Saravanan666/pypi-org huggingface dataset:
Package: pipeadapter
Pipedream Adapter for eonsThis library allows you to call "fittings" from yourPipdream(or compatible) workflow.UsageUsing Pipe Adapter is pretty easy:frompipeadapterimportconnectinput={}input["key"]="value"returnconnect('fitting',input)You need to know:What Fitting you want to useThe inputs to pass to that FittingWhat values to provide to those inputs.FittingsThe modules used by Pipeadapter are functors based on theeons library.Among other features, Fittings support:#All necessary args that *this cannot function without.this.requiredKWArgs=[]#For optional args, supply the arg name as well as a default value.this.optionalKWArgs={}Each of these "KWArgs" will become a member in the Fitting. For examplethis.requiredKWArgs.append("my_arg")setsthis.my_argto the value ofthis.Fetch("my_arg"). This system allows users to provide inputs in multiple ways while allowing developers to code in a standard format.Developers also have access to all theeons.UserFunctor utilities.Example FittingimportosimportloggingfromdatetimeimportdatetimefrompipeadapterimportFittingclasstimestamp_to_date(Fitting):def__init__(this,name="Timestamp to Date"):super().__init__(name)this.requiredKWArgs.append("timestamp")this.optionalKWArgs["output_format"]='%m/%d/%Y'# Required Fitting method. See that class for details.defRun(this):dt=datetime.fromtimestamp(this.timestamp)this.output["date"]=dt.strftime(this.output_format)
Package: pipebio
PipeBio Python SDKA Python 3.9+ SDK for thePipeBioplatform.The integrated bioinformatics platform for large molecule and peptide discoveryPipeBio is an integrated cloud-based platform for biologics discovery that allows wet lab scientists to easily analyze antibody and peptide sequences with functional assay data and bioinformaticians to deploy their own code and run workflows.Use PipeBio to configure standard analysis workflows and create SOPs with the range bioinformatics tools on PipeBio forAntibody sequence analysis and discoveryAntibody engineeringShort peptide discoveryNGS, Sanger, PacBio and single cell sequence analysisInstallationpip install pipebioExamplesExample usage of the sdk can be found atapi-examples.SupportPlease contactsupport@pipebio.comfor help.
Package: pipecapacity
IntroductionEvaluate pipe capacity per industry codes.SummaryUsageA quick way to running code is:
- Create a virtual environment:
- Useconda yaml fileto create a new environment
- (or) by installingpipecapacitypackage in an environmentRun the following batch filesDownload thispipecapacity repositoryactivate environmentChange command line to "pipecapacity" (outside not in src) folderRun the following python files in tests:python src\digitalmodel\tests\ {change_to_relevant}.pyi.e. for catenary riser, python src\digitalmodel\tests\test_catenary_riser.pyUnitsverticalDistance: m
declinationAngle: deg with vertical
Temperature: deg F
Pressure: psi
EffectiveTension: kN
BendingMoment: kNm
YieldStrength: psi
E: psi
NominalID: inch
NominalOD: inch
NominalWT: inch
Rho: kg/m3
MassPerUnitLength: kg/m
WeightPerUnitLength: N/m
SoilStiffness: kN/m/m
BuoyancyCoverage: Percentage
ThermalExpansionCoefficient: in/in/deg F
Package: pipecash
========PIPECASH========.. image:: https://img.shields.io/pypi/v/pipecash.svg:target: https://pypi.python.org/pypi/pipecash.. image:: https://img.shields.io/travis/Pipe-Cash/pipecash.svg:target: https://travis-ci.org/Pipe-Cash/pipecash.. image:: https://readthedocs.org/projects/pipecash/badge/?version=latest:target: https://pipecash.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/Pipe-Cash/pipecash/shield.svg:target: https://pyup.io/repos/github/Pipe-Cash/pipecash/:alt: Updates* License: OPEN BLOCKCHAIN-SPECIFIC LICENSE* Documentation: https://pipecash.readthedocs.io.