Saravanan666/pypi-org huggingface dataset:
Package: lsst-sphinx-bootstrap-theme
This is a prototype Sphinx theme for LSST Stack documentation, based on the Astropy Sphinx theme.Getting started with developmentGet the repo:git clone https://github.com/lsst-sqre/lsst-sphinx-bootstrap-theme
cd lsst-sphinx-bootstrap-themeInstall JavaScript dependencies:npm install -g gulp-cli
npm installCompile the assets and continue watching for changes:gulpCodebase orientationlsst_sphinx_bootstrap_theme/is the theme’s Python packagestatic/is for static assets deployed onto the site.app.cssis generated withgulp, so don’t edit it directly.layout.htmlis the main Jinja2 template for the HTML. The other HTML files are blocks/partials.scss/contains Sass source files that get compiled intolsst_sphinx_bootstrap_theme/static/app.css.Gulp commandsThis project usesGulpto run its build pipelines.
This section describes the gulp commands you can run.gulpUse this default command for development.
It does the following:sass(compile Sass)Watches for changes and recompiles assets as necessary.gulp scssCompile Sass into CSS (app.css).
We use the following features:Compile Sass.Add prefixes to CSS usingAutoprefixer(viaPostCSS).Clean and compress the CSS using gulp-clean-css.You can run this task alone, but usually throughgulp(default task).gulp prettyAutomatically format code.
SeeCode style via Prettier.Code style via PrettierThis project usesPrettierto make sure the Sass and JavaScript are formatted as you’d expect.
Like most projects, we usePrettiernearly as-is.
A couple minor exceptions are configured in.prettierrc.yaml.You can runPrettiertwo ways:Manually, by runninggulp pretty.Automatically, by committing code.
This is configured as a pre-commit hook inpackage.json.Note thatPrettier’spre-commit hook and chunked git commits don’t mix.
You’ll want to manually runPrettierbefore committing a subset of the changed lines in your files.Release processUpdate the change log (CHANGELOG.rst), commit, and marge work tomaster.Tag the release using aPEP 440-compatible version string:git tag -s X.Y.Z -m "X.Y.Z"Push the tag:git tags --pushThe Travis CI pipeline will create and upload the release to PyPI.
Package: lsst-versions
lsst_versionsThis package is used to calculate a version dynamically from a Git repository when it is being built bypip.
It is not needed for EUPS-only packages, and the calculated package version will bepip-compatible and thus differ from that produced by EUPS’spkgautoversion.
It avoids the need to hard-code and continually update a version string.
It assumes the use of LSST DM release and tagging practices.lsst-versions 1.5.0 2023-11-29Package has been verified to work with python 3.12.lsst-versions 1.4.0 2023-02-08New FeaturesThe calculation of the developer version has been modified.
Previously alpha releases were constructed from weekly release tags.
This approach, 26.0.0a20230500, resulted in confusion in PyPI installs once a formal release was made.
To simplify installations withpipweekly developer release versions are now of the form 25.2023.500 – the weekly is encoded in the minor and patchlevel parts of the version and these are now releases derived from the release currently being worked (and not alphas towards the next release).lsst-versions 1.3.0 2022-07-10API ChangesAdded a new functionget_lsst_version.
This allows to get a version string of a GitHub or metadata directory.lsst-versions 1.2.0 2022-06-27New FeaturesNow falls back to looking atPKG-INFOfile if no git version can be determined.
This allows a source distribution to be built.Thefind_lsst_versionAPI can now run without any parameters.lsst-versions 1.1.0 2022-06-14New FeaturesAdded a newlsst-versioncommand line that can be used to determine the version of a package.
