Saravanan666/pypi-org huggingface dataset:
Package: irisml-tasks-azureml
irisml-tasks-azuremlThis package is a part of IrisML pipeline. It provides utility scripts and tasks to work with AzureML.Installationpipinstallirisml-tasks-azuremlAvailable commandsusage:irisml_run_aml[-h][--envENV][--no_cache][--no_cache_read][--include_local_tasks[INCLUDE_LOCAL_TASKS]][--custom_packagesCUSTOM_PACKAGES[CUSTOM_PACKAGES...]][--requirementREQUIREMENT][--extra_index_urlEXTRA_INDEX_URL][--no_wait][--compute_targetCOMPUTE_TARGET][--subscription_idSUBSCRIPTION_ID][--resourcegroupRESOURCEGROUP][--workspaceWORKSPACE][--experimentEXPERIMENT][--base_docker_imageBASE_DOCKER_IMAGE][--base_docker_image_registryBASE_DOCKER_IMAGE_REGISTRY][--no_docker_build_date_label][--use_sp_on_remote][--very_verbose]job_filepath

positionalarguments:job_filepath

options:-h,--helpshowthishelpmessageandexit--envENV,-eENV--no_cache--no_cache_read--include_local_tasks[INCLUDE_LOCAL_TASKS],-l[INCLUDE_LOCAL_TASKS]--custom_packagesCUSTOM_PACKAGES[CUSTOM_PACKAGES...],-pCUSTOM_PACKAGES[CUSTOM_PACKAGES...]--requirementREQUIREMENT,-rREQUIREMENT--extra_index_urlEXTRA_INDEX_URL--no_wait--compute_targetCOMPUTE_TARGET--subscription_idSUBSCRIPTION_ID--resourcegroupRESOURCEGROUP--workspaceWORKSPACE--experimentEXPERIMENT--base_docker_imageBASE_DOCKER_IMAGE--base_docker_image_registryBASE_DOCKER_IMAGE_REGISTRY--no_docker_build_date_label--use_sp_on_remote,--spUseServicePrincipalidandsecretontheAMLjob.--very_verbose,-vvThis command submits an experiment to a remote AzureML node.If --include_local_tasks option is used, python scripts in the current directory or the specified directory will be sent to AzureML and be loaded as custom tasks.If environment variable AZURE_TENANT_ID, AZURE_CLIENT_ID, and AZURE_CLIENT_SECRET are set, this command will use ServicePrincipalAuthentication. Otherwise, the AzureML's default authentication method will be used.If --use_sp_on_remote flag is used, the environment variables for service principal authentication will be set to the AML job. Note that those information will be visible to anyone who has read access to the job.Exampleirisml_run_amlirisml/docs/example/mobilenetv2_mnist_training.json-pirisml-tasks-torchvisionirisml-tasks-training--compute_target<cluster_name>--subscription_id<subscription_id>--workspace<workspacename>Tasksrun_azureml_childSubmit a new AzureML job as a child of the current run. Raises an exception if the current environment was not on AzureML.
Package: irisml-tasks-azure-openai
irisml-tasks-azure-openaiThis repo contains irisml tasks for Azure OpenAI APIs.ContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
contactopencode@microsoft.comwith any additional questions or comments.TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must followMicrosoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
Package: irisml-tasks-fiftyone
irisml-tasks-fiftyoneIrisML tasks for fiftyone-related integration. Using these tasks, you can view datasets and prediction results on the fiftyone interface.For the detail of fiftyone, please visittheir repository.For the detail of IrisML, please visitIrisML repository.Installationpipinstallirisml-tasks-fiftyoneTaskslaunch_fiftyoneclassInputs:dataset:torch.utils.data.Datasetpredictions:Optional[List]class_names:Optional[List[str]]classConfig:task_type:Literal['multiclass_classification','multilabel_classification','object_detection']duration:intThis task launches a fiftyone app, then sleeps for config.duration seconds. Currently Image Classification and Object Detection tasks are supported.
