Saravanan666/pypi-org huggingface dataset:
Package: acqdp
Alibaba Cloud Quantum Development Platform (ACQDP)IntroductionACQDP is an open-source platform designed for quantum computing. ACQDP provides a set of tools for aiding the development of both quantum computing algorithms and quantum processors, and is powered by an efficient tensor-network-based large-scale classical simulator.Computing EnginePartially inspired by the recent quantum supremacy experiment, classical simulation of quantum circuits attracts quite a bit of attention and impressive progress has been made along this line of research to significantly improve the performance of classical simulation of quantum circuits. Key ingredients includeQuantum circuit simulation as tensor network contraction[1];Undirected graph model formalism[2];Dynamic slicing[3];Contraction tree[4];Contraction subtree reconfiguration[5].We are happy to be part of this effort.Use CasesEfficient exact contraction of intermediate-sized tensor networksDeployment on large-scale clusters for contracting complex tensor networksEfficient exact simulation of intermediate sized quantum circuitClassical simulation under different quantum noise modelsDocumentationSee full documentation here.InstallationInstallation from PyPIpipinstall-UacqdpInstallation from source codegitclonehttps://github.com/alibaba/acqdpcdadqdp
pipinstall-e.ContributingIf you are interested in contributing to ACQDP feel free to contact me or create an issue on the issue tracking system.References[1]Markov, I. and Shi, Y.(2008)
Simulating quantum computation by contracting tensor networks
SIAM Journal on Computing, 38(3):963-981, 2008[2]Boixo, S., Isakov, S., Smelyanskiy, V. and Neven, H. (2017)
Simulation of low-depth quantum circuits as complex undirected graphical models
arXiv preprint arXiv:1712.05384[3]Chen, J., Zhang, F., Huang, C., Newman, M. and Shi, Y.(2018)
Classical simulation of intermediate-size quantum circuits
arXiv preprint arXiv:1805.01450[4]Zhang, F., Huang, C., Newman M., Cai, J., Yu, H., Tian, Z., Yuan, B., Xu, H.,Wu, J., Gao, X., Chen, J., Szegedy, M. and Shi, Y.(2019)
Alibaba Cloud Quantum Development Platform: Large-Scale Classical Simulation of Quantum Circuits
arXiv preprint arXiv:1907.11217[5]Gray, J. and Kourtis, S.(2020)
Hyper-optimized tensor network contraction
arXiv preprint arXiv:2002.01935[6]Huang, C., Zhang, F.,Newman M., Cai, J., Gao, X., Tian, Z., Wu, J., Xu, H., Yu, H., Yuan, B.,Szegedy, M., Shi, Y. and Chen, J. (2020)
Classical Simulation of Quantum Supremacy Circuits
arXiv preprint arXiv:2005.06787
Package: acquantum-connector
Examplefromacquantumconnector.connector.acquantumconnectorimportAcQuantumConnectorfromacquantumconnector.credentials.credentialsimportAcQuantumCredentialsfromacquantumconnector.model.backendtypeimportAcQuantumBackendTypefromacquantumconnector.model.gatesimportXGate,Measureapi=AcQuantumConnector()api.create_session(AcQuantumCredentials('username','password'))# Create Experimentexperiment_id=api.create_experiment(bit_width=4,experiment_type=AcQuantumBackendType.SIMULATE,experiment_name='Demo')print(experiment_id)# Update Experimentgates=[XGate(1,1),Measure(2,1)]api.update_experiment(experiment_id,gates)# Get Experimentexp_res=api.get_experiment(experiment_id)print(exp_res)# List Experimentsexp_list=api.get_experiments()print(exp_list)# Run Experimentapi.run_experiment(experiment_id,AcQuantumBackendType.SIMULATE,2,100)# Get Resultapi.get_result(experiment_id)# Download Resultapi.download_result(experiment_id)api.save_session()LicenseThe AcQuantumConnector isfreeandopen source, released under
theApache License, Version 2.0.
Package: acquantum-qiskit
A qiskit provider for the Alibaba’s quantum computer.InstallationThis plugin requires Python version 3.5 and above, as well as qiskit.
Installation can be done using pip:Install from PyPI$pipinstallacquantum-qiskitGetting startedContributingWe welcome contributions - simply fork the repository of this plugin, and then make apull requestcontaining your contribution.
All contributers to this plugin will be listed as authors on the releases.We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on this project.LicenseThe AcQuantum Qiskit Provider isfreeandopen source, released under
theApache License, Version 2.0.