* Website: https://pipe.cash.A framework for easy automation of Bitcoin related tasks. PipeCash is flexible due to it's plugin system and configuration options. Read more at http://pipe.cash/Description------------PipeCash is a framework for automation of Bitcoin Related tasks.It reads a configuration (json file) and executes the appropriate tasks (called "agents") at the appropriate times, in a way similar to IFTTT.An agent may be scheduled (to run at specific times), controlled (to run when another agent tells it to), receive event data (when another agent produces an event), or just be started once and run in the background.Agents represent simple actions:* Sending an email* Reading a file* Getting specific data from the internet* etc...An agent can have access to a wallet and use the standard wallet interface to perform a money related task.Such an agent should work the same, even if it uses a different wallet, or even a different currency.This package contains the core functionality of PipeCash, needed to run any PipeCash instance.The package does not contain any PipeCash agents or wallets. They must come from separate packages.It does not contain any configurations either.At this stage users will be required to create their own configurations.Quick Start------------Run a scenario:.. code-block:: bashpipecash -s /path/to/scenario.jsonIf the scenario needs secret variables, generate them:.. code-block:: bashpipecash -s /path/to/scenario.json --createSecretsFile > secrets.jsonOnce the file is generated, open it and fill the secret variables.To run the scenario together with the secrets, use:.. code-block:: bashpipecash -s /path/to/scenario.json --secretsPath /path/to/secrets.jsonIssues------To tell us about a bug, please see the issue template : https://github.com/Pipe-Cash/pipecash/issues/21=======History=======0.1.0.4 (2019-02-08)------------------* major bug fixes0.1.0.3 (2019-02-08)------------------* Changed OptionTemplateSolver:* Templates resolve to actual objects, instead of their string representation* Templates can no longer be combined with a string outside the template.* Example Template: '{{'one two'.split()}}' resolves to ['one', 'two'] (array)* Example Invalid Template: 'The answer is {{40+2}}!' does not resolve and remains the same string.0.1.0.2 (2019-02-08)------------------* New Feature: GetNeededSecrets* minor bug fixes0.1.0.1 (2019-02-08)------------------* First release on PyPI.
Package: pipecashagents
===============pipecashagents===============.. image:: https://img.shields.io/pypi/v/pipecashagents.svg:target: https://pypi.python.org/pypi/pipecashagents.. image:: https://img.shields.io/travis/Pipe-Cash/pipecashagents.svg:target: https://travis-ci.org/Pipe-Cash/pipecashagents.. image:: https://readthedocs.org/projects/pipecashagents/badge/?version=latest:target: https://pipecashagents.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/Pipe-Cash/pipecashagents/shield.svg:target: https://pyup.io/repos/github/Pipe-Cash/pipecashagents/:alt: UpdatesA collection of common agents used in PipeCash. Learn more about PipeCash on http://pipe.cash/* Free software: MIT license* Documentation: https://pipecashagents.readthedocs.io.Installation----------------pip install pipecashwalletsUsage--------This package is not intended for direct usage.Simply install it and reference its modules in your PipeCash configurations.Contributing----------------All pull requests are wellcome.The areas that need the most help are:- Write agents- Testing- Documentation=======History=======0.1.2.8 (2019-04-28)------------------* shell command agent0.1.2.7 (2019-04-28)------------------* bug fixes0.1.2.6 (2019-04-28)------------------* bug fixes0.1.2.5 (2019-04-27)------------------* bug fixes0.1.2.4 (2019-04-25)------------------* New agents:- HTML text scrape- WriteFile & WriteEventToFile agents- 3 OpReturn Agents- Read File agent- WatchDirectory Agent- GetHandCashAddress agent- ForEach (list splitter to events) agent- RegexFilter Agent- 13 different Twitter Agents0.1.2.2 (2019-04-14)------------------* Fixed broken assertions0.1.2.1 (2019-04-07)------------------* New agents:- EmailSend- OnNewEmail- GetWalletReceiveAddress- OnWalletBalanceChange- WalletSend- AttributeDifference- NumberDifference- DeDuplicationDetector- DelayedEventQueue- RssChecker0.1.1 (2019-02-08)------------------* First release on PyPI.
