Saravanan666/pypi-org huggingface dataset:
Package: mdfr
mdfrASAM mdf reader in rustCurrently a personal project to learn rust from past experience with python (mdfreader).mdfr is currently able to be used from python interpreter (using pyO3) to read mdf 3.x and 4.x files. You can install it with 'pip install mdfr' command.
Using rayon crate on many parts of the code allows to have faster parsing in a safe and easy way compared to python.
To allow efficient data sharing with many other tools, mdfr stores the data using arrow2. Polars (pandas equivalent) use is therefore also straight forward.
It can be used the following way with python interpreter:importmdfr# load file metadata in memoryobj=mdfr.Mdfr('path/to/file.mdf')# loads all data in memoryobj.load_all_channels_data_in_memory()# loads a set of channels in memory, for instance in case there is not enough free memory or for performanceobj.load_channels_data_in_memory({'Channel1','Channel2'})# Returns the numpy arrayobj.get_channel_data('channel_name')# .get_channel_* methods to retrieve channel description, unit, related master name, type and data# .set_channel_* methods to modify channel description, unit, related master name, type and data# list of channels in file, or lists of channels grouped by master are availableobj.get_master_channel_names_set()# To manipulate the file data (cut, resample, merge, etc.), it is possible to use polars:obj.get_polars_series('channel_name')# returns the a serie# to get complete dataframe including given channel:obj.get_polars_dataframe('channel_name')# add and remove channelobj.add_channel(channel_name,data,master_channel,master_type,master_flag,unit,description)# to plot a single channel with matplotlib:obj.plot('channel_name')# Export to parquet:obj.export_to_parquet('file_name',compression_option)# write to mdf4 file, compressed or notobj.write('file_name',conpression_flag)A C/C++ api is also available allowing to get list of channels, units, description and data using the Arrow CDataInterface.
Package: mdfrag
Have you ever wanted to break a Markdown document into chunks?
You are in luck, because that is what you can do with this package!InstallInstall this package like any other PyPI package.pip install mdfragExamplesThis package can be invoked as a CLI tool, or used as a library in your own project.CLI ExampleTo split this document into chunks of 500 bytes, use the following CLI command:python -m mdfrag docs/user-guide.md 500For more information, runpython -m mdfrag --help. Additionally, see__main__.py.Code ExampleUse themdfrag.split_markdown()function to split your markdown document.See the docstring for more detailed information.
Additionally, see__main__.pyfor an implementation of the below example.import mdfrag

frags = None
with open('docs/user-guide.md', 'r') as f:
    frags = mdfrag.split_markdown(f.read(), 512)

# Walk through the list of fragments and write them to their appropriate files. 
for count, fragment in enumerate(frags):
    with open(f'output_{count}.md', 'w') as dest:
        dest.write(fragment)Performance ConsiderationsIf you select a smallfragment-size, and your document contains large blocks of Markdown, the split_markdown function will struggle!
Package: mdfreader-helper
mdfreader读取MDF/DAT文件后的常用操作my_lib.py封装常用函数，用于机器学习预处理。1. parse_mdf(filename, columns)mdfreader常用操作filename : dat/mdf文件名columns : 需要读取的通道名/缩写merge_to_pd : 将目标列拼接成带时间戳的DataFrame
keyword_merge_to_pd : 用变量关键字查询并拼接成带时间戳的DataFrame
signal_list_merge_to_pd：参考已知信号列表提取出本文件中存在的信号并拼接成带时间戳的DataFrame2. pd_parse(content, label)DataFrame常用操作content : DataFramelabel : 目标标签trace_back : 添加特征前序时间序列
add_MeanVar ：添加特征前序时间的均值和方差等一系列前序时间内的特征
resample : 用采样时间内的均值重采样
initialize : 初始化，为每一行添加上一时刻或上n时刻的标签值
add_col : 添加时间序列新特征
Package: mdfs
MDFS (MultiDimensional Feature Selection) for PythonMDFS is a library to assist in MultiDimensional Feature Selection (MDFS),
i.e. feature selection that accounts for multidimensional interactions
in the dataset. To learn more about MDFS, please visit theMDFS website.This project is the implementation of the MDFS library for Python.
