Saravanan666/pypi-org huggingface dataset:
Package: malachite
Malachite: A Gene Enrichment Meta-Analysis (GEM) Tool for ToppGeneResearchers commonly use software, such as Toppgene and online tools to conduct enrichment analyses on gene expression data. This process does not allow multiple gene data sets to be analyzed and compared at once. Malachite allows researchers to perform enrichment analyses on multiple gene lists and concatenate the resulting enrichment statistics, thus, enabling meta-enrichment analyses across multiple data sets. Requires Windows 10, MacOS 10.11 or Linux Mint 18.How to Use:Command Line Argument Descriptions:1. Full path to input file:ex. "/Desktop/myInput/input.xlsx"2. Input type:Choose one: HGNC, HGNC_SYNONYMS, ENTREZ, ENSEMBL, REFSEQ, UNIPROTex. "HGNC"3. Categories:Options: "GeneOntologyMolecularFunction": "GO: Molecular Function", "GeneOntologyBiologicalProcess": "GO: Biological Process", "GeneOntologyCellularComponent":"GO: Cellular Component", "HumanPheno":"Human Phenotype","MousePheno":"Mouse Phenotype","Domain":"Domain", "Pathway":"Pathway","Pubmed":"Pubmed","Interaction":"Interaction","Cytoband":"Cytoband","TFBS":"Transcription Factor Binding Site","GeneFamily":"Gene Family","Coexpression":"Coexpression","CoexpressionAtlas":"Coexpression Atlas","Computational":"Computational","MicroRNA":"MicroRNA","Drug":"Drug","Disease":"Disease"ex. ["Drug", "Disease", "GeneOntologyMolecularFunction"]4. Individual Toppgene analyses output path:ex. "/Desktop/Results/IndividualResults/"5. Final Concatenated Results Output path:ex. "/Desktop/Results/ConcatenatedResults/"For Example,In terminal type...malachite "/Desktop/myInput/input.xlsx" "HGNC" ["Drug", "Disease", "GeneOntologyMolecularFunction"] "/Desktop/Results/IndividualResults/" "/Desktop/Results/ConcatenatedResults/"*NOTE: quotation marks (“/‘) can be glitchy, make sure you use straight quotes, not curly quotes.*NOTE: input data has to be of type .xlsx
Package: malaffinity
malaffinityCalculate affinity between MyAnimeList usersWhat is this?Calculate the affinity (Pearson’s Correlation * 100) between a “base”
user and another user. Refer to thedocsfor more info.InstallpipinstallmalaffinityDependenciesBeautifulSoup4lxmlRequestsExamplesfrommalaffinityimportMALAffinityma=MALAffinity("YOUR_USERNAME")affinity,shared=ma.calculate_affinity("OTHER_USERNAME")print(affinity)# 79.00545465639877print(shared)# 82orimportmalaffinityaffinity,shared=malaffinity.calculate_affinity("YOUR_USERNAME","OTHER_USERNAME")# ...DocumentationDocumentation athttps://malaffinity.readthedocs.ioLegal StuffLicensed under MIT. SeeLICENSEfor more info.Cat Gif
Package: mal-api
Python MAL APIAn unofficial MyAnimeList API for Python 3.Currently, the API does not feature any kind of rate limiting. Use the API in moderation
and rate limit your queries (0.5 seconds is sufficient to my knowledge). This API uses
cached webpage data to increase efficiency and save bandwidth. If you want to refresh
your data, you must manually refresh the object.The API is currently incomplete. More features are to come.If there are any features that you wish to be supported, please raise an issue. Any
feedback is also appreciated.API DocumentationReadTheDocs DocumentationInstallation and UsageTo install the library:pip install -U mal-apiTo import the library:frommalimport*ExampleTo call the API, you need to create an object.ID Query ExamplefrommalimportAnimeanime=Anime(1)# Cowboy Bebopprint(anime.score)# prints 8.82anime.reload()# reload objectprint(anime.score)# prints 8.81Search Query ExamplefrommalimportAnimeSearchsearch=AnimeSearch("cowboy bebop")# Search for "cowboy bebop"print(search.results[0].title)# Get title of first resultConfigurationTo configure timeout (default timeout is 5 seconds):frommalimportAnimefrommalimportconfigconfig.TIMEOUT=1# Import level configanime=Anime(1,timeout=1)# Object level config
Package: mal-api.py
mal.pyUPDATE: i am working on a major revision of the project, stay tuned!AMyAnimeListAPI wrapper written in Python.The objective for now is to cover queries for the publicly available information, support for taking actions on a user list is not planned.Featuresanime and manga search given a keywordfetch details of anime or manga given its id or its urlaccessing public lists of usersretrieving seasonal animeretrieve rankings for anime and mangaretrieve forum boards and discussionsThe next features that are planned to be implemented are:retrieve more information on recommendations and related entriesInstallationPython 3.8+ is needed.To use this library:(optional) create a virtual environmentpython -m venv .venvand activate itinstall withpip install mal-api.pyto perform requests you need an API token
log in on MAL -> account settings -> API -> create IDUsagefrommalimportclientcli=client.Client('your token here')anime=cli.get_anime(16498)anime.title>>Shingeki_no_KyojinFull documentation availablehere.Code samples can be found in the examples folder.LoggingThis library uses the logging module to keep track of configuration changes and possible
network errors. If you want to see the logs you need to configure the logging module yourself, for example:importlogginglogging.basicConfig(logging.level=INFO)For more information on how to use logging refer to the documentation of python:documentation of logging modulelogging HOWTO guideThis project is still a work in progress, if you have problems or find bugs feel free to open an issue or start a discussion.