Package: pipecashwallets
===============pipecashwallets===============.. image:: https://img.shields.io/pypi/v/pipecashwallets.svg:target: https://pypi.python.org/pypi/pipecashwallets.. image:: https://img.shields.io/travis/Pipe-Cash/pipecashwallets.svg:target: https://travis-ci.org/Pipe-Cash/pipecashwallets.. image:: https://readthedocs.org/projects/pipecashwallets/badge/?version=latest:target: https://pipecashwallets.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/Pipe-Cash/pipecashwallets/shield.svg:target: https://pyup.io/repos/github/Pipe-Cash/pipecashwallets/:alt: UpdatesA collection of wallet modules for PipeCaead http://pipe.cash/ for more information.* Free software: MIT license* Documentation: https://pipecashwallets.readthedocs.io.Installation----------------pip install pipecashwalletsUsage--------This package is not intended for direct usage.Simply install it and reference its modules in your PipeCash configurations.Contributing----------------All pull requests are wellcome.The areas that need the most help are:- Wallet implementations- - see pipecashwallets/examples.py to learn how to write wallets- - many of the current wallets are not fully implemented, or need fixes- TestNet wallets- RegTest wallets (for easy tests of PipeCash configurations)- Testing- Documentation=======History=======0.1.2.1 (2019-02-22)------------------- bug fixes0.1.2.0 (2019-02-22)------------------- Hidden the unfinished wallets (no reference in __init__)- Added BitSV wallet - works with private keys only0.1.1 (2019-02-22)------------------- Basic BSV RPC wallet added- Watch-only BitIndex wallet added0.1.0 (2019-02-22)------------------- First release on PyPI.
Package: pipe-cheops
PIPE is a photometric extraction package for CHEOPS that is complementing
the official Data Reduction Pipeline (DRP). While the DRP is using aperture
photometry on the D=200 pixel subarrays provided by CHEOPS, PIPE is using
PSF photometry on the D=60 pixel imagettes, often provided with a shorter cadence
than the subarrays.Why should you care about PIPE when we already have the DRP? There are a couple
of advantages in using PIPE:The shorter cadence of imagettes increases the time resolution of the
photometry. This can be of a distinct advantage for some applications,
e.g. when stellar activity such as flares need to be time resolved, or
when ingress/egress is desired to be better resolved for timing purposes.Targets with a bright source nearby (e.g. binaries) can have their photometry
individually resolved.For faint targets PSF extraction makes better use of the signal by weighting
it properly over the PSF, so that higher S/N photometry can be extracted.The shorter cadence and producing an accurate PSF model makes it easier to
find and correct for cosmic rays.PSF photometry can properly deal with hot or bad pixels by either giving them
a lower weight or masking them entirely in the fitting process. This may become
critical as the detector ages in space, making it increasingly harder to find
“good” regions on the detector without bad pixels.Are there reasons then to not always use PIPE when extracting photometry? Yes,
because accurate PSF photometry requires accurate PSF modelling. The PSF in turn
depends onthe location on the detectorthe spectral energy distribution of the targetthe temperature of the CHEOPS telescope tubethe motion blur introduced by pointing jitterThese challenges to produce an accurate PSF are addressed in PIPE but also
mean that PIPE is not robust enough to be run blindly, as it can fail if
assumptions about the data are wrong. In principle one could attempt to
anticipate all ways assumptions can fail, but in practice we are quite far
from achieving that. A poorly determined PSF could result in photometric
performance that is worse than that provided by the DRP. Thus, PIPE needs to
be run in a supervised mode and is not a replacement for the DRP.Find the PIPE manual here:https://github.com/alphapsa/PIPE/blob/ad47132ca5afaaf512c9da81759fa5e11c6f3ac1/docs/pipe/PIPE_manual.pdfOriginal author: Alexis Brandeker <alexis@astro.su.se>, Stockholm University.LicenseThis project is Copyright (c) Alexis Brandeker and licensed under
the terms of the GNU GPL v3+ license. This package is based upon
theOpenastronomy packaging guidewhich is licensed under the BSD 3-clause licence. See the licenses folder for
more information.
