Saravanan666/pypi-org huggingface dataset:
Package: hydrah
Hydra Validator LibraryHydrah is a validation library for Python that provides a simple and easy way to validate inputs in your applications. With Hydrah, you can validate user inputs, command-line arguments, or configuration files with just a few lines of code.FeaturesSimple and easy to useSupports multiple data types (strings, integers, lists, objects, booleans, datetime)Can validate inputs and provide detailed error messagesProvides acoercemethod to automatically convert inputs to the desired data typeInstallationh can be installed using pip:pipinstallhydrahUsageTo validate an input, you need to first create a validator instance for the desired data type. For example, to validate a string input:fromhydrahimporthvalidator=h.string()ifvalidator.is_valid("hello"):print("Valid input")else:print(validator.get_error_message("hello"))To validate a list of strings:fromhydrahimporthlist_validator=h.list()string()iflist_validator.is_valid(["hello","world"]):print("Valid input")else:print(list_validator.get_error_message(["hello",1]))To validate an object:fromhydrahimporthobject_validator=h.object({"name":h.string(),"age":h.integer()})ifobject_validator.is_valid({"name":"John","age":30}):print("Valid input")else:print(object_validator.get_error_message({"name":"John","age":"30"}))Custom ValidatorsHydrah can also be extended to support custom data types. To create a custom validator, you need to inherit from the Validator class and implement the is_valid and get_error_message methods.For example, to create a validator for boolean inputs:fromhydrahimportValidatorclassBooleanValidator(Validator):defis_valid(self,value):returnisinstance(value,bool)defget_error_message(self,value):returnf"Expected boolean, but got{type(value).__name__}"CoercionHydrah also provides a coerce method to automatically convert inputs to the desired data type. For example:fromhydrahimporthvalidator=h.integer()value=validator.coerce("10")print(value)# 10print(type(value))# <class 'int'>Creating Objects with Optional SchemasIn some cases, you may want to define a schema that is optional, meaning that it can either be present or absent in the data. To define an optional schema in Hydrah, you can either usehydrah.h.string().optional()orhydrah.h.optional(hydrah.h.string()).Here's an example of how you could define an object with an optional string field using both methods:fromhydrahimporth# Using string().optional()optional_string_validator=h.object({"optional_field":h.string().optional()})# Using optional(hydrah.h.string())optional_string_validator=h.object({"optional_field":h.optional(hydrah.string())})# Both of these validators will accept the following data:data={"optional_field":"Hello, world!"}assertoptional_string_validator.is_valid(data)# And also this data:data={}assertoptional_string_validator.is_valid(data)
Package: hydra-logzio-logger
Hydra Logzio LoggerHydra Logzio Logger is a pluginhydraplugin for logging data into logzioLicenseHydra Logzio Logger in licenced underMITPrerequisiteBefore using set to following environment variableslogzio_format,logzio_token,logzio_drain_timeout,logzio_urlFor more infor about these variables readhere.