This command can also be used to create a version file in the package using the configuration found in apyproject.tomlfile. (DM-35064)API ChangesRenamed thefind_dev_lsst_versionfunction tofind_lsst_versionto reflect the fact that it does more than finding developer versions. (DM-35064)Miscellaneous Changes of Minor InterestReplaced some debug prints with logging.Significantly improved the test coverage. (DM-35064)lsst-versions 1.0.0 2022-04-18New FeaturesInitial release oflsst-versionspackage.
This package can be used as asetuptoolsentry point to determine the version of a package from the Git repository. (DM-32408)
Package: lssvr
lssvrlssvris a Python module implementing theLeast Squares Support Vector Regressionusing the scikit-learn as base.instalationthelssvrpackage is available inPyPI. to install, simply type the following command:pip install lssvror usingPoetry:poetry add lssvrbasic usageExample:importnumpyasnpfromlssvrimportLSSVRfromsklearn.datasetsimportload_bostonfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportmean_squared_errorboston=load_boston()X_train,X_test,y_train,y_test=train_test_split(boston.data,boston.target,test_size=0.2)model=LSSVR(kernel='rbf',gamma=0.01)model.fit(X_train,y_train)y_hat=model.predict(X_test)print('MSE',mean_squared_error(y_test,y_hat))print('R2 Score',model.score(X_test,y_test))contributingthis project is open for contributions. here are some of the ways for you to contribute:bug reports/fixfeatures requestsuse-case demonstrationsto make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!
Package: lstar_lm
L*LMImplementation of L*LM algorithm algorithm. Seeproject
pagefor details.Table of ContentsInstallationUsageInstallationIf you just need to uselstar_lm, you can just run:$ pip install lstar_lmFor developers, note that this project uses thepoetrypython package/dependency
management tool. Please familarize yourself with it and then
run:$ poetry installUsageThe main entry points for using this library are theguess_dfaanddfa_search_with_dissfunctions.guess_dfasupports labeled examples and natural language.dfa_search_with_disssupports labeled examples, natural language, and
demonstrations.fromlstar_lmimportguess_dfaAn invocation ofguess_dfatakes the form.dfa=guess_dfa(positive=...,# List of positive examples. Each example is a list of tuples of tokens.negative=...,# List of negative examples. Each example is a list of tuples of tokens.alphabet=...,# List of (hashable) tokens.task_description=...,# String of task description.allow_unsure=...,# Whether to allow unsure responses (default True).random_iters=...,# Number of random queries to oracle.active_queries=...,# Number of active queries to oracle.use_dfa_identify=...,# True if use SAT based DFA identification. False uses L* + SAT hybrid.llm_endpoint=...,# http endpoint for llama.cpp server (default "http://localhost:8080/completion").)To learn using demonstrations, one can usedfa_search_with_dissto search for low energy DFAs:search=dfa_search_with_diss(alphabet=...,# List of (hashable) tokens.# ---- Passive identification params ---positive=...,# List of positive examples. Each example is a list of tuples of tokens.negative=...,# List of negative examples. Each example is a list of tuples of tokens.# ---- LLM oracle params ----task_description=...,# String of task description.llm_params=...,# Dictionary of llama.cpp parameters.llm_query_callback=...,# Callback sent prompt, response.llm_endpoint=...,# http endpoint for llama.cpp server#    (default "http://localhost:8080/completion").allow_unsure=...,# Whether to allow LLM to output unsure.# --- Active learning params ---random_iters=...,# Number of random queries to oracle.active_queries=...,# Number of active queries to oracle.use_dfa_identify=...,# True if use SAT based DFA identification.# False uses L* + SAT hybrid.# --- Demonstration learning parameters ---demonstrations=...,# List of expert demonstrationsmax_diss_iters=...,# Maximum number of diss iterations.to_chain=...,# Converted dfa concept to annotated markov chain given#   a maximum entropy policy. See DISS documentation.diss_params,# Other DISS parameters to override defaults.)mle=min(search,key=lambda_1,_2,metadata:metadata['energy'])conjectured_examples,concept,metadata=mledfa=concept.dfa# Most likely dfa
