Saravanan666/pypi-org huggingface dataset:
Package: confluent_avro
ConfluentAvroAn Avro SerDe implementation that integrates with theconfluent schema registryand serializes and deserializes data according to the definedconfluent wire format.View Demo·Report Bug·Request FeatureGetting StartedBackgroundTo solveschema managementissues and ensure compatibility in the development of Kafka-based applications, the confluent team introduced the schema registry to store and share the schema between the different apps and apply compatibility checks on each newly registered schema. To make the schema sharing easy, they extend the Avro binary format by prepending the schema id before the actual record instead of including the full schema.-» You can find more about Confluent and Schema Registry inConfluent documentation.ImplementationConfluentAvro implemented according to the above specification. Before publishing to Kafka topic, the library prepends the schema id to the generated Avro binary and when consuming from Kafka, it retrieves the schema id and fetches the schema from the registry before deserializing the actual data.The underline API will automatically register new schemas used for the data serialization and will fetch the corresponding schema when deserializing it. Newly registered schemas and fetched schemas are both cached locally to speed up the process for future records.» The ConfluentAvro's bullet points:Supports the confluent wire formatIntegrates with the confluent schema registryRetries with exponential backoff if connection to registry failedImplements caching at the schema registry levelThe underline decoder/encoder is built once for the same schema and reused for all upcoming recordsCan be integrated with different Kafka clientsBuilt Withfastavro(checkfastavro benchmark)requestsInstallation»pipinstallconfluent-avroUsageCheckexamplesfor a fully working demo.Consumer App Example:fromkafkaimportKafkaConsumerfromconfluent_avroimportAvroKeyValueSerde,SchemaRegistryfromconfluent_avro.schema_registryimportHTTPBasicAuthKAFKA_TOPIC="confluent_avro-example-topic"registry_client=SchemaRegistry("https://myschemaregistry.com",HTTPBasicAuth("username","password"),headers={"Content-Type":"application/vnd.schemaregistry.v1+json"},)avroSerde=AvroKeyValueSerde(registry_client,KAFKA_TOPIC)consumer=KafkaConsumer(KAFKA_TOPIC,group_id="random_group_id",bootstrap_servers=["localhost:9092",])formsginconsumer:v=avroSerde.value.deserialize(msg.value)k=avroSerde.key.deserialize(msg.key)print(msg.offset,msg.partition,k,v)Producer App Example:fromkafkaimportKafkaProducerfromconfluent_avroimportAvroKeyValueSerde,SchemaRegistryfromconfluent_avro.schema_registryimportHTTPBasicAuthKAFKA_TOPIC="confluent_avro-example-topic"registry_client=SchemaRegistry("https://myschemaregistry.com",HTTPBasicAuth("username","password"),headers={"Content-Type":"application/vnd.schemaregistry.v1+json"},)avroSerde=AvroKeyValueSerde(registry_client,KAFKA_TOPIC)producer=KafkaProducer(bootstrap_servers=["localhost:9092"])producer.send(KAFKA_TOPIC,key=avroSerde.key.serialize({...},key_schema),value=avroSerde.value.serialize({...},value_schema),)
Package: confluent-cloud-sdk
SDK to interact with Confluent Cloud APIInstallationpipinstallconfluent-cloud-sdkImportsTo use Confluent Admin API SDK in a projectfromconfluent_cloud_sdk.client_factoryimportConfluentClientfromconfluent_cloud_sdk.confluent_iam_v2importApiKeyfromconfluent_cloud_sdk.confluent_iam_v2importServiceAccountInitialize connectionclient=ConfluentClient("cloud_key_key","cloud_key_secret",)Example with secret in AWS And list all assetsimportjsonfromosimportenvironfromboto3.sessionimportSessionfromconfluent_cloud_sdk.client_factoryimportConfluentClientfromconfluent_cloud_sdk.confluent_iam_v2importServiceAccountfromconfluent_cloud_sdk.confluent_org_v2importConfluentEnvironmentfromconfluent_cloud_sdk.confluent_cluster_v2importKafkaClusterV2fromcompose_x_common.awsimportget_sessiondefget_confluent_admin_secret(secret_arn:str,session:Session=None,key_id:str="ApiKey",secret_id:str="ApiSecret",)->ConfluentClient:session=get_session(session)client=session.client("secretsmanager")value=json.loads(client.get_secret_value(SecretId=secret_arn)["SecretString"])returnConfluentClient(value[key_id],value[secret_id])cclient=get_confluent_admin_secret(environ.get("SECRET_ARN"))envs=cclient.list_all(ConfluentEnvironment)forenvinenvs:print(env.obj_id)clusters=cclient.list_all(KafkaClusterV2,url_args=f"?environment={env.obj_id}")forclusterinclusters:print(cluster.obj_id)svc_accounts=cclient.list_all(ServiceAccount)forsvc_accountinsvc_accounts:print(svc_account.obj_id)svc_account.import_api_keys()print([key.obj_idforkeyinsvc_account.api_keys.values()])Usage examplesFor more details, see docs/usage.rst
