5 Best Ways to Save a Figure Remotely with Pylab in Python

Rate this post

πŸ’‘ Problem Formulation: When working with Pylab in Python, there are scenarios where you may need to save your plots or figures on a remote server instead of locally. This article discusses how to export a graphic generated using Pylab to a remote destination programmatically. Imagine you’ve plotted a graph using your data, and now you need the image file to be stored directly on a cloud storage or a remote server for later analysis or web display purposes.

Method 1: Using the savefig() Method with Remote Filesystem Mounting

One way to save a Pylab figure remotely is by mounting the remote filesystem on your local machine. Once the remote directory is mounted and accessible like a local directory, you can use the savefig() function to save the file directly to this directory.

Here’s an example:

import matplotlib.pyplot as plt

# Plot your figure
plt.plot([0, 1, 2, 3], [0, 1, 4, 9])

# Assume "/mnt/remote_directory/" is the mount point for your remote file system
plt.savefig('/mnt/remote_directory/my_figure.png')

Output: A figure saved remotely at /mnt/remote_directory/my_figure.png

In this code snippet, Pylab’s savefig() function is used to write the figure to the mount point of the remote directory. Mounting might be achieved through various protocols such as SSHFS, Samba, or NFS. Once mounted, the directory behaves like any local directory.

Method 2: Using SCP or SFTP for File Transfer after Local Save

Saving a figure locally and then using Secure Copy Protocol (SCP) or Secure File Transfer Protocol (SFTP) to transfer the file into a remote server is another commonly used method. This method involves two steps, but it’s very secure and versatile.

Here’s an example:

import matplotlib.pyplot as plt
import os

# Plot and save locally
plt.plot([0, 1, 2, 3], [0, 1, 4, 9])
plt.savefig('my_figure.png')

# Use SCP or SFTP to transfer the saved file
os.system('scp my_figure.png username@remotehost:/remote_directory/my_figure.png')

Output: The figure is first saved locally and then uploaded to /remote_directory/my_figure.png on the remote host.

This method conducts the save operation as usual with the savefig() function but then employs the SCP command to move the file to a remote system. You will need SSH access and permissions for the destination directory on the remote server.

Method 3: Direct Saving via Library Supporting Remote Operations

Certain high-level Python libraries that integrate with cloud services allow you to save files directly to a remote location by handling the connection and file transfer for you. Examples include libraries like boto3 for AWS.

Here’s an example:

import matplotlib.pyplot as plt
import boto3
import io

# Plot your figure
plt.plot([0, 1, 2, 3], [0, 1, 4, 9])

# Save the figure to a BytesIO object
buf = io.BytesIO()
plt.savefig(buf, format='png')

# Upload to AWS S3
s3 = boto3.resource('s3')
s3.Bucket('mybucket').put_object(Key='my_figure.png', Body=buf.getvalue())

Output: The figure is saved to the ‘mybucket’ bucket on Amazon S3 with the key ‘my_figure.png’.

In this code, the Pylab figure is saved into a bytes buffer, which is then uploaded to Amazon S3 using the boto3 library. This method avoids saving the file locally and is highly secure, though it requires correct AWS permissions and configurations.

Method 4: Use of Python SSH Libraries for Remote Saving

Python provides libraries like paramiko to programmatically establish an SSH connection for transferring files. With Paramiko, you can save a figure locally and then securely upload it to the remote server using SFTP.

Here’s an example:

import matplotlib.pyplot as plt
import paramiko
from io import BytesIO

# Plot your figure
plt.plot([0, 1, 2, 3], [0, 1, 4, 9])

# Save the figure to a BytesIO object
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)

# Establish an SSH session and upload
ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh.connect('hostname', username='username', password='password')
sftp = ssh.open_sftp()
sftp.putfo(buf, '/remote_directory/my_figure.png')
sftp.close()
ssh.close()

Output: The figure is saved on the remote server in /remote_directory/my_figure.png.

This snippet illustrates saving the figure in memory and then using paramiko to open an SFTP session for file upload. This method is preferable for remote operations without mounting or third-party services.

Bonus One-Liner Method 5: Using Matplotlib’s mpld3 Plugin for Direct Web Display

While not strictly remote saving, mpld3 can render Matplotlib figures as interactive web plots using D3.js, which can be immediately used by web applications without the need to save as a conventional file. Here’s a cool one-liner:

Here’s an example:

import matplotlib.pyplot as plt
import mpld3

# Plot your figure and render it as HTML
plt.plot([0, 1, 2, 3], [0, 1, 4, 9])
mpld3.display()

Output: An interactive HTML version of the plot is displayed in your Jupyter notebook or web interface.

This one-liner takes the plotted figures and generates an interactive HTML representation using mpld3. It’s a seamless way to display figures in web applications.

Summary/Discussion

  • Method 1: Remote Filesystem Mounting. Easy integration with existing codebase. Requires filesystem-level access and may have security implications.
  • Method 2: SCP/SFTP Transfer. Secure and widely compatible. Involves an additional transfer step and requires SSH access.
  • Method 3: Library-Supported Remote Operations. Streamlined for specific services like AWS. May require additional setup and service-specific know-how.
  • Method 4: SSH Libraries for Python. Programmatically robust and good for automated systems. Adds a layer of complexity and dependency on external libraries.
  • Method 5: Direct Web Display using mpld3. Provides an interactive web display without saving a file. Limited to web environments and doesn’t actually “save” the figure.