Package: acquifer
Acquifer-Python-APIPython package providing utilitary functionalities when working with an ACQUIFER Imaging Machine.Functionalities include :metadata persing from filenamescontrol of the microscope (tcpip)Similar functions are available for java programs (e.g Fiji) via the acquifer-core package, distributed via the ACQUIFER update site (upon request).InstallationIn a command prompt, usepip install acquifer.The package can be installed locally for test purpose, by opening a command line in the repository directory :pip install -e .with e for "experimental"This way any change to the code is directly reflected.
Package: acquifer-napari
acquifer-napariThe acquifer-napari plugin allows loading IM04 dataset directory, as multi-dimensional images in napari.Sliders for well, channel, time and Z are automatically rendered when there are more than 1 coordinates along the dimension.The plugin uses Dask-Image for efficient data-loading "on request" similar to the VirtualStack in ImageJ.InstallationVia the napari plugin manager : acquifer-napari.
Or with pip :pip install acquifer-napari.Usepip install -e .to install in developement mode, so any change in the source code is directly reflected.Usenpe2 listto check that the plugin is correctly installed and visible by napari.For instance here, the package defines 1 command, which is a reader.One could have more commands, which would be implement other types.This should output something like following
┌──────────────────────────────┬─────────┬──────┬───────────────────────────────────────────────────────────┐
│ Name                         │ Version │ Npe2 │ Contributions                                             │
├──────────────────────────────┼─────────┼──────┼───────────────────────────────────────────────────────────┤
│ acquifer-napari              │ 0.0.1   │ ✅   │ commands (1), readers (1)The plugin should be installed in an environment with napari installed.Napari can be started with thenaparicommand in a command prompt with a system wide python installation.Once installed, napari can be opened in a IPython interactive session with>>importnapari>>napari.Viewer()ConfigurationsThe filenapari.yamlinacquifer_napari_plugindefines what functions of the python package are visible to napari.The top levelnamefield must be the same than the python package name defined insetup.cfg.
It first define a set of commands, which have a customid, and apython_name, which is the actual location of the function in the python package (or module).Then the napari.yaml has optional subsectionsreaders,writers,widget, to reference some of the commands previously defined, to notify napari that they implemente those standard functions.For instance I first define a command myReader pointing to myPackage.myReader, and I reference that command using the id it in the section readersSeehttps://napari.org/stable/plugins/first_plugin.html#add-a-napari-yaml-manifestIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: acquire
Acquireacquireis a tool to quickly gather forensic artifacts from disk images or a live system into a lightweight container.
This makesacquirean excellent tool to, among others, speedup the process of digital forensic triage.
It usesdissectto gather that information from the raw disk, if possible.acquiregathers artifacts based on modules. These modules are paths or globs on a filesystem which acquire attempts to gather.
Multiple modules can be executed at once, which have been collected together inside a profile.
These profiles (used with--profile) arefull,default,minimalandnone.
Depending on what operating system gets detected, different artifacts are collected.The most basic usage ofacquireis as follows:user@dissect~$sudoacquireThe tool requires administrative access to read raw disk data instead of using the operating system for file access.
However, there are some options available to use the operating system as a fallback option. (e.g--fallbackor--force-fallback)For more information, please seethe documentation.RequirementsThis project is part of the Dissect framework and requires Python.Information on the supported Python versions can be found in the Getting Started section ofthe documentation.Installationacquireis available onPyPI.pipinstallacquireBuild and test instructionsThis project usestoxto build source and wheel distributions. Run the following command from the root folder to build
these:tox-ebuildThe build artifacts can be found in thedist/directory.toxis also used to run linting and unit tests in a self-contained environment. To run both linting and unit tests
using the default installed Python version, run:toxFor a more elaborate explanation on how to build and test the project, please seethe
documentation.ContributingThe Dissect project encourages any contribution to the codebase. To make your contribution fit into the project, please
refer tothe development guide.Copyright and licenseDissect is released as open source by Fox-IT (https://www.fox-it.com) part of NCC Group Plc
(https://www.nccgroup.com).Developed by the Dissect Team (dissect@fox-it.com) and made available athttps://github.com/fox-it/acquire.License terms: AGPL3 (https://www.gnu.org/licenses/agpl-3.0.html). For more information, see the LICENSE file.
Package: acquirest
AcquiRestAcquiRest is a web service that turns PostgreSQL database into RESTful API.Free software: MIT licenseDocumentation:https://acquirest.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2022-02-22)First release on PyPI.
