supplement_elevation_profile_plot.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "73a214db-9299-47c2-86c1-7e574e56936b",
      "metadata": {
        "id": "73a214db-9299-47c2-86c1-7e574e56936b"
      },
      "source": [
        "### Elevation Profile Plot from a GPS Track\n",
        "\n",
        "We will take a GPS track recorded from Strava app and create an elevation profile plot."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Setup and Data Download\n",
        "\n",
        "The following blocks of code will install the required packages and download the datasets to your Colab environment."
      ],
      "metadata": {
        "id": "Dilk9LqQdf-c"
      },
      "id": "Dilk9LqQdf-c"
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "if 'google.colab' in str(get_ipython()):\n",
        "  !apt install libspatialindex-dev\n",
        "  !pip install fiona shapely pyproj rtree mapclassify\n",
        "  !pip install geopandas"
      ],
      "metadata": {
        "id": "kSUgro5ddXPf"
      },
      "execution_count": null,
      "outputs": [],
      "id": "kSUgro5ddXPf"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e59f2b5d-e9d5-4a62-91ef-d583118e6b36"
      },
      "outputs": [],
      "source": [
        "import geopandas as gpd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "import os\n",
        "import pandas as pd\n"
      ],
      "id": "e59f2b5d-e9d5-4a62-91ef-d583118e6b36"
    },
    {
      "cell_type": "code",
      "source": [
        "data_folder = 'data'\n",
        "output_folder = 'output'\n",
        "\n",
        "if not os.path.exists(data_folder):\n",
        "    os.mkdir(data_folder)\n",
        "if not os.path.exists(output_folder):\n",
        "    os.mkdir(output_folder)"
      ],
      "metadata": {
        "id": "q44TVIV_eQhd"
      },
      "execution_count": null,
      "outputs": [],
      "id": "q44TVIV_eQhd"
    },
    {
      "cell_type": "code",
      "source": [
        "def download(url):\n",
        "    filename = os.path.join(data_folder, os.path.basename(url))\n",
        "    if not os.path.exists(filename):\n",
        "        from urllib.request import urlretrieve\n",
        "        local, _ = urlretrieve(url, filename)\n",
        "        print('Downloaded ' + local)"
      ],
      "metadata": {
        "id": "PuH1yQv2eWwN"
      },
      "execution_count": null,
      "outputs": [],
      "id": "PuH1yQv2eWwN"
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'summit.gpx'\n",
        "data_url = 'https://github.com/spatialthoughts/python-dataviz-web/raw/main/data/gps/'\n",
        "\n",
        "download(data_url + filename)"
      ],
      "metadata": {
        "id": "3_qYdsQk3_iG"
      },
      "id": "3_qYdsQk3_iG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Pre-Processing"
      ],
      "metadata": {
        "id": "ekvX6uIP4GdH"
      },
      "id": "ekvX6uIP4GdH"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8803d858-dff2-40df-8f8e-6658dfb2b9b6",
      "metadata": {
        "id": "8803d858-dff2-40df-8f8e-6658dfb2b9b6"
      },
      "outputs": [],
      "source": [
        "gpx_path = os.path.join(data_folder, filename)\n",
        "# GPX files contain many layers. Read the 'track_points' layer\n",
        "gdf = gpd.read_file(gpx_path, layer='track_points')\n",
        "gdf = gdf[['track_fid','ele', 'time', 'geometry']]\n",
        "gdf"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7420fc23-0632-4f9a-abd4-7bcef5b1e959",
      "metadata": {
        "id": "7420fc23-0632-4f9a-abd4-7bcef5b1e959"
      },
      "source": [
        "Let's use the timestamp contained in the 'time' column as the index. This will allow us to filter and plot the time-series data easily. We must first convert the time column to datetime type with an appropriate timezone."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "82031ccc-d64b-44bc-8651-997320a96c5f",
      "metadata": {
        "id": "82031ccc-d64b-44bc-8651-997320a96c5f"
      },
      "outputs": [],
      "source": [
        "gdf['time'] = pd.to_datetime(gdf['time'])\n",
        "gdf = gdf.set_index('time')\n",
        "gdf.index = gdf.index.tz_convert('Asia/Kolkata')\n",
        "gdf"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b63b04d-a454-4d40-83d0-291bb1f40e33",
      "metadata": {
        "id": "7b63b04d-a454-4d40-83d0-291bb1f40e33"
      },
      "source": [
        "Using time as index allows us to filter the data as follows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e7eaa4d-e022-43a5-b359-95626e48ecc1",
      "metadata": {
        "id": "8e7eaa4d-e022-43a5-b359-95626e48ecc1"
      },
      "outputs": [],
      "source": [
        "gdf_subset = gdf['2022-04-04T06:15:00':'2022-04-04T10:45:00']\n",
        "gdf_subset"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4c1b61e7-0ac3-4183-9eef-c38857b94e1b",
      "metadata": {
        "id": "4c1b61e7-0ac3-4183-9eef-c38857b94e1b"
      },
      "source": [
        "We can now plot the elevation against the timestamp using Matplotlib. Since we have a large number of timestamps, we can use `set_major_locator()` to define what labels will be present on the axis.\n",
        "\n",
        "Since our timestamps are timezone aware, we can also plot a timezone name (i.e. IST) along with the label."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10b03a80-1a9e-4d56-b0fe-c79ec1db2113",
      "metadata": {
        "id": "10b03a80-1a9e-4d56-b0fe-c79ec1db2113"
      },
      "source": [
        "We can also use a different style for our plot. You can run `plt.style.available` to see all available styles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "8a9c6ad5-4a32-4203-b363-8313578f46df",
      "metadata": {
        "id": "8a9c6ad5-4a32-4203-b363-8313578f46df"
      },
      "outputs": [],
      "source": [
        "plt.style.use('ggplot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "5db17c65-ad41-41f0-a887-a36e894488ae",
      "metadata": {
        "id": "5db17c65-ad41-41f0-a887-a36e894488ae",
        "outputId": "f352946f-9c36-4e9e-a2e4-06da69cc0b9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "fig.set_size_inches(15,7)\n",
        "gdf_subset['ele'].plot(kind='line', ax=ax, color='#2b8cbe')\n",
        "plt.tight_layout()\n",
        "plt.title('Elevation Profile', fontsize = 18)\n",
        "plt.ylabel('Elevation (meters)', size = 15)\n",
        "plt.xlabel(None)\n",
        "# Show a tick every 30 minute\n",
        "xlocator = mdates.MinuteLocator(interval=30)\n",
        "\n",
        "xformat = mdates.DateFormatter('%H:%M %Z', tz=gdf.index.tz)  # adds some extra formatting, but not required\n",
        "\n",
        "ax.xaxis.set_major_locator(xlocator)\n",
        "ax.xaxis.set_major_formatter(xformat)\n",
        "ax.set_ylim([3200, 3700])\n",
        "ax.fill_between(gdf.index, gdf['ele'].values, color='grey', alpha=0.3)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77996bea-e82a-4f61-9e66-9f5c496c7088",
      "metadata": {
        "id": "77996bea-e82a-4f61-9e66-9f5c496c7088"
      },
      "outputs": [],
      "source": [
        "plt.style.use('default')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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