Saravanan666/pypi-org huggingface dataset:
Package: patroltrigger
PatrolTriggerTrigger custom commands from filesystem events.Patrol uses libuv, which creates event driven hooks to filesystem events using epoll, kqueue or IOCP.You can use it to selectively build your project when files change and run tests as soon as you hit the save button.UseTo install:sudo pip install patroltriggerCreate a patrol.py file in your project’s root directory:#!/usr/bin/pythonfrompatroltriggerimportrunnable,run,trigger@trigger(["*.py",],exclude=['venv/*',])deftrigger1(filenames):print"trigger1"run("""echo first command triggered""")the_output_is_in_this_variable_though=run("echo this command is run, but the output is not displayed",silent=True)@trigger(["*.py",],exclude=['venv/*',])deftrigger2(filenames):print"trigger2 command triggered with filename:{}".format(', '.join(filenames))run("false",ignore_errors=True)# If ignore_errors=False, nothing further will be executed.run("""echo if ignore_errors is False or not specified, you wont ever see this command *or* the results of trigger3.""")@trigger(["*.py",],exclude=['venv/*',])deftrigger3(filenames):print"trigger3 command triggered with filename:{}".format(', '.join(filenames))runnable(__name__)Run like so:$ python patrol.pyOr get help:$ python patrol.py --help
Usage: patrol.py [options]

Options:
-h, --help            show this help message and exit
-r RUN, --run=RUN     Specify a method in patrol.py to run directly.
-a, --all             Run all methods in patrol.py in priority order.
-p POST, --post=POST  Command to run after a trigger (e.g. guake)
-d DIRECTORY, --directory=DIRECTORY
                        Directory to run patrol.py in (default:
                        /home/user/yourproject).FeaturesIf multiple methods are triggered by a changed file, they will runin the order they appearin patrol.py.Queue up triggers by putting a file named ‘lock’ in the directory patrol.py is run from. Once ‘lock’ is removed, the pent up triggers will all be fired.Each time a set of methods are triggered, the total run time is timed.If a command fails (e.g. a unit test), the method is aborted by default at that point to shorten feedback time. Subsequent methods will not run.Run a custom command after each method is finished (e.g. guake, notify-send).Ability to run commands manually.
Package: patron-ElioAmado-expoFP
Package for Printing a Pattern with 3 Possible Forms
This package has only one module named patron_ElioAmado_expoFP.py.This Python module defines a function called elio_Patron() which is responsible for printing a pattern in the console. The generated pattern varies according to the specified input parameters. The function accepts four parameters:alt: Specifies the height of the pattern and must be an integer.
frm: Can take three values: “Up”, “Down”, or “Mixed”. Determines the form of the generated pattern.
rell: Indicates the filling character to be used in the middle of the pattern. Must be a single character.
nom: Is a word or phrase that is printed in the pattern. The way it is printed depends on the value of “frm”.
The elio_Patron() function consists of two internal functions:ptrferiaLine(): This function creates a pattern line according to the provided parameters. It calculates the required spaces, the length of the text to print, and uses a list of special characters to shape the pattern.ptrferiaimpr() and ptrferiaimprMez(): These functions print the pattern in the console according to the specified form (“Up”, “Down”, or “Mixed”). They dynamically adjust the height, the amount of filling, and other parameters to create the desired pattern.The main function elio_Patron() prompts the user to input the necessary parameters and then generates the pattern according to the specifications. If the “Mixed” form is selected, the pattern is generated iteratively, gradually reducing the height and length of the text.Finally, the function uses Python’s standard library to pause execution for a short period of time, resulting in a more animated visualization of the pattern in the console.This script is interactive.