Package: lstcn
The Long Short-term Cognitive Network (LSTCN) model is an efficient recurrent neural network for time series forecasting. It supports both one-step-ahead and multiple-step-ahead forecasting of univariate and multivariate time series. The LSTCN model is competitive compared to state-of-the-art recurrent neural networks such as LSTM and GRU in terms of forecasting error while being much faster.
Package: ls-thrift-py-hadoop
This is an eggified version of the hadoop and hive thrift python
libraries.The libs are extracted from clouderas hadoop distribution and the
version of this package includes the upstream version of the hadoop
distribution.Building the DistribtionTo build the distribution from the source tree, run the following:python bootstrap.py
./bin/buildout
./bin/buildThe above commands download the hadoop and hive tarballs from the
cloudera distribution.Change History2013/06/24 1_cdh4.3.0CDH 4.3.0 update, hive 0.10.02013/01/12 1_cdh4.1.3CDH 4 update, hive 0.9.02012/01/27 1_cdh3u2first release
Package: lstm
lstmPytorch implementation of LSTM variants⚠️ Work In progressDescription•Install•Usage•ContributeDocumentationDescriptionThelstmpackage allows user to easily use various LSTM alternatives, implemented in Pytorch.Currently implemented :NothingRoadmap :LSTM(to ensure we get the same results as Pytorch implementation)GRU(to ensure we get the same results as Pytorch implementation)CIFGLiGRUInstallInstalllstmby running :pip install lstmUsage-> TODOContributeTo contribute, install the package locally, create your own branch, add your code (and tests, and documentation), and open a PR !Pre-commit hooksPre-commit hooks are set to check the code added whenever you commit something.If you never ran the hooks before, install it with :pre-commitinstallThen you can just try to commit your code. If your code does not meet the quality required by linters, it will not be committed. You can just fix your code and try to commit again !You can manually run the pre-commit hooks with :pre-commitrun--all-filesTestsWhen you contribute, you need to make sure all the unit-tests pass. You should also add tests if necessary !You can run the tests with :pytestTests are not included in the pre-commit hooks, because running the tests might be slow, and for the sake of developpers we want the pre-commit hooks to be fast !Pre-commit hooks will not run the tests, but it will automatically update the coverage badge !DocumentationThe documentation should be kept up-to-date. You can visualize the documentation locally by running :mkdocsserve
Package: lstm-preprocess
This is a tool used to reshape dataset in pandas for Keras LSTM modelThis module provides a single function that converts a multivariate time series dataframe into a supervised learning - style dataframe by flattening and shifting each series along a given lagging period. For example, when one tries to use keras on a 2-D tensor of shape (total_time_steps, num_features), it's required to reshape the tensor into a 3-D tensor of input_shape (num_features, time_steps, batch_size)
Package: lstm-reshaper
This is a tool used to reshape dataset in pandas for Keras LSTM modelThis module provides a single function that converts a multivariate time series dataframe into a supervised learning - style dataframe by flattening and shifting each series along a given lagging period. For example, when one tries to use keras on a 2-D tensor of shape (total_time_steps, num_features), it's required to reshape the tensor into a 3-D tensor of input_shape (num_features, time_steps, batch_size)
Package: lstm-variants
LSTM VariantsInstall:pip install rnn-classifiersfromlstm_variantsimportLightweightLSTMimporttorchmodule=LightweightLSTM(input_size=5,hidden_size=32,batch_first=True)batch_size,seq_len,input_size=8,35,5x=torch.rand(size=(batch_size,seq_len,input_size))output,(h_t,c_t)=module(x)
Package: lstr