Functionality-wise, it is aligned with theR version of the MDFS library, but the interface differs to
make it more native to the Python ecosystem (i.e.pythonic) and to
free it from early assumptions carried on for backward compatibility in R.LicenseThis software is released the same as the R MDFS library: under theGNU General Public License (GPL) v3.CopyrightThe copyrights are held by Radosław Piliszek (the package maintainer
and author), Abraham Kaczmarski (major contributor to the new interface),
Krzysztof Mnich and Witold Rudnicki (authors of the MDFS method).ChangelogSeethe common changelog.Library structureThe library consists of a single package module:mdfs, which exports
all the user-facing functionality.Introduction for beginnersThemdfspackage module needs to be imported. Then, the main function
to run is, aptly named,run. It accepts a numpy data matrix data and
its corresponding decision, and returns a dictionary with the details of
analysis, including the entry forrelevant_variableswhich gives the
indices of variables deemed relevant under chosen conditions.Interface differences between R and PythonFunction namesThe following list gives the translation between R functions and their
Python counterparts.MDFS=runComputeMaxInfoGains=compute_max_igComputeInterestingTuples=compute_tuplesComputePValue=fit_p_valueDiscretize=discretizeGetRange=get_suggested_rangeGenContrastVariables=gen_contrast_variablesFunction parameter namesFunction parameter names have been adjusted to avoid the dot (.),
replacing it with an underscore (_).No global seed in PythonThere is no global seed in use. All functions depending on PRNG take
aseedparameter.QuirksDue to the way the Python-C interface is implemented in this library withnumpyviews, there is one quirk to be aware of. Functions returning
aStructuresubclass object do so without incurring a copy. Properties
present on such objects return views, not copies. These views do not protect
the result from being garbage collected (i.e., think of them as weak
references to the underlying data). Thus, to avoid freed memory reads,
keep the original structures around when using these views or copy
data elsewhere as necessary.
This quirk might be lifted in the future.
Package: mdfy-esa
mdfy-esamdfy plugin for esaDocumentation:https://argonism.github.io/mdfy-esaGitHub:https://github.com/argonism/mdfy-esaPyPI:https://pypi.org/project/mdfy-esa/Free software: MITPre-requirementYou need set esa.io API token and set it to environment veriable ESA_ACCESS_TOKEN.exportESA_ACCESS_TOKEN=YOUR_ACCESS_TOKENUsageThe mdfy-esa feature supports uploading of local images and files.
With the EsaMdfier, images or files designated with MdImage or MdLink are uploaded automatically.
Simply pass the MdImage with the local image path, and voila - it’s done!"frommdfyimportMdImage,MdLink,MdTextfrommdfy_esaimportEsaMdfieresa_team="your esa team name"post_fullname="post name as you like"contents=[MdText("This is a test article."),MdImage(src="examples/test_image.png"),MdLink(url="examples/dummy.pdf"),]mdfier=EsaMdfier(post_fullname=post_fullname,esa_team=esa_team)created_post_info=mdfier.write(contents=contents)# created_post_info = {'number': 4418, 'name': 'My Test Article', 'full_name': 'note/me/My Test Article', 'wip': True, 'body_md': 'This is a test article.\n', 'body_html': '<p data- ...}# see esa.io api document for detail# https://docs.esa.io/posts/102#POST%20/v1/teams/:team_name/postsYou can also update an existing post using its post number!frommdfyimportMdImage,MdLink,MdTextfrommdfy_esaimportEsaMdfieresa_team="your esa team name"post_number=4930contents=[MdText("NEW! This post is updated!"),MdText("This is a test article."),MdImage(src="examples/test_image.png"),MdLink(url="examples/dummy.pdf"),]mdfier=EsaMdfier(post_number=post_number,esa_team=esa_team)updated_post_info=mdfier.write(contents=contents)FeaturesTODOCreditsThis package was created withCookiecutterand thewaynerv/cookiecutter-pypackageproject template.
Package: mdgen
random-markdown-generatorA library to generate random markdown text.But why?I was making a blog web-app that lets its users write blog posts using markdown
syntax. I couldn't find any python package that does this easily. Using thefakerlibrary, I created this package to
make a highly configurable markdown post generator. Additionally, it also
provides an API for creating markdown files using python.Getting StartedThese instructions will get you a copy of the project up and running on your
local machine for development and testing purposes.InstallingYou must havepythonandpipinstalled on your system, and in youPATH.
An activated virtual environment is also recommended.Usingpip:pipinstallmdgenOr using poetry:poetryaddmdgenSample usagefromfakerimportFakerfrommdgenimportMarkdownPostProviderfake=Faker()fake.add_provider(MarkdownPostProvider)fake_post=fake.post(size='medium')# available sizes: 'small', 'medium', 'large'print(fake_post)The output from the above command:Drop question writer.

> After step respond support argue issue western movie.