Package: patsy
PatsyNotice:patsyis no longer under active development. As of August 2021,
Matthew Wardrop (@matthewwardrop) and Tomás Capretto (@tomicapretto) have taken
on responsibility from Nathaniel Smith (@njsmith) for keeping the lights on, but
no new feature development is planned. The spiritual successor of this project
isFormulaic, and we
recommend that usersmigratewhen possible. For the time being, until major software packages have successfully
transitioned, we will attempt to keeppatsyworking in its current state with
current releases in the Python ecosystem.Patsy is a Python library for describing statistical models
(especially linear models, or models that have a linear component) and
building design matrices. Patsy brings the convenience ofR"formulas" to Python.Documentation:https://patsy.readthedocs.io/Downloads:http://pypi.python.org/pypi/patsy/Code and issues:https://github.com/pydata/patsyMailing list:pydata@googlegroups.com(http://groups.google.com/group/pydata)DependenciesPython (2.6, 2.7, or 3.3+)sixnumpyOptional:pytest/pytest-cov: needed to run testsscipy: needed for spline-related functions likebsInstallationpip install patsy(or, for traditionalists:python setup.py install)License2-clause BSD, see LICENSE.txt for details.
Package: patter
patterPipe stdout directly toMattermost.Install$ pip install patterThen set the following environment variables based on your Mattermost server's config:MATTERMOST_TEAM_NAME
MATTERMOST_URL
MATTERMOST_USERNAME
MATTERMOST_PASSWORD
MATTERMOST_PORTUsageSend a message to a user.echo "testing" | patter -u some_userSend a message to a channel.echo "testing" | patter -c town-squareSend a file.patter -c town-square -f some_file.txtTodoadd unit tests via pytestallow message/files that are not UTF-8 to be sentadd option of reading from a .patter file instead of using env vars.remove prints and add logging via verbose flagContributePRs accepted.LicenseMIT © Christopher HranjNotice: This project was developed in part during my 10% time atTruveris, Inc.
Package: pattern-analysis
pattern_analysisA package for analyzing patterns in biological data.Free software: MIT licenseDocumentation:https://pattern-analysis.readthedocs.io.FeaturesCount patterns of nucleotide interest in a genome sequenceGenerate the complement for a genome sequenceUsagefrompattern_analysisimportpattern_analysis# Estimate the number of patterns equal to "TGATCA" in the ori sequenceori="ATCAATGATCAACGTAAGCTTCTAAGCATGATCAAGGTGCTCACACAGTTTATCCACAACCTGAGTGGATGACATCAAGATAGGTCGTTGTATCTCCTTCCTCTCGTACTCTCATGACCACGGAAAGATGATCAAGAGAGGATGATTTCTTGGCCATATCGCAATGAATACTTGTGACTTGTGCTTCCAATTGACATCTTCAGCGCCATATTGCGCTGGCCAAGGTGACGGAGCGGGATTACGAAAGCATGATCATGGCTGTTGTTCTGTTTATCTTGTTTTGACTGAGACTTGTTAGGATAGACGGTTTTTCATCACTGACTAGCCAAAGCCTTACTCTGCCTGACATCGACCGTAAATTGATAATGAATTTACATGCTTCCGCGACGATTTACCTCTTGATCATCGATCCGATTGAAGATCTTCAATTGTTAATTCTCTTGCCTCGACTCATAGCCATGATGAGCTCTTGATCATGTTTCCTTAACCCTCTATTTTTTACGGAAGAATGATCAAGCTGCTGCTCTTGATCATCGTTTC"pat="TGATCA"res=pattern_analysis.pattern_count(ori,pat)print("A total of{}{}are found.".format(res,pat))# Return "A total of 8 TGATCA are found."CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.1 (2023-12-17)Restructure the pattern analysis package using cookiecutter-pypackage.0.1.0 (2023-12-12)First release on PyPI.
