n",
    "        return [transform(e) for e in gpsevents]\n",
    "\n",
    "    @staticmethod\n",
    "    def transformRawACC(accevents, unit='m/s2', sensitivity='high'):\n",
    "        #  Transforms raw tri-axial acceleration from X Y Z X Y X Y Z to [(ts_interpol,\n",
    "        # deployment, X', Y', Z'),...]\n",
    "        #  X', Y', Z' are in m/s^2 or g. Assumes e-obs acceleration sensors.\n",
    "        #  Acknowledgments to Anne K. Scharf and her great moveACC package, see\n",
    "        # https://gitlab.com/anneks/moveACC\n",
    "\n",
    "        ts_format = '%Y-%m-%d %H:%M:%S.%f'\n",
    "        out = []\n",
    "\n",
    "        if unit == 'g':\n",
    "            unitfactor = 1\n",
    "        else:\n",
    "            unitfactor = 9.81\n",
    "\n",
    "        tag_local_identifier = int(accevents[0]['tag_local_identifier'])\n",
    "        slope = 0.001  # e-obs 1st generation, high sensitivity\n",
    "\n",
    "        if tag_local_identifier <= 2241:\n",
    "            if sensitivity == 'low':\n",
    "                slope = 0.0027\n",
    "        elif 2242 <= tag_local_identifier <= 4117:  # e-obs 2nd generation\n",
    "            slope = 0.0022\n",
    "        else:\n",
    "            slope = 1 / 512\n",
    "\n",
    "        for event in accevents:\n",
    "            deploym = event['deployment_id']\n",
    "            seconds = 1 / float(\n",
    "                event['acceleration_sampling_frequency_per_axis'])\n",
    "            parsedts = datetime.strptime(event['timestamp'],\n",
    "                                         ts_format)  # start timestamp\n",
    "            raw = list(map(int, event['accelerations_raw'].split()))\n",
    "\n",
    "            #  derive in-between timestamps:\n",
    "            ts = [\n",
    "                parsedts + timedelta(seconds=seconds * x)\n",
    "                for x in range(0, int(len(raw) / 3))\n",
    "            ]\n",
    "\n",
    "            #  transform XYZ list to list of (ts, deployment, x, y, z) tuples\n",
    "            it = iter(raw)\n",
    "            transformed = [(a.strftime(ts_format), deploym,\n",
    "                            (b[0] - 2048) * slope * unitfactor,\n",
    "                            (b[1] - 2048) * slope * unitfactor,\n",
    "                            (b[2] - 2048) * slope * unitfactor)\n",
    "                           for (a, b) in list(zip(ts, list(zip(it, it, it))))]\n",
    "            out.append(transformed)\n",
    "        return out\n",
    "\n",
    "    @staticmethod\n",
    "    def _pprint(list_):\n",
    "        print(json.dumps(list_, indent=4))\n",
    "\n",
    "    @staticmethod\n",
    "    def to_pandas(list_, sensor_type=None, save_to=None, transformed=False):\n",
    "        if sensor_type and sensor_type.lower() == 'acc' and transformed:\n",
    "            arr = np.array(list_)\n",
    "            m, n, r = arr.shape\n",
    "            out_arr = np.column_stack((np.repeat(np.arange(m),\n",
    "                                                 n), arr.reshape(m * n, -1)))\n",
    "            df = pd.DataFrame(\n",
    "                out_arr,\n",
    "                columns=[\n",
    "                    'idx', 'timestamp', 'deployment_id', 'AccX', 'AccY', 'AccZ'\n",
    "                ],\n",
    "            )\n",
    "            df.drop(columns=['idx'], inplace=True)\n",
    "            df = df.astype({\n",
    "                'timestamp': 'datetime64[ns]',\n",
    "                'deployment_id': 'int32',\n",
    "                'AccX': 'float32',\n",
    "                'AccY': 'float32',\n",
    "                'AccZ': 'float32'\n",
    "            })\n",
    "        elif sensor_type and sensor_type.lower() == 'gps' and transformed:\n",
    "            df = pd.DataFrame(list_,\n",
    "                              columns=[\n",
    "                                  'timestamp', 'deployment_id', 'location_lat',\n",
    "                                  'location_long'\n",
    "                              ])\n",
    "            df = df.astype({\n",
    "                'timestamp': 'datetime64[ns]',\n",
    "                'deployment_id': 'int32',\n",
    "                'location_lat': 'float32',\n",
    "                'location_long': 'float32'\n",
    "            })\n",
    "        else:\n",
    "            df = pd.DataFrame(list_)\n",
    "        if save_to:\n",
    "            df.to_csv(save_to, index=False)\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ccac011",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleMovebankAPI:\n",
    "    def __init__(self, mb):\n",