[First memory suffer yard.](https://www.simmons.com/)

        1. Possible career speak another believe realize analysis.
|First fear enter surface hospital nothing raise condition.|Name quickly deep free before if.|Rather church provide walk power thank student.|
|----------------------------------------------------------|---------------------------------|-----------------------------------------------|
|Box seem hotel picture popular politics century.|Side simple daughter central suggest.|Campaign nation Republican economy perform require.|DocumentationThe documentation for this project can be found athttps://ignisda.github.io/python-random-markdown-generator/.Running the testsThe project usespytestto automate its test suite. To install all
dependencies, ensure you havepoetryinstalled on your system and then run:poetryinstallThis will install all dependencies for testing this project in a virtual
environment. Then run all tests using:poetryshell
pytestTo see test coverage, run:pytest--cov--cov-report=html--cache-clearThen openhtmlcov/index.htmlin your browser to see the test coverage.Coding styleThis project follows thepep8specifications. The maximum
line length has been increased to 90 characters. For the flake8 configuration
used, seetox.ini. The test suite automatically runs the linters by
default, but you can run just the linting tests.flake8Built WithPoetry- Dependency ManagementSphinx- Used to generate this project's
documentation.ContributingPlease readCONTRIBUTING.mdfor details on our code of
conduct, and the process for submitting pull requests to us.VersioningWe useSemVerfor versioning.AuthorIgnisDa(Diptesh Choudhuri) -Initial
workSee also the list ofcontributorswho participated in this project. If you
have made any contribution to this project, please add it incontributors.md.LicenseThis project is licensed under the Apache-2.0 License - see theLICENSE.mdfile for details.AcknowledgmentsHat tip to anyone whose code was used
Package: mdgenerator
MD Generator (mdgenerator)mdgenerator is a package to generate different kinds of markdown textsFollowing details the functionality provided by the package:Generate File Structure Trees in MarkdownGenerate Tables in Markdown from pandas dataframe or python arraysInstallationThere are two ways to install mdgenerator:Install mdgenerator from PyPI (recommended):pip install mdgeneratorInstall mdgenerator from the Github source:git clone https://github.com/nilansaha/mdgenerator.git
cd mdgenerator
pip install .UsageGenerate File Tree Structure in MarkdownUsing Pythonfrom mdgenerator import generate_file_structure

 generate_file_structure(target_dir='/path/to/directory', output_dir='/output/directory')Using the terminalmdgenerator --target_dir "/path/to/directory" --output_dir "/output/directory"Outputis stored infile_structure.mdin the specifiedoutput_dirSample Output -├── .DS_Store
 ├── mdgenerator.py
 ├── __init__.py
 ├── test.py
 ├── cli.py
 ├── file-structure.md
 ├── ABC
 |   ├── a.txt
 |   ├── BCD
 |   |   ├── b.txt
 ├── __pycache__
 |   ├── mdgenerator.cpython-37.pycGenerate Markdown Table using PythonUsing Python listsfrom mdgenerator import generate_table

 data = [['Word_1','Word_2'],['Happy','Sad'],['Nice','Bad']]
 table = generate_table(data)
 print(table)OutputWord_1Word_2HappySadNiceBadUsing Pandas DataFrameimport pandas as pd
 from mdgenerator import generate_table

 df = pd.DataFrame([['Happy','Sad'], ['Nice','Bad']], columns = ['Word_1','Word_2'])
 table = generate_table(df)
 print(table)OutputWord_1Word_2HappySadNiceBad
Package: mdgo
An all-in-one code base for the classical molecualr dynamics (MD) simulation setup and results analysis.1. Installation1.1 Installing from PyPITo install the latest release version of mdgo:pip install mdgo1.2 Installing from source codeMdgo requires numpy, pandas, matplotlib, scipy, tqdm, statsmodels, pymatgen>=2022.0.9, pubchempy, selenium, MDAnalysis (version 2.0.0-dev0 prefered) and their dependencies.Getting Source CodeIf not available already, use the following steps.Installgit, if not already packaged with your system.Download the mdgo source code using the command:git clone https://github.com/htz1992213/mdgo.gitInstallationNavigate to mdgo root directory:cd mdgoInstall the code, using the command:pip install .The latest version MDAnalysis==2.0.0.dev0 is recommended, you may download the source code of the latest MDAnalysis from github and install using pip to replace an existing version.Installation in development modeNavigate to mdgo root directory:cd mdgoInstall the code in "editable" mode, using the command::pip install -e .The latest version MDAnalysis==2.0.0.dev0 is recommended, you may download the source code of the latest MDAnalysis from github and install using pip to replace an existing version.2. FeaturesRetrieving compound structure and information from PubChemSupported searching