A tool for calculating distortion parameters in coordination complexes.

Overview

Python version PyPI-Server Python Wheel Code size Repo size License

Github Download All releases Github Download Latest version Platform

OctaDist

Octahedral distortion calculator: A tool for calculating distortion parameters in coordination complexes. https://octadist.github.io/

molecule

Register for OctaDist

To get notified when we release new version of OctaDist, please register at https://cutt.ly/regis-octadist.

OctaDist Forum

The users can post questions in our Google Groups: OctaDist Forum

Standard abilities

OctaDist is computer software for inorganic chemistry and crystallography program. OctaDist can be used for studying the structural distortion in coordination complexes. With the abilities of OctaDist, you can:

  • analyze the structure and conformation of coordination complexes.
  • compute the octahedral distortion parameters.
  • explore tilting distortion in perovskite and metal-organic framework.
  • display 3D molecule for graphical analysis.
  • implement OctaDist's module into your or other program.
  • access the program core directly via an interactive scripting language.

Development and Release

OctaDist is written entirely in Python 3 binding to Tkinter GUI toolkit. It is cross-platform program which can work on multiple operating systems. The stable version and development build of OctaDist are released at here. A standalone executable for graphical user interface (GUI) and source code for command line interface (CLI) are available for as follows:

Platform Description Status
Windows windows Travis-CI Test
Linux latest-release Travis-CI Test
macOS latest-release Travis-CI Test
PyPI library PyPI-Server Travis-CI Test
Anaconda cloud Conda-Server Travis-CI Test
Nightly build Development build Travis-CI Test

Branch:

  1. master
  2. nightly-build

Git Clone

git clone https://github.com/OctaDist/OctaDist.git
git checkout nightly-build
git pull origin nightly-build

Documents

User manual : https://octadist.github.io/manual.html.

Reference manual :

Version Status Docs
Stable Doc-Latest-Badge HTML / PDF / Epub
Dev Build Doc-Nightly-Badge HTML / PDF / Epub

Download and Install

For Windows users, we strongly suggest a standalone executable:

Click Here to Download OctaDist-3.0.0-Win-x86-64.exe

For Linux or macOS users and already have Python 3 installed on the system, the easiest way to install OctaDist is to use pip.

pip install octadist

or use conda for those who have Anaconda:

conda install -c rangsiman octadist

Starting OctaDist

The following commands can be used to start OctaDist in different ways:

Graphical User Interface (GUI)

To start GUI program:

octadist

Screenshots of program:

OctaDist GUI XYZ coordinates Computed distortion parameters

Command Line Interface (CLI)

To start program command line:

octadist_cli

To calculate distortion parameters:

octadist_cli --inp EXAMPLE_INPUT.xyz

To calculate distortion parameters and show formatted output:

octadist_cli --inp EXAMPLE_INPUT.xyz --out

Supporting input format

Running the tests

Example 1: OctaDist as a package

import octadist as oc

# Prepare list of atomic coordinates of octahedral structure:

atom = ['Fe', 'O', 'O', 'N', 'N', 'N', 'N']

coord = [[2.298354000, 5.161785000, 7.971898000],  # <- Metal atom
         [1.885657000, 4.804777000, 6.183726000],
         [1.747515000, 6.960963000, 7.932784000],
         [4.094380000, 5.807257000, 7.588689000],
         [0.539005000, 4.482809000, 8.460004000],
         [2.812425000, 3.266553000, 8.131637000],
         [2.886404000, 5.392925000, 9.848966000]]

dist = oc.CalcDistortion(coord)
zeta = dist.zeta             # 0.228072561
delta = dist.delta           # 0.000476251
sigma = dist.sigma           # 47.92652837
theta = dist.theta           # 122.6889727

Example 2: Display 3D structure of molecule

import os
import octadist as oc

dir_path = os.path.dirname(os.path.realpath(__file__))
input_folder = os.path.join(dir_path, "../example-input/")
file = input_folder + "Multiple-metals.xyz"

atom_full, coord_full = oc.io.extract_coord(file)

my_plot = oc.draw.DrawComplex_Matplotlib(atom=atom_full, coord=coord_full)
my_plot.add_atom()
my_plot.add_bond()
my_plot.add_legend()
my_plot.save_img()
my_plot.show_plot()

# Figure will be saved as Complex_saved_by_OctaDist.png by default.

molecule

Other example scripts and octahedral complexes are available at example-py and example-input, respectively.

Citation

Please cite this project when you use OctaDist for scientific publication.

