Top 5 Methods for Saving a 3D Plot to a PDF with Python

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💡 Problem Formulation: Python users frequently require the ability to save 3D visualizations generated with plotting libraries into a PDF format with embedded interactivity (3D PDF). This article aims to provide methods for converting a 3D plot into a fully interactive PDF file, where the input is a 3D plot object, and the desired output is a PDF file containing the visual representation of the plot.

Method 1: Using matplotlib with mpl_to_pdf

This method involves creating 3D plots with matplotlib and using the mpl_to_pdf package to export the plot as a PDF. The mpl_to_pdf package is designed to take a matplotlib figure object and save it directly as a PDF.

Here’s an example:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_to_pdf import export_pdf

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter([1, 2, 3], [4, 5, 6], [7, 8, 9])
export_pdf(fig, 'output_3d_plot.pdf')

The output of this code is a file named ‘output_3d_plot.pdf’ containing the 3D scatter plot.

The code snippet above creates a simple 3D scatter plot using matplotlib, then exports the figure to a PDF using the mpl_to_pdf package. This allows for the creation of static 3D plots within a PDF file.

Method 2: Using Pyvista and PyPDF2

PyVista is a helper module for the Visualization Toolkit (VTK) that provides a streamlined workflow for 3D plotting. PyPDF2 can be used to embed the PyVista plot into a PDF.

Here’s an example:

import pyvista as pv
from PyPDF2 import PdfFileWriter, PdfFileReader

# generate a 3D plot
plot = pv.Sphere()

# save plot as a temporary HTML file
plotter = pv.Plotter()
plotter.add_mesh(plot)
plotter.show(screenshot='temp_plot.png')

# Create a PDF from PNG
writer = PdfFileWriter()
writer.addAttachment('3d_plot.png', 'temp_plot.png')
output_pdf = open('3d_plot.pdf', 'wb')
writer.write(output_pdf)
output_pdf.close()

The output is a PDF file named ‘3d_plot.pdf’ with the 3D plot attached as an image.

While the snippet demonstrates how to create a 3D plot with PyVista, save it as an image, and then embed it within a PDF file using PyPDF2, it does not produce an interactive 3D PDF, only an image embedded in the document.

Method 3: Using ReportLab and matplotlib

ReportLab is a robust PDF generation library. This method combines ReportLab with matplotlib to save the 3D plot as a rasterized image within a PDF document.

Here’s an example:

from reportlab.pdfgen import canvas
import matplotlib.pyplot as plt

# Create a 3D plot using matplotlib
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot([1, 2, 3], [1, 2, 3], [1, 2, 3])

# Save matplotlib plot to PDF using ReportLab
c = canvas.Canvas('3d_plot_with_reportlab.pdf')
fig.savefig("temp_plot.png", format='png')
c.drawImage("temp_plot.png", 100, 100, width=400, height=300)
c.showPage()
c.save()

The result is a PDF file named ‘3d_plot_with_reportlab.pdf’ that includes the 3D plot as a static image.

This method utilizes ReportLab to create a new PDF and then inserts the matplotlib-generated plot image into the PDF. However, similar to Method 2, the plot is not interactive within the PDF.

Method 4: Using Mayavi and img2pdf

Mayavi is a 3D scientific data visualization and plotting software that can render high-quality 3D plots. img2pdf allows for the conversion of images to PDF without quality loss.

Here’s an example:

from mayavi import mlab
import img2pdf

# Creating a sphere using Mayavi
mlab.figure(bgcolor=(1, 1, 1), size=(400, 300))
s = mlab.test_plot3d()
mlab.savefig('3d_plot.png')
mlab.close()

# Convert the image to PDF
with open("3d_mayavi_plot.pdf", "wb") as f:
    f.write(img2pdf.convert('3d_plot.png'))

This will create a PDF named ‘3d_mayavi_plot.pdf’ that features the 3D plot as a non-interactive image.

This code snippet demonstrates how to generate a 3D plot with Mayavi and convert the saved image to PDF format using img2pdf. It is simple and retains image quality but, again, the plot will not be interactive in the PDF.

Bonus One-Liner Method 5: Using matplotlib’s PDF backend

Matplotlib has a built-in backend for directly exporting figures to PDF format.

Here’s an example:

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

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot([1, 2, 3], [1, 2, 3], [1, 2, 3])
fig.savefig('simple_3d_plot.pdf', format='pdf')

The output is a PDF named ‘simple_3d_plot.pdf’ containing the plot as a static image.

This one-liner uses matplotlib’s capability to save plots directly to PDF format. It’s quick and easy but does not offer interactivity within the PDF.

Summary/Discussion

  • Method 1: matplotlib with mpl_to_pdf. Allows for easy export of matplotlib figures to PDF. Strengths: straightforward implementation. Weaknesses: the 3D plot is static in PDF.
  • Method 2: Pyvista and PyPDF2. Provides a way to work with more advanced 3D plots and embed them as images in PDFs. Strengths: suitable for more complex visualizations. Weaknesses: no interactive plotting capabilities in PDF.
  • Method 3: ReportLab and matplotlib. ReportLab is a powerful tool for creating PDFs with fine control. Strengths: flexible PDF creation with complex layouts. Weaknesses: 3D plot remains a static image within the PDF.
  • Method 4: Mayavi and img2pdf. Great for high-quality 3D visualizations. Strengths: high-quality plots. Weaknesses: output is non-interactive.
  • Bonus Method 5: matplotlib’s PDF backend. Simplest method with a single line of code to save the plot. Strengths: extremely quick and easy. Weaknesses: plot is non-interactive and it has limited customization of the PDF output.