Saravanan666/pypi-org huggingface dataset:
Package: plimsolls
plimsollsPresentation helpers based on PlimUsageFile: example.plim:-namespace module="plimsolls.formatters.code2html" name="code2html"

h2 Pygments Example:
pre
  -py
    code = """
    #Python 2.x:
    python -m SimpleHTTPServer

    #Python 3.x:
    python -m http.server 8000
    """

  == code2html.reformat('bash', code)Plimsolls ChangelogVersion 1.0Release forpip install.Version 1.0-betaUpdate and fix for Python 3.Mokey support line indent.Version 0.1(codename Jinja, released on August 12th 2013)First draft of Plimsolls: added formatters module.Keith Yang <yang@keitheis.org>
Package: plind
plind.py DocumentationIntroductionplind.py (Picard-Lefschetz Integration in N-Dimensions) is a Python package that implements the Picard-Lefschetz method for integrating highly oscillatory functions in any number of dimensions.DescriptionThis code is for people interested in computing integrals of the form
$$I=\int_\Omega \text{d}^n\mathbf{x}\exp i S(\mathbf{x};\mathbf{\mu}).$$
These integrals occur in quantum theory and wave optics.
Such integrals are not possible to do using traditional numerical methods due to their highly oscillatory nature.However, Picard-Lefschetz (PL) theory gives a recipe for computing them by analytically continuing the integration domain to $\mathbb{C}^n$, deforming the integration domain according to certain rules, then integrating on the new domain.
This package is an implementation of the PL algorithm in Python.For details, see papers in theadditional informationsection.InstallationTo use plind.py, runpip install plind.UsageTo use the code, follow the examples inExample Notebook.ipynb.Additional InformationFor more information on the code and its usage, refer to the docstrings and comments within the code files, or email the authors. See alsoarXiv:2103.08687andarXiv:1909.04632for details on the method.
Package: pling-publisher
Pling PublisherTool to upload tognome-look.org,store.kde.org,www.pling.com.You can find this python module onhttps://pypi.org/project/pling-publisher/Installpip install pling-publisherHow to usepling build # build an archive from the current folder.pling publish --username <USERNAME> --password <PASSWORD> --project-id <PROJECT_ID> --file <PATH>pling --help # for help :)You can also provide your username, password and project_id via environment variables (PLING_USERNAME, PLING_PASSWORD, PLING_PROJECT_ID).SupportFeel free to submit a pull request.
Package: plink
PLink is a full featured graphical editor for knot and link projections,
using the cross-platform GUI toolkit Tk.  The primary focus is on
piecewise-linear link projections, but it also supports a “smooth
mode” and can export images in PostScript, PDF, SVG, and TikZ formats.
See thePLink home pagefor complete details.This is a pure Python module and you can install it viapip.
Seethe documentationfor more.You can browse the source codehere.LicenseCopyright 2008-present by Marc Culler, Nathan Dunfield, and others.All parts of this package are released under theGNU General Public License, version 2or (at your discretion) any later version as
published by the Free Software Foundation.AcknowledgementsThe development of this program was partially supported by the US
National Science Foundation under grants DMS0608567, DMS0504975,
DMS0204142, and others.
