Saravanan666/pypi-org huggingface dataset:
Package: chemkinlib11
ChemKinLib [![Build Status](https://travis-ci.org/cs207-group11/cs207-FinalProject.svg?branch=master)](https://travis-ci.org/cs207-group11/cs207-FinalProject.svg?branch=master) [![Coverage Status](https://coveralls.io/repos/github/cs207-group11/cs207-FinalProject/badge.svg?branch=master)](https://coveralls.io/github/cs207-group11/cs207-FinalProject?branch=master)===================### Group #11: Karan R. Motwani, Shiyu Huang, Hannah Sim, and Haixing Yin.ChemKinLib (Chemical Kinetics Library) consists of classes and functions to allow the user to interpret the kinetics of a system of chemical reaction by computing the associated reaction rates. The library currently supports:- Elementary reactions: irreversible and reversibleAnd of these reactions, the following reaction rate coefficients are supported:- Constant- Arrhenius- Modified ArrheniusThis Python library is designed to exhibit flexibility, extensibility, and ease of use. The initial input provided by the user is an `.xml` file while other individual parameters (temperature and concentrations) are entered during runtime.## InstallationThere are two options for installing ChemKinLib:1) Clone the repository into you local system.2) Execute in terminal: `python3 setup.py install` or `python3 -m pip install -e .`OR1) Execute in terminal: `pip3 install chemkinlib11`2) Try executing `import chemkinlib` to test the installation in your terminal.## ExamplesThere are two examples in the `examples` subdirectory: (a) `rev_rxns.py` (system of reversible, elementary reactions) and (b) `irrev_rxns.py` (system of irreversible, elementary reactions). They show the user how to set up a calculation using ChemKinLib to compute the reaction rate of each specie involved in the reactions. In addition, there is an iPython notebook tutorial (`basic-tutorial.ipynb`) that goes through the steps to compute reaction rates for a set of irreversible reactions.## DocumentationThe documentation for the library can be found [here](https://github.com/cs207-group11/cs207-FinalProject/blob/master/CS207_Documentation.pdf).## DependenciesSee [requirements](https://github.com/cs207-group11/cs207-FinalProject/blob/master/requirements.txt) for dependencies.## LicenseSee [LICENSE](https://github.com/cs207-group11/cs207-FinalProject/blob/master/LICENSE) file distributed with ChemKinLib for more information.## ContributingContributions are welcome (post project evaluation). If you wish to contribute, please take a few moment to review the [branching model](http://nvie.com/posts/a-successful-git-branching-model/) this repository utilizes.## SupportIf you have questions or need help with using or contributing to the repository, feel free to ask questions through: https://github.com/cs207-group11/cs207-FinalProject/issues
Package: chem-kit
ChemKitDocumentation:http://chem-kit.metwork.science/Source Code:https://github.com/metwork-project/chem-kitChemKit is a chemical toolbox based onRDKitwith currently 2 main purposes :Facilitate the usage of theRDKIt Python APIwith some more easy to use classes that can occasionally fix some bug (especially with Jupyter rendering).Provide tailored methods for theMetWorkprojectUsageManipulate Moleculesfromchem_kitimportMoleculemol=Molecule("CCO")Manipulate Transformationfromchem_kitimportTransformationtsf=Transformation("[#6:1]-[#8:2]-[#1:3]>>[#6:1]-[#8:2]-[#6:3](-[#1])(-[#1])-[#1]")More examples withJupyter notebookInstallLike RDKit, ChemKit needsConda:condaenvcreate-fconda-env.yml
condaactivatechem_kitTo manage other required Python packages,
the better way is to usePoetryon top of Conda :cd/path/to/chem_kit
poetryinstallPoetry manipulate Python packages directly on Conda env.
Package: chemlab
chemlab is a python library and a set of utilities built to ease the
life of the computational chemist. It takes inspiration from other
python scientific library such as numpy scipy and matplotlib, and aims
to bring a consistent and simple API by following the python
guidelines.Computational and theoretical chemistry is a huge
field, and providing a program that encompasses all aspects of it is an
impossible task. The spirit of chemlab is to provide a common ground
from where you can build specific programs. For this reason it
includes an easily extendable molecular viewer and flexible data
structures field-independent.
Package: cheMLearning
cheMLearninglibrary provides of high-level, user-friendly tools to perform common chemometrics analysis in untargeted metabolomics.
