Saravanan666/pypi-org huggingface dataset:
Package: imap-processing
IMAP (Interstellar Mapping and Acceleration Probe)The Interstellar Mapping and Acceleration Probe (IMAP) is an exciting project aimed at studying the interstellar medium and investigating the acceleration mechanisms of particles within our galaxy. IMAP utilizes cutting-edge technology and advanced instrumentation to gather valuable data and expand our understanding of space.The IMAP project's Git repository serves as a centralized hub for collaboration among researchers, scientists, and developers involved in the project. It provides a platform for version control, code sharing, issue tracking, and documentation, fostering a collaborative environment for advancing the goals of the IMAP mission.Through the Git repository, contributors can access project resources, explore the codebase, propose enhancements or bug fixes, and participate in discussions with other team members. This collaborative approach promotes efficient development and fosters innovation in the pursuit of interstellar mapping and particle acceleration research.Join the IMAP Git repository to be part of an exciting scientific endeavor, contribute your expertise, and help unlock the mysteries of the interstellar medium and particle acceleration in our galaxy. Together, we can advance our knowledge of the cosmos and pave the way for future discoveries in space exploration.IMAP WebsiteIMAP Processing DocumentationCreditsLASP (Laboratory of Atmospheric and Space Physics)Contributors ✨Thanks goes to these wonderful people (emoji key):Greg Lucas💻📖🤔🚇🚧👀Tenzin Choedon💻🤔🚇💡🚧👀📖Laura Sandoval👀💻🚇🤔Sean Hoyt💻🤔🚇👀📖🚧Gabriel M.💻🤔🚇👀🚧📖Matthew Bourque💻📖🤔🚇👀🚧Maxine Hartnett💻🤔🚇👀📖🚧Bryan Harter💻📖mstrumik👀This project follows theall-contributorsspecification. Contributions of any kind welcome!
Package: imaprelay
imaprelayis a simple tool designed to scratch a very specific itch.
Presented with an institutional email account that he had an obligation
to check, but lacking desire to use the Outlook Web Access interface, the
author was dismayed to find that he was unable to forward his email.The solution isimaprelay, a python program that logs into an IMAP
account and relays emails from the Inbox to a specified email address,
via an SMTP server. Once relayed, emails are “archived” – moved out of
the inbox into a different folder.Although usable programmatically[1], it is expected that most people
will  want to use theimaprelaycommand-line tool that this package
provides.By default, this program will look for a configuration file in~/.secret/imaprelay.cfg– its location should indicate that it needs
to contain plain-text passwords for IMAP and SMTP servers, and thus the
program will exit immediately if the file is group- or world-readable.The available configuration options are listed below:# IMAP server connection details
[imap]
hostname=imap.exchange.megacorp.com
username=j.bloggs
password=123password

# SMTP server connection details
[smtp]
hostname=mail.recipient.com
username=joe_bloggs
password=passw0rd

# Relay configuration
[relay]
# Who should we relay the emails to?
to=onward@recipient.com
# Where should we look for emails to be relayed?
inbox=INBOX
# Where should we move the emails once successfully relayed?
archive=ArchiveOnce you’ve written a config file, all you need to do is run:imaprelayFor verbose logging, use:imaprelay -vBug reportingPlease report bugson Github.[1]See theimaprelay.Relayclass.
Package: imap-storage
imap-storageUse your Email-account as Storage for data structures and filesGetting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Have a look at thedocumentation.Introducing notesBefore reaching version 1.0, the storage layout may change when updatingPrerequisitespython3.5 or later.