Package: malariagen_data
malariagen_data- analyse MalariaGEN data from PythonThis Python package provides methods for accessing and analysing data from MalariaGEN.InstallationThemalariagen_dataPython package is available from the Python
package index (PyPI) and can be installed viapip, e.g.:pipinstallmalariagen-dataDocumentationDocumentation of classes and methods in the public API are available
from the following locations:Ag3 API
docsAmin1 API
docsPf7 API
docsPv4 API
docsRelease notes (change log)SeeGitHub releasesfor release notes.Developer setupTo get setup for development, seethis
videoand the instructions below.Fork and clone this repo:gitclonegit@github.com:[username]/malariagen-data-python.gitInstall Python 3.8 (current recommended version for local development), e.g.:sudoadd-apt-repositoryppa:deadsnakes/ppa
sudoaptinstallpython3.8python3.8-venvInstall pipx, e.g.:python3.8-mpipinstall--userpipx
python3.8-mpipxensurepathInstallpoetry, e.g.:pipxinstallpoetry==1.4.1--python=/usr/bin/python3.8Create development environment:cdmalariagen-data-python
poetryuse3.8
poetryinstallActivate development environment:poetryshellInstall pre-commit and pre-commit hooks:pipxinstallpre-commit--python=/usr/bin/python3.8
pre-commitinstallRun pre-commit checks (isort, black, blackdoc, flake8, ...) manually:pre-commitrun--all-filesRun tests:poetryrunpytest-vTests will run slowly the first time, as data required for testing
will be read from GCS. Subsequent runs will be faster as data will be
cached locally in the "gcs_cache" folder.Release processCreate a new GitHub release. That's it. This will automatically
trigger publishing of a new release to PyPI and a new version of
the documentation via GitHub Actions.The version switcher for the documentation can then be updated by
modifying thedocs/source/_static/switcher.jsonfile accordingly.
Package: malbrid
The Malbrid library is a Python library for simulating linear hybrid systems modelled as linear linear hybrid automata.While no full documentation is yet available, the example Jupyter notebooks show how to model automata and how to simulate them.Table of contentsContentsTable of contentsInstallationGetting startedNotes on SoundnessInstallationWhile the library is not available to be installed via PyPI, it can be installed by checking out the repository and running:pip install -e .Getting startedHave a look at the examples in the “examples” directory which run with Jupyter Notebook. They describe a bouncing ball example as well as a ball-with-a-paddle example. The latter also shows how to construct the product of two linear hybrid automata on-the-fly.Notes on SoundnessThis examples of the library  contains a couple of checks that test if numerical imprecision during the simulation can cause the wrong discrete transitions to be taken. In such a case, an exception is thrown, and the library contains an exception class so that the respective exception can be caught in your own program.
Package: malcarve
Obfuscated payload extractor for malware samplesOverviewmalcarveis a tool for detecting and extracting obfuscated, embedded content
from files.  In particular it is targeted at extracting malicious payloads such
as those contained in malware attack documents and droppers.A command-line utility is included alongside a simple Python API.
Further, a web API is provided as an example scanning web service.Getting StartedInstall usingpip:pip install malcarveCommand-line usage:malcarve [--extract [--output-dir <output_dir>]] <file1> <file2> ...Example Webservice:malcarve-web [-H <interface_address>] [-p port_number]Config can be updated by copying malcarve/conf/malcarve.conf to
~/.malcarve/malcarve.conf and changing settings as desired.Running from SourceTo run from source code, you will need to manually build the C components too.