Package: plink-url
plinkRecursive link analyser, written in PythonInstallationTo install plink, run the following command in your terminal:python-mpipinstall--upgradepipwheel
python-mpipinstallplink-urlTo upgrade plink, run the following command in your terminal:python-mpipinstall--upgradepipwheel
python-mpipinstall--upgradeplink-urlIf this does not work, you may have to use thepython3orpip3commands instead.ExamplesBasic example:plink-urlhttps://github.com/Jessicaward/plinkLimit analysis to a single domain:plink-urlhttps://jessica.im/Blog/--whitelisthttps://jessica.imBlock multiple domains:plink-urlhttps://jessica.im/--blacklisthttps://last.fmhttps://stackoverflow.comInclude extra information:plink-urlhttps://jessica.im/--verboseSpecify a depth limit:plink-urlhttps://jessica.im/--depth=3Print extra details about the tool and it's usage:plink-urlhttps://jessica.im/--help
Package: plin-linux
Python PLIN libraryThis library provides a Python interface for interacting with PEAK devices such as the PCAN-USB Pro and PLIN-USB on Linux using the chardev API provided by the PEAK LIN Linux Beta. The PEAK Linux beta driver is required to use this library and is availablehere.InstallationTheplin-linuxpackage is available onPyPIand can be directly installed withpip install plin-linux.ExamplesRunnable examples are located in theexamples/directory.Master Examplefromplin.plinimportPLINfromplin.enumsimport(PLINFrameChecksumType,PLINFrameDirection,PLINFrameFlag,PLINMode)# Initializes /dev/plin0 as a LIN mastermaster=PLIN(interface="/dev/plin0")master.start(mode=PLINMode.MASTER,baudrate=19200)# Get firmware version of the PEAK deviceprint(master.get_firmware_version())# Get current status of the PEAK deviceprint(master.get_status())# Add a publisher frame entrymaster.set_frame_entry(id=0x22,direction=PLINFrameDirection.PUBLISHER,checksum_type=PLINFrameChecksumType.CLASSIC,data=bytearray([0x01,0x02,0x03,0x04]))# Add a subscriber frame entrymaster.set_frame_entry(id=0x23,direction=PLINFrameDirection.SUBSCRIBER_AUTO_LEN,checksum_type=PLINFrameChecksumType.CLASSIC)# Get frame entryprint(master.get_frame_entry(id=0x22))# Add unconditional slot to schedule 0master.add_unconditional_schedule_slot(schedule=0,delay_ms=10,id=0x22)master.add_unconditional_schedule_slot(schedule=0,delay_ms=10,id=0x23)# Get schedule 0 slotsprint(master.get_schedule_slots(0))# Start schedule 0master.start_schedule(0)# Set ID filtermaster.set_id_filter(bytearray([0xff]*8))# Read frameswhileTrue:result=master.read()print(result)Slave Examplefromplin.plinimportPLINfromplin.enumsimportPLINMode# Initializes /dev/plin0 as a LIN slaveslave=PLIN(interface="/dev/plin0")slave.start(mode=PLINMode.SLAVE,baudrate=19200)# Set ID filterslave.set_id_filter(bytearray([0xff]*8))# Read frameswhileTrue:result=slave.read()print(result)Unit TestsUnit tests are located in theunit_tests/directory and require a PEAK LIN device connected to run.LicenseCopyright 2023 Rivian Automotive, Inc.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: plinn
plinn: Partition Like It's 1999The partition functionThis module implements a function called "partition."It is an optimized version of the following python function:defpartition(pred,it):f=[]t=[]foriteminit:(tifpred(item)elsef).append(item)returnf,tBenchmarksHere are some benchmarks on my i7-6600U laptop with python 3.9.0 comparing
against several pure-python variants of the same function:VariantTime for 32 iterations (seconds)stdlib3.2970685160253197mariy3.5445828151423484trevor2.6459597239736468byers2.3595181389246136scara2.2327406019903720scara22.1951448819600046plinn1.5203454510774463
Package: plinq
Plinq is a library for python, implementing the Language Integrated Query (LINQ) from the C# language.
Of course it is not a full port of the feature, it is only working with in memory iterable objects, including:listssetsdictionariesfilesxml elementsgeneratorsand every class which implements the iterable/iterator protocol (
__iter__ and __next__)Plinq tries to mimic the Enumerable<T> interface from C# as much as possible.It has the same method names, wherever possible, and the same
parameters tooPlinq operators are lazy, meaning calling one, will not start the actual work.
It will only happen, when the iteration over the returned object is started.Plinq operators returning a new Linq object, so operator calls can be chained into a fluent API like call.How do I get set up?Summary of set upThere are several ways to set up plinq:The simplest way is to invoke pip install plinqYou can also check out the repository, and add the path to it to your python pathCombining the two above steps, you can check out the repository, create a distribution package
by executing the setup.py file, and then install that package with pipAfter these steps you can simply import plinq and start using the libraryConfigurationNo additional configuration is neededDependenciesplinq has no external dependencies. The only requirement is to have at least Python 3.5.2How to run testsAfter you have checked out the repository,
you can run the python unit tests by invoking the following command from the root directory of the repository:python -m unittest discover test/Who do I talk to?Repo owner and adminHeszele Rudolfheszele@gmail.comExample usageimport plinq
linq = plinq.from_iterable([1, 2, 3, 4, 5]).where(lambda item, index: item % 2 == 0).select(lambda item, index: item ** 2)
for item in linq:
    print(item)This will result in the following output:4
16Version History0.1: Never released externally0.2: Initial release on pypi. Includes almost all the linq operators, except then_by and then_by_descending0.3: Added then_by and then_by_descending linq operarors, and max_item and min_item operators.