    "        self.mb = mb\n",
    "\n",
    "    def get_cat_id(self, simple_id=None, all_=False):\n",
    "        individuals = mb.getIndividualsByStudy()\n",
    "        if all_:\n",
    "            return individuals\n",
    "        else:\n",
    "            individual_id = [\n",
    "                x for x in individuals if x['local_identifier'] == simple_id\n",
    "            ][0]['id']\n",
    "            return individual_id\n",
    "\n",
    "    def get_gps(self, individual_id, save=None):\n",
    "        gpsevents = mb.getIndividualEvents(individual_id=individual_id,\n",
    "                                           sensor_type_id=653,\n",
    "                                           transform=False)\n",
    "        gps_df = mb.to_pandas(gpsevents, sensor_type='gps', transformed=False)\n",
    "        if save:\n",
    "            ps_df.to_csv(save, index=False)\n",
    "        return gps_df\n",
    "\n",
    "    def get_acc(self, individual_id, save=None):\n",
    "        accevents = mb.getIndividualEvents(\n",
    "            individual_id=individual_id,\n",
    "            sensor_type_id=2365683,\n",
    "            transform=True,\n",
    "        )\n",
    "        acc_df = mb.to_pandas(accevents, sensor_type='acc', transformed=True)\n",
    "        return acc_df\n",
    "    \n",
    "    @staticmethod\n",
    "    def acc_section(acc_df, start_timestamp, duration, save=None):\n",
    "        # start_timestamp format: 2021-09-29 14:10:12\n",
    "        duration = [int(x) for x in '.'.join(duration.split(':')).split('.')]\n",
    "        if len(duration) < 4:\n",
    "            duration = duration + [0]\n",
    "        end_timestamp = (dateparser.parse(start_timestamp) + \\\n",
    "         timedelta(\n",
    "          hours=duration[0],\n",
    "          minutes=duration[1],\n",
    "          seconds=duration[2],\n",
    "        milliseconds=duration[3])\n",
    "         ).strftime('%Y-%m-%d %H:%M:%S.%f')\n",
    "\n",
    "        acc_section = acc_df.loc[(acc_df['timestamp'] >= start_timestamp)\n",
    "                            & (acc_df['timestamp'] <= end_timestamp)]\n",
    "        if save:\n",
    "            acc_section.to_csv(save, index=False)\n",
    "        return acc_section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7dfb7e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_video_time(df, video_date=None):\n",
    "    \"\"\"Adds a time column that shows the video time relative to clock time.\n",
    "\n",
    "    Args:\n",
    "        video_date (str): The date in which the video was recorded, formatted as `YYYY-mm-dd`.\n",
    "    \"\"\"\n",
    "    df = df.astype({'timestamp': 'datetime64[ns]'})\n",
    "    df = df.reset_index()\n",
    "#     df['video_time'] = (df['timestamp'] - df['timestamp'][0])\n",
    "    df['video_time'] = [str(x - df['timestamp'][0]).replace('0 days', video_date)\n",
    "                        for x in df['timestamp']]\n",
    "    df['video_time'] = df['video_time'].astype('datetime64[ns]')\n",
    "    df = df[['timestamp', 'video_time', 'AccX', 'AccY', 'AccZ']]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2986d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "mb = MovebankAPI('malyetama', keyring.get_password('movebank', 'malyetama'), 1748526129)\n",
    "smb = SimpleMovebankAPI(mb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a2c4227",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Request https://www.movebank.org/movebank/service/direct-read?entity_type=individual&study_id=1748526129\n"
     ]
    }
   ],
   "source": [
    "cat_id = smb.get_cat_id(simple_id='CAT_001')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a2d12a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Request https://www.movebank.org/movebank/service/direct-read?entity_type=event&study_id=1748526129&individual_id=1788706997&sensor_type_id=653&attributes=all\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>individual_id</th>\n",
       "      <th>deployment_id</th>\n",
       "      <th>tag_id</th>\n",
       "      <th>study_id</th>\n",
       "      <th>sensor_type_id</th>\n",
       "      <th>individual_local_identifier</th>\n",
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       "      <th>comments</th>\n",
       "      <th>gps_hdop</th>\n",
       "      <th>gps_satellite_count</th>\n",
       "      <th>ground_speed</th>\n",
       "      <th>heading</th>\n",
       "      <th>height_above_ellipsoid</th>\n",
       "      <th>location_lat</th>\n",
       "      <th>location_long</th>\n",
       "      <th>tag_voltage</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>event_id</th>\n",
