Saravanan666/pypi-org huggingface dataset:
Package: ravpy
Ravpy - The Provider's LibraryIntroducing Raven Protocol's Python SDK for Providers that allows compute nodes across the world to participate in the Ravenverse.Providers are those who are willing to contribute their local system's idle compute power to execute the requester's computational requirements in return for rewards (Raven Tokens).Ravpy is a python SDK that allows providers to intuitively participate in any ongoing graph computations in the Ravenverse. Ravpy nodes are authenticated based on their Ravenverse tokens which must be first generated by visiting theRavenverse website.Ravpy connects to Ravsock (backbone server) from which it receives subgraphs (small groupings of Ravop operations) based on a benchmarking procedure. During this procedure, Ravpy client systems are rigorously tested on a variety of operations and the results are utilized by Ravsock for efficiently assigning complex subgraphs to the provider.InstallationWith PippipinstallravpyUsageThis section details how to join Ravenverse as a Provider. The relevant scripts are available in theRavenverse Repository. It is recommended to clone the Ravenverse repo and navigate to theProviderfolder, where scripts for following steps are available.Setting Environment VariablesCreate a .env file and add the following environment variables:RAVENVERSE_URL=http://server.ravenverse.aiRAVENVERSE_FTP_HOST=server.ravenverse.aiRAVENVERSE_FTP_URL=server.ravenverse.aiLoad environment variables at the beginning of your code using:fromdotenvimportload_dotenvload_dotenv()Participate in Distributed ComputingA Provider can choose which graph to participate in. In order to list which graphs are available, the following command can be used:fromravpy.utilsimportlist_graphslist_graphs(approach="distributed")This command displays a list of executed graphs along with the minimum requirements to participate in their computation, including system requirements and minimum stake required. A provider can participate in a graph only if their system meets these requirements and they have sufficient tokens in their account to meet the minimum stake amount.The following command can be run to participate in a graph (the Provider must note the id of the graph they wish to participate in):fromravpy.distributed.participateimportparticipateparticipate(token='<token>',graph_id=4)Upon participation, the Provider will be assigned a number of subgraphs to execute. Once they have been executed, the results will be sent to the Ravsock server. The full staked amount will be returned along with their earnings on successful computation of all assigned graphs.However if the provider disconnects before the computation of their share of subgraphs, a portion of the staked amount will be deducted.Participate in Federated Analyticsfromravpy.federated.participateimportparticipateparticipate(graph_id=3,file_path="<file_path>")LicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: raw2fits
raw2fitsA Python package to convert camera raw images to astronomical fits files.Installationpipinstallraw2fitsUsageTo convert a raw image to a fits file.fromraw2fits.imageimportImageimg=Image(path='path/to/raw/image',debayer_method="VNG")img.save_fits(image_type="LIGHT",path='path/to/save/fits/image')wheredebayer_methodcan be one of the following: "VNG", "Bilinear". We recommend using "VNG" for better results.Comparsion between different debayer methodsThe following images are the result of converting a raw image to a fits file using different debayer methods.Debayer MethodImageBilinearVNGVNG (by Pixinsight)Debayer MethodImageBilinearVNGVNG (by Pixinsight)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMITAcknowledgementsThis extraction of the bayer image is based on therawpy.
Package: raw2mzml
raw2mzmlis a Python package for wraping common use cases ofmsconvert.execommandline tool.msconvert.exeis part of Proteowizard (<http://proteowizard.sourceforge.net/>)
and allows conversion from Thermo Fisher .raw files to open source formats
.mzML and .mzXML. It has many options and getting them right is not always easy and wrong usage
results in wrong conversion results. Insteadraw2mzmlhas only a configurable options which
cover common use cases.Beyond the package it ships with a command line tool having the same name.
After installation you might run$ raw2mzml –helpto learn about its usage.
Package: raw2tmap
raw2tmapConvert OME-Zarr files to TMAP formatRequirementsPython3.9 or laterInstallationUse the package managerpipto install raw2tmap:pip install raw2tmapTo enable the--dzioption, install with thedziextra (requires libvips):pip install "raw2tmap[dzi]"UsageTo convert an OME-Zarr file to TMAP format:❯ raw2tmap --help
Usage: raw2tmap [OPTIONS] RAW_FILE_OR_URL TMAP_FILE

  Convert OME-Zarr files to TMAP format.

Options:
  -t, --time INTEGER RANGE    Time index.  [x>=0]
  -c, --channel INTEGER/TEXT  Channel index or name.