It should work Python3.4+ but tests are running on 2.7, 3.5 and 3.7.This project makes use of the following libraries:IMAPClient- easy-to-use, Pythonic and complete IMAP client library.lxml- the most feature-rich and easy-to-use library for processing XML and HTML in the Python languageInstallingYou can install the latest release from pip:pip install imap-storageShort examplefromimap_storageimportAccount,Configconfig=Config()config.imap.user='email@example.com'config.imap.password='123'config.imap.host='imap.example.com'# config.imap.port default is 993account=Account(config,1)directory=account.storage.directory_by_path(account.config.directory)email=directory.new_email('Your_first_item')email.add_item('TestMessage',text='Your first message')email.save()email.delete()account.close()Running the testsRename 'secrets.sample.py' in tests directory to 'secrets.py' and include your e-mail account for testing.
Then run this inside root directory:python -m unittest  # tests all
python -m unittest tests.test_account  # tests only accountor run it with coverage:coverage run --source='imap_storage' -m unittest && coverage report -m --skip-coveredAnd coding style testsCode style is not finished, mostly because of missing docstrings.pylint imap_storageDeploymentThis library is not ready to be deployed productiveContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.VersioningWe useSemVerfor versioning.
During beta development minor versions may be incompatible, too.AuthorsMe-Initial work-Snake-SoftSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the GNU General Public License v3.0 - see theLICENSE.mdfile for details
Package: imarac
imaracimarac(forimageratiochecker) is a CLI tool developed to check the ratio of an image. It's really useful to print photo with the good ratio!DescriptionThis tool is useful to walk into a directory and check the ratio of every image file.Dependenciesclickto easily create a CLIrichto create a beautiful CLIfiletype.pyto detect file typeimagesizeto detect image sizeInstallationpip install imaracUsageUsage: imarac [OPTIONS]Options:-d, --dir, --directory DIRECTORY  Directory to check.  [required]-r, --ratio FLOAT RANGE           Image desired ratio.  [x>0; required]-v, --verbose                     Add more information during processing.--help                            Show this message and exit.ExampleFor a normal use:imarac -d my-holyday-pictures -r 1.5📂 /path/to/my-holyday-pictures┣━━ 📂 thecat┗━━ 📂 nature┗━━ beautifulmontain.jpg (ratio: 1.3)With verbose option:imarac -d my-holyday-pictures -r 1.5 -v📂 /path/to/my-holyday-pictures┣━━ 📂 thecat┃   ┣━━ mycat.jpg (ratio: 1.5)┃   ┗━━ mycatwithme.jpg (ratio: 1.5)┗━━ 📂 nature┣━━ awesomecliff.jpg (ratio: 1.5)┣━━ beautifulmontain.jpg (ratio: 1.3)┗━━ infinitesea.jpg (ratio: 1.5)
Package: imas2xarray
imas2xarrayImas2xarrayis a library that makes it as simple and intuitive as possible to read and write IMAS data in HDF5 format with Python. There is no need to manually define the paths or fiddle with the different dimensions and long keys.Reading>>>fromimas2xarrayimportto_xarray>>>>>>path='/pfs/work/g2aho/public/imasdb/test/3/92436/1/'>>>ids='equilibrium'>>>>>>ds=to_xarray(path,ids)>>>ds<xarray.Dataset>Dimensions:(time:1,rho_tor_norm:101,ion:4)Coordinates:*time(time)float6450.04*rho_tor_norm(rho_tor_norm)float640.00.010.020.03...0.980.991.0Dimensionswithoutcoordinates:ionDatavariables:(12/14)q(time,rho_tor_norm)float640.78870.7888...4.2624.845collisionality(time,rho_tor_norm)float6423.237.554...7.3110.19t_i_ave(time,rho_tor_norm)float641.036e+041.036e+04...508.4......n_e(time,rho_tor_norm)float647.976e+19...1.742e+19p_i(time,ion,rho_tor_norm)float641.242e+05...0.2948n_e_tot(time,rho_tor_norm)float647.976e+19...1.742e+19Writing>>>fromimas2xarrayimportto_imas>>>>>>ds['t_e']*=1.1>>>>>>to_imas(>>>path,>>>dataset=ds,>>>ids=ids>>>)For more advanced use-cases and examples, please see thedocumentation.Installing imas2xarrayTo install:pip install imas2xarrayImas2xarray is supported on Python versions 3.9 or newer.DevelopmentCheck out ourContributing Guidelinesto get started with development.Suggestions, improvements, and edits are most welcome.
