Saravanan666/pypi-org huggingface dataset:
Package: paragoo
paragoois a static site generator, written in Python. It takes a
simpleyamlfile with the structure of the site, the source
Markdown/HTML content files and theJinja2based templates and builds
a directory structure with the final website.UsageRunpython paragoo.pyto get help. Typically, a command line looks
like this:python paragoo.py generate_site -s /path/to/site_config -t /path/to/site_templates -o /path/to/output_dirA more involved command including a Secure CoPy to a remote server can
look like this:python paragoo.py generate_site --clean -s ../aquariusoft.org/site -t ../aquariusoft.org/templates -o /tmp/aqs --clean; scp -pqr /tmp/aqs/* vps01:/srv/aquariusoft.org/

or:

python paragoo.py generate_site --clean --cachebuster -s ../../../website/mydomain.net/site -t ../paragoo-theme-material/build/material-grey --pathprefix page --makerooturi -o /srv/mydomain.netTo see what commands are available, run paragoo with--help:python paragoo.py --help

or:

python paragoo.py generate_site --helpTemplatesSome pre-made templates to go with paragoo:paragoo material theme.parawah?The name is derived from παράγω, which is Greek for ‘generate’,
‘produce’.
Package: paragrep
paragrep - Paragraph Grep utilityparagrepis a paragraph grep utility. It searches for a series of regular
expressions in a text file (or several text files) and prints out the
paragraphs containing those expressions. Normallyparagrepdisplays a
paragraph if it contains any of the expressions; this behavior can be modified
by using the-aoption.By default, a paragraph is defined as a block of text delimited by an empty or
blank line; this behavior can be altered with the-poption.If no files are specified on the command line,paragrepsearches
standard input.This is the third implementation ofparagrep. The first implementation,
in 1989, was in C. The second implementation, in 2003, was in perl. This is
the latest and greatest.For help, run with-h.For detailed documentation, see <http://software.clapper.org/paragrep/>This software is released under a BSD license.
Package: paraguay
# Python Paraguay community packageA simple ‘easter egg’ package for the PythonPy community inspired in Python’santigravitymodule.### Installing` pip install paraguay `End with an example of getting some data out of the system or using it for a little demo### How to use` from paraguay import community `Opens Python Paraguay website` from paraguay import meetup `Opens Python Paraguay Meetup Group` from paraguay import telegram `Opens Python Paraguay Telegram Group## TODOimport thismodule with the Zen of Python in guaraní## ContributingFork it!Create your feature branch:git checkout -b my-new-featureCommit your changes:git commit -am ‘Add some feature’Push to the branch:git push origin my-new-featureSubmit a pull request :D## AuthorsMarcelo Elizeche Landó-Initial work- [melizeche](https://github.com/melizeche)## LicenseThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
Package: paralang-cli
paralang_cli- The Python Module for the Para Compiler CLICommand Line Interface Implementation for the Para-C programming languageDocsOur documentation can be foundhere.Installationpython3-mpipinstall-Uparac-ext-cliWith specific version:python3-mpipinstall-Uparac-ext-cli==versionCopyright and LicenseCopyright (C) 2021-2022 Luna KlatzerThis program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with
this program. If not, seehttps://www.gnu.org/licenses/.See theLICENSEfor information on terms & conditions for usage.FOSSA License Report
Package: paralelocs-beam
Paraleo CS Custom packages to work with Apache Beam on Python SDKFree software: Apache Software License 2.0version = v1.0.4.Installationpip install paralelocs-beamYou can also install the in-development version with:pip install https://gitlab.com/lucasmagalhaes/paralelocs_beam/-/archive/master/paralelocs_beam-master.zipDocumentationTo use the project:importparalelocs_beamparalelocs_beam.longest()DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog1.0.0 (2019-11-13)First release on PyPI.