Ketkaew, R.; Tantirungrotechai, Y.; Harding, P.; Chastanet, G.; Guionneau, P.; Marchivie, M.; Harding, D. J. 
OctaDist: A Tool for Calculating Distortion Parameters in Spin Crossover and Coordination Complexes. 
Dalton Trans., 2021,50, 1086-1096. https://doi.org/10.1039/D0DT03988H

BibTeX

@article{Ketkaew2021,
  doi = {10.1039/d0dt03988h},
  url = {https://doi.org/10.1039/d0dt03988h},
  year = {2021},
  publisher = {Royal Society of Chemistry ({RSC})},
  volume = {50},
  number = {3},
  pages = {1086--1096},
  author = {Rangsiman Ketkaew and Yuthana Tantirungrotechai and Phimphaka Harding and Guillaume Chastanet and Philippe Guionneau and Mathieu Marchivie and David J. Harding},
  title = {OctaDist: a tool for calculating distortion parameters in spin crossover and coordination complexes},
  journal = {Dalton Transactions}
}

Bug report

If you found issues in OctaDist, please report it to us at here.

Project team

Comments
  • Bug in screen out the unwanted angle for theta parameter

    Bug in screen out the unwanted angle for theta parameter

    The \theta angle (Ligand-Metal-Ligand) on the same plane that is greater than 60 degree would be changed to 60 degree. The angle value can be less than, equal to, and greater than 60 degree. This condition for removing the unwanted angles is wrong.

    opened by rangsimanketkaew 1
  • Merge Dev v3.0.0 to master

    Merge Dev v3.0.0 to master

    v3.0.0 is the next version of OctaDist that we plan to release by March 2021.

    New features:

    • CIF (experiment) is now supported. (see #22)
    • A new visualizer by Plotly for drawing molecule. 10x faster than Matplotlib.

    Rangsiman

    opened by rangsimanketkaew 0
  • Merge v2.6.2 from nightly-build to master.

    Merge v2.6.2 from nightly-build to master.

    This pull request contains several commits which mainly

    • improve coding style (make it more pythonic)
    • fix CLI runner
    • update documentation and docstring
    • correct typos
    opened by rangsimanketkaew 0
  • Octadist 2.3 beta

    Octadist 2.3 beta

    • Switched to use Mathieu's algorithm
    • This version provides a reasonable Theta value for both regular and irregular octahedral complexes
    • Unable to compile this version as a standalone executable
    • Having a problem with molecular visualization
    opened by rangsimanketkaew 0
  • v2.3_alpha_pull_rq

    v2.3_alpha_pull_rq

    • Decorated program GUI
    • Removed RMSD
    • Improved code performance
    • Added hide/show button for showing sub-window of stdout and stderr progress information
    • Added box to show min, max, and mean Theta values
    enhancement 
    opened by rangsimanketkaew 0
  • Improve the GUI of OctaDist

    Improve the GUI of OctaDist

    Hi OctaDist's developers & users,

    Thanks all for using & supporting OctaDist. OctaDist joins Hacktoberfest this year and we welcome all contributions to make OctaDist better. One of the contributions you can make is the improvement of the GUI of OctaDist. Feel free to send your PR with the hashtag #hacktoberfest !

    Best, Rangsiman

    Hacktoberfest 
    opened by rangsimanketkaew 0
  • ASE integration

    ASE integration

    Dear colleagues, thanks for the nice tool! Are there any plans to integrate with the atomic simulation environment Python framework which is very widely used? The integration seems to be relatively straightforward.

    opened by blokhin 2
  • tkinter.filedialog linked with Tk 8.6.11 crashes on macOS 12 Monterey, breaking IDLE saves

    tkinter.filedialog linked with Tk 8.6.11 crashes on macOS 12 Monterey, breaking IDLE saves

    OctaDist's open file dialog failed on macOS Monterey. Please refer to this thread https://bugs.python.org/issue44828 for more details. However, macOS users are still able to use OctaDist via the command-line interface (CLI):

    octadist_cli -i file.xyz -o
    
    opened by rangsimanketkaew 0
  • Cannot read an XYZ file that saved by OctaDist

    Cannot read an XYZ file that saved by OctaDist

    OctaDist can save the Cartesian coordinate of a molecule as an XYZ file. However, OctaDist fails to read this file.