Package: irisml-tasks-llava
irisml-tasks-llavaThis repository containsIrisMLpackage forLLaVAmodel.Available taskscreate_llava_modelContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
contactopencode@microsoft.comwith any additional questions or comments.TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must followMicrosoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
Package: irisml-tasks-onnx
irisml-tasks-onnxThis is a package for tasks related to onnx models.Seeirisml repositoryfor the detail of irisml framework, andirisml-tasks-trainingfor tasks related to model training, prediction, and export that are referenced here. Note that these tasks are for working with already-traced onnx models, for tracing seeexport_onnxtask.Taskspredict_onnxRun prediction on a dataset with basic onnx model. Uses the same inputs and outputs as thepredict task.ONNX Format Details'Basic' ONNX FormatThe onnx format generated by theexport_onnx task. Very straightforward inference API that expects one already-preprocessed image tensor as input with a fixed size. Currently does not support dynamic image sizes or batching.ContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
contactopencode@microsoft.comwith any additional questions or comments.TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must followMicrosoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
Package: irisml-tasks-timm
irisml-tasks-timmTimm-related tasks for IrisML pipeline.Please visitIrisML repositoryfor the details.Installationpipinstallirisml-tasks-timmTaskscreate_timm_modelCreate a timm model. Please use timm.list_models() method to find available model names.classConfig:name:strnum_classes:intpretrained:bool=Falsetask_type:typing.Literal['multiclass_classification','multilabel_classification']='multiclass_classification'create_timm_transformCreate a timm transform. The task output contains train_transform and val_transform.classConfig:input_size:intcrop_pct:float=0.875# Center crop the image with this ratio.interpolation:typing.Literal['bilinear','bicubic']='bilinear'
Package: irisml-tasks-torchmetrics
irisml-tasks-torchmetricsIrisML tasks that require torchmetrics library.ContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
contactopencode@microsoft.comwith any additional questions or comments.TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must followMicrosoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
Package: irisml-tasks-torchvision
irisml-tasks-torchvisionA collections of IrisML tasks that depends on torchvision library.Seeirismlrepository for the detail.Available taskscreate_torchvision_modelCreate a vision model from the torchvision library.create_torchvision_transformCreate a transform function using the torchvision library.load_torchvision_datasetLoad a Dataset instance from the torchvision library.
Package: irisml-tasks-transformers
irisml-tasks-transformersThis is a MLTest task package for Huggingface's transformers package.For the detail of IrisML framework, visitirisml repositoryTaskscreate_transformers_modelCreate a model using transformers library. Currently only CLIPModel is supported.classConfig:name:strpretrained:bool=FalseclassOutputs:model:torch.nn.Modulecreate_transformers_tokenizerCreate a tokenizer using transformers library.classConfig:name:strclassOutputs:tokenizer:typing.Callable
Package: irismodel
Iris Model AppWelcome to this beautiful  app.Try calling it:curl -H "Content-Type: application/json" -X POST -d "[{"x1":5.1, "x2":3.5, "x3":1.4, "x4":0.2}]"https://larsk-flask.herokuapp.com/predict/curl -H "Content-Type: application/json" -X POST -d "[{"x1":5.1, "x2":3.5, "x3":1.4, "x4":0.2}, {"x1":6.2, "x2":3.4, "x3":5.4, "x4":2.3}]"https://larsk-flask.herokuapp.com/predict/blaaah
Package: irispreppy
irispreppyFor radiometrically calibrating and PSF deconvolving IRIS data.To install runpip install irispreppy.I dislike how I need to own proprietary software (IDL) just to simply prepare my data. I use Python for my analysis, why can't I radiometrically calibrate and deconvolve with it?
This has been a passion project of mine during my PhD (and beyond). The radiometric calibration keeps itself up to date with the response files by checkinghttps://hesperia.gsfc.nasa.gov/ssw/iris/response/every time it is run. If it finds new files, it downloads them before continuing.These scripts should be general purpose and "just work". No janky hacks are present.This remains untested on Mac. However, I expect it to work on UNIX-like OSes.tl;dr usageirispreppy takes a single HDU object. To calibrate and deconvolve,fromastropy.ioimportfitsimportirispreppyasipraw=fits.open("path/to/file.fits")#Raw datarc=ip.radcal(raw)#Radiometrically calibratedrc_d=ip.deconvolve(rc)#Radiometrically calibrated and deconvolvedTo calibrate and deconvolve, and save,fromastropy.ioimportfitsimportirispreppyasipraw=fits.open("path/to/file.fits")#Raw dataip.radcal(raw,save=True)#Radiometrically calibratedrc=fits.open("path/to/file_rc.fits")#Radiometrically calibrated dataip.deconvolve(rc,save=True)#Radiometrically calibrated and deconvolvedMore in depth documentation will be added in the future.AcknowledgementsThank you toDr Graham S. Kerrfor IRIS_SG_deconvolve.py and IRIS_SG_PSFs.pkl.Special thanks toDr C.M.J. Osbornefor putting up with my incessant and innane questions.Makes use of the excellent WENO4 algorithm (Janett et al. 2019) implemented in Python3by Dr C.M.J. Osborne here.
