trajectory_example.ipynb

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      "metadata": {
        "id": "4s2-4ByrxLVS"
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      "source": [
        "## Setup Environment"
      ]
    },
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      "execution_count": null,
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        "colab": {
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      "source": [
        "import pandas as pd\n",
        "import ecoscope\n",
        "import os\n",
        "import fsspec\n",
        "import json\n",
        "\n",
        "ecoscope.init(selenium=True)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "c3xIpIhzxVJN"
      },
      "source": [
        "## Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "parameters"
        ]
      },
      "outputs": [],
      "source": [
        "config_path = \"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with fsspec.open(config_path, mode='rt',) as file:\n",
        "    config = json.loads(file.read())\n",
        "\n",
        "config"
      ]
    },
    {
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        "tags": []
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      "source": [
        "# EarthRanger Credentials\n",
        "ER_SERVER = \"https://mep-dev.pamdas.org\"\n",
        "ER_USERNAME = \"yun.wu\"\n",
        "ER_PASSWORD = os.getenv(\"ER_PASSWORD\", \"\")\n",
        "\n",
        "# SubjectGroup\n",
        "subjectgroup_name = \"Elephants\"\n",
        "\n",
        "# Analysis Time Range\n",
        "start_date = config.get('start_date')\n",
        "end_date = config.get('end_date')\n",
        "\n",
        "# Relocations Bounding Box Filter Coordinates\n",
        "relocs_coord_filter_min_x = -5.0\n",
        "relocs_coord_filter_max_x = 1.0\n",
        "relocs_coord_filter_min_y = 12.0\n",
        "relocs_coord_filter_max_y = 18.0\n",
        "\n",
        "# Relocations Filter Point Coordinates\n",
        "relocs_filter_point_coords = [[180, 90], [0, 0]]\n",
        "\n",
        "# Trajectory Filter Settings\n",
        "traj_min_length_meters=0.0\n",
        "traj_max_length_meters=10000\n",
        "traj_min_time_secs=120.0\n",
        "traj_max_time_secs=4 * 60 * 60\n",
        "traj_min_speed_kmhr=0.000001\n",
        "traj_max_speed_kmhr=8.0"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "iZvlzjOixgKh"
      },
      "source": [
        "## Logic"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "255a9jjzxqQq"
      },
      "source": [
        "### Connect to EarthRanger"
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/"
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      "outputs": [],
      "source": [
        "er_io = ecoscope.io.EarthRangerIO(\n",
        "    server=ER_SERVER,\n",
        "    username=ER_USERNAME,\n",
        "    password=ER_PASSWORD,\n",
        "    tcp_limit=5,\n",
        "    sub_page_size=4000,\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "6DumqDjVzhqk"
      },
      "source": [
        "### Relocations"
      ]
    },
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        "%%time\n",
        "relocs = er_io.get_subjectgroup_observations(\n",
        "    group_name=subjectgroup_name,\n",
        "    include_inactive=True,\n",
        "    include_subject_details=True,\n",
        "    include_source_details=False,\n",
        "    include_subjectsource_details=False,\n",
        "    filter=0,\n",
        "    since=pd.to_datetime(start_date).isoformat(),\n",
        "    until=pd.to_datetime(end_date).isoformat(),\n",
        "    include_details=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "uJduM4fOPf9r"
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      "source": [
        "relocs = relocs[relocs.extra__subject__name != 'Habiba'] # temporary hack until relocs_filter method is fixed..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "k9QcsI4113o5"
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      "source": [
        "# coord_filter = ecoscope.base.RelocsCoordinateFilter(\n",
        "#     min_x=relocs_coord_filter_min_x,\n",
        "#     max_x=relocs_coord_filter_max_x,\n",
        "#     min_y=relocs_coord_filter_min_y,\n",
        "#     max_y=relocs_coord_filter_max_x,\n",
