Saravanan666/pypi-org huggingface dataset:
Package: ifeatpro
ifeatpro (Physicochemical Feature Encoder for Protein Sequences)A python package that generates 21 numerically encoded feature representation for protein sequences based on their
physicochemical properties.Note: ifeatpro is based on iFeature, a python based toolkit available atlink. Here, we have packaged 21 alignment free feature encoding functions
available in iFeature into a pip installable module for easy usage and improved accessibility of a protein feature
encoding tool.ifeatpro installationpipinstallifeatproifeatpro usagefromifeatpro.featuresimportget_feature,get_all_featuresGenerating some random protein sequences and storing them in fasta formatimportrandomAA="ACDEFGHIKLMNPQRSTVWY"sequences=["".join([random.choice(AA)for_inrange(150)])for_inrange(5)]!mkdir-pifeatpro_datafasta_file="ifeatpro_data/seq.fa"withopen(fasta_file,'w')asf:fori,seqinenumerate(sequences):f.write(f">enz_{i}")f.write("\n")f.write(seq)f.write("\n")Getting all 21 feature encodings from protein sequences using ifeatproifeatpro contains 21 features which are capable of numerically encoding protein sequences based on their physicochemical
properties. They are:aacapaaccksaagpcksaapctdcctddctdtctriadddedpcgaacgdpcgearygtpcksctriadmorannmbrotopaacqsordersocnumbertpcUsingget_all_featuresfunction, an user can create all the 21 physicochemical encoding based feature extraction techniques provided by ifeatpro. The first argument of this function denotes the fasta file that contains protein sequences while the second argument denotes the output directory where the files will be stored as csv files.get_all_features(fasta_file,"./ifeatpro_data/")Creating a single feature encoding using ifeatproAn user can also create any one of the 21 feature extraction techniques available in ifeatpro using theget_featurefunction. The function takes the fasta file as the first argument, feature encoding type as the
second argument and output directory where the file will be stored as the third argument. For example if an user
wants to create aac type feature encoding using the fasta_file that we created above and would like to store it in
ifeatpro_data directory, they can run the following command:get_feature(fasta_file,"aac","ifeatpro_data/")feature extraction techniques descriptionTo get a detailed description of the feature extraction techniques used in ifeatpro, please refer to the
Supplementary Document of the paperlink to be added soon.similar modules to encode protein sequencesOther modules that can be used to generate numerical encoding of protein sequences are:ngramprolinkpssmprolink
Package: ifeature
特征工程Installpipinstallgit+https://github.com/Jie-Yuan/iFeature.git_clf = LGBMClassifier(n_estimators=1)
X = iris.data[:100, :]
y = iris.target[:100]
_clf.fit(X, y)
show_info = ['split_gain', 'internal_value', 'internal_count', 'leaf_count']
lgb.plot_tree(_clf.booster_, figsize=(60, 80), show_info=show_info)

model = _clf.booster_.dump_model()
tree_infos = model['tree_info'] # xgb_._Booster.get_dump()
Package: ifexd
Indian Facial Expression DatasetLinks:Homepage|Github|PyPi - project|Indian Facial Expression Dataset12K images including three views - Front, Left, RightImages from 345 Indians7 Expressions + Natural pose:A: NaturalB: HappyC: SurpriseD: SadE: AngerF: ContemptG: DisgustH: FearContacts:Nikesh Bajajhttp://nikeshbajaj.inPhD Student: Queen Mary University of London
Package: ifgen
ifgen (3.2.0)An interface generator for distributed computing.DocumentationGeneratedBysphinx-apidoc(What'ssphinx-apidoc?)Bypydoc(What'spydoc?)Python Version SupportThis package is tested with the following Python minor versions:python3.11python3.12Platform SupportThis package is tested on the following platforms:ubuntu-latestmacos-latestwindows-latestIntroductionCommand-line Options$ ./venv3.11/bin/ig -h

usage: ig [-h] [--version] [-v] [-q] [--curses] [--no-uvloop] [-C DIR]
          {gen,svd,noop} ...

An interface generator for distributed computing.