       "      <th>visible</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>9</td>\n",
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       "    <tr>\n",
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       "      <td>0.94</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>35.919627</td>\n",
       "      <td>-78.912408</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2021-09-29 14:10:16.000</td>\n",
       "      <td>20412009546</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1788706997</td>\n",
       "      <td>1788737103</td>\n",
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       "      <td>35.919682</td>\n",
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       "      <td>2021-10-15 15:07:56.000</td>\n",
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       "    <tr>\n",
       "      <th>77449</th>\n",
       "      <td>1788706997</td>\n",
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       "      <td>653</td>\n",
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       "      <td>3847</td>\n",
       "      <td>Felis catus</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>35.919683</td>\n",
       "      <td>-78.912257</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2021-10-15 15:07:58.000</td>\n",
       "      <td>20412596169</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77450</th>\n",
       "      <td>1788706997</td>\n",
       "      <td>1788873621</td>\n",
       "      <td>1788820431</td>\n",
       "      <td>1748526129</td>\n",
       "      <td>653</td>\n",
       "      <td>CAT_001</td>\n",
       "      <td>3847</td>\n",
       "      <td>Felis catus</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>35.919683</td>\n",
       "      <td>-78.912262</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2021-10-15 15:08:00.000</td>\n",
       "      <td>20412596170</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77451 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      individual_id deployment_id      tag_id    study_id sensor_type_id  \\\n",
       "0        1788706997    1788737103  1788703553  1748526129            653   \n",
       "1        1788706997    1788737103  1788703553  1748526129            653   \n",
       "2        1788706997    1788737103  1788703553  1748526129            653   \n",
       "3        1788706997    1788737103  1788703553  1748526129            653   \n",
       "4        1788706997    1788737103  1788703553  1748526129            653   \n",
       "...             ...           ...         ...         ...            ...   \n",
       "77446    1788706997    1788873621  1788820431  1748526129            653   \n",
       "77447    1788706997    1788873621  1788820431  1748526129            653   \n",
       "77448    1788706997    1788873621  1788820431  1748526129            653   \n",
       "77449    1788706997    1788873621  1788820431  1748526129            653   \n",
       "77450    1788706997    1788873621  1788820431  1748526129            653   \n",
       "\n",
       "      individual_local_identifier tag_local_identifier  \\\n",
       "0                         CAT_001                 3870   \n",
       "1                         CAT_001                 3870   \n",
       "2                         CAT_001                 3870   \n",
       "3                         CAT_001                 3870   \n",
       "4                         CAT_001                 3870   \n",
       "...                           ...                  ...   \n",
       "77446                     CAT_001                 3847   \n",
       "77447                     CAT_001                 3847   \n",
       "77448                     CAT_001                 3847   \n",
       "77449                     CAT_001                 3847   \n",
       "77450                     CAT_001                 3847   \n",
       "\n",
       "      individual_taxon_canonical_name comments gps_hdop gps_satellite_count  \\\n",
       "0                         Felis catus        0     0.94                   9   \n",
       "1                         Felis catus        0     0.94                   9   \n",
       "2                         Felis catus        0     0.94                   9   \n",
       "3                         Felis catus        0     0.94                   9   \n",
       "4                         Felis catus        0     0.94                   9   \n",
       "...                               ...      ...      ...                 ...   \n",
       "77446                     Felis catus        0     1.11                   8   \n",
