Solving the Mystery of Cartopy Non-PlateCarree Projections: A Guide to Avoiding Empty Plots

Are you tired of staring at a blank canvas, wondering why your Cartopy non-PlateCarree projections refuse to yield a beautiful map? You’re not alone! In this article, we’ll delve into the common pitfalls and provide step-by-step solutions to ensure your maps are rendered correctly.

The Problem: Cartopy Non-PlateCarree Projections Result in Empty Plots

Cartopy, a powerful Python library for geospatial data visualization, offers a wide range of projection options. However, when venturing beyond the default PlateCarree projection, many users encounter a frustrating issue: empty plots.

This phenomenon can be attributed to several factors, including:

• Incorrect projection settings
• Inadequate data preprocessing
• Incompatibility with specific projection libraries

Understanding Cartopy Projections

Before diving into the solutions, let’s take a brief look at the different projection options in Cartopy:

Projection	Description
PlateCarree	Default projection, suitable for most use cases
Mercator	Conformal cylindrical projection, useful for navigation
Azimuthal Equidistant	Projection suitable for visualizing global phenomena
Orthographic	Perspective projection, useful for creating 3D-like visualizations

Now that we’ve covered the basics, let’s tackle the issues causing empty plots.

Solution 1: Verify Projection Settings

One of the most common mistakes is misconfiguring the projection settings. Make sure to:

• Specify the correct projection type using the proj parameter
• Set the appropriate projection parameters, such as central longitude (central_longitude) and latitude (central_latitude)

Example code:

import cartopy.crs as ccrs

# Define the Mercator projection with central longitude and latitude
proj = ccrs.Mercator(central_longitude=0, central_latitude=0)

# Create a new figure with the specified projection
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)

Solution 2: Preprocess Data for Non-PlateCarree Projections

When working with non-PlateCarree projections, it’s essential to preprocess your data to ensure it’s compatible with the chosen projection. This may involve:

• Converting coordinate systems using tools like GDAL or Fiona
• Reprojecting data using Cartopy’s built-in functionality

Example code:

import cartopy.crs as ccrs
import numpy as np

# Sample data in PlateCarree coordinates
lons = np.array([-100, -90, -80, -70])
lats = np.array([40, 30, 20, 10])

# Reproject data to Mercator coordinates
proj = ccrs.Mercator()
x, y = proj.transform_points(ccrs.PlateCarree(), lons, lats)

# Create a new figure with the reprojected data
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)
ax.plot(x, y, transform=proj)

Solution 3: Ensure Compatibility with Projection Libraries

Some Cartopy projections rely on external libraries, such as PROJ.4 or GDAL. Make sure to:

• Install the required libraries and their dependencies
• Verify that the libraries are correctly configured and imported

Example code:

import cartopy.crs as ccrs
import os

# Verify that PROJ.4 is installed and configured
if not os.environ.get('PROJ_LIB'):
    os.environ['PROJ_LIB'] = '/usr/share/proj'

# Define the Azimuthal Equidistant projection
proj = ccrs.AzimuthalEquidistant()

# Create a new figure with the specified projection
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(1, 1, 1, projection=proj)

Additional Tips and Tricks

To avoid common pitfalls and ensure successful rendering of non-PlateCarree projections:

1. Use the latest version of Cartopy and its dependencies
2. Verify that your data is correctly formatted and compatible with the chosen projection
3. Avoid mixing different coordinate systems and projections within the same plot
4. Experiment with different projections and settings to find the optimal configuration for your use case

By following these guidelines and troubleshooting steps, you should be able to overcome the hurdle of Cartopy non-PlateCarree projections resulting in empty plots. Remember to stay patient, persistent, and creative in your exploration of the vast world of geospatial data visualization!

I hope this article has been helpful in resolving your Cartopy-related issues. If you have any further questions or would like to share your own experiences, please don’t hesitate to reach out.

Frequently Asked Question

Struggling with Cartopy and its non-PlateCarree projections? We’ve got you covered! Check out these frequently asked questions and find the solution to your empty plot woes.