Package: ima-search
https://user-images.githubusercontent.com/90014847/235641562-67a09617-c1b6-4438-a38c-d1533326a43c.mp4imaImage searcher and downloaderIntroductionInstallationDocumentationLicenseAuthorsima is an extremely tiny python library command line utility which permits you to query search engines(e.g DuckDuckGo)
and download images from web pages.ima might be a utility of choice for the following purpose:Downloading images from the internet to build up your datasetSearching on the internet with Google, DuckDuckGo or Yahoo. You would want to
use multiple search engines to obtain better results.Caching your search results to later on use them locally.Installationpip install ima-searchDocumentationRead the documentation fromhereLicenseima is licensed under GPLv3AuthorsKueppo Tcheukam,tcheukueppo@tutanota.com
Package: imate
imate, short forImplicitMatrixTraceEstimator, is a modular and high-performance C++/CUDA library distributed as a Python package that provides scalable randomized algorithms for the computationally expensive matrix functions in machine learning.LinksDocumentationPyPIAnacondaDocker HubGithubInstallInstall withpippip install imateInstall withcondaconda install -c s-ameli imateDocker Imagedocker pull sameli/imateSupported PlatformsSuccessful installation and tests performed on the following operating systems, architectures, and Python andPyPyversions:PlatformArchDevicePython VersionPyPy Version1Continuous
Integration3.93.103.113.123.83.93.10LinuxX86-64CPU✔✔✔✔✔✔✔GPU✔✔✔✔✔✔✔AARCH-64CPU✔✔✔✔✔✔✔GPU✔✔✔✔✔✔✔macOSX86-64CPU✔✔✔✔✔✔✔GPU2✖✖✖✖✖✖✖ARM-64CPU✔✔✔✔✖✔✔GPU2✖✖✖✖✖✖✖WindowsX86-64CPU✔✔✔✔✖✖✖GPU✔✔✔✔✖✖✖Python wheels forimatefor all supported platforms and versions in the above are available throughPyPIandAnaconda Cloud. If you needimateon other platforms, architectures, and Python or PyPy versions,raise an issueon GitHub and we build its Python Wheel for you.1. Our wheels for PyPy are exclusively available through pip and cannot be installed using conda.2. MacOS does not natively support NVIDIA GPUs.Supported GPU Architecturesimatecan run on CUDA-capablemulti-GPU devices. Using thedocker containeris the easiest way to runimateon GPU devices. The supported GPU micro-architectures and CUDA version are as follows:Version \ ArchFermiKeplerMaxwellPascalVoltaTuringAmpereHopperCUDA 9✖✖✖✖✖✖✖✖CUDA 10✖✔✔✔✔✔✔✔CUDA 11✖✖✖✔✔✔✔✔CUDA 12✖✖✖✔✔✔✔✔DocumentationSeedocumentation, including:What This Packages Does?Comprehensive Installation GuideHow to Work with Docker Container?How to Deploy on GPU Devices?API ReferenceInteractive Notebook TutorialsPublicationsPerformanceimateis scalable tovery large matrices. Its core library for basic linear algebraic operations isfaster than OpenBLAS, and itspseudo-random generatoris a hundred-fold faster than the implementation in the standard C++ library.Read about the performance ofimatein practical applications:Performance on GPU FarmComparison of Randomized AlgorithmsComparison With and Without OpenBLASInterpolation of Affine Matrix FunctionsHow to ContributeWe welcome contributions viaGitHub’s pull request. If you do not feel comfortable modifying the code, we also welcome feature requests and bug reports asGitHub issues.How to CiteIf you publish work that usesimate, please consider citing the manuscripts availablehere.LicenseThis project uses aBSD 3-clause license, in hopes that it will be accessible to most projects. If you require a different license, please raise anissueand we will consider a dual license.
