Saravanan666/pypi-org huggingface dataset:
Package: insights-sandbox
Insights SandboxRun rules with access only to necessary binaries and the archive working
directory.StrategyCreate a sandbox usingbubblewrap (bwrap).Enable only required linux namespaces (seeman namespacesfor more info).Communicate with the sandbox usingzeromqover named pipes.zmq hides underlying buffer details (man 7 pipefor fifos).ArchitectureClient <-> runner adapter proxy <-> named pipes <-> [controller <-> runner adapter <-> runner]TheClientcreates named pipes and a child process that usesbwrapto
invokeinsights_sandbox.consumer.The client creates aRunnerAdapterProxyand configures it withzmqfunctions for sending and recieving messages over the pipes. The proxy is
used to setup theRunnervia theControllerandRunnerAdapter.InstallationCentOS 7:
yum install epel-release
yum install bubblewrap

Fedora:
dnf install epel-release
dnf install bubblewrap

Both:
python3 -m venv .
. bin/activate
pip install -e .[develop]Test an archive./driver.py -p examples.rules <archive>Example Code#!/usr/bin/env python3"""This script is only for testing the sandbox."""importargparsefrominsightsimportdr,parse_pluginsfrominsights_sandbox.clientimportClientdefparse_args():p=argparse.ArgumentParser()p.add_argument("--plugins","-p",help="plugins to load",default="")p.add_argument("archive",help="pass an archive to analyze.")returnp.parse_args()defmain():args=parse_args()broker=dr.Broker()packages=parse_plugins(args.plugins)withClient(packages=packages)asclient:doc=client.process(args.archive,broker=broker)print(doc["results"].decode("utf-8"))if__name__=="__main__":main()
Package: insight-workshop-commons
Commons is a Django app that includes commonly used
serializer, models, pagination and mixins while developing an
API with DjangoRestFramework .Quick startAdd “commons” to your INSTALLED_APPS setting like this:INSTALLED_APPS = […
‘commons’,]Include the polls URLconf in your project urls.py like this:path(‘commons/’, include(‘commons.urls’)),Runpython manage.py migrateto create the polls models.Start the development server
Package: insilicho
InSiliCHOA model to capture CHO dynamics in-silico.  An accompanying streamlit app to try the model out isavailable.Model is based on the following primary sources:Pörtner, Ralf, ed. Animal Cell Biotechnology: Methods and Protocols. Vol. 2095. Methods in Molecular Biology. New York, NY: Springer US, 2020.https://doi.org/10.1007/978-1-0716-0191-4.Möller, Johannes, Tanja Hernández Rodríguez, Jan Müller, Lukas Arndt, Kim B. Kuchemüller, Björn Frahm, Regine Eibl, Dieter Eibl, and Ralf Pörtner. “Model Uncertainty-Based Evaluation of Process Strategies during Scale-up of Biopharmaceutical Processes.” Computers & Chemical Engineering 134 (March 2020): 106693.https://doi.org/10.1016/j.compchemeng.2019.106693.Additional sources include:Parolini, Dott Nicola, and Susanna Carcano. “A model for cell growth in batch bioreactors,” 2009, Thesis.Frahm, Björn. “Seed Train Optimization for Cell Culture.” In Animal Cell Biotechnology, edited by Ralf Pörtner, 1104:355–67. Methods in Molecular Biology. Totowa, NJ: Humana Press, 2014.https://doi.org/10.1007/978-1-62703-733-4_22.Möller, Johannes, Kim B. Kuchemüller, Tobias Steinmetz, Kirsten S. Koopmann, and Ralf Pörtner. “Model-Assisted Design of Experiments as a Concept for Knowledge-Based Bioprocess Development.” Bioprocess and Biosystems Engineering 42, no. 5 (May 2019): 867–82.https://doi.org/10.1007/s00449-019-02089-7.UsageThis repo serves as a standalone package, available to install using (or added as a dependency) using:pip install insilichoExamplefrominsilichoimportrundefT(time):"""returns temperature in degC"""return36.4defF(time):"""returns flow rate in L/hr"""return0.003model=run.GrowCHO({"parameters":{"K_lys":"0.05 1/h"}},feed_fn=F,temp_fn=T,)model.execute(plot=True,initial_conditions={"V":"50 mL"})final_vol=model.full_result.state[-1,8]print(final_V)# 0.914L
Package: insilicodna
InSilicoDNANote: This package is still in a planning phase and is not intended for external usageWhy create synthetic molecular data when there is an ever-growing body of real data to use? Simply stated, control. For the Engineer or Data Engineer, it is critical to have data when building pipelines. For the Scientist, Data Scientist, or Machine Learning Engineer, it is critical to have data when modeling and testing assumptions. While there are now many sets of real molecular data to select from, there are few cases wherealltypes of data exist for a population of individuals in an unrestricted manner.Getting StartedFrom CLIReview options with--help% insilicodna --helpPrint common usage% insilicodnaCreate .fasta and .gff3 synthetic data for 50 genes% insilicodna --gene-count 50 --fasta --gff3Frompythonimport insilicodna