text:cid, name, smiles, inchi, inchikey or formulaSupported output format:smiles code, PDB, XML, ASNT/B, JSON, SDF, CSV, PNG, TXTRetrieving water and ion modelsSupported water models:SCP, SPC/E, TIP3P_EW, TIP4P_EW, TIP4P_2005Supported ion models:alkali, ammonium, and halide monovalent ions by Jensen and Jorgensenalkali and halide monovalent ions by Joung and Cheathamalkali and alkaline-earth metal cations by ÅqvistWrite OPLS-AA forcefield file from LigParGenSupported input format:mol/pdbSMILES codeSupported output format:LAMMPS(.lmp)GROMACS(.gro, .itp)Write OPLS-AA forcefield file from MaestroSupported input format:Anyformat that Maestro supportSupported output format:LAMMPS(.lmp)Others pending...Packmol wrapperSupported input format:xyzOthers pending...Basic simulation propertiesInitial box dimensionEquilibrium box dimensionSalt concentrationConductivity analysisGreen--Kubo conductivityNernst--Einstein conductivityCoordination analysisThe distribution of the coordination number of single speciesThe integral of radial distribution function (The average
coordination numbers of multiple species)Solvation structure write outPopulation of solvent separated ion pairs (SSIP), contact ion
pairs (CIP), and aggregates (AGG)The trajectory (distance) of cation and coordinating species as
a function of timeThe hopping frequency of cation between binding sitesThe distribution heat map of cation around binding sitesThe averaged nearest neighbor distance of a speciesDiffusion analysisThe mean square displacement of all speciesThe mean square displacement of coordinated species and
uncoordinated species, separatelySelf-diffusion coefficientsResidence time analysis
-   The residence time of all species
Package: mdgt
mdgtis a microservice which parses microdata.mdgtis still
in the initial design and development phase.Quick InstructionsCommand Line Output:python -m mdgt [options] [provider] [query][provider]The name of the provider, must be in/providers/[query]The query the provider will use to obtain outputOptions:
*–console,-c: Output to stdout in a readable format (default)
*–config,-f: Path to configuration file to use
*–json,-j: Output to stdout as json
*–providers,-p: Output to stdout a list of available providers
*–provider-dir,-pd: Define a different providers directoryRESTful Web Server:python -m mdgt -w [port(optional)]This starts a WSGI-compliant web server at the specified port (default8181).  The
api is invoked withhttp://domain:port/provider/query, orhttp://domain:port/providers(for a list of available providers).Configuration file structureIt is possible to use an optional configuration file by using the–config/- fflag or placing a file calledmdgt.confin the current working
directory. It can have the following values:`conf console = true|false json = true|falseprovider-dir= path/to/dir `Note that command-line arguments will override those present in the configuration
file.Development instructionsWindows:Use the handy win_init.ps1 script to set up the virtual
environment and install dependencies to it. PyPI doesn’t have 64-bit binaries
for lxml, so the whl file is included and installed from the script.Linux/Mac users:Set up a virtual environment withpython -m venv venv, activate it,
then install the dependencies withpip install requirements.txt.
Package: mdh
Modified Denavit–Hartenberg (mdh)Modified Denavit-Hartenberg parametersWork in progress, use one the other libraries below (Inspiration)Build kinematic chains using the modified Denavit-Hartenberg paramtersd: offset along previous z to the common normaltheta: angle about previous z, from old x to new xa: length of the common normal, assuming a revolute joint, this is the radius about previous z.alpha: angle about common normal, from old z axis to new z axisInspirationYou should probably use one of these, they inspired me to write a simpler
module for my needs:pyboticspytransform3d, some matplotlib 3d examplesrobopy, has some good matplotlib 3d examples, but seems rather brittle and difficult to work withtinyik, usesopen3dto visualize the mechanismExampleimportnumpyasnpfrommdh.kinematic_chainimportKinematicChainfrommdhimportUnReachable# exception# make it print betternp.set_printoptions(suppress=True)# modified DH parameters: alpha a theta d# types: revolute=1, prismatic=2 (not implemented yet)dh=[{'alpha':0,'a':0,'theta':0,'d':0,'type':1},{'alpha':pi/2,'a':52,'theta':0,'d':0,'type':1},{'alpha':0,'a':89,'theta':0,'d':0,'type':1},{'alpha':0,'a':90,'theta':0,'d':0,'type':1},{'alpha':0,'a':95,'theta':0,'d':0,'type':1}]kc=KinematicChain.from_parameters(dh)# forward kinematicsangles=np.deg2rad([-45.00,77.41,-98.15,-69.27,0])t=kc.forward(angles)print(f">>{t}")# inverse kinematicspt=[110,0,-70]deg=kc.inverse(pt)rad=np.rad2deg(deg)print(f">>{rad}")MIT LicenseCopyright (c) 2019 Kevin J. WalchkoPermission 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.