Modelling is done using scikit-learn framework, for machine learning; while visualizations are mainly done with Plotly.Main modules are:
- unsupervised_modelling
- supervised_modelling
- utils## Installation`python pip install cheMLearning `within a Jupyter Notebook cell`python !pip install cheMLearning `# Unsupervised modellingUnsupervised class works aroundPrincipal Component Analysis, facilitating the visualization of score and loading plots. Moreover,Hierarchical Clustering Analysiscan be called on the
principal components and visualize a score plot where the color code is given by the clusters of the HCA.Visualizations can be obtained without having to manipulate scores or loadings. However, if one would like to access to these outputs, they can be accessed via class modules.## Import`python from cheMLearning.unsupervised_modelling import unsupervised_class `# Supervised modellingSupervised class includesPartial Least SquaresandRandom Forestwhich can be used for either regression or classification tasks. PLS is computed with the NIPALS algorithm.In the PLS modelling, tools to compute goodness metrics, i.e. R² and Q², VIPs, are provided. In addition to a static functions to ease feature extraction VIP for a
given VIP threshold.For RF modelling, it can be computed the distance matrix for a RF-classifier model. It allows tracking where the observations land in the forest.Analytical tools for classifiers are also provided by facilitating a classification report.## Import`python from cheMLearning.unsupervised_modelling import supervised_class `# UtilsHere, tools to create confusion matrices and ROC curves are provided.
In case one is working with MetaboRank (MR) tool, a function to read MR’s output is provided.## Import`python from cheMLearning import utils `Change Log0.0.1 (09/11/2022)First Release
Package: chemlib
chemlib⚗️🔬👨‍🔬🧪Chemistry LibraryA pure Python package that supports a variety of functions pertaining to the vast field of chemistry.Documentation|Report a Bug|Submit a Pull RequestInstallationUse the Python Package Installer (PyPI):$pipinstall-UchemlibFeaturesProperties of all ElementsCompoundsFormulaMolar MassPercentage Composition by MassStoichiometric AmountsOxidation States/NumbersEmpirical FormulaeEmpirical Formula by Percentage CompositionCombustion Analysis of HydrocarbonsAqueous SolutionsSoluteMolarity (mol/L)Stoichiometric AmountsDilutionsChemical ReactionsFormulaBalancing the EquationCombustion ReactionsStoichiometric AmountsLimiting ReagentElectrochemistryGalvanic (Voltaic) CellsAnode, Cathode, Cell Potential, DiagramElectrolysisQuantum MechanicsElectromagnetic WavesFrequency, Wavelength, Energy per photonEnergy in nth Hydrogen OrbitalRydberg EquationGalvanic Cell DiagramsElements>>>fromchemlibimportElement>>>boron=Element('B')#Declare Element from its symbol>>>boron.properties{'AtomicNumber':5.0,'Element':'Boron','Symbol':'B','AtomicMass':10.811,'Neutrons':6.0,'Protons':5.0,'Electrons':5.0,'Period':2.0,'Group':13.0,'Phase':'solid','Radioactive':False,'Natural':True,'Metal':False,'Nonmetal':False,'Metalloid':True,'Type':'Metalloid','AtomicRadius':'1.2','Electronegativity':2.04,'FirstIonization':'8.298','Density':'2.34','MeltingPoint':'2573.15','BoilingPoint':'4200','Isotopes':6.0,'Discoverer':'Gay-Lussac','Year':'1808','SpecificHeat':'1.026','Shells':2.0,'Valence':3.0,'Config':'[He] 2s2 2p1','MassNumber':11.0}>>>boron.AtomicMass10.811Compounds>>>fromchemlibimportCompound>>>nitric_acid=Compound("HNO3")>>>nitric_acid.occurences{'H':1,'N':1,'O':3}>>>nitric_acid.molar_mass()63.01>>>nitric_acid.percentage_by_mass('O')#Get percentage composition by mass of a constituent element of choice76.174Stoichiometric conversions with compoundsAccepted inputs: grams, moles, and molecules>>>fromchemlibimportCompound>>>water=Compound('H2O')>>>water.formula'H₂O₁'>>>water.get_amounts(grams=2){'Compound':'H₂O₁','Grams':2,'Moles':0.111,'Molecules':6.685e+22}>>>water.get_amounts(moles=1){'Compound':'H₂O₁','Grams':18.01,'Moles':1,'Molecules':6.02e+23}>>>water.get_amounts(molecules=1.0e+24){'Compound':'H₂O₁','Grams':29.917,'Moles':1.6611,'Molecules':1e+24}Balancing Chemical Reactions>>>fromchemlibimportCompound,Reaction>>>H2=Compound('H2')>>>O2=Compound('O2')>>>H2O=Compound('H2O')>>>r=Reaction(reactants=[H2,O2],products=[H2O])>>>r.formula'1H₂ + 1O₂ --> 1H₂O₁'>>>r.is_balancedFalse>>>r.balance()>>>r.formula'2H₂ + 1O₂ --> 2H₂O₁'>>>r.is_balancedTrueCitingIf you usechemlibin your scientific work, please consider citing:|     H. R. Ambethkar,chemlib- A Python chemistry library , 2020-- . Available at:https://github.com/harirakul/chemlib.The reference in theBibLaTeXformat:@software{chemlib2020,