Package: confluentfucci
ConfluentFUCCIA suite of tools for analyzing large scale confluent FUCCI experimentsOverviewThis repo includes an integartion and automation layer for running CellPose (person-in-the-loop ML driver cell segentation) and TrackMate (cell tracking). Furthermore, the repo includes a set ofanalysis and visualization tools for studying confluent cellular dynamics using a FUCCI stain.UsageThis project is set up using poetry. To install the dependencies, runpoetry installfrom the root of the project.poetryinstallTo add a new dependency, runpoetry add <dependency>from the root of the project.poetryadd<dependency>Pre-Commit HooksThis project usespre-committo run linting and formatting tools before each commit. To install the pre-commit hooks, runpre-commit installfrom the root of the project.poetryrunpre-commitinstallTo run the pre-commit hooks manually, runpre-commit run --all-filesfrom the root of the project.poetryrunpre-commitrun--all-filesTestingThis project usespytestfor testing. To run the tests, runpytestfrom the root of the project in the poetry shell.poetryrunpytestThere are sensible defaults for pytest setup in thepyproject.tomlfile. You can override these defaults by passing in command line arguments. For example, to run the tests with debug logging enabled, runpytest --log-cli-level=DEBUGfrom the root of the project.poetryrunpytest--log-cli-level=DEBUG
Package: confluent-kafka-stubs
confluent-kafka-stubsThis package only contains type hints for theconfluent-kafkapython package. It may be useful to add type checking formypy, or autocompletion in yourlanguage server.This package is not endorsed by Confluent.InstallationThe package can be installed fromPyPI, and needs to be installed in a location that tools likemypycan access. In my experience, installing it as a user package works well, at least withneovimand the officialmypypackage onArch Linux. You can install it as a user package as follows:pipinstall--userconfluent-kafka-stubsUpdatingThe package can be updated like any other PyPI package.pipinstall--user--upgradeconfluent-kafka-stubsStatusThis package should be considered a work in progress.In thecimplmodule, I believe all constants have been ported along with the following classes or functions:KafkaError: classKafkaException: classMessage: classNewPartitions: classNewTopic: classTopicPartition: classProducer: classConsumer: classlibversion(): functionversion(): functionYet to be implemented in thecimplmodule:_AdminClientImpl?: classNote: unlisted modules have not yet been processed.
Package: conflumb
ConflumbWhile writing markdown technical documents, we usually need to synchronize updates to confluence. This helps the team to easily jump between different documents through confluence's search function. Manually copying and uploading attachments to confluence is a very boring process that needs to be repeated for even a very small change.conflumb can help convert markdown formatting to confluence page content formatting, and the images inserted in the document are automatically submitted to confluence as attachments while maintaining a consistent display format
Package: conflupy
conflupyA Python library for Atlassian Confluence REST APIVersion with 1.0.x ~ 1.2.x are for distribution tests and have bugs. Please use conflupy >= 1.3.0RequirementsPython >= 3.9Atlassian Confluence (REST API docs)This library is tested inAtlassian Confluence 7.19.0Confluence REST API examplesConfluence Storage FormatInstallationpipinstallconflupyUsagefromconfluenceimportConfluence# Initialize Confluenceconfluence=Confluence(base_url:'https://confluence.example.com',account:(USER_ID,USER_PW))# Get Pagespages=confluence.get_pages(space_key='TEST')print('Pages :',pages)# Get Specific Pagepage=confluence.get_content(content_id='1349141')print('Page :',page)# Create a new Pagebody='<h1>Hello</h1><br /><p>This is a page created with REST API</p>'new_page=create_page(space_key='TEST',title='Test Page',body=body)print('New Page :',new_page)Build & Deploy (PyPi)# Buildpipinstallbuild
python-mbuild# whl file & archived src(tar.gz) file will be generated.# Deploypipinstalltwine
