Saravanan666/pypi-org huggingface dataset:
Package: liquidhandling
liquidhandlingThis repository contains the source code for a python library developed to allow programmatic control of the Hudson Robotics systems installed in Argonne National Laboratory's Secure BIO Lab.This library is currently inactive development. As such, there are no guarantess of API stability, feature completeness, or test coverage. Any code generated by this library should be considered untested, and when run on the physical lab automation systems should be carefully monitored.InstallationPip:pip install liquidhandlingConda:conda install -c conda-forge -c luckierdodge liquidhandling(For development install, see below)DocumentationDocumentation for the interfaces contained in this library can be found in thedocsdirectory. In addition, example code is available underexample.DevelopmentInstalling For Development/TestingInstallPython 3.8.5+, making sure to include pip in the installGit clone this repository:git clone https://github.com/AD-SDL/hudson-liquidhandling.gitRunpip install -r requirements.txtin the repository rootRunpip install -e .in the repository rootTestsRun all:python -m pytest ./testin the repo's root directory (or with the last argument adjusted to point to thetestdirectory)Formatting the CodeTo automatically format the code for style and readability, runblack .in the repo's root directory. This keeps all python code stylistically consistent.Recommended Visual Studio Code ExtensionsBetter Comments by Aaron BondGitLens by Eric AmodioPylance by MicrosoftPython by MicrosoftVisual Studio IntelliCode by Microsoft
Package: liquid-jsonpath
Liquid JSONPathJSONPathselectors forPython Liquid.Table of ContentsInstallationLinksExamplesLicenseInstallationInstall JSONPath for Liquid usingpip:python -m pip install -U liquid-jsonpathOrpipenv:pipenv install liquid-jsonpathLinksDocs:https://jg-rp.github.io/liquid/jsonpath/introductionChange log:https://github.com/jg-rp/liquid-jsonpath/blob/main/CHANGES.mdPyPi:https://pypi.org/project/liquid-jsonpath/Issue tracker:https://github.com/jg-rp/liquid-jsonpath/issuesExamplesFilterThis example adds thefindfilter to a Liquid environment. You can think offindas an advanced alternative to the standardmapandwherefilters. It takes a JSONPath string argument and applies it to the filter's left value.fromliquidimportEnvironmentfromliquid_jsonpathimportFindenv=Environment()env.add_filter("find",Find())data={"users":[{"name":"Sue","score":100,},{"name":"John","score":86,},{"name":"Sally","score":84,},{"name":"Jane","score":55,},]}template=env.from_string("{{ data | find: '$.users.*.name' | join: ' ' }}")print(template.render(data=data))# Sue John Sally JaneTagThis example replaces the standard{% for %}tag with one that supports piping an iterable through a JSONPath expression.fromliquidimportEnvironmentfromliquid_jsonpathimportJSONPathForTagenv=Environment()env.add_tag(JSONPathForTag)data={"users":[{"name":"Sue","score":100,},{"name":"John","score":86,},{"name":"Sally","score":84,},{"name":"Jane","score":55,},]}template=env.from_string("{% for name in data | '$.users.*.name' %}""{{ name }}, ""{% endfor %}")print(template.render(data=data))# Sue, John, Sally, Jane,Licenseliquid-jsonpathis distributed under the terms of theMITlicense.
Package: liquidluck
Felix Felicis (akaliquidluck) is a simple lightweight static blog generator
written in Python.There are so many static blog generators, why create a new one?The main design pattern of Felix Felicis:Beautiful is better than ugly.Nothing is better than everything.Documentationis available on RTD.CommandsCreate a site:liquidluck initSearch for a theme:liquidluck searchInstall a theme:liquidluck install momentBuild articles:liquidluck buildPreview server:liquidluck serverMore information withliquidluck help.Write a postA post in markdown:# Hello World

- date: 2012-06-11
- tags: python, javascript

-------

Hello World in Python

```python
def hello(name="World"):
    print "Hello %s" % name
```