Package: md-harmonize
md_harmonize package provides facilities for creation and harmonization of compounds and reactions.CitationPlease cite the GitHub repository until our manuscript is accepted for
publication:https://github.com/MoseleyBioinformaticsLab/md_harmonize.gitInstallationmd_harmonize runs under Python 3.7+ and is available through python3-pip.
Install via pip or clone the git repo and install the following dependencies and
you are ready to go!Install on LinuxPip installationpython3-mpipinstallmd-harmonizeNote:Please add ~/.local/bin/ to the environmental PATH variant in bash_profile if you install the package with –user.python3-devel  is required for package installation.GitHub Package installationgitclonegit@github.com:MoseleyBioinformaticsLab/md_harmonize.gitLicenseMade available under the terms of The modified Clear BSD License. See full license inLICENSE.AuthorsHuan JinHunter N.B. Moseley
Package: mdhub
MDHubEasily convert Markdown files to styled HTML. Perfect for delivering projects to clients who might not be familiar with.mdfiles.Table of ContentsMDHubTable of ContentsWhy MDHUB?The nameFeaturesInstallationUsageMy linksWhy MDHUB?Sometimes when delivering projects to clients, they might not be familiar with Markdown or might not have the necessary tools to render it. Online converters often don't replicate the GitHub styling, and might not support dark/light modes.MDHUBfills this gap by providing a simple tool to convert Markdown files to styled HTML that resembles GitHub's own rendering, with support for both dark and light themes.The nameThe name "mdhub" is a nod to the central theme of our tool – making your Markdown files look and feel like they're on GitHub. "md" stands for Markdown, and "hub" alludes to a central place or repository, reminiscent of GitHub where a lot of Markdown content is commonly hosted and viewed. Our goal is to bring that familiar and stylish GitHub appearance to your Markdown files, no matter where they're viewed from, hence the name "mdhub".FeaturesConverts.mdfiles to.htmlwith GitHub-like styling.Supports both dark and light modes.Simple CLI interface for easy use.InstallationYou can easily installMDHUBusing pip:pip3installmdhubUsageRun the tool using the following command:mdhub<inputfile.md(defaultisREADME.md)><optionaloutputfilename(defaultisREADME.html)><lightordarkmode(defaultisdark)>For example, to convertsample.mdtooutput.htmlin dark mode:mdhubsample.mdoutput.htmlOr to convertREADME.mdtoREADME.htmlin dark mode (using default values):mdhubMy linksMedium ArticleWebsiteGitHubTwitterMediumEmail
Package: mdict-utils
MDict pack/unpack toolNoteSupport Reading with MDict Version 3.0Support Reading and Writing with MDict Version 2.0All files must be UTF-8 encoding, include HTML and TXTInstallpip install mdict-utilsUsageMeta information:mdict -m dict.mdxAll key list:mdict -k dict.mdxQuery key:mdict -q <word> dict.mdxNote只用于测试词典打包是否正确。UnpackUnpack MDX:mdict -x dict.mdx -d ./mdxUnpack MDX/MDD and split into 5 files:mdict -x dict.mdx -d ./mdx --split-n 5Unpack MDX/MDD and split into a…z files:mdict -x dict.mdx -d ./mdx --split-azUnpack MDD:mdict -x dict.mdd -d ./mddUnpack MDX/MDD to sqlite3 DB:mdict -x dict.mdx --exdb
mdict -x dict.mdd --exdbUnpack MDX/MDD to sqlite3 DB with zip compress:mdict -x dict.mdx --exdb-zipPackPack MDX:mdict --title title.html --description description.html -a dict.txt dict.mdxPack MDX with many TXT files:mdict --title title.html --description description.html -a dict.part1.txt -a dict.part2.txt dict.mdxor:mdict --title title.html --description description.html -a txt_dir dict.mdxPack MDD:mdict --title title.html --description description.html -a mdd_dir dict.mddOtherConvert TXT to sqlite3 DB:mdict --txt-db dict.txtConvert sqlite3 DB to TXT:mdict --db-txt dict.dbReferencehttps://bitbucket.org/xwang/mdict-analysishttps://github.com/zhansliu/writemdictDonate 捐赠