insilicodna.generate_contig(
	output_prefix="MySyntheticData",
	n_genes=50,
	fasta_file=True,
	gff3_file=True)
Package: in-silico-fate-mapping
in silico fate mappingInteractive in silico fate mapping from tracking data.This napari plugin estimates the cell fates from tracking data by building a radial regression model per time point. The user can select an area of interest using aPointslayer; the algorithm will advent the probed coordinates forward (or backward) in time, showing the estimated fate.Video example below:https://user-images.githubusercontent.com/21022743/216478216-89c1c35f-2ce4-44e8-adb8-9aeea75b5833.mp4InstallationYou can installin-silico-fate-mappingviapip:pip install in-silico-fate-mappingTo install the latest development version :pip install git+https://github.com/royerlab/in-silico-fate-mapping.gitIO file formatThis plugin does not depend on a specific file format, the only requirement is using aTracklayer from napari.Despite this, we ship a reader and writer interface. It supports.csvfiles with the following readerTrackID, t, (z), y, x,zis optional.
Such that each tracklet has a uniqueTrackIDand it's composed of a sequence o time and spatial coordinates.This is extremely similar to how napari store tracks, more information can be foundhere.Divisions are not supported at the moment.Usage ExampleMinimal exampleMinimal example using a track file following the convention described above.importnapariimportpandasaspdfromin_silico_fate_mapping.fate_mappingimportFateMappingtracks=pd.read_csv("tracks.csv")fate_map=FateMapping(radius=5,n_samples=25,bind_to_existing=False,sigma=1)fate_map.data=tracks[["TrackID","t","z","y","x"]]source=tracks[tracks["t"]==0].sample(n=1)tracks=fate_map(source[["t","z","y","x"]])napari.view_tracks(tracks)napari.run()Zebrahub exampleZebrafish embryo tail example. This example requires the packagenapari-ome-zarr.importnapariimportpandasaspdfromin_silico_fate_mappingimportFateMappingWidgetimage_path="http://public.czbiohub.org/royerlab/zebrahub/imaging/single-objective/ZSNS001_tail.ome.zarr"tracks_path="http://public.czbiohub.org/royerlab/zebrahub/imaging/single-objective/ZSNS001_tail_tracks.csv"viewer=napari.Viewer()viewer.window.add_dock_widget(FateMappingWidget(viewer))viewer.open(image_path,plugin="napari-ome-zarr")tracks=pd.read_csv(tracks_path)viewer.add_tracks(tracks[["TrackID","t","z","y","x"]])viewer.add_points(name="Markers")napari.run()CitingIf used please cite:@article{lange2023zebrahub,
  title={Zebrahub-Multimodal Zebrafish Developmental Atlas Reveals the State Transition Dynamics of Late Vertebrate Pluripotent Axial Progenitors},
  author={Lange, Merlin and Granados, Alejandro and VijayKumar, Shruthi and Bragantini, Jordao and Ancheta, Sarah and Santhosh, Sreejith and Borja, Michael and Kobayashi, Hirofumi and McGeever, Erin and Solak, Ahmet Can and others},
  journal={bioRxiv},
  pages={2023--03},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}IssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: insilicosv
insilicoSV (WIP): a framework for structural variant simulationOverviewinsilicoSV generates synthetic diploid genome sequences, given a reference genome and a configuration file.It supports the following functionality:19 types of structural variants (simple and complex), indels, and SNPsa grammar to define custom structural rearrangement signaturesrandom, context-aware (e.g., in repeat regions), or fixed-mode genome placementInstallationPrerequisite: Python 3.8 and up -InstallInstallation using pip:$ pip install insilicosvInstallation using conda:Install and configurebiocondaInstall insilicosv withconda install insilicosvTo RunThe recommended workflow for running insilicoSV is as follows:Create a new directoryPopulate a simulation config file and place it in the directoryRun insilicoSV providing the config file as input:insilicosv <config_yml>Results will be produced in this directoryThe config file syntax is detailed in theInput guidelinessection of the