Hello World in JavaScript

```javascript
function hello(name) {
    alert('Hello ' + name);
}
```A post in restructuredText:Hello World
============

:date: 2012-06-11
:tags: python, javascript

Hello World in Python::

    def hello(name="World"):
        print "Hello %s" % name

Hello World in JavaScript:

.. sourcecode:: javascript

    function hello(name) {
        alert('Hello ' + name);
    }Both of these two posts are valid in their markup language, even without Felix Felicis
they should be displayed well.And in this case, your documents will always be yours, not tied to Felix Felicis.FeaturesCategory, Tag, Post, Page, Feed and everything a blog should haveBeautiful and valid syntax supportsFull customizable theme supportsGlobal theme gallery, site theme gallery, site templateNo extension system (Is this a feature? YES!)GitHub and BitBucket web hook serviceLive preview serverCompatibilityMarkup:restructedTextmarkdownPython:2.62.73.13.2
Package: liquidnet
LiquidNetThis is a simple implementation of the Liquid net official repo translated into pytorch for simplicity.Find the original repo here:Installpip install liquidnetUsageimporttorchfromliquidnet.mainimportLiquidNet# Create an LiquidNet with a specified number of unitsnum_units=64ltc_cell=LiquidNet(num_units)# Generate random input data with batch size 4 and input size 32batch_size=4input_size=32inputs=torch.randn(batch_size,input_size)# Initialize the cell state (hidden state)initial_state=torch.zeros(batch_size,num_units)# Forward pass through the LiquidNetoutputs,final_state=ltc_cell(inputs,initial_state)# Print the shape of outputs and final_stateprint("Outputs shape:",outputs.shape)print("Final state shape:",final_state.shape)VisionLiquidNetSimple model with 2 convolutions with 2 max pools, alot of room for improvementimporttorchfromliquidnet.vision_liquidnetimportVisionLiquidNet# Random Input Imagex=torch.randn(4,3,32,32)# Create a VisionLiquidNet with a specified number of unitsmodel=VisionLiquidNet(64,10)# Forward pass through the VisionLiquidNetprint(model(x).shape)Citation@article{DBLP:journals/corr/abs-2006-04439,author={Ramin M. Hasani andMathias Lechner andAlexander Amini andDaniela Rus andRadu Grosu},title={Liquid Time-constant Networks},journal={CoRR},volume={abs/2006.04439},year={2020},url={https://arxiv.org/abs/2006.04439},eprinttype={arXiv},eprint={2006.04439},timestamp={Fri, 12 Jun 2020 14:02:57 +0200},biburl={https://dblp.org/rec/journals/corr/abs-2006-04439.bib},bibsource={dblp computer science bibliography, https://dblp.org}}LicenseMITTodo:Implement LiquidNet for vision and train on CIFAR
Package: liquidpy
liquidpyA port ofliquidtemplate engine for python, on the shoulder ofjinja2Installpipinstall-UliquidpyPlaygroundPowered bypyscript:https://pwwang.github.io/liquidpy/playgroundBaisic usageLoading a templatefromliquidimportLiquidliq=Liquid('{{a}}',from_file=False)ret=liq.render(a=1)# ret == '1'# load template from a fileliq=Liquid('/path/to/template.html')Using jinja's environmentfromjinja2importEnvironment,FileSystemLoaderenv=Environment(loader=FileSystemLoader('./'),...)liq=Liquid.from_env("/path/to/template.html",env)Switching to a different modeliq=Liquid("""{% python %}from os import pathfilename = path.join("a", "b"){% endpython %}{{filename}}""",mode="wild"# supported: standard(default), jekyll, shopify, wild)liq.render()# 'a/b'Changing default optionsfromliquidimportdefaults,Liquiddefaults.FROM_FILE=Falsedefaults.MODE='wild'# no need to pass from_file and mode anymoreliq=Liquid('{% from_ os import path %}{{path.basename("a/b.txt")}}')liq.render()# 'b.txt'DocumentationLiquidpy's documentationLiquid documentation (standard)Liquid documentation (jekyll)Liquid documentation (shopify-extended)Jinja2's documentation
Package: liquidSVM
Welcome to the Python bindings for liquidSVM.Summary:Install it using any of the following variants:pip install --user --upgrade liquidSVM
easy_install --user --upgrade liquidSVMIf you want to compile liquidSVM for your machine downloadhttp://www.isa.uni-stuttgart.de/software/python/liquidSVM-python.tar.gz.
For Windows there are binaries atliquidSVM-python.win-amd64.zip,
for Mac atliquidSVM-python.macosx.tar.gzThen to try it out issue on the command linepython -m liquidSVM covtype.1000 mc --display=1

