Calib-RT is designed for RT (retention time) calibration.
Project description
Calib-RT
Overview
Calib-RT is an open-source Python software package designed for RT (retention time) calibration. This package provides a flexible and robust solution for achieving accurate RT calibration across various data scales while handling a certain level of noise interference.
The workflow diagram is below, providing an overview of the process. For a comprehensive and in-depth explanation, please refer to the associated paper for detailed insights and analysis.
Installation
To use calib_rt, make sure you have the following dependencies installed:
- Python (>= 3.10)
- numpy (>= 1.26.0)
- pandas (>= 2.1.1)
- networkx (>= 3.1)
- statsmodels (>= 0.14.0)
- scipy (>= 1.11.3)
You can install the calib_rt package using pip:
pip install pycalib_rt
Usage
Here is an example of how to use calib_rt for RT calibration:
import calib_rt
# basic information of all built-in datasets
calib_rt.RTdatasets.get_datasets_list()
sample_type datasets_num
0 distort_left 2
1 distort_right 2
2 exp 2
3 linear 2
4 S 2
# use first of "S" type datasets
datasets = calib_rt.RTdatasets.get_pandas(sample_type="S",index_in_group=1)
x = datasets["Spectral library RT"]
y = datasets["Measured RT"]
# fit and predict
model = calib_rt.Calib_RT()
model.fit(x,y)
y_pred = model.predict(x)
Performance test
Code for performance test is available in the analysis. For a detailed analysis of the test conclusion, please refer to our paper.
References
License
This project is licensed under the MIT License. See the LICENSE file for details.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file pycalib_rt-0.1.1.tar.gz.
File metadata
- Download URL: pycalib_rt-0.1.1.tar.gz
- Upload date:
- Size: 2.6 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d2c7dd1a75516a572cd0c873d6bb667505b8deeec0c735ab97512452b21077c9
|
|
| MD5 |
81f5529e256f9483f96b5cdaeccec6a0
|
|
| BLAKE2b-256 |
569716593784812fdeddcc73dcfe98b1a712f191e3bad7f6b510c9280d7e1d73
|