Package: hydra-lsp
LSP for Hydra config filesInstallationUsing pypi:pip install hydra-lspUsing poetry:poetry installHow to useTo try it out, use the following code snippet in neovim.Note: If you are VS Code user - check out theextension.locallspconfig=require("lspconfig")localconfigs=require("lspconfig.configs")localon_attach=function(client,bufnr)localnmap=function(keys,func,desc)desc="LSP: "..descvim.keymap.set("n",keys,func,{buffer=bufnr,desc=desc,noremap=true})endnmap("gd",vim.lsp.buf.definition,"[G]oto [D]efinition")nmap("gD",vim.lsp.buf.declaration,"[G]oto [D]eclaration")nmap("gr",require("telescope.builtin").lsp_references,"[G]oto [R]eferences")nmap("gI",vim.lsp.buf.implementation,"[G]oto [I]mplementation")nmap("K",vim.lsp.buf.hover,"Hover Documentation")nmap("<C-k>",vim.lsp.buf.signature_help,"Signature Documentation")nmap("<leader>rn",vim.lsp.buf.rename,"[R]e[n]ame")nmap("<leader>ca",vim.lsp.buf.code_action,"[C]ode [A]ction")endifnotconfigs.hydralspthenconfigs.hydralsp={default_config={cmd={"hydra-lsp"},root_dir=lspconfig.util.root_pattern(".git"),filetypes={"yaml"},},}endlspconfig.hydralsp.setup({on_attach=lsp.on_attach,})Note: make sure to install hydra-lsp so that nvim can find an executable (poetry install)
Package: hydra-notebook
# hydra-notebooksJupyter notebooks integration for Django.Features:- Expose and API to handle notebooks,- Import as modules- Execute by notebook- Embedd to Django-extensions shell plus### ConfigurationJupyter configuration has to be add to the site directory, aside the ```settings.py```### Usage#### Importing and using notebooksAssume, we have a notebook called ```my_notebook``` with the following cell:```pythonclass SampleClass(object):def sample_method(self):print('execute sample_method')passdef sample_method_with_output(self):print('execute sample_method_with_output')return 'output'def sample_method_with_inoutput(self, param, *args, **kwargs):print('execute sample_method_with_inoutput(%s, %s, %s)' % (param, args, kwargs))return 'output'def __str__(self):return 'sample_class'``````python# To use the module capabilitiesimport hydra_notebookimport notebooks.my_notebook as mynbnew_instance = mynb.SampleClass()new_instance.sample_method_with_output()```As result:```execute sample_method_with_output```#### Executing notebooks```pythonimport hydra_notebookimport oswith hydra_notebook.NotebookExecutor(path=os.path.join('.', 'notebooks'), fullname='demo_notebook') as e:e()```#### Displaying notebooks as HTML5 page```/notebook/index```: List all notebooks```/notebook/detail/<notebook_name>```: Show a notebook#### Displaying notebooks as JSON```/notebook/```: List all notebooks```/notebook/<notebook_name>/```: Show a notebook```/notebook/<notebook_name>/execute```: Execute a notebook
Package: hydrant
Redirects stdin to Amazon Kinesis Firehose.The application submits each record one at a time,
which limits the throughput.
A future version might batch records together for performance,
but until then
beware of using high throughput streams
(15 records per second or more).
Running from within EC2 will allow higher throughput.InstallationInstall into a virtualenv.$pipinstall--editable.UsagePipe newline separated records into hydrant,
which will send each record to the delivery stream:$producer|hydrantmy-firehose-streamSpecify a region if not usingus-east-1:$producer|hydrant--region='us-west-2'my-firehose-streamRead the help text for more information:$hydrant--help
Package: hydra-openapi-parser
hydra-openapi-parserThis library contains the OpenAPI parser implemntaion in Python to be used withhydrusandhydra-python-agent.Currently the library consists of openapi_parser module which helps hydrus parse OpenAPI standard docs.To install the library:pipinstallgit+https://github.com/HTTP-APIs/hydra-openapi-parser.git#egg=hydra_openapi_parserNote :- If using hydrus, the library doesn't need to be installed separately as it is already a part of requirements.txt for hydrus.