Package: irispy-lmsal
irispy-lmsalis a library that provides the tools to read in and analyze data from Interface Region Imaging Spectrograph (IRIS).IRIS is a NASA-funded Small Explorer which uses a high-frame-rate ultraviolet imaging spectrometer to make observations of the Sun.
For more information see the mission/instrument paper which is availableonline for free.The data is publicly available.Documentation is hosted on RTD
Package: irisreader
IRISreaderTheIRISreaderlibrary provides functionality to read IRIS level 2 data
by theIRIS satelliteand to process it for big data applications.
IRISreader works best with a Jupyter notebook.If you came here to look for something that provides a good python
replacement for IDL code and is well-integrated into SunPy, have a look atIRISpy.Warning:IRISreader is under constant development and a stable version cannot always be guaranteed. Documentation is available but might not reflect the newest
additions.InstallationTo install the latest stable release from pypi, runpip install irisreaderTo install the current development version, clone the repositorygit clone https://github.com/i4Ds/IRISreader.gitand runsudo python3 setup.py installto install the library.To test it go to the Python console, cd to the IRISreader directory and run:from irisreader.data import sample_sji
sji_data = sample_sji()
sji_data.plot(0)DocumentationThe documentation is availablehere.ContactFor questions, suggestions and feedback please contact me viaemail.
Package: irisrelay
Iris relayStateless reverse proxy for thirdparty service integration with Iris API.Setup dev environmentcreate & source your virtualenvrunpython setup.py developrunpip install -r dev_requirements.txtedit ./configs/config.dev.yaml to setup API credential and other settingsTestsRun tests:makeunit# unit testsmakee2e# e2e testsmaketest# all tests, e2e + unitNOTE: e2e tests requires a running API instance. You can tweak the api host,
port and key setting inconfigs/config.dev.yaml.Generate test coverage reports:makee2e-cov
makeunit-cov
Package: iris-sample-data
This repository is a store for the data used in the iris examples.See the iris repository’s INSTALL file for instructions of installation.Data found in this repository are licensed under the UK’s Open Government Licence:Documentation, example and data license© British Crown copyright, 2018.You may use and re-use the information featured in this repository (not including logos) free of
charge in any format or medium, under the terms of theOpen Government Licence.
We encourage users to establish hypertext links to this website.Any email enquiries regarding the use and re-use of this information resource should be
sent to:psi@nationalarchives.gsi.gov.uk.
Package: irisSeg
irisSegSegementation of iris and pupil.Daugman algorithm:where I(x,y) is the eye image, r is the radius to searches over the image (x,y), G(r) is a Gaussian smoothing function. The algorithm starts to search from the pupil, in order to detect the changing of maximum pixel values (partial derivative).Installationpip install irisSegUsagefrom irisSeg import irisSeg
import matplotlib.pyplot as plt
#
# you can also view using the argument in irisSeq function
#
coord_iris, coord_pupil, output_image = irisSeg('UBIRIS_200_150_R/Sessao_1/1/Img_1_1_1.jpg', 40, 70)
print(coord_iris) # radius and the coordinates for the center of iris 
print(coord_pupil) # radius and the coordinates for the center of pupil 
plt.imshow(output_image)
plt.show()use as a commandline tool>>iris-seg file_path min_radius max_radiusReferenceshttps://www.diva-portal.org/smash/get/diva2:831173/FULLTEXT01.pdfhttps://uk.mathworks.com/matlabcentral/fileexchange/15652-iris-segmentation-using-daugman-s-integrodifferential-operator
Package: iris-stage
Iris Staging ClientModified: 2021-03QuickstartSave AWS certs and IRIS machine certs to your home directory. See.aws guideand.secrets guidefor instructions.Install the latestiris-stagepackage:python3-mpipinstall--upgradepip
...
