Metadata-Version: 1.2
Name: pyhrms
Version: 0.1.1
Summary: A powerful GC/LC-HRMS data analysis tool
Home-page: https://github.com/WangRui5/PyHRMS.git
Author: Wang Rui
Author-email: wtrt7009@gmail.com
License: UNKNOWN
Description:   
          
        PyHRMS: Tools For working with High Resolution Mass Spectrometry (HRMS) data in Environmental Science  
        =====================================================================================================
          
          
        PyHRMS is a python package for processing  high resolution Mass Spectrometry data coupled with gas  
        chromatogram (GC) or liquid chromatogram (LC).  
          
        It aims to provide user friendly tool to read,  process and visualize LC/GC-HRMS data for environmental scientist.  
          
        Contributers: Rui Wang  
        ======================
        Release date: Nov.15.2021  
          
        Update
        ======
        Nov.12.2021: First release for pyhrms  
          
          
          
          
        Installation & major dependencies  
        pyhrms can be installed and import as following:  
          
        ```
        pip install pyhrms  
        ```
          
        pyhrms requires major dependencies: 
        ===================================
          
        * numpy>=1.19.2  
          
        * pandas>1.3.3  
          
        * matplotlib>=3.3.2  
          
        * pymzml>=2.4.7  
          
        * scipy>=1.6.2  
          
        * numba>=0.53.1  
          
        * molmass>=2021.6.18  
          
          
          
        Features 
        ========
        PyHRMS provides following functions:  
          
        * Read raw LC/GC-HRMS data in mzML format;  
        * Powerful and accurate peak picking function for LC/GC HRMS;  
        * retention time (rt) and mass over Z stands for charge number of ions (m/z) will be aligned based on user defined error range.  
        * Accurate function for comparing response between/among two or more samples;  
        * Covert profile data to centroid  
        * Parallel computing to improve efficiency;  
        * Interactive visualizations of raw mzML data;  
        * Supporting searching for Local database and massbank;  
        * MS quality evaluation for ms data in profile.  
          
          
        Licensing
        =========
          
        The package is open source and can be utilized under MIT license. Please find the detail in licence file.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