Package: pattern-buffer
Pattern Buffer 🧮Fast pattern counting on small-alphabet sequences with GPU acceleration, built on PyTorch.Originally designed to count patterns in DNA sequences with ambiguous bases as defined by theIUPAC code, but can be extended to any pattern counting task where the original sequences and target patterns can be converted into a sensible categorical encoding.[!NOTE]
The preface of "small-alphabet" sequences is given as the additional memory used by categorical encoding grows with the number of unique values in the alphabet. With DNA and IUPAC encoding this is tractable because many of the codes represent combinations of DNA bases, so despite there being 15 DNA nucleotide codes we only need to embed in a vector of length 4 (A, C, G, T).Usage[!IMPORTANT]
While the query sequences can all be different lengths, currentlyPattern Bufferonly supports counting these patterns on sequences that are all the same length.Pattern Buffercan be used with a broadly functional or object-oriented (OO) interface, with the functional interface geared towards one-time use and the OO interface for use multiple times (i.e. file parsing or PyTorch DataLoaders).To demonstrate, we'll first create some sample sequences and queries. As we're using IUPAC nucleotide sequences, we can use the providedgenerate_iupac_embeddingfunction to provide the embedding tensor.frompattern_bufferimportgenerate_iupac_embeddingsequences=["AACGAATCAAAAT","AACAGTTCAAAAT","AACAGTTCGYGGA","AACAAGATCAGGA"]queries=["AAA","AGT","AAACA","AAR","GYGGA"]embedding=generate_iupac_embedding()From here, we can use the functioncount_queriesto count the queries in a single function
call:frompattern_bufferimportcount_queriescount_queries(sequences,queries,embedding)# tensor([[2, 0, 0, 2, 0],#         [2, 1, 0, 2, 0],#         [0, 1, 0, 0, 0],#         [0, 0, 0, 1, 0]])or create an instance of thePatternBufferclass, and use the.countmethod to count occurrences in new sequences. This has the advantage of not re-calculating the query embeddings or thesupporttensor each time, so is well suited for fast repeated counting:frompattern_bufferimportPatternBufferpb=PatternBuffer(query_strings=queries,embedding=embedding)pb.count(sequences)# tensor([[2, 0, 0, 2, 0],#         [2, 1, 0, 2, 0],#         [0, 1, 0, 0, 0],#         [0, 0, 0, 1, 0]])LimitationsCurrently, the program expects all input sequences to have the same length, but queries can already be different lengths.If all of your patterns contain unique (non-ambiguous) characters then this encoding scheme is likely overkill and other software would be more efficient.The software is designed for use with GPU acceleration, and will likely under-perform on CPU when compared to CPU-optimised counting schemes.Future workAllow dynamic input lengths with paddingAutomatic encoding detection from pattern analysisFFT-based convolutions for long patterns
Package: pattern-cli
CliCli is a Python library to help create command-line interfaces with ease.Installationpipinstallgit+https://github.com/HashLDash/cliUsagefromcliimportcli@cli("hello")defhello():''' This is a test command '''print("Hello, World!")@cli("add <int:a> <int:b>")defadd(a,b):print(f'Adding{a}+{b}={a+b}')This would add two commands (and automagically a help command) that can be called like:$pythoncommand.pyhello
Hello,World!
$pythoncommand.pyadd54Adding5+4=9$pythoncommand.pyhelpcommand.pyhelphello-ThisisatestcommandYou can also add shebangs, ommit the extension and link to your path to create a command for using like:$commandhello
Hello,World!