   author = {Ambethkar, Hari},
   title = {{chemlib} -- A Python chemistry library},
   url = {https://github.com/harirakul/chemlib},
   version = {2.1.9},
   date = {2020--},
}
Package: chemmltoolkit
Copyright (c) 2019 Andrew WilkinsonPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Description: # ChemMLToolkitA set of useful Python functionality for machine learning in chemistryPlatform: UNKNOWN
Description-Content-Type: text/markdown
Package: chemname
ChemnameThis package converts any eligible text to its equivalent using chemical element names.Usagefrom chemname import chemResults are return as a list of lists, each containing a possible alternative. If there is one possiblity:chem.chemname("Ash")>>> [['As', 'H']]The matching process is case insensitive.chem.chemname("ash")>>> [['As', 'H']]If there are more possibilities:chem.chemname("Practice")>>> [['P', 'Ra', 'C', 'Ti', 'Ce'], ['Pr', 'Ac', 'Ti', 'Ce']]
Package: chemner
ChemNerChinese Chemical Named Entity Recognition本文针对化学术语构成的领域特征，从语素的角度入手，构建了化学领域语素分类表，并进行了有无语素特征的对比实验。CRF-baseline-model该实验选择当前字的上下文窗口为5，当前输出标签与上一输出标签的关系作为CRF的特征输入，进行模型训练和预测。基于N-gram特征的CRF识别结果术语 长度术语 总数术语识别数正确识别数正确率召回率F值1911100.00%11.11%20.00%234262388.46%67.65%76.67%312912511692.80%89.92%91.34%411011110392.79%93.64%93.21%550464495.65%88.00%91.67%631272385.19%74.19%79.31%721212095.24%95.24%95.24%816171588.24%93.75%90.91%914131292.31%85.71%88.89%>=1034362980.56%85.29%82.86%all44842338691.25%86.16%88.63%使用工具CRF++: Yet Another CRF toolkitBiLSTM-CRF-baseline-model依赖模块pytorch=1.13.0python3.7+运行方式运行下列命令，进行模型训练：pythonrun_lstm_crf.py--do_train运行下列命令，进行模型预测pythonrun_lstm_crf.py--do_predictAccRecallF10.90620.88970.8979修改自CLUENER2020Hmm-model把语素和语素类建模为简单稳定的HMM模型，利用改进的前向算法规避术语过长的问题，最终达到了91.58%的较好效果。CRF-model实验选择当前字的上下文窗口为5，当前输出标签与上一输出标签的关系为特征的基础上，加入当前化学语素类的上下文窗口为5作为CRF的特征输入基于上下文N-gram特征 + 语素类特征的CRF识别结果术语长度术语总数识别出的术语个数正确识别的术语个数正确率召回率F值192150.00%11.11%18.18%234353188.57%91.18%89.86%312913112393.89%95.35%94.62%411011110897.30%98.18%97.74%550464495.65%88.00%91.67%631312890.32%90.32%90.32%721212095.24%95.24%95.24%8161515100.00%93.75%96.77%914141392.86%92.86%92.86%>=1034373491.89%100.00%95.77%all44844341794.13%93.08%93.60%使用工具CRF++: Yet Another CRF toolkit
Package: chemnlp
ChemNLPTable of ContentsIntroductionInstallationExamplesWeb-appReferenceIntroductionChemNLP is a software-package to process chemical information from the scientific literature.InstallationFirst create a conda environment:
Install miniconda environment fromhttps://conda.io/miniconda.htmlBased on your system requirements, you'll get a file something like 'Miniconda3-latest-XYZ'.Now,bash Miniconda3-latest-Linux-x86_64.sh (for linux)
bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac)Download 32/64 bit python 3.8 miniconda exe and install (for windows)
Now, let's make a conda environment, say "chemnlp", choose other name as you like::conda create --name chemnlp python=3.8
source activate chemnlpMethod 1 (using setup.py):Now, let's install the package:git clone https://github.com/usnistgov/chemnlp.git
cd chemnlp
python setup.py develop
cde data downloadMethod 2 (using pypi):As an alternate method, ChemNLP can also be installed usingpipcommand as follows:pip install chemnlp
cde data downloadExamplesParse chemical formularun_chemnlp.py --file_path="chemnlp/tests/XYZ"Text classification examplepython chemnlp/classification/scikit_class.py --csv_path chemnlp/sample_data/cond_mat_small.csvGoogle Colab example for installation and text classificationGoogle Colab example for Text Generation with HuggingFaceUsing the webappThe webapp is available at:https://jarvis.nist.gov/jarvischemnlpReferenceChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