python3-mpipinstall--no-cacheiris-stageLaunch the staging client:stageHalt the client:unstageIris Stage Client DeploymentThe recommended deployment method for the iris staging client is hosted on theIncuvers/automationrepository using ansible. Follow the setup instructions and proceed with the iris staging client deployment.Client RequirementsThis client is designed to be run on an iris production machine. All the hardware and peripherals should be mounted and ready for integration testing.AWS ServicesThe AWS Python SDKboto3requires AWS user tokens and region information. To avoid passing these tokens at runtime and increasing the complexity of the build process I have required that the AWS credentials and configuration are bound to the staging client. Therefore these credentials will exist in the$HOMEdirectory as describedhereIRIS Machine CredentialsThe IRIS snap requires production grade machine credentials. I have required that the machine credentials are bound to the staging client in the$HOMEdirectory as describedhere. These credentials must be production grade as the snap is built pointing to our production api.DevelopmentA brief development guide is detailedhere
Package: iris-ued
Iris - Ultrafast Electron Scattering Data ExplorationIris is both a library for interacting with ultrafast electron
diffraction data, as well as a GUI frontend for interactively exploring
this data.Iris also includes a plug-in manager so that you can explore your data.Contents:InstallationUsageTest DataDocumentationCitationsSupport / Report IssuesLicenseInstallationTo interact withirisdatasets from a Python environment, theirispackage must be installed.irisis available on PyPI; it can be installed withpip.:python -m pip install iris-uedirisis also available on the conda-forge channel:conda config --add channels conda-forge
conda install iris-uedTo install the latest development version fromGithub:python -m pip install git+git://github.com/LaurentRDC/iris-ued.gitEach version is tested against Python 3.8+. If you are using a different
version, tests can be run using thepytestpackage.Windows InstallersFor Windows, installers are available on theReleasespage. You will still need to installirisviapiporcondato use the scripting functionality.UsageOnce installed, the package can be imported asiris.The GUI component can be launched from a command line interpreter aspython -m irisorpythonw -m iris(no console window). See the documentation
for a visual guide.DocumentationTheDocumentation on readthedocs.ioprovides API-level documentation, as well as tutorials.CitationsIf you find this software useful, please consider citing the following
publication:L. P. René de Cotret, M. R. Otto, M. J. Stern. and B. J. Siwick,An open-source software ecosystem for the interactive exploration of ultrafast electron scattering data, Advanced Structural and Chemical Imaging 4:11 (2018)DOI: 10.1186/s40679-018-0060-y.If you are using the baseline-removal functionality of iris-ued,
please consider citing the following publication:L. P. René de Cotret and B. J. Siwick,A general method for baseline-removal in ultrafast electron powder diffraction data using the dual-tree complex wavelet transform, Struct. Dyn. 4 (2017)DOI: 10.1063/1.4972518.Support / Report IssuesAll support requests and issue reports should befiled on Github as an
issue.Licenseiris is made available under the GPLv3 License. For more details, seeLICENSE.txt.
Package: iris-validation
Iris-ValidationA Python package for interactive all-in-one graphical validation of 3D protein model iterations.metricsCalculates comprehensive per-residue metrics from a model (and reflections) file.interfaceGenerates the HTML validation report.utilsA number of functions that are utilised extensively within the library and are also useful on their own.Iris-ToolsCompanion scripts for the main module that include tests, timings, and the scripts used to generate data for the metrics module.InstallationThe package can now be installed via pip, with the commandpip install iris-validation.
For now, the best way to install and run Iris is to use the Python environment that comes with CCP4, by runningccp4-python -m pip install iris-validationYou may first need to install pip, by downloading the install script fromhttps://bootstrap.pypa.io/get-pip.pyand runningccp4-python get-pip.pyThis is because Clipper-Python, upon which Iris depends, is currently available in a number of different forms, most of which are broken in some way or another. As a consequece, only the Python 2 version of Iris is available. This will be remedied soon.
Package: iris-webhooks-module
IrisWebHooksModuleAn interface module that allows to call webhooks from IRIS.Module type:ProcessorMin IRIS version required:> 1.4.0The module is not yet provided with IRIS. It can however be installed manually - please see the Installation section of this readme.ConfigurationPlease refer to theIRIS documentation.InstallationThe module can be installed manually by running the following command:Get an interactive shell on the docker :docker exec -it <iris_web_app> /bin/bashInstall the new packagepip3 install iris_webhooks_module
Package: iriusrisk-apishell-v1
API Python ModuleThis module is designed to aid calling the IriusRisk v1 APIs. Use it to create
scripts that create, call and process API calls.Besides the helper methods this module provides, it also parses the command
line and searches for configuration file entries useful for making IriusRisk
API calls. Most of the command line arguments are also configuration file
entries, thereby allowing default entries that are valid for multiple script
calls.For further information, install this module locally and run the following:python3 -c 'import iriusrisk.autoinit' --helpThis describes in greater detail how to call the program shell.Usage exampleCreate the file main.py, consisting of the following:import iriusrisk.autoinit
  from iriusrisk.v1 import *

  (resp, json) = do_get("products")
  for i in json:
      print(i["name"])Call the program from the command line:python3 main.py --key {valid API key} --subdomain {SaaS sub-domain}This will list out the names of all the projects in the specified IriusRisk
instance, available to the user of the API key.