  -z, --depth INTEGER RANGE   Depth (z) index.  [x>=0]
  --layers DIRECTORY          Path to layer images, relative to TMAP_FILE.
                              Defaults to '.{TMAP_FILE}/layers'.
  --compression ALGORITHM     Compression algorithm.  [default: none]
  --tilesize INTEGER RANGE    Tile size in pixels.  [default: 256; x>0]
  --format [0.1|0.2|0.3|0.4]  OME-Zarr format version.  [default: 0.4]
  --dzi                       Write DZI file (requires pyvips).
  -q, --quiet                 Quiet mode (hide progress bar).
  --version                   Show the version and exit.
  --help                      Show this message and exit.Example:raw2tmap -t 0 -z 10 https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0052A/5514375.zarr 5514375.tmapOME-Zarr files can for example be created usingbioformats2raw.SupportIf you find a bug, pleaseraise an issue.ContributingPull requests are welcome.For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.ChangelogChangelogAuthorsSciLifeLab BioImage Informatics Facility (BIIF)LicenseMIT
Package: rawake
Remote Computer Awake (rawake)Installation$pipinstall-Urawake# install latest version$rawake--version# verify installationConfigurationTherawakeconfiguration is a python-base configuration file. Configuration should be stored at~/.config/rawake/config.pyExample:fromrawake.configimportConfig,ComputerDEFAULT_SSH_SUSPEND_COMMAND="sudo systemctl suspend"CONFIGURATION=Config(computers=[Computer(name="remote-server",ip_address="192.168.0.10",mac_address="22:1b:5c:44:12:6b",ssh_port=2222,ssh_suspend_command=DEFAULT_SSH_SUSPEND_COMMAND,),Computer(name="other-server",ip_address="192.168.0.200",mac_address="a4:44:c3:61:10:b8",ssh_suspend_command="sudo shutdown -h now",),],)Listing the configuration$rawake--list[{"name":"other-server","mac_address":"a4:44:c3:61:10:b8","ip_address":"192.168.0.200","ss_suspend_command":"sudo shutdown -h now","ssh_port":22},{"name":"remote-server","mac_address":"22:1b:5c:44:12:6b","ip_address":"192.168.0.10","ss_suspend_command":"sudo systemctl suspend","ssh_port":2222}]Suspend remote computer:rawakerequires a username:password SSH connection to the remote host to be able to execute the configured suspend command.$rawake--suspendremote-server
SSHauthentication:
username:username
password:<password>Awake remote computer:For awaking a remote computer,rawakesends aWake-On-Lan magic packet.rawake--awakeremote-serverDevelopmentPython dev environment:Create new Python virtual environment:pyenvvirtualenv3.11rawakeActivate the virtualenv:pyenvactivaterawakeInstall dev and test dependencies:pip install .[dev]pip install .[test]Install git pre-commit hookspre-commit installpre-commit autoupdateRunning the tests:pytest.
Package: rawbuilder
an elegant datasets factoryFree software: MIT licenseDocumentation:https://rawbuilder.readthedocs.io.FeaturesSchema oriented datasets builderHow to Use itTerminal:# Import the package into any python app
import rawbuilder as rw# Init the dataset object as ds
ds = rw.DataSet(
size=1000,
task=’user’,
)# Build the dataset
ds.build()# Optionals
ds = rw.DataSet(
size=1000,
task=’user’,
schema_path=’where/to/read/schema/from’,
schema_dict=’{‘user’:{‘id’:’int’}}’
)df = ds.build(
output_path=’your/output/directory’,
export_csv=True,
return_df=True
)SchemaThe Schema is a JSON object that describes three main components.Themodel names, thecolumn names, and thedata typesper column.Note the below code-block, The model name is “Student”, and it contain 4 columns [id,first_name,email,math_test_results].Each property of the model “student” is called a task and it has its columns and data description.Student data model example:“student”: {
“id”: “int”,
“first_name”: “first_name”,
“last_name”: “last_name”,
“email”: “email”,
“math_test_results”: “random_int between,0,30”
}Data types to use in the schemaint: build a column of integers between 1 and requested dataset size.decrement: build a column of decremented integers between the requested size and 1.random_int: build a column of random integers between 0 and 100 by default.random_float: build a column of random floats between 0 and 1 by default.first_name: build a column of first names.last_name: build a column of last names.email: build a column of fake emails.password: build a random string passwords with default length of 12 characters.Data ModifiersCombine Data Modifiers to the above data types, it can adjust values, change the data nature, and gives more control over the final output.Modifiers syntax is simple:“modifier,argument_1,arg_2,arg_*”Use the modifierbetweento generate random integer column between 0 and 30:“math_test_results”: “random_int between,0,30”All Modifiers1)RangesUse this modifier to set the high-end and low-end for a specific data typeSyntax:“between,10,1000”Supported withrandom_int:“math_test_results”: “random_int between,0,30”random_float:“heights”: “random_float between,1.30,1.80”password:“password”: “password between,12,12”History0.0.4 (2021-11-13)Data modifiers0.0.3 (2021-11-05)Migrate to JSONGenerate simple datasets0.0.2 (2021-11-05)Proof of concept0.0.1 (2021-10-24)First release on PyPI.
