Skip to main content

Tool for automatic analysis of multiple HPLC results

Project description

PyGCMS

A Python tool to manage multiple GCMS qualitative tables and automatically split chemicals into functional groups.

GA An open-source Python tool that can automatically:

  • handle multiple GCMS semi-quantitative data tables (derivatized or not)
  • duild a database of all identified compounds and their relevant properties using PubChemPy
  • split each compound into its functional groups using a published fragmentation algorithm
  • apply calibrations and/or semi-calibration using Tanimoto and molecular weight similarities
  • produce single sample reports, comprehensive multi-sample reports and aggregated reports based on functional group mass fractions in the samples

Example

In the GitHub repo, download the example folder and run the example_code using the example_data. The necessary documents are available. You will need to ensure your project format matches that of the example.

Documentation

The full description of the algorithm capabilities will be provided (link not available now). Comments are exahustive and shoud provide a full description of the code.

A scheme of the algorithm is provided here.

Algorithm

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

gcms_data_analysis-0.1.2.tar.gz (28.4 kB view details)

Uploaded Source

Built Distribution

gcms_data_analysis-0.1.2-py3-none-any.whl (27.8 kB view details)

Uploaded Python 3

File details

Details for the file gcms_data_analysis-0.1.2.tar.gz.

File metadata

  • Download URL: gcms_data_analysis-0.1.2.tar.gz
  • Upload date:
  • Size: 28.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for gcms_data_analysis-0.1.2.tar.gz
Algorithm Hash digest
SHA256 4b10dd7840e73cb019fcb0759c882e256e4afca0315e4830ce91ec43586ee4fe
MD5 ca1842fd98ca702e56195f2791611f2f
BLAKE2b-256 47fd1cdee6b0a1da4c725a8470e6bf86bbf2314aa112ebb9bdfb9c77fbabc271

See more details on using hashes here.

File details

Details for the file gcms_data_analysis-0.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for gcms_data_analysis-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 e3850147750b72312277e481b497d3bf75408d250377d6bdb615b00c1888c741
MD5 de7c291cb85a874e3d46c604717de4fc
BLAKE2b-256 d5e0514acac618eff0ed52be71d40bc8debf292cfd6b0ff441ed3ce104d03de3

See more details on using hashes here.

Supported by

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