Package: paralelocs-qlikapi
Python Library to work with Qlik APIFree software: Apache Software License 2.0v0.2.6.Installationpip install paralelocs-qlikapiYou can also install the in-development version with:pip install git+ssh://git@paralelo/pyqlikapi/.git@masterDocumentationTo use the project:importparalelocs_qlikapiparalelocs_qlikapi.longest()DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (2019-10-25)Adding the tag and stream0.0.0 (2019-10-23)First release on PyPI.
Package: paralex
Paralex: lexicons of morphological paradigmsParalexis a standard for morphological lexicons which document inflectional paradigms.This package contains:The paralex specificationThe full documentationUtilities to generate metadata and to validate paralex lexiconsGenerating metadataTo generate paralex metadata (more in the docs):fromparaleximportparalex_factorypackage=paralex_factory("Vulcan Verbal Paradigms",{"forms":{"path":"vulcan_v_forms.csv"},})package.to_json("vulcan.package.json")The package returned is africtionless Package objectand can be manipulated as such as needed.Validation of paralex datasetsTo validate a paralex lexicon:paralex validate <mypackagename>.package.jsonThis checks that the data is valid using frictionless, as well as a number of statements to match the paralex standard.
For a detailed report on frictionless metadata, do:frictionless validate <mypackagename>.package.jsonServing the paralex siteFirst, build the standard package from files:paralex make_standardSecond, build the specs files from the standard package:paralex make_docThird, use mkdocs (which needs to be installed) to build this site:mkdocs serve
Package: parallel-adb
ParallelADBEnable adb command parallel run in all connected devices.Install$pipinstallparallel-adbExamplefromparalleladbimportParallelADBoutputs=ParallelADB.run('pm list packages')foroinoutputs:print(o)# Output: ADBOutputs(serial='emulator-x', results=['...'])outputs=ParallelADB.run('forward tcp:4274 tcp:4724',is_shell_cmd=False,print_result=True)foroinoutputs:print(o)outputs=ParallelADB.run('cd /sdcard && ls',serials=['emulator-5554'])foroinoutputs:print(o)
Package: parallel-augustus
Efficiently run Augustus in multi-core environmentsparallel_augustuscuts the input genome in chunks to feed to Augustus. The number of chunks and Augustus processes launched in parallel are configurable.Installationpip install --user parallel_augustusRequirementsPython >= 3.8AugustusExecutionBefore launchingparallel_augustus, please make sure that Augustus is available in your path. If you runaugustus -hand do not encounter an error, you are good to go.parallel_augustusfirst divides the genome (-gargument) in the desired number of chunks (-cargument, it can be any number > 1). After that it launches-pprocesses of Augustus in parallel until there is no chunks left. You can pass parameters to Augustus via the--extraflag. At least a--species=thingis required by Augustus.IMPORTANT: the--extraargument should be the last one in the command line due to shortcomings in theargparsepython module.With all that in mind, a typicalparallel_augustuscommand line will look like this:parallel_augustus-ggenome.fasta\-oaugustus_results\-c500\-p20\--extra'--species=human'This command creates the output directoryaugustus_results, divides the genome into 500 chunks and launches 20 processes of Augustus in parallel. At the end, results are concatenated intoaugustus_results/augustus.gff.