Package: pipeclip
PIPE-CLIPPipeline for CLIP-seq Analysis.
This is a fork version (2.0.x) ofPIPE-CLIP.Requirement:Python >=3.6;Python packages:pysam,pybedtoolsandrpy2. (Python packages will be installed automaticallly)R >=3.0;R packages:MASS,VGAMand their dependencies. (R packages will be installed automatically)Perl >=5.0Other packages:HOMER(annotatePeaks.pl) and annotation filesMake sure HOMER are in your PATH. You can test this by type "annotatePeaks.pl" from anywhere and you should get help information of this command.How to install:pipinstallpipeclipHow to use:pipeclip-iinput.bam-ooutput_prefix-cCLIP_type-lminimum_matchlength-mmaximum_mismatchcount-rRemove_PCR_duplicate-MFDR_for_mutations-CFDR_for_clusters-sspecies-iinput BAM-tcontrol BAM-ooutput prefix-cCLIP type,[0,1,2,3] (0)HITS-CLIP; (1)PAR-4SU; (2)PAR-6SG; (3)iCLIP-rmethod to remove PCR duplicate,[0,1,2] (0)No removal; (1)Remove by read start; (2)Remove by sequence-lminimum match length-mmaximum mismatch count-MFDR to get significant mutations-CFDR to get enriched clusters-sspecies. (species might be hg19, mm10, mm9.) Leave blank to skip annotation step.FootnoteCite:Chen, B., Yun, J., Kim, M.S. et al. PIPE-CLIP: a comprehensive online tool for CLIP-seq data analysis. Genome Biol 15, R18 (2014).https://doi.org/10.1186/gb-2014-15-1-r18
Package: piped-api
Piped API client (Python)A Python API wrapper forPiped. This can essentially be used as an alternative way to access YouTube's API, without needing to use an API key.Installationpipinstallpiped-apiQuickstartGetting started is very easy:frompiped_apiimportPipedClientCLIENT=PipedClient()# Print out the first audio stream URL for a video:video=CLIENT.get_video(video_id)audio_stream=video.get_streams('audio')[0]print(f"Audio stream URL:{audio_stream.url}({audio_stream.mime_type})")You can find more examples in thetestsfolder.Why?The creation of this package was primarily fueled by the same type of motivationPiped has.Google's API is not very easy-to-use - you must obtain some JSON thingy to use it, and it is very low-level and not very user-friendly.
On the other hand, this package accessed thePiped API, which has a much more high-level API and doesn't need an account or API keys.It is not meant to be a replacement for the official YouTube API, but it can help you to cut the strings that Google attaches to you when using their API.Useful linksPiped's official API documentationDocumentation for this package🎁 Support meI create free software to benefit people.
If this project helps you and you like it, consider supporting me by donating via cryptocurrency:CryptoAddressBitcoinE-mail meEthereum0x12C598b3bC084710507c9d6d19C9434fD26864CcLitecoinLgHQK1NQrRQ56AKvVtSxMubqbjSWh7DTD2DashXe7TYoRCYPdZyiQYDjgzCGxR5juPWV8PgZZcash:t1Pesobv3SShMHGfrZWe926nsnBo2pyqN3fDogecoin:DALxrKSbcCXz619QqLj9qKXFnTp8u2cS12Ripple:rNQsgQvMbbBAd957XyDeNudA4jLH1ANERLMonero:48TfTddnpgnKBn13MdJNJwHfxDwwGngPgL3v6bNSTwGaXveeaUWzJcMUVrbWUyDSyPDwEJVoup2gmDuskkcFuNG99zatYFSBitcoin Cash:qzx6pqzcltm7ely24wnhpzp65r8ltrqgeuevtrsj9nEthereum Classic:0x383Dc3B83afBD66b4a5e64511525FbFeb2C023DbMore cryptocurrencies are supported. If you are interested in donating with a different one, pleaseE-mail me.
No other forms of donation are currently supported.