documentation.DocumentationFor documentation outlining the different features of insilicoSV along with usage examples and data resources, please refer to the following sections:Input guidelinesExample Use CasesSV GrammarExample SV VisualizationsTutorial Jupyter notebookAuthorsChris Rohlicek -crohlice@broadinstitute.orgNick Jiang -nickj@berkeley.eduIlya Shlyakhter -ilya@broadinstitute.orgVictoria Popic -vpopic@broadinstitute.org
Package: insipid-sphinx-theme
Quick StartInstall the Python packageinsipid-sphinx-themeAddhtml_theme = 'insipid'to yourconf.pyRun Sphinx!Online documentation (and example of use)https://insipid-sphinx-theme.readthedocs.io/Source code repository (and issue tracker)https://github.com/mgeier/insipid-sphinx-theme/LicenseBSD-2-Clause (same as Sphinx itself),
for more information take a look at theLICENSEfile.SVG icons fromFont Awesome,CC BY 4.0.
Package: inskrib
✒️ InskribInskrib - маленькая библиотека для получения подписей из документов с печатью. Для документов без печати она также сработает, но не всегда корректно))🛠️ Инструменты:PythonOpenCV🚀 Установка:pip install inskribЗапускаем, не работет, ура! 🗿🚬📺 Демо:Пример использования -example⚡ Немного дополнительной информации:У библиотеки есть ограничения:библиотека не может автоматически переворачивать документы, но для этого есть отдельный метод в Autographsбибилиотека плохо работает с документами, в которых есть несколько подписей в разных частях документа, или если есть текст такого же цвета что и подписьесли печать имеет много зазоров или она обрезана, то метод удаления печати может не сработать или сработать плохоP.S. Все баги и недочеты - это фичи
Package: insnail_ai_tools
Insnail Ai Tools蜗牛ai团队 python的一些小工具，自用Installpipinstall--upgradeinsnail-ai-tools如果使用了豆瓣、阿里等非官方源，可能出现未同步，使用以下方式安装最新版本:pipinstall-ihttps://pypi.org/simple--upgradeinsnail-ai-toolsrequires-extra本模块依赖的包相对较多，所以对需要安装额外依赖包的模块做了若干拆分。
比如如果只需要oss上传的功能，只需要类似如下的安装：pipinstall--upgrade"insnail_ai_tools[oss]"如果只需要fastapi相关的模块：pipinstall--upgrade"insnail_ai_tools[fastapi]"如果同时需要fastapi和sso相关的模块：pipinstall--upgrade"insnail_ai_tools[fastapi,sso]"目前有的模块为:oss: 使用阿里云上传功能sso: 登录验证模块fastapi: fastapi相关模块django: django相关模块flask: flask 相关模块wechat: 跟微信调用相关的模块development# 拉取代码gitclonehttps://git.woniubaoxian.com/ai/insnail_ai_tools.git# 安装flitpipinstallflit==3.0.0# 安装开发环境flitinstall--deps=develop
Package: insolater
A simple version control system for small personal projects.
insolater has an easy to use interface to save, open, and transfer versions
of your work.Installationpip install insolaterExamplesIn a python script:importinsolaterinsolater.init()insolater.save_version('v1')insolater.change_version('v1')# Modify some filesinsolater.push_version('user@host:path_to_dir_for_version')insolater.pull_version('user@host:path_to_dir_for_version','pulled_ver')insolater.current_version()insolater.change_version('original')# Changes are not present.insolater.change_version('pulled_ver')# Changes are back.insolater.delete_version('v1')insolater.all_versions()insolater.exit(True)# .insolater_repo is deleted and files are in their original condition.Running from command line:$ ls *
fa  fb  test_scipt.sh