options:
  -h, --help         show this help message and exit
  --version          show program's version number and exit
  -v, --verbose      set to increase logging verbosity
  -q, --quiet        set to reduce output
  --curses           whether or not to use curses.wrapper when starting
  --no-uvloop        whether or not to disable uvloop as event loop driver
  -C DIR, --dir DIR  execute from a specific directory

commands:
  {gen,svd,noop}     set of available commands
    gen              generate interfaces
    svd              process CMSIS-SVD files
    noop             command stub (does nothing)Internal Dependency GraphA coarse view of the internal structure and scale ofifgen's source.
Generated usingpydeps(viamk python-deps).
Package: ifitpy
Pyfitpip install ifitpyThis fit package permits to, well, fit a given x,y data. It encapsulates both iminuit and curve_fit.
There are two type of functions. Simple (linear, expo) and Complex(gaussian, gaussian2d, poly).
For Simple functionfit(x,y)arefit(x,y,p0)are valid inputs. In the first option the fit attempts to estimate the initial starting point. In the second option, a list of parameters (p0) is used to initialized the fit.For Complex functionsfit(x,y, n)arefit(x,y,p0)are valid inputs. If n is used, then the algorithm will use this value to generate the fitting function with n components. For example,fit(xx,yy,n=3)will fit a sum of two gaussian(2d) or a 3-degree polonimal function. And p0 zero is also estimated. Iffit(x,y,n,p0)is used, then p0 will be the initialization parameters. Note thatlen(p0) = n*parameters_to_fit.There's also af.fitBinned(xx,yy,bins=50)option which allows fitting a profile histogram instead of the raw data. This option is often faster and is the recommended one as it takes into account the statistical fluctuation of the data.
Don't forget to check the Tutorial notebook.To extract the fit results usefrom ifitpy import Fitter
f = Fitter("linear") #linear, expo, gaussian, gaussian2d, poly
f.fit([0,10], [0,-10])
p = f.getParams()
print(p) # prints a string with the available variables.
print(p.vars) # list of the results
print(p.m) # slope for the “linear” type
print(p.b) # slope for the “intercept” typeThis an ongoing project!
Package: ifixit2zim
iFixitifixit2zimis anopenZIMscraper to create offline versions ofiFixitwebsite, in all its supported languages.This scraper downloads the iFixit resources (categories, guides, ...) and puts them in a ZIM file, a clean and user friendly format for storing content for offline usage.Usageifixit2zimworks off alanguage versionthat you must provide via the--languageargument. The list of supported languages is visible in the--helpmessage.Dockerdockerrun-vmy_dir:/outputopenzim/ifixitifixit2zim--helpPythonifixit2zimis a Python3 (3.6+) software. If you are not using theDockerimage, you are advised to use it in a virtual environment to avoid installing software dependencies on your system. In addition to Python3, you also need to have an up-to-date installation of pip, setuptools and wheel as recommandedhere(wheel is important since you will have to build some dependencies).python3-mvenv.venvsource.venv/bin/activate# using published versionpip3installifixit2zim
ifixit2zim--help# running from sourcepip3install-rrequirements.pip
python3ifixit2zim/--helpCalldeactivateto quit the virtual environment.Seerequirements.txtfor the list of python dependencies.ContributingAll contributions are welcome!Please open an issue on Github and/or submit a Pull-request.GuidelinesDon't take assigned issues. Comment if those get staled.If your contribution is far from trivial, open an issue to discuss it first.Ensure your code passedblack formatting,isortandflake8(88 chars)Create an appropriate Python environmentFirst time:python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.pipNext times:source .venv/bin/activateNOTA : there is some limitations to the execution of the underlying libzim library on
MacOS with some known bugs. The main issue is that the full-text index is not working,
so this shouldn't be a problem for quick tests. In doubt, execute the scraper in a
Docker container as explained below.Test the scraper in a Docker containerFirst, build the Docker image (to be ran in the main folder of this repo):docker build -t openzim/ifixit:local .Then run the scraper with CLI arguments needed for your test (everything afterifixit2zimin the example below).For instance, if you want to run a scrape of only theApple_PDAcategory, including its guides,