Package: pipedata
pipedataChained operations in Python, applied to data processing.InstallationTo install with all optional dependencies:pipinstallpipedata[ops]If you only want the core functionality (building pipelines), and not
the data processing applications, then:pipinstallpipedataExamplesChaining Data Operationspipedata.ops provides some operations for streaming data through memory.importjsonimportzipfileimportpyarrow.parquetaspqfrompipedata.coreimportStreamfrompipedata.opsimportjson_records,parquet_writer,zipped_filesdata1=[{"col1":1,"col2":"Hello"},{"col1":2,"col2":"world"},]data2=[{"col1":3,"col2":"!"},]withzipfile.ZipFile("test_input.json.zip","w")aszipped:zipped.writestr("file1.json",json.dumps(data1))zipped.writestr("file2.json",json.dumps(data2))result=(Stream(["test_input.json.zip"]).then(zipped_files).then(json_records()).then(parquet_writer("test_output.parquet")).to_list())table=pq.read_table("test_output.parquet")print(table.to_pydict())#> {'col1': [1, 2, 3], 'col2': ['Hello', 'world', '!']}Alternatively, you can construct the pipeline as a chain:importpyarrow.parquetaspqfrompipedata.coreimportChain,Streamfrompipedata.opsimportjson_records,parquet_writer,zipped_files# Running this after input file created in above examplechain=(Chain().then(zipped_files).then(json_records()).then(parquet_writer("test_output_2.parquet")))result=Stream(["test_input.json.zip"]).then(chain).to_list()table=pq.read_table("test_output_2.parquet")print(table.to_pydict())#> {'col1': [1, 2, 3], 'col2': ['Hello', 'world', '!']}Core FrameworkThe core framework provides the building blocks for chaining operations.Running a stream:frompipedata.coreimportStream,opsresult=(Stream(range(10)).then(ops.filtering(lambdax:x%2==0)).then(ops.mapping(lambdax:x^2)).then(ops.batched(lambdax:x,2)).to_list())print(result)#> [(2, 0), (6, 4), (10,)]Creating a chain and then using it, this time using the
pipe notation:importjsonfrompipedata.coreimportChain,Stream,opschain=(Chain()|ops.filtering(lambdax:x%2==0)|ops.mapping(lambdax:x^2)|ops.batched(lambdax:sum(x),2))print(Stream(range(10)).then(chain).to_list())#> [2, 10, 10]print(json.dumps(chain.get_counts(),indent=4))#> [#>     {#>         "name": "_identity",#>         "inputs": 10,#>         "outputs": 10#>     },#>     {#>         "name": "<lambda>",#>         "inputs": 10,#>         "outputs": 5#>     },#>     {#>         "name": "<lambda>",#>         "inputs": 5,#>         "outputs": 5#>     },#>     {#>         "name": "<lambda>",#>         "inputs": 5,#>         "outputs": 3#>     }#> ]Similar FunctionalityPython has built in functionality for building iteratorsLangChainimplements chained operations using itsRunnable protocol
Package: pipedi
PipediA simple Python package that enables piping iterables to functions with the pipe|character like in a shell.withopen('input.txt','r')asinput_file:res=list(input_file\|strip('\n')\|tail(10)\|rev()\|grep('x',invert=True)\|cut(delimiter=';',fields=[1,0,3])\|sort(delimiter=';',fields=[2,1])\|tac())print(res)Installpip install pipediUsageIt provides two decorators that enable a function to be used in a pipe.frompipediimportpiped,pipableUse thepipabledecorator for simple functions that edit items in the stream (i.e. don't remove or add items). These functions must take an items as the first parameter. Rest of the parameters can be anything.@pipabledefwrap(item,pre,post):returnpre+item+postUse thepipeddecorator for functions that edit the stream (e.g. filter, add, or enumerate items). These functions must take an iterable as the first parameter. The rest can be anything.@pipeddefgrep(iterable,regex):forlineiniterable:ifre.search(regex,line)isnotNone:yieldline@pipeddefduplicate(iterable):fori,lineinenumerate(iterable):yieldlineyieldline@pipeddefenum(iterable):fori,lineinenumerate(iterable):yield(i,line)When used in a pipe, the first parameters (item or iterable) are omitted. Rest of the arguments are provided to the function.['apple','banana','cocos']|grep('^.[aeiouy]')|wrap('[',']')See more examples in the tests directory and the src/pipedi/util directory.The result of a pipe is a normal iterable, so you can use existing tools on it.For example to duplicate a stream, you can useitertools.dup.fromitertoolsimportdupwithopen('input.txt','r')asinput_file:lines=input_file|strip('\n')input1,input2=dup(lines)lines_with_x=input1|grep('x')lines_without_x=input2|grep('x',invert=True)Building# Clone this repository$gitclonegit@github.com:akupar/pipedi.git
