Python Elevation Profile With Example

To create an elevation profile in Python, you can use various libraries and data sources. Here, I’ll provide a simple example using Python and the Matplotlib library to visualize elevation data along a path. We’ll also use the SRTM (Shuttle Radar Topography Mission) dataset, which provides elevation data for most of the Earth’s landmasses.

1. Install required libraries: First, you need to install the following libraries if you haven’t already:
   • Matplotlib: for plotting.
   • Numpy: for numerical operations.
   • OpenTopography API (optional): for downloading elevation data. You can also use other sources or local elevation data.
   You can install them using pip:

pip install matplotlib numpy opentopography-pyCode language: Python (python)

2. Create a Python script to generate an elevation profile:

import numpy as np
import matplotlib.pyplot as plt
import opentopography

def get_elevation_data(latitude, longitude, distance_km):
    # You can replace this with your own elevation data source or API.
    # Here, we are using OpenTopography to download data.
    elevation_data = opentopography.get_elevation([longitude], [latitude], meters=True, sources="SRTM1")
    elevation_profile = elevation_data[0]["elevationProfile"]

    # Convert the distance to kilometers.
    distances_km = np.linspace(0, distance_km, len(elevation_profile))
    
    return distances_km, elevation_profile

def plot_elevation_profile(distances_km, elevation_profile):
    plt.figure(figsize=(10, 5))
    plt.plot(distances_km, elevation_profile)
    plt.title("Elevation Profile")
    plt.xlabel("Distance (km)")
    plt.ylabel("Elevation (meters)")
    plt.grid()
    plt.show()

if __name__ == "__main__":
    # Replace with your desired latitude, longitude, and path distance.
    latitude = 37.7749  # Example: San Francisco, CA
    longitude = -122.4194
    path_distance_km = 10

    distances_km, elevation_profile = get_elevation_data(latitude, longitude, path_distance_km)
    plot_elevation_profile(distances_km, elevation_profile)
Code language: Python (python)

3. Replace the latitude, longitude, and path_distance_km values with your desired location and path distance. This script will download elevation data using OpenTopography (you can use other sources) and then plot the elevation profile.Make sure you have an active internet connection when running this script, as it fetches data from a remote source (OpenTopography in this case).

Remember that there are many sources of elevation data, and you may need to adapt the data retrieval part of the code to match your specific requirements or data sources if needed.

Visualize digital elevation models in 3D using python

Visualizing Digital Elevation Models (DEMs) in 3D using Python can be achieved using libraries like Matplotlib and mayavi. In this example, I’ll show you how to visualize a DEM in 3D using Matplotlib:

1. Install Dependencies: First, make sure you have the necessary libraries installed. You can install them using pip:

pip install numpy matplotlibCode language: Python (python)

2. Load DEM Data: You should have a DEM file in GeoTIFF or similar format. Use the GDAL library (as mentioned in the previous response) to load the DEM data into a NumPy array.
3. 3D Visualization with Matplotlib:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Load your DEM data into a NumPy array
# Replace 'elevation_array' with your actual DEM data
# For example, elevation_array = dataset.ReadAsArray()

# Create a grid of coordinates for the DEM
x, y = np.meshgrid(np.arange(0, elevation_array.shape[1]), np.arange(0, elevation_array.shape[0]))

# Create a 3D figure
fig = plt.figure()
ax = fig.gca(projection='3d')

# Plot the surface using Matplotlib's plot_surface function
surf = ax.plot_surface(x, y, elevation_array, cmap='terrain', rstride=1, cstride=1, linewidth=0, antialiased=False)

# Add a color bar which maps values to colors
fig.colorbar(surf, shrink=0.5, aspect=5)

# Set labels and title
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Elevation (meters)')
ax.set_title('3D DEM Visualization')

# Show the plot
plt.show()
Code language: Python (python)

In this code:

• We create a grid of x and y coordinates that correspond to the rows and columns of the DEM data.
• We use Matplotlib’s plot_surface function to create the 3D surface plot.
• The cmap='terrain' argument specifies a colormap for the elevation data, which gives a terrain-like appearance.
• Finally, we set labels, a title, and display the 3D plot using plt.show().

Make sure to replace elevation_array with your actual DEM data loaded from the file. You can further customize the visualization by adjusting parameters like colormap, lighting, and viewpoint.

Note that this is a basic example of 3D DEM visualization. Depending on your specific needs and data, you can explore more advanced visualization techniques and libraries like Mayavi for more interactive 3D visualizations.

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