Package: plinth
A better place to start might behttp://telehash.orgThis will break. You were warned. :)If you aren’t already using virtualenvs, it is highly recommended for
playing with plinth:http://virtualenvwrapper.readthedocs.org/You will also need libevent (for gevent) which should be easily found in
most Linux distributions, or installed on Mac OS X fromhttp://brew.sh/Next you’ll want tomkdir $HOME/.plinthand put in a seeds.json which
you can find at:https://github.com/telehash/thjs/blob/master/seeds.jsonNow, from within your virtualenv:$ pip install plinth$ examples/seed.py -vNow behold as your terminal fills with indecipherable debug details.
Package: pliny
PlinyPliny is averyopinionated web framework and set of tools to help teams build modern web services and applications.CommandsPlatform commandspliny platform:setupBootstraps the local pliny development platform. Installs and runs the core platform services and developer tools. You must have Docker Desktop installed first.pliny platform:startStartup the pliny platform services. You must have runplatform:setupbefore.pliny platform:stopStop the pliny platform services.pliny platformShow the current status of the local dev platform.App commandspliny create [app|service]Creates the skeleton for a new app or service. An "app" is full-stack with a React front-end and Python backend. A service is just a Python backend that serves an API.pliny runRuns the app or service in the current directory. By default the app frontend and backend are run together in the foreground.The runtime platformThe Pliny framework assumes that services run on a faily very rich runtime platform. This runtime allows us to build rich applications easily, rather than having to cobble together a service component, client library, and stitch it into our application.The core services provided by the platform include:- Database
- Message queue
- Config and secrets store
- Service discovery
- Email gatewayIn order to support a faithful environment for local development and testing, Pliny includes alocal devversion of the runtime platform. Services run in Docker on the local machine. You can use thepliny platformcommands to setup and manage the local runtime.
Package: plipy
Plipy is a suite of tools for building and managing a portable linux python
environment.  It aims for a plipy environment to run on most major linux
dsitros in use today, as well as a good number that should have been phasedoutof use many years ago.  It’s similar in spirit toportable pythonbut
targeting linux instead of Windows.The secret sauce is basically:Using theautopackage build toolsto hide newer glibc symbols, so that
compiled libs can be used unmodified on older linux boxen.Setting the rpath on all shared libs to a sensible value, so that their
deps can be found regardless of where the plipy env is located.Building a plipy environmentTo build a plipy environment, you’ll need at least a recent gcc version and the
“chrpath” tool.  Initialise a new plipy env with the following command:#> plipy PATH/TO/ENV initThis will build and set up a basic python installation (currently python 2.6.5)
along with setuptools and pip.  Most python packages  can be installed directly
using pip.  For packages with more complex needs a plipy “recipe” is provided,
and you can install them using e.g.:#> plipy PATH/TO/ENV install py_wxpythonThis would build and install a custom wxPython version that is patched to
be more portable.Using a plipy environmentIn the top level of a plipy environment there are three shell scripts named
“python”, “plipy” and “shell”.   These set up some relevant environment vars
and then chainload the appropriate command.Here’s how you might install a third-party package using pip:#>
#> PATH/TO/ENV/shell
plippy(ENV):$
plippy(ENV):$ pip install esky
...lots of output as esky is installed...
plippy(ENV):$
plippy(ENV):$ <ctrl-D>
#>What is it good for?Why, everything that something likeportable pythonis good for, except
on linux instead of Windows!  Use it as a convenient portable scripting or
testing environment, or to run  multiple python versions side-by-side.One thing it’s particular good for (actually, the reason it was created) is
building frozen Python apps on linux.  Plipy comes with recipes for patched
versions of cx-freeze and bbfreeze that will build stand-alone applications
having the same portability as the plipy env itself - meaning they should
run anywhere from ancient Red Hat distros to the latest Ubuntu release.References
Package: plists
# What is this?This is a package for reading, writing and managing [Apple plist files](https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man5/plist.5.html).# Installation```pip install plists```# Examples## Reading plist files### Reading V1 (Old Style) plists from file```import plistsobj = plists.v1parser.parseFile("path_to_file")# ... use obj as a normal python object```### Reading V1 (Old Style) plists from string```import plistsobj = plists.v1parser.parse("some_loaded_string")# ... use obj as a normal python object```### Reading XML plists from file```from plists import xmlparserobj = xmlparser.parseFile("path_to_file")# ... use obj as a normal python object```### Reading XML plists from string```from plists import xmlparserobj = xmlparser.parse("some_loaded_string")# ... use obj as a normal python object```### Reading Binary plistsComing Soon## Writing objects to plist files### Writing to Old style plistsObjects can be written to old style plists with:```from plists import v1parserfrom plists import v1writerobj = v1parser.parseFile(<path_to_plist_file>)v1writer.write(obj, outstream, indentString, level)```The parameters are:* obj         -   The object being serialized* outstream   -   The output stream to which the object will be serialized.  If this is None, then a new string outputstream is written to and returned.* indentString  - Indentation string to be used.  If this value is None then no indentation or pretification is applied.  Otherwise this is used.* level       -   The level to start with when serializing.  Each child node is indented an extra level (if indentString is not None).### Writing to XML plistsObjects can be written to xml plists with:```from plists import xmlparserfrom plists import xmlwriterobj = xmlparser.parseFile(<path_to_plist_file>)xmlwriter.write(obj, outstream, indentString, level)```The parameters are:* obj         -   The object being serialized* outstream   -   The output stream to which the object will be serialized.  If this is None, then a new string outputstream is written to and returned.* indentString  - Indentation string to be used.  If this value is None then no indentation or pretification is applied.  Otherwise this is used.* level       -   The level to start with when serializing.  Each child node is indented an extra level (if indentString is not None).### Writing to Binary plistsComing Soon.