lstr: partially lockable Python stringslstris a Python package for partially lockable strings.Installationlstrrequires Python 3.8 or later.pipinstalllstrExamplesCreating an lstrfromlstrimportlstrgreeting=lstr("Hello, world!")print(greeting)Hello, world!Getting help with indexesfromlstrimportlstrgreeting=lstr("Hello, world!")print(repr(greeting))0  1  2  3  4  5  6  7  8  9 10 11 12
  H  e  l  l  o  ,     w  o  r  l  d  !Inserting textfromlstrimportlstrgreeting=lstr("Good morning, Bobby!")greeting.write("Captain ",index=14)print(greeting)Good morning, Captain Bobby!Overwriting textfromlstrimportlstrgreeting=lstr("Good morning, Captain Bobby!")greeting.write("Fleet Admiral",index=14,length=7)print(greeting)Good morning, Fleet Admiral Bobby!Substituting textfromlstrimportlstrgreeting=lstr("Good morning, Fleet Admiral Bobby!")greeting.sub("morning","evening")print(greeting)Good evening, Fleet Admiral Bobby!Substituting text with a regular expressionfromlstrimportlstrgreeting=lstr("Good evening, Fleet Admiral Bobby!")greeting.sub(r"(Fleet Admiral)",r"🎉\g<1>🎉")print(greeting)Good evening, 🎉Fleet Admiral🎉 Bobby!Locking a rangefromlstrimportlstrgreeting=lstr("Good morning, Fleet Admiral Bobby!")greeting.lock(index=14,length=13)greeting.write("Ensign",index=14,length=13)print(greeting)Good morning, Fleet Admiral Bobby!Locking a substitutionfromlstrimportlstrgreeting=lstr("Good morning, Captain Bobby!")greeting.sub("Captain","Fleet Admiral",lock=True)greeting.sub("Fleet Admiral","Ensign")print(greeting)Good morning, Fleet Admiral Bobby!Equalitylstrinstances are considered equal only if their string value and locks are identical.lstr("f",locks=[Lock(index=1,length=2)])==lstr("f",locks=[Lock(index=1,length=2)])lstr("f",locks=[Lock(index=1,length=2)])!=lstr("f",locks=[Lock(index=3,length=4)])lstrandstrinstances are considered equal if the string value is identical, regardless of the locks.lstr("f")=="f"lstr("f")!="g"Thank you! 🎉My name isCariad, and I'm anindependent freelance DevOps engineer.I'd love to spend more time working on projects like this, but--as a freelancer--my income is sporadic and I need to chase gigs that pay the rent.If this project has value to you, please consider☕️ sponsoringme. Sponsorships grant me time to work onyourwants rather thansomeone else's.Thank you! ❤️
Package: ls-trace
ls-trace-pyDatadog has generously announced thedonationof their tracer libraries
to theOpenTelemetyproject. Auto-instrumentation
is a core feature of these libraries, making it possible to create and
collect telemetry data without needing to change your code. LightStep
wants you to be able to use these libraries now! We've forked the Datadog
libraries into the LightStep repo as agents. You can install and use these agents to take advantage of
auto-instrumentation without waiting for OpenTelemetry. Each LightStep agent
is "pinned" to a Datadog release and is fully supported by LightStep's
Customer Success team.Simply install the agent, configure it to communicate with LightStep
Satellites, run your app, and then anyframeworks,data stores,
andlibrariesincluded in your app will send data to LightStep as distributed traces.
Package: lstree
lstreelstree is for ls what pstree is for ps, and some more …The idea struck when I had just started using leiningen for creating a clojure project. I wanted a way to see what all files/folders/things are created when leiningen creates a project. So I wrote this tool. It helps you visually see the folder hierarchy, and allows you to do some basic filtering on the tree.Free software: MIT licenseDocumentation:https://lstree.readthedocs.org.FeaturesShow a folder (or many, if specified) in tree structureShow/hide hidden filesIgnore empty directoriesShow (filter for) only certain filesIgnore certain files/foldersInstallationUse pip to install lstree:pip install lstreeBasic Usagelstree when used without any arguments, shows the current tree for $PWD:tochukasui:hello-world$ lstree
|- ./
   |- doc/
      |- intro.md