Package: rawdata
Generate realistic raw datasets with optional DQ issuesTo install runpipinstallrawdataBasic UsageCreate a random tableimportrawdata.generatecolLabel=['Year','Name','Born','Details','Amount']colTypes=['DATE','PEOPLE','PLACE','WORD','CURRENCY']tbl=rawdata.generate.TableGenerator(3,colTypes,colLabel)print(tbl)>Year,name,Age,Born,Details,Amount>2013,Douglas,34,Scandinavia,BowlingBall,$34.95>1999,Hunter,65,SierraLeone,Fish,12.00>2005,Shubha,18,Madagascar,screenplay,-$231.00Adding Errors to a tableimportrawdata.errorst=rawdata.errors.TableWithErrors(tbl,'BAD_STRING')t.add_errors(3)print(t.tbl)And after adding 3 random errors there are additional spaces in Douglas, a fake string in Douglas Born column, and the Born column is missing for HunterYearNameBorn------------------------2013DouglasBAD_STRING1999Hunter2005ShubhaMadagascarYou can use columns generated via a custom listcustom_list=['Carved Statue','1984 Volvo','2 metre Ball of string']tbl=TableGenerator(5,['PEOPLE','INT',custom_list],['Name','Age','Fav Possession'])print(tbl)>Name,Age,FavPossession>Inez,58,CarvedStatue>Zane,50,2metreBallofstring>Jered,49,1984Volvo>Tameron,55,2metreBallofstring>Wyatt,68,CarvedStatueOther functionsimportrawdata.generaten=rawdata.generate.NumberGenerators=rawdata.generate.StringGeneratorprint('Random Number    = ',n.random_int(1,100))>RandomNumber=84print('Random Letters   = ',s.random_letters(40))>RandomLetters=T1CElkRAGPAmWSavbDItDbFmQIvUh26SyJE58x49print('Random Password  = ',s.generate_password())>RandomPassword=peujlsmbf19966YKCXwords=rawdata.generate.get_list_words()print(len(words),' words : ',words[500:502])>10739words:['architeuthis','arcsine']places=rawdata.generate.get_list_places()print(len(places),' places : ',places[58:60])>262places:['Brazil','British Virgin Islands']List of Column Types (Table Generator)'INT'-returnsanumber'CURRENCY'-returnsacurrencythatmayhavestrings$/pounds'STRING'-returnsarandomstring'WORD'-returnsawordfromnouns.csv'DATE'-returnsadate'YEAR'-returnsayear.Bothyearanddatecanhaverangessetviaset_range()'PLACE'-returnsalocationfromcountry.csv'PEOPLE'-returnsanamefromnames.csv[list]-passanylisttoreturnarandomchoicefromit(e.g.my_colours=['Blue','Green','Orange'])More information is athttps://github.com/acutesoftware/rawdata
Package: raw-database
Simple Module For DatabaseThis is a simple Python module that provides functions to perform basic insert, update, and delete actions on a database.InstallationTo use this module, you need to have Python installed on your system. You can install the module using pip:pipinstallraw-databaseUsageImport the module into your Python script:from raw_database import Insert, Update, QuerySetInsert ActionInsert(connection, is_commit=False).into(table).values(**values).returning(return_key).fetchone()connection: Connection to databaseis_commit: Determine whether to commit this datatable: The name of the table where the data will be inserted.values: A dictionary containing the data to be inserted, with column names as keys and corresponding values.return_key: A key will be return if insert is successExample usage:Insert(connection_, is_commit=False).into("accounts").values(user_id=6, username="Tom B", email="test email").returning("user_id").fetchone()Update ActionUpdate(connection, is_commit=False)(table).set(**values).where(**conditions).returning(return_key).fetchone()connection: Connection to databaseis_commit: Determine whether to commit this datatable: The name of the table where the data will be inserted.values: A dictionary containing the data to be inserted, with column names as keys and corresponding values.conditions: A dictionary containing conditions to updatereturn_key: A key will be return if insert is successExample usage:Update(connection_, is_commit=False)(table="accounts").set(email="test email update", username="test username update").where(user_id=1).returning("user_id").fetchone()Get ActionQuerySet(connection).select(*fields).from_(table).where(**condition).fetchone()connection: Connection to databasefields: A list containing the selected from tabletable: The name of the table where the data will be inserted.conditions: A dictionary containing conditions to updateExample usage:QuerySet(connection_).select("user_id", "username").from_("accounts").where(user_id=1).fetchone()