in French :docker run -it -v $(pwd)/output:/output --rm openzim/fixit:local ifixit2zim --language fr --output /output --tmp-dir /tmp --category Apple_PDAThis will produce a ZIM in the output folder of your current directory.Test the ZIM producedTo test if the ZIM produced is OK, you should run kiwix-serve, once more with Docker.For instance, if you produced a file namedifixit_fr_selection_2022-04.zimin theoutputsubfolder, and port 1256 is unused on your machine, you might run:docker run -it --rm -v $(pwd)/output:/data -p 1256:80 kiwix/kiwix-tools kiwix-serve /data/ifixit_fr_selection_2022-04.zimAnd then navigate to (https://localhost:1256) on your favorite browser.Once test are complete, you might stop the Docker container by pressing Ctrl-C
Package: iflearner
iFLearner - A Powerful and Lightweight Federated Learning FrameworkDOCS|中文iFLearner is a federated learning framework, which provides a secure computing framework based on
data privacy security protection, mainly for federated modeling in deep learning scenarios. Its security bottom
layer supports various encryption technologies such as homomorphic encryption, secret sharing, and differential
privacy. The algorithm layer supports various deep learning network models, and supports mainstream frameworks
such as Tensorflow, Mxnet, and Pytorch.ArchitectureThe design of iFLearner is based on a few guiding principles:Event-driven mechanism: Use an event-driven programming paradigm to build federated learning, that is,
to regard federated learning as the process of sending and receiving messages between participants,
and describe the federated learning process by defining message types and the behavior of processing messages.Training framework abstraction: Abstract deep learning backend, compatible with support for multiple
types of framework backends such as Tensorflow and Pytorch.High scalability: modular design, users can customize aggregation strategies, encryption modules,
and support algorithms in various scenarios.Lightweight and simple: The framework is Lib level, light enough, and users can simply transform their deep
learning algorithms into federated learning algorithms.DocumentationiFLeaner Docs:InstallationQuickstart (TensorFlow)Quickstart (PyTorch)Quickstart (MXNet)Quickstart (keras)ContributoriFLeaner FAQFAQiFLeaner FAQLicenseApache License 2.0
Package: iflearner-flow-client
Iflearner Flow ClientInteractive tools forIflearner Flow, includingflow sdkandflow cliInstallationYou can execute the following command to quickly install:pipinstalliflearner-flow-clientAfter the installation is complete, you can refer to the sdk package for interface calls,
and you can also directly use the compiled cli toolsflow_federate_cliandflow_server_clito operate.Usage tutorialSDKFlow Server SDKFlow Federate SDKCLIFlow Server CLIFlow Federate CLI
Package: ifm3dpy
ifm3d OverviewLibrary and utilities for working with ifm pmd-based 3D ToF Perception devices. Compatible with the O3R, O3D and O3X platforms.
This library is available in python, c++, and has wrappers for ROS and ROS2.C++ API ReferencePython API ReferenceComprehensive documentation is available onifm3d.com.3D cloudDistanceRGBReleased Versions⚠️ Note that themainbranch is generally in a work in progress state and you probably want to use a
taggedrelease versionfor production.⚠️ Note that O3D and O3X support is currently experimental- Use at your own risk!.Current RevisionThe table below show the compatibility between the current ifm3d version and the firmware versions for the O3X, O3D and O3R devices. Other combinations than the ones listed below might work but have not been thoroughly tested.ifm3d versionSupported O3D Firmware VersionSupported O3X Firmware VersionSupported O3R Firmware VersionSupported Ubuntu Linux VersionNotes1.3.41.30.53091.1.1901.0.1420.04, 22.04See the changelog and the migrating guide.Note that a full compatibility matrix is availableherefor older versions.Organization of the SoftwareThe ifm3d software is organized into modules, they are:Module nameDescriptiondeviceProvides an implementation of the XMLRPC protocol for configuring the camera and pmd imager settings.framegrabberProvides an implementation of the PCIC protocol for streaming pixel data and triggered image acquisition.swupdaterProvides utilities for managing the SWUpdate subsystem of the camera.pcicclientDirect access to PCIC to, for example, actuate digital IO.toolsProvides the ifm3d command line tool for manipulating and introspecting the hardware interactively. It is also suitable for usage within shell scripts to, for example, manage fleets of cameras.pybind11Provides python bindings throughpybind11to the native C++ API. Supports all general camera functionality as well as a zero-copy interface to image data, exposed as NumPy arrays.deserializeProvides definitions and functions for deserializing structs sent over PCIC.Installation instructionsPlease refer to the corresponding section onifm3d.com.Supported docker containersDocker containers are available for the ifm3d library, both onghcrand on thedockerhub.