Package: acr122u-websocket
ACR122U WebsocketThis project enables you to connect an usb ACR122U NFC card scanner to a computer and access it using Socket.IO.FeaturesRead UUID from nfc cards and send them over Socket.IO.Websocket messages to start and stop the polling for cards.Websocket messages to give a confirmation or error beep and light signal.Automatically reconnect to the reader when interrupted.InstallationYou can install this package fromPyPI.pipinstallacr122u-websocketUsageConnect the ACR122U reader to the computerRun the apppython-macr122u_websocket.appAPIYou can connect to the webserver usingSocket.IO.
These are the available events:pollingStart or stop the polling.Requeststartto start the polling.stopto stop the polling.Replyno card reader connectedif no card reader is connected.polling startedif the polling has (already) started.polling stoppedif the polling has (already) stopped.invalid messageif the message is neitherstartnorstop.status indicatorSet the status indicator.Requestconfimto play the confirming status.errorto play the error status.Replyno card reader connectedif no card reader is connectedconfirm status setif the confirm beep and light have been showerror status setif the error beep and light have been showinvalid messageif the message is neitherconfirmnorerrorcard scannedBroadcasts when a card has been scannedBroadcast{"uuid": [..]}- An object containing the uuid in the form of a list of integers.ExampleSee an example webpage attest.html.This page is also served onhttp://localhost:8080/test.
Package: acrawler-cfscrape
acrawler-cfscrapeThe handler works withaCrawlerandcloudscraperInstallation$pipinstallacrawler_cfscrapeUsageAdd Handler:classMyCrawler(Crawler):middleware_config={"acrawler_cfscrape.CfscrapeHandler":True,}config={"CFS_COOKIES_FILE":Path.home()/".cfscookies","CFS_URL":"http://www.example.com","CFS_PROXIES":None,}
Package: acrawler-prometheus
acrawler-prometheusThe handler works withaCrawlerandPrometheus.Export statistics for:Concurrent requestsTask (Requests, Items) countsQueue statusInstallation$pipinstallacrawler_prometheusUsageAdd Handler:classMyCrawler(Crawler):middleware_config={"acrawler_prometheus.PromExporter":100,}config={"PROMETHEUS_INTERVAL":5}Avaliable Config:PROMETHEUS_ADDR="localhost"PROMETHEUS_PORT=8000PROMETHEUS_INTERVAL=1# exporting interval, in second
Package: acrcasppi-ml
acrcasppi_ml predicts protein-protein interaction between Anti-CRISPR protein (Acr) and CRISPR-associated protein (Cas),
based on the machine learning algorithm.Input file:The user need to provide pairs of acr and cas protein as input. The first and second column should contain acr and cas
protein, respecticely.
An example input file can be obtained from the given linkhttps://github.com/snehaiasri/acrcasppi/blob/main/example.csv.Usage:After installation, perform the following steps to use the packageimport the packageimport acrcasppi_ml as acpsave the input file as dataframe. Provide the full path of the input file.df = pd.read_csv("example.csv")convert the df into numpy arrayvar = df.to_numpy()call the predict functionx = acp.predict(var)to call the predict_proba functiony = acp.predict_proba(var)call gen_file function to generate the output file (output.txt) in your current working directoryacp.gen_file(var)HelpTo see the documentation of each function, use help command. For example, help(acr_cas_ppi) or help(predict).RequirmentsPython >3.9,
numpy,
pickle-mixin,
sklearn.
Package: acrclient
Python ACR Client moduleContains a simple client for calling the v2 endpoints of theACRCloudAPI.Installationpoetryaddacrclient# or on old setup style projectspip-minstallacrclientUsage>>>fromacrclientimportClient>>>client=Client(bearer_token="bearer-token")Development# setup a dev envpython-mvenvenv
.env/bin/activate# install a modern poetry versionpython-mpipinstallpoetry>=1.2.0# install deps and dev versionpoetryinstall# make changes, run testspoetryrunpytestRelease ManagementThe CI/CD setup uses semantic commit messages following theconventional commits standard.