QuerySet(connection_).select().from_("accounts").fetchall()
QuerySet(connection_).select().from_("accounts").limit(2).fetchall()
QuerySet(connection_).select().from_("accounts").order_by("user_id DESC").fetchall()
QuerySet(connection_).select("user_id").from_("accounts").where(user_id__in=(1, 2, 3), username='Tom B. Erichsen').fetchall()
QuerySet(connection_).select("user_id").from_("accounts").where(user_id__in=(1, 2, 3)).fetchall()
QuerySet(connection_).select("password", "COUNT(*) AS same_password").from_("accounts").where(user_id__in=(1, 2, 3, 4)).group_by("password").fetchall()ContributingContributions are welcome! If you'd like to contribute to this module, please follow these steps:Fork the repository atGitHub.Create a new branch for your feature/bug fix.Make your changes and commit them.Push your changes to your fork.Submit a pull request.
Package: raw-databases
Simple Module For DatabaseThis is a simple Python module that provides functions to perform basic insert, update, and delete actions on a database.InstallationTo use this module, you need to have Python installed on your system. You can install the module using pip:pipinstallraw-databaseUsageImport the module into your Python script:from raw_database import Insert, Update, QuerySetInsert ActionInsert(connection, is_commit=False).into(table).values(**values).returning(return_key).fetchone()connection: Connection to databaseis_commit: Determine whether to commit this datatable: The name of the table where the data will be inserted.values: A dictionary containing the data to be inserted, with column names as keys and corresponding values.return_key: A key will be return if insert is successExample usage:Insert(connection_, is_commit=False).into("accounts").values(user_id=6, username="Tom B", email="test email").returning("user_id").fetchone()Update ActionUpdate(connection, is_commit=False)(table).set(**values).where(**conditions).returning(return_key).fetchone()connection: Connection to databaseis_commit: Determine whether to commit this datatable: The name of the table where the data will be inserted.values: A dictionary containing the data to be inserted, with column names as keys and corresponding values.conditions: A dictionary containing conditions to updatereturn_key: A key will be return if insert is successExample usage:Update(connection_, is_commit=False)(table="accounts").set(email="test email update", username="test username update").where(user_id=1).returning("user_id").fetchone()Get ActionQuerySet(connection).select(*fields).from_(table).where(**condition).fetchone()connection: Connection to databasefields: A list containing the selected from tabletable: The name of the table where the data will be inserted.conditions: A dictionary containing conditions to updateExample usage:QuerySet(connection_).select("user_id", "username").from_("accounts").where(user_id=1).fetchone()
QuerySet(connection_).select().from_("accounts").fetchall()
QuerySet(connection_).select().from_("accounts").limit(2).fetchall()
QuerySet(connection_).select().from_("accounts").order_by("user_id DESC").fetchall()
QuerySet(connection_).select("user_id").from_("accounts").where(user_id__in=(1, 2, 3), username='Tom B. Erichsen').fetchall()
QuerySet(connection_).select("user_id").from_("accounts").where(user_id__in=(1, 2, 3)).fetchall()
QuerySet(connection_).select("password", "COUNT(*) AS same_password").from_("accounts").where(user_id__in=(1, 2, 3, 4)).group_by("password").fetchall()ContributingContributions are welcome! If you'd like to contribute to this module, please follow these steps:Fork the repository.Create a new branch for your feature/bug fix.Make your changes and commit them.Push your changes to your fork.Submit a pull request.
Package: rawdatarinator
RawDatarInatorPython bindings for BART’stwixreadfunction ([1]).  This new
version of rawdatarinator replaces the old Python port of a MATLAB
script.  Consequently, this one runs much faster and breaks less
often.  A cleaner interface is provided which will break all old
code.  See usage for examples.  In general, this new release has
fewer features that work better.The name of the project pays homage to the naming convention of the
main antagonist in Disney’s hit cartoon “Phineas and Ferb” ([2]).InstallationThis package is known to work with Python 3.6.8 using Ubuntu 18.04.