Here is an example (assuming virtualenv) with dependencies for Ubuntu 18.04:sudo apt-get install build-essential gcc-multilib mingw-w64
git clone https://github.com/shendo/malcarve.git
cd malcarve/malcarve/ext
make
cd ../..
pip install -e .HistoryMalcarve was originally written several years ago.  It was predominantly
targeted at extracting XOR’ed PE files from Flash, Word and PDF documents,
which were commonly being exploited at the time.After needing a similar capability again recently, I’ve started reviving the
code (it’s still a bit of a mess) along with migrating to python3.  This also
comes with a newer focus towards macro’ed documents and embedded urls/other
file types and obfuscation techniques.This is still a work in progress but has been released in the hope
that others may find it useful (no warranty given or implied).Existing ToolsThere are many great tools and published literature already in this space.malcarveborrows heavily, and is inspired from techniques
discussed or available in the following:XORSearchPlaying With Others BlogDeobfuscating Embedded Malware using Probable-Plaintext AttacksunXORbalbuzardThe motivation in writing yet another deobfuscator was the need to not only
detect obfuscated patterns and payloads but to also extract/carve that content
automatically.Some tools already handled this but would only perform a subset of the schemes
or file types needed.FeaturesDeobfuscation/carving of Windows PE Files, Zip and Ole2 filesExperimental carving of PDF and other formats (check config file)Obfuscated and embedded URL extractionMultibyte XOR deobfuscationXOR modifiers countup, countdown, preserve nullsScans inside common stream encodings like base64 and deflateFuture Work:More than happy for feedback, discussion and pull requests…Handle various other obfuscation techniques used in VBA macrosPerformance enhancements by overhauling pattern checks into single passExtraction of general obfuscated scripts (eg. powershell, javascript)Fix poor performance of deflate and ascii based stream handlingWrite proper documentationIssuesSource code formalcarveis hosted onGitHub. Any bug reports or feature
requests can be made using GitHub’sissues system.
Package: malclient-upgraded
MAL-API-Client-UpgradedA third party object-oriented python3 client library for MyAnimeList's official REST API.
Originally created by@JFryy, dropped around 2 years ago, picked up and rewritten by ModerNews to fit more modern standards and new REST API functions.DocumentationUnsure what to do?Check out our documentationThere will be quick guide coming up soon as well!InstallationPython 3.9 or newer required, this is due to changes in type hinting guidelines, for more info regarding this issue readPEP 585Install the latest stable version fromPyPIpip install malclient-upgradedOr current unstable version directly from GitHub:pip install git+https://github.com/ModerNews/MAL-API-Client-UpgradedAuthenticationClient library uses OAuth2 authorization, all you need to do is register your apphere.
Main auth requires access token:importmalclientmalclient.client(access_token=token)Alternatively can authenticate using your client IDimportmalclientmalclient.client(client_id=id)You can generate token old-fashioned way usingthis tutorialOr you can use function implemented in APIimportmalclientprint(malclient.generate_token("<YOUR_CLIENT_ID>","<YOUR_CLIENT_SECRET>"))Although remember to call it only once and, then use the token generated this way, optionally withClient.refresh_bearer_tokenmethodAs mentioned previously, if you're scared that your token will time out you can also utilizeClient.refresh_bearer_tokenfunctionclient.refresh_bearer_token(client_id="<your-client-id>",client_secret="<your-client-secret>",refresh_token="<your-refresh-token>")For any other issues regarding authentication,please refer to the following guide.Quick Start ExamplesI contained some examples of usage of this wrapper, note that all responses are converted to python objects using pydanticimportmalclient# nsfw filter is enabled by default, although it's recommended to disable it if your results are missing titles,#  you can also enable/disable it for every query individuallyclient=malclient.Client(access_token="<your-access-token>",nsfw=True)# search anime, returns listanime=client.search_anime("cowboy",limit=20)foriteminanime:# prints only titlesprint(item)# prints all attributes of objectprint(repr(item))# search anime, returns listmanga=client.search_manga("Monogatari",limit=20)foriteminmanga:# prints only titlesprint(manga)# prints all attributes of objectprint(repr(manga))# Get individual anime by IDanime=client.get_anime_details(1)print(anime)print(repr(anime))# Update anime List based off of search resultsanime=client.search_anime("Monogatari",limit=1)Most Imoprtant To-Do'sRewrite boards (currently disabled)Implement additional search endpointUseful resourcesMAL auth guideUnofficial API DiscordOfficial MAL API ClubP.S.If anything bugs you, you can always reach me out at discord Gruzin#0911 as well
Package: malcolm
Malcolm is a middlelayer framework that implements high level configure/run
behaviour of control system components like those used in continuous scans.