Package: plit
plitplitis aMatplotlibwrapper that automates the
undifferentiated heavy-lifting of writing boilerplate code while maintaining
the power and feel of Matplotlib.There are two components toplit:Wrappersaround core chart types for standard line, scatter, histograms, and
bar charts.Templatesthat are built from these primatives for specific analytic tasks.Here is an example chart created withplit:See thedocsfor more information.Note: if you are looking for the project that manages a set of git
repositories, that project has been renamed toglit(GitHub,PyPi).Installpip install plitQuick StartThe best place to get started is the wrappers. There are three main wrappers
included inplit. The naming is consistent with matplotlib. They work with
multi-series by default.plot: for line and scatter charts.hist: for histograms.bar: for bar charts.Create a line chartCreate a line and scatter chart using theplotfunction.importnumpyasnpx=[np.arange(10)]y=[np.random.random(size=(10,1))for_inrange(4)]fromplitimportplotplot(x,y,list("ABCD"),'X','Y');Create a scatter chartBy simply changing themarker_type='o'you switch from line to scatter chart.fromplitimportplotx=[np.random.random(size=(10,1))for_inrange(4)]plot(x,y,list("ABCD"),'X','Y',marker_type='o')Create a histogramCreate a histogram using thehistfunction.fromplitimporthistx=[np.random.normal(size=(100,1)),np.random.gamma(shape=1,size=(100,1))-2]hist(x,list("AB"),'X',title='Histogram',bins=20)Create a bar chartCreate a grouped bar chart with thebarfunction.fromplitimportbarx=[f"Group{i+1}"foriinrange(6)]y=[np.random.random(size=(6))for_inrange(2)]bar(x,y,list("AB"),'X','Y',colors=list("kb"),title='Bar Chart')Example notebooksThe best way to go deeper is to look at the examples notebooks:quick-start notebookgives an overview of core
functionality including creating core chart types.plit-vs-matplotlibshows the difference
between matplotlib and plit with a simple example.creating-templates-filedemonstrates
how to use partial functions to simplify and streamline your visualization
workflow.accuracy-vs-coverageshows an illustrative
example using a template created for visualizing accuracy and coverage.precision-vs-recallshows an illustrative
example using a template created for choosing a threshold using precision and
recall.softmax-calibrationshows an illustrative
example using a template created for evaluating the calibration for softmax
output.