Package: imaugtools
imaugtoolsimaugtools contains tools used for image augmentation: translate, rotate, crop. This library is for you if you do NOT want to stretch or skew, or pad pixels that would make your images look strange when doing any of these operations.InstallationTo install imaugtools, simply run in your terminalpipinstallimaugtoolsUsageExample imageTranslationfromimaugtoolsimporttranslate_imagemy_image_translated=translate_image(my_image,0.1,0.2)my_image_translated_uncropped=translate_image(my_image,0.1,0.2,crop=False)imshow(my_image_translated,my_image_translated_uncropped,mode='BGR')imshowfunction used here is to simply display the images, you can use it fromimshowtoolslibrary.With and without crop:Rotationfromimaugtoolsimportrotate_imagemy_image_rotated=rotate_image(my_image,30)# angle in degreesmy_image_rotated_uncropped=rotate_image(my_image,30,crop=False)imshow(my_image_rotated,my_image_rotated_uncropped,mode='BGR')With and without crop:Croppingfromimaugtoolsimportcenter_crop,crop_around_centermy_image_center_cropped=center_crop(my_image,(150,200))my_image_cropped_around_center=crop_around_center(my_image,(150,200))imshow(my_image_center_cropped,my_image_cropped_around_center,mode='BGR')Aspect ratio preserving center crop and crop around center:print(my_image_center_cropped.shape,my_image_cropped_around_center.shape)# Output: (150, 200, 3) (150, 200, 3)Advanced UsageStrided TranslationStrided translation is one of the powerful image augmentation techniques used in training neural networks.tx_max = 1andty_max = 1is equivalent to a stride of 1 in both directions. After you specifytx_max, you can specifytx(translation in x-axis) from -tx_maxto +tx_max. The same applies totyandty_max.my_images_translated=[]forjinrange(-1,2):foriinrange(-1,2):my_images_translated.append(translate_image(my_image,i,j,tx_max=1,ty_max=1))imshow(*my_images_translated,mode='BGR')tx_max = 0.5andty_max = 0.5is equivalent to a stride of 0.5 in both directionsmy_images_translated=[]forjinrange(-2,3):foriinrange(-2,3):my_images_translated.append(translate_image(my_image,i/4,j/4,tx_max=0.5,ty_max=0.5))imshow(*my_images_translated,mode='BGR')ContributingPull requests are very welcome.Fork the repoCreate new branch with feature name as branch nameCheck if things work with a jupyter notebookRaise a pull requestLicencePlease see attachedLicence
Package: imax
imaxImage augmentation library for Jax.InstallationpipinstallimaxUsagefromjaximportrandomimportjax.numpyasjnpfromPILimportImagefrommatplotlibimportpyplotaspltfrommpl_toolkits.axes_grid1importImageGridfromimaximporttransforms,color_transforms,randaugment# Setuprandom_key=random.PRNGKey(32)random_key,split_key=random.split(random_key)image=jnp.asarray(Image.open('./test.jpeg').convert('RGBA')).astype('uint8')# Geometric transforms:transform=transforms.rotate(rad=0.7)# create transformation matrixtransformed_image=transforms.apply_transform(image,# apply transformationtransform,mask_value=jnp.array([0,0,0,255]))# multiple transformations can be combined through matrix multiplication# this makes multiple sequential transforms much fastermulti_transform=transform@transform@transformmulti_transformed_image=transforms.apply_transform(image,multi_transform,mask_value=-1)# Color transforms:adjusted_image=color_transforms.posterize(image,bits=2)# Randaugment:randomized_image=randaugment.distort_image_with_randaugment(image,num_layers=3,# number of random augmentations in sequencemagnitude=10,# magnitude of random augmentationsrandom_key=split_key)# Show results:results=[transformed_image,multi_transformed_image,adjusted_image,randomized_image]fig=plt.figure(figsize=(10.,10.))grid=ImageGrid(fig,111,nrows_ncols=(2,2),axes_pad=0.1)forax,iminzip(grid,results):ax.axis('off')ax.imshow(im)plt.show()
Package: imaxt-mosaic
IMAXT Mosaic PipelineThe IMAXT mosaic pipeline produces stitched images from tile observations.