UsageTo import the modules:fromhydra_openapi_parserimportopenapi_parserPorting out from hydrus the hydraspecs directorySample use-cases of openapi_parser moduleOnce the OpenAPI YAML document is assigned into the variabledocas a Python dictionary, you can do the following:Parse the OpenAPI doc into a HydraDocparsed_dict=openapi_parser.parse(doc)Generate an empty objectobject=openapi_parser.generate_empty_object()Test if an endpoint is validpath='A/B/{id}/C/D'openapi_parser.valid_endpoint(path)# Falsepath='A/B/{id}'openapi_parser.valid_endpoint(path)# Collectionpath='A/B/C'openapi_parser.valid_endpoint(path)# TrueExtract class name from the pathpath="A/B/C/Pet"path_list=path.split('/')openapi_parser.get_class_name(path_list)# PetFetch data from locationpath='#/definitions/Order'path_list=path.split('/')data=openapi_parser.get_data_at_location(path_list,doc)# data is of type dictRemove variables from pathpath="A/B/C/{id}"output=openapi_parser.sanitise_path(path)Identify if an object is a collectionschema_block={'type':'array','items':{'$ref':'#/definitions/Pet'}}method="/Pet"openapi_parser.check_collection(schema_block,method)# True
Package: hydra-orion-sweeper
Provides a mechanism for Hydra applications to use Orion
algorithms for the optimization of the parameters of any experiment.Seewebsitefor more informationInstallpipinstallhydra-orion-sweeperSearch SpaceOrion defines 5 different dimensions that can be used to define your search space.uniform(low, high, [discrete=False, precision=4, shape=None, default_value=None])loguniform(low, high, [discrete=False, precision=4, shape=None, default_value=None])normal(loc, scale, [discrete=False, precision=4, shape=None, default_value=None])choices(*options)fidelity(low, high, base=2)Fidelity is a special dimension that is used to represent the training time, you can think of it as theepochdimension.DocumentationFor in-depth documentation about the plugin and its configuration options
you should refer toOrionas the plugin
configurations are simply passed through.algorithmworkerstorageparametrizationExampleConfigurationdefaults:-overridehydra/sweeper:orionhydra:sweeper:params:a:"uniform(0, 1)"b:"uniform(0, 1)"experiment:name:'experiment'version:'1'algorithm:type:randomconfig:seed:1worker:n_workers:-1max_broken:3max_trials:100storage:type:legacydatabase:type:pickleddbhost:'database.pkl'# Default valuesa:0b:0NoteIf the orion database path is relative; orion will create one database per multirun,
this is because hydra is in charge of the working directory, and hydra creates a new directory per run.To share a database between multiruns you can use an absolute path.This also means, that if the database path is relative the HPO runs are never resumed
and always start from scratch.You can use an absolute path to resume a previous run.CodeimporthydrafromomegaconfimportDictConfig@hydra.main(config_path=".",config_name="config")defmain(cfg:DictConfig)->float:"""Simple main function"""a=cfg.ab=cfg.breturnfloat(a+b)if__name__=="__main__":main()RunningTo run the hyper parameter optimization process you need to specify the--multirunargument.pythonmy_app.py--multirunThe search space can also be tweaked from the command linepythonmy_app.py--multirunbatch_size=4,8,12,16optimizer.name=Adam,SGD'optimizer.lr="loguniform(0.001, 1.0)"'NoteWhen specifying overrides you need to be careful with your bash/zsh/fish environment and escape the arguments correctly.
Package: hydra-py
Hydra-PyHydra for Python. A light-weight library for building distributed applications such as microservices.- NOTE THIS PROJECT IS IN VERY EARLY DEVELOPMENT- From version 0.3.0 HydraPy only supports the use of short-form UMF see: https://github.com/pnxtech/umf/blob/master/umf.md#Short-form-syntax- Starting from version 0.5.0 HydraPy expects a Redis URL formatted connection string in the following format, where unused fields may be ignored:  redis://[user]:[password]@[host:port]/[database_number]Hydra-Py is changing quickly and not recommended for serious use at this timeAs a microservices approach, Hydra-Py depends on the presence of a Redis database server as its single infrastructual dependencyHydra-Py requires Python 3.7+ and aioredis==1.3.1 or greaterIntroductionHydra is an approach to building light-weight microservices by leveraging the awesome power of the Redis database platform.Hydra-Py is a nextgen port of the NodeJS implementation of Hydra with a goal of offering the same level of ease of use to the Python community.  We hope this will empower data scientists to build containerized microservices for their AI/ML applications.DocumentationVisit theproject documentationfor a quick start guide and additional documentation.