If it doesn’t work on your system, please submit an issue and we can
try to figure something out.  The goal is to provide a simple
pip-installable Python interface for BART’s twixread function.  The
Cythonized BART code is compiled during the installation, so you will
need a C compiler installed.Use pip to install:pipinstallrawdatarinatorUsageYou get a few functions:# BART's twixread function can be invoked right from a Python# script:fromrawdatarinatorimporttwixreadtwixread('path/to/input/data.dat','path/to/output')# A pair of .cfl, .hdr will be created: path/to/output.[cfl/hdr]# as BART normally does.  However, if you're using these Python# bindings, you might want to just grab the data straight away.# You can do this by not providing any output file:data=twixread('path/to/data.dat',A=True)# You can read existing BART files like this:fromrawdatarinatorimportreaddata=read('path/to/file/without/extentsion')# If you've used BART's Python interface, this is a modified# verison of readcfl.  It works just like that.  Similarly,# BART's writecfl functionality can be found:fromrawdatarinatorimportwritewrite('path/to/out',data)References[1]Uecker, Martin, et al. “Berkeley advanced reconstruction
toolbox.” Proc. Intl. Soc. Mag. Reson. Med. Vol. 23. 2015.[2]List of Doofenshmirtz’s schemes and inventions
Package: rawdatx
rawdatx is a Python 2.7, 3.4, 3.5 converter that generates Excel xlsx files
from TOA5 comma-separated text files produced by Campbell Scientific LoggerNet.
Sensor input, processing instructions, and output structure are specified
in a single XML Definition File that also serves as documentation.rawdatx is available under the MIT license.
The code of the project is hosted athttps://github.com/cpetrich/rawdatxand packages are available on PyPI athttps://pypi.python.org/pypi/rawdatx/.
Documentation is available athttps://rawdatx.readthedocs.org/.InstallationThe following prerequisites need to be installed:Python 2.7, 3.4, or 3.5numpy 1.9 or higherxlsxwriteroptionally (recommended):lxmlastevalThe easiest way to install rawdatx is through pip:pip install rawdatxAlternatively, download the latest version from the repositoryhttps://github.com/cpetrich/rawdatxand install withpython setup.py install.UsageTo convert a TOA5 file to XLSX, run the following script:import rawdatx.read_TOA5 as read_raw_data
import rawdatx.process_XML as process_XML

config = './config.cfg'
read_raw_data.main(config)
process_XML.main(config)Input and output files are specified in an UTF-8 encoded
configuration fileconfig.cfg:[RawData]raw_data_path=./raw-data/mask=CR1000_*.datlogger_time_zone=UTC+1[Metadata]Project=My project name[Files]xml_map_path=./xml_map=data_map.xmldata_path=./processed_data_xlsx=processed_data.xlsxxml_dtd_out=data_map.dtdraw_data=consolidated_raw_data.npyprocessed_data_npy=processed_data.npyThe[RawData]section specifies the location of the logger input files,
the[Metadata]section defines metadata entries copied into the
XLSX file, and the[Files]section specifies path and file names of
output and intermediate files (data_path) and input
XML Definition File (xml_map_pathandxml_map).The XML Definition File (data_map.xml) may look like this:<?xml version="1.0" encoding="UTF-8" ?><measurementsfrom="2015/05/03 11:45"><groupname="Logger"><mapname="Battery Voltage"unit="V"src="Batt_V"/><mapname="Internal Temperature"unit="°C"src="T_panel"/></group><groupname="Weather"><mapname="Air Temperature"unit="°C"src="T_air"/><mapname="Relative Humidity"unit="%"src="RH"/><mapname="Wind Speed"unit="m/s"src="Wind_speed"/><mapname="Wind Direction"unit="°"src="Wind_direction"/></group></measurements>See also examples and test files in the repository athttps://github.com/cpetrich/rawdatx.
Package: rawdisk
rawdiskExperimental python code to explore different volume formatsFree software: BSD licenseDocumentation:https://rawdisk.readthedocs.org.FeaturesTODOHistory0.2.0 (2017-05-13)Cleared spiderwebsFixed failing buildsConverted project to Python 3.5Updated usage examples0.1.2 (2014-11-21)Added couple filesystem binary samplesNew filesystem diagramsNew rawdisk class diagramsEnabled code coverage for Travis CIAdded waffle.io badgeAdded more testsCouple fixes for plugin filesRemoved hurry.filesize dependencyRemoved Py2.6 compatibility0.1.1 (2014-11-10)Moved to the project template generated by cookiecutter.