Package: parallelbam
Parallelizing operations on SAM/BAM filesSAM/BAM files are typically large, thus, operations on these files are time intensive. This project provides tools to parallelize operations on SAM/BAM files. The workflow follows:Split BAM/SAM file innchunksPerform operation in each chunk in a dedicated process and save resulting SAM/BAM chunkMerge results back into a single SAM/BAM fileDepends on:SamtoolsInstallationpip3 install parallelbamorGit clone projectcd to cloned project directorysudo python3 setup.py installBetter to install within an environment, such as a conda environment, to avoid
path conflicts with the included bash scripts.UsageThere is one main function namedparallelizedBAMoperation. This function takes as mandatory arguments:path to original bam file (should be ordered)a callable function to perform the operation on each bam file chunkThe callable function must accept the following two first arguments:path to input bam file andpath to resulting output bam filein this order.TODOThe current way to include bash scripts in the package, while working, seems awkward. Perhaps including bash code directly in subprocess would be simplerHaving permission error in some installations upon calling splitBAM.sh, can one make it executable during installation?fromparallelbam.parallelbamimportparallelizeBAMoperation,getNumberOfReadsAs an example, let's create a function that simply copies a bam file to another directory (does nothing to the bam file). When calling this function inparallelizeBAMoperationit will imply split the BAM file in chunks and the merge them back into a single BAM, whih sould be identical to the first one. We will split the BAM file in 8 chunks, and are dummy function will be called in separate process for each chunk.importshutildeffoo(input_bam,output_bam):shutil.copyfile(input_bam,output_bam)parallelizeBAMoperation('sample.bam',foo,output_dir=None,n_processes=8)To check that the processed bam file, after merging the 8 chunks, contains the same number of reads we can callgetNumberOfReads.getNumberOfReads('sample.bam')11825588getNumberOfReads('processed.bam')11825588
Package: parallelblast
Parallelize BLAST searches by splitting the input and submitting jobs to a SLURM cluster.The blast jobs are run on a SLURM queue with parallelization by input chopping. Not database chopping which requires message passing across the nodes like mpiblast does (when and if it works).
Input chopping is fine as long as the database to search against fits in the RAM of the nodes. If the input is small and the database is large you can always switch them one for the other (in most cases) and apply input splitting.For a usage example look at the test.py file.
Package: parallel-conveyor
Conveyor - Intuitive Python pipelinesConveyor is a multiprocessing framework for creating intuitive data pipelines. With Conveyor, you can easily create stream-based pipelines to efficiently perform a series of operations on data, a task especially useful in the fields of machine learning and scientific computing. Creating a pipelined job is as easy as writing a function.Why use Conveyor?The answer is simple: throughput.  It's like putting a second load of laundry in the washer while a previous load is in the dryer. By breaking down a problem into smaller serial tasks, we perform the smaller tasks in parallel and increase the efficiency of whatever problem we're trying to solve.InstallationInstall Conveyor withpip install parallel-conveyor.A quick and trivial exampleLet's say we wanted to build a short pipeline that computed the fourth root of a number (done in two steps) and the cube of a number (in one step). In this case, we would describe this pipeline visually as such:To express it with Conveyor, we simply build the pipeline as followsfromconveyor.pipelineimportPipelinefromconveyor.stagesimportProcessor,Pipe,ReplicatingFork,Joinfrommathimportsqrtdefsquare_root(arg):returnsqrt(arg)defcube(arg):returnarg**3withPipeline()aspl:# Duplicate the inputpl.add(ReplicatingFork(2))# On first copy, compute the sqrt, on the second, the cubepl.add(Processor(square_root),Processor(cube))# On first copy, compute the sqrt, on the second, do nothingpl.add(Processor(square_root),Pipe())# Join the two data streamspl.add(Join(2))# Print the resultspl.add(Processor(print))# Run the pipeline with three different inputspl.run([16,3,81])$python3sample.py2.01.31607401295249243.0409627531441Other linksDocumentationPyPI project pageA note on stabilityConveyor is currently considered in alpha. Specifications will change, potentially in breaking ways.TestingTo run the tests, ensure nose is installed and run nosetests from the project directorypip3 install nose && nosetestsBuilding from sourceTo build the distribution archives, you will need the latest version of setuptools and wheel.python3 -m pip install --user --upgrade setuptools wheelRunsetup.pyto build using the following command:python3 setup.py sdist bdist_wheelThe compiled.whland.tar.gzfiles will be in the/distdirectory.