Currently we support two instruments: Serial Two-Photon Tomography (STPT)
and AXIOScan.Note that raw data (typically in the form of TIFF images) need to be converted
to Zarr format first using e.g.stpt2zarroraxio2zarr.Pipeline Definition FileIn order to run the pipeline you need a YAML file as follows:version: 1

name: imaxt-mosaic

input_path: /storage/imaxt/raw/stpt/20211020_PDX_STG139_GFP_029621_100x15um
output_path: /storage/imaxt/processed/stpt/20211020_PDX_STG139_GFP_029621_100x15um

stitcher: stpt

recipes:
  - calibration
  - mosaic
  - beadfit

resources:
  cpu: 16
  workers: 1
  memory: 48Execute locally with OwlIn order to run the pipeline locally, install as follows:pip install imaxt-mosaic owl-pipeline-clientand execute:owl execute pipeline.yamlRemote executionTo execute the pipeline using a remote Owl server it is not needed to install
the module locally. Just submit as:owl submit pipeline.yaml
Package: imaxt-multiscale-plugin
IMAXT multiscale napari pluginA napari plugin to visualize multi-resolution images created with the IMAXT mosaic pipeline.InstallationYou can installimaxt-multiscale-pluginviapip:pip install imaxt-multiscale-pluginUsageRunnapariwith the name of the sample to visualize either a local path:napari /storage/imaxt/eglez/processed/stpt/20220606_PDX_AB559_GFP_005503_100x15umor a sample in S3 storage:napari s3://imaxtgw/stpt/20220608_DI_PDX_SA535_Tum_5223_04280_100x15umScreenshotsContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theGNU LGPL v3.0license,
"imaxt-multiscale-plugin" is free and open source softwareIssuesIf you encounter any problems, please [file an issue] along with a detailed description.
Package: imaxt-registration-tools
IMAXT Registration ToolsInstall InstructionsCreate virtual environmentpython -m venv ~/Python/registrationActivate environmentsource ~/Python/registration/bin/activateInstall with pippip install -U imaxt-registration-toolsUsageusage: imaxtreg axio2stpt [-h] --stpt STPT --axio AXIO --output OUTPUT --pdf PDF [--verbose] [--nprocs NPROCS]
                          [--sections SECTIONS]

optional arguments:
  -h, --help           show this help message and exit
  --stpt STPT          Full path to STPT dataset
  --axio AXIO          Full path to AXIO dataset
  --output OUTPUT      Destination of output correspondence table (csv, parquet, npy, pkl)
  --pdf PDF            Destination of outout PDF quality control file
  --verbose            Verbose output
  --nprocs NPROCS      Number of processes to run in parallel (default 6)
  --sections SECTIONS  Comma separated list of AXIO sections to do (default is all)
  --nblobs NBLOBS      Maximum number of blobs for mask detection (default 1)
  --blobsize BLOBSIZE  Relative blob size for mask detection (default 0.01)To run, e.g.imaxtreg axio2stpt --axio /storage/processed/axio/20220208_MPR_NSG_GFP_4T1_TdTOM_inflated_lung_Day21_200x15um_Axio \
                   --stpt /storage/processed/stpt/20220208_MPR_NSG_GFP_4T1_TdTOM_inflated_lung_Day21_200x15um \
                   --out 20220208_MPR_NSG_GFP_4T1_TdTOM_inflated_lung_Day21_200x15um.csv \
                   --pdf 20220208_MPR_NSG_GFP_4T1_TdTOM_inflated_lung_Day21_200x15um.pdf \
                   --verbose
Package: imbaedit
imbaeditImage Batch EditorInstallationpip install imbaeditUsageimbaedit {files to edit}Adjust available sliders to change filters and update the preview of the first image. PressRunto apply the filters to all images.Original images will be overwritten. It is recommended to not use this program on your original images or to have a backup of you images available in case of error.Referencehttps://pysimplegui.readthedocs.io/en/latest/call%20reference/https://pillow.readthedocs.io/en/5.1.x/reference/index.html
Package: imbalanced-learn
imbalanced-learnimbalanced-learn is a python package offering a number of re-sampling techniques
commonly used in datasets showing strong between-class imbalance.