**NOTE**: it might be possible that there is a problem with the last
line if there are files called ``liquidSVM*`` in the current
directory, so change to some other or a newly created one.Or use it in an interactive shellfromliquidSVMimport*model=mcSVM(iris,iris_labs,display=1,threads=2)result,err=model.test(iris,iris_labs)result=model.predict(iris)reg=LiquidData('reg-1d')model=lsSVM(reg.test,display=1)result,err=model.test(reg.test)More Information can be found in thedemo[jupyter
notebook]and infromliquidSVMimport*help(SVM)help(doc.configuration)Both liquidSVM and these bindings are provided under the AGPL 3.0
license.Native Library CompilationliquidSVM is implemented in C++ therefore a native library needs to be
compiled and included in the Python process. Binaries for Windows are
included, however if it is possible for you, we recommend you compile it
for every machine to get full performance.To set compiler options use the the environment variableLIQUIDSVM_CONFIGURE_ARGS. The first word in it can be any of the
following:nativeusually the fastest, but the resulting library is usually not
portable to other machines.genericshould be portable to most machines, yet slower (factor 2 to 4?)debugcompiles with debugging activated (can be debugged e.g. with gdb)emptyNo special compilation options activated.The remainder of the environment variable will be passed to the
compiler. Extracthttp://www.isa.uni-stuttgart.de/software/python/liquidSVM-python.tar.gzand change into the directory. On Linux and MacOS X command line use for
instance:LIQUIDSVM_CONFIGURE_ARGS="native -mavx2" python setup.py bdist
LIQUIDSVM_CONFIGURE_ARGS=generic python setup.py bdistMacOS:Install Xcode and then the optional command line tools are installed
from therein.Windows:If you have VisualStudio installed then you should have an
environment variable like%VS90COMNTOOLS%(for VisualStudio
2015). Still it seems that setup.py needs to have this information
in%VS90COMNTOOLS%so copy that environment variable or use for
example:set VS90COMNTOOLS=%VS140COMNTOOLS%