Package: hydra-python-core
hydra-python-coreThis library provides the core functions to implement Hydra Official Specification in Python.Currently the library mainly consists of 2 modulesdoc_writeranddoc_makerwhich help hydrus generalise a lot of things. The documentation is available athttps://hydra-python-core.readthedocs.io/en/develop/->doc_writercreates a new API Documentation as well as aHydraDocobject while,
->doc_makeruses an existing API Documentation to create aHydraDocobjectInstallationTo install the library:pipinstallgit+https://github.com/HTTP-APIs/hydra-python-core.git#egg=hydra_python_coreNote :-If using hydrus, the library doesn't need to be installed separately as it is already a part ofrequirements.txtfor hydrus.UsageTo import the modules:fromhydra_python_coreimportdoc_writerfromhydra_python_coreimportdoc_makerPorting out from hydrus the hydraspecs directory
Package: hydra-slayer
Hydra SlayerHydra Slayeris a 4th level spell in the School of Fire Magic.
Depending of the level of expertise in fire magic,
slayer spell increases attack of target troop by 8 against
behemoths, dragons, hydras, and other creatures.What is more, it also allows configuring of complex applications just by config and few lines of code.InstallationUsing pip you can easily install the latest release versionPyPI:pipinstallhydra-slayerExampledataset:_target_:torchvision.datasets.CIFAR100root:./datatrain:falsedownload:trueimporthydra_slayerimportyamlwithopen("dataset.yaml")asstream:raw_config=yaml.safe_load(stream)config=hydra_slayer.get_from_params(**raw_config)config["dataset"]# Dataset CIFAR100#     Number of datapoints: 10000#     Root location: ./data#     Split: TestPlease checkdocumentationfor more examples.DocumentationFull documentation for the project is available athttps://catalyst-team.github.io/hydra-slayerCommunicationGitHub Issues: Bug reports, feature requests, install issues, RFCs, thoughts, etc.Slack: TheCatalyst Slackhosts a primary audience of moderate to experienced Hydra-Slayer (and Catalyst) users and developers for general chat, online discussions, collaboration, etc.Email: Feel free to usefeedback@catalyst-team.comas an additional channel for feedback.CitationPlease use this bibtex if you want to cite this repository in your publications:@misc{catalyst,
    author = {Sergey Kolesnikov and Yauheni Kachan},
    title = {Hydra-Slayer},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/catalyst-team/hydra-slayer}},
}
Package: hydra-smac
Hydra-SMAC 0.1.11A minimal Python re-implementation ofHydra.Getting startedpipinstallhydra-smacExampleFor more information on how to use Scenario objects, please refer to theSMACdocumentation.fromConfigSpaceimportConfiguration,ConfigurationSpace,FloatfromhydrasmacimportHydrafromsmacimportScenarioinstances=["a","b","c"]features={"a":[0.0],"b":[1.0],"c":[2.0]}cs=ConfigurationSpace()cs.add_hyperparameters([Float("x",(1.0,5.0)),Float("y",(1.0,5.0)),Float("z",(1.0,5.0)),])deftarget_function(config:Configuration,instance:str,seed:int=0)->float:config_dict=config.get_dictionary()x,y,z=config_dict["x"],config_dict["y"],config_dict["z"]ifinstance=="a"andx<2.5andy>2.5andz>2.5:return0.001ifinstance=="b"andy<2.5andx>2.5andz>2.5:return0.01ifinstance=="c"andz<2.5andy>2.5andx>2.5:return0.1return1scenario=Scenario(configspace=cs,instances=instances,instance_features=features,n_trials=500,)hydra=Hydra(scenario,target_function,hydra_iterations=3,smac_runs_per_iter=1,incumbents_added_per_iter=1,stop_early=True,)portfolio=hydra.optimize()print("====== Resulting portfolio ======")print(portfolio)print("=================================")
Package: hydraspa
# Hydraspa[![Build Status](https://travis-ci.org/richardjgowers/hydraspa.svg?branch=master)](https://travis-ci.org/richardjgowers/hydraspa)[![codecov](https://codecov.io/gh/richardjgowers/hydraspa/branch/master/graph/badge.svg)](https://codecov.io/gh/richardjgowers/hydraspa)Hydraspa (hydra + Raspa) is a Python package for preparing GCMC gas adsorption simulation inputs for the [Raspa](https://github.com/numat/RASPA2) simulation package.Permission to use the structures from the [CoRe MOF Database](http://gregchung.github.io/CoRE-MOFs/) has kindly been given by [Dr. Yongchul G. Chung](https://github.com/gregchung) of [Pusan National University](http://gregchung.github.io/).## Installing hydraspa```bashgit clone https://github.com/richardjgowers/hydraspa.gitcd hydraspapip install -r requirements.txt .