Package: chemometrics
chemometricschemometricsis a free and open source library for visualization, modeling, and prediction of multivariate data.The package streamlines chemometric workflows by providing powerful preprocessing algorithms combined with simple
functional calls for generating fully calibrated chemometric models. A variety of analytical visualizations help to
understand the data and build trust into the generated models. Looking for further extending the models? chemometrics is
fully compatible with scikit-learn. Use advanced machine learning algorithms to get most out of your data.The documentation is hosted athttps://chemometrics.readthedocs.ioInstallationchemometrics is distributed over PyPI. The simplest way to install chemometrics is by runningpython -m pip install chemometricsExample applicationsPlotting with reference coloring:importnumpyasnpimportmatplotlib.pyplotaspltimportchemometricsascmplt.figure(figsize=[12,5])lines=cm.plot_colored_series(D.T,x=wavenumbers,reference=C[:,1])plt.xlabel(r'Wavenumber / $\mathrm{cm^{-1}}$')plt.ylabel('Intensity / AU')Preprocessing by smoothing and performing a second derivative:X_deriv=cm.Whittaker(constraint_order=3,deriv=2).fit_transform(X)cm.plot_colored_series(X_deriv.T,reference=Y)plt.xlabel('Wavenumber / nm')plt.ylabel('$d^2A/dl^2$ / $mAU/nm^2$')A working PLS model is just one function call away:cm.fit_pls(X_deriv,Y)Interested in more? Check out following pages:Full workflowfrom loading the data to interpreting the calibrated PLS modelA variety ofexamplesOverview of the APIRequirementsPython >= 3.8NumPy >= 1.19.2SciPy >= 1.5.2scikit-learn >= 0.23.2matplotlib >= 3.3.2Earlier versions of the required libraries may work but have not been tested.Copyright and licensechemometrics is released under GPLv3.Copyright 2021, 2022 Matthias Rüdt
Package: chemonaut
[banner goes here][Badges for pypi version (0.1.0), crates version (0.1.0), python versions supported (3.8-3.12), code style (black), license (MIT), tests (passing), perhaps a doi eventually]chemonaut: An open-source library for exploring chemical and reaction spaceCreate molecular simulation and machine learning workflows to predict chemical properties and reactivity.pipinstallchemonautSome example code:importchemonautascn# example python code
Package: chemosanitizer
chemo_sanitizerA set of script using RDKit and MolVS to proceed to the standardization , sanitization and different format conversion. Parallelized.In this state it take only InChI as inputs and returns sanitized SMILES, InChI, InChIKeys, Short InChIkeys, MolecularFormula, ExactMass and XLogP.RequirementsInstall the conda environment byconda env create -f environment.ymlSpecific requirementNote that some modules of MolVS are not loaded by default.
You will need to edit the MolVSinit.py in ~/opt/anaconda3/lib/python3.7/site-packages/molvs/ accordingly.
Add these lines to theinit.pyfrom .fragment import LargestFragmentChooser, FragmentRemover
from .charge import UnchargerUsageGo to the src folder
Then add input and output file path as first and second argument, InChI column header as third argument and finally the number of cpus you want to use.Example :cd src
python chemosanitizer.py ~/translatedStructureRdkit.tsv ./test.tsv structureTranslated 6
Package: chemo-standardizer
# standardiserThis is a tool designed to provide a simple way of standardising molecules
as a prelude to e.g. molecular modelling exercises.To install, download this project and ensure that Python can find
the standardise/ directory.
Note that RDKit must also be installed and accessible to Python.Or, install via pip:` pip install standardiser `Please see the IPython Notebooks in docs/ or the HTML versions in docs/html/
for more information. The HTML version is also available for browsing athttps://wwwdev.ebi.ac.uk/chembl/extra/francis/standardiser/.This work was funded by the IMI eTOX project.This code is released under the Apache 2.0 license.