**Note:** At the moment the Visual Studio for Python only gives
Version 9.0 and this is too old for compilation.
Package: liquidswap-sdk
liquidswap-sdk-pythonInstallhttps://pypi.org/project/liquidswap-sdk/pip install liquidswap-sdkFunctionsimportfrom liquidswap_sdk.client import LiquidSwapClientnew a clientliquidswap_client = LiquidSwapClient(node_url, tokens_mapping, wallet_path)get the output amount from given input amountliquidswap_client.calculate_rates("APTOS", "USDT", 1)swap tokenliquidswap_client.swap("APTOS", "USDT", 1, usdt_out)get token balanceliquidswap_client.get_token_balance("APTOS")register tokenliquidswap_client.register("USDT")How to usecreate yourconfigadd token addresses you want to trade totokens_mappingmake sure there is some APT in your wallet ofwallet_pathmake yout own script! (check:example)if you are ready tomainnt, change thenode_urltohttps://fullnode.mainnet.aptoslabs.com/v1WIP:convert_to_decimalscalculate_ratesswapcreate pypl package
Package: liquidtap
LiquidTap Python ClientTo manage API tokens, refer to:https://app.liquid.com/settings/api-tokensTo learn more about Liquid Api token management, see the help links below:https://help.liquid.com/connect-to-liquid-via-apiGetting Startedpip3 install liquidtapThe library can connect to public channels without token credentials.importliquidtapdefupdate_callback(data):print(data)defon_connect(data):tap.pusher.subscribe("price_ladders_cash_btcjpy_buy").bind('updated',update_callback)if__name__=="__main__":tap=liquidtap.Client()tap.pusher.connection.bind('pusher:connection_established',on_connect)tap.pusher.connect()AuthenticationAuthenticate on initializationimportliquidtapdefupdate_callback(data):print(data)defon_connect(data):print(data)tap.pusher.subscribe("price_ladders_cash_btcjpy_buy").bind('updated',update_callback)if__name__=="__main__":tap=liquidtap.Client("insert token id","insert token secret")tap.pusher.connection.bind('pusher:connection_established',on_connect)tap.pusher.connect()
Package: liquify
the Python 2 minifier behindliquenalmost totally based onminipyby Gareth Rees <http://garethrees.org/>Introductionliquifyis a minifier for Python 2 focused on maintaining usability of the minified code as an imported module (which means it preserves functions and variables that could have been of use for external code calling the minified code). For example:$ cat fib.py
cache = {0: 0, 1: 1, 2: 1, 3: 2}
def fibonacci(n):
    """Return the nth Fibonacci number."""
    if n not in cache:
        a = n // 2
        r = n % 2
        m = (r * 2) - 1
        cache[n] = fibonacci(a + 1) ** 2 + m * fibonacci(a + r - 1) ** 2
    return cache[n]

$ liquify --module --docstrings fib.py
cache={0:0,1:1,2:1,3:2}
def fibonnacci(a):
 if a not in b:d=a//2;e=a%2;f=e*2-1;b[a]=fibonnacci(d+1)**2+f*c(d+e-1)**2
 return b[a]Command lineUsage: liquify [options] [-o OUTPUT] FILE

Options:
  --version             show program's version number and exit
  -h, --help            show this help message and exit
  -o OUTPUT, --output=OUTPUT
                        output file (default: stdout)
  -D, --docstrings      remove docstrings and other statements with no side
                        effects (implies --noselftest)
  -R, --rename          aggressively rename non-preserved variables
  -i INDENT, --indent=INDENT
                        number of spaces per indentation level
  -p PRESERVE, --preserve=PRESERVE
                        preserve words from renaming (separate by commas)
  -m, --module          preserve top level object names (for use as imported