Package: parallel-delaunay
delaunay-triangulationThis library is used for computing theDelaunay triangulationof 2D point sets.An example Delaunay triangulation output for 32 points is shown below.Why is this library special?Speed! This is a pure python implementation of the Guibas & Stolfi's divide and conquer algorithm. The divide and conquer algorithm has been shown to be thefastestDT generation technique, with O(nlogn) running time. Furthermore, this code has been parallelised using the MPI for Python (mpi4py) library to utilise multiple CPU cores. This allows the algorithm to be efficiently scaled for distributed computing across supercomputer nodes.InstallationParallel Delaunay can be installed by runningpip install parallel-delaunay. It requires Python 3.8+ to run.To import and use the library see scipi_comparison.py for an example of generating a random set of 2D points and running the delaunay triangulation algorithm.BenchmarkingSingle core resultsAs seen in the graph below, this Delaunay triangulation implementation scales just as efficiently as the SciPy implementation. The SciPy library is written in C and wrapped in Python for ease of use. As a result, the SciPy DT algorithm is consistently around 40x faster for a given number of points. However, the benefit of my library is that it can take advantage of multiple CPU cores, offering a performance advantage unavailable to SciPy users. By utilising the multiple cores available in modern computers, the run time can be reduced linearly by a factor approximately equal to the number of available threads.This graph can be reproduced by runningpython src/triangulation_benchmarks.py.StructureThis repository is currently structured as follows.├── src
    ├── triangulation_core
        ├── points_tools
            ├── generate_values.py
            └── split_list.py
        ├── edge_topology.py
        ├── linear_algebra.py
        ├── triangulation.py
        └── triangulation_primitives.py
    ├── utilities
        └── settings.pyReferences[1]Guibas, Leonidas and Stolfi, Jorge
'Primitives for the Manipulation of General Subdivisions and the Computation of Voronoi'
In: ACM Trans. Graph.4.2  (Apr.1985),  pp.  74–123.
issn:  0730-0301
doi:10.1145/282918.282923
Package: parallel-es2csv
This project is to just have a simple cli command to export data from ES using the CPU’s,
and Elasticsearch’s Sliced Scroll Search for fetching large datasets.
It’s intended to be used in Data workflow for extracting data out.The performance seems better whenno_of_workers == no_of_shards_for_the_index.NoteThis is still early in the development and a bit rough around the edges.
Any bug reports, feature suggestions, etc are greatly appreciated. :)Installation and usageInstallationSince this is a Python package available on PyPi you can install it like
any other Python package.# on modern systems with Python you can install with pip$pipinstallparallel-es2csv# on older systems you can install using easy_install$easy_installparallel-es2csvUsageThe commands should be mostly self-documenting in how they are defined,
which is made available through thehelpcommand.$parallel-es2csvusage:parallel-es2csv-u<elasticsearch_url>-i<[list_of_index]>[-n<no_of_workers>][-o<output_folder>]arguments:-h,--helpshowthishelpmessageandexit-iINDICES[INDICES...],--indicesINDICES[INDICES...]ESindicestoexport.-uURL,--urlURLElasticsearchhostURL.Defaultishttp://localhost:9200.-aAUTH,--authAUTHElasticsearchbasicauthenticationintheformofusername:pwd.-DDOC_TYPE[DOC_TYPE...],--doc_typesDOC_TYPE[DOC_TYPE...]Documenttype(s).-oOUTPUT_FOLDER,--output_folderOUTPUT_FOLDEROutputfolderpath.-fFIELDS[FIELDS...],--fieldsFIELDS[FIELDS...]Listofselectedfieldsinoutput.Defaultis['_all'].-mINTEGER,--maxINTEGERMaximumnumberofresultstoreturn.Defaultis0.-sINTEGER,--scroll_sizeINTEGERScrollsizeforeachbatchofresults.Defaultis100.-tINTEGER,--timeoutINTEGERTimeoutinseconds.Defaultis60.-e,--meta_fieldsAddmeta-fieldsinoutput.-nNO_OF_WORKERS,--no_of_workersNO_OF_WORKERSNo.orparallelscrollfromElasticsearch,usingMultiprocess-v,--versionShowversionandexit.--debugDebugmodeon.