$commandadd54Adding5+4=9TODOThis is still in development. The following is planned:Handling of keyword argumentsIncrease supported typesFeel free to open issues for bugs and feature requests.PRs are welcome too!Enjoy :)
Package: pattern-clustering
Pattern clusteringThis tool clusterizes lines of text given a collection of input patterns modeled using regular expressions.This work has been published to:[ICPR’2022] A novel pattern-based edit distance for automatic log parsing, Maxime Raynal, Marc-Olivier Buob, Georges Quénot.FeaturesForms groups of homogeneous line using a pattern based distance, based on customizable patterns.Configured by default to use common patterns (IP addresses, numeric values, etc.)LicenseThis project is licensed under the BSD-3-Clause license - see theLICENSE.More about pattern-clusteringFor more information, feel free to visit thewiki:OverviewInstallationTestsTutorialDocumentationConfigurationPackagingAcksThe skeleton package was created withCookiecutterand thefrancois-durand/package_helper_2project template.The sphinx part is inspired fromSphinx-Autosummary-Recursion.History0.1.0 (2022-05-11): First releaseFirst release on PyPI.0.2.0 (2022-06-02): CIUpdated tox.ini and GitHub actions, work in progress.0.3.0 (2022-06-22): Bug fixes and CI improvementsFixed sphinx local buildFixed bumpversionAdd experiments notebooks and datasetsImproved test suite0.3.1 (2022-06-22): Bug fixes and CI improvementsFixed readthedoc build0.4.1 (2022-06-24): Bug fixes and CI improvementsFixed readthedoc buildImplemented console script (cli)ReworkedPatternClusteringEnvclassBug fixesUpdated documentation0.4.2 (2022-06-24): Added entry pointsAddedpattern-distanceentry point, seepattern-distance –help.Addedpattern-clustering-mkconfentry point. The resulting json may be passed topattern-distanceandpattern-clusteringcommands.0.5.0 (2022-06-25): Added entry pointsBug fixes innotebooks/Removed unused patterns1.0.0 (2022-07-01): checked experimentsChecked experiments innotebooks/Fixed warning related to documentation buildImproved tests
Package: patterncounter
PatternCounterFeaturesThis tool allows to count patterns in lists of sequential groups usingrulesandvariables.For the following examples, consider the following file (sequences.txt):A -1 -2
B -1 -2
A B -1 -2
A -1 B C -1 -2
B -1 A B -1 A -1 C -1 -2Example 1: Count how many sequences contain both the elements A and B:$patterncountercount"A B"-n-fsequences.txt
Supp((AB))=0.6|3lines:2,3,4Example 2: Count how many sequences contain elements A and B at the same group:$patterncountercount"A & B"-n-fsequences.txt
Supp(A&B)=0.4|2lines:2,4Example 3: Count how many sequences have an element B that after after A:$patterncountercount"A -> B"-n-fsequences.txt
Supp(A->B)=0.2|1lines:3Example 4: Count in how many sequences the element B was removed within an interval of A:$patterncountercount"[A OutB]"-n-fsequences.txt
Supp([AOutB])=0.2|1lines:4Example 5: Count in how many sequences there is an element C that occurs after an interval of A:$patterncountercount"[A] -> C"-n-fsequences.txt
Supp([A]->C)=0.4|2lines:3,4Example 6: Show results even when the pattern does not exist:$patterncountercount"Z"-n-fsequences.txt-z
Supp(Z)=0.0|0linesIn addition to using simple rules, it is possible to define multiple rules and calculated association rules metrics among them:Example 7: Count both how many sequences have an interval of A, and how many sequences have an interval of A with an element B inside:$patterncountercount"[A]""[A B]"-n-fsequences.txt
Supp([A],[AB])=0.4|2lines:2,4AssociationRule:[A]==>[AB]Supp([A])=0.8|4lines:0,2,3,4Supp([AB])=0.4|2lines:2,4Conf=0.5Lift=1.25
AssociationRule:[AB]==>[A]Supp([AB])=0.4|2lines:2,4Supp([A])=0.8|4lines:0,2,3,4Conf=1.0Lift=1.25It is also possible to define variables.Example 8: Count how many sequences have groups with two distinct elements:$patterncountercount"x & y"-v"x"-v"y"-n-fsequences.txt-z
Supp(x&y)=0.6|3lines:2,3,4[BINDING:x=B;y=A]Supp(B&A)=0.4|2lines:2,4[BINDING:x=A;y=B]Supp(A&B)=0.4|2lines:2,4[BINDING:x=B;y=C]Supp(B&C)=0.2|1lines:3[BINDING:x=A;y=C]Supp(A&C)=0.0|0lines[BINDING:x=C;y=B]Supp(C&B)=0.2|1lines:3[BINDING:x=C;y=A]Supp(C&A)=0.0|0linesNote that the result first shows the combined metrics (union).Finally, given a file of sequences, it is also possible to select its lines (0-indexes):$patterncounterselect-fsequences.txt-n40|A-1-22|AB-1-24|B-1AB-1A-1C-1-2InstallationYou can installPatternCounterviapipfromPyPI:$pipinstallpatterncounterUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,PatternCounteris free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.