       "77447                     Felis catus        0     1.11                   8   \n",
       "77448                     Felis catus        0     1.11                   8   \n",
       "77449                     Felis catus        0      1.1                   8   \n",
       "77450                     Felis catus        0      1.1                   8   \n",
       "\n",
       "      ground_speed heading height_above_ellipsoid location_lat location_long  \\\n",
       "0              0.0   200.0                  116.0    35.919627    -78.912407   \n",
       "1              0.0   200.0                  116.0    35.919627    -78.912408   \n",
       "2              0.0   200.0                  116.0    35.919627    -78.912408   \n",
       "3              0.0   200.0                  116.0    35.919628    -78.912408   \n",
       "4              0.0   200.0                  116.0    35.919628     -78.91241   \n",
       "...            ...     ...                    ...          ...           ...   \n",
       "77446          0.0   200.0                  117.0    35.919688    -78.912253   \n",
       "77447          0.0   200.0                  117.0    35.919683     -78.91225   \n",
       "77448          0.0   200.0                  117.0    35.919682    -78.912247   \n",
       "77449          0.0   200.0                  117.0    35.919683    -78.912257   \n",
       "77450          0.0   200.0                  117.0    35.919683    -78.912262   \n",
       "\n",
       "      tag_voltage                timestamp     event_id visible  \n",
       "0          1000.0  2021-09-29 14:10:12.000  20412009544    true  \n",
       "1          1000.0  2021-09-29 14:10:14.000  20412009545    true  \n",
       "2          1000.0  2021-09-29 14:10:16.000  20412009546    true  \n",
       "3          1000.0  2021-09-29 14:10:18.000  20412009547    true  \n",
       "4          1000.0  2021-09-29 14:10:20.000  20412009548    true  \n",
       "...           ...                      ...          ...     ...  \n",
       "77446      1000.0  2021-10-15 15:07:52.000  20412596166    true  \n",
       "77447      1000.0  2021-10-15 15:07:54.000  20412596167    true  \n",
       "77448      1000.0  2021-10-15 15:07:56.000  20412596168    true  \n",
       "77449      1000.0  2021-10-15 15:07:58.000  20412596169    true  \n",
       "77450      1000.0  2021-10-15 15:08:00.000  20412596170    true  \n",
       "\n",
       "[77451 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gps_df = smb.get_gps(individual_id=cat_id)\n",
    "gps_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1deae86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Request https://www.movebank.org/movebank/service/direct-read?entity_type=event&study_id=1748526129&individual_id=1788706997&sensor_type_id=2365683&attributes=all\n"
     ]
    }
   ],
   "source": [
    "acc_df = smb.get_acc(individual_id=cat_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1234bdac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "                       timestamp                 video_time       AccX  \\\n",
       "0     2021-10-10 15:51:33.000000 2021-10-10 00:00:00.000000 -43.228745   \n",
       "1     2021-10-10 15:51:33.062500 2021-10-10 00:00:00.062500 -43.595638   \n",
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       "4     2021-10-10 15:51:33.250000 2021-10-10 00:00:00.250000 -44.329430   \n",
       "...                          ...                        ...        ...   \n",
       "45393 2021-10-10 16:38:59.062500 2021-10-10 00:47:26.062500 -43.185581   \n",
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       "45397 2021-10-10 16:38:59.312500 2021-10-10 00:47:26.312500 -43.185581   \n",
       "\n",
       "            AccY       AccZ  \n",
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       "2     -41.674843 -43.185581  \n",
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       "45396 -43.315075 -44.005699  \n",
       "45397 -43.315075 -44.005699  \n",
       "\n",
       "[45398 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "section_df_1 = smb.acc_section(\n",
    "    acc_df,\n",
    "    start_timestamp='2021-10-10 15:51:33',\n",
    "    duration='00:47:26.365')\n",
    "section_df_1 = add_video_time(df=section_df_1, video_date='2021-10-10')\n",
    "section_df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "486849ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>31222</th>\n",
       "      <td>2021-10-10 17:21:13.375000</td>\n",
       "      <td>2021-10-10 01:19:58.750000</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.444565</td>\n",
