# nanomath
This module provides a few simple math and statistics functions for other scripts processing Oxford Nanopore sequencing data
[](https://twitter.com/wouter_decoster) [](https://anaconda.org/bioconda/nanomath) [](https://tracker.debian.org/pkg/python-nanomath)
## FUNCTIONS
Calculate read N50 from a set of lengths get_N50(readlenghts)
Remove extreme length outliers from a dataset remove_length_outliers(dataframe, columname)
Calculate the average Phred quality of a read ave_qual(qualscores)
Write out the statistics report after calling readstats function write_stats(dataframe, outputname)
Compute a number of statistics, return a dictionary calc_read_stats(dataframe)
As of v1.3.0, nanomath calculates the average quality differently, by first converting per-read phred scale averages to error rates, take the average, and converting back ([nanostat#40](<https://github.com/wdecoster/nanostat/issues/40>))
## INSTALLATION
`bash pip install nanomath `
or [](https://anaconda.org/bioconda/nanomath)
` conda install -c bioconda nanomath `
## CONTRIBUTORS
[@alexomics](https://github.com/alexomics) for fixing the indentation of the printed stats
## CITATION
If you use this tool, please consider citing our [publication](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty149/4934939).
Metadata
Release files for nanomath 1.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| nanomath-1.4.0.tar.gz | 18.8 kB | Without provenance | Details |
Release files / nanomath-1.4.0.tar.gz
| Download URL | nanomath-1.4.0.tar.gz |
|---|---|
| Size | 18.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
ed7a38fbb156d9a68a95c2570fe3c2035321d0a3e234580496750afca4927ced
|
|
BLAKE2b-256 checksum How to use checksums |
fde54ce733aa2f62470fd79523a56f55033589394b49b2ee746521db16e5846a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.8.10
|
Security for nanomath 1.4.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.