   |- resources/
   |- src/
      |- hello_world/
         |- core.clj

   |- target/
      |- base+system+user+dev/
         |- classes/
            |- META-INF/
               |- maven/
                  |- hello-world/
                     |- hello-world/
                        |- pom.properties

         |- stale/
            |- leiningen.core.classpath.extract-native-dependencies

      |- classes/
         |- META-INF/
            |- maven/
               |- hello-world/
                  |- hello-world/
                     |- pom.properties

      |- stale/
         |- leiningen.core.classpath.extract-native-dependencies

      |- hello-world-0.1.0-SNAPSHOT.jar

   |- test/
      |- hello_world/
         |- core_test.clj

   |- CHANGELOG.md
   |- LICENSE
   |- project.clj
   |- README.mdApparently this was a hello-world lein project after alein build. Too much clutter. I don’t care of about anything inside the target folder anyway. Let’s cut it out:tochukasui:hello-world$ lstree -i target
|- ./
   |- doc/
      |- intro.md

   |- resources/
   |- src/
      |- hello_world/
         |- core.clj

   |- test/
      |- hello_world/
         |- core_test.clj

   |- CHANGELOG.md
   |- LICENSE
   |- project.clj
   |- README.mdMuch better. We ‘-i gnored’ the target folder. How about just focusing on the clojure source files?:tochukasui:hello-world$ lstree -i target -f '*.clj'
|- ./
   |- doc/
   |- resources/
   |- src/
      |- hello_world/
         |- core.clj

   |- test/
      |- hello_world/
         |- core_test.clj

   |- project.cljNice. But what are those ‘doc’ and ‘resources’ folders doing there? They don’t have any clj files; why clutter the view?:tochukasui:hello-world$ lstree -i target -f '*.clj' --ignore-empty
|- ./
   |- src/
      |- hello_world/
         |- core.clj

   |- test/
      |- hello_world/
         |- core_test.clj

   |- project.cljAha!There are a few more useful tools lstree offers. For more info, check out the usage section of the documentation:https://lstree.readthedocs.io/en/latest/usage.htmlHistory0.1.0 (2016-05-01)First release on PyPI.
Package: lsvine
lsvineList contents of directories in a vine-like format.I builtlsvineto be liketreebut with the first-level directories distributed horizontally (and dangling downwards, hence like a vine).This format compacts the information vertically and displays it in atrello-like format.Installationpip3 install lsvineUsage# lsvine --version
lsvine, version 0.1.0

# lsvine .
0_root         build               dist                           lsvine       lsvine.egg-info
-------------  ------------------  -----------------------------  -----------  --------------------
CHANGELOG      bdist.linux-x86_64  lsvine-0.1.2-py3-none-any.whl  __init__.py  PKG-INFO
DEVELOPER.md   lib                 lsvine-0.1.2.tar.gz            __pycache__  SOURCES.txt
LICENSE                                                           cli.py       dependency_links.txt
README.md                                                                      entry_points.txt
mk_testdir.sh                                                                  requires.txt
setup.py                                                                       top_level.txt

screenshots    testdir
-------------  ---------
ls.png         d1
lsvine.png     d2
tree.png       d3
               f1
               f2
               f3Comparison withlsandtreelsis available by default on all linux distributions.treecan be installed withapt-get install tree# ls testdir/
d1  d2  d3  f1  f2  f3

# tree testdir/
testdir/
├── d1
│   ├── f4
│   ├── f5
│   └── f6
├── d2
│   ├── f7
│   ├── f8
│   └── f9
├── d3
│   ├── d4
│   │   └── f15
│   ├── f10
│   ├── f11
│   ├── f12
│   ├── f13
│   └── f14
├── f1
├── f2
└── f3