You can pull them with:dockerpullghcr.io/ifm/ifm3d:stableORdockerpullifmrobotics/ifm3d:stableNote that we provide 2 tags,stablealways points to the latest tagged version, andlatestis built nightly with the latest changes on the o3r/main-next branch. Thelatesttag is typically a work in progress.For more details on the available containers, seehere.For more details on docker and the O3R platform seehere.Report a bug and check the known issuesPlease see theGithub Issue Tracker, or contactsupport.robotics@ifm.com.LICENSEPlease see theLICENSEfile.
Package: ifm-contrib
ifm_contribDescriptionifm_contribis an Open-Source Extension of the IFM, the Python API of the groundwater modellierung softwareFEFLOWbyDHI.It extents the namespace of the classic IFM to facilitate using FEFLOW in combination with the popular Python datascience librariespandas,geopandasandmatplotlib.Theifm_contribnamespace is a superset of the classicifmnamespace, thus scripts written for the latter will be back-wards compatible and only one library import statement is required.Project statusThe library has been developed since 2019, and reached maturity around 2021 with most features for (geo-)pandas as matplotlib integration in place.ifm_contribis published under the MIT license. It is free to use, share and modify. Users are encouraged to contribute their own code back to the project.Please note that the maintainer of this project is not associated with DHI.Development setupClone this repoInside the repo:python-mvenv.venvsource.venv/bin/activate
pipinstall-e".[test]"Run unit test withpytestIf you want to create the documentation:source.venv/bin/activate
pipinstall-e".[test,doc]"cddoc/sphinx
makehtml# Open doc/sphinx/build/html/index.html in a browserInstallationThe recommended way of installingifm_contribis by copying or cloning the repository directly into the FEFLOW program director next to the classic IFM.Installation viapipshould be done by:pipinstallhttps://github.com/red5alex/ifm_contrib/archive/refs/heads/master.zipAn installation of FEFLOW (version 7.2 or higher) is required.Getting startedFor a detailed introduction and installation notes, seeifm_contrib: An IntroductionIts not a pretty one, but you can find an (unregularly updated) help system including API reference here](https://red5alex.github.io/ifm_contrib/ifm_contrib.contrib_lib.html).Troubleshooting and ContributionIf you run into issues please open anissue. Please note that the maintainer may not dedicate significant time to the project at this time but will try its best to respond.GalleryVisualize FEFLOW Results directly in JupyterCreate inline plots with the look-and-feel of FEFLOWs directly within Jupyter. The plots can be exported as GeoDataFrames and saved to shape-files easily.ifm_contribadds light support for coordinate systems to FEFLOW.Process FEFLOWs Time Series with PandasTime Series (aka Power functions) and History charts can be easily exported to pandas DataFrames. Automatic conversion to DateTime based on FEFLOWs Reference Time. In-Built Synchronization to observation point reference data.Pandas-access to Nodal and Elemntal Data, Multi-Layer Wells, and much moreRead more inifm_contrib: An Introduction
Package: ifmO3r
ifmO3rTools to configure the ifmO3r and receive images, etc.https://ifm3d.com/New python environmentTo get the best experience, we recommend to use a blank python environment. There are several tools like the built invenv(python >= 3.2),virtualvenvwith more features or evenanacondato create these virtual environments. Usingvenvyou can do following:python -m venv [path/name_of_venv]Now you can choose to either use the local, wheel or pip installation.3D viewerThe 3D viewer is also a package on PyPI. You can install the 3D viewer bypip install ifmO3rViewerFurther informationIf you need documentation or simple guides/tutorials, take a look on the official homepage:https://ifm3d.com
Package: ifmO3rViewer
ifm O3R 3D viewerWhat is this package for?This package contains a 3D viewer, based on PyQt. You can use
this viewer in combination with the ifmO3r package for receiving
images from the VPu and displaying it via the viewer.Installationpip install ifmO3rViewerStart the viewerFrom the CLI:python -m ifmO3rViewer.startViewerFrom the REPL:> import ifmO3rViewer.startViewer as v> v.start()Further informationTo get further information about the ifmO3r etc. please visit theofficial homepage
Package: ifnude
ifnudeIt is a nudity detection library that actually works, on both on humans and drawings.. It tells you exactly what NSFW parts of the body are visible. Optionally, you can censor the said parts.InstallationpipinstallifnudeExamplefromifnudeimportdetect# use mode="fast" for x3 speed with slightly lower accuracyprint(detect('/path/to/nsfw.png'))Output[{'box': [164, 188, 246, 271], 'score': 0.8253238201141357, 'label': 'EXPOSED_BREAST_F'}, {'box': [252, 190, 335, 270], 'score': 0.8235630989074707, 'label': 'EXPOSED_BREAST_F'}]CreditsThis is fork ofNudeNetlibrary which doesn't work anymore. I have taken the liberty to remove the video detection functionality as it was prone to crashes. It will be re-implemented in future.