python-mtwineuploaddist/*# package will be uploaded to PyPi registry# https://pypi.org/project/conflupyHowever, this project use GitHub Actions workflow to automatically publish the package to PyPI when a tag pushed.To DoUnittestAuthorsDaeyeol RyuLicenseApache License 2.0
Package: conflutools
Atlassian Confluence ToolsGeneric Setuppipinstallconflutools# copy the confluence-configuration file and adjust its content:cpconfconf-example.yamlconfconf.yaml
vimconfconf.yamlRecursive page exportAfter the preparation steps above:# create a directory for storing the exports:$EXPORT_DIR="/path/for/storing/wiki-exports"mkdir$EXPORT_DIRcd$EXPORT_DIR# export by page ID:export-pagetree--configconfconf.yaml--page53838601# export by page title (requires the space KEY as well):export-pagetree--configconfconf.yaml--page"Project XY Pages"--space"FOO"
Package: conflux-web3
IntroductionREADME|中文文档IntroductionOverviewQuickstartDocumentationsRun Code Examples Online!LocalizationOverviewPython-conflux-sdk helps to interact with Conflux network using python. It is built overweb3.pyand most of its APIs are consistent withweb3.py.QuickstartRequirements: python version >= 3.7$pip3installconflux-web3fromconflux_web3importWeb3w3=Web3(Web3.HTTPProvider("https://test.confluxrpc.com"))acct=w3.account.from_key("0xxxxxxxxxxxxxx")w3.cfx.default_account=acctw3.cfx.contract(name="Faucet").claimCfx().transact().executed()w3.cfx.send_transaction({'to':w3.address.zero_address(),'value':10**18,}).executed()Or you can also use API as you do inweb3.py:# modified from https://web3py.readthedocs.io/en/stable/middleware.html#signingfromconflux_web3importWeb3w3=Web3("https://test.confluxrpc.com")fromconflux_web3.middlewareimportconstruct_sign_and_send_raw_middlewarefromcfx_accountimportAccountacct=Account.create('KEYSMASH FJAFJKLDSKF7JKFDJ 1530')w3.middleware_onion.add(construct_sign_and_send_raw_middleware(acct))w3.cfx.default_account=acct.addresstransaction={'to':w3.address.zero_address(),'value':22,}w3.cfx.send_transaction(transaction)DocumentationsMore detailed code examples are provided in thedocumentation.Run Code Examples Online!All code examples can be run online inmybinder. You can click🚀->Binderon the top bar to activate the running environment. All dependencies wil be installed and the example can be run immediately.LocalizationCurrently this documentation supports:English versionChinese versionAnd welcome to provide translation inGitLocalize.
Package: conflux-web3py-signer
IntroductionThis is a library used to useweb3.pyon conflux-bridge(?). This library hacks the signing machanism ofweb3.py.InstallThis library requires python >= 3.7It is recommended to use this library in a virtual environment.python-mvenvvenvsource./venv/bin/activateorcondacreate-nvenvpython=3.7
condaactivatevenvThen install in the virtual environmentpipinstallconflux-web3py-signerHow to useBasic UsageImportconflux_we3py_signerbefore importweb3.importconflux_web3py_signerimportweb3Use with BrownieFirstly, you are supposed to install brownie and addconflux-bridge endpointsto brownie networkspipinstallconflux_web3py_signer[brownie]cfx-brownienetworksaddConfluxcfx-testnet-bridgehost=https://cfx2ethtest.nftrainbow.cnchainid=1Then use with command-line with target network.cfx-brownie--networkcfx-testnet-bridgeWhat is DoneTransaction CastWhen the modifiedconstruct_sign_and_send_raw_middlewareis going to sign a transaction, it will convert an EIP-1559 transaction to conflux transaction following the rule:IfgasPriceis missing, usemaxFeePerGasas gas price.FillepochHeightwithw3.eth.block_number, which correspondes toepoch_numberin conflux.Estimate the transaction and fillstorageLimitfrom estimate result.Address CastEOA account addresses are all converted to begin with0x1and is encoded in checksum format
Package: confmanager
# conf_man
Python程序的配置处理## 安装pip install confmanager## 使用显示设置的值环境变量可以设置前缀
如果设置前缀prefix，那么绑定xxx之后，将会查找PREFIX_XXX的环境变量
如果没有设置前缀，那么绑定xxx之后，将会查找XXX的环境变量CLI选项仅仅支持选项flag的绑定，不支持参数绑定
绑定xxx之后，将会映射到–xxx的值配置文件可以多个配置文件，每个配置文件在不同目录，但是文件名称必须一致，文件类型仅仅支持JSON
JSON文件内容格式需要满足{str: str}，不支持嵌套默认值
Package: confmap