Package: patternengine
patternenginetl;drA bullet hell pattern engineConsult the embedded docs for class descriptions and usage.Check outthe demo.
Watchthe demo runningLook into src/patternengine/example.pyPreparation######################################################################### Relevant part here## A "ring" of 180 degrees aiming towards the top devided int 18 parts# A heartbeat over 5 seconds with 4 emits and 4 gaps# A BulletSource fetching 18 bullets at every heartbeat#ring=pe.Ring(100,18,aim=-90,width=180)heartbeat=pe.Heartbeat(5,'#.#.#.#.')emitter=pe.BulletSource(18,ring,heartbeat)#########################################################################Later in the game loop##################################################################### Relevant part here#forbulletinnext(emitter):Bullet(group,position=bullet[0]+anchor,momentum=bullet[1]*bullet_speed,lifetime=10)#####################################################################with Bullet being a simple subclass of pygame.sprite.SpriteTutorialRead TUTORIAL.md for exhaustive examples of all the parts.Additionally, study the sources of the included tutorial scripts and the
demos, both in the package folder.
Package: pattern-feedback-tool
pattern_feedback_toolDesign Pattern Feedback ToolInstallationThis package is built only for playingthe DesignPatternsAdventure/gameFor developers working ongamethere are hiddendoittasks to help with development:poetryrundoit--continue_format_test_check_check_types_update_build_diagramsUsageSeethe README of the game, which utilizespft'sdoittasks, such asdoit checkordoit build_diagrams.Seetestsfor example code and output.Project StatusSee theOpen Issuesand/or theCODE_TAG_SUMMARY. For release history, see theCHANGELOG.ContributingWe welcome pull requests! For your pull request to be accepted smoothly, we suggest that you first open a GitHub issue to discuss your idea. For resources on getting started with the code base, see the below documentation:DEVELOPER_GUIDESTYLE_GUIDECONTRIBUTINGCode of ConductWe follow theContributor Covenant Code of Conduct.Responsible DisclosureIf you have any security issue to report, please contact the project maintainers privately. You can reach us atdev.act.kyle@gmail.com.LicenseLICENSE
Package: patternly
Info:Paper draft link will be posted hereAuthor:Drew Vlasnik, Ishanu ChattopadhyayLaboratory:The Laboratory for Zero Knowledge Discovery, The University of Chicagohttps://zed.uchicago.eduDescription:Discovery of emergent anomalies in data streams without explicit  prior models of correct or aberrant behavior, based on the modeling of ergodic, quasi-stationary finite valued processes as probabilistic finite state automata (PFSA).Documentation:https://zeroknowledgediscovery.github.io/patternlyInstallation:pip install patternly --user -UUsage:Seeexamples.from patternly.detection import AnomalyDetection, StreamingDetection
Package: pattern-matching
Tail call optimization(TCO) has been removed from this package for