There is a GitHub Action in.github/workflows/semantic-release.yamlthat usesgo-semantic-committo create new
releases.The commit message should be structured as follows:<type>[optional scope]: <description>

[optional body]

[optional footer(s)]The commit contains the following structural elements, to communicate intent to the consumers of your library:fix:a commit of the typefixpatches gets released with a PATCH version bumpfeat:a commit of the typefeatgets released as a MINOR version bumpBREAKING CHANGE:a commit that has a footerBREAKING CHANGE:gets released as a MAJOR version bumptypes other thanfix:andfeat:are allowed and don't trigger a releaseIf a commit does not contain a conventional commit style message you can fix
it during the squash and merge operation on the PR.Once a commit has landed on themainbranch a release will be created and automatically published topypiusing the GitHub Action in.github/workflows/release.yamlwhich usespoetryto publish the package to pypi.LicenseThis package is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, version 3 of the License.CopyrightCopyright (c) 2023Radio Bern RaBe
Package: acrcloudclient
acrcloudACR Cloud Python ClientAPI ACRCloud Dochttps://docs.acrcloud.com/reference/console-apiACRClient Examples UsageList and remove audios that are created on 2021fromacrcloudimportACRCloudClientimportosACR_TOKEN=os.getenv("ACR_TOKEN")deflog(bucket,msg):"""Local log function"""print(f"B:{bucket.name}-{msg}")acr=ACRCloudClient(ACR_TOKEN)forbucketinacr.list_buckets(search="my-bucket-"):print("Processing bucket:",bucket.name)audios=bucket.list_audios(order="asc")forainaudios:if"2021"ina.created_at:a.delete()log(bucket,f"Delete audio:{a.id}-{a.created_at}")else:log(bucket,"Not found any audio from 2021")breakEnvironment VariablesACR_CLOUD_TOKEN =Roadmap planHandle date fields with datetime objectHandle TOKEN through default environment variablesPublish to pip
Package: acrl
almgren-chrissDeep reinforcement learning for optimal execution of portfolio transactions.InstallationpipinstallacrlUsagefromcollectionsimportdequeimportnumpyasnpimportacrlasscafromacrl.agentimportAgentenv=sca.MarketEnvironment()agent=Agent(state_size=env.observation_space_dimension(),action_size=env.action_space_dimension(),random_seed=0,)liquidation_time=60n_trades=60risk_aversion=1e-6episodes=10000shortfall_hist=np.array([])shortfall_deque=deque(maxlen=100)forepisodeinrange(episodes):current_state=env.reset(seed=episode,liquid_time=liquidation_time,num_trades=n_trades,lamb=risk_aversion,)env.start_transactions()foriinrange(n_trades+1):action=agent.act(current_state,add_noise=True)new_state,reward,done,info=env.step(action)agent.step(current_state,action,reward,new_state,done)current_state=new_stateifinfo.done:shortfall_hist=np.append(shortfall_hist,info.implementation_shortfall)shortfall_deque.append(info.implementation_shortfall)breakif(episode+1)%100==0:print("\rEpisode [{}/{}]\tAverage Shortfall: ${:,.2f}".format(episode+1,episodes,np.mean(shortfall_deque)))print("\nAverage Implementation Shortfall: ${:,.2f}\n".format(np.mean(shortfall_hist)))
Package: acrm
Arch Linux Custom Repository ManagerThis program is intended to manage an Arch Linux custom repository hosted on a server accessible throughrsync(for example, on a VPS or a NAS with a/path/to/wwwfolder served over the web by a web server).It works by synchronizing the remote repository on the local machine usingrsync, and then mainly usesrepo-addto
manage this repository to finally synchronizing back to the remote server.It requires to run on an Arch Linux distribution, and simply behaves as a wrapper around some common programs:ProgramFrom packageUsed forunamecore/coreutilsDetecting the architecture of the local machinersyncextra/rsyncSynchronizing the repositorytarcore/tarReading inside the repository databaseGnuPGcore/gnupgSigning the packages and the repositoryrepo-addcore/pacmanManaging the packages in the repository[!NOTE]
The CLI of this program is made withcleo,
used bypoetry.Global optionsOptionShortRequiredDescriptionhostHyesSpecify the host on which the repository is hostedremote_rootryesThe path to the repository, on the remote hostuserunoThe remote user who owns the repositoryDefaults to the current local userrepositorydnoThe name of the repositoryDefaults to the name of the directory[!IMPORTANT]
Currently, some options are required by the CLI as it is the only way to pass information to the ACRM.CommandslsList all the packages with their version in the repository.acrm-Hmy_vps_or_nas-r/home/vps_user/path/to/my/repositoryls+------ repository ------+
| Package name | version |
+--------------+---------+
| acrm         | 0.1.0-1 |
+--------------+---------+
Package: acro
ACRO: Tools for the Automatic Checking of Research OutputsStatistical agencies and other custodians of secure facilities such as Trusted
Research Environments (TREs) routinely require the checking of research outputs
for disclosure risk. This can be a time-consuming and costly task, requiring
skilled staff.ACRO (Automatic Checking of Research Outputs) is an open source
tool for automating the statistical disclosure control (SDC) of research
outputs. ACRO assists researchers and output checkers by distinguishing between
research output that is safe to publish, output that requires further analysis,
and output that cannot be published because of substantial disclosure risk.It does this by providing a light-weight 'skin' that sits over well-known
analysis tools, in a variety of languages researchers might use. This adds
functionality to:identify potentially disclosive outputs against a range of commonly used
disclosure tests;suppress outputs where required;report reasons for suppression;produce simple summary documents TRE staff can use to streamline their
workflow.See the projectwikifor details.Coding standardsAre also described in the projectwikiThis work was funded by UK Research and Innovation under Grant Number MC_PC_23006 as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme (https://dareuk.org.uk/), delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK). The specific project was Semi-Automatic Checking of Research Outputs (SACRO).