d:
fa  fc
$ inso init
Initialized repository with versions: original
$ inso list
* original
$ echo data > f
$ rm fb
$ echo data >> fa
$ echo data >> d/fa
$ inso save changes
Version changes created and opened
$ ls *
f  fa  test_scipt.sh

d:
fa  fc
$ inso open original
Switched to original
$ ls *
fa  fb  test_scipt.sh

d:
fa  fc
$ cat fa
old data a
$ cat d/fa
old data da
$ inso open changes
Switched to changes
$ ls *
f  fa  test_scipt.sh

d:
fa  fc
$ cat fa
old data a
data
$ cat d/fa
old data da
data
$ cat f
data
$ ls ~/test_changes
$ inso save changes2
Version changes2 created and opened
$ inso list
  original
* changes2
  changes
$ inso open changes
Switched to changes
$ inso rm changes2
Version changes2 deleted
$ inso list
  original
* changes
$ inso push $USER@localhost:~/test_changes/
user@localhost's password:
f     transfered
fa    transfered
d     transfered
test_scipt.sh     transfered

$ inso exit
Do you want to discard all changes (y/[n]): y
Session Ended
$ ls ../test_changes/ ../test_changes/d
../test_changes/:
d  f  fa  test_scipt.sh

../test_changes/d:
fa  fc
$ ls *
fa  fb  test_scipt.sh

d:
fa  fc
$ cat d/fa
old data da
$ inso init $USER@localhost:~/test_changes/
user@localhost's password:
Initialized repository with versions: original
$ inso list
* original
$ ls *
f  fa  test_scipt.sh