Python classes for transformations of pictures and videos (withmoviepy) using conformal mappings of the complex plane and hyperbolic tessalations of Poincaré’s disk.Author : François CoulombeauExampleIn this example, we open an image and build a (∞;6;4;6) hyperbolic tessalation :importconfmapascmimportnumpyasnpim=cm.HyperbolicTiling('./Sources/Reflets.jpg',0,'./Exports/',600,600)im.transform(sommets=(np.inf,6,4,6),nbit=20,backcolor=[255,255,255])which leeds to this image :InstallationConfMap depends on the Python modulesNumpy,Matplotlibandmoviepywhich will be automatically installed during ConfMap’s installation. The software FFMPEG should be automatically downloaded/installed (by imageio) during your first use of MoviePy (installation will take a few seconds). If you want to use a specific version of FFMPEG, see Moviepy’s documentation.Installation by hand:download the sources, either fromPyPIor, if you want the development version, fromGitHub, unzip everything into one folder, open a terminal and type:$(sudo)pythonsetup.pyinstallInstallation with pip:if you havepipinstalled, just type this in a terminal:$(sudo)pipinstallmoviepyMaintainersOioi(owner)
Package: confoid
ConfoidConfiguration Management for Python.Install$pipinstallconfoidLoading a single config fileimportconfoidnew_settings=confoid.Config("application.yml")Loading multiple  config fileMultiple files can be provided inorderand will merge with the previous configuration.importconfoidconfig_files=["application.yml","application.development.yml"]new_settings=confoid.Config(config_files,base_dir="config",)Autoload based on provided environmentimportconfoidnew_settings=confoid.Config("application.yml",current_env="development")# this will load application.yml and application.{current_env}.ymlReading the settingssettings.username=="admin"# dot notation with multi nesting supportsettings['password']=="secret123"# dict like accesssettings.get("nonexisting","default value")# Default values just like a dictsettings.databases.name=="mydb"# Nested key traversingConfig mergingapplication.ymldefault:
  prefix:
    otp: "user-otp"
    auth: "auth-tokens"
  type: inmemory
  redis:
    url:application.development.ymldefault:
  type: redis
  redis:
    url: redis://saviof.com
    encoding: "utf8"Will resolve to the following final configdefault:
  prefix:
    otp: "user-otp"
    auth: "auth-tokens"
  type: redis
  redis:
    url: redis://saviof.com
    encoding: "utf8"Config environment varsAll fields can use environment variables with a fallback default valuepassword: ${TEST_SERVICE_DEFAULT_PASSWORD:test}Validators can be added for pre checks on loaded configfromconfoidimportValidatorconfig_files=["application.yml","application.development.yml"]new_settings=confoid.Config(config_files,base_dir="config",validators=[Validator("default","default.redis.password",must_exist=True)Validator("otp.length",gte=6,lte=20)Validator("default.type",values=["inmemory","redis"])])
Package: confopt
ConfOptConfOpt is an inferential hyperparameter optimization package designed to
speed up model hyperparameter tuning.The package currently implements Adaptive Conformal Hyperparameter Optimization (ACHO), as detailed
inthe original paper.InstallationYou can install ConfOpt fromPyPIusingpip:pipinstallconfoptGetting StartedAs an example, we'll tune a Random Forest model with data from a regression task.Start by setting up your training and validation data:fromsklearn.datasetsimportfetch_california_housingX,y=fetch_california_housing(return_X_y=True)split_idx=int(len(X)*0.5)X_train,y_train=X[:split_idx,:],y[:split_idx]X_val,y_val=X[split_idx:,:],y[split_idx:]Then import the Random Forest model to tune and define a search space for
its parameters (must be a dictionary mapping the model's parameter names to
possible values of that parameter to search):fromsklearn.ensembleimportRandomForestRegressorparameter_search_space={"n_estimators":[10,30,50,100,150,200,300,400],"min_samples_split":[0.005,0.01,0.1,0.2,0.3],"min_samples_leaf":[0.005,0.01,0.1,0.2,0.3],"max_features":[None,0.8,0.9,1],}Now import theConformalSearcherclass and initialize it with:The model to tune.The raw X and y data.The parameter search space.An extra variable clarifying whether this is a regression or classification problem.Hyperparameter tuning can be kicked off with thesearchmethod and a specification
of how long the tuning should run for (in seconds):fromconfopt.tuningimportConformalSearchersearcher=ConformalSearcher(model=RandomForestRegressor(),X_train=X_train,y_train=y_train,X_val=X_val,y_val=y_val,search_space=parameter_search_space,prediction_type="regression",)searcher.search(runtime_budget=120# How many seconds to run the search for)Once done, you can retrieve the best parameters obtained during tuning using:searcher.get_best_params()Or obtain an already initialized model with:searcher.get_best_model()More information on specific parameters and overrides not mentioned
in this walk-through can be found in the docstrings or in theexamplesfolder of the main repository.