Package: ifoam
🟢⬜🟩⬜🟩⬜⬜⬜⬜⬜🟩⬜🟩⬜🟩⬜⬜⬜⬜⬜🟩⬜🟩⬜🟩OpenFOAM.YAMLPython Interface to OpenFOAM Case (Configured Using YAML)Explore the docs »ViewDemo/Tutorial·Report Bug·Request FeatureTable of ContentsAbout The ProjectGetting StartedInstallationDemoContributingLicenseContactAbout The ProjectThis repository was originally designed to solve the problem of complex OpenFOAM case structure, and the solution was to re-present the original cases using the common configuration file format YAML. Later, since there is a corresponding package for the YAML format in Python, I wrote this Python interface package for OpenFOAM, and then I added progress bars to most OpenFOAM solvers by analyzing log files in real time. Although there are still many details to be specified in this repository, its function of generating cases and calling solvers is ready for preliminary use, for example, I used this package to generate cases in batch in my own project. In the future I would like to integrate the post-processing steps into this interface package as well.(back to top)Getting StartedThis project currently uses Poetry to manage Python dependencies. I've heard good things aboutPDMso far, and may provide PDM support subsequently.Installationpip3installifoam[full]DemoSave the following demo code as a separate file (e.g.demo.py).fromfoamimportFoamfoam=Foam.fromDemo('cavity')foam['foam']['system','controlDict','endTime']=1.0foam.save('cavity')foam.cmd.all_run()Running the demo code in the virtual environment results in the following output.$pythondemo.py

Foam.fromPath('.../of.yaml/foam/demo/7/cavity.yaml',warn=False)RunningblockMeshon.../of.yaml/cavityusing1processesifinparallel
RunningicoFoamon.../of.yaml/cavityusing1processesifinparallel100%|█████████████████████████████████████|1.0/1.0[00:02<00:00,2.24s/it]ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repository and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request(back to top)LicenseDistributed under the GPL-3.0 License. SeeLICENSE.txtfor more information.(back to top)ContactIydon Liang -@iydon- liangiydon_AT_gmail.com(back to top)
Package: ifolder
# ifolder
ifolder is a python package for transferring files and folders with tcp socket.receiverfromifolderimportReceiver# replace this path to a valid one on this machinetoPATH='/Users/xxxx/Desktop/copyto'rx=Receiver()whileTrue:rx.save_files_to(toPATH)senderfromifolderimportSenderip=('127.0.0.1',10086)# files or folders to be sent. should be a listfiles=['/Users/xxx/Desktop/TEST.pdf',]tx=Sender(ip)tx.send(files)
Package: ifood
IfoodTo installpip install ifoodTo import the libraryfrom ifood import IfoodDefining the zip codeapp = Ifood('59082311')or addressapp = Ifood('rua+buzius+47+brazil')Creating object to search addressprint(app.searchAddress())All functions of addressprint(app.city())
print(app.state())
print(app.country())
print(app.streetName())
print(app.postalCode())
print(app.latitude())
print(app.longitude())Creating object to search productapp.searchItems('energetico+reign')All functions of productprint(app.listItems())
print(app.listItems()[0]['id'])
print(app.listItems()[0]['name'])
print(app.listItems()[0]['category'])
print(app.listItems()[0]['price'])
print(app.listItems()[0]['merchant'])
print(app.listItems()[0]['slug'])
print(app.listItems()[0]['distance'])
print(app.listItems()[0]['deliveryTime'])
print(app.listItems()[0]['deliveryValue'])
print(app.listItems()[0]['mainCategoryName'])LinksSee myGitHub.
Package: ifood-kube-login
A sample Python projectA sample project that exists as an aid to thePython Packaging User
Guide'sTutorial on Packaging and Distributing
Projects.This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.Most of the configuration for a Python project is done in thesetup.pyfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written usingreStructuredTextormarkdownwith the appropriatekey set. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.