Copyright [2014] is retained by the EMBL-EBI.
Package: chemparse
chemparseA lightweight package for parsing chemical formula strings into python dictionaries.FeaturesConvert a chemical formula string into a python dictionary.example:"CH4"returns{"C":1.0, "H":4.0}Handles fractional stoichiometry.example:"C1.5O3"returns{"C":1.5, "O":3.0}example:"H2e-1O1e-1"returns{"H":0.2, "O":0.1}Handles groups with parentheses.example:"(CH3)2(CH2)4"returns{"C":6.0, "H":14.0}Note: chemparse currently only handles non-nested paretheses. A formula with nested parentheses like"CH3(C2(CH3)2)3CH3"will not work properly.InstallationInstallchemparsewith pip:$ pip install chemparseUsageImport chemparse in python and use the parse_formula function:import chemparse

print(chemparse.parse_formula("C6H12O6"))
Package: chemparseplot
Table of ContentsAboutFeaturesSupported Engines [WIP]RationaleLicenseAboutApure-python1project to provide unit-aware uniform visualizations
of common computational chemistry tasks. Essentially this means we provide:Plotting scripts for specific workflowsParsers for various software outputsThis is a spin-off fromwailord(here) which is meant to handle aggregated
runs in a specific workflow, while here the goal is to do no input handling and
very pragmatic output parsing, with the goal of generating uniform plots.FeaturesScientific color mapsfor the plotsCamera readyUnit preservingViapintSupported Engines [WIP]ORCA (5.x)Scanning energies over a degree of freedom (OPTscans)Nudged elastic band (NEB) visualizations (over the "linearized" reaction
coordinate)Rationalewailordis for production runs, however often there is a need to collect
"spot" calculation visualizations, which should nevertheless be uniform, i.e.
either Bohr/Hartree or Angstron/eV or whatever.Also I couldn't find (m)any scripts using the scientific colorschemes.LicenseMIT. However, this is an academic resource, soplease citeas much as possible
via:The Zenodo DOI for general use.Thewailordpaper for ORCA usageFootnotes1To distinguish it from my other thin-python wrapper projects
Package: chemrecommender
recommendation_engineRepo for the recommendation engine that was part of the DRP projectRecommender PipelineSteps to implement a recommender pipeline
(Specific implementation of this pipeline is available in ./recommender/recommender_pipeline.py)Generate reaction featuresGet the chemicals in a reaction. For DRP these are referred to as triplesGenerate descriptors for each of the chemicals in the reactionGenerate a sampling grid of reaction parametersExpand grid by associating descriptors with each point on the gridRun trained models with the reaction SieveGet a trained machine learning modelFilter sampling grid by running it through the ML modelMake a list of all the potentially successful reactions
as predicted by the ML modelRecommend reactionsCalculate the mutual information of the potential reactions
as compared to the already completed reactionsSelect the top 'k' reactions with the highest MIProgressGenerate Reaction featuresReaction SieveReaction RecommenderTest and evaluate against Nature paper
Package: chemreps
chemrepschemreps is a Python package for the creation of molecular representations for the purpose of machine learning. The molecular representations included in this library are implemented/adapted from current literature. The aim of chemreps is to provide an easy to use library for making molecular representations that can be then used with machine learning packages such as Scikit-Learn and Tensorflow.Current ImplementationsCoulomb MatrixBag of BondsBonds/Nonbonding, Angles, TorsionsJust BondsThe citations for the literature from which the representations are implemented/adapted from can be found in the source code for each representation.Representation requestsRequests for new representations to be added can be made by raising an issue and labeling it as a feature request. Before requesting a new representation, please check under the Representation project in the Projects tab to see if that representation is included in the current work or progress.InstallThe latest release version can be installed with:pip install chemrepsThe latest development version can be installed by:git clone https://github.com/chemreps/chemreps
cd chemreps
pip install -e .Dependencieschemreps requires:Python (>=3.6)NumPy (>=1.12)cclib (>=1.5)ContributingIf you are interested in helping develop for this project, please check outContributing to chemrepsin the wiki for a guide on how to get started.TestingTests can be run in the top-level directory with the commandpytest -v --cov=chemreps tests/For helpIf you need any help using chemreps, feel free to post in ourGitter.Disclaimers:These are attempts at the recreation of molecular representations from literature and may not be implemented properly.If we do not implement something properly, feel free to make an issue.This is solely a representation library and will not perform machine learning.