  --nojoinlines         put each statement on its own line
  --noselftest          skip the self-test
  --debug               dump the parse treeLicenseliquify 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 theGNU
General Public Licensefor more details.
Package: liquimigrate
Liquibase migrations for DjangoLicense: BSDDocumentation:https://liquimigrate.readthedocs.org.History0.5.2 (2015-10-22)addedmanagementtoMANIFEST.in0.5.1 (2015-10-22)handle liquibase return codes as an exception0.5.0 (2015-08-06)add documentation and better package layout0.4.1 (2015-08-06)fix for disabling signalsadd possiblity to disable emitting pre- and postmigrate signalsadd example project configured with liquibase migrations0.4.0 (2014-09-28)more detailed messages in makemigrations and squashmigrations commandsDjango 1.7+ compatibility + wrapped migration commands0.3.0 (2014-02-17)Update README.rstremoved version from __init__Update README.rstback to previous versioning schemecleaned up settings headerChanged Makefile:pypi to not use iw.distUpdate README.rstpossibility to change liquibase jar and connectors via settingsdb name, host, port were overriden by defaults0.2.8 (2012-02-28)Updated documentation about new LIQUIMIGRATE_CHANGELOG_FILES directive Renamed README.txt to README.rst & included into MANIFEST.in Print additional info only in –verbosity 1 model Minor changes for PEP8full multidb support0.2.7 (2011-06-01)emit post_sync signal, call loaddata and initial_datasupport for singledb django versionsfixed driver option0.2.6 (2011-05-20)accepting –driver option from command line0.2.5 (2011-05-18)Better README.txt - link to liquibase0.2.4 (2011-05-17)FIXED: project path0.2.3 (2011-05-17)Updated README.txt about mysql driver & about BSD license0.2.2 (2011-05-17)ADDED: pass other args as liquibase args0.2.1 (2011-05-17)FIXED: too small tuple when using not supported db driver0.2.0 (2011-04-29)ADDED: mysql connector0.1.2 (2011-04-28)ADDED: custom syncdb commandADDED: ensure command was given & more README.txtFIXED: run cmdline than just print them ;)FIXED: missing vendor in egg0.1.1 (2011-04-28)REMOVED: old dependencies/namespaces0.1.0 (2011-04-28)
Package: liquisource
liquibase数据源驱动liquibase引入python3脚本，统一管理管理mongo、clickhouse的库表结构。changelog记录还是选在记录到mysql中，这样业务上会更加灵活<changeSetid="xxxxx"author="xxxxxx"labels="mongo"><comment>xxxxx</comment><executeCommandexecutable="python3"><argvalue="script/db_tag/creat_collection.py"/></executeCommand></changeSet>#!/usr/bin/env python3# -*- coding: utf-8 -*-fromliquibase_datasourceimport*defcreate_tag_database():# 获取mongo链接实例client=get_client(filepath)returnclient[db]defcreate_tag_collection():# 获取mongo链接实例db_name=get_tenant_shard(tag_database)client=get_client()db=client[db_name]# db开启分片client.admin.command("enableSharding",db_name)db.create_collection(tag_collection)# 创建索引coll=db[tag_collection]coll.create_index([("id",1),("name",1)])if__name__=="__main__":# 创建标签集合create_tag_database()create_tag_collection()发布包python3-mpipinstall--upgradebuildpython3-mbuildpython3-mpipinstall--upgradetwinepython3-mtwineuploaddist/*
Package: liquor
=====Liquor=====Liquor is a simple library that will copy the lcbo's products api to your database.This was created from a workshop assignment for the YMC (www.theymc.com).The purpose is to avoid creating fake data, when building sample applications,Using manage.py steal_booze, we can now populate our database with live data,and build a sample site with accurate information.http://git.io/NM2urAQuick start-----------1. `pip install liquor`2. Add "liquor" to your INSTALLED_APPS setting like this::INSTALLED_APPS = (...'liquor',)3. Run `python manage.py syncdb` to create the LcboProduct models.4. Run `python manage.py steal_booze` to get data from the LCBO API, and store it in your models.5. Run `python manage.py analyze_booze` to get some insights.Accessing the data-----------------1. Run `python manage.py shell` to launch the shell2. `from liquor import models`3. `models.LcboProduct.objects.all()`
Package: lir
LIR Python Likelihood Ratio LibraryThis library provides a collection of scripts to aid calibration, and
calculation and evaluation of Likelihood Ratios.A simple score-based LR systemA score-based LR system needs a scorer and a calibrator. The most basic setup
uses a training set and a test set. Both the scorer and the calibrator are