        "#     filter_point_coords=relocs_filter_point_coords,\n",
        "# )\n",
        "# relocs.apply_reloc_filter(coord_filter, inplace=True)\n",
        "# relocs.remove_filtered(inplace=True)"
      ]
    },
    {
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      "execution_count": null,
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      "source": [
        "%%time\n",
        "## Clean up columns that are not compatible with a GPKG format\n",
        "relocs.drop(\n",
        "    columns=relocs.columns[relocs.applymap(lambda x: isinstance(x, list)).any()],\n",
        "    errors=\"ignore\",\n",
        "    inplace=True,\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "3B0uusJrXbbm"
      },
      "source": [
        "## Trajectory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
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      "outputs": [],
      "source": [
        "%%time\n",
        "## create a trajectory\n",
        "traj = ecoscope.base.Trajectory.from_relocations(relocs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G0ibrot0ikMM"
      },
      "outputs": [],
      "source": [
        "## filter the trajectory\n",
        "traj_seg_filter = ecoscope.base.TrajSegFilter(\n",
        "    min_length_meters=traj_min_length_meters,\n",
        "    max_length_meters=traj_max_length_meters,\n",
        "    min_time_secs=traj_min_time_secs,\n",
        "    max_time_secs=traj_max_time_secs,\n",
        "    min_speed_kmhr=traj_min_speed_kmhr,\n",
        "    max_speed_kmhr=traj_max_speed_kmhr,\n",
        ")\n",
        "traj.apply_traj_filter(traj_seg_filter, inplace=True)\n",
        "traj.remove_filtered(inplace=True)"
      ]
    },
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      "outputs": [],
      "source": [
        "%%time\n",
        "## remove any incompatible columns for geopackage export\n",
        "traj.drop(\n",
        "    columns=traj.columns[traj.applymap(lambda x: isinstance(x, list)).any()],\n",
        "    errors=\"ignore\",\n",
        "    inplace=True,\n",
        ")"
      ]
    },
    {
      "attachments": {},
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      "metadata": {
        "id": "n7h5Ft2q0ioq"
      },
      "source": [
        "## Outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDwxF_7oPN4Z",
        "outputId": "4f7ccd0b-46ee-4cd4-e79c-ec98901d7f82"
      },
      "outputs": [],
      "source": [
        "# Calculate the number of rows in the geodatabse\n",
        "len(relocs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "su0fey6bNp_m",
        "outputId": "3e6151a8-1bf1-46ac-93aa-f98bfdc20d29"
      },
      "outputs": [],
      "source": [
        "len(traj)"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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        },
        "id": "cyumS3xJOGFb",
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      },
      "outputs": [],
      "source": [
        "# Map of the data\n",
        "m = ecoscope.mapping.EcoMap(static=True)\n",
        "m.add_tile_layer(\n",
        "    url=\"https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}\",\n",
        "    name=\"Google Satellite\",\n",
        "    attribution=\"Google\",\n",
        "    opacity=1.0,\n",
        ")\n",
        "m.add_speedmap(trajectory=traj, classification_method=\"equal_interval\", num_classes=6, bins=None)\n",
        "m.add_north_arrow(position=\"topright\")\n",
        "m.add_title(title=\"Elephant Speed Map\", align=\"center\", font_size=\"18px\")\n",
        "m.zoom_to_gdf(traj)\n",
        "m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Export to HTML\n",
        "source_file_name = \"/tmp/ecomap.html\"\n",
        "m.to_html(source_file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Upload to Google Cloud Storage\n",
        "from google.cloud import storage\n",
        "\n",
        "storage_client = storage.Client()\n",
        "bucket = storage_client.get_bucket(\"us-central1-ecoscope-pipeli-92c4ecc9-bucket\")\n",
        "blob = bucket.blob(\"website/ecomap.html\")\n",
        "\n",
        "blob.upload_from_filename(source_file_name)\n",
        "\n",
        "print('File {} uploaded to {}.'.format(\n",
        "    source_file_name,\n",
        "    \"website\"))"
      ]
    }
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