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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elliptical Time Density (ETD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ecoscope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ECOSCOPE_RAW = \"https://raw.githubusercontent.com/wildlife-dynamics/ecoscope/master\"\n",
    "\n",
    "!pip install 'ecoscope[analysis,mapping,plotting] @ git+https://github.com/wildlife-dynamics/ecoscope@v1.8.12' &> /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import ecoscope\n",
    "from ecoscope.analysis.UD import calculate_etd_range\n",
    "from ecoscope.analysis.percentile import get_percentile_area\n",
    "\n",
    "ecoscope.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Google Drive setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir = \"Ecoscope-Outputs\"\n",
    "\n",
    "if \"google.colab\" in sys.modules:\n",
    "    from google.colab import drive\n",
    "\n",
    "    drive.mount(\"/content/drive/\", force_remount=True)\n",
    "    output_dir = os.path.join(\"/content/drive/MyDrive/\", output_dir)\n",
    "\n",
    "os.makedirs(output_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Relocations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecoscope.io.download_file(\n",
    "    f\"{ECOSCOPE_RAW}/tests/sample_data/vector/movebank_data.csv\",\n",
    "    os.path.join(output_dir, \"movebank_data.csv\"),\n",
    ")\n",
    "\n",
    "df = pd.read_csv(os.path.join(output_dir, \"movebank_data.csv\"), index_col=0)\n",
    "gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df[\"location-long\"], df[\"location-lat\"]), crs=4326)\n",
    "\n",
    "relocs = ecoscope.base.Relocations.from_gdf(gdf, groupby_col=\"individual-local-identifier\", time_col=\"timestamp\")\n",
    "relocs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Trajectory from Relocations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj = ecoscope.base.Trajectory.from_relocations(relocs)\n",
    "traj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count unique Subjects in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj[\"groupby_col\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter the segments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj_seg_filter = ecoscope.base.TrajSegFilter(\n",
    "    min_length_meters=0.0,\n",
    "    max_length_meters=float(\"inf\"),\n",
    "    min_time_secs=0.0,\n",
    "    max_time_secs=4 * 60 * 60,\n",
    "    min_speed_kmhr=0.0,\n",
    "    max_speed_kmhr=8.0,\n",
    ")\n",
    "traj.apply_traj_filter(traj_seg_filter, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj.remove_filtered(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Individual ETD Ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View maximum speed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj.speed_kmhr.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define RasterProfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raster_profile = ecoscope.io.raster.RasterProfile(\n",
    "    pixel_size=250.0,  # may need to reduce grid size if you receive a warning\n",
    "    crs=\"ESRI:102022\",  # Albers Africa Equal Area Conic\n",
    "    nodata_value=np.nan,\n",
    "    band_count=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute ETD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(trajectory_gdf):\n",
    "    output_path = os.path.join(output_dir, f\"{trajectory_gdf.name}.tif\")\n",
    "\n",
    "    calculate_etd_range(\n",
    "        trajectory_gdf=trajectory_gdf,\n",
    "        output_path=output_path,\n",
    "        max_speed_kmhr=1.05 * trajectory_gdf.speed_kmhr.max(),  # Choose a value above the max recorded segment speed\n",
    "        raster_profile=raster_profile,\n",
    "        expansion_factor=1.3,\n",
    "    )\n",
    "    return output_path\n",
    "\n",
    "\n",
    "etd = traj.groupby(\"groupby_col\").apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate ETD Percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentiles = pd.concat(\n",
    "    [get_percentile_area(percentile_levels=[50, 99.9, 100.0], raster_path=v, subject_id=k) for k, v in etd.items()]\n",
    ").reset_index(drop=True)\n",
    "\n",
    "percentiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentiles.to_file(os.path.join(output_dir, \"percentiles.gpkg\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentiles.to_csv(os.path.join(output_dir, \"percentiles.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "salif = get_percentile_area(\n",
    "    percentile_levels=[50, 60, 70, 80, 90, 99.9], raster_path=etd.at[\"Salif Keita\"], subject_id=\"Salif Keita\"\n",
    ")"
   ]
  }
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