```## Using hydraspahydraspa is designed to be used via the command line to prepare and analyse simulation inputs:```bash# Create a simulation template of CO2 in IRMOF-1hydraspa create -s IRMOF-1 -g CO2 -f UFF -o myCO2sim# Split the template into many temperatures and pressureshydraspa split myCO2sim/ -p 10k,20k,30k -T  278.0,298.0 -c 20k# Gather the results once the simulation has been donehydraspa gather myCO2sim/```## CitingIf this software is useful in your research, please consider citing the following sources:[D. Nazarian, J. Camp, Y.G. Chung, R.Q. Snurr, D.S. Sholl, "Large-Scale Refinement of Metal Organic Framework Structures Using DFT," Chemistry of Materials, 2016](https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.6b04226)[Y.G. Chung, J. Camp, M. Haranczyk, B.J. Sikora, W. Bury, V. Krungleviciute, T. Yildirim, O.K. Farha, D.S. Sholl, R.Q. Snurr, "Computation-Ready, Experimental Metal-Organic Frameworks: A Tool to Enable High-Throughput Computation of Nanoporous Crystals," Chemistry of Materials, 2014, 26 (21), pp 6185–6192](https://pubs.acs.org/doi/abs/10.1021/cm502594j)[A. K. Rappe, C. J. Casewit, K. S. Colwell, W. A. Goddard III, and W. M. Skiff "UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations" Journal of the American Chemical Society, 1992, 114 (25) pp 10024–10035](https://pubs.acs.org/doi/abs/10.1021/ja00051a040)## ContributingHydraspa is actively being used and developed, if you encounter any issues drop me a line on the issue tracker.
Package: hydra-submitit-extension
hydra_submitit_extensionA more flexible Hydra Submitit Launcher pluginThis launcher is a extension ofSubmiti Launcher Plugin.Features:Iterative scheduling of jobs:Max Number for Active and Pending JobsAutomatically Schedule Jobs and Wait depending on LimitsDrawbacks:Single Jobs are submitted instead of Job arraysHydra must be active to schedule jobsNo Naming Scheme for Slurm jobs. This makes manual canceling difficultInstallationpipinstallhydra-submitit-extensionQuickstartpython main.py hydra/launcher=submitit_slurm_extended -mAll Parameter:# @package hydra.launcher# No changes to Submitit Launcher pluginsubmitit_folder:${hydra.sweep.dir}/.submitit/%jtimeout_min:30cpus_per_task:1gpus_per_node:nulltasks_per_node:1mem_gb:1nodes:1name:${hydra.job.name}partition:"dev_single"qos:nullcomment:nullconstraint:nullexclude:null#gres: "gpu:1"cpus_per_gpu:nullgpus_per_task:nullmem_per_gpu:nullmem_per_cpu:nullaccount:nullsignal_delay_s:120max_num_timeout:0additional_parameters:{}array_parallelism:256setup:null# Hydra Submitit Extension_target_:hydra_plugins.hydra_submitit_extension.submitit_launcher.ExtendedSlurmLauncher# Time between reschedule tries in s.# Min is 60sreschedule_interval:60# Maximum number of total active jobs in slurm account.max_jobs_in_total:5# Maximum number of active jobs in current partition (e.g dev_single).max_jobs_in_partition:4# Maximum number of active jobs in current sweep.max_jobs_in_sweep:3RoadmapIterative SchedulingGreedy Partition Selection
Package: hydrat
hydrat - Classifier Comparison Framework==========================This is the hydrat classifier comparison framework.Credits-------- `Distribute`_- `Buildout`_- `modern-package-template`_.. _Buildout: http://www.buildout.org/.. _Distribute: http://pypi.python.org/pypi/distribute.. _`modern-package-template`: http://pypi.python.org/pypi/modern-package-templateNews====0.9.4-----*Release date: 8-11-2012** First "release" of hydrat in a long time. Development has continued, but there has not been an "official release" in awhile.* This release is intended to support my publication of a langid test harness.0.9.2.4-------*Release date: 16-10-2010** Developed the cherrypy-based store browser. The browser is invoked via 'hydrat browse <file.h5>'** Split into subcomponents** Added result deletion via the browser** Added result-set comparison, showing instances misclassified in a set of results** Added intelligent class-space reduction in displaying a result's class-class confusion matrix* Added an xmlrpc server to the online framework. Classification as a service!* Added merge capabilities to store* Fixed a missing dependency on pexpect0.9.2.3-------*Release date: 12-10-2010** Removed ~ from the paths considered in autoconfigure* Made weka easier to autoconfigure* Added -s to 'hydrat configure' to allow user to specify directories to scan0.9.2.2-------*Release date: 12-10-2010** Improved the CLI options for 'hydrat browse'* Simplified the Store management in Framework* Added example code for the ALTW2010 Programming Competition0.9.2.1-------*Release date: 11-10-2010** Fixed a subtle bug in the computation of cosine values.0.9.2-----*Release date: 10-10-2010** Extensive work done on many fronts.