fitted on the training set.importlirimportnumpyasnpfromsklearn.linear_modelimportLogisticRegressionfromsklearn.model_selectionimporttrain_test_split# generate some data randomly from a normal distributionX=np.concatenate([np.random.normal(loc=0,size=(100,1)),np.random.normal(loc=1,size=(100,1))])y=np.concatenate([np.zeros(100),np.ones(100)])# split the data into train and testX_train,X_test,y_train,y_test=train_test_split(X,y)# initialize a scorer and a calibratorscorer=LogisticRegression(solver='lbfgs')# choose any sklearn style classifiercalibrator=lir.KDECalibrator()# use plain KDE for calibrationcalibrated_scorer=lir.CalibratedScorer(scorer,calibrator)# fit and predictcalibrated_scorer.fit(X_train,y_train)lrs_test=calibrated_scorer.predict_lr(X_test)# print the quality of the system as log likelihood ratio cost (lower is better)print('The log likelihood ratio cost is',lir.metrics.cllr(lrs_test,y_test),'(lower is better)')print('The discriminative power is',lir.metrics.cllr_min(lrs_test,y_test),'(lower is better)')# plot calibrationimportlir.plottingwithlir.plotting.show()asax:ax.pav(lrs_test,y_test)The log likelihood ratio cost (Cllr) may be used as a metric of performance.
In this case it should yield a value of around .8, but highly variable due to
the small number of samples. Increase the sample size to get more stable
results.
Package: lira
liraTutorial interactivo de Python en tu terminal.Learn, Imagine, Repeat, AnalizeCaracterísticasTutorial de línea de comandos (100% texto)MultiplataformaFácil de instalarContenido para todos los nivelesAutocompletado (argumentos del programa)Fácil de agregar nuevos tutorialesMulti-idiomas, enfocado al españolEdita el código con tu editor de preferenciaGuardado de avanceNo-CaracterísticasNo es un tutorial de webNo tiene soporte para móvilNo tiene soporte para multiusuarioNo es un editor o IDE para PythonNo soporta otros lenguajesNo es un remplazo de Jupyter NotebookNo es un libro o documento de referenciaIdeas random a futuroCompartir el scoreExpandirse a otro tipo de tutoriales (física, matemática, estadística)Modo contra relojModo quiz
Package: lirc
This is a python package that allows you to interact with the daemon in theLinux Infrared Remote Controlpackage. By interacting
with this daemon, it allows you to programmatically send IR signals from a
computer.This package is for emitting IR signals, but it does not support listening to
IR codes. If you’d like to monitor the IR signals you recieve on
Linux, which has built-in support in the kernel for recieving IR signals, you
can try usingpython-evdev.
They have atutorial on reading the events.More information on the lircd daemon, socket interface,
reply packet format, etc. can be found athttps://www.lirc.org/html/lircd.htmlMaintainance StatusThis project is maintained, but it is not actively developed.
It is feature complete for my purposes.InstallationThis package is hosted on PyPI and can be installed
through pip.$pipinstalllircHowever since this is a wrapper around the LIRC daemon, it
is expected that LIRC is installed and setup on the given
system as well.More information on that can be found in theinstallationportion of the full documentation.Quick StartUsing the Clientimportlircclient=lirc.Client()print(client.version())>>>'0.10.1'To use this package, we instantiate aClient. By initializing it
with no arguments, theClientwill attempt to connect to the lirc
daemon with the default connection parameters for your operating system.These defaults depend on your operating system and can be looked up in the
full documentation if you need different parameters.However, if you’ve instantiated theClientwithout any arguments,
you don’t get any errors, and you recieve a response from theversion()command, you are connected to the daemon. Most people should not need to
change the default parameters.Customizing the ClientAs previously stated, we can customize these defaults if needed.importsocketimportlircclient=lirc.Client(connection=lirc.LircdConnection(address="/var/run/lirc/lircd",socket=socket.socket(socket.AF_UNIX,socket.SOCK_STREAM),timeout=5.0))For the client in the example above, we set it up using the defaults for a Linux machine.
While this example illustrates what is customizable, it is not a practical example since
you could callClient()with no arguments if you’re on Linux and achieve the same outcome.SeeOverriding LIRC Defaults on Initializationfor more information.Sending IRimportlircclient=lirc.Client()client.send_once("my-remote-name","KEY_POWER")# Go to channel "33"client.send_once("my-remote-name","KEY_3",repeat_count=1)With sending IR, we can use thesend_oncemethod and optionally,
send multiple by using therepeat_countkeyword argument.Handling Errorsimportlircclient=lirc.Client()try:client.send_once('some-remote','key_power')exceptlirc.exceptions.LircdCommandFailureErroraserror:print('Unable to send the power key!')print(error)# Error has more info on what lircd sent back.If the command was not successful, aLircdCommandFailureErrorexception will be thrown.Further DocumentationMore information on how to setup the system installed LIRC, how to use
this python library, and a full API specification can be found athttps://lirc.readthedocs.io/