4 directories, 15 files


# lsvine testdir/
0_root    d1    d2    d3
--------  ----  ----  ----
f1        f4    f7    d4
f2        f5    f8    f10
f3        f6    f9    f11
                      f12
                      f13
                      f14ScreenshotsLicenseApache License 2.0. Check fileLICENSEDev notesCheckDEVELOPER.md
Package: lswapi
[![Actions Status](https://github.com/nrocco/lswapi/workflows/Python%20package/badge.svg)](https://github.com/nrocco/lswapi/actions)Python module to talk to LeaseWeb’s API.For more information refer to the documentation available at
[http://developer.leaseweb.com]InstallationInstall the module using pippip install lswapiUsageThelswapi.get_leaseweb_apifunction creates an instance of the LeaseWeb Api
object with theX-Lsw-Authkey. You can provide the api key as an argument
toget_leaseweb_api$ python
>>> import lswapi
>>> client = lswapi.get_leaseweb_api(api_key=”xxxx-xxx-xxxxxx”)
>>> response = client.get(“/bareMetals/v2/servers”)
>>> servers = response.json()or as an environment variableLSW_API_KEY$ LSW_API_KEY=xxxx-xxxx-xxxxx python
>>> import lswapi
>>> client = lswapi.get_leaseweb_api()
>>> response = client.get(“/bareMetals/v2/servers”)
>>> servers = response.json()ContributeFork itCreate your feature branch (git checkout -b my-new-feature)Commit your changes (git commit -am ‘Add some feature’)Make sure that tests pass (make test)Push to the branch (git push origin my-new-feature)Create new Pull Request
Package: ls-wechat
描述这是一个定时自动发送微信消息的脚本参数介绍：optional arguments:-h, --help     show this help message and exit--r            是否使用上次的记录--sr       查看上次的记录--time TIME    发送时间，格式为: 12:00:00--delay DELAY  提前 or 延迟 多少秒发送，满了就减去，快了就加上--who WHO      发送给谁，例如 张三--msg MSG      消息内容，例如 今天天气真好pc端时间一般有误差。可以进入 https://time.is/zh/， 点击左上角 TIME.IS logo，可以查看本机时间差距。   慢了就将 delay 设置为负数，快了就设置为正数。如下所示:示例：# 发送一条消息wechat_auto.py--time=12:00:00--delay=-0.3--who=文件传输助手--msg=今天天气真不错# 执行上一次的命令wechat_auto.py--r# 查看上一次的命令wechat_auto.py--sr
Package: lswmi
lswmiUtility to retrieve information about WMI devices on Linux.RequirementsPython >= 3.11 is required to use this utility.Installationpython3-mpipinstalllswmiExamplesListing all available WMI devices:python3 -m lswmi
05901221-D566-11D1-B2F0-00A0C9062910: Data   (Instances: 1)
ABBC0F6A-8EA1-11D1-00A0-C90629100000: Method (Instances: 1)
05901221-D566-11D1-B2F0-00A0C9062910: Data   (Instances: 1)
ABBC0F72-8EA1-11D1-00A0-C90629100000: Event  (Instances: 1)
97845ED0-4E6D-11DE-8A39-0800200C9A66: Method (Instances: 1)
C3021213-D0BC-41A2-BA17-816CD5ED7744: Method (Instances: 1)
05901221-D566-11D1-B2F0-00A0C9062910: Data   (Instances: 1)
B82BB115-43AE-4B35-B79D-BD6416ABC381: Method (Instances: 1)
05901221-D566-11D1-B2F0-00A0C9062910: Data   (Instances: 1)
A0485619-3E07-4ABE-BE6B-0AB67E2A92E6: Method (Instances: 1)Enable verbose output:python3 -m lswmi -V
05901221-D566-11D1-B2F0-00A0C9062910: Data   (Instances: 1)
    Identification: MO
    Expensive: False
    Setable: False
    Driver: wmi-bmof

ABBC0F6A-8EA1-11D1-00A0-C90629100000: Method (Instances: 1)
    Identification: AA
    Expensive: False

05901221-D566-11D1-B2F0-00A0C9062910: Data   (Instances: 1)
    Identification: MO
    Expensive: False
    Setable: False
    Driver: wmi-bmof

ABBC0F72-8EA1-11D1-00A0-C90629100000: Event  (Instances: 1)
    Identification: D2
    Expensive: False

97845ED0-4E6D-11DE-8A39-0800200C9A66: Method (Instances: 1)
    Identification: BC
    Expensive: False

C3021213-D0BC-41A2-BA17-816CD5ED7744: Method (Instances: 1)
    Identification: BC
    Expensive: False

05901221-D566-11D1-B2F0-00A0C9062910: Data   (Instances: 1)
    Identification: BA
    Expensive: False
    Setable: False
    Driver: wmi-bmof

B82BB115-43AE-4B35-B79D-BD6416ABC381: Method (Instances: 1)
    Identification: BC
    Expensive: False

05901221-D566-11D1-B2F0-00A0C9062910: Data   (Instances: 1)
    Identification: MO
    Expensive: False
    Setable: False
    Driver: wmi-bmof

A0485619-3E07-4ABE-BE6B-0AB67E2A92E6: Method (Instances: 1)
    Identification: BC
    Expensive: False