following reasons:TCO is easy to implement.Guaranteeing TCO dynamically in any situations is really expensive.If you do want to use TCO in Python, checkhttps://zhuanlan.zhihu.com/p/42684997.The documents have been migrated to README now:DocsThese are all you need to import.frompattern_matchingimportvar,_,T,t,when,Match,overwriteType Matching@when(var[T==int])# T means the type would be capture.deff(v,type_of_v):print(v,type_of_v)f(1)# => (1, int)Remark: UsingMatchis similar towhen/overwrite:m=Match(1)res=m.case(var[T==int])ifres:[a,b]=res.getassert[a,b]==[1,int]If the pattern matched,Match.casereturns aResultobject.classResult:__slots__='get'def__init__(self,_):self.get=_Otherwise the return isNone.Value Matching@when(_==1)deff():return12@when(_==2)deff():return0@when(var)deff(arg):returnarg**3f(1),f(2),f(3)# => 12, 0, 27Wildcard for types@when(var[t==float])# the lowercase, "t", which indicates that the type just be matched without capture.deff(v):print(v)f(1.0)# => 1.0Wildcard for values@when(_)deff():return1f(1)==f("...")==f(1e-3)# => TrueType BoundaryclassMyList(list):passfromcollectionsimportIterable@when(var[Iterable<=T<=MyList].when(lambdax:1inx))deff(x,T):return(x,T)f([1,2,3])# => ([1, 2, 3], list)f({1,2,3})# => UnsolvedCase: No entry for args<({1, 2, 3},)>, kwargs:<{}>Overloading functionsOverloading functions are introduced through the following simple cases:@overwrite(_==[])defsummary():return0@when([var[int],*(_==[])])defsummary(head):returnhead@when([var[int],*var[list]])defsummary(head,tail):returnhead+summary(tail)summary([1,2,3])# => 6Note that above code is definitely useless for it doesn’t use tail call
optimization.Union Type@when(var[(t==int)|(t==str)])defdisp(x):print(x)disp(1)# => 1disp('1')# => '1'Intersection TypeclassA:passclassB:passclassC(A,B):pass@when(_[(T==A)|(T==B)])defdisp(ty):print(ty)disp(C())# => <class __main__.C>Difference TypeclassA:passclassB:passclassC(A,B):pass@when(_[T!=A])defdisp(ty):print(ty)disp(C())# => <class __main__.C>disp(B())# => <class __main__.B>disp(A())# => UnsolvedCase: No entry for args<(<__main__.A object at ...>,)>, kwargs:<{}>Type ContractsYou can apply.when(predicate)methods onpattern_matching.T/t.To avoid subclassing.classA:passclassB:passclassC(A,B):pass@overwrite(_[T.when(lambda_:notissubclass(_,A))])defdisp(ty):print(ty)disp(C())# => <class __main__.C># => UnsolvedCase: No entry for args<(<__main__.C object at ...>,)>, kwargs:<{}>Match Argument Numbers@when(var/2)deff(g):returng(1,2)f(lambdaa,b:a+b)# => 3f(lambdaa,b,c:a+b)# => UnsolvedCase: No entry for args<(<function <lambda> at ...>,)>, kwargs:<{}>classF:defapply(self,arg):returnarg+1@when(var/1)deff2(g):returng(1)f2(lambdaa,b:a+b)# => UnsolvedCase: No entry for args<(<function <lambda> at ...>,)>, kwargs:<{}>f2(F().apply)# => 2
Package: pattern-observer
The observer pattern is a software design pattern in which an object, called
the subject, maintains a list of its dependents, called observers, and notifies
them automatically of any state changes, usually by calling one of their
methods[1].Theobserver.pyimplementation has a topic-based system, a message filtering
type of the publish–subscribe pattern (an observer pattern variation)[2].
Therefore, a subject can be subdivided into topics and observers can express
interest in one (or more) topic and only receive notifications (with any
message or not) from that.The implementation is also based on the jquery publish–subscribe model[3][4]. So, for convenience, observers will be calledhandlersand topics
will be calledevents. The observer module has only two members: Event and
Observable. AnObservableinstance allows you connect different handlers to
its events. A handler can be any function, method or callable object.A simpler implementation of the pattern can be found atold.py.References[1]https://en.wikipedia.org/wiki/Observer_pattern[2]https://en.wikipedia.org/wiki/Publish%E2%80%93subscribe_pattern[3]http://api.jquery.com/trigger/[4]http://api.jquery.com/on/