Package: hydration
HydrationThis software has been designed for you, with much joy, byMichael ShustinYou can find the complete documentationhereWhat is Hydration?Hydration is a library used to define python objects that can be converted to (and from) bytes.Installationpip install hydrationIntroductionThis guide assumes you are familiar with low-level primitive data types (like signed/unsigned int, int64, etc.)FieldFields are primitive objects that hold data,
and can be converted to (and from) bytes:>>>fromhydrationimport*>>>f=UInt16(1512)>>>bytes(f)b'\xe8\x05'>>>UInt16().from_bytes(b'\xe8\x05')UInt16(1512)StructA struct is a composite of fields.
To create a struct, Inherit fromStruct:fromhydrationimport*classMyStruct(Struct):a=UInt8b=UInt8(value=3)# You can set the default value>>>st=MyStruct(a=10)# Structs can receive field values as keyword arguments>>>print(st)MyStructa:	UInt8(10)b:	UInt8(3)>>>bytes(st)b'\n\x03'>>>print(MyStruct.from_bytes(b'\n\x03'))MyStruct:a:	UInt8(10)b:	UInt8(3)MessageA message is a list-like composite of structs:fromhydrationimport*classHeader(Struct):magic=UInt32(0xDEADBEEF)classBody(Struct):param1=Float(2.875)param2=Int8(-128)>>>msg=Header()/Body()# Create a message by using the division operator on structs>>>print(msg)Header:magic:	UInt32(3735928559)Body:param1:	Float(2.875)param2:	Int8(-128)>>>bytes(msg)b'\xef\xbe\xad\xde\x00\x008@\x80'Advanced featuresFor more advanced usage, be sure to check thedocumentation.SupportWant to report a bug? Request a feature? Please do sohereMaintainersMichael Shustin(Author)Aviv Atedgi
Package: hydratk
HydraTK (Hydra Toolkit) is fully extendable object oriented application toolkit with nice modular architecture.It’s useful mainly for creating utilities for system analysis and general testing.It has decent portfolio of features:nice commandline interfaceextension interfaceevents and functionality hooksservicesmessagingparallel processinginternalization supportOS and Python versions supportCurrently the Linux platform with CPython 2.6, 2.7, 3.3, 3.4, 3.5, 3.6, PyPy 2.7 is supported,but the final version is planned to be crossplatform and targeted also to the other popular systemsincluding Windows and MacOS and possibly other Python versions such as Jython and IronPython
Package: hydratk-client
GUI client for HydraTKFeatures:Project explorerEditor for Python and Jedi files - syntax highlighting, text formatting, code autocompleteJedi test files designMulti-language supportPlugins: SyntaxChecker, GitClientOS and Python versions supportCurrently the Windows and Linux platform with CPython 2.6, 2.7, 3.3, 3.4, 3.5, 3.6, PyPy 2.7 is supported.