$cdpipedi# Create virtual environment.../pipedi$python-mvenvvenv# Activate virtual environment.../pipedi$.venv/bin/activate# Install requirements(venv).../pipedi$pipinstall-rrequirements.txt# Install the pipedi package in editable mode(venv).../pipedi$pipinstall-e.# Run tests(venv).../pipedi$python-mpytest# Preview readme(venv).../pipedi$grip-bREADME.md# Build package(venv).../pipedi$python-mbuild``´
Package: pipedrivepy
pipedrivepyPipedrive API generic client for PythonRequirementspipedrivepy tested under Python 3.6+. Synchoronous version requiresrequestspackage. Asynchoronous version requiresaiohttppackage.InstallationYou can install pipedrivepy with:pip install pipedrivepy[sync]for synchoronous version;pip install pipedrivepy[async]for asynchoronous version;UsagePipedrive API Referencepipedrivepy uses chain technique to build endpoint path.For example,client.deals(100).followersmakes path to/deals/100/followers.To send request need call one of control methods:get(**query)to GET request;add(**payload)to POST request;update(**payload)to PUT request;delete(**payload)to DELETE request;You must first check API method signature to build right path and call right method.Example usage of synchoronous client"""Add a dealhttps://developers.pipedrive.com/docs/api/v1/#!/Deals/post_deals"""frompipedriveimportPipedriveErrorfrompipedrive.sioimportClientclient=Client('your-company-domain','your-token')defadd_deal(title):try:data=client.deals.add(title='Test deal')exceptPipedriveErroraserror:print(error.code)# Show HTTP coderaiseValueError(str(error))print(data['data']['id'])Example usage of asynchoronous client"""Merging two dealshttps://developers.pipedrive.com/docs/api/v1/#!/Deals/put_deals_id_merge"""frompipedriveimportPipedriveErrorfrompipedrive.aioimportClientclient=Client('your-company-domain','your-token')asyncdefmerge_deals(deal_id,merge_with_id):try:data=awaitclient.deals(42).merge.update(merge_with_id=24)exceptPipedriveErroraserror:print(error.code)# Show HTTP coderaiseValueError(str(error))print(data['data']['id'])LicenseMIT
Package: pipedrive-py
Pipedrive Python================A Python library to interact with the Pipedrive REST API.Other libs were available, but they tend to be very thing wrappers, basically json in and out.While this works, as the integration grows the lack of structure and models will make using the API harder and take longer.This lib aims to have rich types for all resources.Design Goals-------------We are aiming at:* Rich models and schema. All properties listed and full fledged when possible.* Good test coverage, partly why models and resources are separated.* Good hooks for logging and metrics.Usage-----Sample usage:```pythonfrom pipedrive import PipedriveAPIapi = PipedriveAPI('your api token')print api.deal.detail(200).user.email # someone@example.com```Creating an activity:```pythonfrom activity import Activityactivity = Activity(raw_data={'subject': "Do something", 'type': "Call me!"})created_activity = api.activity.create(activity)print created_activity.id```Current Status--------------The API is pretty big, and has tons of resources and each one has tons of fields.We have to be pragmatic: we're only creating the models and resources for the objects we are currently using.This means that right now very few entities are actually supported. It is, however, awfully easy to add new ones. See for example the activity.Activity model and activity.ActivityResource for a pointer.TODO----Major points:* Implement errors and normalized response for objects* Implement metrics and logging callbacksI needed this, so I made this.Arthur Debert <arthur@loggi.com>Topic :: Software Development :: Libraries :: Python Modules