This repository contains a Python implementation allowing the creation of
Malcolm servers and clients.DocumentationFull documentation is available athttps://dls-controls.github.io/pymalcolmSource CodeAvailable fromhttps://github.com/dls-controls/pymalcolmInstallationTo install the latest release, type:pip install malcolmTo install the latest code directly from source, type:pip install git+git://github.com/dls-controls/pymalcolm.gitChangelogSeeCHANGELOGContributingSeeCONTRIBUTINGLicenseAPACHE License. (seeLICENSE)
Package: malcontent
A cross-platform content editor.Malcontent requires Python 3THIS IS A PLACEHOLDER PROJECTAt present, it has no functionality - it exists purely to reserve the PyPI namespace.CommunityMalcontent is part of theBeeWare suite. You can talk to the community through:@pybeeware on TwitterThepybee/generalchannel on Gitter.ContributingIf you experience problems with this backend,log them on GitHub. If you
want to contribute code, pleasefork the codeandsubmit a pull request.
Package: maldi-nn
maldi-nnDeep learning tools and models for MALDI-TOF mass spectra analysis.Package features:Reading and preprocessing functions for MALDI-TOF MS spectra.Model definitions to process SMILES strings with state-of-the-art techniques (for feature-based AMR prediction).Model definitions to pre-train state-of-the-art Transformer networks on MALDI-TOF MS dataModel definitions and scripts to train AMR models on theDRIAMS database.Model definitions and scripts to train species identification models.Installmaldi-nnis distributed on PyPI.pipinstallmaldi-nnYou may need toinstall PyTorchbefore running this command in order to ensure the right CUDA kernels for your system are installedAcademic ReproducibilityThis package contains all code and scripts to reproduce:"An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks", and "Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction" (in draft).
All information regarding reproducing our results can be found in thereproduce folder READMECreditsImplementations of many MALDI reading and processing functions were based on the R packageMaldiQuant.Topological Peak Filtering was taken from theTopf package.Citation@article{de2023antimicrobial,
  title={An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks},
  author={De Waele, Gaetan and Menschaert, Gerben and Waegeman, Willem},
  journal={bioRxiv},
  pages={2023--09},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}
Package: malduck
:duck: MalduckInstallation ⚙️|Docs 📚Malduck is your ducky companion in malware analysis journeys. It is mostly based onRoachproject, which derives many concepts frommliblibrary created byMaciej Kotowicz. The purpose of fork was to make Roach independent fromCuckoo Sandboxproject, but still supporting its internalprocmemformat.Malduck provides many improvements resulting from CERT.pl codebase, making scripts written for malware analysis purposes much shorter and more powerful.FeaturesCryptography(AES, Blowfish, Camelie, ChaCha20, Serpent and many others)Compression algorithms(aPLib, gzip, LZNT1 (RtlDecompressBuffer))Memory model objects(work on memory dumps, PE/ELF, raw files and IDA dumps using the same code)Extraction engine(modular extraction framework for config extraction from files/dumps)Fixed integer types (like Uint64) and bitwise utilitiesString operations (chunks, padding, packing/unpacking etc)Hashing algorithms (CRC32, MD5, SHA1, SHA256)Usage examplesAESfrommalduckimportaeskey=b'A'*16iv=b'B'*16plaintext=b'data'*16ciphertext=aes.cbc.encrypt(key,iv,plaintext)Serpentfrommalduckimportserpentkey=b'a'*16iv=b'b'*16plaintext=b'data'*16ciphertext=serpent.cbc.encrypt(key,plaintext,iv)APLib decompressionfrommalduckimportaplib# Headerless compressed bufferaplib(b'T\x00he quick\xecb\x0erown\xcef\xaex\x80jumps\xed\xe4veur`t?lazy\xead\xfeg\xc0\x00')Fixed integer typesfrommalduckimportDWORDdefsdbm_hash(name:bytes)->int:hh=0forcinname:# operations on the DWORD type produce a dword, so a result# is also a DWORD.hh=DWORD(c)+(hh<<6)+(hh<<16)-hhreturnint(hh)Extractor engine - module examplefrommalduckimportExtractorclassCitadel(Extractor):family="citadel"yara_rules=("citadel",)overrides=("zeus",)@Extractor.string("briankerbs")defcitadel_found(self,p,addr,match):log.info('[+] `Coded by Brian Krebs` str @%X'%addr)returnTrue@Extractor.stringdefcit_login(self,p,addr,match):log.info('[+] Found login_key xor @%X'%addr)hit=p.uint32v(addr+4)print(hex(hit))ifp.is_addr(hit):return{'login_key':p.asciiz(hit)}hit=p.uint32v(addr+5)print(hex(hit))ifp.is_addr(hit):return{'login_key':p.asciiz(hit)}Memory model objectsfrommalduckimportprocmempewithprocmempe.from_file("notepad.exe",image=True)asp:resource_data=p.pe.resource("NPENCODINGDIALOG")How to startInstall it by runningpipinstallmalduckMore documentation can be found onreadthedocs.