Package: plitlib
plitplitis aMatplotlibwrapper that automates the
undifferentiated heavy-lifting of writing boilerplate code while maintaining
the power and feel of Matplotlib.There are two components toplit:Wrappersaround core chart types for standard line, scatter, histograms, and
bar charts.Templatesthat are built from these primatives for specific analytic tasks.Here is an example chart created withplit:See thePRFAQfor more information.Installpip install plitlibQuick StartThe best place to get started is the wrappers. There are three main wrappers
included inplit. The naming is consistent with matplotlib. They work with
multi-series by default.plot: for line and scatter charts.hist: for histograms.bar: for bar charts.Create a line chartCreate a line and scatter chart using theplotfunction.importnumpyasnpx=[np.arange(10)]y=[np.random.random(size=(10,1))for_inrange(4)]fromplitimportplotplot(x,y,list("ABCD"),'X','Y');Create a scatter chartBy simply changing themarker_type='o'you switch from line to scatter chart.fromplitimportplotx=[np.random.random(size=(10,1))for_inrange(4)]plot(x,y,list("ABCD"),'X','Y',marker_type='o')Create a histogramCreate a histogram using thehistfunction.fromplitimporthistx=[np.random.normal(size=(100,1)),np.random.gamma(shape=1,size=(100,1))-2]hist(x,list("AB"),'X',title='Histogram',bins=20)Create a bar chartCreate a grouped bar chart with thebarfunction.fromplitimportbarx=[f"Group{i+1}"foriinrange(6)]y=[np.random.random(size=(6))for_inrange(2)]bar(x,y,list("AB"),'X','Y',colors=list("kb"),title='Bar Chart')Example notebooksThe best way to go deeper is to look at the examples notebooks:quick-start notebookgives an overview of core
functionality including creating core chart types.plit-vs-matplotlibshows the difference
between matplotlib and plit with a simple example.creating-templates-filedemonstrates
how to use partial functions to simplify and streamline your visualization
workflow.accuracy-vs-coverageshows an illustrative
example using a template created for visualizing accuracy and coverage.precision-vs-recallshows an illustrative
example using a template created for choosing a threshold using precision and
recall.softmax-calibrationshows an illustrative
example using a template created for evaluating the calibration for softmax
output.
Package: plj
pljLuaJIT utilities in pure PythonWARNING: This repo is in development. It was automatically generated withmkpylib. If you're reading this message, it means that I use this repo for my own purposes right now. It might not do anything at all; the default functionality isprint('TODO').If you really want to try it out, feel free. I recommend reading through the code and commit history to see if it does what you need, orask mefor status updates.Stay tuned!Installpython3 -m pip install -U pljUsageimportpljprint('TODO')LicenseMITContactA library byShawn Presser. If you found it useful, please considerjoining my patreon!My Twitter DMs are always open; you shouldsend me one! It's the best way to reach me, and I'm always happy to hear from you.Twitter:@theshawwnPatreon:https://www.patreon.com/shawwnHN:sillysaurusxWebsite:shawwn.com
Package: plmbr
plmbrReusable pipes for data stream processing.fromitertoolsimportzip_longestimportjsonfromplmbr.pipeimportpipefromplmbr.pipesimport*classvalidate(Pipe):def__init__(self,*vals):self.vals=valsdefpipe(self,items:Iterator)->Iterator:forexpected,actualinzip_longest(self.vals,items):print(f'expecting{expected}got{actual}')assertactual==expectedyieldactualdeftest_null():(range(3)-null()>validate(0,1,2))deftest_json_loads():items=[{'a':2},{'b':4}]((json.dumps(i)foriinitems)-json_loads()>validate(*items))deftest_json_dumps():items=[{'a':2},{'b':4}](items-json_dumps()>validate(*[json.dumps(i)foriinitems]))deftest_batch():(range(3)-batch(batch_size=2)>validate([0,1],[2]))([0,1,2]-batch(batch_size=2)>validate([0,1],[2]))deftest_unbatch():([range(2),range(3)]-unbatch()>validate(0,1,0,1,2))deftest_to():(range(3)-to(lambdai:i+1)>validate(1,2,3))deftest_keep():(range(3)-keep(lambdai:i>0)>validate(1,2))deftest_drop_fields():(({'a':i,'b':i,'c':i}foriinrange(3))-drop_fields('b','c')>validate({'a':0},{'a':1},{'a':2}))deftest_uniq():(({'a':0,'b':i//2,'c':i}foriinrange(3))-uniq('a','b')>validate({'a':0,'b':0,'c':0},{'a':0,'b':1,'c':2}))deftest_sample():(range(10)-sample(prob=.5)>validate(1,4,8,9))deftest_window():(range(4)-window(size=2)>validate((0,1),(1,2),(2,3)))([0,1,2,3]-window(size=2)>validate((0,1),(1,2),(2,3)))deftest_append():res=[8](range(4)-log()>append(res))assertres==[8,0,1,2,3]deftest_tee():([1,2,3]-tee(keep(lambdai:i<3)-to(lambdai:i*2),to(lambdai:i*10))>validate(2,4,10,20,30))deftest_catch():defbad_func(items):foriinitems:ifi%2:raiseException(i)yieldierr=[](range(3)-pipe(bad_func)-validate(0,2)>catch(lambdae:err.append(e.args[0])))print(err)asserterr==[1]