Package: acrocord
A python API for managing postgresql databaseInstall and setupInstall third party packages:sudoaptinstallpython3-devlibpq-devunixodbc-devTo install the packagepython3-mpipinstallacrocorduse python in the proper environment e.g. in conda powershellSetup database configuration and connectionSSL connection:if SSL connection is required, put the certificates given by administrator in/home/$USER/.postgresqlcreate the folder if it does not already existDefault connection configuration can be saved inconnections.cfgin the folder/home/$USER/.postgresql/for linux
user or typicallyC:\Users\$USER\.postgresqlfor windows user.Example ofconnections.cfg:[connection-name]user=USERNAMEdbname=DATABASENAMEport=PORThost=HOSTssh=Falsepassword=PASSWORD[!TIP]thehostfield does not recognize ssh alias, use ip addresstheportfield is typically 5432 or 5433the name of the database isdbnameThen in python the connection can directly be instantiate using the keywordconnection-namefromacrocordimportConnectDatabasedb=ConnectDatabase()db.connect(connection="connection-name")Alternatively, you can use the following syntaxfromacrocordimportConnectDatabasedb=ConnectDatabase()connection=dict(user="USERNAME",print_sql_cmd=True,dbname="DATABASENAME",port="PORT",host="HOST",ssh=False)db.connect(print_sql_cmd=True,connection=connection)Simple usageimportpandasaspd# create schema (i.e. an independent database: requires privileges)# write table in schema# read table as pandas dataframedb.create_schema("SCHEMA")db.write_table(pd.DataFrame(1,index=[1,2,3],columns=[1,2,3]),"SCHEMA.NAME")db.read_table("SCHEMA.NAME")[!CAUTION]If the password is trivial (for local connection), add password field to the dictionaryconnectionPassword field can be added inconnections.cfgfileIf no password is provided python will open an log in windowNo password is needed with ssl connectionOther topicsDeploy database: install postresgql server and create database on premiseManage spatial data using postgis: how to install postgis and manipulate spatial dataAuthorEurobios Mews labs
Package: acrolib
InstallationFirst install cython, wichsetup.pyneeds to build the package.pipinstallcythonIn some cases you also have to install additional dependencies.sudoaptinstallpython3-dev
pipinstallwheelUsing pipThen install the package.pipinstallacrolibFrom sourcegitclonehttps://github.com/JeroenDM/acrolib.gitcdacrolib
pythonsetup.pybuild
pythonsetup.pyinstallIf you want to edit the package and test the changes, you can replace the last line with:pythonsetup.pydevelopAcrolibGeneral utilities for writing motion planning algorithms atACRO.
This library is aimed at miscellaneous functions and classes that cannot be grouped in a larger package.Dynamic ProgrammingSolve a specific type of Deterministic Markov Decision Process.
It uses a value function that must be minimized instead of maximized.
It assumes a sequential linear graph structure.QuaternionExtension to thepyquaternionpackage.SamplingA sampler class to generate uniform random or deterministic samples.
Deterministic samples are generated using aHalton Sequence.
Package: acrosort-tex
acrosort-texacrosort-texis a Python Command Line App to sort your acronyms in your.texby their shortform.InstallationYou can install acrosort-tex using pip (note the underscore):pipinstallacrosort_texUsageTo useacrosort-tex, you first need to create a.texfile with a list of acronyms (see inexamplesfor an example file).It doesn't matter if there are other TeX commands before or after theacronymblock.To sort the acronyms, run the following command:acrosort<input_file.tex><output_file.tex>For example:acrosortexamples/List_Of_Abbreviations.texsorted_acronyms.texThis will create a new.texfile calledsorted_acronyms.texwith the sorted acronyms, while everything else isn't touched.It will also find the longest key to set the width of the shortform column in the acronym block.Licenseacrosort_texis licensed under the MIT License. See the LICENSE file for more information.