notebooks/03 - Creating a Dataset from Raw, DataFrame, GeoDataFrame.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The `Dataset` class is the main class that is maniupulated in the `trackio` library. It contains reference to each individual `Agent`, as well as various attributes and methods that allow one to operate on the data stored in each `Agent`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There are a few ways to instantiate a `Dataset`, which are shown in this notebook\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import trackio as tio\n",
    "import glob\n",
    "import pandas as pd\n",
    "import geopandas as gp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## The first, is by passing the `trackio.read` function a list of raw data (CSV) files. Let's continue with the clipped AIS data from the last notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./files\\\\AIS_2021_01_01_clipped_to_polygon.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define raw data files\n",
    "data_path = './files'\n",
    "files = glob.glob(f'{data_path}/*clipped_to_polygon*.csv')\n",
    "\n",
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When you create a `Dataset`, you also need to specify a `data_path` keyword argument. \n",
    "\n",
    "## This is where trackio will store the processed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "No dataset.db found in c:\\code\\trackio\\notebooks\\example0. \n",
      "Using default units/crs. \n",
      "Edit self.meta and then run self.refresh_meta to update.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#create the Dataset\n",
    "ds = tio.read(raw_files=files,\n",
    "              data_path='./example0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This warning just means that no `dataset.db` file was found in the `data_path` yet. This file will save metadata about the `Dataset` to disk for later reading.\n",
    "\n",
    "## You can view metadata from the `Dataset` by using the `repr`, or by accessing the `Dataset.meta` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type:\n",
       "    <class 'trackio.Dataset.Dataset'>\n",
       "Status:\n",
       "    1 Unprocessed CSV Files\n",
       "    0 Processed CSV Files\n",
       "    0 Unsplit Agent Files\n",
       "    0 Split Agent Files\n",
       "Static Data Fields:\n",
       "    []\n",
       "Dynamic Data Fields:\n",
       "    ['X', 'Y']\n",
       "Metadata:\n",
       "    X: degrees\n",
       "    Y: degrees\n",
       "    CRS: EPSG:4326\n",
       "Data Path:\n",
       "    c:\\code\\trackio\\notebooks\\example0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the repr\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X': 'degrees',\n",
       " 'Y': 'degrees',\n",
       " 'CRS': <Geographic 2D CRS: EPSG:4326>\n",
       " Name: WGS 84\n",
       " Axis Info [ellipsoidal]:\n",
       " - Lat[north]: Geodetic latitude (degree)\n",
       " - Lon[east]: Geodetic longitude (degree)\n",
       " Area of Use:\n",
       " - name: World.\n",
       " - bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       " Datum: World Geodetic System 1984 ensemble\n",
       " - Ellipsoid: WGS 84\n",
       " - Prime Meridian: Greenwich,\n",
       " 'Static Data': [],\n",
       " 'Dynamic Data': ['X', 'Y']}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the metadata dict - this can be edited manually to keep track of data information\n",
    "ds.meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `Dataset` is now ready for the raw data to be processed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## You can also instantiate a `Dataset` using a pandas DataFrame by using the `trackio.from_df` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "No dataset.db found in c:\\code\\trackio\\notebooks\\files. \n",
      "Using default units/crs. \n",
      "Edit self.meta and then run self.refresh_meta to update.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#generate a dataframe\n",
    "df = pd.read_csv(files[0])\n",
    "\n",
    "#instantiate the Dataset\n",
    "ds = tio.from_df(df, data_path='./files')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## You can also start from a geopandas GeoDataFrame.\n",
    "\n",
    "## The GeoDataFrame must include a geometry column with ***only*** LineStrings, as well as at least one other column which can be used as a unique identifier for each geometry feature. E.g. a GeoDataFrame with MMSI and geometry columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
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       "    }\n",
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       "    .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>identifier</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>LINESTRING (-74.03934 40.59620, -74.04682 40.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>LINESTRING (-74.04377 40.59590, -74.04666 40.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>LINESTRING (-74.04270 40.59666, -74.04865 40.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LINESTRING (-74.04712 40.59941, -74.05246 40.6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   identifier                                           geometry\n",
       "0           1  LINESTRING (-74.03934 40.59620, -74.04682 40.6...\n",
       "1           2  LINESTRING (-74.04377 40.59590, -74.04666 40.6...\n",
       "2           3  LINESTRING (-74.04270 40.59666, -74.04865 40.6...\n",
       "3           4  LINESTRING (-74.04712 40.59941, -74.05246 40.6..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read the raw data\n",
    "gdf = gp.read_file(f'{data_path}/raw_tracks.gpkg')\n",
    "\n",
    "gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the data quickly\n",
    "gdf.plot(column='identifier')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "No dataset.db found in c:\\code\\trackio\\notebooks\\files. \n",
      "Using default units/crs. \n",
      "Edit self.meta and then run self.refresh_meta to update.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#create the Dataset\n",
    "ds = tio.from_gdf(gdf, data_path=data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## ***Very importantly***, creating a `Dataset` from a GeoDataFrame means that there is no temporal data associated with the tracks. Because of this, every track is stretched over a 1 second duration. This means, technically, they are all overlapping in time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Note, you cannot run `ds.refresh_meta()` until you raw data has been processed. See  [`04 - Grouping Points and Splitting Tracks.ipynb`](https://github.com/derekeden/trackio/blob/main/notebooks/04%20-%20Grouping%20Points%20and%20Splitting%20Tracks.ipynb) to see how this is done.\n",
    "___"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "trackio",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}