d:
fa  fc
$ cat d/fa
old data da
data
$ inso -f exit
Session Ended
$ cat d/fa
old data da
Package: insolver
InsolverInsolver is a low-code machine learning library, originally created for the insurance industry, but can be used in any other. You can find a more detailed overviewhere.Installation:Insolver can be installed via pip from PyPI. There are several installation options available:DescriptionCommandRegular installationpip install insolverInstallation with all heavy requirementspip install insolver[full]Installation with development requirementspip install insolver[dev]Insolver is already installed in the easy access cloud via the GitHub login. Tryhttps://mset.spacewith a familiar notebook-style environment.Examples:Binary Classification Example - Rain in Australia PredictionThis tutorial demonstrates how to createclassification modelsfor theweatherAUSdataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.Data Preprocessing Example I - New York City AirbnbThis tutorial demonstrates how to use thefeature_engineeringmodule and all themain features of each class. For this, theAB_NYC_2019dataset is used.Data Preprocessing Example II - New York City AirbnbThis tutorial also demonstrates how to use thefeature_engineeringmodule, but it covers theautomated data preprossesingclass and all of its features. For this, theAB_NYC_2019dataset is used.Gradient Boosting Example - Lending ClubThis tutorial demonstrates how to createclassification modelsfor theLending Clubdataset using theGradient Boosting librariesand theInsolverGBMWrapperclass.Transforms Inference ExampleThis tutorial demonstrates how to loadInsolverTransformtransforms from a file using theload_transformsfunction.InsolverDataFrame and InsolverTransform ExampleThis tutorial demonstrates main features of theInsolverDataFrameclass and theInsolverTransformclass.Regression Example - FreeMLPThis tutorial demonstrates how to createregression modelsfor thefreMPL-Rdataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.Regression Example - US AccidentsThis tutorial demonstrates how to createregression modelsfor theUS Traffic Accidentdataset: getting and preprocessing data, transformations, creating models, plotting SHAP values and comparing models.Report ExampleThis tutorial demonstrates how to create aHTML reportwith different models using theReportclass.Documentation:AvailablehereSupported libraries:GLMBoosting modelsServing (REST-API)Model interpretation- sklearn- h2o- XGBoost- LightGBM- CatBoost- Flask- FastAPI- Django- shap plotsRun tests:python-mpytesttests with coverage:python-mpytest--cov=insolver;coveragehtml;xdg-openhtmlcov/index.htmlContributing to Insolver:Please, feel free to open an issue or/and suggest PR, if you find any bugs or any enhancements.DemoExample of creating models using the InsolverExample of a model production serviceExample of an elyra pipeline built with the Insolver insideContactsfrank@mind-set.ru+79263790123
Package: insoundz-api
insoundz-api PackageInsoundz API client implementation to produce audio enhancement.InstallationPlease make sure you are running python3.7 or later.pip install insoundz-apiGetting started: Audio file enhancementSending the original file for audio enhancement processing and download the enhanced file to our local machine.frominsoundz_api.enhancerimportAudioEnhancerenhancer=AudioEnhancer(client_id="my_client_id",secret="my_secret")enhancer.enhance_file(src="/home/example_user/my_audio_files/example.wav",dst="/home/example_user/my_enhanced_files_dir")$cd/home/example_user/my_enhanced_files_dir$lsexample_file_enhanced.wav
Package: inspace
# inspace-------Jump into space with Nasa's photo of the day, simply type:>>> inspaceit will fetch the image of the day from Nasa's website and will make it your desktop wallpaper.if you want a random pic from the last N years you can sepcify this using:>>> inspace -rif you want a pic from a specific date you can use this:>>> inspace -d YYMMDD>>> # 2016 April 03>>> inspace -d 160403
Package: inspec
inspecVisually inspect audio files and images from the command line by displaying them as unicode characters.Tested onpng,jpg,mp4, andwavfiles.Quick startpip install inspecFrom command lineUsage: inspec [OPTIONS] COMMAND [ARGS]...