    Steps to reproduce

    1. Browse a molecule
    2. Save its coordinate as a new file called, e.g., octahedron.xyz
    3. Browse octahedron.xyz file and OctaDist yields the following error
    Exception in Tkinter callback
    Traceback (most recent call last):
      File "C:\Users\Nutt\miniconda3\lib\tkinter\__init__.py", line 1705, in __call__
        return self.func(*args)
      File "C:\Users\Nutt\Desktop\github\OctaDist\octadist\main.py", line 415, in open_file
        self.search_coord()
      File "C:\Users\Nutt\Desktop\github\OctaDist\octadist\main.py", line 451, in search_coord
        total_metal, atom_metal, coord_metal = molecule.find_metal(atom_full, coord_full)
      File "C:\Users\Nutt\Desktop\github\OctaDist\octadist\src\molecule.py", line 778, in find_metal
        21 <= number <= 30
    TypeError: '<=' not supported between instances of 'int' and 'NoneType'
    
    opened by rangsimanketkaew 0
Releases(v.3.0.0)
Owner
OctaDist
Octahedral Distortion Calculator
OctaDist
CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes

CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes. CHERRY is based on a deep learning model, which consists of a graph convolutional encoder and a link

Kenneth Shang 12 Dec 15, 2022
ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton (AAAI'22)

ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton (AAAI 2022) Official code of the paper ShuttleN

Wei-Yao Wang 11 Nov 30, 2022
Apache Spark - A unified analytics engine for large-scale data processing

Apache Spark Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an op

The Apache Software Foundation 34.7k Jan 04, 2023
NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

NVIDIA Merlin NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs. It enables data scientists, machine

419 Jan 03, 2023
Generate image analogies using neural matching and blending

neural image analogies This is basically an implementation of this "Image Analogies" paper, In our case, we use feature maps from VGG16. The patch mat

Adam Wentz 3.5k Jan 08, 2023
Faster RCNN pytorch windows

Faster-RCNN-pytorch-windows Faster RCNN implementation with pytorch for windows Open cmd, compile this comands: cd lib python setup.py build develop T

Hwa-Rang Kim 1 Nov 11, 2022
A knowledge base construction engine for richly formatted data

Fonduer is a Python package and framework for building knowledge base construction (KBC) applications from richly formatted data. Note that Fonduer is

HazyResearch 386 Dec 05, 2022
The official PyTorch code implementation of "Personalized Trajectory Prediction via Distribution Discrimination" in ICCV 2021.

Personalized Trajectory Prediction via Distribution Discrimination (DisDis) The official PyTorch code implementation of "Personalized Trajectory Predi

25 Dec 20, 2022
Gif-caption - A straightforward GIF Captioner written in Python

Broksy's GIF Captioner Have you ever wanted to easily caption a GIF without havi

3 Apr 09, 2022
TensorFlow tutorials and best practices.

Effective TensorFlow 2 Table of Contents Part I: TensorFlow 2 Fundamentals TensorFlow 2 Basics Broadcasting the good and the ugly Take advantage of th

Vahid Kazemi 8.7k Dec 31, 2022
Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

Weakly Supervised Segmentation with TensorFlow This repo contains a TensorFlow implementation of weakly supervised instance segmentation as described

Phil Ferriere 220 Dec 13, 2022
Omniverse sample scripts - A guide for developing with Python scripts on NVIDIA Ominverse

Omniverse sample scripts ここでは、NVIDIA Omniverse ( https://www.nvidia.com/ja-jp/om

ft-lab (Yutaka Yoshisaka) 37 Nov 17, 2022
LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)

LV-BERT Introduction In this repo, we introduce LV-BERT by exploiting layer variety for BERT. For detailed description and experimental results, pleas

Weihao Yu 14 Aug 24, 2022
Attendance Monitoring with Face Recognition using Python

Attendance Monitoring with Face Recognition using Python A python GUI integrated attendance system using face recognition to take attendance. In this

Vaibhav Rajput 2 Jun 21, 2022
A Simulated Optimal Intrusion Response Game

Optimal Intrusion Response An OpenAI Gym interface to a MDP/Markov Game model for optimal intrusion response of a realistic infrastructure simulated u

Kim Hammar 10 Dec 09, 2022
Code for ICML 2021 paper: How could Neural Networks understand Programs?

OSCAR This repository contains the source code of our ICML 2021 paper How could Neural Networks understand Programs?. Environment Run following comman

Dinglan Peng 115 Dec 17, 2022
PyTorch code of paper "LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering"

LiVLR-VideoQA We propose a Lightweight Visual-Linguistic Reasoning framework (LiVLR) for VideoQA. The overview of LiVLR: Evaluation on MSRVTT-QA Datas

JJ Jiang 7 Dec 30, 2022
Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch

SRDenseNet-pytorch Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch (http://openaccess.thecvf.com/content_ICC

wxy 114 Nov 26, 2022
Immortal tracker

Immortal_tracker Prerequisite Our code is tested for Python 3.6. To install required liabraries: pip install -r requirements.txt Waymo Open Dataset P

74 Dec 03, 2022
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022