Package: conformal-region-designer
Conformal Region DesignerWelcome to the Conformal Region Designer package! This package provides a set of tools and utilities for designing and visualizing conformal regions.InstallationTo install the Conformal Region Designer package, you can use pip:pipinstallconformal-region-designerUsageThe following code creates a conformal region creator with a target of 90% coverage,
using a kernel density estimator to estimate the density of the data,
a mean shift algorithm to estimate the center of the data, and an ellipse to define the region.importconformal_region_designerascrdpcr=crd.ConformalRegion('kde','meanshift','ellipse',0.90)pcr.fit(Z_cal_one)pcr.conformalize(Z_cal_one)The regions can be visualized using the following code:fig,ax=plt.subplots()pcr.plot(ax=ax)
Package: conformer-rl
conformer-rlAn open-source deep reinforcement learning library for conformer generation.DocumentationDocumentation can be found athttps://conformer-rl.readthedocs.io/.Platform SupportSince conformer-rl can be run within a Conda environment, it should work on all platforms (Windows, MacOS, Linux).Installation and Quick StartPlease see the documentation forinstallation instructionsandgetting started.Issues and Feature RequestsWe are actively adding new features to this project and are open to all suggestions. If you believe you have encountered a bug, or if you have a feature that you would like to see implemented, please feel free to file anissue.Developer DocumentationPull requests are always welcome for suggestions to improve the code or to add additional features. We encourage new developers to document new features and write unit tests (if applicable). For more information on writing documentation and unit tests, see thedeveloper documentation.
Package: conformer-tf
ConformerThis repo implementsConformer: Convolution-augmented Transformer for Speech Recognitionby Gulati et al. in TensorFlow.Conformerachieves the best of both worlds (transformers for content-based global interactions and CNNs to  exploit local features) by studying how to combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way.It also significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies.InstallationRun the following to install:pipinstallconformer-tfDeveloping conformer-tfTo installconformer-tf, along with tools you need to develop and test, run the following in your virtualenv:gitclonehttps://github.com/Rishit-dagli/Conformer.git# or clone your own forkcdConformer
pipinstall-e.[dev]To run rank and shape tests run the following:pytest--verboseUsageIn this section, I show a minimal example of creating a Convolutional Module, one of the main contributions of the paper and a Conformer block as well.Create a Convolutional Moduleimporttensorflowastffromconformer_tfimportConformerConvModulelayer=ConformerConvModule(dim=512,causal=False,# whether it is auto-regressiveexpansion_factor=2,# what multiple of the dimension to expand for the depthwise convolutionkernel_size=31,dropout=0.)x=tf.random.normal([1,1024,512])x=layer(x)+x# (1, 1024, 512)Create a Conformer Blockimporttensorflowastffromconformer_tfimportConformerBlockconformer_block=ConformerBlock(dim=512,dim_head=64,heads=8,ff_mult=4,conv_expansion_factor=2,conv_kernel_size=31,attn_dropout=0.,ff_dropout=0.,conv_dropout=0.)x=tf.random.normal([1,1024,512])conformer_block(x)# (1, 1024, 512)Want to Contribute 🙋‍♂️?Awesome! If you want to contribute to this project, you're always welcome! SeeContributing Guidelines. You can also take a look atopen issuesfor getting more information about current or upcoming tasks.Want to discuss? 💬Have any questions, doubts or want to present your opinions, views? You're always welcome. You canstart discussions.Citation@misc{gulati2020conformer,title={Conformer: Convolution-augmented Transformer for Speech Recognition},author={Anmol Gulati and James Qin and Chung-Cheng Chiu and Niki Parmar and Yu Zhang and Jiahui Yu and Wei Han and Shibo Wang and Zhengdong Zhang and Yonghui Wu and Ruoming Pang},year={2020},eprint={2005.08100},archivePrefix={arXiv},primaryClass={eess.AS}}Phil Wang's PyTorch implementationwas super helpful while building this.LicenseCopyright 2020 Rishit Dagli

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.