Package: parallelexec
ParallelexecParallelexec is a Python module that provides decorators and methods to easily run multiple functions concurrently.InstallationUse pippip install parallelexecUsageThreads:fromparallelexecimportParallelExec@ParallelExec.thread(join=True)deffun():...Processes:fromparallelexecimportParallelExecdeffunc()->None:...deffunc2()->None:...deffunc3()->None:...if__name__=="__main__":ParallelExec.cores_limited_processor([func,func2,func3])Better yet, checkexamples/LicenseThis is under thePublic Domain
Package: parallel-execute
Python wrappers for easy multiprocessing and threading.Run multiple functions in parallel using parallel-executeInstallationpip install parallel-executeUsage Example1. Create a loom:This takes a number of tasks and executes them using a pool of
threads/process.To use threadingfrompexecute.threadimportThreadLoomloom=ThreadLoom(max_runner_cap=10)To use multiprocessingfrompexecute.processimportProcessLoomloom=ProcessLoom(max_runner_cap=10)max_runner_cap: is the number of maximum threads/processes to run at a
time. You can add as many as functions you want, but onlynfunctions will run at a time in parallel,nis the max_runner_cap2. Add tasks in loomAdd a function in loom usingadd_functionloom.add_function(f1,args1,kw1)loom.add_function(f2,args2,kw2)loom.add_function(f3,args3,kw3)Add multiple functions together usingadd_workmethodwork=[(f1,args1,kwargs1),(f2,args2,kwargs2),(f3,args3,kwargs3)]loom.add_work(work)3. Execute all tasksAfter adding tasks, calling execute will return a dictionary of results
corresponding to the keys or the order in which the tasks were added.output=loom.execute()key is the order in which the function was added and value is the return data of the function.# Example:deffun1():return"Hello World"deffun2(a):returnadeffun3(a,b=0):returna+bloom.add_function(fun1,[],{})loom.add_function(fun2,[1],{})loom.add_function(fun3,[1],{'b':3})output=loom.execute()>>>output{0:{'output':'Hello World','got_error':False,'error':None,'started_time':datetime.datetime(2019,6,28,19,44,58,395002),'finished_time':datetime.datetime(2019,6,28,19,44,58,396500),'execution_time':0.001498,},1:{'output':1,'got_error':False,'error':None,'started_time':datetime.datetime(2019,6,28,19,44,58,396590),'finished_time':datetime.datetime(2019,6,28,19,44,58,397651),'execution_time':0.001061},2:{'output':4,'got_error':False,'error':None,'started_time':datetime.datetime(2019,6,28,19,44,58,400323),'finished_time':datetime.datetime(2019,6,28,19,44,58,401749),'execution_time':0.001426}}We can also provide akeyto store the function return data.# Example:loom.add_function(fun1,[],{},'key1')loom.add_function(fun2,[1],{},'fun2')loom.add_function(fun3,[1],{'b':3},'xyz')output=loom.execute()>>>output{'key1':{'output':'Hello World','got_error':False,'error':None,'started_time':datetime.datetime(2019,6,28,19,44,58,395002),'finished_time':datetime.datetime(2019,6,28,19,44,58,396500),'execution_time':0.001498,},'fun2: {'output': 1,'got_error':False,'error':None,'started_time':datetime.datetime(2019,6,28,19,44,58,396590),'finished_time':datetime.datetime(2019,6,28,19,44,58,397651),'execution_time':0.001061},'xyz':{'output':4,'got_error':False,'error':None,'started_time':datetime.datetime(2019,6,28,19,44,58,400323),'finished_time':datetime.datetime(2019,6,28,19,44,58,401749),'execution_time':0.001426}}
Package: parallel-executor
# Parallel ExecutorThe purpose of this library is to execute bunch of tasks to run independently in parallel## Version1.1## Installationpip install parallel-executor==1.1## Basic usage example
task1 = Task(‘Task1’, lambda: 2+2)
task2 = Task(‘Task2’, lambda: 2*2)
tasks = [task1, task2]
e = Executor(3, 3, tasks)
e.executeTasks()
print(task1.getResult())
print(task2.getResult())## History
- 1.1 Not closing thread bug was fixed
- 1.0 Initial release## AuthorDovydas Jančauskas
Package: parallel-feature-selector
Parallel Feature SelectorA feature selection module that works in parallel among the processorsInstallationpip install parallel-feature-selectorDependencies:pandasscikit-learnmpi4pyarff2pandasopenpyxlMPI needs to be installed on the computer.
