Metadata-Version: 2.1
Name: cctk
Version: 0.2.4
Summary: computational chemistry toolkit
Home-page: https://github.com/ekwan/cctk
Author: Corin Wagen and Eugene Kwan
Author-email: corin.wagen@gmail.com
License: Apache 2.O
Download-URL: https://github.com/ekwan/cctk/archive/v0.2.4.tar.gz
Description: 
        # cctk
        
        *a Python-based computational chemistry toolkit*
        
        *cctk* simplifies the computational modeling of organic reactions and small molecule structures by automating routine interactions with quantum chemistry software packages:
        
         - **input file creation**: conformer enumeration, job keyword manipulations, constrained potential energy surface creation
         - **method screening**: creating jobs that screen grids of DFT methods and basis sets
         - **job monitoring**: identification of job status, progress of optimizations, and resubmission of failed jobs
         - **data extraction**: geometries, energies, molecular properties (e.g. charges or NMR shieldings), or geometric parameters (distances, angles, dihedrals) from output files
         - **data analysis**: easy export to [pandas](https://pandas.pydata.org/) for statistical analysis or visualization
        
        A quick-start guide is [available](https://cctk.readthedocs.io/en/latest/quick-start.html). More documentation is [here](https://cctk.readthedocs.io/).
         
        *cctk* is primarily designed for use with [Gaussian 16](https://gaussian.com). Some support is provided for other file formats (`.xyz`, `.mol2`, `.pdb`, [Schrodinger](https://www.schrodinger.com) `mae`, and [Orca]() `.inp`).
        
        ## Installation
        
        [![PyPI version](https://badge.fury.io/py/cctk.svg)](https://badge.fury.io/py/cctk)
        [![Doc status](https://readthedocs.org/projects/pip/badge/)](https://cctk.rtfd.io)
        
        ### First Time
        
        *cctk* is easy to install! It should work on any system where Python works.
        
        With Python 3.7 or later, type:
        
        ```
        pip install cctk
        ```
        
        If you don't have [pip](https://pypi.org/project/pip/) or virtual environments available on your system, then we recommend installing Anaconda first:
        
        1. Go to [https://www.anaconda.com/distribution/](https://www.anaconda.com/distribution/). Download the Python 3 installer appropriate to your system and run it.
        
        2. Create a virtual environment to use with *cctk*:
        
         ```
         conda create --name cctk python=3.8
         ```
        
        3. Now activate the virtual environment:
        
         ```
         conda activate cctk
         ```
        
        To use *cctk*, you will need to place this command at the beginning of your Python scripts:
        
        ```
        import cctk
        ```
        
        The [documentation](https://cctk.readthedocs.io/) contains many examples of how to write *cctk* scripts.
        
        ### Upgrading
        
        *cctk* is undergoing active development. To upgrade to the latest stable release:
        
        ```
        pip install --upgrade cctk
        ```
        
        To install the development version, which may be unstable, run:
        
        ```
        $ pip install --upgrade git+git@github.com:ekwan/cctk.git@master 
        ```
        
        Alternatively, clone the repository. Then, from within the repository folder, run:
        
        ```
        pip install --upgrade .
        ```
        
        ### Building Documentation
        
        If you want to read the *cctk* documentation locally, you can build it by going to the `docs` folder and typing:
        
        ```
        make html
        ```
        
        This command will require the `sphinx` and `sphinx-bootstrap-theme` packages to be installed first. Once generated, the documentation will be available locally at: `docs/_build/html/index.html`.
        
        ## Fine Print
        
        ### Package Details 
        
        - `cctk/` contains the Python modules for *cctk* and the accompanying static data files.  
        - `docs/` contains the code needed to generate the documentation.  
        - `scripts/` contains pre-defined scripts that use *cctk* to quickly analyze and manipulate one or many output files.  
        - `test/` contains code to test *cctk* and accompanying files.  
        - `tutorial/` contains detailed tutorials on how to use *cctk* on complex, real-world problems.  
        
        *cctk* requires Python 3.7+, [`numpy`](https://numpy.org/), and [`networkx`](https://networkx.github.io/).
        A full list of requirements can be found in `env.yml`. 
        
        ### External Data:
        
        *cctk* depends on some external data (`cctk/data/`):
        
        - Atomic weights are taken from the 
        [NIST website](https://physics.nist.gov/cgi-bin/Compositions/stand_alone.pl?ele=&all=all&ascii=ascii2&isotype=some) 
        and stored in `cctk/data/isotopes.csv`.
        - Covalent radii are taken from 
        [**Dalton Trans.** *2008*, 2832&ndash;2838](https://pubs.rsc.org/en/content/articlelanding/2008/dt/b801115j#!divAbstract) 
        and stored in `cctk/data/covalent_radii.csv`.
        (When multiple atomic types were specified, the one with longer bond distances was adopted for simplicity).
        
        ### Authors:
        
        *cctk* was written by Corin Wagen (Harvard University) and Eugene Kwan.
        Please email `cwagen@g.harvard.edu` or `ekwan16@gmail.com` with any questions or bug reports; we will do our best! We also welcome contributors!
        
        ### How to Cite:
        
        Wagen, C.C.; Kwan, E.E. *cctk* **2020**, [www.github.com/ekwan/cctk](https://www.github.com/ekwan/cctk).
        
        ### License:
        
        This project is licensed under the Apache License, Version 2.0.  Please see `LICENSE` for full terms and conditions. 
        
        *Copyright 2020 by Corin Wagen and Eugene Kwan*
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