Package: maldump
maldumpMaldump makes it easy to extract quarantined files of multiple AVs from a live system or a mounted disk image.FeaturesSupports extraction from the following AV productsAvast AntivirusAvira AntivirusEset NOD32FortiClientG DataKaspersky for Windows ServerMalwarebytesMicrosoft DefenderMcAfeeAVGInstallationUsing pip (Recommended)$pipinstallmaldumpOr alternatively using git and Virtual Environment$ git clone https://github.com/NUKIB/maldump
$ cd maldumpCreate new environment and activate it$ python3 -m venv venv
$ . venv/bin/activateInstall dependencies(env) $ pip install -r requirements.txtRun it as a module(env) $ python3 -m maldumpUsageusage: maldump [-h] [-l] [-q] [-m] [-a] [-v] root_dir

Multi-quarantine extractor

positional arguments:
  root_dir       root directory where OS is installed (example C:\)

optional arguments:
  -h, --help     show this help message and exit
  -l, --list     list quarantined file(s) to stdout (default action)
  -q, --quar     dump quarantined file(s) to archive 'quarantine.tar'
  -m, --meta     dump metadata to CSV file 'quarantine.csv'
  -a, --all      equivalent of running both -q and -m
  -v, --version  show program's version number and exit
  -d, --dest     destination for exported filesExamplesOn WindowsList quarantine files located on disk C$ maldump C:\Dump quarantine files from disk C into archivequarantine.tar$ maldump C:\ --quarExport quarantine metadata from disk C intoquarantine.csv$ maldump C:\ --metaExport both files and metadata from a mounted disk F$ maldump F:\ --allOn LinuxList quarantine files from a windows partition mounted on/mnt/win$ maldump /mnt/winDisclaimerKeep in mind, all timestamps are in UTCexceptfor "Kaspersky for Windows Server" which stores timestamps in a local timezone.For optimal results, admin privileges are required when running on Windows system. Linux does not require admin rights.ContributingTo contribute to this project, please follow theCONTRIBUTING.LicenseThis software is licensed under GNU General Public License version 3.Copyright (C) 2022National Cyber and Information Security Agency of the Czech Republic (NÚKIB)
Package: malee
MaleeThemaleepackage contains a collection of financial modeling functions.The major goal of this package is to provide you an easy to use library that implements most of the financial modeling formulas like risk and return ready to be used. This package is usingnumpyas its backend so everything is fast enough as you might expect. There are similar projects likenumpy-financial, but most of the have these two problems of 1) not having everything you might need 2) not being maintained actively. Hence this packages aims to fill this gap and be your goto library when you want financial modeling stuff.maleein Farsi meansFinancial. Tried so many other cool names, but I was late in the game
and they were already taken.The source code of this package is available athttps://github.com/RezaBehzadpour/maleeInstallTo install the current release ofmalee, type the following command in terminal:$pipinstall-UmaleeHow to use?Here's how you can calculate simpleArithmetic Return:>>>importmalee>>>malee.ar(100,140)0.4For more information, see thedocumentation.LicenseBSD 3-Clause
Package: maleo
MaleoWrapper library for text cleansing, preprocessing and POS Tagging in NLPDocshttps://jakartaresearch.github.io/maleo/Overview of features- Scanner : get insight about your text dataset (ex: number of chars, words, emojis, etc)
- Remove hyperlink, punctuation, stopword, emoticon, etc
- Extract hashtags, price from text
- Convert email, phone number, date to <TAG>
- Convert Indonesian slang to formal word
- Convert emoji to word or <TAG>
- Convert word to number
- Predict Part-of-Speech (POS) tagsInstallationpip install maleoGetting Startedfrommaleo.wizardimportWizardfrommaleo.pos_tagimportPOSwiz=Wizard()pos=POS()wiz.scanner(df,'text')wiz.emoji_to_word(df.text)wiz.slang_to_formal(df.text)pos.predict('saya mau pergi beli makan siang dulu',output_pair=False)Universal POS tagshttps://universaldependencies.org/u/pos/index.htmlContributor:Ruben Stefanus