For Windows:Install both Microsoft MPI v10.0 and Microsoft MPI SDKSet up environment variables
-- C:\Program Files (x86)\Microsoft SDKs\MPI
-- C:\Program Files\Microsoft MPI\BinInstall mpi4py with "conda install -c intel mpi4py" in Anaconda PromptUsage (Module)Example Code:import parallel_feature_selector as pfs
pfs.bruteForce(data, estimator, 0.2, 42, 10, 10, True, 'outputTimeAnalysis.csv', 'outputSubsetAnalysis.csv')Function Parameters and Default Arguments:def bruteForce(data, estimator, testSize=0.2, randomState=42, cv=5, topScoreNo=5, shuffle=True, timeResultFile='outputTimeAnalysis.csv', scoreResultFile='outputSubsetAnalysis.csv')ParameterDescriptiondatapandas.DataFrame with column names and numerical valuesesimatorscikit-learn esimator objecttestSizefloat, test size between 0 and 1 for train_test_split (Default = 0.2)randomStateint or None, random state for train_test_split (Default = 42)cvint, fold number for cross-validation (Default = 5)topScoreNoint, specifies how many best-scoring subsets to print (Default = 5)shufflebool, used to shuffle the data set before splitting (Default = True)timeResultFileOutput file path for elapsed time results in csv format (Default = 'outputTimeAnalysis.csv')scoreResultFileOutput file path for score and subset results in csv format (Default = 'outputSubsetAnalysis.csv')Usage (Script)You can also run the code in terminal with the following arguments:mpiexec -n [PROCESS_NO] python parallel_feature_selector.py [DATA_SET_PATH] [ESTIMATOR_NAME] [TEST_SIZE] [RANDOM_STATE] [CROSS_VALIDATION] [TOP_SCORE_NO] [SHUFFLE] [TIME_RESULT_PATH] [SCORE_RESULT_PATH][RANDOM_STATE] must be given as integer ('None' can be given in module function)[SHUFFLE] must be given as integer, 1 for 'True' and 0 for 'False' ('True' or 'False' in module function)Example:mpiexec -n 4 python parallel_feature_selector.py C:\Users\BILGISAYAR\Desktop\AcademicPerformance.csv gnb 0.2 1 10 10 1 outputTimeAnalysis.csv outputSubsetAnalysis.csvWhen running the script in terminal:.csv .xlsx .arff files can be used for input data setAvailable estimator arguments: gnb, bnb, knnMinkowski, knnEuclidean, svcRbf, svcPoly, logReg, dtEntropy, dtGini, rfEntropy, rfGiniTerminal ArgumentScikit-Learn EstimatorgnbGaussianNB()bnbBernoulliNB()knnMinkowskiKNeighborsClassifier(n_neighbors=5, metric='minkowski')knnEuclideanKNeighborsClassifier(n_neighbors=5, metric='euclidean')svcRbfSVC(kernel = 'rbf', gamma='scale')svcPolySVC(kernel = 'poly', gamma='scale')logRegLogisticRegression(solver='liblinear', multi_class='auto')dtEntropyDecisionTreeClassifier(criterion='entropy', random_state = 1)dtGiniDecisionTreeClassifier(criterion='gini', random_state = 1)rfEntropyRandomForestClassifier(n_estimators=10, criterion='entropy', random_state=1)rfGiniRandomForestClassifier(n_estimators=10, criterion='gini', random_state=1)