Skip to main content

A Python package for EM data analysis

Project description

Description

This project provides a useful tool for analyzing electron microscopy data. More specifically, the morph_analysis.py module offers various functions that iteratively analyze several morphological properties of networks (such as porosity, tortuosity, fractal dimension, etc.) obtained from in-situ measurement movies. This module is highly valuable as it facilitates the investigation of how specific properties evolve over time, thereby reducing the time required for data analysis.

Features

  • Normalization for a better image contrast (optional)
  • Scalebar calibration
  • ROI selection
  • Plotting a specific property value as a function of time
  • Generating a .csv file for a subsequent analysis with other programs (such as curve fitting)
  • Provides images and corresponding tables for the segmentation measurements

Installation

EM_data_analysis requires Python 3.6 or above.

Installation using pip (recommended):

pip install EM_data_analysis

Usage

Create an empty directory (will be used to store all the frames created from the movie). Then, simply input the path of the movie and the directory created to store the frames.

from EM_data_analysis import morph_analysis as ma

ma.Network_analysis("path-of-the-movie", "path-of-the-directory") 

The movie format must be explicit and could be in AVI or MP4 format.

Contributions

EM_data_analysis is created by Mattia Lizzano

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

EM_data_analysis-1.0.5.tar.gz (14.1 kB view details)

Uploaded Source

File details

Details for the file EM_data_analysis-1.0.5.tar.gz.

File metadata

  • Download URL: EM_data_analysis-1.0.5.tar.gz
  • Upload date:
  • Size: 14.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.9

File hashes

Hashes for EM_data_analysis-1.0.5.tar.gz
Algorithm Hash digest
SHA256 fcca2d7472462ab54ab06bd6020662a3024a5994bd87f150ccd11b5564a36c15
MD5 d05e5137764c2d7c9fdf34f11bbc2466
BLAKE2b-256 5797339b14fe057edb116ef477e8bd052019bfa93e7bf50bd53b7888f61caa99

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page