       "      <td>-44.005699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31223</th>\n",
       "      <td>2021-10-10 17:21:13.437500</td>\n",
       "      <td>2021-10-10 01:19:58.812500</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.444565</td>\n",
       "      <td>-44.005699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31224</th>\n",
       "      <td>2021-10-10 17:21:13.500000</td>\n",
       "      <td>2021-10-10 01:19:58.875000</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.444565</td>\n",
       "      <td>-44.005699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31225</th>\n",
       "      <td>2021-10-10 17:21:13.562500</td>\n",
       "      <td>2021-10-10 01:19:58.937500</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.444565</td>\n",
       "      <td>-44.005699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31226 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       timestamp                 video_time       AccX  \\\n",
       "0     2021-10-10 16:48:41.000000 2021-10-10 00:47:26.375000 -44.156773   \n",
       "1     2021-10-10 16:48:41.062500 2021-10-10 00:47:26.437500 -44.156773   \n",
       "2     2021-10-10 16:48:41.125000 2021-10-10 00:47:26.500000 -44.092026   \n",
       "3     2021-10-10 16:48:41.187500 2021-10-10 00:47:26.562500 -44.135189   \n",
       "4     2021-10-10 16:48:41.250000 2021-10-10 00:47:26.625000 -44.199936   \n",
       "...                          ...                        ...        ...   \n",
       "31221 2021-10-10 17:21:13.312500 2021-10-10 01:19:58.687500 -43.077671   \n",
       "31222 2021-10-10 17:21:13.375000 2021-10-10 01:19:58.750000 -43.077671   \n",
       "31223 2021-10-10 17:21:13.437500 2021-10-10 01:19:58.812500 -43.077671   \n",
       "31224 2021-10-10 17:21:13.500000 2021-10-10 01:19:58.875000 -43.077671   \n",
       "31225 2021-10-10 17:21:13.562500 2021-10-10 01:19:58.937500 -43.077671   \n",
       "\n",
       "            AccY       AccZ  \n",
       "0     -42.883434 -44.005699  \n",
       "1     -42.797108 -43.940952  \n",
       "2     -42.861851 -44.005699  \n",
       "3     -42.969761 -43.984116  \n",
       "4     -42.948181 -43.897789  \n",
       "...          ...        ...  \n",
       "31221 -43.444565 -44.005699  \n",
       "31222 -43.444565 -44.005699  \n",
       "31223 -43.444565 -44.005699  \n",
       "31224 -43.444565 -44.005699  \n",
       "31225 -43.444565 -44.005699  \n",
       "\n",
       "[31226 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_microseconds = dateparser.parse(str(section_df_1['video_time'][1])).microsecond\n",
    "\n",
    "section_df_2 = smb.acc_section(\n",
    "    acc_df,\n",
    "    start_timestamp='2021-10-10 16:48:41',\n",
    "    duration='00:32:32.602')\n",
    "\n",
    "df_2_vid = add_video_time(df=section_df_2, video_date='2021-10-10')\n",
    "\n",
    "section_df_2 = section_df_2.reset_index()\n",
    "video_date = str(list(section_df_1['timestamp'])[-1].date())\n",
    "ts_1 = dateparser.parse(str(list(section_df_2['timestamp'])[0])).time()\n",
    "\n",
    "section_df_2['video_time'] = section_df_2['timestamp'] - timedelta(hours=ts_1.hour, minutes=ts_1.minute, seconds=ts_1.second)\n",
    "\n",
    "ts_2 = dateparser.parse(str(list(section_df_1['video_time'])[-1])).time()\n",
    "section_df_2['video_time'] = section_df_2['video_time'] + timedelta(minutes=ts_2.minute, seconds=ts_2.second, microseconds=ts_2.microsecond + default_microseconds)\n",
    "\n",
    "section_df_2 = section_df_2[['timestamp', 'video_time', 'AccX', 'AccY', 'AccZ']]\n",
    "section_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7bee9ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2021-10-10 00:00:00.000000\n",
       "1    2021-10-10 00:00:00.062500\n",
       "2    2021-10-10 00:00:00.125000\n",
       "3    2021-10-10 00:00:00.187500\n",
       "4    2021-10-10 00:00:00.250000\n",
       "5    2021-10-10 00:00:00.312500\n",
       "6    2021-10-10 00:00:00.375000\n",
       "7    2021-10-10 00:00:00.437500\n",
       "8    2021-10-10 00:00:00.500000\n",
       "9    2021-10-10 00:00:00.562500\n",
       "10   2021-10-10 00:00:00.625000\n",
       "11   2021-10-10 00:00:00.687500\n",
       "12   2021-10-10 00:00:00.750000\n",
       "13   2021-10-10 00:00:00.812500\n",
       "14   2021-10-10 00:00:00.875000\n",
       "15   2021-10-10 00:00:00.937500\n",
       "Name: video_time, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "section_df_1['video_time'][:16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e3ba8339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>video_time</th>\n",
       "      <th>AccX</th>\n",