Commands:
  imshow  Print an image to stdout
  listen  Listen to audio and print to stdout
  open    Open interactive GUI
  show    Print an audio file to stdoutFrom pythonimportinspecinspec.imshow("path/to/image/file.png")inspec.ashow("path/to/audio/file.wav")inspec.vshow("path/to/video/file.mp4")inspec.listen()Dev setupgit clone git@github.com:kevinyu/inspec.git
cd inspec
python -m venv env
source bin/activate
pip install -e .You might needffmpegfor video support not sure tho.Run testspytest srcExamplesinspecshowdemo/warbling.wav
inspecimshowdemo/mandrill.jpg
Package: inspec-ai
inspec-aiThis library contains all the prototypes that were developped by the Moov AI product team.InstallationTo add inSpec to your project, you can run the following command:pip install inspec-aiNOTE: inspec-ai was developed using Python 3.9.DocumentationFor more information on each specific function, please consult their specific documentation page in thedocs/folder on the repository.LicenseCopyright (c) 2022 MoovAI Technologies Inc.Permission is hereby granted, free of charge, to any person or organization
obtaining a copy of this software and associated documentation files
(the "Software"), to use, copy and distribute the Software within the
internal operations of aforementioned person or organization. It is
strictly forbidden to publish, externally distribute, sublicense, lease or
sell copies or use of the Software or any derived work, without explicit
authorization from MoovAI Technologies Inc.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: inspect2
inspect2A backport of the Python 3.6 inspect module to Python 2.7-3.5.This module was based on the code of the inspect module and its tests in the Python 3.6
standard library and modified to pass tests on earlier Python versions.I have tried to follow these rules in writing the code:Minimize changes relative to the 3.6 source. This should make backporting of future
changes easier.No support for old-style classes. Python 2 is hard enough without them.All functions should be available on all versions.inspect2.iscoroutinefunction()just always returns False on Python versions that don't haveasync def.
Package: inspecta
inspectaA colorized object pretty printer - for Python.IntroductionThe defaultpprintis not human friendly enough - color syntax highlighting to the rescue.InstallInstall usingpip:$pipinstallinspectaUseVery basicexample:importinspectadata={'foo':{'bar':{'baz':[{'a':1,'b':'two','c':('three',4,'five')}]}},'bar':[1,2,3],'baz':True}print('\n.inspect(data)\n')print(inspecta.inspect(data))print('\n.print(data)\n')inspecta.print(data)Run this with optional environment variablesCOLORS/ERROR_COLORSset too truthy or falsy values, so see various error info formatting in terminal.Something like this (imagine some colorized formatting):.inspect(data){'bar':[1,2,3],'baz':True,'foo':{'bar':{'baz':[{'a':1,'b':'two','c':('three',4,'five')}]}}}.print(data){'bar':[1,2,3],'baz':True,'foo':{'bar':{'baz':[{'a':1,'b':'two','c':('three',4,'five')}]}}}TestClone down source code:$makeinstallRuncolorful tests, with only native environment (dependency sandboxing up to you):$maketestRunless colorful tests, withmulti-environment(usingtox):$maketest-toxAboutThis project was mainly initiated - in lack of solid existing alternatives - to be used at our work atMarkable.aito have common code conventions between various programming environments wherePython(research, CV, AI) is heavily used.LicenseReleased under the MIT license.
Package: inspectcall
The functionsupdate_wrapperandwrapsimprove onfunctools.update_wrapperandfunctools.wrapsby persisting information
about function signatures. The functionget_argspecis similar toinspect.get_argspec, except it first tries to use the metadata provided
byupdate_wrapper.The functionget_callargsis similar toinspect.getcallargs, except
thatit can access the metadata created byupdate_wrapperit does not raise when a call is illegal but rather makes a best
guess, which makes it suitable for logging and error handling in
decorators.
Package: inspect-dense
inspect-dense byAI Modelsinspect-dense is a Python package for recursively describing all python files in a directory.
Provides a simple way to get a high-level overview of a project's codebase in a conscise format to minimize tokens used when developing code with AI systems.InstallationYou can install inspect-dense using pip:Copy codepip install inspect-denseUsageTo use inspect-dense, simply import thedescribe_directoryfunction from the package and pass it the directory path you want to describe:python codefrominspect-denseimportdescribe_directorydirectory="/path/to/directory"output=describe_directory(directory)Thedescribe_directoryfunction returns a dictionary with the following structure:{"/path/to/file.py":{"functions":{"function_name(args)":"Function docstring"},"classes":{"Class1":{"methods":{"method1(args)":"Method docstring","method2(args)":"Method docstring"}},"Class2":{"methods":{"method3(args)":"Method docstring","method4(args)":"Method docstring"}}}}}Command Line Interfaceinspect-dense also comes with a command line interface. You can use it to describe a directory and output the results to a file:inspect-dense <directory> [--no-gitignore]For example:inspect-dense /path/to/directory --no-gitignoreThis will output the results.LicenseThis project is licensed under the Apache 2 License - see theLICENSEfile for details.