It is compatible withscikit-learnand is part ofscikit-learn-contribprojects.DocumentationInstallation documentation, API documentation, and examples can be found on thedocumentation.InstallationDependenciesimbalanced-learnrequires the following dependencies:Python (>= 3.8)NumPy (>= 1.17.3)SciPy (>= 1.5.0)Scikit-learn (>= 1.0.2)Additionally,imbalanced-learnrequires the following optional dependencies:Pandas (>= 1.0.5) for dealing with dataframesTensorflow (>= 2.4.3) for dealing with TensorFlow modelsKeras (>= 2.4.3) for dealing with Keras modelsThe examples will requires the following additional dependencies:Matplotlib (>= 3.1.2)Seaborn (>= 0.9.0)InstallationFrom PyPi or conda-forge repositoriesimbalanced-learn is currently available on the PyPi’s repositories and you can
install it viapip:pip install -U imbalanced-learnThe package is release also in Anaconda Cloud platform:conda install -c conda-forge imbalanced-learnFrom source available on GitHubIf you prefer, you can clone it and run the setup.py file. Use the following
commands to get a copy from Github and install all dependencies:git clone https://github.com/scikit-learn-contrib/imbalanced-learn.git
cd imbalanced-learn
pip install .Be aware that you can install in developer mode with:pip install --no-build-isolation --editable .If you wish to make pull-requests on GitHub, we advise you to install
pre-commit:pip install pre-commit
pre-commit installTestingAfter installation, you can usepytestto run the test suite:make coverageDevelopmentThe development of this scikit-learn-contrib is in line with the one
of the scikit-learn community. Therefore, you can refer to theirDevelopment Guide.AboutIf you use imbalanced-learn in a scientific publication, we would appreciate
citations to the following paper:@article{JMLR:v18:16-365,
author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
journal = {Journal of Machine Learning Research},
year    = {2017},
volume  = {18},
number  = {17},
pages   = {1-5},
url     = {http://jmlr.org/papers/v18/16-365}
}Most classification algorithms will only perform optimally when the number of
samples of each class is roughly the same. Highly skewed datasets, where the
minority is heavily outnumbered by one or more classes, have proven to be a
challenge while at the same time becoming more and more common.One way of addressing this issue is by re-sampling the dataset as to offset this
imbalance with the hope of arriving at a more robust and fair decision boundary
than you would otherwise.You can refer to theimbalanced-learndocumentation to find details about
the implemented algorithms.
Package: imbdata
Balance_libraryEsta es una libreria de python que proporciona herramientas para el preprocesamiento y equilibrio de datos desequilibrados.Las caracteristicas que tiene esta libreria son las siguientes:Preprocesamiento de datos: Limpieza de datos eliminando filas con valores faltantes (NAs).Equilibrio de datos: Ofrece métodos para equilibrar conjuntos de datos, incluyendo undersampling, oversampling y una combinación de ambas estrategias.Generación de datos sintéticos: Permite la generación de datos sintéticos para abordar problemas de oversampling utilizando técnicas como CTGANSynthesizer.