       "      <th>AccY</th>\n",
       "      <th>AccZ</th>\n",
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       "  <tbody>\n",
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       "      <td>2021-10-10 15:51:33.000000</td>\n",
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       "      <td>2021-10-10 15:51:33.062500</td>\n",
       "      <td>2021-10-10 00:00:00.062500</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-10 15:51:33.125000</td>\n",
       "      <td>2021-10-10 00:00:00.125000</td>\n",
       "      <td>-43.876205</td>\n",
       "      <td>-41.674843</td>\n",
       "      <td>-43.185581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-10-10 15:51:33.187500</td>\n",
       "      <td>2021-10-10 00:00:00.187500</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.293491</td>\n",
       "      <td>-44.199936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-10-10 15:51:33.250000</td>\n",
       "      <td>2021-10-10 00:00:00.250000</td>\n",
       "      <td>-44.329430</td>\n",
       "      <td>-44.005699</td>\n",
       "      <td>-44.199936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>31221</th>\n",
       "      <td>2021-10-10 17:21:13.312500</td>\n",
       "      <td>2021-10-10 01:19:58.687500</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.444565</td>\n",
       "      <td>-44.005699</td>\n",
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       "    <tr>\n",
       "      <th>31222</th>\n",
       "      <td>2021-10-10 17:21:13.375000</td>\n",
       "      <td>2021-10-10 01:19:58.750000</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.444565</td>\n",
       "      <td>-44.005699</td>\n",
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       "    <tr>\n",
       "      <th>31223</th>\n",
       "      <td>2021-10-10 17:21:13.437500</td>\n",
       "      <td>2021-10-10 01:19:58.812500</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.444565</td>\n",
       "      <td>-44.005699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31224</th>\n",
       "      <td>2021-10-10 17:21:13.500000</td>\n",
       "      <td>2021-10-10 01:19:58.875000</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.444565</td>\n",
       "      <td>-44.005699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31225</th>\n",
       "      <td>2021-10-10 17:21:13.562500</td>\n",
       "      <td>2021-10-10 01:19:58.937500</td>\n",
       "      <td>-43.077671</td>\n",
       "      <td>-43.444565</td>\n",
       "      <td>-44.005699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76624 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       timestamp                 video_time       AccX  \\\n",
       "0     2021-10-10 15:51:33.000000 2021-10-10 00:00:00.000000 -43.228745   \n",
       "1     2021-10-10 15:51:33.062500 2021-10-10 00:00:00.062500 -43.595638   \n",
       "2     2021-10-10 15:51:33.125000 2021-10-10 00:00:00.125000 -43.876205   \n",
       "3     2021-10-10 15:51:33.187500 2021-10-10 00:00:00.187500 -43.077671   \n",
       "4     2021-10-10 15:51:33.250000 2021-10-10 00:00:00.250000 -44.329430   \n",
       "...                          ...                        ...        ...   \n",
       "31221 2021-10-10 17:21:13.312500 2021-10-10 01:19:58.687500 -43.077671   \n",
       "31222 2021-10-10 17:21:13.375000 2021-10-10 01:19:58.750000 -43.077671   \n",
       "31223 2021-10-10 17:21:13.437500 2021-10-10 01:19:58.812500 -43.077671   \n",
       "31224 2021-10-10 17:21:13.500000 2021-10-10 01:19:58.875000 -43.077671   \n",
       "31225 2021-10-10 17:21:13.562500 2021-10-10 01:19:58.937500 -43.077671   \n",
       "\n",
       "            AccY       AccZ  \n",
       "0     -44.005699 -44.307846  \n",
       "1     -42.171227 -43.185581  \n",
       "2     -41.674843 -43.185581  \n",
       "3     -43.293491 -44.199936  \n",
       "4     -44.005699 -44.199936  \n",
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       "31222 -43.444565 -44.005699  \n",
       "31223 -43.444565 -44.005699  \n",
       "31224 -43.444565 -44.005699  \n",
       "31225 -43.444565 -44.005699  \n",
       "\n",
       "[76624 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ = pd.concat([section_df_1, section_df_2])\n",
    "df_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f6369db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_.to_csv(\n",
    "    '10-10-2021_kraken-769a9b6b_ACC_LS_unlabeled.csv',\n",
    "    index=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6677f7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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