The PYthon Microscopy Environment

The PYthon Microscopy Environment is an open-source package providing image acquisition and data analysis functionality for a number of microscopy applications, but with a particular emphasis on super-resolution techniques (PALM/STORM/PAINT etc ...). The package is multi platform, running on Windows, Linux, and OSX.

Get PYME or read the documentation.


Data Acquisition - PYMEAcquire

PYME offers a data acquisition module which performs the microscope and camera control functionality similar to that offered by e.g. micro-manager, but optimised for PALM/STORM type imaging. Some features include:

  • Support for a range of common hardware including Andor, Hamamatsu and PCO sCMOS cameras
  • Streaming of video to disk with on the fly compression and large (>4 GB) file size support
  • Customisable acquisition protocols.
  • Extensive meta-data capture.
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High bandwidth streaming and distributed storage for microscopy data

The combination of high frame rates and large file sizes produced by modern microscopy experiments can easily overwhelm local storage. PYME offers a distributed storage system which allows microscope data to be compressed in real time and streamed in parallel to multiple storage nodes as it is acquired. Our system is designed to operate on inexpensive consumer-grade commodity hardware, whilst being scalable to 100s of TB of attached storage and GB/s of streaming bandwidth.

  • Inexpensive hardware requirements
  • REST based HTTP api for easy integration with other software
  • High compression ratios (~5x)
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Recipe based description of complex analysis tasks

Allows complex data processing workflows to be defined without writing code by assembling pre-defined processing modules. The recipe specification removes allows reproducible workflows which can be run both locally and distributed across a cluster.

  • Abstracts IO and focuses on data flow
  • Automated propagation of metadata
  • Large number of modules available, including filtering, tracking, deconvolution, segmentation, machine learning
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Real time distributed analysis infrastructure

Our distributed analysis infrastructure allows scalable distribution of both single-molecule localization and more conventional image processing and quantification tasks. It can be used on a single-computer to divide processing between multiple cores, on HPC systems, or across a cluster of commodity computers.

  • Support for a range of different localization modalities (including 2D, 3D, & ratiometric multi-colour)
  • PSF type agnostic 3D localization fitting (using measured PSF)
  • Respects data locality, both on disk and in memory, and avoids copies between nodes
  • High throughput, low overhead, scheduling allowing thousands of tasks/s
  • Suitable for analyising streaming data in real timeas it comes in
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PYMEVisualize data exploration and visualisation tool

Offers a fast and flexible tool for exploring and visualising the list of fluorophore locations that is produced by PALM/STORM analysis algorithms.

  • OpenGL accelerated point cloud display
  • Points colourable by any fitted parameter (or parameters derived from these)
  • Filtering by fitted parameters or derived measures
  • Extensive support for ratiometric multi-colour datasets
  • Point chaining for single particle tracking or to collapse multiple localisations of a single fluorophore.
  • Multiple rendering options including Gaussian, histogram, and jittered triangulation.
  • Export as .tif or other standard image formats.
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PYMEImage feature rich image viewer

A slice based image viewer for 2D, 3D, and 4D data sets, supporting many basic image processing tasks.

  • Widefield and confocal deconvolution.
  • Guided PSF extraction
  • Filtering, cropping and projections
  • Segmentation
  • Colocalisation and distance transforms
  • 3D isosurface & volume rendering
  • Data import and export
  • Vectorial chromatic shift calibration and correction
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Contact us:

Whatever your experience with PYME, we want to hear from you! If you want to know if PYME will work for you we're happy to help. If you've run into problems we want to know so we can fix things. If you are a happy user, we also want to know as this helps us secure continued funding.

Get in touch with us at support@python-microscopy.org, connect with us on the image.sc forum using the "pyme" tag, or post an issue on our github repository.

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