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 for a mountain trek recorded from Strava app and create an elevation profile plot. We also explore some advanced charting functions to add labels on X-axis at precise time intervals."
      ]
    },
    {
      "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",
      "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",
        "import requests"
      ],
      "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",
        "      with requests.get(url, stream=True, allow_redirects=True) as r:\n",
        "          with open(filename, 'wb') as f:\n",
        "              for chunk in r.iter_content(chunk_size=8192):\n",
        "                  f.write(chunk)\n",
        "      print('Downloaded', filename)"
      ],
      "metadata": {
        "id": "PuH1yQv2eWwN"
      },
      "execution_count": null,
      "outputs": [],
      "id": "PuH1yQv2eWwN"
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'summit.gpx'\n",
        "\n",
        "data_url = 'https://github.com/spatialthoughts/python-dataviz-web/releases/' \\\n",
        "  'download/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": null,
      "id": "8a9c6ad5-4a32-4203-b363-8313578f46df",
      "metadata": {
        "id": "8a9c6ad5-4a32-4203-b363-8313578f46df"
      },
      "outputs": [],
      "source": [
        "plt.style.use('ggplot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "5db17c65-ad41-41f0-a887-a36e894488ae",
      "metadata": {
        "id": "5db17c65-ad41-41f0-a887-a36e894488ae",
        "outputId": "4a2f8c38-5bed-4832-a871-0a45f6e8402b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "fig.set_size_inches(15,7)\n",
        "\n",
        "# We show a subset of track for the summit in a blue line\n",
        "gdf_subset['ele'].plot(kind='line', ax=ax, color='#2b8cbe')\n",
        "# The full track is shown with a grey fill\n",
        "ax.fill_between(gdf.index, gdf['ele'].values, color='grey', alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.title('Elevation Profile', fontsize = 18)\n",
        "plt.ylabel('Elevation (meters)', size = 15)\n",
        "plt.xlabel(None)\n",
        "\n",
        "# Show a tick every 30 minute\n",
        "xlocator = mdates.MinuteLocator(interval=30)\n",
        "xformat = mdates.DateFormatter('%H:%M %Z', tz=gdf.index.tz)\n",
        "\n",
        "ax.xaxis.set_major_locator(xlocator)\n",
        "ax.xaxis.set_major_formatter(xformat)\n",
        "\n",
        "ax.set_ylim([3200, 3700])\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.10.5"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}